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With the File > Save dialog you simply select the. So you don’t have to remember that Job Category (jobcat) 1 is “Clerical,” 2 is “Custodial,” and 3 is “Managerial. SPSS lets you also save to a large number of other file types: spreadsheets, other statistical software etc. It lists out the labels for all the values for each variable. I find the information I use the most are the labels and the missing data codes.Įven more useful, though, is the Value Label table. The first includes the following information on the variables. Simply choose Display Data File Information and Working File.ĭoing this gives you two tables. There is a nice little way to get a few tables with a list of all the variable metadata. Or even just to print them out for yourself for easy reference. But sometimes you need to just print them all out–to create a code book for another analyst or to include in the output you’re sending to a collaborator. 1Old Value: Specify the type of value you wish to recode (e.g., a specific value, missing data, or a range of values) and the specific value to be recoded (e.g., a value of 1 or a range of 1-5). Spending the time to set up variable information makes data analysis much easier–you don’t have to keep looking up whether males are coded 1 or 0, for example.Īnd having them all in the variable view window makes things incredibly easy while you’re doing your analysis. Once you click Old and New Values, a new window where you will specify how to transform the values will appear. This includes variable labels, missing data codes, value labels, and variable formats. Download the first file archive from the SPSS Syntax section, Diabetes.zip. # p <- ggplot(x, aes(y = mean(PF), x = W.One of the nice features of SPSS is its ability to keep track of information on the variables themselves. That being said save your syntax after every successful cluster of code. # p <- ggplot(x, aes(y = PF, x = W.L)) + geom_errorbar() # p <- ggplot(x, aes(y = PA, x = PF)) + geom_point()+ labs(title = 'Points Scored by Points Against') # p <- ggplot(x, aes(y = mean(PF), x = W.L)) + geom_line() count., x = PF, fill = W.L)) + geom_bar(position = 'dodge') count., x = PF)) + geom_bar(position = 'dodge')+ labs(title = 'Points Scored by WinLoss') # p <- ggplot(x, aes(ymax = max(PF), ymin = min(PF), x = W.L)) + geom_ribbon() # p <- ggplot(x, aes(ymax = max(PF), ymin = min(PF), y = mean(PF), x = W.L)) + geom_pointrange() # p <- ggplot(x, aes(x = PF)) + geom_histogram()+ labs(title = 'Points Scored') density.), stat = 'bin')+ stat_function(geom='line', fun = dnorm, arg = list(mean = mean(PF), sd = sd(PF)))+ labs(title = 'Points Scored') Most of these are described in various publications, and I recommend you read the corresponding publication before using the macro. # p <- ggplot(x, aes(x = PF)) + geom_histogram(aes(y =. My Macros and Code for SPSS, SAS, and R On this page you will find information about many of the macros for SPSS and SAS that I have written. |
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