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A research alignment matrix maps the connections among important parts of a study so you can inspect whether each research question has the evidence, methods, and analysis needed to answer it. It is useful as a diagnostic tool, but it is not mandatory for every study.
Read Guide →A useful alignment matrix does more than place research components in adjacent cells. Learn how to build one by testing the reasoning between the cells and using mismatches as signals to revise the study.
Read Guide →Not every research problem can be repaired by changing an instrument, sample, or statistical test. Learn when recurring methodological difficulties suggest that the question, framework, assumptions, or scope of the study needs reconsideration.
Read Guide →Methods are the procedures you use to conduct research; methodology concerns the reasoning, principles, and assumptions that inform how those procedures are selected and used. The distinction matters because rigorous research requires more than reporting what you did.
Read Guide →Quantitative, qualitative, and mixed methods approaches answer different kinds of research questions and produce different forms of evidence. The appropriate choice depends on what you need to know, not on which approach appears more rigorous.
Read Guide →Exploratory, descriptive, explanatory, and evaluative usually describe what a study is trying to accomplish rather than the specific procedures it uses. Distinguishing these purposes helps align the research question, evidence, design, and conclusions.
Read Guide →A study can legitimately be both descriptive and explanatory when its questions require both an account of what is happening and an investigation of how or why it happens. The key is ensuring that each type of claim is supported by appropriate evidence.
Read Guide →Experimental, quasi-experimental, and observational studies differ mainly in what researchers do with the exposure or intervention and how participants enter comparison conditions. Understanding those differences helps you choose a design that fits both your research question and the strength of inference you hope to make.
Read Guide →Cross-sectional research examines a population or phenomenon within a defined point or period, whereas longitudinal research incorporates observations across time to investigate change, development, or temporal patterns. The better choice depends on what your research question requires you to observe.
Read Guide →Prospective and retrospective research differ in how the study is positioned relative to the data and events being investigated. The distinction affects measurement control, available data, bias, feasibility, and how researchers should describe their design.
Read Guide →Case study research investigates a clearly bounded case in depth and in relation to its real-world context. Studying one person, classroom, institution, event, or site does not automatically make a project a case study because the design depends on how the case is defined and investigated.
Read Guide →Phenomenology, ethnography, grounded theory, and narrative research are not interchangeable labels for interview-based qualitative studies. Each organizes the inquiry around a different purpose, from understanding lived experience to culture, explanatory processes, or stories.
Read Guide →Mixed methods research requires more than collecting qualitative and quantitative data in the same project. The defining issue is whether the components are deliberately related and integrated to produce an understanding that neither component would provide independently.
Read Guide →Adding research sites can broaden settings, increase recruitment, and reveal contextual variation, but more sites do not automatically make a study stronger or more generalizable. The benefit depends on what additional settings contribute to the research question.
Read Guide →When you collect data affects what relationships, changes, and temporal patterns your study can observe. The number, sequence, and spacing of measurements should follow the process your research question is trying to understand.
Read Guide →Correlation, association, prediction, and causation answer different research questions. Learn how to match the claim you want to make with the evidence your study is actually designed to provide.
Read Guide →Not every research question needs two groups, two treatments, or a before-and-after comparison. The comparison should follow from what you want to know, not from the assumption that stronger research always compares something.
Read Guide →A control group can make some research questions answerable, but not every study needs one. Whether you need a control depends on the contrast required by your question and the claim you intend to make.
Read Guide →Control group and comparison group are sometimes used interchangeably, but not always. The more important issue is what the group represents, how it was formed, and whether it provides the comparison your research question requires.
Read Guide →An appropriate comparison group is not simply a group that looks similar to the intervention group. It must represent the alternative required by the research question and support a credible interpretation of the resulting contrast.
Read Guide →Random assignment strengthens causal inference by using chance rather than choice to allocate study conditions. It does not guarantee identical groups, representative samples, perfect implementation, or an unbiased study by itself.
Read Guide →Between-subjects designs compare different participants across conditions, while within-subjects designs compare conditions within the same participants. The better choice depends on what is being studied and whether experiencing one condition can influence another.
Read Guide →A causal question asks what would have happened under an alternative condition. That unobserved alternative is the counterfactual, and constructing a credible substitute for it is central to causal research.
Read Guide →Observational data do not automatically restrict researchers to non-causal questions. Causal inference may be possible when the causal contrast, design, assumptions, timing, confounding strategy, and analysis are explicitly aligned.
Read Guide →Community-based participatory research (CBPR) is a collaborative approach in which researchers and community partners work together across the research process. It emphasizes equitable partnership, co-learning, community strengths, locally relevant problems, and connecting knowledge with action.
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