Designing a Study You Can Defend
Remote recruitment can expand a study's geographical reach while changing who encounters the invitation, qualifies to participate, and completes data collection. Learn how digital access, platform membership, self-selection, and recruitment procedures influence sample composition, and how to assess whether the resulting participants support the intended research conclusions.
Read Guide →
Designing a Study You Can Defend
Poor internet access can prevent otherwise eligible participants from joining remote studies, interrupt assessments, and create systematic missing data. Learn how to design low-bandwidth and offline alternatives, support participants without compromising measurement validity, and distinguish accessibility accommodations from changes that alter the research evidence.
Read Guide →
Designing a Study You Can Defend
Remote research can reach participants across geographical boundaries, but it may systematically exclude people with limited connectivity, unsuitable devices, low digital literacy, or accessibility barriers. When these characteristics are related to the outcomes being studied, the resulting sample may produce biased estimates. Learn how digital exclusion operates through recruitment and data collection, why large samples do not eliminate the problem, and how to evaluate and reduce its consequences.
Read Guide →
Designing a Study You Can Defend
Combining remote and in-person participant data may increase sample size and improve access, but differences in measurement conditions and participant selection can complicate interpretation. Learn when pooling is defensible, why adding collection mode as a covariate may be insufficient, and how to choose between pooled, adjusted, stratified, and mode-specific analyses.
Read Guide →
Designing a Study You Can Defend
Remote data collection allows participants to contribute research evidence from different locations, but variations in devices, instructions, environments, timing, and technical support can affect measurement quality. Learn how to establish standardized remote procedures, distinguish essential controls from acceptable flexibility, document protocol deviations, and maintain comparability without unnecessarily excluding participants.
Read Guide →
Designing a Study You Can Defend
Online research can produce large samples quickly and inexpensively, but convenient recruitment may systematically favor participants who are accessible, digitally engaged, or particularly interested in the topic. Learn when convenience becomes a threat to representativeness, why sample size cannot eliminate selection bias, and how to determine whether online findings support population-level conclusions.
Read Guide →
Designing a Study You Can Defend
A diverse sample can help researchers study variation that a narrower sample might miss, but diversity is not automatically the same as representativeness. What matters is which differences are relevant to the research question and the conclusions being drawn.
Read Guide →
Designing a Study You Can Defend
Representation asks whether relevant people or groups are present in research. Representativeness asks how adequately the sample reflects a defined target population for the inference being made.
Read Guide →
Designing a Study You Can Defend
Sex and gender can both matter in research, but they do not necessarily represent the same constructs. Distinguishing them helps researchers measure the variable they actually need and interpret observed differences more carefully.
Read Guide →
Designing a Study You Can Defend
Analyzing results by sex or gender can reveal important differences that an overall average conceals, but not every dataset supports a meaningful subgroup comparison. The decision should follow from the research question, study design, sample size, and relevant scientific or reporting requirements.
Read Guide →
Designing a Study You Can Defend
Research participation becomes more accessible when researchers examine what the study requires of participants and remove barriers that are not necessary to answer the research question. Accessibility can involve communication, disability accommodations, scheduling, location, technology, language, and participant burden.
Read Guide →
Designing a Study You Can Defend
Adding participants from more groups can broaden whose experiences appear in a study, but diversity alone does not establish representativeness. Representativeness depends on the target population, selection process, and inference researchers want to make.
Read Guide →
Designing a Study You Can Defend
Inclusive research does not mean including everyone. Researchers need a population broad enough to capture relevant variation but sufficiently defined to answer the research question safely and meaningfully.
Read Guide →
Designing a Study You Can Defend
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 →
Designing a Study You Can Defend
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 →
Designing a Study You Can Defend
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 →
Designing a Study You Can Defend
A method can be implemented perfectly and still be wrong for the question it is supposed to answer. When that mismatch becomes apparent, reassess what evidence the question requires before deciding whether to adapt the method, narrow the question, or redesign the study.
Read Guide →
Designing a Study You Can Defend
Not every methodological change creates a new study, but some changes alter the research question, evidence, population, comparison, or inferential logic so substantially that treating the project as unchanged becomes misleading. The boundary is conceptual, not a fixed percentage of procedures changed.
Read Guide →
Designing a Study You Can Defend
Not every tiny departure from a research plan deserves equal space in a publication, but deviations that affect interpretation, participant protections, planned analyses, or confidence in the findings generally should be visible. The goal is to let readers understand the study that was actually conducted.
Read Guide →
Designing a Study You Can Defend
Recruitment problems do not automatically mean that a study has failed. Diagnose why recruitment is falling short before changing the strategy, because some solutions can alter the sample, introduce bias, or change the study itself.
Read Guide →
Designing a Study You Can Defend
Falling short of a planned sample size does not automatically make a study worthless, but it may substantially change what the data can establish. The appropriate response is to assess the consequences of the smaller sample rather than retroactively making the original target disappear.
Read Guide →
Designing a Study You Can Defend
The people you can recruit are not always the people you originally intended to study. Before broadening eligibility or changing recruitment settings, determine whether the accessible population can still provide evidence relevant to the original research question.
Read Guide →
Designing a Study You Can Defend
Not every change to a research sample creates a different study. The critical issue is whether the revised sample still provides evidence about essentially the same population and research question, or whether the change alters what the study is actually about.
Read Guide →
Designing a Study You Can Defend
A control or comparison group can fail to function as intended even when recruitment and data collection continue normally. The key is to determine what actually happened in each group, why it happened, and whether the intended comparison still supports the study's research question.
Read Guide →
Designing a Study You Can Defend
Every real study operates under constraints, so some methodological compromise is unavoidable. The critical question is whether the remaining design can still support the claims you intend to make after those compromises are considered together.
Read Guide →
