Child Care and Early Education Research Connections
Descriptive research studies.
Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?
Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.
A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.
Descriptive studies have an important role in early care and education research. Studies such as the National Survey of Early Care and Education and the National Household Education Surveys Program have greatly increased our knowledge of the supply of and demand for child care in the U.S. The Head Start Family and Child Experiences Survey and the Early Childhood Longitudinal Study Program have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.
Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.
Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).
Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.
Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."
Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.
Limitations:
Descriptive studies cannot be used to establish cause and effect relationships.
Respondents may not be truthful when answering survey questions or may give socially desirable responses.
The choice and wording of questions on a questionnaire may influence the descriptive findings.
Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.
Psychological Research
Descriptive Research
Learning objectives.
- Differentiate between descriptive, experimental, and correlational research
- Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.
The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.
Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.
Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.
The three main types of descriptive studies are case studies, naturalistic observation, and surveys.
Case Studies
In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.
Link to Learning
To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.
The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.
These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).
In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.
If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.
Naturalistic Observation
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?
This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.
Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).
It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).
It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).
The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.
The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.
Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.
Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.
There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.
Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.
Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).
Think It Over
A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?
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- Approaches to Research. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-2-approaches-to-research . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.
- Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike
research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured
tests whether a relationship exists between two or more variables
tests a hypothesis to determine cause and effect relationships
observational research study focusing on one or a few people
observation of behavior in its natural setting
inferring that the results for a sample apply to the larger population
when observations may be skewed to align with observer expectations
measure of agreement among observers on how they record and classify a particular event
list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people
the collection of individuals on which we collect data.
a larger collection of individuals that we would like to generalize our results to.
General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
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- Descriptive Research Design | Definition, Methods & Examples
Descriptive Research Design | Definition, Methods & Examples
Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how questions , but not why questions.
A descriptive research design can use a wide variety of research methods to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.
Table of contents
When to use a descriptive research design, descriptive research methods.
Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.
It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.
- How has the London housing market changed over the past 20 years?
- Do customers of company X prefer product Y or product Z?
- What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
- What are the most popular online news sources among under-18s?
- How prevalent is disease A in population B?
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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .
Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:
- Describing the demographics of a country or region
- Gauging public opinion on political and social topics
- Evaluating satisfaction with a company’s products or an organisation’s services
Observations
Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.
Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.
Case studies
A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.
Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .
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Descriptive studies: what they can and cannot do
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- PMID: 11809274
- DOI: 10.1016/S0140-6736(02)07373-7
Descriptive studies often represent the first scientific toe in the water in new areas of inquiry. A fundamental element of descriptive reporting is a clear, specific, and measurable definition of the disease or condition in question. Like newspapers, good descriptive reporting answers the five basic W questions: who, what, why, when, where. and a sixth: so what? Case reports, case-series reports, cross-sectional studies, and surveillance studies deal with individuals, whereas ecological correlational studies examine populations. The case report is the least-publishable unit in medical literature. Case-series reports aggregate individual cases in one publication. Clustering of unusual cases in a short period often heralds a new epidemic, as happened with AIDS. Cross-sectional (prevalence) studies describe the health of populations. Surveillance can be thought of as watchfulness over a community; feedback to those who need to know is an integral component of surveillance. Ecological correlational studies look for associations between exposures and outcomes in populations-eg, per capita cigarette sales and rates of coronary artery disease-rather than in individuals. Three important uses of descriptive studies include trend analysis, health-care planning, and hypothesis generation. A frequent error in reports of descriptive studies is overstepping the data: studies without a comparison group allow no inferences to be drawn about associations, causal or otherwise. Hypotheses about causation from descriptive studies are often tested in rigorous analytical studies.
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How to Write a Great Hypothesis
Hypothesis Format, Examples, and Tips
Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.
Verywell / Alex Dos Diaz
- The Scientific Method
Hypothesis Format
Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.
- Collecting Data
Frequently Asked Questions
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study.
One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."
This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.
The Hypothesis in the Scientific Method
In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:
- Forming a question
- Performing background research
- Creating a hypothesis
- Designing an experiment
- Collecting data
- Analyzing the results
- Drawing conclusions
- Communicating the results
The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.
In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.
Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.
In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.
In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."
In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."
Elements of a Good Hypothesis
So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:
- Is your hypothesis based on your research on a topic?
- Can your hypothesis be tested?
- Does your hypothesis include independent and dependent variables?
Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.
To form a hypothesis, you should take these steps:
- Collect as many observations about a topic or problem as you can.
- Evaluate these observations and look for possible causes of the problem.
- Create a list of possible explanations that you might want to explore.
- After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.
In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.
Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.
One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.
A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.
For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.
These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.
Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.
In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.
Hypothesis Checklist
- Does your hypothesis focus on something that you can actually test?
- Does your hypothesis include both an independent and dependent variable?
- Can you manipulate the variables?
- Can your hypothesis be tested without violating ethical standards?
The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:
- Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
- Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
- Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
- Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
- Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
- Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.
A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .
The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."
A few examples of simple hypotheses:
- "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
- Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
- "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
Examples of a complex hypothesis include:
- "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
- "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."
Examples of a null hypothesis include:
- "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
- "There will be no difference in scores on a memory recall task between children and adults."
Examples of an alternative hypothesis:
- "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
- "Adults will perform better on a memory task than children."
Collecting Data on Your Hypothesis
Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.
Descriptive Research Methods
Descriptive research such as case studies , naturalistic observations , and surveys are often used when it would be impossible or difficult to conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.
Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.
Experimental Research Methods
Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).
Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.
A Word From Verywell
The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.
Some examples of how to write a hypothesis include:
- "Staying up late will lead to worse test performance the next day."
- "People who consume one apple each day will visit the doctor fewer times each year."
- "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."
The four parts of a hypothesis are:
- The research question
- The independent variable (IV)
- The dependent variable (DV)
- The proposed relationship between the IV and DV
Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401
Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
- Descriptive Research Designs: Types, Examples & Methods
One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.
This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.
In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.
What is Descriptive Research?
Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.
This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place.
For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.
What Are The Types of Descriptive Research?
Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:
- Descriptive-survey
Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.
For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer.
This way, the researcher can describe the qualifications possessed by the employed demographics of this community.
- Descriptive-normative survey
This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.
For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.
If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.
- Descriptive-status
This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.
A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.
- Descriptive-analysis
The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.
A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.
- Descriptive classification
This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.
- Descriptive-comparative
In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.
A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.
- Correlative Survey
Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.
Examples of Descriptive Research
There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.
- Comparing Student Performance:
An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.
Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.
- Scientific Classification
During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.
For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc.
All these classifications are made a result of descriptive research which describes what they are.
- Human Behavior
When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.
This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.
What are the Characteristics of Descriptive Research?
The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:
- Quantitativeness
Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.
- Qualitativeness
It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.
- Uncontrolled variables
In descriptive research, researchers cannot control the variables like they do in experimental research.
- The basis for further research
The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.
This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.
Why Use Descriptive Research Design?
Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.
- Define subject characteristics :
It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.
For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.
- Measure Data Trends
It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.
Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.
Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.
This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?
Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.
- Validate existing conditions
When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.
- Conducted Overtime
Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.
What are the Disadvantages of Descriptive Research?
- Response and Non-response Bias
Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.
- The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
- A case-study or sample taken from a large population is not representative of the whole population.
- Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.
What are the Data Collection Methods in Descriptive Research?
There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.
1. Observational Method
The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.
It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.
Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods.
Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.
2. Case Study Method
A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.
This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.
3. Survey Research
This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.
Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.
Survey research can be carried out both online and offline using the following methods
- Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
- Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.
What Are The Differences Between Descriptive and Correlational Research?
Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.
Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).
Correlational research may be used in 2 situations;
(i) when trying to find out if there is a relationship between two variables, and
(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables.
Below are some of the differences between correlational and descriptive research:
- Definitions :
Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables.
- Characteristics :
Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.
- Predictions :
Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.
Descriptive Research vs. Causal Research
Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation.
It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.
Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.
Descriptive Research vs. Analytical Research
Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor.
It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors.
It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.
Descriptive Research vs. Exploratory Research
Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause.
Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic.
It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.
Read More – Exploratory Research: What are its Method & Examples?
Descriptive Research vs. Experimental Research
Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs.
Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.
Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects.
Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.
Related – Experimental vs Non-Experimental Research: 15 Key Differences
Descriptive Research vs. Explanatory Research
Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context.
Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms.
It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.
Descriptive Research vs. Inferential Research
Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study.
Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.
Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample.
It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.
Related – Inferential Statistics: Definition, Types + Examples
Conclusion
The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.
Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .
It is also very useful in solving real-life problems in various fields of social science, physical science, and education.
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Descriptive Research: Definition, Characteristics, Methods + Examples
Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.
The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.
What is descriptive research?
Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.
The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.
Characteristics of descriptive research
The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.
Some distinctive characteristics of descriptive research are:
- Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
- Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
- Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
- The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.
Applications of descriptive research with examples
A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:
- Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
- Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
- Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
- Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
- Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.
Advantages of descriptive research
Some of the significant advantages of descriptive research are:
- Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
- Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
- Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
- Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.
Descriptive research methods
There are three distinctive methods to conduct descriptive research. They are:
Observational method
The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.
A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .
Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.
Case study method
Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.
Survey research
In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.
Examples of descriptive research
Some examples of descriptive research are:
- A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
- Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.
Some other research problems and research questions that can lead to descriptive research are:
- Market researchers want to observe the habits of consumers.
- A company wants to evaluate the morale of its staff.
- A school district wants to understand if students will access online lessons rather than textbooks.
- To understand if its wellness questionnaire programs enhance the overall health of the employees.
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Hypothesis Testing | A Step-by-Step Guide with Easy Examples
Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
There are 5 main steps in hypothesis testing:
- State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a or H 1 ).
- Collect data in a way designed to test the hypothesis.
- Perform an appropriate statistical test .
- Decide whether to reject or fail to reject your null hypothesis.
- Present the findings in your results and discussion section.
Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.
Table of contents
Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.
After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.
The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.
- H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.
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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.
There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).
If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.
Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.
Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .
- an estimate of the difference in average height between the two groups.
- a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.
Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.
In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.
In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).
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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .
In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.
In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.
However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.
If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”
These are superficial differences; you can see that they mean the same thing.
You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.
If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Normal distribution
- Descriptive statistics
- Measures of central tendency
- Correlation coefficient
Methodology
- Cluster sampling
- Stratified sampling
- Types of interviews
- Cohort study
- Thematic analysis
Research bias
- Implicit bias
- Cognitive bias
- Survivorship bias
- Availability heuristic
- Nonresponse bias
- Regression to the mean
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.
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Psychological Research
Descriptive research, learning objectives.
- Differentiate between descriptive, experimental, and correlational research
- Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys
There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.
The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research, it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not. Table 1 displays a quick overview of the characteristics of each research design.
Table 1. Characteristics of Descriptive, Experimental, and Correlational Research
Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.
Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.
The three main types of descriptive studies are case studies, naturalistic observation, and surveys.
Case Studies
In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.
Link to Learning
To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.
The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.
These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).
In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.
If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.
Naturalistic Observation
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?
This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.
Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).
Figure 1 . Seeing a police car behind you would probably affect your driving behavior. (credit: Michael Gil)
It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).
It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).
Figure 2 . (a) Jane Goodall made a career of conducting naturalistic observations of (b) chimpanzee behavior. (credit “Jane Goodall”: modification of work by Erik Hersman; “chimpanzee”: modification of work by “Afrika Force”/Flickr.com)
The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.
The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.
Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.
Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population. Generally, researchers will begin this process by calculating various measures of central tendency from the data they have collected. These measures provide an overall summary of what a typical response looks like. There are three measures of central tendency: mode, median, and mean. The mode is the most frequently occurring response, the median lies at the middle of a given data set, and the mean is the arithmetic average of all data points. Means tend to be most useful in conducting additional analyses like those described below; however, means are very sensitive to the effects of outliers, and so one must be aware of those effects when making assessments of what measures of central tendency tell us about a data set in question.
Figure 3 . Surveys can be administered in a number of ways, including electronically administered research, like the survey shown here. (credit: Robert Nyman)
There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.
Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.
Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).
Think It Over
A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?
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Descriptive Epidemiology
Introduction
The image above illustrates the ten essential functions of public health. Epidemiology plays a particularly important role for three of the functions: monitoring, investigating, and evaluating. The 10 Essential Public Health Services describe the public health activities that all communities should undertake. Public health systems should
- Monitor health status to identify and solve community health problems.
- Diagnose and investigate health problems and health hazards in the community.
- Inform, educate, and empower people about health issues.
- Mobilize community partnerships and action to identify and solve health problems.
- Develop policies and plans that support individual and community health efforts.
- Enforce laws and regulations that protect health and ensure safety.
- Link people to needed personal health services and assure the provision of health care when otherwise unavailable.
- Assure competent public and personal health care workforce.
- Evaluate effectiveness, accessibility, and quality of personal and population-based health services.
- Research for new insights and innovative solutions to health problems.
Disease surveillance systems and health data sources provide the raw information necessary to monitor trends in health and disease. Descriptive epidemiology provides a way of organizing and analyzing these data in order to understand variations in disease frequency geographically and over time, and how disease (or health) varies among people based on a host of personal characteristics (person, place, and time). This makes it possible to identify trends in health and disease and also provides a means of planning resources for populations. In addition, descriptive epidemiology is important for generating hypotheses (possible explanations) about the determinants of health and disease. By generating hypotheses, descriptive epidemiology also provides the starting point for analytic epidemiology, which formally tests associations between potential determinants and health or disease outcomes. Specific tasks of descriptive epidemiology are the following:
- Monitoring and reporting on the health status and health related behaviors in populations
- Identifying emerging health problems
- Alerting us to potential threats from bioterrorism
- Establishing public health priorities for a population
- Evaluating the effectiveness of intervention programs and
- Exploring potential associations between "risk factors" and health outcomes in order to generate hypotheses about the determinants of disease.
Learning Objectives
After successfully completing this unit, the student will be able to:
- Explain the role of descriptive studies for identifying problems and establishing hypotheses.
- Explain how the characteristics of person, place, & time are used to formulate hypotheses in acute disease outbreaks and in studies of chronic diseases.
- Identify case reports and case series and explain their uses and their limitations.
- Describe the design features of an ecologic study and discuss their strengths and weaknesses.
- Explain the concept of ecologic fallacy both in general and in the context of a study. Identify the strengths and limitations of an ecologic study.
- Describe the design features of a cross-sectional study and describe their uses, strengths, and limitations.
Hypothesis Formulation – Characteristics of Person, Place, and Time
Descriptive epidemiology searches for patterns by examining characteristics of person, place, & time . These characteristics are carefully considered when a disease outbreak occurs, because they provide important clues regarding the source of the outbreak.
Hypotheses about the determinants of disease arise from considering the characteristics of person, place, and time and looking for differences, similarities, and correlations. Consider the following examples:
- Differences : if the frequency of disease differs in two circumstances, it may be caused by a factor that differs between the two circumstances. For example , there was a substantial difference in the incidence of stomach cancer in Japan & the US. There are also substantial differences in genetics and diet. Perhaps these factors are related to stomach cancer.
- Similarities : if a high frequency of disease is found in several different circumstances & one can identify a common factor, then the common factor may be responsible. Example : AIDS in IV drug users, recipients of transfusions, & hemophiliacs suggests the possibility that HIV can be transmitted via blood or blood products.
- Correlations: If the frequency of disease varies in relation to some factor, then that factor may be a cause of the disease. Example: differences in coronary heart disease vary with cigarettes consumption.
Descriptive epidemiology provides a way of organizing and analyzing data on health and disease in order to understand variations in disease frequency geographically and over time and how disease varies among people based on a host of personal characteristics (person, place, and time). Epidemiology had its origins in the desire to understand the determinants of acute infectious diseases, but its methods and applicability have expanded to include chronic diseases as well.
Descriptive Epidemiology for Infectious Disease Outbreaks
Outbreaks generally come to the attention of state or local health departments in one of two ways:
- Astute individuals (citizens, physicians, nurses, laboratory workers) will sometimes notice cases of disease occurring close together with respect to time and/or location or they will notice several individuals with unusual features of disease and report them to health authorities.
- Public health surveillance systems collect data on 'reportable diseases'. Requirements for reporting infectious diseases in Massachusetts are described in 105 CMR 300.000 (Link to Reportable Diseases, Surveillance, and Isolation and Quarantine Requirements).
Clues About the Source of an Outbreak of Infectious Disease
When an outbreak occurs, one of the first things that should be considered is what is known about that particular disease. How can the disease be transmitted? In what settings is it commonly found? What is the incubation period? There are many good summaries available online. For example, Massachusetts DPH provides this link to a PDF fact sheet for Hepatitis A, which provide a very succinct summary. With this background information in mind, the initial task is to begin to characterize the cases in terms of personal characteristics, location, and time (when did they become ill and where might they have been exposed given the incubation period for that disease. In sense, we are looking for the common element that explains why all of these people became ill. What do they have in common?
"Person"
Information about the cases is typically recorded in a "line listing," a grid on which information for each case is summarized with a separate column for each variable. Demographic information is always relevant, e.g., age, sex, and address, because they are often the characteristics most strongly related to exposure and to the risk of disease. In the beginning of an investigation a small number of cases will be interviewed to look for some common link. These are referred to as "hypothesis-generating interviews." Depending on the means by which the disease is generally transmitted, the investigator might also want to know about other personal characteristics, such as travel, occupation, leisure activities, use of medications, tobacco, drugs. What did these victims have in common? Where did they do their grocery shopping? What restaurants had they gone to in the past month or so? Had they traveled? Had they been exposed to other people who had been ill? Other characteristics will be more specific to the disease under investigation and the setting of the outbreak. For example, if you were investigating an outbreak of hepatitis B, you should consider the usual high-risk exposures for that infection, such as intravenous drug use, sexual contacts, and health care employment. Of course, with an outbreak of foodborne illness (such as hepatitis A), it would be important to ask many questions about possible food exposures. Where do you generally eat your meals? Do you ever eat at restaurants or obtain foods from sources outside the home? Hypothesis generating interviews may quickly reveal some commonalities that provide clues about the possible sources.
"Place"
Assessment of an outbreak by place provides information on the geographic extent of a problem and may also show clusters or patterns that provide clues to the identity and origins of the problem. A simple and useful technique for looking at geographic patterns is to plot, on a "spot map" of the area, where the affected people live, work, or may have been exposed. A spot map of cases may show clusters or patterns that reflect water supplies, wind currents, or proximity to a restaurant or grocery store.
In 1854 there was an epidemic of cholera in the Broad Street area of London. John Snow determined the residence or place of business of the victims and plotted them on a street map (the stacked black disks on the map below). He noted that the cases were clustered around the Broad Street community pump. It was also noteworthy that there were large numbers of workers in a local workhouse and a brewery, but none of these workers were affected - the workhouse and brewery each had their own well.
On a spot map within a hospital, nursing home, or other such facility, clustering usually indicates either a focal source or person-to-person spread, while the scattering of cases throughout a facility is more consistent with a common source such as a dining hall. In studying an outbreak of surgical wound infections in a hospital, we might plot cases by operating room, recovery room, and ward room to look for clustering.
- Link to more on the outbreak of cholera in the Broad Street area of London
- Link to an enlarged version of Snow's spot map
"Time"
When investigating the source of an outbreak of infectious disease, Investigators record the date of onset of disease for each of the victims and then plot the onset of new cases over time to create what is referred to as an epidemic curve . The epidemic curve for an outbreak of hepatitis A is shown in the illustration below. Begriming in late April, the number of new cases rises to a peak of twelve new cases reported on May 12, and then the number of new cases gradually drops back to zero by May 21. Knowing that the incubation period for hepatitis A averages about 28-30 days, the investigators concluded that this was a point source epidemic because the cluster of new cases all occurred within the span of a single incubation period (see explanation on the next page). This, in conjunction with other information, provided important clues that helped shape their hypotheses about the source of the outbreak.
Video Summary: Person, Place, and Time (10:42)
Epidemic Curves
An "epidemic curve" shows the frequency of new cases over time based on the date of onset of disease. The shape of the curve in relation to the incubation period for a particular disease can give clues about the source. There are three basic types of epidemic curve.
Point source outbreaks (epidemics) involve a common source, such as contaminated food or an infected food handler, and all the exposures tend to occur in a relatively brief period. Consequently, point source outbreaks tend to have epidemic curves with a rapid increase in cases followed by a somewhat slower decline, and all of the cases tend to fall within one incubation period. The graph above from a hepatitis outbreak is an example of a point source epidemic. The incubation period for hepatitis ranges from 15-50 days, with an average of about 28-30 days. In a point source epidemic of hepatitis A you would expect the rise and fall of new cases to occur within about a 30 day span of time, which is what is seen in the graph below.
Continuous common source epidemics may also rise to a peak and then fall, but the cases do not all occur within the span of a single incubation period. This implies that there is an ongoing source of contamination. The down slope of the curve may be very sharp if the common source is removed or gradual if the outbreak is allowed to exhaust itself. The epidemic curve below is from the cholera outbreak in the Broad Street area of London in 1854 that was investigated by Dr. John Snow. Cholera has an incubation period of 1-3 days, and even though residents began to flee when the outbreak erupted, you can see that this outbreak lasted for more than a single incubation period. This suggests an ongoing source of infection, in this case the Broad Street pump.
Propagated (or progressive source) epidemic . The epidemic curve shown below is from an outbreak of measles that began with a single index case who infected a number of other individuals. (The incubation period for measles averages 10 days with a range of 7-18 days.) One or more of the people infected in the initial wave infected a group of people who become the second wave of infection. So here transmission is person-to-person, rather than from a common source. Propagated epidemic curves usually have a series of successively larger peaks, which are one incubation period apart. The successive waves tend to involve more and more people, until the pool of susceptible people is exhausted or control measures are implemented. This is an ideal example, however; in reality, most of these epidemics do not produce the classic pattern.
For some outbreaks the descriptive information is all that is needed to figure out the source, and control measures can be undertaken rapidly. In other cases, this descriptive information (person, place, and time) helps generate hypotheses about the source, but it isn't obvious what the source is. When this occurs, it is necessary to test the hypotheses by conducting an analytical study, i.e. either a case-control study or a cohort study. This means collecting data and analyzing it in order to identify the source. After the hepatitis outbreak in Marshfield, DPH conducted a case-control study. After an outbreak of Giardia in Milton, MA, a retrospective cohort study was conducted. However, it is important to recognize that you can't test a hypothesis unless you have one to test. So, the descriptive studies that generate hypotheses are essential.
Use the graph below to answer this "Quiz Me."
(Optional) - Two Methods for Creating an Epidemic Curve in Excel
Method 1 - video
Method 2 - video
(Optional) - Steps in the Investigation of a Disease Outbreak
Most outbreak investigations involve the following steps:
- Preparation for the investigation
- Verifying the diagnosis and establishing the existence of an outbreak
- Establishing a case definition and finding cases
- Conducting descriptive epidemiology to determine the personal characteristics of the cases, changes in disease frequency over time, and differences in disease frequency based on location.
- Developing hypotheses about the cause or source
- Evaluating the hypotheses & refining the hypotheses and conducting additional studies if necessary
- Implementing control and prevention measures
- Communicating the findings
Some of these steps may be conducted simultaneously, and the order may vary depending on the circumstances. For example, if new cases are continuing to occur and there are steps that can be taken to control the outbreak and prevent more cases, then certainly control and prevention measures would take top priority.
Optional Additional Resources
General Information on Outbreak Investigations
For an overview of outbreak investigations for foodborne illness see the CDC web page linked here. Other good general sources of information on how to conduct outbreak investigations can be found in the University of North Carolina (UNC) online Focus on Field Epidemiology series. The following links to online articles may be of interest:
Issue #1: Overview of Outbreak Investigations
Issue #2: Anatomy and Physiology of an Outbreak Team
Issue #3: Embarking on an Outbreak Investigation
Issue #4: Case Finding and Line Listing: A Guide for Investigators
Issue #5: Epidemic Curves Ahead with a Focus Flash on Creating an Epidemic Curve in Excel
Issue #6:Hypothesis Generation During Outbreaks
Issue #1: Hypothesis-Generating Interviews
Issue #2: Developing a Questionnaire
Issue #3: Interviewing Techniques
Another good general resource is "Hepatitis in Sparta." This is an online interactive teaching case that thrusts the student into the role of investigator trying to determine the source for an outbreak of hepatitis cases in the town of Sparta.
Descriptive Epidemiology for Chronic Diseases
The same questions about person, time, and place can be applied to chronic diseases. Who are the people who have the disease? What are their characteristics? What is their occupation? Where do they live and work? How did disease occurrence vary over time?
Personal Characteristics
Personal characteristics also provide clues about the causes of chronic diseases. Many disease vary in relation to age and gender, but many other characteristics are also important, such as occupation, diet, sexual activity, travel history, and personal behaviors (exercise, smoking, etc.)
Age-specific Rates of Disease
Because so many diseases vary in relation to disease, one frequently sees disease rates categorized this way - so-called "age-specific rates of disease." Mortality rates are very low in the youngest age groups & similar in males and females. In adulthood the mortality rates rise sharply and become higher in males. Although the mortality rate continues to rise into old age, the gender difference begins to narrow. One might describe this as a chronic, progressive disease in which the gender differences raise the question of whether sex hormones play a role, particularly since females begin to catch up after menopause occurs.
Table - Death Rates from Coronary Artery Disease (Age-Specific Rates)
Differences by Race and Ethnicity
In addition to age and gender one might want to examine how disease rates differ with respect to other characteristics, such as race. The table below summarizes. annual mortality rates per 100,000 in whites and blacks in the United States in 1967. Ethnic and racial differences in disease rates sometimes have a genetic basis, e.g., sickle cell anemia in people of African descent or beta thalassemia in people of Mediterranean descent, but in other cases racial differences are due to environmental or socioeconomic factors.
- Link to more on sickle cell anemia
- Link to more on beta thalassemia
Table - Annual Mortality Rates per 100,000 population in the US, 1967
Other Personal Characteristics
Besides age, gender and race/ethnicity, other personal characteristics that might be important to consider are:
- Religious practices, e.g. dietary restrictions or restrictions on drinking alcohol or tobacco use
- Leisure activities, e.g., exercise
Place: Variation by Location
Differences in disease frequency by location provides important clues about the determinants of chronic diseases. Where does the disease tend to occur?
- Does the frequency of disease vary from country to country? Or state to state?
- Does it vary among cities or neighborhoods?
- Does it vary within different parts of a large workplace?
Example 1: Stomach Cancer by Location in the US
These maps show death rates from stomach cancer in females (top) and males (below) in different US counties. The darkness of shading of each county indicates how its stomach cancer rate compares with the national average. The darkest shading indicates rates well above average, and white shading indicates rates below average; the gray shading indicates intermediate levels. Note that rates of stomach cancer tend to be high in counties in the north-central part of the country in both males and females. Investigators speculated that these clusters might correlate with populations of German or Scandinavian descent who have a tradition of eating smoked fish. Could the high rates of stomach cancer be the result of their consumption of smoked fish or other traditional methods of food preservation?
Source: Atlas of Cancer Mortality for U.S. Counties: 1950-1969, TJ Mason et al, PHS, NIH, 1975
Example 2: Differences in Rates of Stomach Cancer in Japan and US
Rates of stomach cancer also vary among countries. Japanese have a higher rate of stomach cancer than Caucasians in California. Is this due to a genetic difference? A dietary difference? The rate among Japanese people diminishes after they move to US, and diminishes even more in their offspring. One possibility is that once the Japanese move here, they begin to shift to an American diet, and this trend is even stronger in their children. Are there important dietary differences? Could consumption of large amounts of smoked fish be a cause of stomach cancer?
Variation in Disease Over Time
- Has the frequency of disease changed over several decades?
- Does frequency of disease vary in a cyclic way that relates to the seasons?
- Has it changed over the course of days?
Changes in disease rate over time can also provide clues for chronic diseases.
Example 1: Annual Mortality from Pulmonary Tuberculosis in England and Wales
Tuberculosis (TB) is one of the great killers of all times. The graph on the right shows the mortality rate from TB from 1855-1955 in England and Wales. The remarkable downward trend began well before the development of antibiotics. The steady improvement was probably a direct result of "the sanitary idea" which resulted in concerted efforts to improve working and living conditions, nutrition, ventilation, and waste management. Also, note the increases in TB mortality that occurred during World War I and World War II. This suggests that nutritional deficiencies, translocation, crowding, and other adverse circumstances associated with war are contributing factors to the causation of TB.
Example 2: Toxic Shock and Rely Tampons
In January 1980 there were several reports of toxic shock syndrome due to infection with Staphylococcus aureus bacteria, and the descriptive epidemiology indicated that the problem was occurring primarily in menstruating women. A CDC task force investigated and eventually traced the outbreak to the introduction of Rely tampons, a super absorbent product marketed by Proctor and Gamble. The monthly cases of toxic shock syndrome in 1980-1981 are shown in the graph below [from A. Reingold et al., Toxic shock syndrome surveillance in the United States, 1980-1981. Ann. Intern. Med 96:875, 1982]. The graph shows that prior to 1978 there were just occasional cases of toxic shock syndrome in the United States. After Rely tampons were introduced in 1978, there was a steady increase in toxic shock cases which peaked at about 125 per month in 1980. Shortly after that, Rely tampons were taken off the market, and the incidence declined sharply.
There were actual two pieces of evidence related to time variations that supported Rely tampons as the cause. First, descriptive epidemiology suggested a link to menstruation, leading doctors to take bacterial cultures from the vagina. This provided a key clue suggesting a link to certain brands of tampons. In addition, the frequency of toxic shock syndrome clearly correlated with the introduction and subsequent removal of Rely tampons from the market.
- Link to more on toxic shock syndrome
Other Factors That Can Produce Changes in Disease Frequency Over Years or Decades
If the frequency of a disease or mortality from a disease changes over time, there are several factors which could be responsible:
- Changes in incidence due to environmental or life-style changes.
- Improvements in diagnosis may increase cases reported even though the incidence may not be changing.
- Changes in record keeping (accuracy) can create what appear to be changes in disease rates.
- Improved treatment may decrease mortality rates
- Changes in the age distribution of a population can produce changes in the overall rate of disease, even though age-specific rates are not changing.
Two Fundamental Types of Study Questions
Specifying the research questions is essential to selection of an appropriate study population, and infinite questions exist. Nevertheless, Keyes and Galea stress two fundamental types of research questions which have important implications selecting an appropriate study design.
1. Questions whose goal is accurate estimation of population parameters
- What proportion of high school students smoke? Or use drugs?
- What is the frequency of death from coronary artery disease among black and white males and females, and how have those rate changed over the past 20 years?
Questions like these require samples that are representative of the population being studied, that is comparable to the population in their characteristics (and they require adequate sample size in order to minimize sampling error).
2. Questions whose goal is to identify and quantify exposures that have causal effects on health outcomes.
- Does use of cell phones cause cancer?
- Do "brain exercises prevent cognitive decline with advancing age?
- Do childhood vaccinations cause autism?
Questions like these also require an adequate sample size to precisely assess the magnitude of an effect, but they differ from questions aimed at parameter estimation in that that they require making comparisons, e.g., comparing risk between exposed and non-exposed persons. When trying to answer questions like these regarding etiology, it is not so important that the samples be representative of the overall population, but for accurate assessment of the effect the groups being compared must be comparable to each other with respect to other factors that affect the outcome.
Fundamental Study Designs for Both Representative and Purposive Studies
Keyes and Galea identify three fundamental approaches to study design that can be applied regardless of whether one's goal is to take representative samples to estimate population parameters or to take purposive samples in order to determine whether a given exposure or factor causes one or more health outcomes.
- One can study the sample at a particular point in time.
- One can follow the sample forward in time to compare the frequency of health indicators among two or more exposure groups.
- One can examine the retrospective exposure history of a sample.
The second option will only be utilized in analytical studies, which will be covered in a separate module, but the first two options will be seen in the next section describing several types of descritive studies.
Categories of Descriptive Epidemiology
Case reports.
A case report is a detailed description of disease occurrence in a single person. Unusual features of the case may suggest a new hypothesis about the causes or mechanisms of disease.
Example: Acquired Immunodeficiency in an Infant; Possible Transmission by Means of Blood Products
Link to article by Ammann AJ et al: Acquired immunodeficiency in an infant: possible transmission by means of blood products. The Lancet 1:956-958, 1983.
In April 1983 it had not yet been shown that AIDS could be transmitted by blood or blood products. An infant born with Rh incompatibility; required blood products from 18 donors over 8 weeks and subsequently developed unusual recurrent infections with opportunistic agents such as Candida. The infant's T cell count was low, suggesting AIDS. There was no family history of immunodeficiency, but one of the blood donors was found to have died of AIDS. This led the investigators to hypothesize that AIDS could be transmitted by blood transfusion.
Example: Survival after Treatment of Rabies with Induction of Coma.
Link to article by Willoughby R, Jr., et al: N Engl J Med 2005;352:2508-14.
Rabies is almost uniformly fatal once it develops. As of 2005 there had been only four survivors, each of whom received rabies prophylaxis after the bite, but before symptoms developed. Willoughby et al. reported on a 15 year-old girl who rescued and released a bat that had struck an interior window. The bat bit her left index finger. The wound was washed with peroxide, but medical attention was not sought, and no rabies prophylaxis was administered. One month later she began to experience progressive neurological symptoms that were eventually diagnosed as rabies. The mainstay of her treatment was medically induced coma. Eight days later blood tests demonstrated that she had begun to develop an immune response to the rabies virus. Eventually the coma was reversed, and the patient gradually regained consciousness. She had severe neurological deficits, but gradually improved. She was discharged to her home after 76 days. Five months after her initial hospitalization, she was alert and communicative, but had persistent slurred speech and an unsteady gait.
The report by Willoughby et al. is an example of a case report – a detailed description of a single subject. The report is important because it demonstrates that it is possible for victims of rabies to survive, even without post-exposure prophylaxis. However, we have no idea how effective this treatment might be.
Case Series
A case series is a report on the characteristics of a group of subjects who all have a particular disease or condition. Common features among the group may suggest hypotheses about disease causation. Note that the "series" may be small (as in the example below) or it may be large (hundreds or thousands of "cases"). However, the chief limitation is that there is no comparison group. Consequently, common features may suggest hypotheses, but these need to be tested with some sort of analytical study before an association can be accepted as valid.
Example: Pneumocystis carinii pneumonia and mucosal candidiasis in previously healthy homosexual men: evidence of a new acquired cellular immunodeficiency.
Link to article by Gottlieb MS, et al: N Engl J Med 1981;305:1425-1431.
In 1980 –1981 four previously healthy young men were diagnosed with Pneumocystis carinii pneumonia, an unusual "opportunistic" infection that had only been seen in immune compromised people with hereditary disorders or in people with immune compromise due to chemotherapy. The medical histories didn't suggest any preexisting immunodeficiency, but all had decreased immune responses and low T cell counts. These unusual infections suggested the possibility of a previously unknown disease. It was noted that all four men were sexually active homosexuals, and in the case series which was published in the New England Journal of Medicine the authors speculated that the immune dysfunction was due to a sexually transmitted infectious agent.
This was an extraordinarily important case series (a detailed description of characteristics of a series of people who all have the same disease) that suggested that this new syndrome was associated with sexual activity in male homosexuals. Alerting the medical establishment and proposing a hypothesis was an important milestone in the AIDS epidemic, however, the association could not be securely established based on this small case series. It was not known how many other individuals might be suffering from this new syndrome. It was also not known what the prevalence of homosexuality might be in others with this syndrome or how this might compare to the overall prevalence of homosexuality in the population that gave rise to the cases. As a result, this case series could not securely establish a valid association. Nevertheless, it laid the ground work for subsequent case-control studies and cohort studies (analytic studies) that did establish the risk factors for this disease.
Example: Oral Contraceptives and Hepatocellular Carcinoma?
There had been a number of case reports of liver cancers in young women taking oral contraceptives. A study was undertaken by contacting all of the cancer registries collaborating with the American College of Surgeons. The investigators wanted to collect information on as many of these rare liver tumors as possible across the US.
Table - Oral Contraceptive Use Among Women Who Developed Liver Cancer
What conclusions can you draw from these data regarding a possible increased risk of liver cancer in woman taking oral contraceptives? Think about it before you look at the answer.
Answer
Video Summary: Case Reports and Case Series (6:59)
Cross-Sectional Surveys
Cross-sectional surveys assess the prevalence of disease and the prevalence of risk factors at the same point in time and provide a "snapshot" of diseases and risk factors simultaneously in a defined population. For example, US government agencies periodically send out large surveys to random samples of the US population, asking about health status and risk factors and behaviors at that point in time. The Health Interview Survey (HIS) and the National Health and Nutrition Examination Survey (NHANES) are good examples.
The health questionnaires you are asked to fill out when you go to a new physician or being processed for a new job, or prior to entry into military service are similar to cross-sectional surveys in that they ask about the health problems that you have (heart disease? diabetes? asthma?) and your current behaviors and risk factors (e.g., How old are you? Do you smoke? What is your occupation?).
Cross-sectional surveys ask people their current status with respect to both exposures and diseases. This results in two main disadvantages.
- The temporal relationship between exposure and disease outcomes can be unclear, i.e., which came first.
- Cross-sectional studies tend to identify prevalent cases of long duration , since people who die quickly or recover quickly or who are no longer employed in a particular occupation are less likely to be identified.
Consider the following example in which a survey was conducted among white male farm workers. The survey asked many questions, but among them were the questions: "Have you been told you have coronary heart disease (CHD)?" And "How would you classify your level of physical activity?" The table below summarizes the findings.
Table - Current Coronary Heart Disease Among Male Farm Workers
Note that the investigators did not follow these subjects over a period of time, so they did not assess the "incidence" of heart disease. Instead, they asked the subjects questions designed to determine the prevalence of heart disease, i.e., the proportion of the study population that had heart disease at this particular point in time. When they divided the sample into physically active and inactive farmers and computed the prevalence of heart disease in each of these, they found that CHD was much more prevalent among the inactive farmers. However, this was a cross-sectional study that related the prevalence of disease to the prevalence of activity at a point in time. They did not follow subjects over time to track the development of heart disease (i.e., the incidence). Consequently, the temporal relationship between the risk factor of interest (physical inactivity) and the outcome (CHD) is unclear. Had the farmers been physically active prior to developing CHD? Or, did they begin to limit their physical activity after they developed CHD? Consequently physical inactivity could have been either a cause of heart disease, or it could have been a consequence of CHD.
Large cross-sectional surveys are important for monitoring health status and health care needs of the population over time, and they are sometimes useful for suggesting possible associations between risk factors and diseases. However, the temporal relationship between the risk factor and disease is frequently unclear. Under these circumstances, they can generate hypotheses, but these associations need to be tested by appropriate analytical studies.
However, note that under some circumstances, the temporal relationship is clear on a cross-sectional survey. For example, if one conducted a survey of salaries of male and female professors to see if gender was associated with salary inequities, we could regard this as an analytical study, because it is clear that gender was established long before salary level. In this situation the temporal relationship between the "exposure" of interest (gender) and outcome (salary paid) is clear; we know that gender was established before the salary was negotiated. So, in a sense cross-sectional studies (and ecological studies can be thought of as an intermediate category between descriptive and analytic studies.
Video Summary on Cross-Sectional Surveys (8:25)
Ecological Studies (Correlational Studies)
These studies are distinguished by the fact that the unit of observation is not a person; rather it is an entire population or group. In essence, these studies examine the correlation between the average exposure in various populations with the overall frequency of disease within the populations.
In the study below investigators used commerce data to compute the overall consumption of meat by various nations. They then calculated the average (per capita) meat consumption per person by dividing total national meat consumption by the number of people in a given country. There is a clear linear trend; countries with the lowest meat consumption have the lowest rates of colon cancer, and the colon cancer rate among these countries progressively increases as meat consumption increases.
Note that in reality, people's meat consumption probably varied widely within nations, and the exposure that was calculated was an average that assumes that everyone ate the average amount of meat. This average exposure was then correlated with the overall disease frequency in each country. The example here suggests that the frequency of colon cancer increases as meat consumption increases. The characteristic of ecological studies that is most striking is that there is no information about individual people. If the data were summarized in a spread sheet, you would not see individual level data; you would see records with data on average exposure in multiple groups .
Morgenstern notes that, "Individual level variables are properties of individuals, and ecologic variables are properties of groups. To be more specific, ecologic measures may be classified into three types:
- Aggregate measures are summaries (e.g. means or proportions) of observations derived from individuals in each group (e.g. the proportion of smokers or median family income).
- Environmental measures are physical characteristics of the place in which members of each group live or work (e.g. air-pollution level or hours of sunlight). Note that each environmental measure has an analogue at the individual level, and these individual exposures, or doses, usually vary among members of each group, though they may remain unmeasured.
- Global measures are attributes of groups or places for which there is no distinct analogue at the individual level. Unlike aggregate and environmental measures (e.g. population density, level of social disorganization. or the existence of a specific law).
Morgenstern goes on to note: "Ecologic study designs may be classified on two dimensions: (a) whether the primary group is measured (exploratory vs analytic study); and (b) whether subjects are grouped by place (multiple-group study), by time (time-trend study), or by place and time (mixed study). Despite several practical advantages of ecologic studies, there are many methodologic problems that severely limit causal inference, including ecologic and cross-level bias, problems of confounder control, within-group misclassification, lack of adequate data, temporal ambiguity, collinearity, and migration across groups."
For a detailed review of ecologic studies see follow the link to an article by Morgenstern H: Ecologic Studies in Epidemiology: Concepts, Principles, and Methods. Annual Review of Public Health 1995;16:61-81.
To see an extraordinary example of an ecologic study, play the video below created by Hans Rosling. This is a magnificent example that examines the correlation between income and life expectancy in the countries of the world over time. It is also a terrific example of a creative, engaging, and powerful way to display a vast quantity of data.
Advantages of Ecological Studies:
- The data required is frequently readily available. Commerce data can be used to estimate a population's total consumption of products (possible risk factors) such as meat, tobacco, fish, etc. So, these studies are quick & inexpensive.
- The " correlation coefficient " or an "r" value provides a measure of how closely the observed data points conform to a straight line. Some authors say that the "r" value is a measure of the association between the risk factor and the disease, but this is incorrect. The slope of the line would be a measure of the strength of association. (See the course spreadsheet "Epi_Tools. XLSX" for a worksheet that calculates correlation coefficients). The value of a correlation coefficient is from +1 (a perfect positive correlation) and –1 (a perfect negative correlation). See the tabbed activity below for examples.
Limitations of Ecological Studies: It is important to bear in mind that the exposure in correlational studies is the average exposure for an entire population or group. This results in major limitations:
- Since you don't have any information about the risk factor status or the outcome status of individual people, you can't directly link the risk factor to the disease, i.e., it is not clear that the people who ate the most meat were the ones who got colon cancer. This is sometimes referred to as "ecological bias" or the "ecological fallacy."
- Another limitation is that there is no effective way of taking into account, or adjusting for, other factors that influence the outcome (confounding factors). As a result, an apparent correlation, or the lack of a correlation could be misleading. For example, one might find a strong correlation between the average number of hours of TV viewing & the rate of coronary artery disease among different countries. However, this doesn't necessarily mean that TV per se is a risk factor for CAD. There may be a number of other differences between the populations that are associated with higher rates of TV viewing: e.g., greater industrialization, less exercise, greater availability of processed foods and saturated fat, and so forth. And conversely, the lack of a correlation doesn't necessarily imply that there is no association.
- Since the exposure levels represent average exposure in a large number of people, correlational studies can mask more complicated relationships, as illustrated below.
When a correlational study compared per capita alcohol consumption to death rates from coronary heart disease in different countries, it appeared that there was a fairly striking negative correlation.
However, a meta-analysis of prospective cohort studies which determined mortality rates in subjects for whom they had estimates of individual alcohol consumption, showed that there was actually a "J" shaped relationship. The people who drank the most actually had the highest mortality rates; moderate drinkers had the lowest mortality. This relationship was masked in the correlational study, because of the small percentage of people who have more than three drinks per day.
Adapted from: Di Castelnuovo A, Costanzo S, et al.: Alcohol Dosing and Total Mortality in Men and Women:
An Updated Meta-analysis of 34 Prospective Studies. Arch Intern Med. 2006;166(22):2437-2445.
Video Summary for Ecological Studies (7:48)
Summary & Self-Check
Descriptive studies are useful for:
Other Resources
- University of North Carolina (UNC) -Torok M and Anderson M: "Focus on Field Epidemiology: Volume 5; Issue 5:Introduction to Public Health Surveillance."
- University of North Carolina (UNC) - Anderson M: "Focus on Field Epidemiology: Volume 5; Issue 6: Public Health Surveillance Systems".
- Trifonov V, Khiabanian H, Rabadan R: Geographic Dependence, Surveillance, and Origins of the 2009 Influenza A (H1N1) Virus. Perspective article in: N. Engl. J. Med. 2009;361(2):115-119.
- Scallan E, Hoekstra RM, Angulo FJ, et al. Foodborne Illness Acquired in the United States - Major Pathogens. Emerging Infectious Diseases 2011;17(1):7-15. [Volume 17, Number 1, January 2011, pages 7-15]
- Marsden-Haug N, Foster VB, Gould PL, Elbert E, Wang H, Pavlin JA. Code-based syndromic surveillance for influenzalike illness by International Classification of Diseases, ninth revision. Emerg Infect Dis, Feb. 2007;13(2):207-216.
- Open access
- Published: 31 January 2024
Exploring factors that affect nurse staffing: a descriptive qualitative study from nurse managers’ perspective
- Xiaoyan Yu 1 ,
- Miqi Li 1 ,
- Meichen Du 1 ,
- Ying Wang 1 ,
- Yu Liu 1 &
- Hui Wang 1
BMC Nursing volume 23 , Article number: 80 ( 2024 ) Cite this article
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Metrics details
The appropriate nurse staffing reflects the situation of nursing management of human resources. Nurse managers have a pivotal role in determining a competent and sufficient number of nurses. It is important to understand the factors influencing nurse staffing to promote appropriate staffing levels. The study aimed to explore the factors affecting nurse staffing from the perspective of nursing managers.
Purposive sampling was adopted to recruit 14 nurse managers from secondary and tertiary hospitals located in the central region of China, and semi-structured interviews via telephone were conducted from April to May 2022. Interview transcripts were analyzed and collated using thematic analysis.
This research identified four themes and ten subthemes influencing nurse staffing. Extracted themes include: government level (inadequacy of mandatory policies, budgetary constraints), hospital level (hospital characteristics, the control of nurse labor costs, inadequate support on nursing), patient level (patient characteristics, increasing care needs), and nurse level (nurse shortage, skill-mix, individual high-level needs).
The findings indicate that it is crucial for decision-makers or policymakers to legislate for safe nurse staffing and establish effective supervision and funding incentives. Tailored interventions are also needed to improve the organizational context, address the nurse workforce and balance the structure of nurse staff.
Peer Review reports
Many countries have achieved significant development in healthcare personnel resources in recent years [ 1 , 2 ]. The nurse staff is considered an integral part of the healthcare workforce and makes up approximately 60% worldwide [ 3 ]. While the world has credibly acknowledged nurse staff as vital in helping prevent adverse outcomes, promoting patients’ health and improving their satisfaction with healthcare services, one of the main challenges faced is the global shortage of nursing workforce [ 3 , 4 , 5 ]. By the end of 2020, the number of registered nurses (RNs) in China was 4.70 million, or 3.34 RNs per 1,000 population [ 6 ], which is much lower than the average of 9.4 RNs per 1,000 population in the member countries of the Organization for Economic Co-operation and Development (OECD) [ 7 ]. Moreover, it reported that the bed-to-nurse ratio in general units was 1:0.53, and over 70% of nurses had a junior college degree or above [ 6 ]. With the development of the economy, increased coverage of medical insurance networks, the three-child policy, and an ageing population with growing healthcare needs, the healthcare system in China faces enormous challenges, such as the shortage of nursing workforce and the imbalanced skill structure of nurse staff [ 6 , 8 ].
Nurse staffing is referred to the number of nurses, professional qualification of nurses, nurse-to-patient ratio or skill-mix [ 5 , 8 ]. Optimal nurse staffing is the concern of most nurse leaders worldwide and is essential for patient safety and quality of care [ 9 ]. In 2016, the World Health Organization (WHO) raised a vision for health care: accessible, acceptable, quality, and cost-effective health care with the staffing of nurses according to patients’ expectations and needs [ 10 ]. Studies have shown that appropriate nurse staffing helps to ensure better patient outcomes and improve the quality of healthcare, including shorter length of hospital stay, lower levels of in-hospital mortality and hospital-acquired infections, as well as fewer omissions of nursing care [ 11 , 12 , 13 ]. To support nurses empowered to create staffing plans specific to each unit, some parts of the world, such as California, the United States, Victoria, and Queensland state, Australia, have passed the law for the minimum nurse-to-patient ratio, which has been proven to have benefits for patients and healthcare system [ 9 , 14 ]. In China, there are two standards for nurse staffing in clinical settings, including the bed-to-nurse and nurse-to-patient ratios, which are requirements for the total allocation of nurses to a ward and for given shifts in a ward, respectively [ 15 ]. In 2012, the Ministry of Health of China recommended that the patient-nurse ratio in general wards should be no more than eight [ 15 ]. The requirement for the bed-to-nurse ratio in general units should be not less than 0.55 nurses per bed [ 8 ]. Though there are three shifts for nurses in most Chinese hospitals, nurse staffing standards have not been established for different shifts [ 16 ]. Furthermore, these two indices are the only requirements on the number of nurses, without considering nurses’ working experience, professional titles, or educational levels.
Nurse staffing is essential to the ever-evolving healthcare system, and high baseline staffing levels are needed to enhance patient health. Previous studies reported several factors, such as the increasingly aging workforce, changing workplace climate, and high nursing workloads, continue to drive insufficient nurse staffing in most hospitals across the world [ 17 , 18 ]. In China, the healthcare system is different from most Western countries in terms of government healthcare expenditure, public health insurance, and a tiered healthcare delivery system. The government healthcare expenditure in China is underfunded, accounting for 6.5% of gross domestic product [ 19 ]. Hospitals may not enroll the healthcare workforce, particularly the nurse staff, when facing financial challenges. The nurse manager is pivotal in determining the unit’s nurse number and skill mix of nurses [ 20 ]. Those managers should be able to logically suggest the requisite number of nurses who can give enough care to patients. While studies consistently emphasize the importance of sufficient nurse staffing levels [ 21 , 22 ], the staffing of nurses in hospital settings is more complex than solely acting on evidentiary support. Therefore, the objective of this study was to explore the factors influencing nurse staffing from nurse managers’ perspective. This result could help identify the obstacles in the allocation of nursing resources and highlight the need for an appropriate nurse staffing plan involving all relevant stakeholders.
A descriptive qualitative with semi-structured interviews via telephone was used to explore the factors influencing nurse staffing from nurse managers’ perspective. The consolidated criteria for reporting qualitative research (COREQ) checklist was followed to ensure the quality of research [ 23 ] (see Table S1 ).
Setting and sample
This qualitative descriptive study was conducted in 5 secondary and 9 tertiary hospitals in the central region of China. Purposive sampling was used to achieve a maximum variation in nurse managers until data reached theoretical saturation (i.e., no new themes were identified) [ 24 ]. The eligible criteria were nurse managers with at least five years of experience in that position at a public general hospital. Participants were excluded if they were not willing to participate in the research. Before the data collection, one researcher (MQL) introduced the research purposes and procedures for eligible participants, who were contacted by E-mail or telephone. They were invited to participate in the interview if they showed interest in this research.
Data collection
A semi-structured interview guide based on research purposes was developed to direct conversations toward the research topic through an extensive review of relevant literature, following the process recommended by Kallio et al. [ 25 ]. A meeting with the research team was held to revise the interview guide by removing ambiguous questions. Next, an ethics specialist was invited to assess the appropriate wording. The final interview questions in the interview guide are presented in Table S2 . The interview guide was piloted with two participants prior to the formal interview. There was no need to reformulate the questions, so the pilot data were included in the analysis. Telephone interviews were conducted by two authors (XYY and MQL), and the time for interviews was set according to the participants’ convenience and preferences. All enrolled participants were asked to complete a written informed consent form and provide their relevant demographic data before being interviewed. The interviews lasted from 15 to 40 min. The interview data were collected from April to May 2022.
Data analysis
We used thematic analysis to scrutinize data [ 26 ]. The data collection and data analysis were simultaneously conducted. Two independent researchers repeatedly read transcripts and notes to understand participants’ exact meanings (XYY and MQL). Key lines and condensed meaning units were highlighted in the text, which were coded to generate initial codes. Similar codes were clustered to create subcategories and categories, which were grouped into themes. The data analysis was ongoing throughout data collection. The first author determined the initial coding, and the others read a sample of coded interviews to check the coding. All authors discussed the assigned codes several times until they reached a consensus. Following data analysis, the emerged themes and interview excerpts were translated into English by the researcher (bilingual in English and Chinese), and then back-translated into Chinese by a translator to ensure their exact meaning were consistent with the original transcripts [ 27 ].
The research was conducted and reported by the following four criteria to ensure rigor: credibility, transferability, confirmability, and dependability [ 24 ]. For the credibility, the whole interviews were held using a semi-structured interview question, and field notes were taken throughout the interviews. To ensure the confirmability, data were analyzed by two independent researchers [ 28 ]. We also conducted member checks with two participants separately to review and comment on interpretive notes via online face-to-face meetings [ 28 ]. To establish dependability, verbal data were recorded and interpreted. In the meantime, relevant quotations were also attached to elaborate on each theme and subtheme. Lastly, transferability was supported by providing contextual information such as age, hospital type, and a detailed description of the interview data collection process [ 28 ].
Ethical consideration
The hospital granted permission to conduct the study (number: TJ-IRB20220454). All enrolled participants were informed about the study procedure. Written informed consent was obtained from participants before the interview. They were informed that participation was entirely voluntary and had the right to withdraw from the research at any time without negative consequences. Besides, all data were confidentially maintained. Only the researchers and research team had access to the data in a password-protected computer.
A total of 14 participants participated in the research, and none refused or withdrew. All participants were women with a mean age of 48.71 years (43 to 58 years) and an average work experience of 29.5 years (24 to 36 years). Half of the participants held a master’s degree. Nice participants came from tertiary and five from secondary hospitals. The participants’ demographic characteristics are shown in Table 1 . Four themes emerged from the interviews: government level, hospital level, patient level, and nurse level. Participant’s quotations were used as exemplars to illustrate the critical issues experienced by participants and to support each theme and subtheme.
Government level
Inadequacy of mandatory policies.
Nurse staffing policy requires the hospital to determine a minimum average staffing level in a ward. However, the policy’s detailed operation, including the punishment detail and special operation, may affect the enforceability.
Seven interviewees in the study mentioned that some mandatory measures should be adopted to ensure sufficient staffing levels. One participant said:“We want to comply with the nurse staffing policy, …it seems that there are no mandatory or punishment measures, for example, if it could not achieve the nurse staffing levels…”(NM11). Another participant stated: “If the nurse staffing levels are related to…the evaluation of hospital quality indicators, the hospital managers would pay some attention to nurse workforce …”.(NM 13).
Besides, the nurse staffing standards have some limitations in the complex clinical context. Accordingly, the following narratives were recorded:
For example, some departments such as the cardiology department, which belongs to internal medicine, have set its bed-to-nurse ratio… However, there is no detail in the policy document …. (NM 10) The bed-to-nurse ratio was 1:0.4, which was published in 1978. Until high-quality care was proposed in 2010, it recommended the ratio was 1:0.5. (NM 4)
Budgetary constraints
Financial support from the government was the primary incentive mechanism for hospitals to increase nurse staffing levels. However, budgetary constraints at a governmental level may affect the nurse staffing. The following narrative highlighted these findings:
Our hospital (secondary hospital) has a relatively low level of nurse staffing, and it is necessary for the hospital to control labor costs for further development…without enough economic support, and now it adopt contract-based employment, these nurses will feel the same as a part-time job…they would rather find a good job outside the hospital, and may not feel so burnout….(NM 5) If nurses’ salary is subsidized by the government, we are willing to… increase nursing human resources.(NM 9)
Hospital level
Hospital characteristics.
Many factors are necessary to be considered in the rational allocation of nursing human resources, such as the hospital size, hospital types, and hospital service capacity. Accordingly, the following narratives were recorded:
We thought the most important factors were the hospital size and its workload, when we set nurse staffing standards. (NM 7) It is also necessary to consider the hospital type; if the hospital provides service for more elderly people, women or children, the bed-to-nurse ratio may be different. (NM 9)
The control of nurse labor costs
In the market-oriented healthcare system, hospitals tend to consider human resource costs, especially the nurse labor costs, which account for the majority of hospital expenditure. The following narrative highlighted these findings:
Heads of department are unwilling to recruit nurses because of its labor costs. (NM 3) The hospital boards will take it (labor cost) into consideration when in developing stage, … if the total number of patients does not reach a certain level, the hospital boards will control the labor costs and not enroll enough nurses at once. (NM 8)
Inadequate support on nursing
Nurse staff is considered an integral part of the healthcare system. However, hospital leaders’ perspectives and their support may affect nurse staffing. The following narratives were recorded:
First of all, hospital leaders’ perceptions on nursing is crucial, some still hold on the view that the nursing care is less important so that priority will be given to…control nurse workforce.(NM 3) For example, in the anti-COVID stage, the bed-to-nurse ratio reached 1:0.4, because of the deployment of nurse personnel from other medical institutions. However, after that, with the deployment of nurse personnel decreasing, all hospitals began to restore normal order and needed more nurses to deliver care service; however…the hospital leaders thought that they could ensure the hospital’s normal operation, it was not necessary to recruit more nurses. (NM 4)
Patient level
Patient characteristics.
The basic characteristics of patients, including total number, age, education level, and economic conditions …, affect nursing workload, so there are requirements for nursing human resources. The following narrative highlighted these findings:
There are many elderly people hospitalized in our hospital. Some of them have low education level, with more healthcare demands, so our nurses spend more time caring for them…. (NM 5) However, due to the increasing patients and their demand for healthcare, we have to enroll more nurses, and increase labor costs… as a result. (NM 7)
Increasing care needs
With the change in health concepts, more patients tend to pursue high-quality nursing services. Moreover, the specific nursing service is also required according to the patient’s disease condition. Accordingly, the following narratives were recorded:
Nursing personnel should be allocated …according to patients’ disease risk and condition, as well as bed-to-nurse ratio.(NM 12) If there are more serious patients in the department, it means that more nurses are needed, such as advanced practical nurses.(NM 5) Patients hope to get better quality of care in the hospital…, so they have the feeling of security. Thus…the quality of nursing care has to be improved.(NM 6)
Nurse level
Nurse shortage.
The limited nurse number may restrict the reasonable allocation of nurse staff, which is also the main dilemma of the current distribution of nurse human resources.
First of all, the total number of nurses is relatively inadequate, so no matter how we allocate nurses, it could not meet the clinical care needs. (NM 8) However, the annual nurse recruitment plan is not adopted. In fact, it is very difficult to recruit nurses, so that the units have low baseline staffing. (NM 10)
Nurses’ age, work experience, professional levels and professional title are essential embodiments of nurse structure, which can promote the rational allocation of nursing human resources.
For nurses’ competency level, not just about Level 1, level 2, … we have to consider, such as specialist nurses….(NM 10) Nurse’ age, working experience, education level, professional title, etc., should be considered in the overall staffing. (NM 11)
Individual high-level needs
Nurse individual needs and choices were essential in directing their professional development. The following narrative highlighted these findings:
Some nurses with three-year college degrees are enrolled in our hospital. However, once they complete their undergraduate education, they intend to resign…. (NM 3) In recent two years, nurses with higher education level are more likely to leave because…they can get better opportunities. (NM 4)
This study offers insights into the multiple factors influencing nurse staffing from nurse managers’ perspectives. Analysis of our qualitative data from 14 nurse managers showed that four factors influenced the complex and dynamic organization of nurse staffing: government level, hospital level, patient level, and nurse level.
The results of this study showed that nurse staffing was affected by mandatory policies and budgetary constraints at a governmental level. Nurse staffing policy could secure a sufficient number of nurses in hospital, which is the vane of nursing human resource allocation. Though the Ministry of Health of China has developed standards, planning or guidance on nurse staffing [ 6 , 15 , 29 ], there is no safe staffing legislation similar to that in Queensland and California where the ratio of no more than five or four (in Queensland) patients per registered nurse is required on medical and surgical wards for the day shift [ 30 ]. Moreover, a lack of supervision measures would weaken the policy’s mandate to allocate nurses [ 31 ]. On the other hand, budgetary constraints at a governmental level are one of the main factors hampering nurse staffing and the long-term development of the nurse team. In the future, while safe nurse staffing should be legislated, it is also necessary to establish supervisory mechanisms to ensure the effectiveness of policy implementation and to promote the sound development of the nurse workforce. Furthermore, there is a need to establish incentive mechanisms for medical institutions to improve the baseline staffing levels.
The results of this study showed that hospital level, which constituted of hospital characteristics, the control of nurse labor costs, and inadequate support on nursing, was considered an essential factor influencing nurse staffing. Providing nurse staffing levels that match patient healthcare needs is vital to deliver cost-effective health services [ 13 ]. However, the nurse staff is the largest staff group, accounting for a large proportion of the hospital’ variable costs, which was sometimes regarded simply as a costly labor input [ 22 , 32 ]. Furthermore, nursing care may not contribute direct benefits to medical institutions; those leaders may control costs by reducing the number of nurses, decreasing nurses’ income, etc. Therefore, nurse staffing decisions need to address the baseline staff establishment to roster and better respond to fluctuating nurse care demand among patients [ 13 ]. Previous studies have reported that the benefits of better nurse staffing extend to nurses as well; those nurses in better-staffed hospitals report less job dissatisfaction, burnout, or intention to leave their jobs [ 16 , 33 ]. Griffiths et al. found that a higher baseline staffing plan, which was planned to meet 90% of demand, was more resilient in the face of variation and may also be highly cost-effective, due to much of the increased additional staff costs being offset by savings from reduced length of stay in hospital [ 22 ].
Nurses constitute the backbone of the health care system, and sufficient nurse personnel is essential to human resource allocation. This study found that nurse shortage, skill-mix, and individual high-level needs were the factors affecting nurse staffing. The nurse shortage is considered a critical global problem, and this concern is further exacerbated by the trend of nurses leaving their positions. Nurse turnover rates vary across countries, with 15.1% in Australia [ 34 ], 17.8% in the United States [ 35 ], and 23% in Israel [ 36 ]. Liu et al. reported that the turnover rate of nurses was 0.64%~12.7% across 22 secondary and 26 tertiary hospitals in Jiangsu Province of China [ 37 ]. In such circumstances, nurses are struggling to meet the demands of patients and organizations. A recent study found one third of the Chinese nurses were overworked [ 8 ], which may affect the quality of nursing care and cause nurses to increase job dissatisfaction, exhaustion and intention to leave [ 8 , 16 , 38 ]. In addition, due to the rapidly changing healthcare context, it poses a challenge for nurses to update their knowledge and skill, and adapt to the newest medical technology to deliver comprehensive health-care needs for patients [ 39 , 40 ]. Nurses with different work experience and educational levels may be equipped with distinctive abilities to provide healthcare, which should be considered in nurse staffing [ 22 ]. Thus, further consideration should be paid to achieving the optimization of nurse staffing.
Implications for nursing management
Exploring the factors influencing nurse staffing could present evidence for decision-makers or policymakers to address nurse shortages and promote appropriate nurse staffing to ensure high-quality patient care. An effort should be made to provide supportive measures by reinforcing policy, investing in the nurse workforce, improving the organizational context, and offering nurses’ professional development needs, which, in turn, would increase baseline nurse staff, improve nurses’ work attitudes, and their intention to stay in the medical institution.
Limitations
Our study has several limitations. First, although we purposively sampled participants to ensure diversity of opinions and experiences, our study conducted a semi-structured interview via telephone, and some information, such as non-verbal data, may be missing during the interview. Second, the interview data were translated from Chinese to English, it is still a risk to misinterpret and mislay some of the code meaning while translating data.
Conclusions
This study uncovered multiple factors on governmental, hospital, patient and nurse level that may affect nurse staffing. Results illustrate the complexity of the implementation process for nurse staffing, highlighting the need for a well-thought-out nurse staffing plan with the involvement of all relevant stakeholders. Nurse staffing levels across all sectors and settings, and for all shift patterns, should be legislated for safe nurse staffing, and its supervision and funding incentive mechanism should also be established. Tailored interventions focused on improving the organizational context, addressing the nurse workforce and balancing the structure of nurse staff, are needed to improve quality of care and nurse and patient outcomes.
Data availability
The data being used and analyzed during the current study are available from the corresponding authors upon reasonable request.
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This work is supported by Nursing Research Sub- Program of China Health Personnel Training Program (grant number: 2021-HLYJ-010).
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XYY, MQL, and HW designed the present study. XYY, MQL and MCD analyzed the data. XYY and MQL wrote the first draft of the manuscript. XYY, MCD, YW, YL and HW revised the manuscript. All authors have read and approved the final version for submission.
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Yu, X., Li, M., Du, M. et al. Exploring factors that affect nurse staffing: a descriptive qualitative study from nurse managers’ perspective. BMC Nurs 23 , 80 (2024). https://doi.org/10.1186/s12912-024-01766-7
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ORIGINAL RESEARCH article
The impact of incentives on job performance, business cycle, and population health in emerging economies.
- 1 Department of Business Administration, International College, Rajamangala University of Technology Krungthep, Bangkok, Thailand
- 2 Department of Global Buddhism, Institute of Science Innovation and Culture, Rajamangala University of Technology Krungthep, Bangkok, Thailand
In the past, different researchers have conducted studies on incentives and how they are linked to employee motivation, influencing emerging economies. This study addresses two gaps as outlined in previous studies. One research gap exists in examining employee loyalty and employee engagement in relation to the business cycle. The other gap is observed in the recommendation that future researchers use different moderators between incentives, the health of employees, and job performance with population health. This focus was explored in the present study by identifying the responses of hospitals and physicians to the business cycle to examine the impact of incentives on job performance and health of workers in public and private sector hospitals in Shandong, Eastern China. Data were collected in the form of questionnaires that consisted of close-ended questions. These questionnaires were then filled out by 171 doctors and 149 nurses working in both public and private sectors in Shandong, Eastern China. The results showed that there is a relation between different variables. Some variables have more impact on other variables such as transformational leadership, which has a significant impact on the job performance and business cycle, whereas monetary incentives also impact job performance and population health, but this impact was lower than that of transformational leadership in terms of how job performance influences emerging economies.
Introduction
The population of China is continuously increasing day by day. In 2019 it was 1.4 billion and is growing at a rate of 0.43% ( 1 ). With this population growth, there is a need to focus on the health facilities that are being provided to people and the employees and workers of the health sector. Public sector hospitals are trying their best to provide the best facilities to citizens and their employees but they are lacking in resources compared to the private sector, particularly in terms of technology and staff ( 2 – 4 ).
Through this comparison of both public and private sector hospitals, we can also see the reasons why researchers believe that the public sector has low productivity. These include unfriendly and unprofessional care and an absence of performance based incentives ( 5 , 6 ). This might be linked to how hospital staff feel about their own health and organization. The present study explores employees' general health and the loyalty of employees regarding the hospital sector ( 7 ). This will help the government sector to adopt some techniques that are being used in the private sector and create a working environment that is more conducive to the health of employees, enabling them to be more productive.
Organizations are trying to adapt according to the changes in the environment such as those caused by the Covid-19 pandemic ( 8 ). For that purpose, they are focusing on a resource-based view. They are trying to get a competitive advantage by creating human capital that is valuable, rare, and difficult to replace. In doing so, they are making sure that the employees' needs, i.e. health, a healthy environment, and incentives are being fulfilled ( 9 ). Organizations believe that fulfilling these needs will lead to employee loyalty and later on affect their general health and job performance. Leaders also play a role in creating an environment that promotes the good health of employees and loyalty of employees leading toward job performance ( 10 , 11 ). Therefore, this study focuses on exploring whether incentives are the reason for the increase or decrease in an employee's general health and loyalty. It also tries to evaluate the impact that incentives have on job performance and the health of workers. This study also explored the role of leaders in creating employee loyalty and in job performance ( 1 , 12 ). This study also explores the impact that leaders have on incentives, and how personal care can affect job performance.
Employees are assets of an organization and influence sustainability in organizations. Organizations use various strategies to retain employees and for that purpose, they have different policies in the organization that recognize the efforts of those employees ( 13 , 14 ). They provide them with rewards or incentives so they may live a healthy life that enables them to contribure to the organization in positive ways. These incentives are there in order to make employees motivated and increase productivity. The rewards system is one beneficial policy for employees, encouraging them to improve and maintain their general health and job performance ( 15 ).
Different researchers have examined motivational incentive and reward systems for employees. Some researchers believe that these incentives are the reason employees feel energetic and motivated toward their work. However, some researchers have also focused on the effect that a positive environment has on employees. If organizations provide them with this environment, then employees help them in achieving their goals. When an organization is developing, they try to motivate employees by training or providing them with different facilities including healthcare ( 14 , 16 – 19 ). Thus they utilize the skills of employees in different ways. A number of past studies have focused either on the incentive perspective or the environment and how these are linked to determining job performance. Environmental factors are important when it comes to job satisfaction as most employees are satisfied if they have more developmental opportunities rather than extrinsic rewards ( 20 – 22 ). If they get the best out of the prevailing conditions, for example staying healthy during Covid-19, they will certainly do their best ( 3 , 4 , 23 ).
In the changing environment, organizations are trying to adapt. They need to understand the importance of both working environments and extrinsic rewards ( 24 , 25 ). If one of them is missing, then employees will not be satisfied with their work and that dissatisfaction will lead to bad health and an increase in turnover. Therefore, this research focuses on both perspectives side-by-side, exploring incentives and the environment in relation to leadership as these determine general health, employee loyalty, and the job performance of employees within an organization ( 12 , 21 , 26 ).
This research was conducted in public and private hospitals in Shandong Province, Eastern China. This research will help in determining employees' perspectives regarding incentives and leadership and how they affect their health and loyalty toward the hospital as well as their performance. This research will help different HR managers working in the hospital sector and in other sectors to understand that it is important to focus on both these perspectives and what things are needed to focus more on job performance, better health, and how these are linked to organizational failure or success ( 27 – 29 ). This research furthers understanding of employee perception and weightage and how they influence incentives and leadership in the workplace.
This research is based on knowledge gaps identified in two previously published articles. The first article examined “The effects of organizational culture and leadership style on employee engagement and what their impact on employee loyalty is.” In that article, the researcher recommended that future researchers further explore different perspectives of employee loyalty and employee engagement, whether it is in an organizational or an individual context. They also outline that different factors need to be encouraged that lead to employee engagement, such as the leadership styles, as employee engagement has a positive correlation with employee loyalty. They also mentioned that future researchers can use different moderating variables in research. The second article was on “Employee participation, performance metrics, and job performance: A survey study based on self-determination theory” ( 30 – 32 ) published in 2017. This article recommended that future researchers concentrate on the different moderators that may explain the different relationships between incentives, general health, and performance ( 29 , 33 – 35 ). They also recommend that future researchers provide an overview of different tasks or their environment as moderators.
Literature Review
Concepts and definitions.
Incentives are defined as concrete incentives or any kind of compensation that is given to an employee in the form of cash. Incentives can also be defined as the objective criteria where an individual simply wants to establish quantifiable standards for performance. Some researchers have divided incentives into two types, namely concrete and moral incentives. Moral incentives refer to indirect compensation through certification, for example appraising someone ( 36 ). Concrete incentives refer to a direct way of compensating one's effort by giving a bonus. In this research, we will be talking about the concrete incentives that are being given to workers. According to the American Compensation Association, compensation is defined as the cash and non-cash remuneration provided by an employer in exchange for the services that are provided by the employee ( 37 ). This research discusses concrete or cash remuneration.
A compensation package is when an incentive is used as a strategic tool to compensate an employee for their performance and retain them by achieving employee satisfaction and improving their health for achieving the best job performance at the same time. Some researchers believe that incentives are used by employers to trigger and influence the motivation of employees ( 38 ). When they motivate employees, it leads to improvements in general health, skill and they will also be satisfied with their work. Most organizations perceive incentives as a way of achieving their goals ( 30 , 36 , 39 ). Some researchers have outlined that compensation was an important factor in providing health allowances, job satisfaction, and employee empowerment are also considered to be important factors in cases of employee loyalty ( 28 , 30 , 39 – 41 ).
Some previous research found that the alignment of the reward system must follow the organization's design, otherwise, they send mixed messages ( 42 ). Let us take an example of an automobile company, which designs distinct working activities as project assignments for the workforce in departments. The main objectives are that employee pay must be analyzed by the respective managers of departments and decided with a finance manager. Consequently, workers will be satisfied and motivated to meet their functional goals. Although, all the departments must communicate and work cross-functionally. Everyone is responsible and accountable for the responsibilities for which they were hired. The direct supervisor is linked with the incentives that are being provided to the employees rather than a person who has no knowledge of the work performed by the employee. In this way, the employee is motivated and puts effort into working efficiently to gain rewards ( 43 ). Therefore, choosing a leader who will measure the performance of the employees is also important.
The reward in satisfying the needs of employees, for example, the healthcare of their family, as stated by Victor H. Vroom under the theory of expectancy ( 44 ). Incentives were considered as a form of payment that is directly linked to the performance of employees. The more profits or incentives the better the performance of employees. This system of providing monetary incentives to employees is another way of compensating them other than their salaries. This system of compensating employees is based on performance ( 35 , 37 , 45 , 46 ). Different research also shows that employees nowadays are much more motivated by extrinsic rewards. If they have a greater sense of entitlement, then they want to work hard to achieve goals. Employees are not motivated by intrinsic rewards and are more sensitive about monetary compensation for the work that they do ( 39 , 40 , 44 , 47 ).
Employee Loyalty
Employee loyalty is defined as the commitment or psychological attachment of employees toward the organization. Employee loyalty is also defined as the capability of the employee to stay in an organization. It might also depend on how much time they have spent in an organization and what type of work they do in an organization ( 46 , 48 , 49 ).
In the past, employee loyalty was defined as the time an employee remains in an organization but due to the environmental changes during the pandemic, its definition was updated to the time an employee remains committed toward the organization and is said to be loyal. Previously, employee loyalty consisted of two major divisions: firstly, loyalty is in the employer's best interest; and secondly, loyalty is when an employee remains with the same employer ( 50 ). Employee loyalty has evolved. Traditionally, it was known as a trust and bond relationship between employee and employer, which means the longer the time an employee spends on their job the more loyal they will be but in recent times leaders do not equate longevity with loyalty, rather they define loyalty as the commitment and dedication that an employee gives it to their organization ( 51 , 52 ). Employee loyalty is considered to be linked with the survival and success of any organization and if the employers recognize the importance of an individual, then it means that they will try to ensure that the employee remains loyal to that organization ( 53 – 55 ).
Some researchers believe that employee loyalty leads to job satisfaction. If the employee's expectations are met, then their level of satisfaction also increases. This loyalty will develop into a generalized emotional attitude toward that organization for which the employee is working ( 56 – 59 ). The more satisfied they are the more healthy their life, and the more loyal they will be toward that organization. When employees develop affection toward their organization, they show loyal behavior by improving productivity and that helps them in achieving organizational goals i.e. they provide a better quality of services to employees whereas, some researchers have mentioned that employee loyalty is generated due to the presence of job satisfaction ( 57 , 58 , 60 , 61 ). Job satisfaction and the general health of employees are important variables when it comes to employee loyalty. The positive or negative feelings of employees might later determine employee loyalty and eventually affect their performance. When employees have satisfaction they become committed to that organization ( 8 , 23 – 25 , 62 ) and will remain loyal. A study showed that if the banking sector continued to provide proper compensation, training, and appraisals then the employees will remain committed to the organization and the chances of turnover will also be less. In the past, different studies have found out that different employee loyalty behaviors were linked with how much employees were satisfied with their job, which lead to a commitment to the job ( 63 ). Job security was also one of the reasons employee loyalty is generated in some individuals ( 64 ).
Past research has outlined that employee loyalty has nothing to do with the human resource management policies and practices, environmental conditions such as a healthy atmosphere. How much an employee is satisfied with their job will later determine the employee loyalty that they develop ( 65 – 67 ). Employee satisfaction is quite important when an employee is from services or sales departments as if these employees are not satisfied with their job then customers are not satisfied with the services they are providing, which will affect the company's goals and impact loyalty toward the organization ( 64 ). In this case, employee satisfaction leads to them being loyal toward the organization. If they are not healthy and satisfied with their work not only do they fail to develop employee loyalty, it will harm the organization's performance as well ( 68 , 69 ). Thus, job satisfaction influences employee loyalty, and later on impacts organizational commitment as well.
Leadership can be defined by the behaviors employees possess and how they process their decision-making. A leader is one or more people who either select, train, or influence their employees. They have a diverse set of abilities, skills, and knowledge that helps them to align employee goals with organizational goals. A leader is a person who influences the behaviors of employees or their followers ( 70 , 71 ). A leader is also defined as a person who has the ability to understand and work within a culture, which makes a leader effective. Leadership style is defined by the psychological latitude and the behavior they possess during interaction with employees or while they are handling their operations or activities ( 72 , 73 ). In this research, we examine transformational leaders in order to explain the leadership style used in organizations. Transformational leaders are defined as those who motivate their followers to achieve goals through inspirational motivation, intellectual stimulation, idealized influence, and individual consideration ( 70 , 71 , 74 ). They help the followers perform better than expected. They possess good visioning and use their skills to develop a strong bond with followers. Transformational leadership is used to describe the situation where leaders and followers help each other reach higher levels of morality and motivation ( 75 – 79 ).
Past research has focused on the leadership personality traits that made people successful leaders but some researchers believe that leaders have some innate qualities that distinguish them from other people. Research has started to focus on the different behavioral aspects of leader's personality and the different contingency theories that support situational leadership outline that leader effectiveness depends upon situational factors ( 75 ). Other researchers have defined leaders based on two types, including effective leaders and transformational leaders. Transformational leadership is when leaders motivate their employees to increase or strengthen their perceptions, behaviors, commitment, motivation, and beliefs to stay aligned with organizational goals. Whereas, an effective leader is someone who influences followers in such a way that leads to an organizational vision that sets an example by performing a job in such a way that inspires the followers. In other words, an effective leader is someone who leads through their actions ( 11 , 20 , 80 ).
Different leadership styles are seen to be highly supportive and engage employees in their decision making. This engagement in different activities of work makes employees happy that their decision and participation are valued in the organization, which makes them more loyal ( 81 ). Different research has shown that employee engagement leads to organizational success ( 7 ). Therefore, leader communication strategies have a significant impact on employee general health and their loyalty or commitment ultimately affects job performance in an organization ( 82 ). Today, worker loyalty is one of the utmost factors in the success of an organization. For this reason, key leader communication strategies are taken into account in the present study to determine how a worker's motivation loyalty can be increased through different leadership communication strategies, which results in an increase in job and organizational performance ( 13 ).
Job Performance
Job performance is defined as a certain behavior that organizations expect an individual to carry out. Different researchers have defined job performance as a multidimensional concept that includes both task performance as well as contextual performance ( 81 ). Task performance is defined as an employee's contribution toward the organization i.e., their technical competencies and job proficiency, whereas contextual performance is not linked with the formal job requirements of an employee ( 32 ). This article focuses on the task performance of the employee. Here, performance includes the outcomes of a particular job that an employee is performing at their workplace. Thus, it is more linked with the task performance of an employee. It is also linked with the employees' behavior toward their work.
Performance is composed of many other different concepts but on a basic level. It can be described as behavioral engagement with an expected outcome, where behavior shows the action people perform to complete the work, outcomes exhibit the results of individual job behavior. Performance is considered a multi-dimensional concept. Job performance has received research attention in the last few decades ( 41 ). Effectiveness of job tasks involves evaluating the results of employee performance (i.e., financial value of sales). In comparison, productivity is defined as the ratio of effectiveness to the cost of attaining the outcome. For example, the ratio of hours of work that an employee is investing as input and the product they assemble as output both describe the productivity of an employee. Therefore, performance must be evaluated separately from efficiency and effectiveness in productivity ( 29 ).
Leadership as a Moderator Between Employee General Health, Loyalty, and Performance
Different leadership styles and strategies are used by organizations to improve employee loyalty and the overall performance of the organization. Different past studies have examined the impact of leadership and these impacts vary according to their styles and the effect the employee has on commitment or loyalty, which significantly affect job performance ( 40 ). Different researchers have also explored how leadership style has an impact on organizational culture and organizational performance. Different studies have shown that in the past, leaders inspire followers to accomplish certain organizational goals but in recent years it has been observed that leaders have failed to motivate employees as employees are much more focused on the concept of working to live and are thus more focused on rewards ( 45 ). This is due to the fact that employees are so involved in their work that they forget to take care of their health. The researcher has also talked about different research that has claimed that employees growing value is much more on extrinsic rewards and they are not motivated by the charisma of any kind of leader as they want to seek outcome. About 70 % of employees thought that they would get promoted within 2 years in a firm ( 83 ). This also shows that employees have high expectations from the firm where they work. Other researchers have found that servant leadership style has a positive relationship between employee loyalty and servant leadership style. Different leadership styles might have a different relationship with job performance depending upon the situation or the context that employees and supervisors are in. This also shows that without a leadership style the link between employee loyalty and job performance can decrease ( 37 , 38 ). Leaders can create an environment for them to be motivated toward the achievement of goals. If leaders do not guide employees, they might be de-motivated and think that they are not getting incentives, but a leader tries to make sure that employees understand their work and that their contribution will pay off in the form of incentives.
Incentives and Employee Loyalty
Different researchers have examined the loyalty of employees. Some of them have used hotel managers and supervisors to check loyalty. They found out that loyalty was associated with intangible aspects. This intangible aspect can be the working environment of the organization and this environment was linked with their peers, supervisors as well as customers. They were satisfied with their jobs because they had opportunities for personal healthcare, development, and to use strengths that helped them in achieving their work objectives ( 31 ). They also said that employee turnover was related to there being no opportunities for development. This study focused on how employee loyalty was linked to environmental factors that were creating motivation among employees, leading to them achieving their goals. In the past, it was believed that performance and employee loyalty was linked with promotions. But later on, the focus shifted toward the relationship between the employee and employer and became much more focused on flexible environments to get better performance from employees, as they felt more energetic and healthy when working. Employee loyalty reduces the turnover intentions among employees. The hospital industry has suffered an increasing rise in employee turnover, with the main reason being poor wages ( 45 ). Another study showed that employee loyalty was used as a mediator between commitment and employee retention and the results of that study showed that it had a significant effect on both commitment and employee retention. The study mentioned that compensation and different social benefits have a greater impact on employee commitment rather than on retention ( 37 ). One of the most important factors should be taking care of the health of employees in changing environments such as during the Covid-19 pandemic. Most of the time, organizations focus on giving financial rewards to their employees but they sometimes forget that non-financial rewards are also important to keep employees motivated toward work. Therefore, both incentives are important when trying to motivate employees and we need leaders to create an environment for the employees that enables them to feel motivated toward work ( 84 ).
Some researchers have tried to explain incentives and employee loyalty from a different perspective. They have talked about how employment and unemployment rates can change an organization's point of view ( 85 ). One study mentioned that when there is a higher rate of unemployed in the country then the chances of the potential job loss become more, at that time the employer gives employees incentives to gain loyalty. Organizations try to avoid layoffs and at that time they need more loyal employees and for that purpose, they provide stronger incentives. This shows that incentives depend on the leader or the organizations and much they are encouraging employees to perform better. This can also depend on the availability of human capital and when human capital is not easily available in the market, meaning the employer tries to gain employee loyalty by giving out benefits to retain talent in the organization ( 76 ).
Employee Loyalty and Job Performance
Employees' work attitudes can predict their outcomes. The main dimensions linked to employee loyalty are incentives, healthcare facilities, salaries, promotions, and different individual characteristics such as the age of the employee, job tenure, and position. Different studies have focused on perceived organizational support, customer participation and perceived that supervisory support can lead to an increase in job satisfaction and later on, also improve the service quality of employees ( 34 ). Employee loyalty is determined through leadership, human relations, personal development, better health, creativity, and job satisfaction. The better these determinants are handled the more employee loyalty indirectly affects job performance in other ways, as there is a positive relationship between employee loyalty and job performance ( 38 ).
Employees Incentives and Job Performance
It is suggested in literature on human resource management and organizational behavior that nonmonetary incentives act as a tool for motivating employees. When organizations pay attention to different monetary tools i.e., paid leave, giving bonuses for having an eye on their health and their family healthcare or other family, then employees start to perceive that the organization is supporting them. Therefore, monetary incentives increase the motivation of employees leading to increased job performance ( 78 ). Non-monetary tools can be appraised by the leader or environment that leaders provide to their employees. These non-monetary tools keep employees motivated for a certain time but if organizations do not give proper incentives to employees then it will affect their work. Different studies also show that incentives play a part in the job performance of employees. A study investigated the link between incentive packages and employees' attitudes concluded that several different types of incentives (monetary, tangible, and non-tangible non-monetary) play important roles in enhancing employees' attitudes toward their work. Different studies have found that there was a linear correlation between employee loyalty and job performance ( 72 ).
Another researcher discusses how job satisfaction impacts employee loyalty ( 76 – 78 ). They state that different underlying factors affect job satisfaction, including healthy working environment, healthy activities, chances of career growth, and motivation, all of which lead to employees being loyal. Some researchers believe that job analysis, compensation, and career planning help to determine employee loyalty. These factors motivate employees in the workplace and further lead to an increase in job performance. However, an absence of employee loyalty can create different issues like an increase in turnover rate among employees ( 71 ). Organizations might not be concerned about losing bad performers but if they lose good performers then that is a major concern for the organization. Therefore, the organizations are more focused on retaining human talent in the organization by giving out different facilities to employees and their families. Later on, the benefits incurred by employee loyalty will be more from the cost that was invested in them. Various studies have also shown that employee loyalty is linked to customer services and developing customer loyalty to ensure long term profitability for the organization ( 73 ).
Expectancy theory by Victor H. Vroom suggests that people put effort into work when they start to perceive that it will lead to an increase in their performance, which will eventually increase the chances of them receiving rewards. Consequently, an increase in these financial incentives also enhances employee loyalty, which increases the employee's performance and reduces the turnover rate ( 86 ). Employees can only be loyal when their desires are being satisfied by organizations. The organization also pays attention to these things, as they also believe humans are an asset and that they need to fulfill their needs to utilize their skills. In the hierarchy of needs (1954), Maslow concluded that humans have five basic wants (physiological, safety needs, love, and belonging, self-esteem, and self-actualization), which can be satisfied through financial incentives and rewards. Employees with a sense of recognition from their employers fall under the heading of self-esteem and, as their needs are being fulfilled, they will experience increased job satisfaction as well ( 87 – 89 ).
Theoretical Reflection
Two micro theories support the conceptual framework of our study. These motivational theories explain why incentives influence employee loyalty, leading toward employee performance. The first motivational theory is “Maslow's hierarchy of need,” which classifies human needs into two types. First, lower order needs, which are physiological and connected to safety and security, and second, higher order needs, which include socialization, self-esteem, and self-actualization. In this theory, incentives and this type of recognition are given to employees, and are related to the self-esteem of employees. These create motivation among them to work hard within the organization. The second motivational theory is by Herzberg, who explains two types of factors i.e., motivational factors and hygiene factors ( 90 ). Employees would like to grow in an organization and if there are chances for growth, advancements, and recognition they feel motivated to work but hygiene factors like working environment, quality of interpersonal relation, and salary are also important along with the motivational factor ( 91 ). Without any one of them, an employee will start to feel dissatisfied with their job, which will impact their behavior toward work and can also lead to high turnover among employees ( 92 ).
Another important theory that is highly relevant to our study is “Vroom expectancy theory.” This theory suggests that behavior will develop certain attitudes among employees, which will lead to further actions. This theory outlines that job performance is based on certain things i.e., skill, personality, experience, abilities, and knowledge regarding that particular field ( 93 ). The effort an employee puts into work; performance and motivation are all linked to employee motivation. This model uses three variables, including expectancy, valence, and instrumentality. Expectancy is defined as the belief about how much effort an employee puts into their work that will lead toward increased performance ( 90 ). Valance is defined as the importance an employee gives or places on the expected outcome. Instrumentality is defined as an employee's belief that if they do well in an organization, a valued outcome will be received. In this study, an organization motivates employees to work well and when they do so they receive incentives for the work they have done. Employees feel motivated by these rewards, which creates employee loyalty among them as they think organizations care about their contribution and their needs at the same time. If organizations do not provide incentives to employees who have performed well in the organization, they will feel demotivated and their performance will also decrease, and they will not trust the organization's rules and procedures, potentially decreasing employee loyalty as well ( 94 ). Organizations try to motivate their employees by either providing them with a good healthy working environment and in this case, with a leader who will make sure that the working environment motivates an employee to increase loyalty and job performance. Therefore, there is a continuous cycle connecting staying healthy, job performance, incentives, and rewards to future job performance. The incentives or rewards determine whether the employees are motivated enough or, if they are not motivated by the rewards given or think that the incentives given to them do not reflect what they have contributed to the organization, then their performance will decrease in the future ( 95 ). Based on the discussed literature, we formulated a conceptual framework, shown in Figure 1 .
Figure 1 . Conceptual framework.
Conceptual Framework
H1: Incentives have a positive effect on employee loyalty.
H2: Employee loyalty has a positive effect on job performance.
H3: Leadership can have a positive moderating effect on employee loyalty leading toward job performance.
H4: Leadership has a moderating effect on incentives and job performance.
H5: Incentives have a positive effect on job performance.
Research Methodology
This research focuses on a quantitative methodology. Quantitative methods are focused on a systematic way of collecting data either through questionnaires or surveys. Quantitative research explains the phenomenon according to numerical data. It is also defined as the empirical research that explains a social phenomenon by testing a theory consisting of different variables. Researchers want to explain the perception of employees regarding incentives, healthcare, employee loyalty, leadership, and job performance in the language of statistics and mathematics. In this research, the researchers have only focused on the hospital staff's point of view regarding this matter and have collected data from them for this research. This research was a quantitative, descriptive, and cross-sectional study. The overall methodology of this study is positivism, as it describes the study through different statistics that are gathered by collecting data.
Research Design
The research design provides an outline for research. It also provides a guideline for the researchers who are performing that research ( 96 ). While this research focuses on how incentives can lead to employee loyalty and further contribute to job performance, this research also focuses on the impact leaders have on employee loyalty and job performance. Thus, it tries to describe the relationship between different variables, how they impact each other, and which variable has the most impact on the other variables. Therefore, this research used quantitative methods of collecting data by giving out questionnaires to the hospital staff. Quantitative research was used for this research to make the data more representative and to generalize the information collected and examine the hypotheses proposed in the literature section. These empirical results could also help future studies of other developing countries with similar work conditions. Figure 2 shows the Levene's test of equality of error variances.
Figure 2 . Normality plot of JPM.
Research Approach
This research used a deductive research approach, which converts specific things into general applications. In the deductive approach, the researcher first finds a theory related to the conceptual framework and then analyzes that data ( 10 ). This will help in interpreting the data but also in explaining data presented in the form of graphs and numbers. This deductive approach will help us explain the results through different theories. It will also help in accepting and rejecting the hypotheses and explain why people thought different variables were affecting each other.
Data Collection
The data for this research were collected from primary and secondary sources. Primary data was collected by giving out questionnaires to hospital staff e.g. doctors and nurses. The questionnaires consisted of close-ended questions. In total, 320 questionnaires were collected from respondents and later analyzed. The reason for using a quantitative method to collect data was to make sure that we gathered enough information from the sample, could easily compile data regarding employee perceptions, and easily analyze the data gathered. Where the secondary data was gathered through several sources i.e., articles, and books, the secondary data were used to formulate the literature review and support the description of findings.
Population and Sampling
A population is defined as a group of individuals that have certain skills, knowledge, or experiences required for research. The population chosen for the present research included staff members working in both public and private sector Hospitals in Shandong, eastern China. The term population is also defined as a wide range of people. Every study is based on a certain population e.g., hospital sector, banking sector, or schools, but collecting data from such a huge number of people is impossible. For that reason, researchers divide this population into a sample to easily collect the required data. Certain techniques are used by researchers to select samples.
The sampling technique used for the collection of data was cluster random sampling. Clustering is a useful method of collecting data and discovering different groups of respondents that represent the population. The sample might be taken from a particular city or a particular sector. These clusters help in dividing the greater population into smaller sections. The later helps in separating people with similar patterns. Clustering does not indicate the desired relations that would be valid among the data beforehand and for this reason, it is thought to be an unsupervised process ( 24 ).
The sample taken for this study is forms of hospital staff i.e. doctors and nurses from both public and private sector hospitals of Shandong, Eastern China.
Data were collected via questionnaire to see what impact incentives have on their job performance or their employee loyalty and what type of leadership was being provided to them. The questionnaire was based on the Likert type scale.
Data Analysis Techniques
For the analysis, data were first screened to see if there were any missing values. Data collected through questionnaires were coded and then analyzed using SPSS. The data gathered from participants were gathered through Likert type scale questions and was dissolved into high and low groupings and positioned on scales 1 = strongly agree and 5 = strongly disagree. The respondents that rated 1–3 were considered high, whereas ratings from 4 to 5 were considered low. The data were analyzed in SPSS by applying different tests such as correlation, descriptive analysis, normality tests, and ANOVA to analyze the gathered data. Furthermore, confirmatory factor analysis (CFA) was also performed to analyze different variables.
Data Analysis
Descriptive analysis.
Descriptive analysis justifies the salient features of the study and provides a comprehensive summary of the data used in the study and also shows different statistical measures. Collectively, with simple graphical analysis, they form the structure of quantitative analysis of data. Descriptive statistics are easy to understand for general readers and show the behavior of the data. It is also used to present the data in a manageable form. Descriptive statistics cover the different aspects of the data i.e., central tendency, the measure of dispersion, the measure of normality, and trends in the data. The section covers the results descriptive statistics for the hospital sector in Shandong, eastern China. In Table 1 data are analyzed using SPSS software, which illustrates the total number of observations, arithmetic mean, standard deviation, the maximum and minimum value of each variable, which provides an entire description of the data used in the study.
Table 1 . Reliability analysis.
Table 1 represents descriptive statistics for the hospitals in Shandong, eastern China for a total of 320 observations. The mean value of the job performance is 2.4948 with a standard deviation of 0.59176. This means that the value of job performance can deviate from the mean to either or both sides of the mean by 0.59176 and the maximum value of job performance is 4.25 with 1.13 as the minimum value. The mean value of monetary incentives is 2.8203 with a standard deviation of 0.717, which again means that the mean value of monetary incentives can deviate to both sides of the average by 0.717 with a maximum and minimum value of 5 and 1. Furthermore, the table shows that the mean value of employee loyalty is 2.1538 with a standard deviation of 0.537 with a minimum and maximum value of 3.67 and 1 in contrast, transformational leadership has a mean value of 2.6167, with a standard deviation of 0.853 and has a minimum value of 1 and maximum value of 5.
The correlation table above clearly shows that the variables used in the study are related to one another, in other words, the independent variable influences the dependent variable. Simultaneously, the mediating and moderating variables also influence the independent and dependent variables. The tables above illustrate that there is a 0.180 correlation between job performance and monetary incentives, which means that if monetary incentives are increased by 1% job performance will increase by 0.180% hence it has an impact on job performance. Furthermore, the correlation matrix states that transformational leadership does influence job performance and has a greater correlation with dependent variables with a value of 0.222, apart from this, employee loyalty also has a positive correlation with job performance, with a value of 0.240 apart from this, the value of correlation is significant because the p value is <0.05. Hence, statistics prove that the variables do have a relationship.
Normality is one of the assumptions of running the ANCOVA model on data. It states the error between observed and predicted values are normally distributed. The hypotheses for the normality are as follows (see Table 1 ):
Ho = the error term is not normally distributed.
H1 = the error term is normally distributed.
To use the 320 observations from the hospital sector, data were analyzed to check whether the concerned variables are normally distributed or not.
The normality assumption was checked and is supported by different tests applied to the study, among them Shapiro wilk test is simple, effective, and is a standard test for checking normality (see Tables 2 , 3 ). Observing the size and nature of the data, Shapiro Wilk and Kolmogorov-Smirnov tests were applied to determine the normality of data, as shown in the table below. The table below showed that the majority of the significant values were <5%, which means the data are not normally distributed, but the table also shows that the p value in terms of nurses is significant and proves that data are normal ( Tables 2 , 3 ). Hence, the null hypothesis has been rejected and an alternate hypothesis is accepted.
Table 2 . Correlation matrix.
Table 3 . Tests of normality.
Test of Normality
To check the homogeneity of variances of the data collected, Levine's test of equality of error variances was applied ( Table 4 ). The table below clearly shows that the results are significant, indicating that the mean p value is <0.05, which proves that homogeneity of variances exists in the collected data (see Figures 3 , 4 ).
Table 4 . Levene's test of equality of error variances.
Figure 3 . Normality plot of JPM.
Figure 4 . CFA model.
Confirmatory Factor Analysis
The following table reports the analysis between our variables. The coefficient value between depending on and independent variable that is job performance and monetary incentives is 0.04, which shows every unit increase in monetary incentives the job performance increase by 0.04 percent at a 5% significance level. The result shows that employee loyalty plays a mediating role in job performance through monetary incentives at a 5% significance level. Furthermore, the coefficient of transformational leadership between monetary incentives and job performance plays a moderating role of 0.09 at a 5% significance level ( Figure 3 ).
The data collected in the study were analyzed using SPSS software, regression, and ANOVA analysis techniques because we are measuring the direct impact of monetary incentives on job performance with employee loyalty as a mediator and transformational leadership as a moderator between job performance and employee loyalty. The results are shown in Table 7 .
Hypothesis Testing
To check the hypothesis, several values were used, from the model, the basic value used for analyzing the hypothesis are the beta coefficients, Adjusted R square, and R square.
Table 5 clearly shows that healthcare, monetary incentives, employee loyalty, and transformational leadership affect job performance. The value of adjusted R square that is 0.097 means that the 100% fluctuations in job performance out of which 9.7% fluctuations are due to monetary incentives, employee loyalty, and transformational leadership, support the hypotheses of this study.
Table 5 . Model summary.
Furthermore, the beta coefficients from the model were used to prove each hypothesis separately.
For testing hypotheses 1 and 5, the beta coefficient 0.132 in Table 6 indicates that when the monetary incentives are increased by 1 unit the job performance increases by 0.132. It showed that the value 0.132 is positive, which also proves that there is a positive relationship between monetary incentives and employee loyalty at a significant level of 0.020 < 0.05, hence we accept our hypotheses 1 and 5 ( Table 6 ).
Table 6 . Coefficients.
Hypothesis 2: there exists a positive relationship between employee loyalty and job performance. The model in Table 6 shows that the beta coefficient between job performance and employee loyalty is 0.193 at a significance level of 0.001 < 0.05, which means that the 1 unit increase in employee loyalty causes the job performance to increase by 0.193 hence our second hypothesis is also accepted ( Table 7 ).
Table 7 . ANOVA.
For testing Hypotheses 3 and 4, the beta coefficient 0.119 at a significance level of 0.045 <0.05 in Table 6 shows that transformational leadership does moderate employee loyalty incentives and job performance. The value 0.119 means that when transformational leadership increases by 1 unit positively the job performance of an employee increases by 0.119, which provides logical proof of Hypotheses 3 and 4.
Discussion and Conclusion
The present study examined the impact of incentives on the healthcare of employees, their loyalty, and job performance. In this study, we used transformational leadership as a moderator to see what impact both incentives and leadership have on the job performance of an employee. The data for this study were collected from doctors and nurses from both the public and private sectors to see the impact that monetary incentives and leadership have on employee loyalty and job performance. The collected data were then analyzed in SPSS through descriptive statistics, correlation matrix, and by doing confirmatory factor analysis and as well as through regression and ANOVA model. After applying these analysis tools several statistics were shown by the model such as the correlation matrix, which proved that a relationship among the variables used in the study exists. All the values in the correlation matrix showed that there was a link between monetary incentives and job performance and that leadership did play a moderating role between incentives and job performance. The correlation matrix also showed that there was a 0.180 % correlation between job performance and incentives including better healthcare. The correlation matrix also showed that transformational leadership had a greater correlation with the dependent variable, apart from the regression model, which gave the healthy justification for accepting all the hypotheses since all the beta coefficients were aligned with the hypotheses and showed a positive relationship among the variables. The key point of the discussion is that this study provides helpful material for managers and employers to understand the behavior of employees regarding their job performance. Organizations could increase employee loyalty by giving meaningful incentives to their employees. Additionally, a good and effective allocation of supervisors to a particular group of employees can increase their job performance and their loyalty toward the organization. Given that the research subjects of this study were health workers from Shandong Province, eastern China, their increased job performance and loyalty to affiliated hospitals could further increase their service quality, enabling higher patient satisfaction.
The first limitation of our study is that it is cross-sectional. The results might be different in the case of a longitudinal study. Therefore, it is recommended that further researchers undertake a longitudinal study on the same variables. Another limitation of our study is that it is quantitative and only describes the relationship between different variables. Future researchers should undertake an in-depth study examining the reasons for the variables affecting each other in this manner. Apart from this, the study was conducted in a few public and private sector hospitals in Shandong and the sample size of the study was small. Thus, future research could use a larger sample size for the same variables. In addition to this, the researchers cannot generalize the findings for this small sample, meaning further research should be conducted in different countries to explore how different factors vary and affect different contexts. Future studies should also compare how these factors affect semi-government hospitals. Further future research could also explore the impact of these variables on administration staff working in the hospital sector.
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author Contributions
WL conceived the main idea and collected the data for analysis. YL suggested the methodology and finalized the manuscript. All authors are agreed on publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords: employee incentives, job performance, service quality, patient satisfaction, business cycle, population health, health performance, economies
Citation: Liu W and Liu Y (2022) The Impact of Incentives on Job Performance, Business Cycle, and Population Health in Emerging Economies. Front. Public Health 9:778101. doi: 10.3389/fpubh.2021.778101
Received: 16 September 2021; Accepted: 16 December 2021; Published: 10 February 2022.
Reviewed by:
Copyright © 2022 Liu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yaoping Liu, yaoping.l@mail.rmutk.ac.th
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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- 29 January 2024
Signs of ‘transmissible’ Alzheimer’s seen in people who received growth hormone
- Carissa Wong
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A coloured computed-tomography scan of a brain affected by Alzheimer’s disease. Credit: Zephyr/SPL
Researchers say they have uncovered more evidence to support a controversial hypothesis that sticky proteins that are a signature of Alzheimer’s disease can be transmitted from person to person through certain surgical procedures.
The authors and other scientists stress that the research is based on a small number of people and is related to medical practices that are no longer used. The study does not suggest that forms of dementia such as Alzheimer’s disease can be contagious.
Still, “we’d like to take precautions going forward to reduce even those rare cases occurring”, says neurologist John Collinge at University College London who led the research 1 , which was published in Nature Medicine on 29 January.
For the past decade, Collinge and his team have studied people in the United Kingdom who, during childhood, received growth hormone derived from the pituitary glands of cadavers to treat medical conditions such as short stature. The latest study finds that, decades later, some of these people developed signs of early-onset dementia. The dementia symptoms, such as memory and language problems, were diagnosed clinically, and in some patients appeared alongside plaques of the sticky protein amyloid-β in the brain, a hallmark of Alzheimer’s disease. The authors suggest that this protein, which was present in the hormone preparations, was ‘seeded’ in the brains and caused the damage.
Contaminated hormone
The work builds on the team’s previous studies of people who received cadaver-derived growth hormone, a practice that the United Kingdom stopped in 1985. In 2015, Collinge’s team described 2 the discovery at post-mortem of amyloid-β deposits in the brains of four people who had been treated with the growth hormone. These people had died in middle age of the deadly neurological condition Creutzfeldt–Jakob disease, which is caused by infectious, misfolded proteins called prions. The prions were present in batches of the growth hormone.
The four people analysed in that study died before clinical signs related to the amyloid-β build-up might have been observed. But the presence of these amyloid plaques in blood vessels in their brains suggested that they would have developed a condition called cerebral amyloid angiopathy (CAA) — which causes bleeding in the brain and is often a precursor to Alzheimer’s disease.
Collinge’s team also located and studied archived batches of the cadaver-derived growth hormone. In a 2018 study 3 , they reported that certain batches of the hormone preparation contained amyloid-β proteins , and that when such preparations were injected into mice, this led to the development of amyloid plaques and caused CAA in the animals.
This led the team to wonder whether the contaminated hormone preparations might also have resulted in people who received it developing Alzheimer’s disease, in which amyloid plaques are thought to cause the loss of neurons and brain tissue.
In the latest study, the researchers found that five out of eight people who had received the hormone treatment in childhood — but did not develop Creutzfeldt–Jakob disease — developed behavioural signs of early-onset dementia later in life, between the ages of 38 and 55. Collinge’s team argues that these five people — whom the researchers studied in the clinic or through medical records and brain scans — met the diagnostic criteria for early-onset Alzheimer’s disease.
Early-onset Alzheimer’s is usually caused by certain genetic variants, but the researchers did not find these variants in three of the people who showed signs of Alzheimer’s and whose DNA samples were available for testing. “This is consistent with these patients having developed a form of Alzheimer’s disease resulting from childhood treatment with this contaminated pituitary hormone,” says Collinge. Taken together, the studies suggest that, in rare cases, Alzheimer’s disease could be transmitted through the transfer of biological material, the authors argue.
However, the study’s small size limits the strength of the findings, says neuroscientist Tara Spires-Jones at the UK Dementia Research Institute at the University of Edinburgh. “Are the amyloid-β seeds from the hormone treatment playing a role in the development of dementia? It’s hard to know with just eight people,” she says.
It cannot be excluded that some of the people might have developed dementia regardless of the hormone treatment, says neuroscientist Mathias Jucker at the German Center for Neurodegenerative Diseases in Tübingen. “These people had many different medical conditions which could have increased the risk of developing a neurodegenerative disease like Alzheimer’s disease,” he says.
Researchers including Spires-Jones also question whether the people with dementia actually had Alzheimer’s, despite the clinical diagnoses.
“There are often errors in diagnosing the type of dementia someone has while they’re alive,” agrees neuroscience researcher Andrew Doig at the University of Manchester, UK.
From a public-health perspective, there is no need to be concerned about ‘transmissible’ dementia today, says Spires-Jones. “This treatment doesn’t exist anymore.”
Despite the study’s limitations, the research furthers our understanding of neurodegenerative diseases, scientists say. “I’m glad that people are doing amazing research to help us better understand seeding of neurodegenerative disease by amyloid-β,” says Spires-Jones.
“I think many other scientists will now look for additional evidence to explore the idea of transmissible Alzheimer’s,” says Jucker.
Nature 626 , 241-242 (2024)
doi: https://doi.org/10.1038/d41586-024-00268-5
Banerjee, G. et al. Nature Med . https://doi.org/10.1038/s41591-023-02729-2 (2024).
Article Google Scholar
Jaunmuktane, Z. et al. Nature 525 , 247–250 (2015).
Article PubMed Google Scholar
Purro, S. A. et al. Nature 564 , 415–419 (2018).
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Wage Growth Disparities by Gender and Race/Ethnicity among Entrants to Mid-Level Occupations in the United States: Findings from the Career Trajectories and Occupational Transitions Study
Publication info, research methodology, country, state or territory, description, other products.
The career pathways approach to workforce development emerged to help workers with lower levels of formal education advance to better paying jobs by earning in-demand postsecondary credentials. The approach involves articulated steps of education, training, and jobs within an industry sector or occupational cluster, combined with other services and employer connections to support participant success. To advance the evidence base in the career pathways field, the Descriptive & Analytical Career Pathways Project (D&A CP Project) includes three sub-studies, each addressing different evidence gaps through distinct data sources and methods.
This paper on wage growth disparities was completed as part of the Career Trajectories and Occupational Transitions (CTOT) Study. This paper presents a new analysis examining gender and racial/ethnic differences in wage growth trends among workers who take jobs in mid-level occupations that require some preparation beyond a high school degree, but less than a four-year college degree—the types of occupations that career pathways programs generally target. Women and workers of color constitute a large proportion of individuals served by career pathways programs, and past research has shown large gaps in employment outcomes by gender and race/ethnicity.
The study used panel surveys that follow individuals for decades to examine wage growth 10 years after workers entered occupations. The study finds that otherwise similar workers entering the same mid-level occupations experience large gender and racial/ethnic disparities in wage growth.
The study also finds: • Wage growth disparities widen steadily over the course of 10 years. • When individuals are grouped by race/ethnicity and gender, Black and Hispanic women experience the least wage growth of all groups. • Wage growth disparities are pervasive across occupational clusters. • Women experience less wage growth than men despite being more likely to go on to obtain additional postsecondary degrees. • Wage growth disparities cannot be explained by differences in other career-related outcomes, such as time spent not working or in advancement to higher-level occupations.
The other two sub-studies in the D&A CP Project include a Meta-Analysis Study and the Career Trajectories and Occupational Transitions (CTOT) Study.
In addition to this paper, the CTOT Study includes a full report from an analysis of career trajectories and occupational transitions; detailed appendices for healthcare, early care and education, information technology, and production/manufacturing; public use data; and a dashboard.
The D&A CP Project also produced a career pathways timeline as well as an early brief describing highlights from a scan of the research and an accompanying research and evaluation matrix.
The other two sub-studies in the D&A CP Project include a Meta-Analysis Study and Machine Learning Study.
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- Place of birth and postnatal transfers in infants with congenital diaphragmatic hernia in England and Wales: a descriptive observational cohort study
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- http://orcid.org/0000-0002-7488-5564 Behrouz Nezafat Maldonado 1 , 2 ,
- Julia Lanoue 1 ,
- Benjamin Allin 3 ,
- Dougal Hargreaves 2 , 4 ,
- http://orcid.org/0000-0002-1984-4575 Marian Knight 3 ,
- http://orcid.org/0000-0003-0707-876X Chris Gale 1 , 2 ,
- http://orcid.org/0000-0002-2898-553X Cheryl Battersby 1 , 2
- 1 Neonatal Medicine, Faculty of Medicine, School of Public Health , Imperial College London, Chelsea and Westminster Campus , London , UK
- 2 Centre for Paediatrics and Child Health , Imperial College London , London , UK
- 3 National Perinatal Epidemiology Unit , University of Oxford , Oxford , UK
- 4 Department of Primary Care and Public Health , Imperial College London , London , UK
- Correspondence to Dr Cheryl Battersby, Medicine, Imperial College London, London SW7 2AZ, UK; c.battersby{at}imperial.ac.uk
Objective To describe clinical pathways for infants with congenital diaphragmatic hernia (CDH) and short-term outcomes.
Design Retrospective observational cohort study using the UK National Neonatal Research Database (NNRD).
Patients Babies with a diagnosis of CDH admitted to a neonatal unit in England and Wales between 2012 and 2020.
Main outcome measures Clinical pathways defined by place of birth (with or without colocated neonatal and surgical facilities), transfers, clinical interventions, length of hospital stay and discharge outcome.
Results There were 1319 babies with a diagnosis of CDH cared for in four clinical pathways: born in maternity units with (1) colocated tertiary neonatal and surgical units (‘ neonatal surgical units ’), 50% (660/1319); (2) designated tertiary neonatal unit and transfer to stand-alone surgical centre (‘ tertiary designated ’), 25% (337/1319); (3) non-designated tertiary neonatal unit (‘ tertiary non-designated’ ), 7% (89/1319); or (4) non-tertiary unit (‘ non-tertiary ’), 18% (233/1319)—the latter three needing postnatal transfers. Infant characteristics were similar for infants born in neonatal surgical and tertiary designated units. Excluding 149 infants with minimal data due to early transfer (median (IQR) 2.2 (0.4–4.5) days) to other settings, survival to neonatal discharge was 73% (851/1170), with a median (IQR) stay of 26 (16–44) days.
Conclusions We found that half of the babies with CDH were born in hospitals that did not have on-site surgical services and required postnatal transfer. Similar characteristics between infants born in neonatal surgical units and tertiary designated units suggest that organisation rather than infant factors influence place of birth. Future work linking the NNRD to other datasets will enable comparisons between care pathways.
- Neonatology
- Child Health Services
Data availability statement
Data may be obtained from a third party and are not publicly available.
This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .
https://doi.org/10.1136/archdischild-2023-326152
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WHAT IS ALREADY KNOWN ON THIS TOPIC
National guidelines recommend that infants with known surgical anomalies are delivered in maternity units with colocated neonatal medical and surgical units.
WHAT THIS STUDY ADDS
Each year, around 147 babies with congenital diaphragmatic hernia (CDH) are admitted to neonatal units in England and Wales. Half of them are born in maternity units without an on-site surgical facility and consequently require postnatal transfer.
Similar characteristics between those born in neonatal surgical units and tertiary designated units suggest that organisation rather than infant factors influence place of birth.
Out of ten babies, around seven will survive neonatal discharge, half are discharged home and a quarter are discharged to other paediatric settings for ongoing care.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
We identified four different clinical pathways in England and Wales that may lead to variation in care and outcomes of babies with CDH.
Data linkage between available health datasets is urgently needed to reliably evaluate factors that influence outcomes including the organisation of services.
This would facilitate future research needed to determine whether variation in place of birth and care pathways observed in this study influence short and long-term outcomes of babies with CDH.
Congenital diaphragmatic hernia (CDH), a defect in the formation of the diaphragm, can result in abdominal organs herniating into the chest compromising lung development. 1 2 This defect may manifest as poor lung function at birth, respiratory failure and death. Advancements in prenatal diagnosis, neonatal interventions and surgical techniques have improved outcomes of infants with CDH. 3 4
Antenatal screening aims to detect CDH early. 5 In the UK, around 60% of CDH cases are antenatally diagnosed. 6 This allows for parental counselling and shared decision-making on place of birth. However, evidence gaps on the optimal timing and place of birth can hinder informed decision-making. 7 It is necessary to evaluate how neonatal services are organised to ensure infants with CDH are cared for in appropriately resourced settings. 7
National guidelines recommend that neonates requiring surgical care are born in maternity centres with colocated neonatal surgery. 8 9 UK neonatal services are organised in networks. 10 In several networks, neonatal surgery is provided in ‘stand-alone surgical units’ without colocated maternity. Babies born in these networks are transferred postnatally. Previous research in England found that lack of colocation leads to avoidable postnatal transfers, with estimates of 31 cases of CDH undergoing an avoidable postnatal transfer annually. 7 11
There is mixed evidence on whether place of birth for CDH impacts outcomes. 12 The role of place of birth in short-term outcomes has not been studied previously in the UK. Examining this relationship is complex, as both place of birth and outcomes are associated with an array of demographic, clinical and organisational factors. However, routine data can potentially be used to examine the relationship between these factors and infant outcomes at a population level.
The National Neonatal Research Database (NNRD) comprises quality-assured data on all infants admitted to UK neonatal units. 13 The NNRD enables us to study admissions of infants from birth to discharge from neonatal care. However, the NNRD does not capture care in other settings such as stand-alone paediatric surgical centres, paediatric intensive care or paediatric wards.
We aimed to describe current care pathways defined by place of birth for infants with CDH born in England and Wales and describe interventions and short-term outcomes.
Study design, setting and participants
We conducted the study in a two-stage process.
Stage 1: Discussions with experts in neonatal medicine, paediatric intensive care and neonatal surgery to explore care pathways for infants with CDH.
Stage 2: A retrospective observational study using routinely recorded data from the NNRD. Data were used to explore care pathways and short-term health outcomes. We report in line with Reporting of Studies Conducted Using Observational Routinely Collected Health Data guidelines. 14
Data were extracted for all infants admitted to a neonatal unit in England and Wales between 1 January 2012 and 31 December 2020 with a diagnosis of CDH (as defined in online supplemental appendix ). We excluded infants with inconsistent transfer patterns and those diagnosed with multiple congenital (non-cardiac) surgical abnormalities, for example, CDH and gastroschisis.
Supplemental material
We used Office for National Statistics data on live births in England and Wales across the study period to estimate the prevalence of CDH. 15
We report primary outcome survival to discharge to home and neonatal discharge to other settings. Secondary outcome measures include postnatal management, transfer patterns and length of stay ( table 1 ).
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Outcome measures of interest, definition and how they were derived
We define postnatal transfer as any transfer that requires ambulance transport. We sought clarification from the expert advisory panel on which units required postnatal transfer between neonatal and surgical units and sought consensus. Transfers were identified from the NNRD discharge destination code.
Statistical analysis
We report findings across clinical pathways and present descriptive statistics using median/IQR and percentage as appropriate. We report survival to neonatal discharge, discharge destination and length of hospital stay. This is a descriptive study and we have not undertaken any formal analysis to compare outcomes between pathways or adjust confounders. All analyses were performed using R V.3.6.
Research ethics and other approvals
We used deidentified data from the NNRD. 13 16 All neonatal units agreed to the inclusion of their data in the study.
Stage 1: establish expert advisory board
Experts from across 6 out of 10 neonatal networks in England and Wales participated in the expert advisory board. Members included six neonatologists, eight neonatal surgeons and one paediatric cardiac intensivist. They worked in either tertiary neonatal units with colocated surgery or tertiary neonatal units without surgery on-site. The four pathways identified were based on birth in a maternity unit with:
Colocated tertiary neonatal and surgical units ( neonatal surgical units ).
Designated tertiary neonatal unit for surgical conditions and transfer to a surgical centre ( tertiary designated ).
Tertiary neonatal unit not designated for surgical conditions and transfer to a surgical centre ( tertiary non-designated ).
Non-tertiary units without surgery and transfer to a surgical centre ( non-tertiary ).
Birth in neonatal surgical units (1) is the only pathway that does not require postnatal transfer in an ambulance to a surgical unit.
Stage 2: routine data analysis using NNRD
Between 1 January 2012 and 31 December 2020, a total of 1335 babies with a diagnosis of CDH were admitted to a neonatal unit in England and Wales of which 16 were excluded from the study ( figure 1 ). There were 6 108 030 live births during this period in England and Wales, resulting in an estimated live birth incidence of 2.2 per 10 000 (95% CI 2.1 to 2.3). The true live birth incidence is likely to be higher as our case number excludes babies born alive who died in the delivery room.
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Study population, clinical pathways defined by place of birth and survival to neonatal discharge . Number and proportion of infants admitted to each type of neonatal unit at birth displayed, together with the outcome of their neonatal stay: survival to neonatal discharge, death or discharge to other settings early not returning to an NNRD unit. CDH, congenital diaphragmatic hernia.
Clinical pathways
We identified 16 neonatal surgical units , 5 tertiary designated units and 5 stand-alone surgical centres without an on-site neonatal unit across England and Wales. During the study period 660/1319 (50%) infants were delivered in neonatal surgical units . The other half required postnatal transfer to a surgical unit ( figure 1 ). We report yearly births across each clinical pathway during the study period ( figure 2 ). We found that a median of 72 (67–76) of 146 cases were born in neonatal surgical units annually.
Number of cases per year born in maternity units with neonatal surgical units (green) and non-colocated units (stacked bar): designated tertiary units (blue), non-designated tertiary intensive care units (red) and non-tertiary units (yellow).
Infant and maternal characteristics
Table 2 summarises infant and maternal characteristics by pathway. Infants were most commonly male with a median gestational age of 38 weeks. Across the cohort, median (IQR) maternal age was 30.5 (26–35) years and half of the group were white British; between 10% and 20% of maternal ethnicity data were missing.
Baseline characteristics and first transfer details by place of birth
Organisational factors and postnatal transfers
More than 95% of infants born in surgical neonatal or tertiary designated units were admitted to the neonatal unit within the first 2 hours after birth compared with 74–78% in tertiary non-designated or non-tertiary units. Transfer to a surgical centre occurred within the first 24 hours in 81% in non-tertiary centre and 60% in a tertiary non-designated centre . Infants in tertiary designated units were transferred later, commonly after day 3 from birth (median 4 (1.8–8) days). Infants in tertiary designated units were transferred to a stand-alone surgical unit without a neonatal unit in 71% (239/337) cases. One tertiary designated centre transferred infants to the surgical unit for surgery and then retrieved them after surgery. Infants born at this unit were not discharged from their electronic health system despite being transferred to a surgical unit as preoperative care and postoperative care occur within the tertiary designated neonatal unit. Across the cohort, 30% of infants (396/1319) were postnatally transferred to a stand-alone surgical unit.
Day 1 postnatal management
Infants in surgical and tertiary designated units received intensive care support at similar rates on day 1 including invasive ventilation (601/660, 91%; 309/337, 92%), inotropes (332/660, 50%; 169/337, 50%) and nitric oxide (287/660, 43%; 149/337, 44%). These interventions were received at lower rates in the tertiary non-designated and non-tertiary groups. Of those infants in tertiary non-designated units, 74% (66/89) received mechanical ventilation on day 1, 30% (27/89) received inotropes and 25% (22/89) received nitric oxide.
Survival to a surgical centre
We found 659 infants born outside a surgical centre, of which 86% (565/659) survived to transfer to be admitted to a surgical centre. Survival to discharge to the surgical centre was higher in the tertiary non-designated (82/89 (92%)) and non-tertiary unit groups (222/233 (95%)) ( online supplemental appendix ).
Discharge destination
We report final neonatal discharge destination for all infants ( figure 3 ). Among the whole cohort, 76% (1000/1319) survived neonatal discharge. 49% (645/1319) were discharged home and 27% (355/1319) were discharged to other settings. Of those discharged to other settings, 40% (143/355) were discharged to a paediatric or cardiac intensive care unit, 39% (138/355) to a stand-alone surgical unit, 17% (59/355) to a paediatric ward and 4% (15/355) to their local hospital ( table 3 ).
Sankey diagram with the final discharge destination recorded in the dataset for all infants across the four pathways (n=1319).
Discharge destination, length of stay and survival to neonatal discharge for whole population (n=1319)
Survival to neonatal discharge and postnatal management during neonatal stay
In the first days after birth, 149 infants were transferred to a stand-alone surgical centre or other paediatric settings and did not return to an NNRD contributing unit. Transfers occurred on a median of day 2 (median (IQR) 2.2. (0.9–4.5) days) ( online supplemental appendix ) and these infants had minimal data. We therefore present in additional data reporting survival to neonatal discharge and postnatal management for 1170 infants (89% of the original cohort) excluding these 149 infants.
We present postnatal management for 1170 infants that received the majority of care in a neonatal unit ( table 2 ). Across all groups, infants commonly received a combination of multiple modes of ventilation during their neonatal stay. We found higher rates of inhaled nitric oxide and sildenafil use in neonatal surgical units and tertiary designated units. Surfactant was given outside the delivery room to a similar proportion of infants across all groups.
Of the 1170 infants, 73% (851/1170) survived neonatal discharge, 55% (645/1170) were discharged home and 18% (206/1170) were discharged to other settings. Of those discharged to other settings, 43% (88/206) were discharged to a paediatric or cardiac intensive care unit, 21% (44/206) went to a stand-alone surgical unit, 29% (59/206) were discharged to a paediatric ward and 7% (15/206) were discharged to their local hospital ( table 4 ). Across this cohort, the median (IQR) hospitalisation was 25.5 (16–43.6) days.
Discharge destination, length of stay and survival to neonatal discharge excluding infants with minimal data transferred early to stand-alone units (n=1170)
Over a 9-year period, 1319 infants with CDH were admitted to neonatal units in England and Wales. We identified four clinical pathways of care for neonates with a diagnosis of CDH. Half of the babies were born in maternity units with colocated neonatal surgical units and a quarter in tertiary designated units requiring postnatal transfer to stand-alone surgical centres. The transfer from tertiary designated centres to a surgical centre occurred at a median age of 4 days. The remaining quarter were born outside of these designated pathways.
Infant characteristics and rates of intensive care support were similar for infants born in neonatal surgical units and in tertiary designated units. This suggests that organisational rather than infant factors influence place of birth and care pathway, particularly for babies with antenatally diagnosed CDH, who would be predominantly cared for across these two designated pathways. We report a survival rate of 73%, consistent with previous data from England which estimated 1-year survival between 68% and 81%. 17 18
While comparison of survival outcomes between pathways is of interest, this was not undertaken formally in this descriptive study due to the unavailability of important confounders and mediators. These include information on fetoscopic endoluminal tracheal occlusion, a procedure which has been shown to improve survival to discharge in infants with severe left-sided CDH. 19 Importantly, we lack information on whether CDH was antenatally or postnatally diagnosed, the laterality of the defect, defect type, lung-head ratio, antenatal treatment, timing of surgery and surgical complications. We speculate, for example, that the population of babies born in the tertiary non-designated and non-tertiary groups are likely to have been postnatally diagnosed due to smaller defects being undiagnosed antenatally and hence born outside a surgical centre. This would explain the more favourable survival to neonatal discharge and the shorter length of stay in the tertiary non-designated and non-tertiary groups.
Previous data from the USA have identified that being ‘inborn’ at the treatment centre is associated with mortality in CDH, 20 this is consistent with our findings. Whether there is a difference seen between the survival to neonatal discharge and length of stay between neonatal surgical unit and the tertiary designated unit groups warrants further exploration but requires data linkage in the UK to obtain additional information to enable case-mix adjustment.
Limitations to the study include missing data beyond the first few days of life for over one-third of babies born in tertiary designated centres transferred early to stand-alone surgical centres. To assess the impact of the missing data, we additionally reported outcomes for a subgroup of 1170 babies, excluding 149 babies (11% of the cohort), length of neonatal stay becomes longer and more consistent across the pathways in the subgroup. However, survival rate to neonatal discharge decreased for infants in tertiary designated units from 75% (260/337) to 64% (137/214). We speculate this is due to the disproportionate representation of deaths due to the inclusion of early mortality before transfer to a surgical centre, but exclusion of survivors transferred early to a surgical centre. The population in the subgroup may represent more severe CDH, particularly in the tertiary designated unit group.
A further limitation of the NNRD is that it captures data on neonatal unit admissions only and therefore while we found an estimated live birth prevalence of 2.2 per 10 000 (95% CI 2.1 to 2.3), this does not consider terminations of pregnancy or delivery room deaths. 17 21
Strengths of our study include the population-level coverage, including all babies with CDH admitted to neonatal units in England and Wales across a 9-year period. In England and Wales, babies with CDH will be admitted to a neonatal unit following birth as their first hospital episode, unless the antenatal plan is for palliative care on the postnatal ward, or the infant does not survive birth or the CDH is not detected prior to postnatal discharge. All other infants, even if they are transferred to a non-neonatal unit for ongoing care, are included thus reducing recruitment bias.
We have demonstrated the feasibility of using routinely collected data to identify the cohort of infants with CDH receiving care in the UK. Future research aimed at informing the configuration of care pathways and determining the optimal place of birth for babies with CDH must include outcome data from stand-alone surgical centres, as well as report on long-term health and education outcomes. To improve the accuracy and completeness of data and allow for more robust conclusions to be drawn, there are plans to link data from the NNRD with other sources of routine health data, such as the National Congenital Anomaly and Rare Disease Registration Service, Hospital Episodes Statistics and educational outcomes from the National Pupil Database for this population. 22 This data linkage will enable future studies to explore the impact of birth location on outcomes of CDH while considering all confounders. To further enhance data quality, we recommend that centres carrying out neonatal surgery, including stand-alone centres, contribute to surgical datasets or registries to enable national audits and service evaluation.
Ethics statements
Patient consent for publication.
Not applicable.
Ethics approval
This study involves human participants and was approved by the East Midlands–Leicester South Research Ethics Committee as part of the neoWONDER research programme (Ref 21/EM/0130, IRAS Project ID 293603).
Acknowledgments
We thank Nigel Hall, Nick Lansdale, Nimish Subhedar, Ingo Jester, Rachel Harwood, Alex Macdonald, Karen Luyt, Elizabeth Pilling, Eleri Adams, Katherine Brown, Fiona Metcalfe, Kathryn Johnson and Simon Hannan for their expert input, as well as the other professionals who contributed to this work. We thank the UK Neonatal Collaborative comprising neonatal units contributing data to the National Neonatal Research Database.
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Supplementary materials
Supplementary data.
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
- Data supplement 1
Twitter @Marianfknight, @DrCGale, @DrCBattersby
Collaborators UK Neonatal Collaborative contributing neonatal units listed in online supplemental file.
Contributors BNM designed the study and undertook analysis under the supervision of CB and CG. JL extracted the data. CB, CG, BA, MK and DH provided clinical interpretation and review of the manuscript. All authors contributed to the interpretation, revised the manuscript critically and approved the final version for submission. CB, as supervisor of BNM, had access to all data and responsibility for the project including the decision for publication, and is the guarantor for this paper.
Funding This research was supported by the National Institute for Health Research (NIHR) grant ACF-2020-21-011 awarded to BNM. Data extraction was supported by a NIHR grant (NIHR127844) awarded to MK. CG was supported by the United Kingdom Medical Research Council through a Transitional Support Fellowship (MR/V036866/1). CB is supported through a UK NIHR Advanced Fellowship personal award (NIHR300617). This study was also supported through the Imperial NIHR Biomedical Research Centre. The views expressed in this publication are those of the authors and not necessarily those of the NIHR, National Health Service or the UK Department of Health and Social Care. None of the funders have had any influence over study design, collection, analysis and interpretation of the data, in writing the report and in the decisions to submit this article for publication. Imperial College London Open Access Fund supported the publication and dissemination of this work.
Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the NHS, NIHR or the Department of Health. None of the funders have had any influence over study design, collection, analysis and interpretation of the data, in writing the report and in the decisions to submit this article for publication.
Competing interests CB reports personal support from NIHR Advanced Fellowship; grants from National Institute for Health Research; personal fees to suport attendance at educational events from Chiesi Pharmaceuticals; she is deputy chair of the NIHR HTA Prioritisation commitee for hospital based care. CG reports personal support from the United Kingdom Medical Research Council Transition Support Fellowship during the conduct of the study; grants from National Institute for Health Research, Rosetrees Foundation, Canadian Institute for Health Research, Action Medical Research outside the submitted work; grants for research outside the submitted work and personal fees to support attendance at educational events from Chiesi Pharmaceuticals; he is chair of the NIHR Research for Patient Benefit London Regional Assessment Panel.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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A descriptive study is one that is designed to describe the distribution of one or more variables, without regard to any causal or other hypothesis. Go to: TYPES OF DESCRIPTIVE STUDIES Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies.
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables.
Descriptive research is often referred to as "hypothesis generating research." Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples. Limitations: Descriptive studies cannot be used to establish cause and effect relationships.
Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to "prove" or "disprove" it within predetermined and widely accepted levels of certainty.
Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies. These studies are used to describe general or specific behaviors and attributes that are observed and measured.
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what, where, when, and how questions, but not why questions. A descriptive research design can use a wide variety of research methods to investigate one or more variables. Unlike in experimental research, the researcher does ...
Step 1. Ask a question Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.
Three important uses of descriptive studies include trend analysis, health-care planning, and hypothesis generation. A frequent error in reports of descriptive studies is overstepping the data: studies without a comparison group allow no inferences to be drawn about associations, causal or otherwise.
The Hypothesis in the Scientific Method In the scientific method, whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps: Forming a question Performing background research Creating a hypothesis
Descriptive Research vs. Exploratory Research. Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause.
hypothesis-generating research; Descriptive Research Examples. Example #1: A company wants to add a free month-long gym membership to its employees as an incentive to meet quarterly company goals ...
A descriptive study is one that is designed to describe the distribution of one or more variables, without regard to any causal or other hypothesis. TYPES OF DESCRIPTIVE STUDIES Descriptive studies can be of several types, namely, case reports, case series, cross-sectional studies, and ecological studies.
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Yes, both qualitative and quantitative studies need hypothesis, a research question you answer. Cite. 1 Recommendation. Madelaine Lawrence. RnCeus Interactive. Generally speaking, hypotheses are ...
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Research studies that do not test specific relationships between variables are called descriptive studies. These studies are used to describe general or specific behaviors and attributes that are observed and measured.
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This is a descriptive study and we have not undertaken any formal analysis to compare outcomes between pathways or adjust confounders. All analyses were performed using R V.3.6. Research ethics and other approvals. We used deidentified data from the NNRD.13 16 All neonatal units agreed to the inclusion of their data in the study.