is the research approach or type of analysis clearly described

Organizing Your Social Sciences Research Paper: Types of Research Designs

Purpose of Guide
Writing a Research Proposal
Design Flaws to Avoid
Independent and Dependent Variables
Narrowing a Topic Idea
Broadening a Topic Idea
The Research Problem/Question
Academic Writing Style
Choosing a Title
Making an Outline
Paragraph Development
The C.A.R.S. Model
Background Information
Theoretical Framework
Citation Tracking
Evaluating Sources
Reading Research Effectively
Primary Sources
Secondary Sources
What Is Scholarly vs. Popular?
Is it Peer-Reviewed?
Qualitative Methods
Quantitative Methods
Common Grammar Mistakes
Writing Concisely
Avoiding Plagiarism [linked guide]
Annotated Bibliography
Grading Someone Else's Paper

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, 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.

General Structure and Writing Style

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 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 far too early, 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 research designs in your paper can vary considerably, but any well-developed design will achieve the following :

Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
Review and synthesize previously published literature associated with the research problem,
Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
Effectively describe the data which will be necessary for an adequate testing of the hypotheses and explain how such data will be obtained, and
Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide 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.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

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:

Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.

What do these studies tell you ?

Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
Replication is possible.
There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.

What these studies don't tell you ?

Not all relationships are casual! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the actual effect.

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.

Cohort Design

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, r ather 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."

Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
Either original data or secondary data can be used in this design.
In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

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 Design

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.

Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
Studies cannot be utilized to establish cause and effect relationships.
This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
There is no follow up to the 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 Design

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.

The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
If the limitations are understood, they can be a useful tool in developing a more focused study.
Descriptive studies can yield rich data that lead to important recommendations in practice.
Appoach collects a large amount of data for detailed analysis.
The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

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. Explorable.com website.

Experimental Design

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.

Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
Approach provides the highest level of evidence for single studies.
The design is artificial, and results may not generalize well to the real world.
The artificial settings of experiments may alter the behaviors or responses of participants.
Experimental designs can be costly if special equipment or facilities are needed.
Some research problems cannot be studied using an experiment because of ethical or technical reasons.
Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

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.

Exploratory Design

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:

Familiarity with basic details, settings, and concerns.
Well grounded picture of the situation being developed.
Generation of new ideas and assumptions.
Development of tentative theories or hypotheses.
Determination about whether a study is feasible in the future.
Issues get refined for more systematic investigation and formulation of new research questions.
Direction for future research and techniques get developed.
Design is a useful approach for gaining background information on a particular topic.
Exploratory research is flexible and can address research questions of all types (what, why, how).
Provides an opportunity to define new terms and clarify existing concepts.
Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

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.

Historical Design

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.

The historical research design is unobtrusive; the act of research does not affect the results of the study.
The historical approach is well suited for trend analysis.
Historical records can add important contextual background required to more fully understand and interpret a research problem.
There is often no possibility of researcher-subject interaction that could affect the findings.
Historical sources can be used over and over to study different research problems or to replicate a previous study.
The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
Interpreting historical sources can be very time consuming.
The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

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.

Longitudinal Design

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.

Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
The data collection method may change over time.
Maintaining the integrity of the original sample can be difficult over an extended period of time.
It can be difficult to show more than one variable at a time.
This design often needs qualitative research data to explain fluctuations in the results.
A longitudinal research design assumes present trends will continue unchanged.
It can take a long period of time to gather results.
There is a need to have a large sample size and accurate sampling to reach representativness.

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.

Mixed-Method Design

Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
The strengths of one method can be used to overcome the inherent weaknesses of another method.
Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

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 .

Observational Design

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.

Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
The researcher is able to collect in-depth information about a particular behavior.
Can reveal interrelationships among multifaceted dimensions of group interactions.
You can generalize your results to real life situations.
Observational research is useful for discovering what variables may be important before applying other methods like experiments.
Observation research designs account for the complexity of group behaviors.
Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
There can be problems with bias as the researcher may only "see what they want to see."
There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
Sources or subjects may not all be equally credible.
Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

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.

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Research Types and Approaches

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There are many ways to classify different types of academic research. The words you use to describe your research depend on your goals, approach, intended methods, strategies, and your field of research.

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Bovaird, J. A., & Kevin A. K. (2010). Sequential design. In N. J. Salkind (Ed.), Encyclopaedia of research design. Thousand Oaks, CA: Sage.

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Brew, A. (2007). Academic autonomy and research decision-making: The researcher’s view. In C. Kayrooz, G. S. Akerlind, & M. Tight (Eds.), Autonomy in social science research: The view from United Kingdom and Australian Universities. UK: Oxford.

Gray, E. D. (2018). Doing research in the real world (4t ed.). Sage.

Greer, S. (2005). Encyclopaedia of social measuremen . Sacred Heart University. https://library.sacredheart.edu/c.php?g=29803&p=185902 .

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A Comprehensive Guide to Different Types of Research

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When embarking on a research project, selecting the right methodology can be the difference between success and failure. With various methods available, each suited to different types of research, it’s essential you make an informed choice. This blog post will provide tips on how to choose a research methodology that best fits your research goals .

We’ll start with definitions: Research is the systematic process of exploring, investigating, and discovering new information or validating existing knowledge. It involves defining questions, collecting data, analyzing results, and drawing conclusions.

Meanwhile, a research methodology is a structured plan that outlines how your research is to be conducted. A complete methodology should detail the strategies, processes, and techniques you plan to use for your data collection and analysis.

a computer keyboard being worked by a researcher

Research Methods

The first step of a research methodology is to identify a focused research topic, which is the question you seek to answer. By setting clear boundaries on the scope of your research, you can concentrate on specific aspects of a problem without being overwhelmed by information. This will produce more accurate findings.

Along with clarifying your research topic, your methodology should also address your research methods. Let’s look at the four main types of research: descriptive, correlational, experimental, and diagnostic.

Descriptive Research

Descriptive research is an approach designed to describe the characteristics of a population systematically and accurately. This method focuses on answering “what” questions by providing detailed observations about the subject. Descriptive research employs surveys, observational studies , and case studies to gather qualitative or quantitative data.

A real-world example of descriptive research is a survey investigating consumer behavior toward a competitor’s product. By analyzing the survey results, the company can gather detailed insights into how consumers perceive a competitor’s product, which can inform their marketing strategies and product development.

Correlational Research

Correlational research examines the statistical relationship between two or more variables to determine whether a relationship exists. Correlational research is particularly useful when ethical or practical constraints prevent experimental manipulation. It is often employed in fields such as psychology, education, and health sciences to provide insights into complex real-world interactions, helping to develop theories and inform further experimental research.

An example of correlational research is the study of the relationship between smoking and lung cancer. Researchers observe and collect data on individuals’ smoking habits and the incidence of lung cancer to determine if there is a correlation between the two variables. This type of research helps identify patterns and relationships, indicating whether increased smoking is associated with higher rates of lung cancer.

Experimental Research

Experimental research is a scientific approach where researchers manipulate one or more independent variables to observe their effect on a dependent variable. This method is designed to establish cause-and-effect relationships. Fields like psychology , medicine, and social sciences frequently employ experimental research to test hypotheses and theories under controlled conditions.

A real-world example of experimental research is Pavlov’s Dog experiment. In this experiment, Ivan Pavlov demonstrated classical conditioning by ringing a bell each time he fed his dogs. After repeating this process multiple times, the dogs began to salivate just by hearing the bell, even when no food was presented. This experiment helped to illustrate how certain stimuli can elicit specific responses through associative learning.

Diagnostic Research

Diagnostic research tries to accurately diagnose a problem by identifying its underlying causes. This type of research is crucial for understanding complex situations where a precise diagnosis is necessary for formulating effective solutions. It involves methods such as case studies and data analysis and often integrates both qualitative and quantitative data to provide a comprehensive view of the issue at hand.

An example of diagnostic research is studying the causes of a specific illness outbreak. During an outbreak of a respiratory virus, researchers might conduct diagnostic research to determine the factors contributing to the spread of the virus. This could involve analyzing patient data, testing environmental samples, and evaluating potential sources of infection. The goal is to identify the root causes and contributing factors to develop effective containment and prevention strategies.

Using an established research method is imperative, no matter if you are researching for marketing , technology , healthcare , engineering, or social science. A methodology lends legitimacy to your research by ensuring your data is both consistent and credible. A well-defined methodology also enhances the reliability and validity of the research findings, which is crucial for drawing accurate and meaningful conclusions.

Additionally, methodologies help researchers stay focused and on track, limiting the scope of the study to relevant questions and objectives. This not only improves the quality of the research but also ensures that the study can be replicated and verified by other researchers, further solidifying its scientific value.

a graphical depiction of the wide possibilities of research

How to Choose a Research Methodology

Choosing the best research methodology for your project involves several key steps to ensure that your approach aligns with your research goals and questions. Here’s a simplified guide to help you make the best choice.

Understand Your Goals

Clearly define the objectives of your research. What do you aim to discover, prove, or understand? Understanding your goals helps in selecting a methodology that aligns with your research purpose.

Consider the Nature of Your Data

Determine whether your research will involve numerical data, textual data, or both. Quantitative methods are best for numerical data, while qualitative methods are suitable for textual or thematic data.

Understand the Purpose of Each Methodology

Becoming familiar with the four types of research – descriptive, correlational, experimental, and diagnostic – will enable you to select the most appropriate method for your research. Many times, you will want to use a combination of methods to gather meaningful data.

Evaluate Resources and Constraints

Consider the resources available to you, including time, budget, and access to data. Some methodologies may require more resources or longer timeframes to implement effectively.

Review Similar Studies

Look at previous research in your field to see which methodologies were successful. This can provide insights and help you choose a proven approach.

By following these steps, you can select a research methodology that best fits your project’s requirements and ensures robust, credible results.

Completing Your Research Project

Upon completing your research, the next critical step is to analyze and interpret the data you’ve collected. This involves summarizing the key findings, identifying patterns, and determining how these results address your initial research questions. By thoroughly examining the data, you can draw meaningful conclusions that contribute to the body of knowledge in your field.

It’s essential that you present these findings clearly and concisely, using charts, graphs, and tables to enhance comprehension. Furthermore, discuss the implications of your results, any limitations encountered during the study, and how your findings align with or challenge existing theories.

Your research project should conclude with a strong statement that encapsulates the essence of your research and its broader impact. This final section should leave readers with a clear understanding of the value of your work and inspire continued exploration and discussion in the field.

Now that you know how to perform quality research , it’s time to get started! Applying the right research methodologies can make a significant difference in the accuracy and reliability of your findings. Remember, the key to successful research is not just in collecting data, but in analyzing it thoughtfully and systematically to draw meaningful conclusions. So, dive in, explore, and contribute to the ever-growing body of knowledge with confidence. Happy researching!

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Types of Research – Explained with Examples

By DiscoverPhDs
October 2, 2020

Types of Research

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

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

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

Classification of Types of Research

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

According to its Purpose

Theoretical research.

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

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

Applied Research

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

This type of research is subdivided into two types:

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

According to your Depth of Scope

Exploratory research.

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

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

Descriptive Research

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

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

Explanatory Research

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

Correlational Research

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

According to the Type of Data Used

Qualitative research.

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

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

Quantitative Research

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

According to the Degree of Manipulation of Variables

Experimental research.

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

Non-Experimental Research

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

Quasi-Experimental Research

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

According to the Type of Inference

Deductive investigation.

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

Inductive Research

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

Hypothetical-Deductive Investigation

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

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

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

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

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

According to The Sources of Information

Primary research.

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

Secondary research

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

According to How the Data is Obtained

Documentary (cabinet).

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

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

From Laboratory

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

Mixed-Method: Documentary, Field and/or Laboratory

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

The Thurstone Scale is used to quantify the attitudes of people being surveyed, using a format of ‘agree-disagree’ statements.

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Statistical Treatment of Data in Research

Statistical treatment of data is essential for all researchers, regardless of whether you’re a biologist, computer scientist or psychologist, but what exactly is it?

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Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

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 :

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

Second, decide how you will analyze the data .

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

Methods for collecting data, examples of data collection methods, methods for 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.

Qualitative vs. quantitative data

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

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

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


Qualitative		to broader populations. .
Quantitative		.

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

Primary vs. secondary research

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

Descriptive vs. experimental data

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

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

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


Descriptive		. .
Experimental

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Research methods for collecting data
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 methods

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

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

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

Quantitative analysis methods

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

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

During an experiment .
Using probability sampling methods .

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 methods for analyzing data
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).

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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.

Chi square test of independence
Statistical power
Descriptive statistics
Degrees of freedom
Pearson correlation
Null hypothesis
Double-blind study
Case-control study
Research ethics
Data collection
Hypothesis testing
Structured interviews

Research bias

Hawthorne effect
Unconscious bias
Recall bias
Halo effect
Self-serving bias
Information 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 .

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

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

Methods are the specific tools and procedures you use to collect and 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.

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Ultimate Guide to the 7 Types of Research: Definitions, Examples, Advantages and Limitations!

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.

It is essential to understand the different types of research before kickstarting your research works.

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

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

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

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

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

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

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

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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v.7(4); 2015 Dec

Choosing a Qualitative Research Approach

Associated data.

Editor's Note: The online version of this article contains a list of further reading resources and the authors' professional information .

The Challenge

Educators often pose questions about qualitative research. For example, a program director might say: “I collect data from my residents about their learning experiences in a new longitudinal clinical rotation. If I want to know about their learning experiences, should I use qualitative methods? I have been told that there are many approaches from which to choose. Someone suggested that I use grounded theory, but how do I know this is the best approach? Are there others?”

What Is Known

Qualitative research is the systematic inquiry into social phenomena in natural settings. These phenomena can include, but are not limited to, how people experience aspects of their lives, how individuals and/or groups behave, how organizations function, and how interactions shape relationships. In qualitative research, the researcher is the main data collection instrument. The researcher examines why events occur, what happens, and what those events mean to the participants studied. 1 , 2

Qualitative research starts from a fundamentally different set of beliefs—or paradigms—than those that underpin quantitative research. Quantitative research is based on positivist beliefs that there is a singular reality that can be discovered with the appropriate experimental methods. Post-positivist researchers agree with the positivist paradigm, but believe that environmental and individual differences, such as the learning culture or the learners' capacity to learn, influence this reality, and that these differences are important. Constructivist researchers believe that there is no single reality, but that the researcher elicits participants' views of reality. 3 Qualitative research generally draws on post-positivist or constructivist beliefs.

Qualitative scholars develop their work from these beliefs—usually post-positivist or constructivist—using different approaches to conduct their research. In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question(s), the method(s) of data collection, and how data are analyzed. 4 , 5

Choosing a Qualitative Approach

Before engaging in any qualitative study, consider how your views about what is possible to study will affect your approach. Then select an appropriate approach within which to work. Alignment between the belief system underpinning the research approach, the research question, and the research approach itself is a prerequisite for rigorous qualitative research. To enhance the understanding of how different approaches frame qualitative research, we use this introductory challenge as an illustrative example.

The clinic rotation in a program director's training program was recently redesigned as a longitudinal clinical experience. Resident satisfaction with this rotation improved significantly following implementation of the new longitudinal experience. The program director wants to understand how the changes made in the clinic rotation translated into changes in learning experiences for the residents.

Qualitative research can support this program director's efforts. Qualitative research focuses on the events that transpire and on outcomes of those events from the perspectives of those involved. In this case, the program director can use qualitative research to understand the impact of the new clinic rotation on the learning experiences of residents. The next step is to decide which approach to use as a frame for the study.

The table lists the purpose of 3 commonly used approaches to frame qualitative research. For each frame, we provide an example of a research question that could direct the study and delineate what outcomes might be gained by using that particular approach.

Methodology Overview

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How You Can Start TODAY

1 Examine the foundations of the existing literature: As part of the literature review, make note of what is known about the topic and which approaches have been used in prior studies. A decision should be made to determine the extent to which the new study is exploratory and the extent to which findings will advance what is already known about the topic.
2 Find a qualitatively skilled collaborator: If you are interested in doing qualitative research, you should consult with a qualitative expert. Be prepared to talk to the qualitative scholar about what you would like to study and why . Furthermore, be ready to describe the literature to date on the topic (remember, you are asking for this person's expertise regarding qualitative approaches—he or she won't necessarily have content expertise). Qualitative research must be designed and conducted with rigor (rigor will be discussed in Rip Out No. 8 of this series). Input from a qualitative expert will ensure that rigor is employed from the study's inception.
3 Consider the approach: With a literature review completed and a qualitatively skilled collaborator secured, it is time to decide which approach would be best suited to answering the research question. Questions to consider when weighing approaches might include the following:
• Will my findings contribute to the creation of a theoretical model to better understand the area of study? ( grounded theory )
• Will I need to spend an extended amount of time trying to understand the culture and process of a particular group of learners in their natural context? ( ethnography )
• Is there a particular phenomenon I want to better understand/describe? ( phenomenology )

What You Can Do LONG TERM

1 Develop your qualitative research knowledge and skills : A basic qualitative research textbook is a valuable investment to learn about qualitative research (further reading is provided as online supplemental material). A novice qualitative researcher will also benefit from participating in a massive online open course or a mini-course (often offered by professional organizations or conferences) that provides an introduction to qualitative research. Most of all, collaborating with a qualitative researcher can provide the support necessary to design, execute, and report on the study.
2 Undertake a pilot study: After learning about qualitative methodology, the next best way to gain expertise in qualitative research is to try it in a small scale pilot study with the support of a qualitative expert. Such application provides an appreciation for the thought processes that go into designing a study, analyzing the data, and reporting on the findings. Alternatively, if you have the opportunity to work on a study led by a qualitative expert, take it! The experience will provide invaluable opportunities for learning how to engage in qualitative research.

Supplementary Material

The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Uniformed Services University of the Health Sciences, the Department of the Navy, the Department of Defense, or the US government.

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Types of Research Methods Explained with Examples

Research methods are the various strategies, techniques, and tools that researchers use to collect and analyze data . These methods help researchers find answers to their questions and gain a better understanding of different topics. Whether conducting experiments, surveys, or interviews, choosing the right research method is crucial for obtaining accurate and reliable results.

In the ever-evolving world of academia and professional inquiry, understanding the various research methods is crucial for anyone looking to delve into a new study or project. Research is a systematic investigation aimed at discovering and interpreting facts , plays a pivotal role in expanding our knowledge across various fields.

Table of Content

What is Research?

Types of research methods, types of research methodology, difference between qualitative and quantitative research.

This article will explore the different types of research methods , how they are used, and their importance in the world of research.

Research is the process of studying a subject in detail to discover new information or understand it better. This can be anything from studying plants or animals, to learning how people think and behave, to finding new ways to cure diseases. People do research by asking questions, collecting information, and then looking at that information to find answers or learn new things.

This table provides a quick reference to understand the key aspects of each research type.

Research Methods	Focus	Methodology	Applications
Qualitative	Human behavior	Interviews, Observations	Social Sciences
Quantitative	Data quantification	Statistical Analysis	Natural Sciences
Descriptive	Phenomenon description	Surveys, Observations	Demographics
Analytical	Underlying reasons	Data Comparison	Scientific Research
Applied	Practical solutions	Collaborative Research	Healthcare
Fundamental	Knowledge expansion	Theoretical Research	Physics, Math
Exploratory	Undefined problems	Secondary Research	Product Development
Conclusive	Decision-making	Experiments, Testing	Market Research

1. Qualitative Research

Qualitative research method is a methodological approach primarily used in fields like social sciences, anthropology, and psychology . It’s aimed at understanding human behavior and the motivations behind it. Qualitative research delves into the nature of phenomena through detailed, in-depth exploration.

Definition and Approach: Qualitative research focuses on understanding human behavior and the reasons that govern such behavior. It involves in-depth analysis of non-numerical data like texts, videos, or audio recordings.

Key Features:

Emphasis on exploring complex phenomena
Involves interviews, focus groups , and observations
Generates rich, detailed data that are often subjective

Applications: Widely used in social sciences, marketing, and user experience research.

2. Quantitative Research

Quantitative research method is a systematic approach used in various scientific fields to quantify data and generalize findings from a sample to a larger population.

Definition and Approach: Quantitative research is centered around quantifying data and generalizing results from a sample to the population of interest. It involves statistical analysis and numerical data .

Relies on structured data collection instruments
Large sample sizes for generalizability
Statistical methods to establish relationships between variables

Applications: Common in natural sciences, economics, and market research.

3. Descriptive Research

Descriptive research is a type of research method that is used to describe characteristics of a population or phenomenon being studied . It does not answer questions about how or why things are the way they are. Instead, it focuses on providing a snapshot of current conditions or describing what exists.

Definition and Approach: This Types of Research method aims to accurately describe characteristics of a particular phenomenon or population.

Provides detailed insights without explaining why or how something happens
Involves surveys and observations
Often used as a preliminary research method

Applications: Used in demographic studies, census, and organizational reporting.

4. Analytical Research

Analytical research is a type of research that s eeks to understand the underlying factors or causes behind phenomena or relationships . It goes beyond descriptive research by attempting to explain why things happen and how they happen.

Definition and Approach: Analytical research method goes beyond description to understand the underlying reasons or causes.

Involves comparing data and facts to make evaluations
Critical thinking is a key component
Often hypothesis-driven

Applications: Useful in scientific research, policy analysis, and business strategy.

5. Applied Research

Applied research is a type of scientific research method that aims to solve specific practical problems or address practical questions . Unlike fundamental research, which seeks to expand knowledge for knowledge’s sake, applied research is directed towards solving real-world issues .

Definition and Approach: Applied research focuses on finding solutions to practical problems.

Direct practical application
Often collaborative , involving stakeholders
Results are immediately applicable

Applications: Used in healthcare, engineering, and technology development.

6. Fundamental Research

Fundamental research, also known as basic research or pure research, is a type of scientific research method that aims to expand the existing knowledge base. It is driven by curiosity, interest in a particular subject, or the pursuit of knowledge for knowledge’s sake , rather than with a specific practical application in mind.

Definition and Approach: Also known as basic or pure research, it aims to expand knowledge without a direct application in mind.

Theoretical framework
Focus on understanding fundamental principles
Long-term in nature

Applications: Foundational in fields like physics, mathematics, and social sciences.

7. Exploratory Research

Exploratory research is a type of research method conducted for a problem that has not been clearly defined. Its primary goal is to gain insights and familiarity with the problem or to gain more information about a topic. Exploratory research is often conducted when a researcher or investigator does not know much about the issue and is looking to gather more information.

Definition and Approach: This type of research is conducted for a problem that has not been clearly defined.

Flexible and unstructured
Used to identify potential hypotheses
Relies on secondary research like reviewing available literature

Applications: Often the first step in social science research and product development.

8. Conclusive Research

Conclusive research, also known as confirmatory research, is a type of research method that aims to confirm or deny a hypotheses or provide answers to specific research questions. It is used to make conclusive decisions or draw conclusions about the relationships among variables.

Definition and Approach: Conclusive research is designed to provide information that is useful in decision-making.

Structured and methodical
Aims to test hypotheses
Involves experiments, surveys, and testing

Applications: Used in market research, clinical trials, and policy evaluations.

Here is detailed difference between the qualitative and quantitative research –


	Focuses on exploring ideas, understanding concepts, and gathering insights.	Involves the collection and analysis of numerical data to describe, predict, or control variables of interest.
	To gain a deep understanding of underlying reasons, motivations, and opinions.	To quantify data and generalize results from a sample to a larger population.
	Non-numerical data such as words, images, or objects.	Numerical data, often in the form of numbers and statistics.
	Interviews, focus groups, observations, and review of documents or artifacts.	Surveys, experiments, , and numerical measurements.
	Interpretive, subjective analysis aimed at understanding context and complexity.	Statistical, objective analysis focused on quantifying data and generalizing findings.
	Descriptive, detailed narrative or thematic analysis.	Statistical results, often presented in charts, tables, or graphs.
	Generally smaller, focused on depth rather than breadth.	Larger to ensure statistical significance and representativeness.
	High flexibility in research design, allowing for changes as the study progresses.	Structured and fixed design, with little room for changes once the study begins.
	Exploratory, open-ended, and subjective.	Conclusive, closed-ended, and objective.
	Social sciences, humanities, psychology, and market research for understanding behaviors and experiences.	Natural sciences, economics, and large-scale market research for testing hypotheses and making predictions.
	Provides depth and detail, offers a more human touch and context, good for exploring new areas.	Allows for a broader study, involving a greater number of subjects, and enhances generalizability of results.
	Can be time-consuming, harder to generalize due to small sample size, and may be subject to researcher bias.	May overlook the richness of context, less effective in understanding complex social phenomena.

Understanding the different types of research methods is crucial for anyone embarking on a research project. Each type has its unique approach, methodology, and application area, making it essential to choose the right type for your specific research question or problem. This guide serves as a starting point for researchers to explore and select the most suitable research method for their needs, ensuring effective and reliable outcomes.

Types of Research Methods – FAQs

What are the 4 main types of research methods.

There are four main types of Quantitative research: Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research . attempts to establish cause- effect relationships among the variables. These types of design are very similar to true experiments, but with some key differences.

What are the 5 main purpose of research?

The primary purposes of basic research (as opposed to applied research) are documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge .

What are 7 C’s of research?

The 7 C’s define the principles that are essential for conducting rigorous and credible research. They are Curiosity, Clarity, Conciseness, Correctness, Completeness, Coherence, Credibility.

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
SQL : A programming language used to manage and manipulate relational databases.
R : An open-source programming language and software environment for statistical computing and graphics.
Python : A general-purpose programming language that is widely used in data analysis and machine learning.
Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
SAS : A statistical analysis software used for data management, analysis, and reporting.
SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
Matlab : A numerical computing software that is widely used in scientific research and engineering.
RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Step 1: Explain your methodological approach. Step 2: Describe your data collection methods. Step 3: Describe your analysis method. Step 4: Evaluate and justify the methodological choices you made. Tips for writing a strong methodology chapter. Other interesting articles.
Research Design
It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.
PDF Presenting Methodology and Research Approach
Then provide a rationale for using a qualitative research approach, as well as a rationale for the particular qualitative tradition/genre you have chosen. Provide a brief overview of your study. 2: Research Sample Describe the research sample and the population from which that sample was drawn. Discuss the sampling strategy used.
Research Types and Approaches
The words you use to describe your research depend on your goals, approach, intended methods, strategies, and your field of research. ... the sampling methods, time frame, analysis methods, etc. Research is grouped into four categories according to their perspectives and interpretations: the 'domino approach' (problem solving ...
What is Research? Definition, Types, Methods and Process
Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study.
A Beginner's Guide to Types of Research
Descriptive Research. Descriptive research is an approach designed to describe the characteristics of a population systematically and accurately. This method focuses on answering "what" questions by providing detailed observations about the subject. Descriptive research employs surveys, observational studies, and case studies to gather ...
Types of Research
Types of Research. Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it. It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions.
How to use and assess qualitative research methods
Abstract. This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions ...
Research Methods
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:
Research Process
Step 2: Develop a Hypothesis Based on the literature review, develop a hypothesis that a plant-based diet positively affects athletic performance in high school athletes. Step 3: Design the Study Design a study to test the hypothesis. Decide on the study population, sample size, and research methods.
Ultimate Guide to the 7 Types of Research: Definitions, Examples
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.
Choosing a Qualitative Research Approach
In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question (s), the method (s) of data collection, and how data are analyzed. 4, 5. Go to:
Research Methods
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Types of Research
Exploratory research is a type of research method conducted for a problem that has not been clearly defined. Its primary goal is to gain insights and familiarity with the problem or to gain more information about a topic. ... Involves the collection and analysis of numerical data to describe, predict, or control variables of interest ...
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Quantitative Research
Here are some key characteristics of quantitative research: Numerical data: Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
Data Analysis
Data Analysis. Definition: Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets.

Organizing Your Social Sciences Research Paper: Types of Research Designs

Introduction

General Structure and Writing Style

Causal Design

Cohort Design

Cross-Sectional Design

Descriptive Design

Experimental Design

Exploratory Design

Historical Design

Longitudinal Design

Mixed-Method Design

Observational Design

Research Types and Approaches

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A Comprehensive Guide to Different Types of Research

Research Methods

Descriptive Research

Correlational Research

Experimental Research

Diagnostic Research

How to Choose a Research Methodology

Understand Your Goals

Consider the Nature of Your Data

Understand the Purpose of Each Methodology

Evaluate Resources and Constraints

Review Similar Studies

Completing Your Research Project

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Types of Research – Explained with Examples

Types of Research

Classification of Types of Research

According to its Purpose

Applied Research

According to your Depth of Scope

Descriptive Research

Explanatory Research

Correlational Research

According to the Type of Data Used

Quantitative Research

According to the Degree of Manipulation of Variables

Non-Experimental Research

Quasi-Experimental Research

According to the Type of Inference

Inductive Research

Hypothetical-Deductive Investigation

According to the Time in Which it is Carried Out

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

According to The Sources of Information

Secondary research

According to How the Data is Obtained

From Laboratory

Mixed-Method: Documentary, Field and/or Laboratory

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Research Methods | Definitions, Types, Examples

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Qualitative vs. quantitative data

Primary vs. secondary research

Descriptive vs. experimental data

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Qualitative analysis methods

Quantitative analysis methods

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Ultimate Guide to the 7 Types of Research: Definitions, Examples, Advantages and Limitations!

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