Write a short description of your dataset, including sample size, sample characteristics, aims of the data, and key measures.
List and describe criminology questions that your dataset is meant to help answer. This may include examples from published research, unpublished research, or hypotheticals. [This section should not describe your or another dataset, but, rather, focus on the background/context that motivates your dataset.]
List and describe existing datasets that are used to answer those criminology questions. In each dataset’s description, include the sample size, sample characteristics, aims of the data, and key measures. Also, describe and explain the relative strengths and weaknesses of those datasets. Then describe and explain how your dataset is different from, better, and worse than the alternatives. For example, this may include new constructs, measures, or populations. [This section should not describe your or another RSP’s methods, but, rather, focus on how they operate—at a high-level—and their utility.]
Describe the sampling frame, how sampling units were selected, response rate and replacement (if applicable). Discuss the process and nature of data collection including the mode or interview format (in-person, telephone, computer), how respondents entered their answers (out loud, on computer keyboard), dates of collection, time length of survey, and other details necessary to place the data collection in context. Note any oversampling and sample weight information. Indicate what population the sample is intended to represent.
Describe key characteristics of the final sample, including basic demographics (as relevant for sample), such as age, sex/gender, race/ethnicity, and distribution of focal variables. If the data are nested/multilevel, provide this information for each level.
Describe the variables included in the data, including the instruments used. If instruments were derived from or modeled after existing ones, cite the original source and note if modified and, if so, how. Describe the scales used in the data, and how these should be coded (cite existing studies that have used these measures if new or adapted).
Describe existing applications of this data to particular criminological topics. Where existing research use is extensive, consider providing a list of studies in tabular form. Investigators should be clear which questions have been asked and answered with these data.
Provide a link to the data and codebook on a repository. We prefer that authors upload their data and codebooks on Zenodo; acceptable alternatives are Dataverse, ICPSR, and OSF. If authors wish to use another repository, they should send an inquiry email to [email protected] to determine if it is acceptable. Post acceptance, the journal will require the data and codebook be included within the article, thereby archiving it. Make sure the data have been deidentified before submission (see e.g., Kayaalp 2017). We prefer the raw data, but where this is not possible due to privacy, describe how the data has been altered from the original raw data, including any out of range or wild codes (e.g., age 121 or weight 1124 pounds) and how these were altered. Note consistency checks, if any, and results. Also, the codebook should include all variable names, coding, skip patterns, and all other information needed to read the dataset. Ensure that all variables are named and coding is clearly and accurately described. Note how missing data are coded. If pre-made scales are provided, include a description of how the scales were made.
Chicago-Style preferred but not necessary for review.
Author 1 is so and so.
Do for each author.
This is as applicable.
This is optional.