Search across guide titles, topics, keywords, and content throughout the Researcher's Field Guide.
Showing 476–500 of 576 results
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.
Read Guide →Participatory research changes who contributes to producing knowledge and how influence is distributed across the research process. It does not abandon research rigor or require every decision to be made collectively.
Read Guide →Stakeholders can make research more relevant by shaping questions, outcomes, methods, interpretation, and communication. Their influence should improve what the study investigates and how findings are understood without giving any group authority to determine what the evidence must show.
Read Guide →The best data collection method is not simply the one you know best or can administer most easily. Learn how to work from your research question to the evidence, source, method, and practical design that can actually answer it.
Read Guide →A research question does more than identify what you want to know. It also implies what evidence must exist before you can answer it. Learn how to identify that evidence before choosing your data source or collection method.
Read Guide →Using interviews and observations, or several other data collection techniques, does not automatically make a study mixed methods. What matters is the methodological nature of the evidence and how the different components are designed, analyzed, and related.
Read Guide →A study does not become mixed methods simply because its dataset contains both numbers and words. What matters is whether substantive qualitative and quantitative components are intentionally designed, analyzed, and integrated to address the research problem.
Read Guide →More sources, methods, or measures can strengthen a study when they address different limitations or provide genuinely useful evidence. Simply adding more, however, does not make weak evidence strong.
Read Guide →Data collection methods make different trade-offs between depth, breadth, standardization, and flexibility. Understanding those trade-offs can help you choose a method that fits what your research question actually needs.
Read Guide →A data collection method may fit your research question conceptually but still be inappropriate if it places unreasonable demands on participants or cannot be implemented well. Learn how to balance evidence quality with burden and feasibility.
Read Guide →Sometimes the evidence that would answer your question most directly cannot realistically or ethically be collected. The solution is not to pretend a convenient substitute is equivalent, but to find the best defensible alternative and adjust the question or claim when necessary.
Read Guide →A defensible sample begins with a clearly defined population. Learn how to move from your research question to the people or units you actually recruit without claiming more than your design can support.
Read Guide →Your data usually come from a sample, but your research question may concern a much larger population. Understanding that distinction is essential for choosing participants and interpreting what your findings actually mean.
Read Guide →