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An analysis is appropriate only when it answers the question your study actually asks using evidence your design can legitimately provide. Learn how to test that alignment before collecting data.
Read Guide →Every testable hypothesis should have a corresponding analytical path, but the relationship is not always one hypothesis to one statistical test. The analysis must evaluate the specific claim the hypothesis makes.
Read Guide →Your research question and inferential goal should drive the study design, not a favorite statistical test. Analysis still belongs in the design process because thinking ahead about statistics can reveal what data the study must collect.
Read Guide →You should usually determine your main analytical approach before collecting data, and often the primary statistical method as well. But good planning does not require pretending that every analytical decision can be made before you see the data.
Read Guide →Collecting data without knowing how to analyze them does not automatically ruin a study, but it can reveal serious mismatches among the research question, design, measurements, and analysis. The solution is to diagnose the problem before choosing a convenient statistical test.
Read Guide →An analysis plan does not need to predict every feature of data that do not yet exist. The important distinction is between decisions that should be made before results are known and flexibility that is genuinely required by the data, methodology, or research purpose.
Read Guide →A statistician or methodologist can often contribute most before data collection begins, when the research question, design, measurements, sampling, sample size, and analysis can still be changed.
Read Guide →A statistical analysis plan should specify enough detail to make primary analyses reproducible and distinguish planned decisions from those influenced by observed results. Learn what to prespecify, which decisions can remain flexible, and how the required detail varies by study design.
Read Guide →Primary analyses should generally be planned before recruitment so study design, sample size, measurement, and inference remain aligned. Learn which decisions need early specification, when later refinements may be acceptable, and how to document legitimate changes.
Read Guide →Secondary analyses can answer important research questions beyond a study's primary objective. Learn which analyses should be planned before data collection, how much detail they require, and how to distinguish prespecified secondary findings from exploratory discoveries.
Read Guide →Missing data can affect statistical precision, bias, and the interpretation of research findings. Learn how to anticipate missingness during study design, select appropriate analytical strategies, collect useful predictors, and plan sensitivity analyses before the data are collected.
Read Guide →Clustering and repeated measurements affect the amount of independent information available in a study. Learn why these design features must be considered before sample size calculations, how correlation influences statistical precision, and why counting observations is not the same as counting independent units.
Read Guide →An analysis that cannot answer the research question may signal a fundamental design problem. Learn when to redesign a study, which analytical difficulties can be corrected, and how to preserve scientific integrity when the original design is no longer defensible.
Read Guide →Qualitative studies benefit from planning analysis before data collection, but the plan should not predetermine what the data must reveal. Good qualitative planning establishes an analytical direction while preserving the flexibility required by the methodology.
Read Guide →You should normally identify a defensible qualitative analysis approach before collecting data, but not every analytic decision needs to be fixed. Learn which commitments matter early and where methodological flexibility is appropriate.
Read Guide →Changing your qualitative analysis approach after data collection begins can be methodologically defensible, but not every change is equally consequential. Learn when a revision is justified, what evidence to assess, and how to preserve the integrity of your study.
Read Guide →A qualitative codebook can be developed before data collection, but not every analysis approach requires one. Learn when advance coding categories are useful, what a preliminary codebook should contain, and how to avoid restricting unexpected findings.
Read Guide →Iterative sampling allows emerging qualitative analysis to inform who or what is studied next. Learn when it is methodologically necessary, how it differs from theoretical sampling, and when a fixed sampling plan may be sufficient.
Read Guide →Using multiple coders can strengthen some forms of qualitative analysis, but it is not a universal requirement for rigor. Learn when collaborative coding is appropriate, how coding agreement should be understood, and what to plan before involving additional researchers.
Read Guide →A qualitative analysis approach may be appropriate for your research question but exceed your team's current expertise. Learn how to identify critical competency gaps, choose between training and collaboration, and decide when a simpler methodological approach is more defensible.
Read Guide →A mixed-methods study needs more than separate quantitative and qualitative analyses. Planning integration before data collection helps ensure the two strands can actually be combined to answer a question that neither would address as fully alone.
Read Guide →Mixed-methods integration should be considered during research design, not postponed until both datasets are complete. Learn which integration decisions must be made early, which can evolve, and how to ensure the combined evidence answers your research question.
Read Guide →Quantitative and qualitative evidence can be integrated during sampling, data collection, analysis, interpretation, or across several stages. Learn what each integration stage accomplishes and how to select the arrangement that best supports your research question.
Read Guide →One dataset can inform the development, explanation, or corroboration of another in mixed-methods research. Learn how these relationships work, when they are appropriate, and why agreement between datasets does not automatically establish validity.
Read Guide →Mixed-methods integration can occur while evidence is being collected, particularly when one strand informs the procedures or questions used in another. Learn when this is appropriate, how to coordinate collection, and why collecting both forms of data simultaneously does not automatically constitute integration.
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