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Setting Up Research and Choosing the Right Method
Research Methods in Psychology · Chapter 3: Research Design and Methodological PlanningSection 3.1 · lesson 16 of 9411 min · free with a free account
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In this lesson
Foundations of research design and methodological decision-making.
- Name the key steps in setting up a study
- Distinguish quantitative, qualitative and mixed-methods research
- Match a research question to an appropriate design
- Weigh internal validity against external validity
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Research Design and Methodological Planning
Choosing the Right Method
This section is about the decision that comes before any data. You have a question about human thought, feeling, or behavior. Now you have to decide how to investigate it. What kind of data, collected from whom, using what procedures. Those choices together are called the research design. They are made early, and they constrain everything that comes after. By the end, you will name the key steps in setting up a study. You will tell quantitative, qualitative, and mixed-methods work apart. You will match a research question to a design that can actually answer it. And you will weigh internal validity against external validity, which pull in opposite directions.
Why Design Matters
The Decision Before the Data
Every psychological study begins with a problem. Something about thought, feeling, or behavior that existing knowledge cannot yet answer fully. But identifying a good question is only the first step. The researcher then has to decide how to investigate it. That means what kind of data to collect, from whom, and using what procedures. Those decisions are the research design, and they shape everything that follows. Here is the failure mode worth naming early. A poorly chosen method can produce data that are technically rigorous, and completely irrelevant. Nothing about the analysis will reveal that. The numbers will look fine. A well-chosen method makes the most efficient and valid use of the resources you have. And the choice is never neutral, because it assumes what counts as knowledge.
The Whole Sequence
Nine Steps, Five Phases
Research rarely follows a perfectly linear path. Studies loop back, questions get sharpened, designs get rewritten in the middle. Even so, most psychological studies move through a recognizable sequence of nine steps. It helps to see them grouped into five phases of work. First you define, by identifying the problem and reviewing the literature. Then you frame, turning that problem into a question and a hypothesis. Then you choose, picking a design and defining the sample. Then you prepare, developing measures and obtaining ethical approval. And finally you deliver, collecting data, analyzing it, and reporting what you found. Knowing the sequence lets you plan systematically. More usefully, it lets you anticipate challenges before they arrive.
Framing the Study
The First Five Steps
Take the first five steps one at a time. Step one is identifying the research problem. It may come from a gap in the existing literature. It may come from a practical problem in an applied setting, or a theoretical inconsistency. Step two is reviewing the existing literature. You establish what is already known, and identify the debates that remain unresolved. A thorough review prevents duplication, and strengthens your justification. Step three turns the broad problem into a specific, answerable question. Where the theory supports it, you derive a testable hypothesis at the same time. Step four selects the overall design - experimental, correlational, qualitative, or mixed. Step five determines who participates, and how they will be recruited.
Running the Study
From Measures to Findings
The last four steps move from planning into execution. Step six develops the measures and procedures. You operationalize the variables, and select instruments that are reliable and valid. Pilot test the procedures wherever you can. Step seven obtains ethical approval. The protocol goes to the review board, and approval comes back, before any data collection begins. That ordering is not a formality. It is a condition on the whole study. Step eight collects and analyzes the data, following the pre-specified protocol. Pre-specified matters here, because deciding the analysis afterward invites you to fit the answer you wanted. Step nine interprets and reports. Draw only the conclusions the data warrant, acknowledge the limitations, and communicate transparently.
Approach One
Quantitative Research
Psychological methods fall into three broad approaches. Each rests on different assumptions about how knowledge is best generated. Quantitative research measures phenomena numerically, and tests hypotheses statistically. It prioritizes objectivity, replicability, and the ability to generalize to larger populations. Its hallmarks are operational definitions, standardized procedures, and inferential statistics. The designs include true experiments, quasi-experiments, correlational studies, and surveys. Quantitative work suits questions asking how much, or how often. It also suits asking whether there is a relationship between two variables. In short, questions you can answer by counting, measuring, or comparing. One concrete example, start to finish. Randomly assign one hundred and twenty participants to an eight week mindfulness program, or a waitlist control. Then compare salivary cortisol at baseline and follow-up, using a between-subjects t test.
Approach Two
Qualitative Research
Qualitative research asks a different kind of question. It seeks to understand phenomena through language, meaning, and experience. Rather than measuring and counting, qualitative researchers interpret. The aim is to understand how people make sense of their lives and relationships. So the priorities shift, toward depth, context, and the participant's own perspective. Common methods include in-depth interviews, focus groups, and ethnographic observation. Also thematic analysis, applied to documents or narratives. These methods suit questions that ask how, or why. Questions about process, meaning, and subjective experience, which resist reduction to numbers. A concrete example. Interview fifteen survivors of workplace burnout about their recovery. What helped, what hindered, and how their sense of identity changed. Then analyze the interviews thematically, looking for recurring patterns of meaning.
Approach Three
Mixed-Methods Designs
Mixed-methods research refuses to choose, and integrates both approaches instead. The statistical power of one, combined with the contextual richness of the other. That combination can answer more complex questions than either approach alone. There are three standard ways to sequence the two kinds of data. In an explanatory sequential design, the quantitative data come first. The qualitative data then explain or contextualize the statistical findings. An exploratory sequential design reverses that order. Qualitative data come first, and inform the development of the quantitative instruments. In a convergent design, both are collected at the same time, and integrated during analysis. Questions asking how and why something occurs are the natural home for this work. Why do some individuals recover from trauma, while others develop PTSD?
Question First
Matching Method to Question
Now the practical part. How do you actually pick? The goal is the design that best answers your specific question. Given, of course, the constraints you are actually working under. If the question is whether X causes Y, you need a true experiment with random assignment. Does caffeine improve working memory performance, for instance. If the question is whether two things are merely related, a correlational study or survey will do. If you want to know how people experience something, use qualitative interviews. If you want to know how something develops over time, you need a longitudinal study. If you want to know how common something is, run a survey. And to test an effect in a real-world setting, use a field experiment or a quasi-experiment. The question type comes first. The method follows from it.
Two Kinds of Validity
Internal and External Validity
Two ideas govern how much a finding is worth. Internal validity is the degree to which a design supports a causal interpretation. It is your confidence that the independent variable caused the outcome. Not some confounding factor that happened to travel alongside it. External validity is about reach, not cause. It asks whether the findings generalize beyond this sample, this setting, this moment. These two goals are often in direct tension with each other. Highly controlled laboratory experiments maximize internal validity. But that control may cost them ecological realism. Naturalistic studies maximize external validity, and make causal inference difficult. You rarely get both at full strength, so decide early which one your question needs.
Before You Collect
Measures, and Ethical Limits
Two more considerations belong before data collection, not after. The first concerns your measures. A measure must be reliable, meaning it produces consistent results. It must also be valid, meaning it measures what it claims to measure. Piloting a measure with a small group first is a standard safeguard. The second consideration is ethics, and it is not a formality. Ethical obligations may rule out certain designs entirely. A researcher interested in the causal effects of childhood trauma faces an absolute limit. You cannot randomly assign children to abuse conditions. There is no version of that study. So quasi-experimental designs, retrospective studies, or natural experiments must substitute. Ethical constraints are not merely hurdles. They shape the knowledge psychology can produce.
Before You Begin
A Checklist for Any Design
Finish with a checklist you can run against any design. Does the design match the question, and can it answer what you are actually asking? Have the major threats to internal validity been identified and addressed? Is the sampling strategy appropriate for the population you want to describe? Are the measures reliable and valid for this specific population? That last phrase matters. A measure validated elsewhere is not automatically valid here. Has the design been reviewed for ethical compliance, and has approval been obtained? Is the planned sample size sufficient for the statistical power you need? Have potential confounds been identified, and either controlled or measured? One idea holds the section together. The choice of method must be driven by the nature of the research question. Not by habit, and not by convenience.
About Research Methods in Psychology at UPI Study
Research Methods in Psychology (Psychology 111) is a self-paced online course of 94 video lessons in 16 chapters, from the scientific method and research ethics to designs, data collection, sampling, measurement, validity, experiments, descriptive statistics and hypothesis testing. It carries a NCCRS credit recommendation for 3 semester credits.
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