SPSS Python Extension Functions
The SPSS Python Extension is an addon to SPSS that allows you to include Python code in SPSS syntax programs, greatly enhancing the programmability of SPSS. The Python extension is completely free, and is available as part of the standard installation of SPSS version 23 and above. If you try to run a macro and get a message indicating that Python is not installed, you should reinstall your SPSS and make sure that you install the Python Extension as part of the setup process.
Those interested in working with Python might also be available in the book "Programming and Data Management in SPSS," which gives a good introduction to using Python in SPSS. It is also free, and is available from the original author (Raynald Levesque) at:
So why should you be interested in Python? Well, basically, it gives you the ability to do a lot of new things in SPSS syntax, such as the following:
|Performs a confirmatory factor analysis within SPSS by using Mplus. The program automatically converts the active dataset to Mplus, writes the necessary Mplus code, runs the analysis in Mplus, and then brings the results into the SPSS output window.
Process model data
Process mean data
|Performs a latent profile analysis or a latent class analysis in Mplus. It can create a dataset containing the variable means from different profiles, and can create a dataset containing model statistics. The "Process model data" syntax will add the LMR test to the model data file. The "Process mean data" syntax will create profile plots based on the means in the mean data file.
|Performs a latent transition analyseis in Mplus. It creates output and data files similar to those produced by the MplusLPA program.
|Reads in a set of multiple imputation datasets generated in Mplus and turns them into a multiple imputation dataset in SPSS.
|Performs a path analysis within SPSS by using Mplus. The program automatically converts the active dataset to Mplus, writes the necessary Mplus code, runs the analysis in Mplus, and then brings the results into the SPSS output window.
|Runs a two-level model from within SPSS by using Mplus. The program automatically converts the active dataset to Mplus, writes the necessary Mplus code, runs the analysis in Mplus, and then brings the results into the SPSS output window.
|Writes a set of "with" statements in the Output window that can be copied into an Mplus program. The program can either intercorrelate all of the items within a list, or it can correlate the items in one list with all of the items in a second list.
|Given a base Mplus input file, this progrm will write and run a collection of additional Mplus input files that each drop one predictor from the model. These can be used to determine the delta R2 for each of the predictors in the model.
|This program takes the output from the rnMplusDeltaR2 program, automatically calculates the delta R2 for each predictor, and puts the results in an SPSS data set.
|Converts an SPSS data set into Mplus format, and also generates a skeleton input file that would read the data set.. It performs a number of transformations to the data to make it consistent with the requirements of Mplus.
|Artificially categorizes a continuous variable. You give the name of the variable and the number of groups, and the function will find the appropriate percentiles and create a new variable that divides people into groups based on their scores on the continuous variable.
|Takes a list of existing binary variables and converts them into a single categorical variable based on an ordered list.
|Provides basic information about all of the variables in a data set. String variables and variables that have value labels are treated categorically. Other variables are treated continuously.
|Assigns value labels for one variable based on values in a second variable.
|Allows you to randomly select a fixed number of participants from each level of a grouping variable. This is useful when you want to graphically examine relations but there are too many participants to make sense of the graphs.
|Provides a correlation matrix for a set of variables that includes a confidence interval.
|Locates all of the CSV files in a source directory and converts them all to SPSS data sets, which are placed in an identified target directory.
|Calculates the d for a single two-group between-subjects comparison.
|Calculates the results for two-group between subjects comparisons and puts them into a new dataset. Allows you to optionally define multiple groupings, multiple outcomes, and a split variable.
|Deletes all variables that have no valid cases.
|Allows you to use a summary statistic, like the mean, standard deviation, or median, as part of a formula so that it automatically updates when the data changes.
|Calculates one or more summary statistics for one or more variables and puts the results into a new data set. Also allows you to optionally define a split variable. Rows in the dataset can be labeled, and the dataset can be appended by issuing the command multiple times, potentially with different labels each time.
|Automatically creates a set of dummy codes for a categorical variable.
|Locates all of the Excel files (either .xls or .xlsx) in a source directory and converts them all to SPSS data sets, which are placed in an identified target directory.
a group of continuous variables and then provides
information about their univariate and bivariate distributions
1. Descriptive statistics for each variable
2. Correlations among variables
3. Univariate histograms
4. Bivariate scatterplots
5. Univariate missingness
6. Patterns of missing data
|Creates a dataset containing the frequencies of a set of variables. Rows in the dataset can be labeled, and the dataset can be appended by issuing the command multiple times, potentially with different labels each time.
|Calculates the intraclass correlation and the design effect for a cluster variable on a list of outcomes.
|Locates all of the SPSS data sets in a source directory and merges them into a single file, which is placed in an identified target directory. Specifically used to merge datasets that have a similar set of variables but different cases.
|Given a long filename, it returns it automatically split into smaller portions so that it doesn't violate SPSS constraints on how many characters are allowed on a single line of syntax.
|Removes cases missing values on the identified variable. Works on both string and numeric variables.
|Sets the missing values for all numeric variables in the data set using a single command.
|Removes variable labels and/or value labels from a list of variables.
|Resolves duplicate cases in a dataset. You specificy a primary row for each case, which is where most of the values are taken from. However, if the primary row is missing a value on the variable, it is filled in from the other rows for that case if they have valid values.
|This program provides users with classification statistics (such as sensitivity and specificity) for the ability of a screener to predict the value of a test.
|Set every string variable in the data set to the smallest size that will fit all its values.
|Takes a set of comma-separated values in a text string and puts each entry into a separate variable. Was originally designed to handlethe responses to Qualtrics checkbox questions.
|Reads a text variable and then creates a number of additional variables separating the contents of the original text variable into different parts based on an identified delimiter. This can be used to take a sentence and have each word put in a different variable.
|Applies a specific suffix to a set of variables in the dataset. You can apply the suffix to all of the variables, a specific list of variables, or all of the variables except a specific list of variables.
|SPSS Python Extension function to calculate the mode of a set of variables.
|Creates a correlation matrix using a regression weight. This function calculates each weighted correlation individually using the regression command (rather than using the WEIGHT command, which artificially increases your sample size), and then combines them into a correlation matrix.
|Similar to the descriptive function, this allows you to use a summary statistic as part of a formula. However, this function restricts the calculation to those cases that are in the same condition as the current case on a split variable.