types research techniques

15 Types of Research Methods

types of research methods, explained below

Research methods refer to the strategies, tools, and techniques used to gather and analyze data in a structured way in order to answer a research question or investigate a hypothesis (Hammond & Wellington, 2020).

Generally, we place research methods into two categories: quantitative and qualitative. Each has its own strengths and weaknesses, which we can summarize as:

Quantitative research can achieve generalizability through scrupulous statistical analysis applied to large sample sizes.
Qualitative research achieves deep, detailed, and nuance accounts of specific case studies, which are not generalizable.

Some researchers, with the aim of making the most of both quantitative and qualitative research, employ mixed methods, whereby they will apply both types of research methods in the one study, such as by conducting a statistical survey alongside in-depth interviews to add context to the quantitative findings.

Below, I’ll outline 15 common research methods, and include pros, cons, and examples of each .

Types of Research Methods

Research methods can be broadly categorized into two types: quantitative and qualitative.

Quantitative methods involve systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques, providing an in-depth understanding of a specific concept or phenomenon (Schweigert, 2021). The strengths of this approach include its ability to produce reliable results that can be generalized to a larger population, although it can lack depth and detail.
Qualitative methods encompass techniques that are designed to provide a deep understanding of a complex issue, often in a specific context, through collection of non-numerical data (Tracy, 2019). This approach often provides rich, detailed insights but can be time-consuming and its findings may not be generalizable.

These can be further broken down into a range of specific research methods and designs:

Combining the two methods above, mixed methods research mixes elements of both qualitative and quantitative research methods, providing a comprehensive understanding of the research problem . We can further break these down into:

Sequential Explanatory Design (QUAN→QUAL): This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
Sequential Exploratory Design (QUAL→QUAN): This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.

Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with qualitative research methods.

Qualitative Research Methods

Qualitative research methods allow for the exploration of phenomena in their natural settings, providing detailed, descriptive responses and insights into individuals’ experiences and perceptions (Howitt, 2019).

These methods are useful when a detailed understanding of a phenomenon is sought.

1. Ethnographic Research

Ethnographic research emerged out of anthropological research, where anthropologists would enter into a setting for a sustained period of time, getting to know a cultural group and taking detailed observations.

Ethnographers would sometimes even act as participants in the group or culture, which many scholars argue is a weakness because it is a step away from achieving objectivity (Stokes & Wall, 2017).

In fact, at its most extreme version, ethnographers even conduct research on themselves, in a fascinating methodology call autoethnography .

The purpose is to understand the culture, social structure, and the behaviors of the group under study. It is often useful when researchers seek to understand shared cultural meanings and practices in their natural settings.

However, it can be time-consuming and may reflect researcher biases due to the immersion approach.

Example of Ethnography

Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.

2. Phenomenological Research

Phenomenological research is a qualitative method focused on the study of individual experiences from the participant’s perspective (Tracy, 2019).

It focuses specifically on people’s experiences in relation to a specific social phenomenon ( see here for examples of social phenomena ).

This method is valuable when the goal is to understand how individuals perceive, experience, and make meaning of particular phenomena. However, because it is subjective and dependent on participants’ self-reports, findings may not be generalizable, and are highly reliant on self-reported ‘thoughts and feelings’.

Example of Phenomenological Research

A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting-point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

3. Historical Research

Historical research is a qualitative method involving the examination of past events to draw conclusions about the present or make predictions about the future (Stokes & Wall, 2017).

As you might expect, it’s common in the research branches of history departments in universities.

This approach is useful in studies that seek to understand the past to interpret present events or trends. However, it relies heavily on the availability and reliability of source materials, which may be limited.

Common data sources include cultural artifacts from both material and non-material culture , which are then examined, compared, contrasted, and contextualized to test hypotheses and generate theories.

Example of Historical Research

A historical research example might be a study examining the evolution of gender roles over the last century. This research might involve the analysis of historical newspapers, advertisements, letters, and company documents, as well as sociocultural contexts.

4. Content Analysis

Content analysis is a research method that involves systematic and objective coding and interpreting of text or media to identify patterns, themes, ideologies, or biases (Schweigert, 2021).

A content analysis is useful in analyzing communication patterns, helping to reveal how texts such as newspapers, movies, films, political speeches, and other types of ‘content’ contain narratives and biases.

However, interpretations can be very subjective, which often requires scholars to engage in practices such as cross-comparing their coding with peers or external researchers.

Content analysis can be further broken down in to other specific methodologies such as semiotic analysis, multimodal analysis , and discourse analysis .

Example of Content Analysis

How is Islam Portrayed in Western Media? by Poorebrahim and Zarei (2013) employs a type of content analysis called critical discourse analysis (common in poststructuralist and critical theory research ). This study by Poorebrahum and Zarei combs through a corpus of western media texts to explore the language forms that are used in relation to Islam and Muslims, finding that they are overly stereotyped, which may represent anti-Islam bias or failure to understand the Islamic world.

5. Grounded Theory Research

Grounded theory involves developing a theory during and after data collection rather than beforehand.

This is in contrast to most academic research studies, which start with a hypothesis or theory and then testing of it through a study, where we might have a null hypothesis (disproving the theory) and an alternative hypothesis (supporting the theory).

Grounded Theory is useful because it keeps an open mind to what the data might reveal out of the research. It can be time-consuming and requires rigorous data analysis (Tracy, 2019).

Grounded Theory Example

Developing a Leadership Identity by Komives et al (2005) employs a grounded theory approach to develop a thesis based on the data rather than testing a hypothesis. The researchers studied the leadership identity of 13 college students taking on leadership roles. Based on their interviews, the researchers theorized that the students’ leadership identities shifted from a hierarchical view of leadership to one that embraced leadership as a collaborative concept.

6. Action Research

Action research is an approach which aims to solve real-world problems and bring about change within a setting. The study is designed to solve a specific problem – or in other words, to take action (Patten, 2017).

This approach can involve mixed methods, but is generally qualitative because it usually involves the study of a specific case study wherein the researcher works, e.g. a teacher studying their own classroom practice to seek ways they can improve.

Action research is very common in fields like education and nursing where practitioners identify areas for improvement then implement a study in order to find paths forward.

Action Research Example

Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing by Ellison and Drew was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

7. Natural Observational Research

Observational research can also be quantitative (see: experimental research), but in naturalistic settings for the social sciences, researchers tend to employ qualitative data collection methods like interviews and field notes to observe people in their day-to-day environments.

This approach involves the observation and detailed recording of behaviors in their natural settings (Howitt, 2019). It can provide rich, in-depth information, but the researcher’s presence might influence behavior.

While observational research has some overlaps with ethnography (especially in regard to data collection techniques), it tends not to be as sustained as ethnography, e.g. a researcher might do 5 observations, every second Monday, as opposed to being embedded in an environment.

Observational Research Example

A researcher might use qualitative observational research to study the behaviors and interactions of children at a playground. The researcher would document the behaviors observed, such as the types of games played, levels of cooperation , and instances of conflict.

8. Case Study Research

Case study research is a qualitative method that involves a deep and thorough investigation of a single individual, group, or event in order to explore facets of that phenomenon that cannot be captured using other methods (Stokes & Wall, 2017).

Case study research is especially valuable in providing contextualized insights into specific issues, facilitating the application of abstract theories to real-world situations (Patten, 2017).

However, findings from a case study may not be generalizable due to the specific context and the limited number of cases studied (Walliman, 2021).

See More: Case Study Advantages and Disadvantages

Example of a Case Study

Scholars conduct a detailed exploration of the implementation of a new teaching method within a classroom setting. The study focuses on how the teacher and students adapt to the new method, the challenges encountered, and the outcomes on student performance and engagement. While the study provides specific and detailed insights of the teaching method in that classroom, it cannot be generalized to other classrooms, as statistical significance has not been established through this qualitative approach.

Quantitative Research Methods

Quantitative research methods involve the systematic empirical investigation of observable phenomena via statistical, mathematical, or computational techniques (Pajo, 2022). The focus is on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

9. Experimental Research

Experimental research is a quantitative method where researchers manipulate one variable to determine its effect on another (Walliman, 2021).

This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.

This type of research is useful in situations where researchers want to determine causal relationships between variables. However, experimental conditions may not reflect real-world conditions.

Example of Experimental Research

A researcher may conduct an experiment to determine the effects of a new educational approach on student learning outcomes. Students would be randomly assigned to either the control group (traditional teaching method) or the experimental group (new educational approach).

10. Surveys and Questionnaires

Surveys and questionnaires are quantitative methods that involve asking research participants structured and predefined questions to collect data about their attitudes, beliefs, behaviors, or characteristics (Patten, 2017).

Surveys are beneficial for collecting data from large samples, but they depend heavily on the honesty and accuracy of respondents.

They tend to be seen as more authoritative than their qualitative counterparts, semi-structured interviews, because the data is quantifiable (e.g. a questionnaire where information is presented on a scale from 1 to 10 can allow researchers to determine and compare statistical means, averages, and variations across sub-populations in the study).

Example of a Survey Study

A company might use a survey to gather data about employee job satisfaction across its offices worldwide. Employees would be asked to rate various aspects of their job satisfaction on a Likert scale. While this method provides a broad overview, it may lack the depth of understanding possible with other methods (Stokes & Wall, 2017).

11. Longitudinal Studies

Longitudinal studies involve repeated observations of the same variables over extended periods (Howitt, 2019). These studies are valuable for tracking development and change but can be costly and time-consuming.

With multiple data points collected over extended periods, it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes a detailed analysis of change possible.

a visual representation of a longitudinal study demonstrating that data is collected over time on one sample so researchers can examine how variables change over time

Perhaps the most relatable example of a longitudinal study is a national census, which is taken on the same day every few years, to gather comparative demographic data that can show how a nation is changing over time.

While longitudinal studies are commonly quantitative, there are also instances of qualitative ones as well, such as the famous 7 Up study from the UK, which studies 14 individuals every 7 years to explore their development over their lives.

Example of a Longitudinal Study

A national census, taken every few years, uses surveys to develop longitudinal data, which is then compared and analyzed to present accurate trends over time. Trends a census can reveal include changes in religiosity, values and attitudes on social issues, and much more.

12. Cross-Sectional Studies

Cross-sectional studies are a quantitative research method that involves analyzing data from a population at a specific point in time (Patten, 2017). They provide a snapshot of a situation but cannot determine causality.

This design is used to measure and compare the prevalence of certain characteristics or outcomes in different groups within the sampled population.

A visual representation of a cross-sectional group of people, demonstrating that the data is collected at a single point in time and you can compare groups within the sample

The major advantage of cross-sectional design is its ability to measure a wide range of variables simultaneously without needing to follow up with participants over time.

However, cross-sectional studies do have limitations . This design can only show if there are associations or correlations between different variables, but cannot prove cause and effect relationships, temporal sequence, changes, and trends over time.

Example of a Cross-Sectional Study

Our longitudinal study example of a national census also happens to contain cross-sectional design. One census is cross-sectional, displaying only data from one point in time. But when a census is taken once every few years, it becomes longitudinal, and so long as the data collection technique remains unchanged, identification of changes will be achievable, adding another time dimension on top of a basic cross-sectional study.

13. Correlational Research

Correlational research is a quantitative method that seeks to determine if and to what degree a relationship exists between two or more quantifiable variables (Schweigert, 2021).

This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.

While correlational research can reveal relationships between variables, it cannot establish causality.

Methods used for data analysis may include statistical correlations such as Pearson’s or Spearman’s.

Example of Correlational Research

A team of researchers is interested in studying the relationship between the amount of time students spend studying and their academic performance. They gather data from a high school, measuring the number of hours each student studies per week and their grade point averages (GPAs) at the end of the semester. Upon analyzing the data, they find a positive correlation, suggesting that students who spend more time studying tend to have higher GPAs.

14. Quasi-Experimental Design Research

Quasi-experimental design research is a quantitative research method that is similar to experimental design but lacks the element of random assignment to treatment or control.

Instead, quasi-experimental designs typically rely on certain other methods to control for extraneous variables.

The term ‘quasi-experimental’ implies that the experiment resembles a true experiment, but it is not exactly the same because it doesn’t meet all the criteria for a ‘true’ experiment, specifically in terms of control and random assignment.

Quasi-experimental design is useful when researchers want to study a causal hypothesis or relationship, but practical or ethical considerations prevent them from manipulating variables and randomly assigning participants to conditions.

Example of Quasi-Experimental Design

A researcher wants to study the impact of a new math tutoring program on student performance. However, ethical and practical constraints prevent random assignment to the “tutoring” and “no tutoring” groups. Instead, the researcher compares students who chose to receive tutoring (experimental group) to similar students who did not choose to receive tutoring (control group), controlling for other variables like grade level and previous math performance.

Related: Examples and Types of Random Assignment in Research

15. Meta-Analysis Research

Meta-analysis statistically combines the results of multiple studies on a specific topic to yield a more precise estimate of the effect size. It’s the gold standard of secondary research .

Meta-analysis is particularly useful when there are numerous studies on a topic, and there is a need to integrate the findings to draw more reliable conclusions.

Some meta-analyses can identify flaws or gaps in a corpus of research, when can be highly influential in academic research, despite lack of primary data collection.

However, they tend only to be feasible when there is a sizable corpus of high-quality and reliable studies into a phenomenon.

Example of a Meta-Analysis

The power of feedback revisited (Wisniewski, Zierer & Hattie, 2020) is a meta-analysis that examines 435 empirical studies research on the effects of feedback on student learning. They use a random-effects model to ascertain whether there is a clear effect size across the literature. The authors find that feedback tends to impact cognitive and motor skill outcomes but has less of an effect on motivational and behavioral outcomes.

Choosing a research method requires a lot of consideration regarding what you want to achieve, your research paradigm, and the methodology that is most valuable for what you are studying. There are multiple types of research methods, many of which I haven’t been able to present here. Generally, it’s recommended that you work with an experienced researcher or research supervisor to identify a suitable research method for your study at hand.

Hammond, M., & Wellington, J. (2020). Research methods: The key concepts . New York: Routledge.

Howitt, D. (2019). Introduction to qualitative research methods in psychology . London: Pearson UK.

Pajo, B. (2022). Introduction to research methods: A hands-on approach . New York: Sage Publications.

Patten, M. L. (2017). Understanding research methods: An overview of the essentials . New York: Sage

Schweigert, W. A. (2021). Research methods in psychology: A handbook . Los Angeles: Waveland Press.

Stokes, P., & Wall, T. (2017). Research methods . New York: Bloomsbury Publishing.

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis, communicating impact . London: John Wiley & Sons.

Walliman, N. (2021). Research methods: The basics. London: Routledge.

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Research methods--quantitative, qualitative, and more: overview.

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Qualitative Research
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
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Last Updated: Apr 3, 2023 3:14 PM
URL: https://guides.lib.berkeley.edu/researchmethods

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Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Exploring Types of Research Methods: A Comprehensive Guide

Grasping the concept of research method is essential for anyone engaged in research or assessing the outcomes of studies. Whether you're an academic student, a dedicated researcher, or just inquisitive about the world, a thorough understanding of the diverse research methods will assist you in sifting through the extensive array of information at your disposal.

Our detailed guide will walk you through the types of research design, including qualitative and quantitative approaches, as well as descriptive, correlational, experimental, and mixed methods research. We will also touch upon the different types of research methodology, ensuring a comprehensive understanding of the various types of methods in research.

This article will also highlight the pivotal factors to consider when crafting a study and the inherent strengths and limitations of different type of research methods.Whether you're embarking on your own research project or looking to enhance your critical thinking skills Armed with the research methods definition, this guide will equip you with the essential knowledge to make well-informed decisions and formulate significant conclusions in the field of research.

Qualitative vs. Quantitative Research Methods

Qualitative and quantitative research methods represent two fundamentally different approaches to data collection and analysis. Qualitative observation delves into non-numerical data, while quantitative observation involves the scrutiny of data that is numerical and quantifiable.

Qualitative Research:

Involves gathering and interpreting non-numerical data, such as text, video, photographs, or audio recordings
Uses sources like interviews, focus groups, documents, personal accounts, cultural records, and observation
Unstructured or semi-structured format
Open-ended questions
Comprehensive perspective on individuals' experiences
Comparison of participants' feedback and input
Focus on answering the "why" behind a phenomenon, correlation, or behavior
Ethnography, for instance, seeks to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified, offering a deep dive into the cultural fabric of a community.
This method is used to understand how an individual subjectively perceives and imparts meaning to their social reality, often revealing underlying bias that can influence the interpretation of social phenomena.
Data analysis techniques include content analysis, grounded theory, thematic analysis, or discourse analysis

Quantitative Research:

Focuses on numerical or measurable data
Uses sources such as experiments, questionnaires, surveys, and database reports
Multiple-choice format
Countable answers (e.g., "yes" or "no")
Numerical analysis
Statistical picture of a trend or connection
To define research methods, one must focus on answering the 'what' or 'how' in relation to a particular phenomenon, correlation, or behavior. This foundational approach is crucial in the realm of empirical inquiry.
Provides precise causal explanations that can be measured and communicated mathematically
The objectives of scientific inquiry often include hypothesis testing to examine causal relationships between variables, making accurate predictions, and generalizing findings to broader populations.
Aims to establish general laws of behavior and phenomenon across different settings/contexts
Used to test a theory and ultimately support or reject it
Empirical research in psychology utilizes examples of quantitative data such as standardized psychological assessments, neuroimaging data, and clinical outcome measures to inform its findings.
Data analysis techniques include descriptive and inferential statistics

When selecting research methodology types, it's important to consider various factors such as the study's primary goal, the nature of the research questions and conceptual framework, the variables involved, the context of the study, ethical issues, and whether the focus is on individuals or groups, or on comparing groups and understanding their relationships.

Research design methods play a pivotal role in determining the appropriateness of qualitative methods for studies involving individuals or groups, while quantitative methods are often chosen for studies aimed at comparing groups or deciphering the relationship between variables.

Descriptive Research

Descriptive research is a methodological approach that aims to accurately and systematically depict a population, situation, or phenomenon. It adeptly addresses 'what', 'where', 'when', and 'how' questions, although it steers clear of exploring 'why'. Employing a descriptive research design means observing and documenting variables without exerting control or manipulation, which is particularly beneficial when exploring new topics or problems to identify characteristics, frequencies, trends, and categories.

Descriptive research methods include:

Surveys: Survey research is a powerful tool that enables researchers to collect extensive data sets, which can then be meticulously analyzed to uncover frequencies, averages, and emerging patterns.
Observations: Utilizing observation allows researchers to collect data on behaviors and phenomena, ensuring the gathered information is not tainted by the honesty or accuracy of respondents.
Case studies: Case study research delves into detailed data to pinpoint the unique characteristics of a narrowly defined subject, providing in-depth insights.

Descriptive research can be conducted in different ways:

Cross-sectional : Observing a population at a single point in time.
Longitudinal : Following a population over a period of time.
Surveys or interviews : When the researcher interacts with the participant.
Observational studies or data collection using existing records : When the researcher does not interact with the participant.

Advantages of descriptive research include:

Varied data collection methods
A natural environment for respondents
Quick and cheap data collection
A holistic understanding of the research topic

Limitations of descriptive research studies:

They cannot establish cause and effect relationships.
The reliability and validity of survey responses can be compromised if respondents are not truthful or tend to provide socially desirable answers.
The choice and wording of questions on a questionnaire may influence the descriptive findings.

Correlational Research

Correlational research, a non-experimental method, delves into the dynamics between two variables, focusing on the strength and direction of their relationship without manipulating any factors, which is pivotal in understanding associations rather than causality.

Researchers may choose correlational research in the following situations

When manipulating the independent variable is impractical, impossible, or unethical
When exploring non-causal relationships between variables
When testing new measurement tools

In correlational research, the correlation coefficient is measured, which can range from -1 to +1, indicating the relationship's direction and strength. A comprehensive meta-analysis can further elucidate these types of correlations.

Positive correlation: Both variables change in the same direction
Negative correlation: Variables change in opposite directions
Zero correlation: No relationship exists between the variables

Data collection methods for correlational research include

Naturalistic observation
Archival research or secondary data

Analytical research methods, such as correlation or regression analyses, are employed to analyze correlational data, with the former yielding a coefficient that clarifies the relationship's intensity and direction, and the latter forecasting the impact of variable changes.

Experimental Research

Experimental research, a methodical scientific approach, manipulates variables to observe their effects and is indispensable for establishing cause-and-effect relationships and making informed decisions in the face of inadequate data.

Pre-experimental research design : Includes One-shot Case Study Research Design, One-group Pretest-posttest Research Design, and Static-group Comparison.
True experimental research design Statistical analysis, a cornerstone in testing hypotheses, is pivotal in research for its accuracy in proving or disproving a hypothesis. It's uniquely capable of establishing a cause-effect relationship within a group, making it an indispensable tool for researchers.
Quasi-experimental design : Similar to an experimental design but assigns participants to groups non-randomly.

Experimental research is essential for various fields, such as:

Developing new drugs and medical treatments
Understanding human behavior in psychology
Improving educational outcomes
Identifying opportunities for businesses and organizations

To conduct experimental research effectively, researchers must consider three key factors:

A Control Group and an Experimental Group
A variable that can be manipulated by the researcher
Random distribution of participants

Experimental research, whether conducted in laboratory settings with high control variables and internal validity or in field settings that boast both internal and external validity, presents a spectrum of advantages and challenges. Researchers must navigate potential threats to internal validity, including history, maturation, testing, instrumentation, mortality, and regression threats.

Mixed Methods Research

Mixed methods research, an approach that synergizes the rigor of quantitative and qualitative research methods, capitalizes on the strengths of each to provide a comprehensive analysis. This integration, which can occur during data collection, analysis, or presentation of results, is a hallmark of mixed methods research designs.

Convergent design
Explanatory sequential design
Exploratory sequential design
Embedded design

The practice of triangulation in mixed methods research enhances the integration of quantitative and qualitative data, offering multiple perspectives and a more comprehensive understanding. It also allows for a deeper explanation of statistical results, as exemplified by the EQUALITY study's exploratory sequential design for patient-centered data collection.

In mixed methods research, the intricate research design and methodology combine qualitative and quantitative data collection and analysis. This purposeful mixing of methods and data integration at strategic stages of the research process can reveal relationships between complex layers of research questions, although it demands significant resources and specialized training.

Explanatory
Exploratory
Nested (embedded) designs

Mixed methods research, characterized by its diverse research design and methods, integrates quantitative and qualitative approaches within a single study. Grounded in positivism and interpretivism, it provides a multifaceted understanding of research topics, despite the challenges of mastering both methodologies and collaborating with multidisciplinary teams.

In sum, a thorough grasp of the various research methodologies is crucial for conducting robust research and critically assessing others' findings. From qualitative to quantitative, descriptive, correlational, experimental, and mixed methods research, each approach offers distinct strengths and limitations, guiding researchers to the most suitable methods for effective data collection and analysis.

As we navigate the vast landscape of information available, understanding what are research methods empowers us to make informed decisions, draw meaningful conclusions, and contribute to the advancement of knowledge across various fields. Embracing the diversity of research methods, whether you're a student, researcher, or simply curious, will enhance your critical thinking skills and enable you to uncover valuable insights that shape our understanding of the world.

What are the seven most commonly used research methods? The seven most commonly used research methods are:

Observation / Participant Observation
Focus Groups
Experiments
Secondary Data Analysis / Archival Study
Mixed Methods (a combination of some of the above)

What does comprehensive research methodology entail?

Comprehensive research methodology involves conducting a thorough and exhaustive investigation on a specific topic, subject, or issue. This approach is characterized by the meticulous collection, analysis, and evaluation of a wide array of information, data, and sources, with the objective of achieving a deep and comprehensive understanding of the subject matter.

What are the three primary methods to investigate a specific research question?

To investigate a specific research question, you can use:

Quantitative methods for measuring something or testing a hypothesis.
Qualitative methods for exploring ideas, thoughts, and meanings.
Secondary data analysis for examining a large volume of readily-available data.

What does exploration mean in the context of research methodology?

Exploration in research methodology signifies a research approach that aims to delve into questions that have not been extensively explored before. Exploratory research, often qualitative and primary in nature, is focused on uncovering new insights and understanding. Nonetheless, it can also adopt a quantitative stance, particularly when it involves analyzing a large sample size, to further the scope of exploratory research.

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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There are various types of research that are classified by objective, depth of study, analysed data and the time required to study the phenomenon etc.

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Research Process

Choosing the Right Research Methodology: A Guide for Researchers

3 minute read

Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories.

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis.

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable.

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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Different Types of Research Methods

Mallika Rangaiah
Dec 22, 2021
Updated on: Nov 21, 2023

Unlike what a layman generally presumes, Research is not just about determining a hypothesis and unraveling a conclusion for that hypothesis. Every research approach that we take up falls under the category of a type of methodology and every methodology is exclusive and intricate in its depth.

So what are these research methodologies and how do the researchers make use of them? This is what we are going to explore through this blog. Before we attempt to understand these methods, let us understand what research methodology actually means.

What are Research Methods ?

Firstly, let's understand why we undertake research? What exactly is the point of it?

Research is mainly done to gain knowledge to support a survey or quest regarding a particular conception or theory and to reach a resolute conclusion regarding the same. Research is generally an approach for gaining knowledge which is required to interpret, write, delve further and to distribute data.

For ensuring that a fulfilling experience is delivered, it is essential that the Research is premium in its quality and that’s where Research Methods come to the rescue.

(Recommended blog - Research Market Analysis )

Types of Research Methods

An area is selected, a specific hypothesis is determined and a defined conclusion is required to be achieved. But how is this conclusion reached? What is the approach that can be taken up? As per CR Kothari’s book “Research Methodology Methods and Techniques” (The Second Revised Edition), the basic types of Research Methods are the following :

Descriptive Research

Descriptive Research is a form of research that incorporates surveys as well as different varieties of fact-finding investigations. This form of research is focused on describing the prevailing state of affairs as they are. Descriptive Research is also termed as Ex post facto research.

This research form emphasises on factual reporting, the researcher cannot control the involved variables and can only report the details as they took place or as they are taking place.

Researchers mainly make use of a descriptive research approach for purposes such as when the research is aimed at deciphering characteristics, frequencies or trends.

Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The descriptive research methods are mainly, observations, surveys as well as case studies.

(Speaking of variables, have you ever wondered - What are confounding variables? )

Analytical Research

Analytical Research is a form of research where the researcher has to make do with the data and factual information available at their behest and interpret this information to undertake an acute evaluation of the data.

This form of research is often undertaken by researchers to uncover some evidence that supports their present research and which makes it more authentic. It is also undertaken for concocting fresh ideas relating to the topic on which the research is based.

From conducting meta analysis, literary research or scientific trials and learning public opinion, there are many methods through which this research is done.

Applied Research

When a business or say, the society is faced with an issue that needs an immediate solution or resolution, Applied Research is the research type that comes to the rescue.

We primarily make use of Applied Research when it comes to resolving the issues plaguing our daily lives, impacting our work, health or welfare. This research type is undertaken to uncover solutions for issues relating to varying sectors like education, engineering, psychology or business.

For instance, a company might employ an applied researcher for concluding the best possible approach of selecting employees that would be the best fit for specific positions in the company.

The crux of Applied Research is to figure out the solution to a certain growing practical issue.

The 3 Types of Applied Research are mainly

Evaluation Research - Research where prevailing data regarding the topic is interpreted to arrive at proper decisions

Research and Development - Where the focus is on setting up fresh products or services which focus on the target market requirements

Action Research - Which aims at offering practical solutions for certain business issues by giving them proper direction, are the 3 types of Applied Research.

(Related blog - Target Marketing using AI )

Fundamental Research

This is a Research type that is primarily concerned with formulating a theory or understanding a particular natural phenomenon. Fundamental Research aims to discover information with an extensive application base, supplementing the existing concepts in a certain field or industry.

Research on pure mathematics or research regarding generalisation of the behavior of humans are also examples of Fundamental Research. This form of research is mainly carried out in sectors like Education, Psychology and Science.

For instance, in Psychology fundamental research assists the individual or the company in gaining better insights regarding certain behaviors such as deciphering how consumption of caffeine can possibly impact the attention span of a student or how culture stereotypes can possibly trigger depression.

Quantitative Research

Quantitative Research, as the name suggests, is based on the measurement of a particular amount or quantity of a particular phenomenon. It focuses on gathering and interpreting numerical data and can be adopted for discovering any averages or patterns or for making predictions.

This form of Research is number based and it lies under the two main Research Types. It makes use of tables, data and graphs to reach a conclusion. The outcomes generated from this research are measurable and can be repeated unlike the outcomes of qualitative research. This research type is mainly adopted for scientific and field based research.

Quantitative research generally involves a large number of people and a huge section of data and has a lot of scope for accuracy in it.

These research methods can be adopted for approaches like descriptive, correlational or experimental research.

Descriptive research - The study variables are analyzed and a summary of the same is seeked.

Correlational Research - The relationship between the study variables is analyzed.

Experimental Research - It is deciphered to analyse whether a cause and effect relationship between the variables exists.

Quantitative research methods

Experiment Research - This method controls or manages independent variables for calculating the effect it has on dependent variables.
Survey - Surveys involve inquiring questions from a certain specified number or set of people either online, face to face or over the phone.
(Systematic) observation - This method involves detecting any occurrence and monitoring it in a natural setting.
Secondary research : This research focuses on making use of data which has been previously collected for other purposes such as for say, a national survey.

(Related blog - Hypothesis Testing )

Qualitative Research

As the name suggests, this form of Research is more considered with the quality of a certain phenomenon, it dives into the “why” alongside the “what”. For instance, let’s consider a gender neutral clothing store which has more women visiting it than men.

Qualitative research would be determining why men are not visiting the store by carrying out an in-depth interview of some potential customers in this category.

This form of research is interested in getting to the bottom of the reasons for human behaviour, i.e understanding why certain actions are taken by people or why they think certain thoughts.

Through this research the factors influencing people into behaving in a certain way or which control their preferences towards a certain thing can be interpreted.

An example of Qualitative Research would be Motivation Research . This research focuses on deciphering the rooted motives or desires through intricate methods like in depth interviews. It involves several tests like story completion or word association.

Another example would be Opinion Research . This type of research is carried out to discover the opinion and perspective of people regarding a certain subject or phenomenon.

This is a theory based form of research and it works by describing an issue by taking into account the prior concepts, ideas and studies. The experience of the researcher plays an integral role here.

The Types of Qualitative Research includes the following methods :

Qualitative research methods

Observations: In this method what the researcher sees, hears of or encounters is recorded in detail.
Interviews: Personally asking people questions in one-on-one conversations.
Focus groups: This involves asking questions and discussions among a group of people to generate conclusions from the same.
Surveys: In these surveys unlike the quantitative research surveys, the questionnaires involve extensive open ended questions that require elaborate answers.
Secondary research: Gathering the existing data such as images, texts or audio or video recordings. This can involve a text analysis, a research of a case study, or an In-depth interview.

Conceptual Research

This research is related to an abstract idea or a theory. It is adopted by thinkers and philosophers with the aim of developing a new concept or to re-examine the existing concepts.

Conceptual Research is mainly defined as a methodology in which the research is conducted by observing and interpreting the already present information on a present topic. It does not include carrying out any practical experiments.

This methodology has often been adopted by famous Philosophers like Aristotle, Copernicus, Einstein and Newton for developing fresh theories and insights regarding the working of the world and for examining the existing ones from a different perspective.

The concepts were set up by philosophers to observe their environment and to sort, study, and summarise the information available.

Empirical Research

This is a research method that focuses solely on aspects like observation and experience, without focusing on the theory or system. It is based on data and it can churn conclusions that can be confirmed or verified through observation and experiment. Empirical Research is mainly undertaken to determine proof that certain variables are affecting the others in a particular way.

This kind of research can also be termed as Experimental Research. In this research it is essential that all the facts are received firsthand, directly from the source so that the researcher can actively go and carry out the actions and manipulate the concerned materials to gain the information he requires.

In this research a hypothesis is generated and then a path is undertaken to confirm or invalidate this hypothesis. The control that the researcher holds over the involved variables defines this research. The researcher can manipulate one of these variables to examine its effect.

(Recommended blog - Data Analysis )

Other Types of Research

All research types apart from the ones stated above are mainly variations of them, either in terms of research purpose or in the terms of the time that is required for accomplishing the research, or say, the research environment.

If we take the perspective of time, research can be considered as either One-time research or Longitudinal Research.

One time Research : The research is restricted to a single time period.

Longitudinal Research : The research is executed over multiple time periods.

A research can also be set in a field or a laboratory or be a simulation, it depends on the environment that the research is based on.

We’ve also got Historical Research which makes use of historical sources such as documents and remains for examining past events and ideas. This also includes the philosophy of an individual and groups at a particular time.

Research may be clinical or diagnostic . These kinds of research generally carry out case study or in-depth interview approaches to determine basic causal relationships.

Research can also be Exploratory or Formalized.

Exploratory Research: This is a research that is more focused on establishing hypotheses than on deriving the result. This form of Research focuses on understanding the prevailing issue but it doesn’t really offer defining results.

Formalized research: This is a research that has a solid structure and which also has specific hypotheses for testing.

We can also classify Research as conclusion-oriented and decision-oriented.

Conclusion Oriented Research: In this form of research, the researcher can select an issue, revamp the enquiry as he continues and visualize it as per his requirements.

Decision-oriented research: This research depends on the requirement of the decision maker and offers less freedom to the research to conduct it as he pleases.

The common and well known research methods have been listed in this blog. Hopefully this blog will give the readers and present and future researchers proper knowledge regarding important methods they can adopt to conduct their Research.

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

Sampling : the process of selecting a representative group from the population under study.
Target population : the total group of individuals from which the sample might be drawn.
Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

The advantages are that your sample should represent the target population and eliminate sampling bias.
The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

This is a quick and easy way of choosing participants (advantage)
It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

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Students must have either completed, or enrol concurrently in, SCIN4002 - Scientific Research Context, Perspective and Methods 1

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Introduces science Honours students to the range of theoretical frameworks which may inform different types of scientific research and to the methods and methodologies which may be employed in the scientific research process. Encourages students to acquire the skills necessary to carry out, produce and report well designed and articulated research proposals and projects.

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Orientation: the nature of research; types of research.

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Published: 05 April 2024

The impact of health insurance on maternal and reproductive health service utilization and financial protection in low- and lower middle-income countries: a systematic review of the evidence

Joseph Kazibwe 1 ,
Phuong Bich Tran 2 ,
Andrea Hannah Kaiser 1 ,
Simon Peter Kasagga 3 ,
Felix Masiye 4 ,
Björn Ekman 1 &
Jesper Sundewall 1 , 5

BMC Health Services Research volume 24 , Article number: 432 ( 2024 ) Cite this article

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Low- and middle-income countries have committed to achieving universal health coverage (UHC) as a means to enhance access to services and improve financial protection. One of the key health financing reforms to achieve UHC is the introduction or expansion of health insurance to enhance access to basic health services, including maternal and reproductive health care. However, there is a paucity of evidence of the extent to which these reforms have had impact on the main policy objectives of enhancing service utilization and financial protection. The aim of this systematic review is to assess the existing evidence on the causal impact of health insurance on maternal and reproductive health service utilization and financial protection in low- and lower middle-income countries.

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search included six databases: Medline, Embase, Web of Science, Cochrane, CINAHL, and Scopus as of 23rd May 2023. The keywords included health insurance, impact, utilisation, financial protection, and maternal and reproductive health. The search was followed by independent title and abstract screening and full text review by two reviewers using the Covidence software. Studies published in English since 2010, which reported on the impact of health insurance on maternal and reproductive health utilisation and or financial protection were included in the review. The ROBINS-I tool was used to assess the quality of the included studies.

A total of 17 studies fulfilled the inclusion criteria. The majority of the studies (82.4%, n = 14) were nationally representative. Most studies found that health insurance had a significant positive impact on having at least four antenatal care (ANC) visits, delivery at a health facility and having a delivery assisted by a skilled attendant with average treatment effects ranging from 0.02 to 0.11, 0.03 to 0.34 and 0.03 to 0.23 respectively. There was no evidence that health insurance had increased postnatal care, access to contraception and financial protection for maternal and reproductive health services. Various maternal and reproductive health indicators were reported in studies. ANC had the greatest number of reported indicators ( n = 10), followed by financial protection ( n = 6), postnatal care ( n = 5), and delivery care ( n = 4). The overall quality of the evidence was moderate based on the risk of bias assessment.

The introduction or expansion of various types of health insurance can be a useful intervention to improve ANC (receiving at least four ANC visits) and delivery care (delivery at health facility and delivery assisted by skilled birth attendant) service utilization in low- and lower-middle-income countries. Implementation of health insurance could enable countries’ progress towards UHC and reduce maternal mortality. However, more research using rigorous impact evaluation methods is needed to investigate the causal impact of health insurance coverage on postnatal care utilization, contraceptive use and financial protection both in the general population and by socioeconomic status.

Trial registration

This study was registered with Prospero (CRD42021285776).

Peer Review reports

Introduction

Low- and middle-income countries (LMICs) have committed to making progress towards universal health coverage (UHC) as part of the Sustainable Development Goals (SDGs). UHC has been defined by the World Health Organization (WHO) as a state where all people and communities receive the quality health services they need, when they need them, without experiencing financial hardship due to health care costs [ 1 ]. Generally, high income countries have attained high levels of service coverage (UHC service coverage index of at least 80 out of 100), however a majority of low- and lower-middle income countries (LLMICs) are still lagging behind (UHC service coverage index of less than 60 out of 100) as of 2022 [ 2 ]. The health service coverage index is the average coverage of essential services based on 14 tracer indicators of health service coverage (encompassing reproductive, maternal, newborn and child health, infectious diseases, non-communicable diseases and service capacity and access) among the general and the most disadvantaged population [ 3 , 4 ]. Similarly, while efforts have been made to decrease catastrophic health expenditure globally, LLMICs continue to face the greatest burden of people being thrust into extreme poverty (spending less than international dollars 1.9 per day) due to out of pocket payments (OOP) on healthcare [ 5 ].

In order to advance towards UHC, several countries especially LLMICs, are planning or implementing health financing reforms with a view to introduce or expand some form of health insurance (i.e. prepayment and pooling of funds). Countries that have opted for health insurance schemes – specifically social health insurance (SHI) – have seen an increment in their health expenditure compared to those that have a tax-based model of financing [ 6 , 7 ]. However, the choice of health financing mechanism does not necessarily have a clear effect on health outcomes (such as increased immunization coverage, reduced under-five mortality) or financial protection [ 7 ]. For example, Wagstaff who looked at Organization for Economic Co-operation and Development (OECD) countries found that neither a tax-funded health system nor a SHI system had a significant effect on health outcomes [ 6 ] while Gabani et al. who looked at over 124 countries found that transitions from predominantly OOP financing to tax-funded health systems yielded significantly better health outcomes than transitions from predominantly OOP financing to health insurance [ 7 ].

An increasing number of LLMICs have started implementing, or are planning to implement health insurance reforms to advance UHC [ 8 , 9 , 10 ]. Health insurance can go by different names including SHI, publicly funded health insurance (PFHI), community-based health insurance (CBHI) and private for-profit health insurance based on the pre-payment arrangement within an insurance scheme [ 11 ]. The intention, however, is the same for all health insurance systems (especially not for profit health insurance), which is to pool the risk of high-cost health care across a large number of people in order to protect individuals from high unexpected medical costs. Through a system of prepayment for guaranteed access to a predetermined package of health benefits, individuals can benefit from more predictable health care expenses and be protected from catastrophic health expenditure. A number of countries are opting for SHI. SHI refers to a health insurance system where contributions in form of premiums are collected from employees, employers and or government and pooled into an insurance fund [ 12 ]. Over time, SHI has been defined to mean insurance schemes where employees and employers both contribute premiums to the insurance fund. In instances where contributions/premiums are paid by government, such insurance has been referred to as PFHI for example in India [ 13 , 14 ]. PFHI has been implemented in some LLMIC settings, where there is a large informal sector, and inability to pay or collect premiums. In some cases, a health insurance scheme can be a combination of tiered contributions by members and subsidies from the government for example contributory and non-contributory.

A core component of UHC is maternal and reproductive health services (MRH), which has received a lot of attention in the past few decades. It was central to the Millennium Development Goals, specifically Goal 5 aimed at improving maternal health [ 15 ]; and it is currently well stipulated within the SDGs. MRH is one of the four categories measured for the UHC service coverage index. The other health services areas under the index are infectious diseases, non-communicable diseases and service capacity and access [ 16 ]. Several interventions have been implemented to improve MRH, including sexual and reproductive health and rights interventions. These endeavors have led to the improvement of MRH globally [ 17 ]. However, several LLMICs continue to face high maternal mortality ratios (accounting for 94% of all maternal deaths globally) [ 18 ], which is far from achieving the target of reducing maternal mortality to 70 deaths per 100,000 live births. Furthermore, women have continued to experience financial barriers when seeking healthcare, and they are found to be more vulnerable to facing financial hardships when accessing care, compared to men [ 19 ].

Despite the increasing interest surrounding health insurance, our understanding of the actual causal impact of the implemented reforms remains limited. Several reviews have examined the existing evidence on the impact of health insurance on service utilization and financial protection, but the results are inconclusive [ 20 , 21 , 22 , 23 ]. A review by Comfort et al. [ 24 ] analyzed the effects of health insurance on maternal health services in LMICs. Insurance (a mix of different types of insurance) was found to be consistently associated with increased utilization of facility-based child delivery and delivery assisted by a skilled health worker.

However, Comfort et al.’s study did not address the impact of health insurance on financial protection. In addition, the study examined various types of insurance schemes, including a mix of both for-profit and not-for-profit models. The study also included conditional cash transfers (or CCTs, a kind of demand-side financing). Therefore, based on Comfort et al.'s findings, it can be challenging to discern the specific impact of individual types of insurance. Our study differs from that of Comfort et al. as we specifically focus on well-specified not-for-profit health insurance as the intervention in LLMICs. Furthermore, we have also examined and reported on the impact of insurance on the financial protection of women of reproductive age. Our review constitutes a contribution to the current evidence base on this topic as no previous review has specifically examined the impact of not-for-profit health insurance on maternal and reproductive service utilization and financial protection in LLMICs, despite the recognition that MRH is among the four core categories of essential health services under UHC [ 4 ].

Our study aims to review the existing evidence of the causal impact of health insurance on maternal and reproductive service utilization and financial protection in LLMICs to inform ongoing health financing reform discussions and identify evidence gaps for future research.

The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 25 ]. In addition, we used the PICO framework [ 26 ] to guide the study scope. The study is registered with PROSPERO, registration number CRD42021285776. We searched electronic databases to identify published articles, and bibliographies of included articles were screened to identify missed articles that fulfilled the inclusion criteria.

To narrow the scope of our study, we employed the PICO framework with the following parameters – Population: Women of reproductive age residing in low- and lower middle-income countries as classified by the World Bank [ 27 ] as of 1st July 2021 – Intervention: health insurance – Comparator: uninsured women – Outcomes: two types of outcomes were considered, i) utilisation of maternal and reproductive health services, and ii) financial protection. Outcomes on utilisation of MRH included contraceptive use, number of antenatal care (ANC) visits, delivery at health facility. Outcomes on financial protection included catastrophic health expenditure and impoverishment impact of out-of-pocket expenditure (OOPE).”

Studies reporting impact

In this study, we reviewed studies that reported impact, i.e. studies that employed a research design enabling the identification of a causal effect of the intervention on an outcome of relevance. We defined studies that report impact as studies that estimate the causal influence the intervention (health insurance) has on a given outcome (MRH and financial protection indicators).

Intervention

The intervention in this study is any not-for-profit health insurance. Health insurance is the protection of registered members (beneficiaries) from high costs of/expenditure on health services by pooling resources through payment of agreed periodic premiums. A person or entity (employer or government) pays a periodic premium to enable them access to health services without requiring them to pay for the services. In some cases where there is a co-payment, the beneficiary pays a small amount or portion of the cost of services they receive. This arrangement aims at sharing the financial risks associated with falling ill and needing medical care.

Inclusion criteria

Our review applied the following inclusion criteria. We included articles that:

Reported on any specific type of not-for-profit health insurance. The reason was to focus on health insurance implemented with the intention of progressing towards UHC.

Reported on the impact of health insurance on MRH service utilization and/or financial protection of people accessing MRH services, and where any MRH service is included in the health benefit package of the insurance scheme. MRH in this study covers contraception, pre- and postnatal services, and delivery care.

Used experimental and/or quasi experimental study designs.

Were published from 2010 onwards and in English. We included studies from 2010 to capture the most recent evidence, as insurance schemes undergo reforms over time with likely implications on their respective performance. Relevant unpublished studies in the form of reports were also considered.

Exclusion criteria

Articles were excluded if they:

Reported on the impact of private for-profit insurance only or aggregated all types of insurance (both for-profit and not-for-profit) as one. Excluding such articles was necessary because grouping different insurance types as a single intervention makes it challenging to differentiate the specific impact of each insurance type.

Adopted a non-experimental study design prohibiting the identification of a causal effect.

Reported on the impact of health insurance qualitatively.

Were published in languages other than English.

Had unavailable full texts.

Databases searched

We searched for published literature in selected electronic databases and bibliographies. Databases included Medline (PubMed), Embase, Web of Science, CINAHL, Cochrane and Scopus. Additionally, we reviewed bibliographies of included articles to find other relevant articles that might have been missed in the search. It should be noted that although no time restrictions were included in the search strategy, studies published before 2010 were excluded at the screening stage.

Search strategy

The six electronic databases were searched on the 31st of October 2021, with an update of the search carried out on 23rd May 2023. The search strategy included all keywords and respective keyword variations for the five keyword domains: health insurance, impact, utilisation, financial protection, and maternal and reproductive health. Search strategies were customised for the respective databases (Supplementary Material 1 : Appendix 1).

Study selection

The PRISMA guidelines [ 28 ] were followed in the articles screening and selection process. The articles retrieved from the search were uploaded to Covidence systematic review software [ 29 ], where duplicates were removed. Initially, at least two independent researchers (PT, SPK, and JK) carried out screening for each title and abstract. Subsequently, full text screening was conducted by PT and JK, following a standard protocol. In the event of any conflict, a fourth researcher (JS) was available to review the conflict and make the final decision.

Data extraction

We developed a data extraction template in Microsoft Excel, which was piloted on ten randomly selected articles and necessary adjustments were made. We extracted data on author, year of publication, target group, study design, country, geographic location, setting (rural/urban/mixed), level of health facility, study participants, type of insurance, year of implementation of insurance, source of data, year of data collection, analysis methods used, description of the insurance, type of membership (voluntary/compulsory), enrolment requirements, services covered by insurance, services received, insurance coverage, premium, reimbursement rates, co-payments, OOPE, indicators used in measuring utilisation, financial protection and their definitions, proportion of households experiencing catastrophic heath expenditure (CHE), measures used for impact, theoretical framework used, reported impact (adjusted and unadjusted), and correction of self-selection among others.

Quality assessment and risk of bias in individual studies

The quality of the evidence was assessed through a two-step process, including: 1) using a tool for assessing the risk of bias in each study and 2) using the GRADE criteria to determine the level of certainty of the evidence.

The study design of an article being assessed determined the quality assessment tool to be used. Since we did not find any randomised studies, we resorted to a tool suitable for non-randomised studies. We used the Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) tool developed by the Development and Evaluation (GRADE) working group [ 30 ]. The tool rates the risk of bias in seven domains, 1) Bias due to confounding, 2) Bias in selection of participants into the study, 3) Bias in classification of interventions, 4) Bias due to departures from intended interventions, 5) Bias due to missing data, 6) Bias in measurement of outcomes, and 7) Bias in selection of reported results [ 31 ]. The study can be rated as low risk of bias, moderate risk of bias, or serious risk of bias based on the respective guiding questions in the tool for each domain. The overall extent of bias of a study is determined by the respective domain ratings, following the algorithm in the guide. The above tool was selected following the findings of a systematic review by Ma et al [ 32 ], describing the ROBINS-I tool as one of the most reliable tools available for quality assessment.

The certainty level of evidence of each study was then determined following the GRADE criteria based on the ROBINS-I tool [ 33 ]. It involved three steps: 1) establishing the initial level of certainty as advised by GRADE, 2) considering lowering or raising the level of certainty and 3) determining the final certainty rating. The certainty level of the evidence could be high, moderate, low, or very low depending on the rating. A non-randomised study’s evidence is initially rated as high level of certainty, which is then downgraded by a level or two depending on how it performs in the following domains: limitations in the detailed study design and execution; inconsistency (or heterogeneity); indirectness (PICO and applicability); imprecision; and publication bias. The downgrade of the certainty level of the evidence is mitigated (increased) by the magnitude of the effect estimates.

Reporting, summary measures and synthesis of results

The reporting was both descriptive and analytical. For extracted quantitative data, we reported summary measures. Indicators reported by the different studies were categorised into MRH service utilisation indicators and financial protection indicators with several subcategories each. Additionally, we elicited the covariates used in the adjustment of results from the studies and categorised these into characteristics including mother’s demographic, households, partners, communities, and mother’s perceptions. The reported impact of the insurance on MRH service utilisation and financial protection was summarised in five groups: positive and significant impact, positive and not significant impact, no impact, negative and not significant impact, and negative and significant impact. The impact findings were summarised in a table showing the proportion of studies that reported a positive significant impact for each respective indicator. Studies that had a high risk of bias were excluded in the calculation of the proportions as shown in Table 3 .

The systematic literature search yielded a total of 11,988 studies after deduplication. Following title and abstract screening and full text review, we included 17 studies that fulfilled our eligibility criteria. Figure 1 shows the flow of selection process including reasons for exclusion of articles at the full text reading stage.

PRISMA flow chart

Characteristics of included studies

Table 1 provides an overview of key characteristics of the included studies.

All the included studies were quasi experimental studies ( n = 17). Quasi experimental studies are non-randomised studies that evaluate an intervention with the aim of demonstrating causality between the intervention and outcome [ 51 ]. Ghana and Indonesia had the most studies with three each. These were followed by the Philippines ( n = 2), and Mauritania ( n = 2). The review included one study from each of the following countries: Tanzania, Egypt, Rwanda, Ethiopia, India, and Senegal (Table 1 ). Additionally, there was a multi-country study that encompassed three countries: Ghana, Rwanda, and Indonesia [ 46 ]. Most studies ( n = 14) were nationally representative of the population, while the remaining three were carried out in specific region(s) within the specified country [ 42 , 48 , 49 ]. Three studies were specifically conducted in rural settings [ 42 , 48 , 49 ], and no studies focused on urban settings exclusively. The rest of the studies ( n = 14) covered both rural and urban areas. All the studies included in the review involved female participants of childbearing age from 15 years old. The majority of the studies ( n = 13) specifically focused on women aged between 15 and 49. The studies focused on three types of health insurance, including social health insurance (e.g. Ghana), community-based health insurance (e.g. Rwanda), and publicly-funded health insurance (e.g. India). The data sources used were mostly secondary data ( n = 14), specifically demographic health survey (DHS) data [ 52 ], Multiple Indicator Cluster Survey (MICS) and Family Life Survey (FLS).

Quality assessment of included studies

The overall quality of assessed studies was rated as moderate. A total of 12 studies were rated as having a high level of certainty of evidence [ 36 , 37 , 38 , 39 , 41 , 43 , 44 , 45 , 46 , 48 , 49 , 50 ], two studies had moderate while those rated low and very low were two [ 40 , 42 ] and one [ 34 ], respectively. This assessment was based on the categorization of the risk of bias using the ROBINS-I tool. Overall, the majority of the studies ( n = 15; 88.2%) were categorised as having moderate risk of bias, and two studies were rated as having serious risk [ 34 , 42 ]. No study was found with an overall low risk of bias. All studies were rated as low risk of bias in three domains: bias in classification of interventions, bias due to departures from intended interventions, and bias due to missing data as shown in Fig. 2 . For the domain of bias in selection of reported results, the majority of the studies ( n = 16, 94.1%) were rated as low risk of bias, while the rest ( n = 1, 5.9%) was rated as moderate risk [ 40 ]. One study showed serious risk of bias due to confounding [ 34 ], and one [ 42 ] study was assessed to have serious risk of bias in the measurement of outcomes. The table with the assessment results is included in Supplementary Material 1 : Appendix 2, and Fig. 2 shows the ratings by domain as well as the overall rating of bias.

Assessment of the risk of bias of the studies according to the seven domains, using the ROBINS-I tool

Indicators used to measure the impact

Table 2 shows the indicators used to measure the impact of health insurance on MRH service utilisation and financial protection. There was a large variation in the number of indicators per category of MRH services, and the frequency to which they were reported in the studies. Regarding the number of indicators per category, ANC had the greatest number of reported indicators ( n = 10), followed by financial protection ( n = 6), postnatal care ( n = 5), and delivery care ( n = 4). Contraception had only one indicator, with three studies reporting on this indicator [ 34 , 40 , 42 ]. Regarding the frequency of use of the indicators, under the ANC category, the most common indicator was having at least four ANC visits during pregnancy ( n = 9, 52.9%), which was also the second most reported MRH indicator in this review. For delivery care, delivery at a health facility was the most reported indicator in the delivery care category (and the most reported indicator in this review) ( n = 14, 82.4%); followed by delivery by skilled attendant ( n = 7, 41.2%). For the postnatal care category, having postnatal care (without specifying the point or time of access) was the most common indicator used in this category ( n = 4, 23.5%) [ 38 , 40 , 41 , 45 ]. For financial protection, six indicators were reported. OOPE on delivery services was reported in four studies (23.5%) [ 34 , 42 , 43 , 50 ]. OOPE due to ANC, OOPE due to C-section, financial distress after C-section, and CHE were each reported in one study. Financial distress was defined by Garg et al. as a situation where a patient, or their household member, borrowed money or sold their assets to cover the OOPE due to seeking maternal health care [ 50 ].

Impact of health insurance on MRH service utilisation and financial protection

Studies reported a positive effect of the health insurances on ANC and delivery care indicators, with a clear significant positive impact reported for the most used indicators (having at least four ANC visits, delivery at a health facility, and delivery assisted by a skilled attendant) as shown in Table 3 . Specifically, 85.7% of the studies found a significant positive impact between health insurance and delivery with assistance from a skilled attendant, 83.3% reported a significant positive impact on delivery at a health facility, and 75.0% indicated a significant positive impact on having at least four ANC visits during pregnancy. In contrast, the evidence on the impact of health insurance on contraceptive use [ 40 ], postnatal care [ 36 , 38 , 40 , 41 , 45 ] and financial protection [ 43 , 48 , 50 ] indicators was scanty, variable and inconclusive.

Certain indicators (ANC at health facility, postnatal care visit at health facility in less than 2 months after delivery, OOPE due to ANC, and OOPE due to PNC) were not included in the analysis, because these indicators were only reported in articles that were excluded due to their serious risk of bias.

Table 4 shows the magnitude of the impact reported by each study for indicators that were reported by more than one study.

ANC: Health insurance increased the chance of a pregnant woman having at least four ANC visits. The magnitude of the positive significant impact of health insurance on receiving at least four ANC visits during a pregnancy ranged between approximately 2% [ 46 ] and 11% [ 36 ]. Insurance increased the total number of ANC visits during pregnancy. The magnitude of positive significant impact of health insurance on the number of ANC during pregnancy ranged from 43% [ 47 ] to 56% [ 48 ]. On the other hand, insurance did not have a significant positive impact on having an ANC visit in the first trimester except for Indonesia [ 46 ].

Delivery care: Health insurance increased chances of having a delivery at a health facility and delivery by a skilled attendant. Studies that reported a significant positive impact of health insurance on delivery at a health facility found a magnitude ranging from approximately 3% [ 38 ] to 34% [ 48 ]. The magnitude of the impact ranged from 3% [ 41 ] to 23% [ 45 ] for having a delivery assisted by a skilled attendant.

Postnatal care: Health insurance showed an increase in the chance of receiving postnatal care but only 50% of the studies reporting on the impact of health insurance on postnatal reported a significant positive increase. The magnitude of the health insurance on postnatal care among studies that reported positive significant impact was 4% [ 41 ] and 9% [ 45 ].

Reduction of OOPE: Evidence suggests that health insurance has generally reduced OOP payments for MRH services. However, of the two studies that reported on OOPE only one found a significant reduction in OOPE of 1,136,966 Indonesian Rupiah (IDR) and 676,402 IDR for non-contributory and contributory health insurance in Indonesia respectively [ 43 ].

Methods used to estimate the impact of health insurance on MRH service utilisation

No study used randomisation in allocating participants to the intervention or control groups.

A wide range of statistical methods were applied in the studies (Table 5 ). Propensity Score Analysis/Matching (PSM) was the most used statistical methods (58.8%), followed by difference-in-difference (DID) analysis (11.8%). Some studies utilised more than one method; for example, Samarakoon et al [ 34 ] used both PSM and DID. The effect measures used were mostly Average Treatment Effect (ATE) (47.1%), and Average Treatment Effect on the Treated (ATT) (35.3%).

Several methods were used to adjust for self-selection, such as PSM, DID, conditional mixed process framework (CMP) (e.g. Agbanyo et al [ 35 ]), entropy balance weighting of observed characteristics (e.g. Aizawa [ 43 ]) and coarsened exact matching (CEM) methods (e.g. Chang et al [ 37 ]). Anindya et al [ 41 ] used more than one method, specifically PSM followed by CEM for sensitivity analysis and robustness check.

Covariates adjusted for in the studies

Table 5 presents the covariates that were adjusted for in the studies. Overall, the most used covariates were age, the education level of the woman, and wealth status of the household; with each being used in 76.5% of all studies. This was followed by place of residence (rural/urban) and marital status, with each at 58.8%. The other covariates were used in less than 50% of the studies that adjusted for covariates.

Our review shows that there is considerable evidence on the impact of health insurance on ANC and delivery care service utilisation. However, there is a scarcity of evidence on the impact of health insurance on the financial protection of women seeking MRH services, utilisation of postnatal care, and contraception. We found that health insurance has a significant positive impact on ANC and delivery care service utilisation specifically having at least four ANC visits, delivery at a health facility and having a delivery assisted by a skilled attendant. However, findings regarding its impact on financial protection, contraception, and postnatal care were inconclusive.

ANC and delivery care utilisation

Among the articles reviewed, recent evidence shows that health insurance generally exhibits a positive impact on ANC and delivery care service utilisation. This is in line with the findings of Spaan et al. and Erlangga et al. reporting that social health insurance and CBHI improved general health service utilisation [ 20 , 21 ]. With comparison to Acharya et al [ 22 ] – who reported inconclusive results on the impact of health insurance on general health service utilisation among the informal sector – the evidence that was reported on MRH service utilisation in that study concurs with our findings.

On the other hand, our findings differ from Comfort et al., who stated that there was no evidence that insurance increased maternal health service utilisation [ 24 ]. The statement was premised on the fact that Comfort et al. did not identify any studies that used randomised methods. Comfort et al. argued that causality could not be established without randomisation of the intervention. However, quasi experimental studies can estimate causation which are the only studies we included in our review. In addition, as shown in Table 1 , all the studies included in our review were published after the publication of Comfort et al.’s review (2013). This indicates that studies which estimated the causal relationship between health insurance and MRH are recent.

For countries that are still experiencing high MMR [ 53 , 54 ], the evidence available on the positive impact of health insurance on at least four ANC visits, delivery at a health facility and having a delivery assisted by a skilled attendant can inform the country’s health financing reforms, encourage implementation, and expansion of such insurance schemes as an intervention to increase access to care and reduce MMR. MRH services such as attending ANC and having a health facility-based delivery have been highlighted as some of the ways to counter occurrence of maternal mortality[ 54 ], and investment in these services was found to be cost-effective [ 17 , 55 ].

Limited evidence on financial protection when accessing MRH

The available evidence suggests that health insurance plays a role in reducing OOPE. However, it is important to note that the evidence in this area is weak, with only a limited number of studies reporting on OOPE indicators. The findings are variable and inconclusive, particularly regarding the likelihood of CHE and the reduction of OOPE specifically related to delivery care. This finding contrasts with the results of a previous systematic review examining financial protection in a broader context [ 20 ]. Health insurance is known to reduce CHE generally. However, we did not find any evidence of a positive impact of health insurance in reducing CHE in the MRH context. It should be noted that this review found very few studies (less than five) that investigated the impact of health insurance on the financial protection of women seeking MRH services in LLMICs. Globally, LMICs bear the highest proportion of OOPE on health. OOPE on health was 43.21% of the total in low-income countries, and 48.17% for lower middle-income countries; meanwhile, the global average is at 18.01% based on the World Bank estimates of 2019 [ 56 ]. Countries that channel larger shares of total health expenditure through prepayment schemes such as health insurance tend to have lower levels of OOPE. As an example, in 2019, the level of OOPE as a proportion of current health expenditure in Indonesia was 34.76%, while in Ghana it was 36.22% which is lower than to the LMIC average. The OOP costs to the patient are found to increase with the increasing level of care. For example in Vietnam, community health facilities had a lower cost for deliveries compared to district and higher-level hospitals [ 57 ]. Health insurance could be key in protecting populations from financial hardship, although, more evidence is necessary to see whether there is substantive impact of health insurance on the financial protection of mothers or women seeking MRH services, especially among the different wealth quintiles, underserved and vulnerable groups of the population.

Inconclusive results on contraception and postnatal care utilisation

The evidence on the impact of health insurance on contraception and postnatal care service utilisation was scarce and inconclusive. Specifically, there was very little evidence on the impact of health insurance on the use of contraception. These findings differ from that of Comfort et al., who found a positive association between health insurance and postnatal care utilisation [ 24 ]. The difference in findings between our study and that of Comfort et al. could potentially be attributed to their inclusion of cross-sectional studies with less rigorous methods.

For contraceptive use, the inconclusive results could be partly explained by the limited insurance coverage for contraceptives in some countries, where the reimbursable contraceptive options are few. Moreover, the reimbursable contraceptive options may not be the most preferred by the society. For example, Ghana has just officially included long-term contraceptive options (such as permanent methods, intrauterine devices (IUDs), implants, and injectables) in the National Health Insurance Scheme benefit package [ 58 ]. On the other hand, cultural, social, and normative practices surrounding postnatal care, as well as the lack of awareness of the clinical postnatal care guidelines may partly explain the inconclusive evidence on the use of postnatal care [ 59 , 60 ]. In addition, despite the importance of postnatal care and contraceptive use in reducing maternal mortality [ 18 ], few studies have evaluated indicators in these areas and the quality of studies examining contraceptive use was moderate to low. The finding regarding the scarcity of evidence on postnatal care in LMICs is not unique to this study, as it has been reported in recent research as well [ 61 ]. Further research is needed to better understand the impact of health insurance on postnatal care and contraceptive utilisation.

Indicators used to measure MRH and the mismatch with international recommendations

Most of the indicators used to measure MRH service utilisation were related to ANC. This may be in part due to the well-established evidence regarding the positive effect of ANC on maternal health related outcomes. Moreover, this aligns with the long standing WHO ANC model (sometimes called basic or focused ANC) introduced in the 1990s, which recommended that a pregnant woman should have at least four ANC visits/contacts during pregnancy [ 62 , 63 ]. However, WHO recently updated their recommendations, increasing the number of ANC visits/contacts to eight [ 64 ]. Unfortunately, our review did not identify any articles that specifically used at least eight visits as an indicator for ANC.

For postnatal care, WHO recommended a minimum of four postnatal care contacts for mothers. These recommended contacts include the first contact within 24 h after delivery, the second contact between 48 and 72 h, the third contact between seven and 14 days, and the fourth contact in the sixth week after delivery [ 65 , 66 ]. However, there was a mismatch between the WHO recommended indicators and the indicators reported in these studies. This indicates that more publicity/sensitization on this important component of the MRH service delivery spectrum is vital. Authors should be encouraged to use recommended indicators to measure the impact of an intervention (health insurance) towards the achievement of global targets and allow for comparison across countries.

Methods used by studies

Propensity score matching was the most popular method used in studies. This conforms to the assertion of Abadie and Cattaneo (2018) that noted an increasing use of matching techniques by researcher partly because of the flexibility of the methods and the failure of ordinary linear regression to estimate conventional treatment effect parameters like ATE and ATET [ 67 ]. In addition, matching makes it possible to estimate treatment effects in the absence of experimental data in evaluation research [ 68 ].Despite the importance of propensity score matching in determining causal inference, it relies on the assumption of conditional independence which may not hold in some instances especially when there are unobservable variables that influence both the treatment and outcome [ 68 ].

Different covariates were used to construct statistical models. Some authors selected covariates based on variable significance level, while others based their selection on the confounding relationship between the exposure and outcome. To have evidence of high certainty, it is necessary to adjust the results based on confounders which can be identified using the directed acyclic graphs [ 67 , 69 ].

Quality of evidence

The quality of the studies included in this review, with regards to the risk of bias, was generally assessed as moderate. It is important to note that increasing the quality of studies in this context can be challenging, as randomised controlled trials are often not feasible or ethically permissible for evaluating policy-related public health interventions, such as health insurance schemes. The absence of randomisation in the allocation of the intervention to participants can introduce various forms of bias, including confounding, which may impact the validity of the study results. Recognizing this, it is essential to thoroughly assess potential drawbacks and biases using appropriate tools [ 30 , 31 ]. The authors tried to overcome this likely consequence of non-randomisation by adjusting for confounders; however, it is difficult to control for all the likely bias. The overall quality of a study can be improved through the randomisation of the intervention (where possible) and the use of causal inference statistical methods that address the potential selection problems that may arise [ 67 , 69 ].

Future research

Although we find that health insurance has a positive impact on the utilisation of ANC; we should be conscious of the intersectionality of evidence. Health insurance interventions may have varying effects across different subgroups within the population. Factors such as age, economic status, and the rural/urban setting can influence how individuals experience and benefit from health insurance coverage [ 47 , 70 ]. A study by Barasa et al. reported that most insurance schemes in sub–Saharan Africa are pro-rich and have minimal benefits for the poor given the low insurance coverage [ 71 ]. The impact of health insurance schemes on utilization and financial protection may vary based on the characteristics/features of the schemes for example organization/design, implementation, enrolment levels, premiums, target population, benefit package [ 21 , 72 ]. If countries are to advance UHC, there is need to understand the intersectionality of the impact, thus conduct more research to investigate the impact of health insurance across geographical domains (rural/urban), across type and level of health providers (private vs public; community-level providers vs secondary- and tertiary-level providers) and vulnerable population subgroups (e.g., people in lower socio-economic quintiles).

Limitations

Our review included studies that were published in English after 2009, which could have led to the omission of studies published in other languages, such as those conducted in French-speaking countries in West Africa or studies before 2010 that may have reported relevant results. We acknowledge that in some contexts, individuals may have private health insurance in addition to the type of health insurance examined in this study, which may have affected the results reported in the included studies. We included studies of various designs, which may have led to variations in the interpretation of results. The use of different covariates in the models employed by the studies could have influenced the magnitude of the reported impact of health insurance.

This study focused on the direction of impact (positive, no change or negative) and significance level of the impact but did not cover the magnitude of the impact. Furthermore, due to the heterogeneity in study design and other characteristics of the included studies, it was not feasible for us to conduct a meta-analysis.

The majority of the included studies used pre-existing datasets to estimate the impact of health insurance. The datasets utilised in this regard were not developed or collected to specifically evaluate health insurance schemes. Such datasets may not be comprehensive in collecting all the relevant data points needed for a robust evaluation of the impact of health insurance.

The quality assessment of the studies was conducted using the ROBINS-I tool – a validated tool recommended by Cochrane for the quality assessment of non-randomised studies [ 20 ]. However the tool does not address problems relating to imprecision of results, where statistical analyses fail to account for clustering or matching of participants [ 31 ]. Such shortfalls may have been overlooked. Therefore, studies that were found to have serious risk of bias were not included in the causal impact analysis, to avoid increasing biases in the summary results.

This review finds evidence supporting the positive impact of health insurance on the utilisation of ANC and delivery care services in low- and lower middle-income settings specially regarding receiving at least four ANC visits, delivery at a health facility and having a delivery assisted by a skilled attendant. Health insurance may contribute to making progress towards UHC, through improving access and utilisation of health services for all. The evidence on financial protection, contraception, and postnatal care is limited and inconclusive. Future evaluations of the impact of health insurance are crucial for countries to identify areas that require improvement, particularly in terms of its impact on vulnerable groups. Further research is needed to assess the impact of health insurance on contraception, postnatal care, and the financial protection of women seeking maternal and reproductive health services. Such work would contribute to a deeper understanding of the potential benefits and limitations of health insurance in these critical areas.

Availability of data and materials

The extracted data analysed during the current study is available from corresponding author on reasonable request.

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This is how your blood type can affect your health

Posted: December 22, 2023 | Last updated: March 13, 2024

<p>In 1930, Dr. Karl Landsteiner won the Nobel Prize for developing the ABO blood group system, the method of classifying blood types. It’s important to know your blood type if you need to receive or <a href="https://www.starsinsider.com/health/466149/the-benefits-of-blood-donation-and-the-celebs-who-support-it" rel="noopener">give blood</a>. A mismatch may cause an immune system reaction that could result in complications such as kidney failure, blood clotting, and, in more extreme cases, death.</p> <p>Fortunately, today’s sophisticated testing techniques have limited these incidents. Nevertheless, research suggests that there are links between blood type and several different diseases. This means that knowing your blood type could also alert you to certain types of diseases, such as multiple sclerosis or diabetes. To find out more about your blood type and its health risks, click through the following gallery.</p><p>You may also like:<a href="https://www.starsinsider.com/n/226683?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686v1en-en"> Britain's secret societies</a></p>

In 1930, Dr. Karl Landsteiner won the Nobel Prize for developing the ABO blood group system, the method of classifying blood types. It’s important to know your blood type if you need to receive or give blood . A mismatch may cause an immune system reaction that could result in complications such as kidney failure, blood clotting, and, in more extreme cases, death.

Fortunately, today’s sophisticated testing techniques have limited these incidents. Nevertheless, research suggests that there are links between blood type and several different diseases. This means that knowing your blood type could also alert you to certain types of diseases, such as multiple sclerosis or diabetes. To find out more about your blood type and its health risks, click through the following gallery.

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<p>Like eye color, blood type is passed on genetically from your parents. <a href="https://www.starsinsider.com/health/421818/the-best-ways-to-reduce-your-blood-pressure" rel="noopener">Blood</a> types are determined by the presence or absence of certain antigens, which can trigger an immune response if they're foreign to the body.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

How blood type is determined

Like eye color, blood type is passed on genetically from your parents. Blood types are determined by the presence or absence of certain antigens, which can trigger an immune response if they're foreign to the body.

<p>The ABO blood group system classifies blood types according to the different types of antigens in the red blood cells and antibodies in the plasma. Alongside the RhD antigen status, they determine which blood type will match for a safe transfusion.</p><p>You may also like:<a href="https://www.starsinsider.com/n/232341?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Diseases and infections that can kill you within 24 hours</a></p>

ABO and the most common blood types

The ABO blood group system classifies blood types according to the different types of antigens in the red blood cells and antibodies in the plasma. Alongside the RhD antigen status, they determine which blood type will match for a safe transfusion.

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<p>The surface of the red blood cells contains A antigen, and the plasma has anti-B antibody. Anti-B antibody attacks blood cells that contain B antigen.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

The surface of the red blood cells contains A antigen, and the plasma has anti-B antibody. Anti-B antibody attacks blood cells that contain B antigen.

<p>The surface of the red blood cells contains B antigen, and the plasma has anti-A antibody. Anti-A antibody attacks blood cells that contain A antigen.</p><p>You may also like:<a href="https://www.starsinsider.com/n/241394?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Serial killers: these are the men who claimed the most lives</a></p>

The surface of the red blood cells contains B antigen, and the plasma has anti-A antibody. Anti-A antibody attacks blood cells that contain A antigen.

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<p>The red blood cells have both A and B antigens, but the plasma doesn't contain anti-A or anti-B antibodies. People with type AB can receive any ABO blood type.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

The red blood cells have both A and B antigens, but the plasma doesn't contain anti-A or anti-B antibodies. People with type AB can receive any ABO blood type.

<p>The plasma contains both anti-A and anti-B antibodies, but the surface of the red blood cells doesn't contain any A or B antigens. Any ABO blood type can receive this type of blood.</p><p>You may also like:<a href="https://www.starsinsider.com/n/250123?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Brainy beauty: meet the world's hottest politicians</a></p>

The plasma contains both anti-A and anti-B antibodies, but the surface of the red blood cells doesn't contain any A or B antigens. Any ABO blood type can receive this type of blood.

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<p>Some red blood cells have the Rh factor or antigen. If the red blood cells contain the Rh antigen, they're Rh positive. If they don't, then they're Rh negative.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

Rhesus factor

Some red blood cells have the Rh factor or antigen. If the red blood cells contain the Rh antigen, they're Rh positive. If they don't, then they're Rh negative.

<p>A blood test can determine an individual’s blood type. In a lab, the blood is mixed with three different substances to see how they react. Each substance will contain A or B antibodies, or Rh factor. Observing these reactions will enable the technician to identify a person’s blood type.</p><p>You may also like:<a href="https://www.starsinsider.com/n/268923?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Why people keep disappearing from these American national parks</a></p>

Testing for blood type

A blood test can determine an individual’s blood type. In a lab, the blood is mixed with three different substances to see how they react. Each substance will contain A or B antibodies, or Rh factor. Observing these reactions will enable the technician to identify a person’s blood type.

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Heart disease

Research shows that O blood types have a lower risk of coronary heart disease. Experts aren’t sure why. But some believe it might be because other types are likely to have higher cholesterol and higher amounts of a protein that’s linked to clotting.

<p>A small study showed that people with AB blood type are more likely to develop thinking and memory problems, which can lead to dementia, than people with other blood types.</p><p>You may also like:<a href="https://www.starsinsider.com/n/339909?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Too cool for school: celebs who never smile on the red carpet</a></p>

A small study showed that people with AB blood type are more likely to develop thinking and memory problems, which can lead to dementia, than people with other blood types.

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<p>Chances are higher you’ll live longer if you have type O blood. Experts believe this is mainly due to a lowered risk of disease in heart and blood vessels.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

Chances are higher you’ll live longer if you have type O blood. Experts believe this is mainly due to a lowered risk of disease in heart and blood vessels.

<p>A, AB, and B blood types are more at risk than type Os, especially people with type A blood. Experts think this might be because of H. pylori, an infection more common with type A blood. It's usually found in the stomach, causing inflammation and ulcers.</p><p>You may also like:<a href="https://www.starsinsider.com/n/439582?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> The best advice from celebrity makeup artists</a></p>

Stomach cancer

A, AB, and B blood types are more at risk than type Os, especially people with type A blood. Experts think this might be because of H. pylori, an infection more common with type A blood. It's usually found in the stomach, causing inflammation and ulcers.

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<p>Stress increases the body's level of cortisol, also known as the stress hormone. People with type A blood tend to have more cortisol, which can make it harder for them to deal with stress.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

Stress increases the body's level of cortisol, also known as the stress hormone. People with type A blood tend to have more cortisol, which can make it harder for them to deal with stress.

<p>The risk is higher if you're types A, AB, or B. Molecules in types A and B red blood cells help the H. pylori bacteria grow in your gut. This can make you more likely to get pancreatic cancer.</p><p>You may also like:<a href="https://www.starsinsider.com/n/444346?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Towering tiaras and more: Extravagant jewelry owned by British royals</a></p>

Pancreatic cancer

The risk is higher if you're types A, AB, or B. Molecules in types A and B red blood cells help the H. pylori bacteria grow in your gut. This can make you more likely to get pancreatic cancer.

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You can get malaria when an infected mosquito bites you. Luckily, if you have blood type O, it's harder for the parasite to attach to the cells.

Peptic ulcers are painful open sores that crop up in the lining of your stomach or upper intestine. They seem to occur more often with blood type O.

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Type 2 diabetes seems to happen more often in people with blood types A and B. However, more research is needed to understand why.

<p>In one study, women with low numbers of healthy eggs were more likely to have type O blood than any other type. However, more research needs to be done to understand why.</p><p>You may also like:<a href="https://www.starsinsider.com/n/461536?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Movies that bankrupted their studios</a></p>

In one study, women with low numbers of healthy eggs were more likely to have type O blood than any other type. However, more research needs to be done to understand why.

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Rheumatic disease

Rheumatic disease is a group of more than 200 conditions that cause pain in your joints, connective tissue, tendons, and cartilage. A 2017 study in Turkey showed that some conditions were more common with blood types A and O.

Blood clots

Venous thromboembolism (VTE) is when your blood clots in a deep vein. Research shows that people with types A, B, or AB blood are at a higher risk of VTE.

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You're at greater risk for a stroke if you have blood type AB. Medical professionals think that’s because it’s more likely to clot than other types.

<p>Lupus is an autoimmune disease that causes inflammation and pain throughout the body. Studies have found those with blood types A and B had more severe symptoms than other blood types.</p><p>You may also like:<a href="https://www.starsinsider.com/n/490041?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Famous people who became celebrities overnight</a></p>

Lupus is an autoimmune disease that causes inflammation and pain throughout the body. Studies have found those with blood types A and B had more severe symptoms than other blood types.

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<p>MS is a condition when your immune system attacks a protective layer around your nerves called myelin. It's a lifelong condition that can cause serious disability. Research has found that those with blood types A and B have an elevated risk for developing MS.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

Multiple sclerosis (MS)

MS is a condition when your immune system attacks a protective layer around your nerves called myelin. It's a lifelong condition that can cause serious disability. Research has found that those with blood types A and B have an elevated risk for developing MS.

<p>There are two main types of IBD: ulcerative colitis, and Crohn’s disease. Studies both in Italy and South Korea showed that those with blood type O had a lower chance of developing Crohn’s disease.</p><p>You may also like:<a href="https://www.starsinsider.com/n/493882?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> Celebs who were rejected by other stars</a></p>

Inflammatory bowel disease (IBD)

There are two main types of IBD: ulcerative colitis, and Crohn’s disease. Studies both in Italy and South Korea showed that those with blood type O had a lower chance of developing Crohn’s disease.

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<p>Losing a large quantity of blood is common after severe trauma, such as after a car accident or other injury. Blood type O has been noted to suffer more from hemorrhage-related deaths. But more research needs to be done to understand why.</p><p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

Losing a large quantity of blood is common after severe trauma, such as after a car accident or other injury. Blood type O has been noted to suffer more from hemorrhage-related deaths. But more research needs to be done to understand why.

<p>The Rh antigen comes into play during pregnancy, if the mother has Rh-negative blood and her fetus’ blood is Rh-positive. Antibodies from an Rh-negative mother may enter the blood stream of her unborn Rh-positive infant, damaging the red blood cells.</p><p>You may also like:<a href="https://www.starsinsider.com/n/494870?utm_source=msn.com&utm_medium=display&utm_campaign=referral_description&utm_content=516686en-us"> The extravagant spending of royals around the world</a></p>

Risks in pregnancy

The Rh antigen comes into play during pregnancy, if the mother has Rh-negative blood and her fetus’ blood is Rh-positive. Antibodies from an Rh-negative mother may enter the blood stream of her unborn Rh-positive infant, damaging the red blood cells.

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<p>Researchers still don’t understand how blood type and certain diseases are related. It’s thought that genetics and environmental factors both play a role in the development of many conditions. But more research is needed to fully understand this link.</p><p>Sources: (<a href="https://www.medicalnewstoday.com/articles/218285#takeaway" rel="noopener">Medical News Today</a>) (<a href="https://www.webmd.com/a-to-z-guides/ss/slideshow-how-your-blood-type-affects-your-health" rel="noopener">WebMD</a>) (<a href="https://www.healthline.com/health/blood-type-and-autoimmune-diseases#bottom-line" rel="noopener">Healthline</a>)</p><p>See also: <a href="https://www.starsinsider.com/health/499757/health-mistakes-you-didnt-know-youre-making">Health mistakes you didn't know you're making</a></p> <p><a href="https://www.msn.com/en-us/community/channel/vid-7xx8mnucu55yw63we9va2gwr7uihbxwc68fxqp25x6tg4ftibpra?cvid=94631541bc0f4f89bfd59158d696ad7e">Follow us and access great exclusive content everyday</a></p>

The bottom line

Researchers still don’t understand how blood type and certain diseases are related. It’s thought that genetics and environmental factors both play a role in the development of many conditions. But more research is needed to fully understand this link.

Sources: (Medical News Today) (WebMD) (Healthline)

See more: Deadly diseases you thought were gone...but aren't

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Cathodoluminescence differentiates sedimentary organic matter types

High-resolution scanning electron microscopy (SEM) visualization of sedimentary organic matter is widely utilized in the geosciences for evaluating microscale rock properties relevant to depositional environment, diagenesis, and the processes of fluid generation, transport, and storage. However, despite thousands of studies which have incorporated SEM methods, the inability of SEM to differentiate sedimentary organic matter types has hampered the pace of scientific advancement. In this study, we show that SEM-cathodoluminescence (CL) properties can be used to identify and characterize sedimentary organic matter at low thermal maturity conditions. Eleven varied mudstone samples with a broad array of sedimentary organic matter types, ranging from the Paleoproterozoic to Eocene in age, were investigated. Sedimentary organic matter fluorescence intensity and CL intensity showed an almost one-to-one correspondence, with certain exceptions in three samples potentially related to radiolytic alteration. Therefore, because CL emission can be used as a proxy for fluorescence emission from sedimentary organic matter, CL emission during SEM visualization can be used to differentiate fluorescent from non-fluorescent sedimentary organic matter. This result will allow CL to be used as a visual means to quickly differentiate sedimentary organic matter types without employing correlative optical microscopy and could be widely and rapidly adapted for SEM-based studies in the geosciences.

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Related content, paul c. hackley, ph.d., research geologist, ryan j mcaleer, ph.d., aaron m. jubb, ph.d., research chemist, brett j. valentine, physical scientist, justin birdwell, research engineer and geochemist.

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Methodology

Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Research Methods | Definition, Types, Examples

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Choosing the Right Research Methodology: A Guide for Researchers

Understanding different research methods:

Qualitative vs quantitative research:

Qualitative research methodology:

Quantitative research methodology:

Analysing qualitative vs quantitative data:

When to use qualitative vs quantitative research: