(*Note: These guidelines are written to address authors directly, but reviewers and editors should consult them as well and ask authors to consider these issues if they are not doing so already in their manuscripts).

In your empirical manuscript, report a more detailed set of demographic information (and any other background information) about the sample and context of the study that are relevant for understanding and interpreting the study's findings, including its generalizability (in quantitative studies) or transferability (in qualitative studies) and limitations thereof. Specifically, take the following into account:

  1. Report Sample Characteristics in More Detail, and Considering the Context: In selecting which demographics and other background variables to report, consider (a) the local context and the relevant social dimensions/categories in the local context (including how those dimensions/categories are labeled in the local context), as well as (b) the research question. For example, different racial categories and labels will be relevant in different contexts (see example M), in some places these questions may be illegal (see example J), and in some places ethnicity, religion, or caste may be more relevant than race (see examples C, G, I, N). Likewise, in a study on relationships, questions about gender, relationship status, and sexual identity will be more relevant, whereas in a study on collective action, political ideology and disability may be more relevant. Other demographics such as social class (see examples L, N, P) or relevant majority/minority group distinctions (whether based on race, ethnicity, religion, nationality – depending on the context - see example H) should always be considered in addition to age and gender. [see all examples in the training materials section for illustrations of more detailed and context-specific sample reporting]
  2. Use Inclusive Language: Use inclusive language when describing demographic information (for example, "xx identified as women, xx as men, and xx as non-binary" instead of "xx males and xx females"). For the U.S. context, APA resources on inclusive and bias-free language are useful: For non-U.S. contexts, different language may be needed. It may also involve asking participants to describe themselves in their own terms and report these, rather than predetermined labels. It is also helpful to explain the context-specific choice of language for readers who are not familiar with the context. This includes the U.S. context, because SPSP journals have an international readership! [For examples see D, E, K, M, O in the training materials section]
  3. Disaggregate Social Categories: Whenever possible and relevant, disaggregate social categories and pay attention to diversity within groups (instead of assuming homogeneity or dominant intersecting identities as the default - see example H). For example, in the U.S. or European context, researchers may want to disaggregate broad racial categories to consider ethnicity (see examples G, P), immigration generation or birth country (see examples D, P), and/or citizenship (see examples B, G), as these variables can be tied to vastly different experiences and outcomes. [See all examples in the training materials section for disaggregation of some social categories]
  4. Explain Exclusions: Explain any exclusions of populations from the sampling. For example, if specific class backgrounds, age groups, or ethnic/racial minority groups are not included or are underrepresented in the sample, this should be explicitly discussed, especially when these backgrounds pertain to the research question. [For examples see E, I, J, K in the training materials section]
  5. Use Tables and Online Supplementary Materials: Because providing additional detailed information often conflicts with journal word limits, authors can use tables (which are not included in the word limit at PSPB) to report this information more concisely. Additionally, online supplementary materials can be used for more detailed reporting of sample and context information. [For examples see A, D, E, G, H, K, L, N, O in the training materials section]
  6. Additional suggestions to highlight context and diversity considerations in sampling: The following suggestions are from the examples in the training materials section below and include various other ways in which authors attended to the context and diversity of their samples in their research design and reported it in the Method section of their published manuscript. These suggestions will not apply to all cases, and the list of suggestions is not exhaustive.
    • Address (in the sample or procedure section) efforts that were made to diversify the sample (see examples A, F, N)
      1. For example, this could include selecting participants from a larger pool, based on purposive diversity-sampling criteria (see example E), or using quota sampling to ensure inclusion of sufficient numbers of participants of certain backgrounds (see example H)
    • Report sample statistics for different intersecting social categories (see examples C, L)
    • Include representative statistics for comparison of the population of interest and the distribution of certain characteristics in the study sample (see examples G, I)
    • Consult with context experts to determine which other social categories are relevant to assess and report (see example H)
    • Consider explaining legal restrictions on collecting and reporting certain demographic data recommended here or common in other contexts (see example J), or explaining context-specific demographic labels or categories (see example M)
    • Point out and make explicit in the Method section (and Discussion) which groups are dominant or overrepresented in the sample, and if possible, also why (see examples I, K)