# Preprocessing All rating variables (i.e., actual choice, friendship, short-term relationship etc.) were corrected for prior acquaintance, which means that dates wih prior acquaintance were excluded (set to missing) on a dyadic basis. These effects are not (!) based on multilevel analyses. These are centered marginal means that were calculated according to the formulae provided by Kenny, Kashy, and Cook (2006).
Codebook spss download#
# Download link () # Personwise datafile The personwise datafile contains individual differences variables and perceiver and target effects according to the social relations model. The respective SPSS files are named \" DarkTriadDate_person.sav \" and \" DarkTriadDate_dyad.sav \". Metadata(darktriad) $name <- "How alluring are dark personalities? The Dark Triad and attractiveness in speed dating" metadata(darktriad) $description <- paste0( "The data to this speed dating study comes in two different formats: Personwise (one record for each individual) and dyadic (pairwise one record for each date). 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.
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)
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