I would like to give special thanks to Dr. If you would like to see the codebook (the variable names and their corresponding questions), you can find this by clicking here. Other users of this guide who wish access the GSS data for practice purposes may do so by clicking here: gss08_1500cases. Undergraduate students studying social science research methods and social science statistics at SUNY Empire use SPSS and the GSS data for some course assignments. The examples and screen shots used in this guide are based on the 2008 General Social Survey (GSS) data set, which contains information on 50 variables for a sample of 1500 U.S. I recommend that you keep a good statistics book handy as you conduct your quantitative analyses. Remember, SPSS will calculate what you request it does not determine the appropriateness or limitations of the test. It is essential that you have a good understanding of the statistical tests that you are conducting. I have written this eTutor to provide a brief introduction to SPSS analysis. Click on the tabs above to find links to specific SPSS instructions. SPSS gives the researcher the tools for describing variable statistics, analyzing two variables together, predicting numerical outcomes, and predicting identifying groups. Originally used with large mainframe computers and data punch cards, SPSS (Statistical Package for the Social Sciences) revolutionized quantitative data analysis. European Journal of Personality, 30(2), 125-138.SPSS, a statistical software package first released in 1968, continues to be a premier software for analysis of quantitative data. 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).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. 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. 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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