![]() ![]() I'm going to very strongly suggest that you never recode into the same variable. Now normally the way you would do this, is you would come up to transform and you would come down to either recode into the same variable or recode into different variables. Another one is with the syntax, and in this video I'm going to look at recoding the categorical variables. As with so many others there are two different ways to do this, one is with the menus in SPSS, which is a great way to get things set up. But let's take a look at how this can work. One's on a zero to six scale, one's on a one to seven and one goes from a negative three to a positive three and all of these are treated in slightly different ways. We have group which is group one all the way up through group nine and then we have three variables about scaled questions. We've got this one right here that says status, and then underneath it are the values one and two which currently are labeled as complete and not complete. If you come and turn off the labels, you'll see that we've got these numbers all underneath and we've got a couple of cryptic variables. Now this one looks a little curious because some of the values have labels, and some don't. ![]() To demonstrate how this works in SPSS, I've got a tiny little toy data set here called Recoding Variables and I want to run through a couple of different ways of manipulating the data so the values reflect what you actually want. The wrangling process includes recoding and re-categorizing data to get it into the format that's going to be most useful for answering the questions that you have. The INDEX subcommand creates a new variable named Gender with integer values that represent the sequential order in which the original variables are specified on the MAKE subcommand.- Getting the data into SPSS is a very significant part of getting ready for analysis but it's hardly the only one. ![]() The MAKE subcommand creates a sinble income variable from the two original income variables. MAKE Income FROM Male_income Female_income There is no known or assumed relationship between male and female values that are recorded in the same row the two columns represent independent (unrelated) observations, and we want to create cases (rows) from the columns (variables) and create new variable that indicate the gender for each case. A simple excel file contains two columns of information: income for males and income for females. VARSTOCASES command creates the exact opposite. The COUNT subcommand will create a new variable that indicates the number of original cases represented by each combined case in the restructured file. SEPARATOR subcommand specifies the character that will be used to separate original variable names and the values appended to those names for the new variable names in the restructured file. E.g.: only values of ID_number will be used to generate new variable. Optional INDEX allocate all unique values of all non-ID variables. In this example, all cases with the same value for ID_number will become a single case in the restructured file. The ID subcommand of the CASETOVARS indicates the variables that will be used to group cases together. The data file must be sorted by the variable specified on the ID subcommand of the CASETOVARS command. Sort Cases sorts the data file by the variable that will be used to group cases in CASETOVARS command. The CASETOVARS command combines the related cases and produces the new variables. If data file contains groups of related cases, you may not be able to use the appropriate statistical techniques (Paired Samples T Test of Repeated Measures GLM) because the data are not organized in the required fashion for those techniques. Some SPSS techiques are based on the assumption that cases (rows) represent independent obervations and/or that related observations are recorded in separate variables rather than separate cases.
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