Codebook spss how to#

European Journal of Personality, 30(2), 125-138. How alluring are dark personalities? The Dark Triad and attractiveness in speed dating. C., Mairunteregger, T., Pemp, S., Sieber, K. NPI = narcissistic personality inventoryĬitation: Jauk, E., Neubauer, A.MACHIV = mach-iv machiavellianism questionnaire.DG = dating group (three groups in this study)._acq = acquaintance (i.e., variables with this suffix are controlled for prior * acquaintance) EAVS: Data Codebook Policy Survey: Data Codebook EAVS Datasets Version 1.1 (released October 8, 2021): CSV, Excel, SPSS, Stata, SAS EAVS Datasets Version 1.0.

European Journal of Personality, 30(2), 125-138.' metadata ( darktriad ) $ url <- '' metadata ( darktriad ) $ temporalCoverage <- '2015' metadata ( darktriad ) $ spatialCoverage <- 'Graz, Austria' metadata ( darktriad ) $ distribution = list ( list ( = 'DataDownload',Ī list of important abbreviations, prefixes and suffixes: Name = 'Karl‐Franzens‐Universität Graz, Austria' ) ) metadata ( darktriad ) $ citation <- 'Jauk, E., Neubauer, A. This file should include descriptive variable labels and values that can be viewed in the variable view window of the main SPSS interface. GivenName = 'Emanuel', familyName = 'Jauk', Metadata ( darktriad ) $ name <- 'How alluring are dark personalities? The Dark Triad and attractiveness in speed dating' metadata ( darktriad ) $ description <- paste0 ( ) metadata ( darktriad ) $ identifier <- '' metadata ( darktriad ) $ datePublished <- '' metadata ( darktriad ) $ creator <- list ( = 'Person', Here, that is not the case, but if you find yourself with such a dataset, the detect_missing function makes it easy to recognise common ways to specify missing data (e.g. negative values, labelled values, 99/999). Often, files imported from SPSS or Stata to R will not have their missings coded properly. The data were shared by Emanuel Jauk in a project called How alluring are dark personalities? The Dark Triad and attractiveness in speed dating. We select a subset of variables, just to keep it short. Here, we’re downloading straight from the Open Science Framework, so we have to specify the file extension. For files with the right file extension, we can automatically pick the right way to import the data.

Codebook spss how to#

In this vignette, you can see how to use the metadata that is often already stored in SPSS and Stata files. For nominal and ordinal variables and multiple response sets, summary statistics include.

Knit_by_pkgdown <- ! is.null ( knitr :: opts_chunk $ get ( 'fig.retina' ) ) ggplot2 :: theme_set ( ggplot2 :: theme_bw ( ) ) knitr :: opts_chunk $ set (warning = TRUE, message = TRUE, error = FALSE, echo = TRUE ) library ( dplyr ) library ( codebook ) (CODEBOOK command) CODEBOOK reports the dictionary information - such as variable names, variable labels, value labels, missing values - and summary statistics for all or specified variables and multiple response sets in the active dataset.

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Codebook spss