Phenomenology
This is concerned with the lived experiences of humans.
Example:
Ethnography
This is concerned with learning about patterns and lifeways of cultural groups. Often these researchers go to the culture itself (fieldwork) to interview the participants in their natural settings.
Example:
The main difference between inductive and deductive reasoning is that inductive reasoning aims at developing a theory while deductive reasoning aims at testing an existing theory.
Think of inductive (theory producing) as to qualitative research and deductive (theory testing) as to quantitative research.
Inductive reasoning moves from specific observations to broad generalizations, and deductive reasoning the other way around.
Both approaches are used in various types of research, and it’s not uncommon to combine them in one large study.
Here is a qualitative study in which the researchers conducted interviews in order to obtain the subjective perspectives of the participants.
Quantitative Research: In quantitative research, the goal is to utilize the statistical data to generalize results to the population studied. Some key features include utilizing the statistics to help answer the clinical question and determine whether the hypothesis is indeed statistically supported.
There are two main types of quantitative research:
We will explore those two types in much detail in the next module.
Quantitative research differs from qualitative research in that:
Experimental Research : In the following article, the researchers introduced an intervention, which was a “Program for Enhancing the Positive Aspects of Caregiving” (a particular education program).
Non-experimental Research : In the following article, the researchers did not introduce an intervention or treatment. They handed out surveys for the participants to complete about their activity and depression levels.
Video: Qualitative Types and Experimental/Nonexperimental Research
In summary, there are two main approaches to research designs: Quantitative and qualitative. They each seek to answer questions, but quantitative research is meant to generalize its findings to the population whereas qualitative research seeks to understand phenomenon and develop theories about the human lived experiences.
References & Attribution
“ Light bulb doodle ” by rawpixel licensed CC0
“ Orange flame ” by rawpixel licensed CC0 .
Chen, P., Nunez-Smith, M., Bernheim, S… (2010). Professional experiences of international medical graduates practicing primary care in the United States. Journal of General Internal Medicine, 25 (9), 947-53.
Haedtke, C., Smith, M., VanBuren, J., Kein, D., Turvey, C. (2017). The relationships among pain, depression, and physical activity in patients with heart failure. Journal of Cardiovascular Nursing, 32 (5), E21-E25.
Pankong, O., Pothiban, L., Sucamvang, K., Khampolsiri, T. (2018). A randomized controlled trial of enhancing positive aspects of caregiving in Thai dementia caregivers for dementia. Pacific Rim Internal Journal of Nursing Res, 22 (2), 131-143.
Evidence-Based Practice & Research Methodologies Copyright © by Tracy Fawns is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.
Before beginning your paper, you need to decide how you plan to design the study .
The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!
De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.
The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.
With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.
The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :
The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.
NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.
Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.
Definition and Purpose
The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.
What do these studies tell you ?
What these studies don't tell you ?
Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA: Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.
A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.
Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.
Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.
Conditions necessary for determining causality:
Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.
Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."
Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.
Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.
Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.
Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.
A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.
An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.
The goals of exploratory research are intended to produce the following possible insights:
Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.
Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .
What these studies don't tell you
The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.
Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58; Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.
A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.
Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.
Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:
Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.
Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .
This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.
Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.
Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:
Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.
Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.
Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods . David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research." Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.
There are many different types of research studies, and each has distinct strengths and weaknesses. In general, randomized trials and cohort studies provide the best information when looking at the link between a certain factor (like diet) and a health outcome (like heart disease).
These are studies done in laboratories on cells, tissue, or animals.
These studies examine the incidence of a certain outcome (disease or other health characteristic) in a specific group of people at one point in time. Surveys are often sent to participants to gather data about the outcome of interest.
These studies look at the characteristics of one group of people who already have a certain health outcome (the cases) and compare them with a similar group of people who do not have the outcome (the controls). An example may be looking at a group of people with heart disease and another group without heart disease who are similar in age, sex, and economic status, and comparing their intakes of fruits and vegetables to see if this exposure could be associated with heart disease risk.
These are observational studies that follow large groups of people over a long period of time, years or even decades, to find associations of an exposure(s) with disease outcomes. Researchers regularly gather information from the people in the study on several variables (like meat intake, physical activity level, and weight). Once a specified amount of time has elapsed, the characteristics of people in the group are compared to test specific hypotheses (such as a link between high versus low intake of carotenoid-rich foods and glaucoma, or high versus low meat intake and prostate cancer).
Two of the largest and longest-running cohort studies of diet are the Harvard-based Nurses’ Health Study and the Health Professionals Follow-up Study.
If you follow nutrition news, chances are you have come across findings from a cohort called the Nurses’ Health Study . The Nurses’ Health Study (NHS) began in 1976, spearheaded by researchers from the Channing Laboratory at the Brigham and Women’s Hospital, Harvard Medical School, and the Harvard T.H. Chan School of Public Health, with funding from the National Institutes of Health. It gathered registered nurses ages 30-55 years from across the U.S. to respond to a series of questionnaires. Nurses were specifically chosen because of their ability to complete the health-related, often very technical, questionnaires thoroughly and accurately. They showed motivation to participate in the long-term study that required ongoing questionnaires every two years. Furthermore, the group provided blood, urine, and other samples over the course of the study.
The NHS is a prospective cohort study, meaning a group of people who are followed forward in time to examine lifestyle habits or other characteristics to see if they develop a disease, death, or some other indicated outcome. In comparison, a retrospective cohort study would specify a disease or outcome and look back in time at the group to see if there were common factors leading to the disease or outcome. A benefit of prospective studies over retrospective studies is greater accuracy in reporting details, such as food intake, that is not distorted by the diagnosis of illness.
To date, there are three NHS cohorts: NHS original cohort, NHS II, and NHS 3. Below are some features unique to each cohort.
NHS – Original Cohort
From these three cohorts, extensive research has been published regarding the association of diet, smoking, physical activity levels, overweight and obesity, oral contraceptive use, hormone therapy, endogenous hormones, dietary factors, sleep, genetics, and other behaviors and characteristics with various diseases. In 2016, in celebration of the 40 th Anniversary of NHS, the American Journal of Public Health’s September issue was dedicated to featuring the many contributions of the Nurses’ Health Studies to public health.
In 1996, recruitment began for a new cross-generational cohort called GUTS (Growing Up Today Study) —children of nurses from the NHS II. GUTS is composed of 27,802 girls and boys who were between the ages of 9 and 17 at the time of enrollment. As the entire cohort has entered adulthood, they complete annual questionnaires including information on dietary intake, weight changes, exercise level, substance and alcohol use, body image, and environmental factors. Researchers are looking at conditions more common in young adults such as asthma, skin cancer, eating disorders, and sports injuries.
Like cohort studies, these studies follow a group of people over time. However, with randomized trials, the researchers intervene with a specific behavior change or treatment (such as following a specific diet or taking a supplement) to see how it affects a health outcome. They are called “randomized trials” because people in the study are randomly assigned to either receive or not receive the intervention. This randomization helps researchers determine the true effect the intervention has on the health outcome. Those who do not receive the intervention or labelled the “control group,” which means these participants do not change their behavior, or if the study is examining the effects of a vitamin supplement, the control group participants receive a placebo supplement that contains no active ingredients.
A meta-analysis collects data from several previous studies on one topic to analyze and combine the results using statistical methods to provide a summary conclusion. Meta-analyses are usually conducted using randomized controlled trials and cohort studies that have higher quality of evidence than other designs. A systematic review also examines past literature related to a specific topic and design, analyzing the quality of studies and results but may not pool the data. Sometimes a systematic review is followed by conducting a meta-analysis if the quality of the studies is good and the data can be combined.
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Table of Contents
Study designs are frameworks used in medical research to gather data and explore a specific research question .
Choosing an appropriate study design is one of many essential considerations before conducting research to minimise bias and yield valid results .
This guide provides a summary of study designs commonly used in medical research, their characteristics, advantages and disadvantages.
A case report is a detailed description of a patient’s medical history, diagnosis, treatment, and outcome. A case report typically documents unusual or rare cases or reports new or unexpected clinical findings .
A case series is a similar study that involves a group of patients sharing a similar disease or condition. A case series involves a comprehensive review of medical records for each patient to identify common features or disease patterns. Case series help better understand a disease’s presentation, diagnosis, and treatment.
While a case report focuses on a single patient, a case series involves a group of patients to provide a broader perspective on a specific disease. Both case reports and case series are important tools for understanding rare or unusual diseases .
Advantages of case series and case reports include:
Disadvantages of case series and case reports include:
A cross-sectional study aims to measure the prevalence or frequency of a disease in a population at a specific point in time. In other words, it provides a “ snapshot ” of the population at a single moment in time.
Cross-sectional studies are unique from other study designs in that they collect data on the exposure and the outcome of interest from a sample of individuals in the population. This type of data is used to investigate the distribution of health-related conditions and behaviours in different populations, which is especially useful for guiding the development of public health interventions .
A cross-sectional study might investigate the prevalence of hypertension (the outcome) in a sample of adults in a particular region. The researchers would measure blood pressure levels in each participant and gather information on other factors that could influence blood pressure, such as age, sex, weight, and lifestyle habits (exposure).
Advantages of cross-sectional studies include:
Disadvantages of cross-sectional studies include:
A case-control study compares people who have developed a disease of interest ( cases ) with people who have not developed the disease ( controls ) to identify potential risk factors associated with the disease.
Once cases and controls have been identified, researchers then collect information about related risk factors , such as age, sex, lifestyle factors, or environmental exposures, from individuals. By comparing the prevalence of risk factors between the cases and the controls, researchers can determine the association between the risk factors and the disease.
A case-control study design might involve comparing a group of individuals with lung cancer (cases) to a group of individuals without lung cancer (controls) to assess the association between smoking (risk factor) and the development of lung cancer.
Advantages of case-control studies include:
Disadvantages of case-control studies include:
A cohort study follows a group of individuals (a cohort) over time to investigate the relationship between an exposure or risk factor and a particular outcome or health condition. Cohort studies can be further classified into prospective or retrospective cohort studies.
A prospective cohort study is a study in which the researchers select a group of individuals who do not have a particular disease or outcome of interest at the start of the study.
They then follow this cohort over time to track the number of patients who develop the outcome . Before the start of the study, information on exposure(s) of interest may also be collected.
A prospective cohort study might follow a group of individuals who have never smoked and measure their exposure to tobacco smoke over time to investigate the relationship between smoking and lung cancer .
In contrast, a retrospective cohort study is a study in which the researchers select a group of individuals who have already been exposed to something (e.g. smoking) and look back in time (for example, through patient charts) to see if they developed the outcome (e.g. lung cancer ).
The key difference in retrospective cohort studies is that data on exposure and outcome are collected after the outcome has occurred.
A retrospective cohort study might look at the medical records of smokers and see if they developed a particular adverse event such as lung cancer.
Advantages of cohort studies include:
Disadvantages of cohort studies include:
A meta-analysis is a type of study that involves extracting outcome data from all relevant studies in the literature and combining the results of multiple studies to produce an overall estimate of the effect size of an intervention or exposure.
Meta-analysis is often conducted alongside a systematic review and can be considered a study of studies . By doing this, researchers provide a more comprehensive and reliable estimate of the overall effect size and their confidence interval (a measure of precision).
Meta-analyses can be conducted for a wide range of research questions , including evaluating the effectiveness of medical interventions, identifying risk factors for disease, or assessing the accuracy of diagnostic tests. They are particularly useful when the results of individual studies are inconsistent or when the sample sizes of individual studies are small, as a meta-analysis can provide a more precise estimate of the true effect size.
When conducting a meta-analysis, researchers must carefully assess the risk of bias in each study to enhance the validity of the meta-analysis. Many aspects of research studies are prone to bias , such as the methodology and the reporting of results. Where studies exhibit a high risk of bias, authors may opt to exclude the study from the analysis or perform a subgroup or sensitivity analysis.
Advantages of a meta-analysis include:
Disadvantages of a meta-analysis include:
An ecological study assesses the relationship between outcome and exposure at a population level or among groups of people rather than studying individuals directly.
The main goal of an ecological study is to observe and analyse patterns or trends at the population level and to identify potential associations or correlations between environmental factors or exposures and health outcomes.
Ecological studies focus on collecting data on population health outcomes , such as disease or mortality rates, and environmental factors or exposures, such as air pollution, temperature, or socioeconomic status.
An ecological study might be used when comparing smoking rates and lung cancer incidence across different countries.
Advantages of an ecological study include:
Disadvantages of an ecological study include:
A randomised controlled trial (RCT) is an important study design commonly used in medical research to determine the effectiveness of a treatment or intervention . It is considered the gold standard in research design because it allows researchers to draw cause-and-effect conclusions about the effects of an intervention.
In an RCT, participants are randomly assigned to two or more groups. One group receives the intervention being tested, such as a new drug or a specific medical procedure. In contrast, the other group is a control group and receives either no intervention or a placebo .
Randomisation ensures that each participant has an equal chance of being assigned to either group, thereby minimising selection bias . To reduce bias, an RCT often uses a technique called blinding , in which study participants, researchers, or analysts are kept unaware of participant assignment during the study. The participants are then followed over time, and outcome measures are collected and compared to determine if there is any statistical difference between the intervention and control groups.
An RCT might be employed to evaluate the effectiveness of a new smoking cessation program in helping individuals quit smoking compared to the existing standard of care.
Advantages of an RCT include:
Disadvantages of an RCT include:
About the article : different types of research.
Research is a fundamental aspect of any field of study, providing a systematic approach to gather and analyze information. It plays a crucial role in expanding knowledge, solving problems, and making informed decisions. Understanding the different types of research is essential for researchers to choose the most appropriate method for their study.
In this article, we will explore the various types of research methods commonly used in academic and professional settings. Each type of research has its own unique characteristics, strengths, and limitations. By gaining a comprehensive understanding of these research types, researchers can effectively design and conduct their studies to achieve their objectives.
Exploratory research is a type of research that is used to investigate a problem that is not clearly defined and gain a better understanding of the existing problem. It is often conducted when a researcher has just begun an investigation and wishes to understand the topic generally.
There are two main methods of conducting exploratory research: primary research and secondary research. Primary research involves collecting new data directly from the source, while secondary research involves analyzing existing data that has already been collected by others.
Under these two broad types, various methods can be employed to gather information. These methods include surveys, interviews , focus groups, observations, and case studies. Each method has its own advantages and disadvantages, and the choice of method depends on the nature of the research question and the available resources.
Exploratory research is valuable because it helps researchers gain insights and generate hypotheses for further investigation. It allows them to explore new areas of study and discover potential relationships between variables. However, it is important to note that exploratory research does not provide definitive answers or conclusive results. Instead, it lays the foundation for more in-depth research and helps researchers refine their research questions and methodologies.
Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. It involves observing and measuring without manipulating variables, allowing researchers to identify characteristics, trends, and correlations. The main goal of descriptive research is to provide a detailed description of the population or phenomenon being studied. This type of research focuses on answering questions such as how, what, when, and where.
There are three basic approaches for gathering data in descriptive research: observational, case study, and survey. Observational research involves observing and recording behavior in its natural setting. Case study research involves in-depth analysis of a single individual, group, or situation. Survey research involves collecting data from a sample of individuals through questionnaires or interviews.
Descriptive research is particularly useful when researchers want to describe specific behaviors, characteristics, or trends as they occur in the environment. It provides a foundation for further research and can help generate hypotheses for future studies.
However, one limitation of descriptive research is that it does not establish causal relationships between variables. It can only provide a snapshot of the current state of the population or phenomenon being studied. Despite this limitation, descriptive research plays a crucial role in understanding and describing various aspects of the world around us.
Experimental research is a quantitative research method with a scientific approach. It is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This type of research design is popular in scientific experiments, social sciences, medical science, etc. Experimental research involves manipulating one or more variables to observe the effect on another variable. It aims to establish cause-and-effect relationships between variables. The researcher carefully controls and manipulates the independent variable(s) while measuring the dependent variable(s).
There are two broad categories of experimental research designs: true experimental designs and quasi-experimental designs. True experimental designs involve random assignment of participants to different groups and manipulation of the independent variable. Quasi-experimental designs lack random assignment but still involve manipulation of the independent variable.
One advantage of experimental research is its ability to establish causal relationships. By manipulating variables and controlling extraneous factors, researchers can determine whether changes in the independent variable(s) cause changes in the dependent variable(s). This allows for a more confident understanding of cause and effect.
Another advantage of experimental research is its versatility. It can be used in various fields and disciplines, allowing researchers to investigate a wide range of phenomena. Whether it’s testing the effectiveness of a new drug, studying the impact of different teaching methods, or exploring the relationship between variables, experimental research provides a powerful tool for scientific inquiry.
However, experimental research also has some limitations. One limitation is the potential for artificiality. In a controlled laboratory setting, variables may be manipulated in a way that does not fully reflect real-world conditions. This can limit the generalizability of the findings to real-life situations. Additionally, experimental research may face ethical considerations. Manipulating variables and potentially exposing participants to certain conditions can raise ethical concerns. Researchers must ensure that the benefits of the study outweigh any potential risks or harm to participants.
Correlational research is a type of non-experimental research that focuses on observing and measuring the relationship between two or more variables. Unlike experimental research, the researcher does not control or manipulate the variables in correlational research. The main purpose of correlational research is to determine if there is a statistical relationship between the variables being studied. It involves comparing two variables and data sources, assessing the relationship between them, and identifying any trends or patterns.
There are several types of correlational studies that can be conducted. One type is positive correlation, which occurs when an increase in one variable is associated with an increase in another variable. For example, there may be a positive correlation between income and education level, meaning that as income increases, education level also tends to increase.
On the other hand, negative correlation refers to a relationship where an increase in one variable is associated with a decrease in another variable. An example of negative correlation could be the relationship between hours spent studying and test scores. As the number of hours spent studying increases, test scores tend to decrease. Lastly, zero correlation indicates that there is no relationship between the variables being studied. This means that changes in one variable do not affect the other variable. For instance, there may be zero correlation between shoe size and intelligence.
Correlational research is commonly used in various fields, including psychology, sociology, and marketing. It provides valuable insights into the relationships between variables and helps researchers understand the patterns and trends in data. However, correlational research has its limitations. Since it does not involve manipulation of variables, it cannot establish causation. It can only identify associations between variables. Additionally, correlational research relies on the accuracy and reliability of the data collected, which can be influenced by various factors.
Causal-comparative research is a methodology used to identify cause-effect relationships between independent and dependent variables. It is a type of research method where the researcher tries to find out if there is a causal effect relationship between two or more groups or variables.
The main objective of causal-comparative research is to determine the cause or reason for pre-existing differences in groups of individuals. This research design involves comparing groups that have already been formed based on a specific characteristic or condition. The researcher then analyzes the differences between these groups to identify any causal relationships.
There are two types of causal-comparative research designs: retrospective and prospective. Retrospective causal-comparative research looks at past events or conditions to determine the cause-effect relationship. On the other hand, prospective causal-comparative research looks at current or future events or conditions to identify the causal relationship.
One example of causal-comparative research is a study comparing the critical thinking skills of students who were taught using the inquiry method versus those who were taught using the lecture method. The researcher would compare the two groups of students and analyze the differences in their critical thinking abilities to determine if the teaching method had a causal effect on their skills.
Causal-comparative research has its advantages and disadvantages. One advantage is that it allows researchers to study cause-effect relationships in situations where it is not possible or ethical to manipulate variables. It also provides valuable insights into the factors that contribute to differences between groups.
However, a limitation of causal-comparative research is that it cannot establish a cause-effect relationship with certainty, as there may be other variables or factors that influence the observed differences between groups.
Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data, qualitative research deals with data types such as text, audio, images, and video, focusing on the variety of human experiences and perspectives.
There are different types of qualitative research methods that researchers can use depending on their study requirements. Some common qualitative research methods include in-depth interviews, focus groups, ethnographic research, content analysis, and case study. In-depth interviews involve conducting one-on-one interviews with participants to gather detailed information about their experiences, opinions, and perspectives. This method allows researchers to delve deep into the thoughts and feelings of individuals and gain a comprehensive understanding of their experiences.
Focus groups involve bringing together a small group of participants to discuss a specific topic or issue. The group dynamic allows for the exploration of different perspectives and the generation of rich and diverse insights. Focus groups are particularly useful for understanding social interactions and group dynamics. Ethnographic research involves immersing the researcher in the natural environment of the participants to observe and understand their behaviors, beliefs, and cultural practices. This method allows for a holistic understanding of the social and cultural context in which individuals operate.
Content analysis involves systematically analyzing textual, audio, or visual data to identify patterns, themes, and meanings. This method is often used to analyze documents, media content, or online discussions to gain insights into societal trends, attitudes, or representations. Case study research involves in-depth investigation of a specific individual, group, or organization. Researchers collect and analyze multiple sources of data to gain a comprehensive understanding of the case under study. Case studies are particularly useful for exploring complex phenomena or unique situations.
Qualitative research provides several advantages. It allows researchers to explore complex and nuanced phenomena in depth, providing rich and detailed insights. It also allows for flexibility and adaptability in the research process, as researchers can adjust their approach based on emerging findings. Additionally, qualitative research is often used to generate hypotheses or theories that can be further tested using quantitative research methods.
However, qualitative research also has some limitations. The findings are often context-specific and may not be generalizable to a larger population. The subjective nature of qualitative data collection and analysis can introduce bias and interpretation challenges. Qualitative research also requires significant time and resources, as data collection and analysis can be time-consuming and labor-intensive.
Quantitative research is a type of research that involves collecting and analyzing numerical data to describe characteristics, find correlations, or test hypotheses. It is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, and reliance on prior studies.
There are two main types of quantitative research: primary and secondary. Primary quantitative research involves collecting data directly from the source, such as through surveys or experiments. Secondary quantitative research, on the other hand, involves analyzing existing data that has been collected by someone else.
Quantitative research methods can be used to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, and more. It provides a systematic and objective approach to studying phenomena and allows for statistical analysis to draw conclusions.
There are several types of quantitative research designs that can be used, depending on the research objectives . These include descriptive research, correlational research, causal-comparative research, and experimental research as per explained above.
In conclusion, understanding the different types of research is essential for conducting effective and meaningful studies. Each type of research has its own strengths and limitations, and researchers must carefully consider which approach is the most appropriate for their specific research question and objectives. It is important to recognize that research is an iterative process, and different types of research may be used at different stages of a study.
In summary, the various types of research offer different perspectives and methodologies for investigating and understanding the world around us. By utilizing a combination of these approaches, researchers can gain a comprehensive understanding of complex phenomena and make meaningful contributions to their fields of study.
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Bernd röhrig.
1 MDK Rheinland-Pfalz, Referat Rehabilitation/Biometrie, Alzey
2 Zentrum für Präventive Pädiatrie, Zentrum für Kinder- und Jugendmedizin, Mainz
3 Interdisziplinäres Zentrum Klinische Studien (IZKS), Fachbereich Medizin der Universität Mainz
4 Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Johannes Gutenberg Universität Mainz
The choice of study type is an important aspect of the design of medical studies. The study design and consequent study type are major determinants of a study’s scientific quality and clinical value.
This article describes the structured classification of studies into two types, primary and secondary, as well as a further subclassification of studies of primary type. This is done on the basis of a selective literature search concerning study types in medical research, in addition to the authors’ own experience.
Three main areas of medical research can be distinguished by study type: basic (experimental), clinical, and epidemiological research. Furthermore, clinical and epidemiological studies can be further subclassified as either interventional or noninterventional.
The study type that can best answer the particular research question at hand must be determined not only on a purely scientific basis, but also in view of the available financial resources, staffing, and practical feasibility (organization, medical prerequisites, number of patients, etc.).
The quality, reliability and possibility of publishing a study are decisively influenced by the selection of a proper study design. The study type is a component of the study design (see the article "Study Design in Medical Research") and must be specified before the study starts. The study type is determined by the question to be answered and decides how useful a scientific study is and how well it can be interpreted. If the wrong study type has been selected, this cannot be rectified once the study has started.
After an earlier publication dealing with aspects of study design, the present article deals with study types in primary and secondary research. The article focuses on study types in primary research. A special article will be devoted to study types in secondary research, such as meta-analyses and reviews. This article covers the classification of individual study types. The conception, implementation, advantages, disadvantages and possibilities of using the different study types are illustrated by examples. The article is based on a selective literature research on study types in medical research, as well as the authors’ own experience.
In principle, medical research is classified into primary and secondary research. While secondary research summarizes available studies in the form of reviews and meta-analyses, the actual studies are performed in primary research. Three main areas are distinguished: basic medical research, clinical research, and epidemiological research. In individual cases, it may be difficult to classify individual studies to one of these three main categories or to the subcategories. In the interests of clarity and to avoid excessive length, the authors will dispense with discussing special areas of research, such as health services research, quality assurance, or clinical epidemiology. Figure 1 gives an overview of the different study types in medical research.
Classification of different study types
*1 , sometimes known as experimental research; *2 , analogous term: interventional; *3 , analogous term: noninterventional or nonexperimental
This scheme is intended to classify the study types as clearly as possible. In the interests of clarity, we have excluded clinical epidemiology — a subject which borders on both clinical and epidemiological research ( 3 ). The study types in this area can be found under clinical research and epidemiology.
Basic medical research (otherwise known as experimental research) includes animal experiments, cell studies, biochemical, genetic and physiological investigations, and studies on the properties of drugs and materials. In almost all experiments, at least one independent variable is varied and the effects on the dependent variable are investigated. The procedure and the experimental design can be precisely specified and implemented ( 1 ). For example, the population, number of groups, case numbers, treatments and dosages can be exactly specified. It is also important that confounding factors should be specifically controlled or reduced. In experiments, specific hypotheses are investigated and causal statements are made. High internal validity (= unambiguity) is achieved by setting up standardized experimental conditions, with low variability in the units of observation (for example, cells, animals or materials). External validity is a more difficult issue. Laboratory conditions cannot always be directly transferred to normal clinical practice and processes in isolated cells or in animals are not equivalent to those in man (= generalizability) ( 2 ).
Basic research also includes the development and improvement of analytical procedures—such as analytical determination of enzymes, markers or genes—, imaging procedures—such as computed tomography or magnetic resonance imaging—, and gene sequencing—such as the link between eye color and specific gene sequences. The development of biometric procedures—such as statistical test procedures, modeling and statistical evaluation strategies—also belongs here.
Clinical studies include both interventional (or experimental) studies and noninterventional (or observational) studies. A clinical drug study is an interventional clinical study, defined according to §4 Paragraph 23 of the Medicines Act [Arzneimittelgesetz; AMG] as "any study performed on man with the purpose of studying or demonstrating the clinical or pharmacological effects of drugs, to establish side effects, or to investigate absorption, distribution, metabolism or elimination, with the aim of providing clear evidence of the efficacy or safety of the drug."
Interventional studies also include studies on medical devices and studies in which surgical, physical or psychotherapeutic procedures are examined. In contrast to clinical studies, §4 Paragraph 23 of the AMG describes noninterventional studies as follows: "A noninterventional study is a study in the context of which knowledge from the treatment of persons with drugs in accordance with the instructions for use specified in their registration is analyzed using epidemiological methods. The diagnosis, treatment and monitoring are not performed according to a previously specified study protocol, but exclusively according to medical practice."
The aim of an interventional clinical study is to compare treatment procedures within a patient population, which should exhibit as few as possible internal differences, apart from the treatment ( 4 , e1 ). This is to be achieved by appropriate measures, particularly by random allocation of the patients to the groups, thus avoiding bias in the result. Possible therapies include a drug, an operation, the therapeutic use of a medical device such as a stent, or physiotherapy, acupuncture, psychosocial intervention, rehabilitation measures, training or diet. Vaccine studies also count as interventional studies in Germany and are performed as clinical studies according to the AMG.
Interventional clinical studies are subject to a variety of legal and ethical requirements, including the Medicines Act and the Law on Medical Devices. Studies with medical devices must be registered by the responsible authorities, who must also approve studies with drugs. Drug studies also require a favorable ruling from the responsible ethics committee. A study must be performed in accordance with the binding rules of Good Clinical Practice (GCP) ( 5 , e2 – e4 ). For clinical studies on persons capable of giving consent, it is absolutely essential that the patient should sign a declaration of consent (informed consent) ( e2 ). A control group is included in most clinical studies. This group receives another treatment regimen and/or placebo—a therapy without substantial efficacy. The selection of the control group must not only be ethically defensible, but also be suitable for answering the most important questions in the study ( e5 ).
Clinical studies should ideally include randomization, in which the patients are allocated by chance to the therapy arms. This procedure is performed with random numbers or computer algorithms ( 6 – 8 ). Randomization ensures that the patients will be allocated to the different groups in a balanced manner and that possible confounding factors—such as risk factors, comorbidities and genetic variabilities—will be distributed by chance between the groups (structural equivalence) ( 9 , 10 ). Randomization is intended to maximize homogeneity between the groups and prevent, for example, a specific therapy being reserved for patients with a particularly favorable prognosis (such as young patients in good physical condition) ( 11 ).
Blinding is another suitable method to avoid bias. A distinction is made between single and double blinding. With single blinding, the patient is unaware which treatment he is receiving, while, with double blinding, neither the patient nor the investigator knows which treatment is planned. Blinding the patient and investigator excludes possible subjective (even subconscious) influences on the evaluation of a specific therapy (e.g. drug administration versus placebo). Thus, double blinding ensures that the patient or therapy groups are both handled and observed in the same manner. The highest possible degree of blinding should always be selected. The study statistician should also remain blinded until the details of the evaluation have finally been specified.
A well designed clinical study must also include case number planning. This ensures that the assumed therapeutic effect can be recognized as such, with a previously specified statistical probability (statistical power) ( 4 , 6 , 12 ).
It is important for the performance of a clinical trial that it should be carefully planned and that the exact clinical details and methods should be specified in the study protocol ( 13 ). It is, however, also important that the implementation of the study according to the protocol, as well as data collection, must be monitored. For a first class study, data quality must be ensured by double data entry, programming plausibility tests, and evaluation by a biometrician. International recommendations for the reporting of randomized clinical studies can be found in the CONSORT statement (Consolidated Standards of Reporting Trials, www.consort-statement.org ) ( 14 ). Many journals make this an essential condition for publication.
For all the methodological reasons mentioned above and for ethical reasons, the randomized controlled and blinded clinical trial with case number planning is accepted as the gold standard for testing the efficacy and safety of therapies or drugs ( 4 , e1 , 15 ).
In contrast, noninterventional clinical studies (NIS) are patient-related observational studies, in which patients are given an individually specified therapy. The responsible physician specifies the therapy on the basis of the medical diagnosis and the patient’s wishes. NIS include noninterventional therapeutic studies, prognostic studies, observational drug studies, secondary data analyses, case series and single case analyses ( 13 , 16 ). Similarly to clinical studies, noninterventional therapy studies include comparison between therapies; however, the treatment is exclusively according to the physician’s discretion. The evaluation is often retrospective. Prognostic studies examine the influence of prognostic factors (such as tumor stage, functional state, or body mass index) on the further course of a disease. Diagnostic studies are another class of observational studies, in which either the quality of a diagnostic method is compared to an established method (ideally a gold standard), or an investigator is compared with one or several other investigators (inter-rater comparison) or with himself at different time points (intra-rater comparison) ( e1 ). If an event is very rare (such as a rare disease or an individual course of treatment), a single-case study, or a case series, are possibilities. A case series is a study on a larger patient group with a specific disease. For example, after the discovery of the AIDS virus, the Center for Disease Control (CDC) in the USA collected a case series of 1000 patients, in order to study frequent complications of this infection. The lack of a control group is a disadvantage of case series. For this reason, case series are primarily used for descriptive purposes ( 3 ).
The main point of interest in epidemiological studies is to investigate the distribution and historical changes in the frequency of diseases and the causes for these. Analogously to clinical studies, a distinction is made between experimental and observational epidemiological studies ( 16 , 17 ).
Interventional studies are experimental in character and are further subdivided into field studies (sample from an area, such as a large region or a country) and group studies (sample from a specific group, such as a specific social or ethnic group). One example was the investigation of the iodine supplementation of cooking salt to prevent cretinism in a region with iodine deficiency. On the other hand, many interventions are unsuitable for randomized intervention studies, for ethical, social or political reasons, as the exposure may be harmful to the subjects ( 17 ).
Observational epidemiological studies can be further subdivided into cohort studies (follow-up studies), case control studies, cross-sectional studies (prevalence studies), and ecological studies (correlation studies or studies with aggregated data).
In contrast, studies with only descriptive evaluation are restricted to a simple depiction of the frequency (incidence and prevalence) and distribution of a disease within a population. The objective of the description may also be the regular recording of information (monitoring, surveillance). Registry data are also suited for the description of prevalence and incidence; for example, they are used for national health reports in Germany.
In the simplest case, cohort studies involve the observation of two healthy groups of subjects over time. One group is exposed to a specific substance (for example, workers in a chemical factory) and the other is not exposed. It is recorded prospectively (into the future) how often a specific disease (such as lung cancer) occurs in the two groups ( figure 2a ). The incidence for the occurrence of the disease can be determined for both groups. Moreover, the relative risk (quotient of the incidence rates) is a very important statistical parameter which can be calculated in cohort studies. For rare types of exposure, the general population can be used as controls ( e6 ). All evaluations naturally consider the age and gender distributions in the corresponding cohorts. The objective of cohort studies is to record detailed information on the exposure and on confounding factors, such as the duration of employment, the maximum and the cumulated exposure. One well known cohort study is the British Doctors Study, which prospectively examined the effect of smoking on mortality among British doctors over a period of decades ( e7 ). Cohort studies are well suited for detecting causal connections between exposure and the development of disease. On the other hand, cohort studies often demand a great deal of time, organization, and money. So-called historical cohort studies represent a special case. In this case, all data on exposure and effect (illness) are already available at the start of the study and are analyzed retrospectively. For example, studies of this sort are used to investigate occupational forms of cancer. They are usually cheaper ( 16 ).
Graphical depiction of a prospective cohort study (simplest case [2a]) and a retrospective case control study (2b)
In case control studies, cases are compared with controls. Cases are persons who fall ill from the disease in question. Controls are persons who are not ill, but are otherwise comparable to the cases. A retrospective analysis is performed to establish to what extent persons in the case and control groups were exposed ( figure 2b ). Possible exposure factors include smoking, nutrition and pollutant load. Care should be taken that the intensity and duration of the exposure is analyzed as carefully and in as detailed a manner as possible. If it is observed that ill people are more often exposed than healthy people, it may be concluded that there is a link between the illness and the risk factor. In case control studies, the most important statistical parameter is the odds ratio. Case control studies usually require less time and fewer resources than cohort studies ( 16 ). The disadvantage of case control studies is that the incidence rate (rate of new cases) cannot be calculated. There is also a great risk of bias from the selection of the study population ("selection bias") and from faulty recall ("recall bias") (see too the article "Avoiding Bias in Observational Studies"). Table 1 presents an overview of possible types of epidemiological study ( e8 ). Table 2 summarizes the advantages and disadvantages of observational studies ( 16 ).
Study of rare diseases such as cancers | Case control studies |
Study of rare exposure, such as exposure to industrial chemicals | Cohort studies in a population group in which there has been exposure (e.g. industrial workers) |
Study of multiple exposures, such as the combined effect of oral contraceptives and smoking on myocardial infarction | Case control studies |
Study of multiple end points, such as mortality from different causes | Cohort studies |
Estimate of the incidence rate in exposed populations | Exclusively cohort studies |
Study of covariables which change over time | Preferably cohort studies |
Study of the effect of interventions | Intervention studies |
Selection bias | N/A | 2 | 3 | 1 |
Recall bias | N/A | 3 | 3 | 1 |
Loss to follow-up | N/A | N/A | 1 | 3 |
Confounding | 3 | 2 | 2 | 1 |
Time required | 1 | 2 | 2 | 3 |
Costs | 1 | 2 | 2 | 3 |
1 = slight; 2 = moderate; 3 = high; N/A, not applicable.
*Individual cases may deviate from this pattern.
Selecting the correct study type is an important aspect of study design (see "Study Design in Medical Research" in volume 11/2009). However, the scientific questions can only be correctly answered if the study is planned and performed at a qualitatively high level ( e9 ). It is very important to consider or even eliminate possible interfering factors (or confounders), as otherwise the result cannot be adequately interpreted. Confounders are characteristics which influence the target parameters. Although this influence is not of primary interest, it can interfere with the connection between the target parameter and the factors that are of interest. The influence of confounders can be minimized or eliminated by standardizing the procedure, stratification ( 18 ), or adjustment ( 19 ).
The decision as to which study type is suitable to answer a specific primary research question must be based not only on scientific considerations, but also on issues related to resources (personnel and finances), hospital capacity, and practicability. Many epidemiological studies can only be implemented if there is access to registry data. The demands for planning, implementation, and statistical evaluation for observational studies should be just as high for observational studies as for experimental studies. There are particularly strict requirements, with legally based regulations (such as the Medicines Act and Good Clinical Practice), for the planning, implementation, and evaluation of clinical studies. A study protocol must be prepared for both interventional and noninterventional studies ( 6 , 13 ). The study protocol must contain information on the conditions, question to be answered (objective), the methods of measurement, the implementation, organization, study population, data management, case number planning, the biometric evaluation, and the clinical relevance of the question to be answered ( 13 ).
Important and justified ethical considerations may restrict studies with optimal scientific and statistical features. A randomized intervention study under strictly controlled conditions of the effect of exposure to harmful factors (such as smoking, radiation, or a fatty diet) is not possible and not permissible for ethical reasons. Observational studies are a possible alternative to interventional studies, even though observational studies are less reliable and less easy to control ( 17 ).
A medical study should always be published in a peer reviewed journal. Depending on the study type, there are recommendations and checklists for presenting the results. For example, these may include a description of the population, the procedure for missing values and confounders, and information on statistical parameters. Recommendations and guidelines are available for clinical studies ( 14 , 20 , e10 , e11 ), for diagnostic studies ( 21 , 22 , e12 ), and for epidemiological studies ( 23 , e13 ). Since 2004, the WHO has demanded that studies should be registered in a public registry, such as www.controlled-trials.com or www.clinicaltrials.gov . This demand is supported by the International Committee of Medical Journal Editors (ICMJE) ( 24 ), which specifies that the registration of the study before inclusion of the first subject is an essential condition for the publication of the study results ( e14 ).
When specifying the study type and study design for medical studies, it is essential to collaborate with an experienced biometrician. The quality and reliability of the study can be decisively improved if all important details are planned together ( 12 , 25 ).
Translated from the original German by Rodney A. Yeates, M.A., Ph.D.
Conflict of interest statement
The authors declare that there is no conflict of interest in the sense of the International Committee of Medical Journal Editors.
Chris Drew (PhD)
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]
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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:
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 .
Research methods can be broadly categorized into two types: quantitative and qualitative.
These can be further broken down into a range of specific research methods and designs:
Primarily Quantitative Methods | Primarily Qualitative methods |
---|---|
Experimental Research | Case Study |
Surveys and Questionnaires | Ethnography |
Longitudinal Studies | Phenomenology |
Cross-Sectional Studies | Historical research |
Correlational Research | Content analysis |
Causal-Comparative Research | Grounded theory |
Meta-Analysis | Action research |
Quasi-Experimental Design | Observational research |
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:
Let’s explore some methods and designs from both quantitative and qualitative traditions, starting with 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.
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.
Pros of Ethnographic Research | Cons of Ethnographic Research |
---|---|
1. Provides deep cultural insights | 1. Time-consuming |
2. Contextually relevant findings | 2. Potential researcher bias |
3. Explores dynamic social processes | 3. May |
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.
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’.
Pros of Phenomenological Research | Cons of Phenomenological Research |
---|---|
1. Provides rich, detailed data | 1. Limited generalizability |
2. Highlights personal experience and perceptions | 2. Data collection can be time-consuming |
3. Allows exploration of complex phenomena | 3. Requires highly skilled researchers |
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.
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.
Pros of Historical Research | Cons of Historical Research |
---|---|
1. | 1. Dependent on available sources |
2. Can help understand current events or trends | 2. Potential bias in source materials |
3. Allows the study of change over time | 3. Difficult to replicate |
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.
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 .
Pros of Content Analysis | Cons of Content Analysis |
---|---|
1. Unobtrusive data collection | 1. Lacks contextual information |
2. Allows for large sample analysis | 2. Potential coder bias |
3. Replicable and reliable if done properly | 3. May overlook nuances |
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.
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).
Pros of Grounded Theory Research | Cons of Grounded Theory Research |
---|---|
1. Helps with theory development | 1. Time-consuming |
2. Rigorous data analysis | 2. Requires iterative data collection and analysis |
3. Can fill gaps in existing theories | 3. Requires skilled researchers |
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.
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.
Pros of Action Research | Cons of Action Research |
---|---|
1. Addresses real-world problems and seeks to find solutions. | 1. It is time-consuming and often hard to implement into a practitioner’s already busy schedule |
2. Integrates research and action in an action-research cycle. | 2. Requires collaboration between researcher, practitioner, and research participants. |
3. Can bring about positive change in isolated instances, such as in a school or nursery setting. | 3. Complexity of managing dual roles (where the researcher is also often the practitioner) |
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.
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.
Pros of Qualitative Observational Research | Cons of Qualitative Observational Research |
---|---|
1. Captures behavior in natural settings, allowing for interesting insights into authentic behaviors. | 1. Researcher’s presence may influence behavior |
2. Can provide rich, detailed data through the researcher’s vignettes. | 2. Can be time-consuming |
3. Non-invasive because researchers want to observe natural activities rather than interfering with research participants. | 3. Requires skilled and trained observers |
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.
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).
Pros of Case Study Research | Cons of Case Study Research |
---|---|
1. Provides detailed insights | 1. Limited generalizability |
2. Facilitates the study of complex phenomena | 2. Can be time-consuming |
3. Can test or generate theories | 3. Subject to observer bias |
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 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.
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.
Pros of Experimental Research | Cons of Experimental Research |
---|---|
1. Allows for determination of causality | 1. Might not reflect real-world conditions |
2. Allows for the study of phenomena in highly controlled environments to minimize research contamination. | 2. Can be costly and time-consuming to create a controlled environment. |
3. Can be replicated so other researchers can test and verify the results. | 3. Ethical concerns need to be addressed as the research is directly manipulating variables. |
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).
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).
Pros of Surveys and Questionnaires | Cons of Surveys and Questionnaires |
---|---|
1. Data can be gathered from larger samples than is possible in qualitative research. | 1. There is heavy dependence on respondent honesty |
2. The data is quantifiable, allowing for comparison across subpopulations | 2. There is limited depth of response as opposed to qualitative approaches. |
3. Can be cost-effective and time-efficient | 3. Static with no flexibility to explore responses (unlike semi- or unstrcutured interviewing) |
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).
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.
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.
Pros of Longitudinal Studies | Cons of Longitudinal Studies |
---|---|
1. Tracks changes over time allowing for comparison of past to present events. | 1. Is almost by definition time-consuming because time needs to pass between each data collection session. |
2. Can identify sequences of events, but causality is often harder to determine. | 2. There is high risk of participant dropout over time as participants move on with 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.
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.
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.
Pros of Cross-Sectional Studies | Cons of Cross-Sectional Studies |
---|---|
1. Quick and inexpensive, with no long-term commitment required. | 1. Cannot determine causality because it is a simple snapshot, with no time delay between data collection points. |
2. Good for descriptive analyses. | 2. Does not allow researchers to follow up with research participants. |
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.
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.
Pros of Correlational Research | Cons of Correlational Research |
---|---|
1. Reveals relationships between variables | 1. Cannot determine causality |
2. Can use existing data | 2. May be |
3. Can guide further experimental research | 3. Correlation may be coincidental |
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.
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.
Pros | Cons |
---|---|
1. It’s more feasible to implement than true experiments. | 1. Without random assignment, it’s harder to rule out confounding variables. |
2. It can be conducted in real-world settings, making the findings more applicable to the real world. | 2. The lack of random assignment may of the study. |
3. Useful when it’s unethical or impossible to manipulate the independent variable or randomly assign participants. | 3. It’s more difficult to establish a cause-effect relationship due to the potential for confounding variables. |
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
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.
Pros | Cons |
---|---|
Increased Statistical Power: By combining data from multiple studies, meta-analysis increases the statistical power to detect effects. | Publication Bias: Studies with null or negative findings are less likely to be published, leading to an overestimation of effect sizes. |
Greater Precision: It provides more precise estimates of effect sizes by reducing the influence of random error. | Quality of Studies: of a meta-analysis depends on the quality of the studies included. |
Resolving Discrepancies: Meta-analysis can help resolve disagreements between different studies on a topic. | Heterogeneity: Differences in study design, sample, or procedures can introduce heterogeneity, complicating interpretation of results. |
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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The research builds on previous findings connecting red and processed meats with Type 2 diabetes.
By Alice Callahan
For sausage, salami and steak lovers, the news has not been good. Scientists have been consistently finding links between red and processed meat consumption and heart disease , some types of cancer and earlier death .
And now, two recent studies have added to the growing body of evidence that a meat-heavy diet may increase the risk of Type 2 diabetes.
In one of the studies, published today in The Lancet Diabetes and Endocrinology , researchers analyzed data from nearly two million adults participating in 31 studies across 20 countries, including the United States and parts of Europe and Asia.
The researchers reviewed survey data on participants’ diets and then looked at their health an average of 10 years later. After adjusting for other risk factors like smoking, a higher body mass index, physical inactivity and a family history of diabetes, they found that for every 1.8 ounces of processed meat the participants ate each day, their risk for Type 2 diabetes increased by 15 percent. (This is equivalent to a medium-sized sausage or two to three slices of bacon.) For every 3.5 ounces of unprocessed red meat they consumed daily, their risk increased by 10 percent. (This is about the size of a small steak.)
The data also suggested that one serving of poultry per day was associated with an 8 percent increase in Type 2 diabetes risk, but this finding was less consistent and only significant in the European studies, so more research is needed, said Dr. Nita Forouhi, a professor of population health and nutrition at the University of Cambridge who led the study.
The takeaway, she said, is that the less red and processed meat you eat, the better.
These findings jibe with previous research, including a large U.S. study published in October .
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Feeling like everything in your body seems to be deteriorating at once? You might not be imagining it if you're at a certain point in your 40s or 60s.
It's often been the belief that your body starts to steadily age right as you're finished growing and maturing. But a new study says that instead of aging being a linear, gradual process, it comes with at least two massive bursts, hitting humans at the average ages of 44 and 60 and often bringing along negative health impacts.
Stanford Medicine researchers came to this conclusion after tracking the levels of more than 135,000 different molecules and microbes in 108 people, aged 25 to 75, for one to nearly seven years. During this time, the researchers gathered blood and stool samples and skin, oral and nasal swabs from the participants every three to six months to test for age-related changes in their molecular profiles.
The results, published in the journal Nature Aging , revealed that the amount of molecules and microbes in the body don't change steadily or chronologically. Rather, humans go through two periods in which these dramatically change in abundance, first at around age 44 and the next at age 60.
RELATED STORY | A year of strength training can provide years of benefits for seniors, study finds
Researchers said that the shift at age 60 wasn't too surprising, as it's known that many age-related disease risks increase at that point in life, but the cluster of change in the mid-40s was a new discovery.
Though at first the scientists thought the number might be related to the average menopausal age for women — which is between 45 and 55 — they later found that the shift happens for men at the same time.
"This suggests that while menopause or perimenopause may contribute to the changes observed in women in their mid-40s, there are likely other, more significant factors influencing these changes in both men and women. Identifying and studying these factors should be a priority for future research," said Dr. Xiaotao Shen, the study's first author.
At the mid-40s aging burst, the study showed significant changes in the levels of molecules linked to skin and muscle, cardiovascular disease and the ability to metabolize alcohol, caffeine and lipids. Then at the 60-year aging burst, researchers also found molecular shifts linked to skin and muscle, cardiovascular disease and caffeine metabolism. But there were also changes linked to immune regulation, kidney function and carbohydrate metabolism.
RELATED STORY | Healthy lifestyle could offset 'bad genes' and extend your life, analysis says
The researchers noted that some of these biological shifts could be tied to lifestyle or behavioral changes: If a person in their mid-40s is consuming more alcohol, they could see a shift in their alcohol metabolism function, for example. But they say this indicates a need for more exploration into why these clusters change.
In any case, the scientists say the existence of the two aging bursts suggests people should pay closer attention to their health, particularly at those two ages. Useful habits could include maintaining muscle mass, keeping your heart and weight healthy, reducing alcohol and caffeine in your mid-40s and more.
"I'm a big believer that we should try to adjust our lifestyles while we're still healthy," said Dr. Michael Snyder, the study's senior author.
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Methodology
Research methods are specific procedures for collecting and analyzing 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 :
Second, decide how you will analyze the data .
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is 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.
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 .
Qualitative | to broader populations. . | |
---|---|---|
Quantitative | . |
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data 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 synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Primary | . | methods. |
---|---|---|
Secondary |
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.
Descriptive | . . | |
---|---|---|
Experimental |
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Research method | Primary or secondary? | Qualitative or quantitative? | When to use |
---|---|---|---|
Primary | Quantitative | To test cause-and-effect relationships. | |
Primary | Quantitative | To understand general characteristics of a population. | |
Interview/focus group | Primary | Qualitative | To gain more in-depth understanding of a topic. |
Observation | Primary | Either | To understand how something occurs in its natural setting. |
Secondary | Either | To situate your research in an existing body of work, or to evaluate trends within a research topic. | |
Either | Either | To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. |
Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
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 and be careful to avoid research bias .
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 was collected either:
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
Research method | Qualitative or quantitative? | When to use |
---|---|---|
Quantitative | To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). | |
Meta-analysis | Quantitative | To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. |
Qualitative | To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. | |
Either | To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). |
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.
Research bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
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.
In statistics, sampling allows you to test a hypothesis about the characteristics of a population.
The research methods you use depend on the type of data you need to answer your research question .
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 analyze data (for example, 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.
Other students also liked, writing strong research questions | criteria & examples.
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There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following ...
Here are six common types of research studies, along with examples that help explain the advantages and disadvantages of each: 1. Meta-analysis. A meta-analysis study helps researchers compile the quantitative data available from previous studies. It's an observational study in which the researchers don't manipulate variables.
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
Other interesting articles. 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. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.
Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the ...
Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...
Figure 2.3. The hierarchy of evidence shows types of research studies relative to their strength of evidence and relevance to real-life nutrition decisions, with the strongest studies at the top and the weakest at the bottom. The pyramid also represents a few other general ideas.
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.
An observational study is a study in which the investigator cannot control the assignment of treatment to subjects because the participants or conditions are not directly assigned by the researcher.. Examines predetermined treatments, interventions, policies, and their effects; Four main types: case series, case-control studies, cross-sectional studies, and cohort studies
This guide provides a high-level overview of research types, study designs, and types of data you may encounter when searching for information on your topic. Understand Evidence-Based Practice. ... Sometimes, a research study will look at the results of many studies, look for trends and draw conclusions. These types of studies include: Meta ...
a form of inquiry in which qualitative research findings about a process or experience are aggregated or integrated across research studies. Aims can involve synthesizing qualitative findings across primary studies, generating new theoretical or conceptual models, identifying gaps in research, or generating new questions.
Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies. Other interesting articles.
The two major classes of research are: Qualitative Research - subjective, seeks a human's experience as a narrative. Quantitative Research - objective, seeks to statistically make inferences about a sample to generalize to the larger population. We need to have a solid understanding of the difference between the two main types of research ...
Before beginning your paper, you need to decide how you plan to design the study.. The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection ...
Cohort Studies. These are observational studies that follow large groups of people over a long period of time, years or even decades, to find associations of an exposure (s) with disease outcomes. Researchers regularly gather information from the people in the study on several variables (like meat intake, physical activity level, and weight).
Example: A researcher examines if and how employee satisfaction changes in the same employees after one year, three years and five years with the same company. 16. Mixed research. Mixed research includes both qualitative and quantitative data. The results are often presented as a mix of graphs, words and images.
A randomised controlled trial (RCT) is an important study design commonly used in medical research to determine the effectiveness of a treatment or intervention. It is considered the gold standard in research design because it allows researchers to draw cause-and-effect conclusions about the effects of an intervention.
It is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, and reliance on prior studies. There are two main types of quantitative research: primary and secondary. Primary quantitative research involves collecting data directly from the source, such as through surveys or experiments.
Quantitative research Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts. about a topic. Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.
What are the main types of qualitative approaches to research? While there are many different investigations that can be done, a study with a qualitative approach generally can be described with the characteristics of one of the following three types: Historical research describes past events, problems, issues and facts. Data are gathered from ...
This article describes the structured classification of studies into two types, primary and secondary, as well as a further subclassification of studies of primary type. This is done on the basis of a selective literature search concerning study types in medical research, in addition to the authors' own experience. ...
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).
Research Questions and Types of Statistical Studies. In a statistical study, a population is a set of all people or objects that share certain characteristics.A sample is a subset of the population used in the study.Subjects are the individuals or objects in the sample.Subjects are often people, but could be animals, plants, or things. Variables are the characteristics of the subjects we study.
The research builds on previous findings connecting red and processed meats with Type 2 diabetes. By Alice Callahan For sausage, salami and steak lovers, the news has not been good. Scientists ...
The aim of this study is to evaluate safety, and tolerability of insulin LY3209590 following a single dose given to children with type 2 diabetes mellitus (T2DM). This will be the first study to evaluate LY3209590 in pediatric patients with T2DM. ScreeningAll participants will be screened up to 28 days prior to dosing.
Identifying and studying these factors should be a priority for future research," said Dr. Xiaotao Shen, the study's first author. At the mid-40s aging burst, the study showed significant changes in the levels of molecules linked to skin and muscle, cardiovascular disease and the ability to metabolize alcohol, caffeine and lipids.
Research methods are specific procedures for collecting and analyzing 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:
The study is the most comprehensive to date showing the link between processed meat and unprocessed red meat with type 2 diabetes, said senior study ... study. But the new research does line up ...
Note that the conditions presented in Study 1 and Study 2 are part of a larger study reflected in the preregistration (i.e., a subset of experimental conditions is presented here), and thus, data analytic plans were altered slightly to accommodate the exclusion of certain parts of the larger studies (e.g., moving to t tests from analyses of ...
The Type 1 Diabetes Grand Challenge, supported by the Steve Morgan Foundation, Diabetes UK, and JDRF, awarded over £2.7 million to various research projects, on August 12, 2024. These funds aim to create advanced insulin therapies that better manage type 1 diabetes by mimicking how a healthy pancreas functions. Understanding Type 1 Diabetes