Complete template: After this, you can simply fill out the template, which creates the codebook.In this template, each column describes different aspects of a single variable, including name, label, type, and so on. Create template: iecodebook first converts the dataset into a template in Excel using iecodebook template.The following steps list how the iecodebook apply works.
Codebook sample code#
The iecodebook apply subcommand allows the research team to perform all of these tasks without writing separate lines of code for each task in Stata. The most common data cleaning tasks include renaming variables, applying variable and value labels, and recoding values. iecodebook template creates an Excel template that describes the current or targeted dataset(s), and prepares the codebook for the other iecodebook subcommands.It can also produce an exportable version of the dataset which only contains the variables used in a particular do-file. iecodebook export creates an Excel codebook that describes the current dataset.As you will see, this is different from simply using the append command in Stata. That is, it harmonizes two or more datasets, and then appends them. iecodebook append allows two or more datasets to have the same variable names, labels, and value labels.iecodebook apply reads an Excel codebook where the user renames, recodes, and/or labels a large number of variables, and applies these changes to the current dataset.There are four subcommands in iecodebook to support its functions: These codebooks in Excel are also easier to read and understand, even if someone does not have knowledge of Stata. In this way, iecodebook creates a metadata record which is easier to write than a long sequence of data cleaning commands in a do-file. The research team can fill these codebooks with data cleaning instructions for Stata. For instructions and available options, type help iecodebook.Īs its name suggests, the iecodebook command creates Excel-based codebooks.
Codebook sample install#
Codebook sample software#
If "9" is a missing value, then the codebook could note "9 = Missing Data." Other examples of missing data labels include "Refused," "Don't Know," "Blank (No Answer)," and "Legitimate Skip." Some analysis software requires that certain types of data be excluded from analysis and designated as "Missing Data," (i.e., inappropriate, not ascertained, not ascertainable, or ambiguous data categories). Missing Data Code: Indicates the values and labels of missing data.In some cases, an expanded version of the Variable Name can be found in a Variable Description List. Variable Label: Indicates an abbreviated variable description (maximum of 40 characters) that can be used to identify the variable.If the variable is a multiple-response type, then the width referenced is that of a single response. Variable Column Location: Indicates the starting location and width of a variable.Variable Name: Indicates the variable number or name assigned to each variable in the data collection.The following elements are generally included for each variable in the data file: The body of a codebook describes the content of the data file. Information on data collection, data processing, and data quality.A description of the survey design and methodology.Other indications of the content and characteristics of each variableĪdditionally, codebooks may also contain:.Exact questions and skip patterns used in a survey.Codes used to indicate nonresponse and missing data.Column locations and widths for each variable.Users are strongly encouraged to review the codebook of a study before downloading the data file(s).Īlthough codebooks vary widely in quality and amount of information given, a typical codebook includes: A codebook provides information on the structure, contents, and layout of a data file.
0 kommentar(er)
