1/28/2024 0 Comments Qualitative codebook templateAs long as the content is complete and self-explanatory, the stylistic touches can match the needs of the research project. Codebooks come in a variety of shapes and formats. This is helpful to the user in locating variables of interest. To enhance usability on complex or larger data collections, researchers sometimes add appendices listing variable names and labels alphabetically, by sample characteristic, or according to the substantive groups to which they belong - e.g., Demographic Variables, Health Status Variables. The order of variable descriptions in the codebook usually matches the order of the data. For measures or questions from copyrighted instruments, the notes field is the appropriate location to cite the source.įor variables that are compiled, created, or constructed, such as the examples below from the Aging of Veterans of the Union Army: Military, Pension, and Medical Records, 1820-1940 3 study and the Welfare, Children, and Families: A Three-City Study 4, fewer details are needed: variable name and label, as well as a description of how the data were compiled or created. Notes: Additional notes, remarks, or comments that contextualize the information conveyed in the variable or relay special instructions.Universe skip patterns: Where applicable, information about the population to which the variable refers, as well as the preceding and following variables.Remember to describe all missing codes, including "system missing" and blank. Missing data can bias an analysis and is important to convey in study documentation. Missing data: Where applicable, the values and labels of missing data.For continuous variables, minimum, maximum, and median values are relevant. For categorical variables, for instance, frequency counts showing the number of times a value occurs and the percentage of cases that value represents for the variable are appropriate. Summary statistics: Where appropriate and depending on the type of variable, provide unweighted summary statistics for quick reference.Value labels: The textual descriptions of the codes.Values: The actual coded values in the data for this variable.Question text: Where applicable, the exact wording from survey questions.Where possible, use the exact question or research wording. Variable label: A brief description to identify the variable for the user.For survey data, try to name variables after the question numbers - e.g., Q1, Q2b, etc. Some researchers prefer to use mnemonic abbreviations (e.g., EMPLOY1), while others use alphanumeric patterns (e.g., VAR001). Variable name: The name or number assigned to each variable in the data collection.These include, as shown in the example below from the National Longitudinal Survey of Youth, 1979 2, the following: The main body of a codebook contains unambiguous variable level details. Some codebooks also include methodological details, such as how weights were computed, and data collection instruments, while others, especially with larger or more complex data collections, leave those details for a separate user guide and/or data collection instrument. A well-documented codebook "contains information intended to be complete and self-explanatory for each variable in a data file 1."Ĭodebooks begin with basic front matter, including the study title, name of the principal investigator(s), table of contents, and an introduction describing the purpose and format of the codebook. A codebook describes the contents, structure, and layout of a data collection.
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