Survey Codebook

[Erminia Scharnberg]

Acodebook describes the contents, structure, and layout of a data collection. A well-documented codebook "contains information intended to be complete and self-explanatory for each variable in a data file1."

Codebooks 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. 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.

Below are links to HTML codebooks for HRS core interviews, off-years studies, health studies, and cross-year data products. Additional biennial and cross-year data products, not shown on this page, have been contributed by the RAND Center for the Study of Aging.

Survey research and Quantitative analysis method for which a researcher poses the same set of questions, typically in a written format, to a sample of individuals. It is a quantitative method whereby a researcher poses predetermined questions to an entire group, or sample, of individuals.

The variable names, especially the first name, must not contain the more extended character. It essential for SPSS and various other data analysis programs. Some programs allow longer names. Some programs allow longer names. The variable names must be meaningful; it must tell something about the nature of the variable. That is especially vital for the longer data sets if you have more than a half dozen variables to keep track.

2. Metadata: Codebooks must also comprise data, which is mainly about the data gathering procedure. It may comprise an explanation of the problems or issues, which may arise while Data collection that impacts the quality of coding of the data. For example, during the second year of a multiyear project, one of the variables may have been dropped. All other consecutive entries would be coded as missing data. Thus, any analysis of this variable must only comprise the cases until data collection was dropped.

The metadata is essential as this information provides essential methodological information that is used to write the reports. Some of the information listed above is also used to ascertain study limitations. Codebooks must be updated when new variables are created or whenever limitations or research issues arise.

The codebook is one of the most important documents created during a research project. This document offers details concerning the variable structure and coding, database generation, and other data quality aspects. Besides, codebooks are often consulted after the data were collected, and as such, it needs to be developed carefully.

The following links provide PDF versions of all available current population survey codebooks. These codebooks do not apply to the completed IPUMS data, but describe the source samples. They are particularly useful in conjunction with the translation tables to review the IPUMS data transformations.

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The NIOSH codebook includes the annual number of observations to occupational mental health questions that were administered in fiscal years 2009 and 2010, by response category. Variables in the NIOSH codebook match the FY 2010 questionnaire.

The EPA codebook contains the annual number of observations to the EPA supplemental questions on hygiene and clothes laundering practices that were administered in fiscal years 2013 and 2014, by response category. Variables in the EPA codebook match the FY 2014 questionnaire.

Codebooks can also contain documentation about when and how the data was created. A good codebook allows you to communicate your research data to others clearly and succinctly, and ensures that the data is understood and interpreted properly.

To get the most out of the Codebooks procedure in SPSS, your dataset should already have variable labels and value labels applied before you run the Codebooks procedure. If you are not familiar with variable properties, such as labels or measurement levels, or concepts like value labeling of category codes in SPSS, you should read the Defining Variables tutorial before continuing.

This codebook method prints most of the information found in the Variable View window. It gives the names, labels, measurement levels, widths, formats, and any assigned missing values labels for every variable in the dataset. It also prints a table with the assigned value labels for categorical variables.

This codebook method includes all of the same information as the simple method, but also includes options for printing summary statistics as well. Unlike the simple method, you can choose which variables are included in the codebook, and you can choose which variable properties are included in the summary. Also unlike the simple method, the summary information for each variable will be printed in its own table.

Note: This procedure was introduced in SPSS version 17 (source: SPSS v23 Command Syntax Reference). If you are using an older version of SPSS, this command is not available - it will not appear in the menus, and running the syntax will return error messages.

To reproduce this example, download the sample SPSS dataset and SPSS syntax file. Run the syntax file on the sample data. This will add all of the appropriate variable labels and value labels for this dataset.

When sharing your data with others, it's important that your variables are properly documented. This includes having succinct but descriptive labels for your variables, and labels for any numeric codes used for categories.

The second table is the Variable Values table. This table will only appear if you have value labels defined for at least one variable in your dataset; otherwise, it is omitted. This table prints the name of each variable with defined value labels, and lists each code and associated label for that variable.

You may consider exporting your survey as word document and selecting to display the recoded values as well. What this does is export the entire survey into a word document and then next to each possible answer selection the numerical value of that option is displayed in parentheses such as Male (1) Female (2). Just make sure to turn on this option when exporting your survey as it is not the default.

The 2022 Survey of Consumer Finances (SCF) is the most recent survey conducted. Below are links to the bulletin article, interactive chartbook, historical bulletin tables, full public dataset, extract dataset, replicate weight files, and documentation.

How to get a notification of changes: If you would like to receive notification about additions to the web page and updates to these surveys, please sign our guest book.

How to send a comment or question: To send a comment about the SCF website or to make technical inquiries about the SCF, please fill out our feedback form. To ensure that your question is properly routed, please select the Survey of Consumer Finances as the "Economic Data" and select no other options above the field labeled "Type your message."

SCF Interactive Chart

The SCF Interactive Chart creates time series charts representing estimates in the historic tables, and covers the period 1989 to the most recent survey year. For each variable and classification group, the charts show the percent of families in the group who have the item and the median and mean amounts of holdings for those who have the item. Users should be aware that because robust techniques were not used to calculate the mean estimates, results in some instances may be strongly affected by outliers. All dollar variables are inflation-adjusted to 2022 dollars.

The following tables are based on those that have historically appeared in the main article. Estimates for all survey years from 1989 to the most recent survey year are included in both nominal and real terms.

WARNING: Please review the following PDF for instructions on how to calculate correct standard errors. As a result of multiple imputation, the dataset you are downloading contains five times the number of actual observations. Failure to account for the imputations and the complex sample design will result in incorrect estimation of standard errors.

Special note to R users: An outside programmer has created scripts for converting and working with SCF data. These scripts are available for download from: -survey-of-consumer-finances-scf.html#survey-of-consumer-finances-scf

SCF Interactive Chart

The SCF Interactive Chart contains time series charts using triennial SCF data covering the period 1989 to 2022. The variables included are ones that appear in a selected set of the tables in the Bulletin article. For each variable and classification group, the charts show the percent of families in the group who have the item and the median and mean amounts of holdings for those who have any. All dollar estimates are given in 2022 dollars. The definitions of the summary variables are given by the SAS program used to create them.

Tables based on public data

The calculations reported in these tables are weighted estimates made from the public data. These calculations may be convenient for users who want to ensure that their estimates align with those made for the writing of the most recent Bulletin article. The program that creates the variables can be found in the documentation column of the table.

Table based on internal data

The calculations reported in these tables are weighted estimates made from the internal data, incorporating any weighting adjustments implemented in the analysis of those data for purposes of the summary articles in the Federal Reserve Bulletin. The program that creates the variables can be found in the documentation column of the table.

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