similarities between descriptive and analytical research pdf

Distinguishing Features and Similarities Between Descriptive Phenomenological and Qualitative Description Research

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• 1 Boston College, Chestnut Hill, MA, USA [email protected].
• 2 New York University, New York City, USA.
• 3 University of North Carolina at Chapel Hill, USA.
• 4 University of Nebraska Medical Center, Omaha, USA.
• PMID: 27106878
• DOI: 10.1177/0193945916645499

Scholars who research phenomena of concern to the discipline of nursing are challenged with making wise choices about different qualitative research approaches. Ultimately, they want to choose an approach that is best suited to answer their research questions. Such choices are predicated on having made distinctions between qualitative methodology, methods, and analytic frames. In this article, we distinguish two qualitative research approaches widely used for descriptive studies: descriptive phenomenological and qualitative description. Providing a clear basis that highlights the distinguishing features and similarities between descriptive phenomenological and qualitative description research will help students and researchers make more informed choices in deciding upon the most appropriate methodology in qualitative research. We orient the reader to distinguishing features and similarities associated with each approach and the kinds of research questions descriptive phenomenological and qualitative description research address.

Keywords: phenomenology; qualitative methods.

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Statistical Approaches for Epidemiology pp 1–18 Cite as

Descriptive and Analytical Epidemiology

• Kiran Sapkota 2  
• First Online: 13 December 2023

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Both descriptive and analytical epidemiology are important for advancing clinical medicine and public health. Descriptive epidemiology assesses the burden and magnitude of health problems in a population, whereas analytical epidemiology identifies the causes and risk factors of health problems. This chapter provides the scopes, designs, data analytics approaches, ethical issues, and examples of various epidemiological studies. Descriptive epidemiological studies include: (1) case reports, (2) case series, (3) descriptive cross-sectional (prevalence) studies, and (4) descriptive cohort (incidence) studies. Analytical epidemiological studies include: (a) observational studies, such as (1) ecological studies (correlational studies), (2) analytical cross-sectional studies, (3) analytical cohort studies (prospective and retrospective), and (4) case–control studies, and (b) experimental studies, such as (1) community-based interventions and (2) clinical trials.

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Sapkota, K. (2024). Descriptive and Analytical Epidemiology. In: Mitra, A.K. (eds) Statistical Approaches for Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-031-41784-9_1

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Study designs: Part 3 - Analytical observational studies

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Director, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India

In analytical observational studies, researchers try to establish an association between exposure(s) and outcome(s). Depending on the direction of enquiry, these studies can be directed forwards (cohort studies) or backwards (case–control studies). In this article, we examine the key features of these two types of studies.

INTRODUCTION

In a previous article[ 1 ] in this series, we looked at descriptive observational studies, namely case reports, case series, cross-sectional studies, and ecological studies. As compared to descriptive studies which merely describe one or more variables in a sample (or occasionally population), analytical studies attempt to quantify a relationship or association between two variables – an exposure and an outcome. As discussed previously, in observational analytical studies, the exposure is naturally determined as opposed to experimental studies where an investigator assigns each subject to receive or not receive a particular exposure.

COHORT STUDIES

A cohort is defined as a “group of people with a shared characteristic.” In cohort studies, different groups of people with varying levels of exposure are followed over time to evaluate the occurrence of an outcome. These participants have to be free of the outcome at baseline. The presence or absence of the risk factor (exposure) in each subject is recorded. The subjects are then followed up over time (longitudinally) to determine the occurrence of the outcome. Thus, cohort studies are forward-direction studies (moving from exposure to outcome) and are typically prospective studies (the outcome has not occurred at the start of the study).

An example of cohort study design is a study by Viljakainen et al ., which investigated the relation between maternal vitamin D levels during pregnancy and the bone health in their newborns.[ 2 ] Maternal blood vitamin D levels were estimated during pregnancy. Children born to these mothers were then followed up until 14 months of age, and bone parameters were evaluated. Based on the maternal serum 25-hydroxy vitamin D levels during pregnancy, children were divided into two groups – those born to mothers with normal blood vitamin D and those born to mothers with low blood vitamin D. The authors found that children born to mothers with low vitamin D levels had persistent bone abnormalities.

Advantages of cohort studies

• For an exposure to be causative, it must precede the outcome. In a cohort study, one starts with subjects who are known to have or not have the exposure and are free of the outcome at the start of the study, and the outcome develops later. Hence, one is certain that the exposure preceded the outcome, and temporality (and therefore probable causality) can be established. In the above example, one can be certain that the maternal vitamin D deficiency preceded the bone abnormalities.
• For a given exposure, more than one outcome can be studied. In the above example, the authors compared not only bone growth but also the age at which the babies born to low and high vitamin D mothers started walking independently.
• In cohort studies, often several exposures can be studied simultaneously. For this, the investigators begin by assessing several 'exposures', for example, age, sex, smoking status, diabetes, and obesity/overweight status in every member of a population. The entire population is then followed for the outcome of interest, for example, coronary artery disease. At the end of the follow-up, the data can then be analyzed for several contrasting cohorts defined by levels of each “exposure” – old/young, male/female, smoker/nonsmoker, diabetic/nondiabetic, and underweight/ideal body weight/overweight/obese, etc.

Limitations of cohort studies

• Cohort studies often require a long duration of follow-up to determine whether outcome will occur or not. This duration depends on the exposure-outcome pair. In the above example, a follow-up of at least 14 months was used. An even longer follow-up over several years or decades may be necessary – for instance, in the above example, if the investigators wanted to study whether maternal vitamin D levels influence the final height of a person, they would have needed to follow the babies till adolescence. During such follow-up, losses to follow-up, and logistic and cost issues pose major challenges.
• It is not uncommon for one or more unknown confounding factors to affect the occurrence of outcome. For example, in a cohort study looking at coffee drinking as a risk factor for pancreatic cancer, people who drink a large amount of coffee may also be consuming alcohol. In such cases, the finding that coffee drinkers have an increased occurrence of pancreatic cancer may lead the investigator to incorrectly conclude that drinking coffee increases the risk of pancreatic cancer, whereas it is the consumption of alcohol which is the true risk factor. Similarly, in the above study, the mothers with low and high vitamin D levels could have been different in another factor, e.g. overall nutrition or socioeconomic status, and that could be the real reason for the differences in the babies' bone health.

Uses of cohort studies

• Since cohort study design closely resembles the experimental design with the only difference being lack of random assignment to exposure, it is considered as having a greater validity compared to the other observational study designs.
• Since one starts with subjects known to have or not have exposure, one can determine the risk of outcome among exposed persons and unexposed persons, as also the relative risk.
• In situations where experimental studies are not feasible (e.g., when it is either unethical to randomize participants to a potentially harmful intervention, such as smoking, or impractical to create an exposure, such as diabetes or hypertension), cohort studies are a reasonable and arguably the best alternative.

Variations of cohort studies

Sometimes, a researcher may look back at data which have already been collected. For example, let us think of a hospital that records every patient's smoking status at the time of the first visit. A researcher may use these records from 10 years ago, and then contact the persons today to check if any of them have already been diagnosed or currently have features of lung cancer. This is still a forward-direction study (exposure traced forward among exposed and unexposed to outcome) but is retrospective (since the outcome may have already occurred). Such studies are known as 'retrospective cohort studies'.

Large cohort studies, such as the Framingham Heart Study or the Nurses' Health Study, have yielded extremely useful information about risk factors for several chronic diseases.

CASE-CONTROL STUDIES

In case-control studies, the researcher first enrolls cases (participants with the outcome) and controls (participants without the outcome) and then tries to elicit a history of exposure in each group. Thus, these are backward-direction studies (looking from outcome to exposure) and are always retrospective (the outcome must have occurred when the study starts). Typically, cases are identified from hospital records, death certificates or disease registries. This is followed by the identification and enrolment of controls.

Identification of appropriate controls is a key element of the case-control study design and can influence the estimate of association between exposure and outcome (selection bias). The controls should resemble cases in all respects, except for the absence of disease. Thus, they should be representative of the population from which the cases were drawn. For instance, if cases are drawn from a community clinic, an outpatient clinic or an inpatient setting, the controls should also ideally be from the same setting.

Sometimes, controls are individually matched with cases for factors (except for the one which is the exposure of interest) which are considered important to the development of the outcome. For example, in a study on relation of smoking with lung cancer, for each case of lung cancer enrolled, one control with similar age and sex is enrolled. This would reduce the risk of confounding by age and sex – the factors used for matching. Sometimes, the number of controls per case may be larger (e.g. two, three, or more).

Furthermore, to minimize assessment bias, it is important that the person assessing the history of exposure (e.g., smoking in this case) is unaware of (blinded to) whether the participant being interviewed is a case or a control.

For example, Anderson et al . conducted a case–control study to look at risk factors for childhood fractures.[ 3 ] They recruited cases from a hospital fracture clinic and individually matched controls (children without fractures) from a primary care research network. The cases and controls were matched on age, sex, height, and season. They found that the history of previous use of vitamin D supplements was significantly higher in the children without fractures, suggesting an inverse association between vitamin D supplementation and incidence of fractures.

Advantages of case–control studies

• Case-control studies are often cheap, and less time-consuming than cohort studies.
• Once cases and controls are identified and enrolled, it is often easy to study the relationship of outcome with not one but several exposures.

Limitations of case–control studies

• In case-control studies, temporality (whether the outcome or exposure occurred first) is often difficult to establish.
• There may be a bias in selecting cases or controls. For instance, if the cases studied differ from the entire pool of cases of a disease in an important characteristic, then the results of the study may apply only to the selected type of cases and not to the entire population of cases. In the above example,[ 3 ] the cases and controls were derived from different sources, and it is possible that the children that attended the hospital fracture clinic had different socioeconomic backgrounds to those attending the primary care facility from where controls were enrolled.
• Confounding factors, as discussed in cohort studies, also apply to case-control studies. For instance, the children with fractures and controls could have had different overall food intake, milk intake, and outdoor play time. These factors could influence both the likelihood of prior use of vitamin D supplements (exposure) and the risk of fracture (outcome), affecting the measurement of their association.
• The determination of exposure relies on existing records or history taking. Either can be problematic. The records may not contain information on exposure or contain erroneous data (e.g., those collected perfunctorily). This is particularly challenging if the missing or unreliable data are more likely to be present in one of the two groups being compared – cases or controls (misinformation bias). During history taking, cases may be more likely to recall exposure than controls (recall bias), for example, the mother of a child with a congenital anomaly is more likely to recall drugs ingested during pregnancy than a mother with a normal child. In the study by Anderson et al,[ 3 ] the mothers of children with fractures could have underestimated the amount of vitamin D their children have received, believing that this was the reason for the occurrence of fracture.
• Finally, since case–control studies are backward-directed, there is no “at risk” group at the start of the study; therefore, the determination of “risk” (and relative risk or risk ratio) is not possible, and one can only estimate “odds” (and odds ratio). For a detailed discussion on this, please refer to a previous article.[ 4 ]

Uses of case–control studies

• Case-control studies are ideal for rare diseases, where identifying cases is easier than following up large numbers of exposed persons to determine outcome.
• Case-control studies, because of their simplicity and need for fewer resources, are often the initial study design used to assess the relationship of a particular exposure and an outcome. If this study is positive, then a study with more complex and robust study design (cohort or interventional) can be undertaken.

A special variation of case–control study design

Nested case-control design is a special type of case-control study design which is built into a cohort study. From the main cohorts, participants who develop the outcome (irrespective of whether exposed or unexposed) are chosen as cases. From among the remaining study participants who have not developed the outcome, a subset of matched controls are selected. The cases and controls are then compared with respect to exposure. This is still a backward-direction (since the enquiry begins with outcome and then proceeds toward exposure) and retrospective study (since outcomes have already occurred when the study starts). The main advantage is that since one knows that the outcome had not occurred when the cohorts were established, temporal relation of exposure and outcome is ensured.

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Chapter 3: Developing a Research Question

3.2 Exploration, Description, Explanation

As you can see, there is much to think about and many decisions to be made as you begin to define your research question and your research project. Something else you will need to consider in the early stages is whether your research will be exploratory, descriptive, or explanatory. Each of these types of research has a different aim or purpose, consequently, how you design your research project will be determined in part by this decision. In the following paragraphs we will look at these three types of research.

Exploratory research

Researchers conducting exploratory research are typically at the early stages of examining their topics. These sorts of projects are usually conducted when a researcher wants to test the feasibility of conducting a more extensive study; he or she wants to figure out the lay of the land with respect to the particular topic. Perhaps very little prior research has been conducted on this subject. If this is the case, a researcher may wish to do some exploratory work to learn what method to use in collecting data, how best to approach research participants, or even what sorts of questions are reasonable to ask. A researcher wanting to simply satisfy his or her own curiosity about a topic could also conduct exploratory research. Conducting exploratory research on a topic is often a necessary first step, both to satisfy researcher curiosity about the subject and to better understand the phenomenon and the research participants in order to design a larger, subsequent study. See Table 2.1 for examples.

Descriptive research

Sometimes the goal of research is to describe or define a particular phenomenon. In this case, descriptive research would be an appropriate strategy. A descriptive may, for example, aim to describe a pattern. For example, researchers often collect information to describe something for the benefit of the general public. Market researchers rely on descriptive research to tell them what consumers think of their products. In fact, descriptive research has many useful applications, and you probably rely on findings from descriptive research without even being aware that that is what you are doing. See Table 3.1 for examples.

Explanatory research

The third type of research, explanatory research, seeks to answer “why” questions. In this case, the researcher is trying to identify the causes and effects of whatever phenomenon is being studied. An explanatory study of college students’ addictions to their electronic gadgets, for example, might aim to understand why students become addicted. Does it have anything to do with their family histories? Does it have anything to do with their other extracurricular hobbies and activities? Does it have anything to do with the people with whom they spend their time? An explanatory study could answer these kinds of questions. See Table 3.1 for examples.

Table 3.1 Exploratory, descriptive and explanatory research differences (Adapted from Adjei, n.d.).

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Health » What is the Difference Between Descriptive and Analytic Epidemiology

What is the Difference Between Descriptive and Analytic Epidemiology

The main difference  between descriptive and analytical epidemiology is that  descriptive epidemiology generates hypotheses on risk factors and causes of disease, whereas analytical epidemiology tests hypotheses by assessing the determinants of diseases, focusing on risk factors and causes as well as, analyzing  the distribution of exposures and diseases.  Furthermore, descriptive epidemiology is comparatively a small and less complex study area, while analytical epidemiology is a larger and more complex study area.  

Descriptive and analytical epidemiology are two main areas of epidemiology that studies the distribution, patterns, and determinants of health and diseases in defined populations .  

Key Areas Covered  

1. What is Descriptive Epidemiology      – Definition, Features, Importance 2. What is Analytical Epidemiology      – Definition, Features, Importance 3. What are the Similarities Between Descriptive and Analytical Epidemiology      – Outline of Common Features 4. What is the Difference Between Descriptive and Analytical Epidemiology      – Comparison of Key Differences

Key Terms  

Analytical Epidemiology, Descriptive Epidemiology, Making Hypotheses, Occurrence of Diseases, Testing Hypotheses

What is Descriptive Epidemiology  

Descriptive epidemiology is one of the two main areas of epidemiological studies. It is responsible for the determination of the patterns of disease occurrence, focusing on clinical information, person, place, and time. Here, the clinical information includes the signs and symptoms of the disease, laboratory results, data on hospitalization, and live or dead numbers. Besides, it uses demographic information, including age, sex, material status, personal habits, etc. Also , it studies socioeconomic information such as education, occupation, income, residence, place of work, etc. Furthermore, cultural information, including ethnicity, dietary habits, and religious preferences, also have an effect on causing diseases. 

Figure 1: Bar Graph of the Incidence of Mild Traumatic Brain Injury by Age Range

Generally, descriptive epidemiologists collect relatively accessible data used for program planning , generating hypotheses, and suggesting ideas for further studies. Moreover, the hypotheses produced by descriptive epidemiological studies are confirmed by the analytical epidemiology. Furthermore, the three main types of descriptive epidemiology are the case report, case studies, and incidence. Case reports describe the person, place, and time of a specific case while case series describes the person, place, and time of a group of cases. Incidence studies, on the other hand, describe the number of new cases during a specific time.  

What is Analytical Epidemiology  

Analytical epidemiology is the second area of epidemiology, and it is a more complex and broader area than descriptive epidemiology. It is responsible for testing the hypotheses built in descriptive epidemiology. Therefore, the main objective of analytical epidemiology is to assess the determinants of diseases, risk factors and causes, as well as, to analyze the distribution of diseases and their exposures. Additionally, the key feature of analytical epidemiology is that it uses comparison groups. 

Figure 2: Table of Comparison of Prostate Screening Results Globally

Moreover, the two main types of analytical epidemiology are the experimental epidemiology and observational epidemiology. In experimental epidemiology , a randomized selection process based on chance is used to study different study groups. Sometimes, it can be clinical procedures, which study new drugs to prevent a particular disease in a community. In contrast, observational epidemiology is based on non-randomized studies. Moreover, they mainly study patterns of exposure. Furthermore, the four types of analytical epidemiology studies are cohort, case-control, cross-sectional, and ecologic.  

Similarities Between Descriptive and Analytical Epidemiology  

• Descriptive and analytical epidemiology are two main study areas of epidemiology.  
• Moreover, both study the distribution, patterns, and determinants of health and diseases in defined populations.  
• Also, their main goals are to identify who is at risk and to provide  clues to the cause of diseases.  
• Therefore, they are a type of important activities in public health authorities.  

Difference Between Descriptive and Analytical Epidemiology  

Definition  .

Descriptive epidemiology refers to the area of epidemiology that focuses on describing disease distribution by characteristics relating to time, place, and people, while analytical epidemiology refers to the area of epidemiology, which measures the association between a particular exposure and a disease, using information collected from individuals, rather than from the aggregate population.

Importance  

While descriptive epidemiology generates hypotheses on risk factors and causes of disease, analytical epidemiology tests hypotheses by assessing the determinants of diseases focusing on risk factors and causes as well as, analyzing  the distribution of exposures and diseases. Thus, this is the main difference between descriptive and analytical epidemiology.

Focuses on  

Another difference between descriptive and analytical epidemiology is that descriptive epidemiology focuses on what, who, when, and where disease can occur, while analytical epidemiology focuses on why and how disease occurs.  

Significance  

Furthermore, descriptive epidemiology is comparatively a small and less complex study area, while analytical epidemiology is a larger and more complex study area.  

Broadness  

Descriptive epidemiology uses individuals or a group of individuals to make hypotheses, while analytical epidemiology uses comparison groups to test hypotheses. Hence, this is also a difference between descriptive and analytical epidemiology.

Moreover, descriptive epidemiology includes case reports, case series, and incidence, while analytical epidemiology includes observational studies and experimental studies.  

As an example, descriptive epidemiology examines case series using person, place, and time of first 100 patients with SARS, while analytical epidemiology measures risk factors for  SARS such as contact with animals and infected people.  

Conclusion  

Descriptive epidemiology is one of the two main areas of epidemiology that produces hypotheses about the risk factors and causes of diseases. Analytical epidemiology, on the other hand, is the area of epidemiology which tests the above hypotheses. Moreover, it assesses the risk factors and analyzes  the distribution of diseases. Therefore, the main difference between descriptive and analytical epidemiology is the type of study.  

References:

1. Kobayashi, John. “Study Types in Epidemiology.”  Nwcphp.org , Northwest Center for Public Health Practice. Available Here .

Image Courtesy:

1. “MTBI incidince bar graph” By self – Own work ( CC BY-SA 3.0 ) via Commons Wikimedia     2. “ Prostate cancer global epidemiology ” By US govt (Public Domain) via Commons Wikimedia   

About the Author: Lakna

Lakna, a graduate in Molecular Biology and Biochemistry, is a Molecular Biologist and has a broad and keen interest in the discovery of nature related things. She has a keen interest in writing articles regarding science.

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Qualitative Research in Psychology, 4, 364-366.

Anna Madill

Keywords in qualitative methods’ is a reassuringly compact (195 page) paperback which sets out in alphabetical order  from Access Negotiations to Writing  entries explaining 62 concepts relevant to qualitative research. Each entry follows a structure of concept definition, distinctive features, examples, evaluation, associated concepts (within the book), and key readings (with a *indicating the, presumably, key-key readings). The book does not set out to be an encyclopaedia but aims ‘to provide some practical assistance’ (p. 1), seeks ‘to be helpful rather than authoritative (p. 3), and values ‘brevity over exhaustiveness’ (p. 3). In general, I did think the book fulfilled these aims. I used it as a resource for a paper I was writing, found it helpful, and thought most entries usefully concise and appropriately structured. However, I also agree that such a wide-ranging authored, not edited, work cannot be authoritative in all areas and found it to lack a certain coverage. But first, more about what I liked. Qualitative research methods have a long a varied history across several academic disciplines and, as a psychologist, I found it particularly educational to read such a wide ranging text which presented my area of expertise from a different perspective. The panel of advisors is drawn from sociology, education, psychology, criminology, anthropology, geography, and linguistics. The stated emphasis is ‘anthropology and sociology first and foremost’ (p. 3) followed by (I’m not sure if the order is significant) ‘education, geography, linguistics, management science, psychology, public health and nursing studies’ (p. 3), with an explicit de-emphasis on ‘commercial research practice’ (p. 4). It was, therefore, refreshing to have familiar concepts explained through a different disciplinary eye using unfamiliar examples and to meet approaches that I had not come across in psychology....

Kevin Meethan

• Data analysis–the examination of research data.• Data collection–the systematic process of collecting data.• Deduction–arriving at logical conclusions through the application of rational processes eg theory testing. Quantitative research tends to be deductive.• Documentary research–the use of texts or documents as source materials (eg historical reports, newspapers, diaries).

Journal of Phenomenological Psychology

James Morley

Rachel Irish Kozicki

Malvin Nana Data IX

Michèle Lamont

Qualitative & Multi-Method Research

Andrew Bennett

I thank my colleagues for their serious and careful reading of Alexander George's and my book, and Jack Levy and John Gerring for organizing this symposium. Publishing a book is always something of a Rohrshach test—you offer up your "ink blots" and wait to see which of the points you were less sure of or committed to will be pounced upon or embraced, which of the arguments you felt the most defensible will garner praise or come in for unexpected criticism, and what patterns will emerge. I am pleased that there seems to be considerable convergence among the critiques on important issues that our book got right, and interested to find a bit more divergence on what it could have done better or differently. Given that research methods are fraught with trade-offs, to achieve any consensus is an accomplishment, and clarifying which points lack consensus helps advance the discourse on research methods. Thus, like our critics, I will focus only briefly on the considerable area...