asdocx codebook mpg price trunk, replace Consider the following example, where we want to create a codebook table for mpg, price, and trunk. Instead, if we wanted a codebook for some specific variables, then we would provide list of those variables. In the above code, the is optional, therefore, if we leave it out, codebook will be generated for all variables. The syntax for the compact codebook is: asdocx codebook, replace asdocx can export the output to MS Word, Excel or LaTeX formats. The codebook command examines the variable names, labels, and data to produce a codebook describing the dataset. To generate a detailed codebook, see this post. This post discusses how to export the output from the codebook command in a compact format. If any kind of imputation took place this should be documented.Asdocx : Stata – Export Codebook to Word, Excel or LaTeXĪsdocx is a Stata package that can export output from almost all Stata commands. For example, the variable “ marital status” is a filter variable if a set of questions is only presented to subjects who stated that they were married. Is this variable a filter variable? Depending on participants’ responses on a filter variable, a set of subsequent items/questions will be presented or not. parents providing information on their child’s behaviour) Note that response unit and analysis unit are not necessarily the same (e.g. The unit that is analysed in the variable. The unit of measurement for continuous variables (e.g. The theoretical construct that is measured by a variable. The measurement instrument to which the variable belongs. The measurement occasion for the variable (e.g. corresponding to the variable (in consideration of third party rights). The exact wording of the questionnaire item, software instruction, etc. The following information should be included in either the variable label or in a separate attribute field if they enhance data intelligibility: measurements of heart rates that are higher than 220 beats per minute). 55 instead of 5 because of typing errors) or measurement errors from analyses (e.g. a missing value range that is defined as 6-99 for a 5-point Likert-scale) facilitates excluding wild codes (e.g. In some cases, it may be useful to define a range of missing values. there is one code for each kind of missing value which is consistently used throughout your dataset). It is important to standardize missing values (i.e. Therefore, you should assign different codes to these missing value patterns and, subsequently, value labels to these codes. pregnancy for male participants), not answered. because some questions were only asked in the control group), not applicable (e.g. Different kinds of missing values should be distinguished: e.g. “Blanks” or “sysmis” values should not be used as missing values because it is not possible to discriminate between fields which were deliberately left blank (items that were not answered or are missing by design) and fields which were just skipped on data entry. The set of values, which were used to code missing data. Value labels provide information on how to interpret valid values for nominal and ordinal categorical variables, as well as, information on how to interpret missing values for all types of variables.To indicate that value labels were not assigned by accident, we recommend to assign value labels to all valid values that are listed. by assigning value labels to minimum and maximum). For continuous variables, a definition of the range of valid values should be given (e.g. The set of valid values, which were used to code categories, for nominal and categorical variables. 5-point rating scale, height, intelligence), (b) strings (any open text item) and (c) dates. At least, you should distinguish between (a) numeric variables (e.g. There is no fixed scheme for describing the variable type. For example, if the variable name was BDI_Q1_T1 the full name could be Becks Depression Inventory, Question 1, Baseline. A short description or the full name of a variable. 8 tokens (digits or letters) and no combination of upper and lower case letters An example for an elaborated naming convention is the naming convention of the GESIS Panel: its assignment rules ensure that every variable name is unique, easily identifiable and meets archive standards (restriction to a length of max. You should provide a ReadMe on naming conventions that were used. Note that programs differ on allowed length, symbols that are supported, and on distinguishing between upper and lower case letters. The name of a variable should only consist of letters, integers and underscores. In general, they describe the contents, structure, and layout of a data collection. Codebooks are an inevitable component of data documentation and data sharing in the social sciences.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |