Create Dummy Variables:  Ever want to create dummy variables for a categorical variable?  Doing so through the SPSS Transform menu is straightforward but inefficient.  ZumaStat creates dummy variables for you with just a click of a few buttons on a single screen.  ZumaStat will do dummy coding or effect coding.

Growth Curve Data:  Many growth curve programs that import SPSS data require that the repeated measure data be structured in rows rather than columns.  ZumaStat will convert your SPSS data file to such a format with ease and will add dummy variables for respondents with just a few mouse clicks.  The ZumaStat programs make it easy to structure data files that are compatible with the popular HLM computer program. ZumaStat also includes a utility that passes data to Excel and then plots growth curves for each individual case in a file. This utility is useful for seeing trends in growth curves for individuals considered separately rather than aggregated.

Mean Center a Variable:  For many regression analyses, it is useful to center a predictor variable about its mean.  ZumaStat allows you to do so with one or two mouse clicks.  You can also easily "center" a variable one standard deviation above or below its mean value with one simple click.

Mean Center a Variable Within Strata. ZumaStat will also mean center variables within strata or as a function of a break variable. For example, if the break variable is gender and it has two values, then the mean for males is calculated and the mean for females is calculated. The male mean is subtracted from all scores of males and the female mean is subtracted from all scores of females.

Product Terms:  Interaction analysis in regression requires product terms.  ZumaStat offers two utilities that greatly simplify the calculation of product terms.  This is really quick and easy to do, whereas it is a cumbersome process in SPSS.  For example, in one routine, you specify the variables you want to focus on, and ZumaStat automatically calculates all possible product terms between them and inserts variable labels to identify them.

Reformat a Correlation Matrix:  Pivot tables are nice, but did you ever try to look at a large correlation matrix and examine the correlations on the right side of the matrix.  This is a great headache in SPSS.  ZumaStat will reformat a correlation matrix for you so you can see all the results on a single screen on your monitor.

Missing Data Dummy Variables:  When you have missing data, you often want to determine if the data are missing at random or if there is bias in the missing data pattern.  One way of approaching this for a given variable, X, is to create a dummy variable for X, where a score of 1 is assigned to cases with missing data and a score of 0 is assigned to cases with non-missing data.  This dummy variable can then be correlated with other variables to determine if it is associated  with them.  For example, if the missing data dummy variable is significantly correlated with gender, then this suggests that the data are not missing at random and that males are more or less likely to have missing data than females.  ZumaStat creates missing data dummy variables for you for as many as 400 variables all with a few mouse clicks.

Change User Missing to System Missing Values: ZumaStat allows you with the click of one button to change user missing values to system missing values for all variables in a data set.

Drop Variables from an Active Data File: ZumaStat allows you to easily drop variables from an active data file, using simple point and click.

Reorder Variables in an Active Data File: ZumaStat allows you to easily reorder variables in an active data file, using simple point and click.

Remove Scientific Notation and Change Decimals in a Pivot Table. With the click of a button, you can change the number of decimals displayed in a Pivot Table and remove scientific notation. ZumaStat does this much more quickly and efficiently than SPSS.

Copy Value Labels. ZumaStat has a routine that allows you to easily copy the value labels from one variable to one or more other variables.

Weighted Combinations of Variables:  This routine creates a new variable that is a weighted sum of other variables in your data file.  For example, if you target three variables, V1, V2 and V3, and specify weights for each (using ZumaStat interface), then ZumaStat creates a new variable (NV) that equalsV = w1*V1 + w2*V2 + w3*V3, where w is a weight.  If you have three variables and you want the new variable to be the sum of the three, set w1=1, w2=1 and w3=1. If you want the new variable to be the average of the three, set w1=.333, w2=.333 and w3=.333. If you want the new variable to be the average of the first two variables minus the third variable, set w1=.5, w2=.5 and w3=-1.  ZumaSta provides shortcut check boxes to generate a new variable that is the mean or sum of a set of variables.

Variable List:  If you have a file with many variables in it, you might have SPSS set so that it shows you the variables in a variable list in the same order that they occur in the data file.  Suppose that you want to locate a certain variable whose name you know, but you can not remember where in the file it occurs.  You could change SPSS so that it shows the variables in alphabetical order, but this is laborious.

Eliminate Cases with Missing Data from the Data File:  Some programs that interface with SPSS require that there be no missing data in a file (e.g., HLM, some applications of AMOS).  Although you can easily invoke listwise deletion of missing data in any of SPSS's statistical routines, it is quite another matter to eliminate cases from a data file based on missing data across multiple variables.  ZumaStat offers a utility that does so with a few mouse clicks.

Cross-Validation: ZumaStat has several utilities that facilitate cross-validation analyses.  One utility uses point and click to randomly divide a sample in half and create two separate data files with each half in them. Another utility randomly divides cases into any number of groups you specify and then creates a break variable identifying the different groups for use of the Split File capability of SPSS.

Date Variables:  ZumaStat provides you a simple pont and click method for working with date variables. Specify two date variables and this utility creates a new variable that is the number of days, months or years between the two dates.

Excel Graphics:  ZumaStat interfaces SPSS and Excel and creates graphs of SPSS data using the Excel graphics programs.  An advantage of this is that Excel offers a wider range of graph types that can be easily cut and pasted into other applications.  If you work with Word, Powerpoint and other Microsoft products, there are advantages of pasting graphs from Excel  due to compatibility and flexible editing issues.  ZumaStat plots SPSS data in Excel for one way frequencies, two way contingency tables, means for single factor designs, means for two factor designs,  scatter plots, and graphs to explore growth curves at the level of single respondents, all with just a few mouse clicks.

Smoothed Scatterplots:  Ever form a scatter plot between two variables that are correlated 0.30 or so and have a blob of points appear before you?  Smoothed scatterplots can be revealing in such instances.  ZumaStat offers the option of a smoothed scatterplot using Excel.  Smoothed scatterplots are not available in SPSS.

Creating Difference Scores:  ZumaStat describes shortcuts you can use to effectively analyze data in repeated measure factorial designs.  One of these relies on the analysis of difference scores.  ZumaStat offers a routine that quickly and easily calculates all possible difference scores among a set of variables. 

Save Filtered Cases:  If you want to save cases to a new SPSS file but apply a filter so that only a subset of the cases are saved, you must again get into pasting and editing syntax.  ZumaStat greatly simplifies this process and circumvents working with syntax.

Web Page Links:  ZumaStat includes some useful links to web pages focused on statistics and SPSS support groups.  

Add or Subtract Constants to Many Variables:  To add or subtract a constant from a variable, you have to go through about a half a dozen clicks for each variable as you navigate the SPSS Transform menu.  With the ZumaStat utility, you can easily accomplish this transformation for up to 10 variables, all from a single screen and with relative ease.  This can particularly be useful for "centering" variables about different values for purposes of interaction analysis.

Exchange Values:  If you rely on the parameter estimates of SPSS's GLM program to isolate single degree of freedom contrasts, then you know that you often have to change the reference group in order to isolate the coefficient that is of theoretical interest to you.  SPSS allows you to change the reference group from being the first to the last group, but this often does not do the trick.  Rather, you must recode and exchange values (e.g., recode scores on a factor so that those individuals with a 2 on the factor now have a score of 1 and vice versa).  You can do this using the recode routine in SPSS, but ZumaStat greatly simplifies the process for you.

Exporting Data:  Exporting a data file to other formats (e.g., ascii, Excel) is straightforward in SPSS but not if you only want to export a subset of the variables in the data set. To accomplish this you must paste syntax and modify the syntax while keeping variable names in mind.  ZumaStat allows you to export subsets of variables with a few mouse clicks.  Exporting is truly simple.

Grouping Scores on a Variable:  Suppose you want to recode scores on a variable so that individuals with values between 1 and 5 are in one group, those with scores of 6 to 10 are in another group, and so on.  ZumaStat makes this operation simple, circumventing the more complicated recode and transformation utilities of SPSS.

Reverse Score Variables:  ZumaStat makes it very easy to reverse score a variable (e.g., change the scoring from 1 to 5 to 5 to 1).

Rename Variables:  Renaming a variable is straightforward but cumbersome in SPSS.  ZumaStat makes it easy to rename many variables with just a few mouse clicks.

Reduce Factorial Design to a One Factor Design: ZumaStat uses simple point and click to reduce a factorial design to a one-way factor. For example, if you have a 3X2 design, this utility will turn it into a one-way design with six groups. A new variable is created with the numbers 1 to 6, each identifying group membership based on the original six groups. This utility is useful so that you can use the SPSS program 'One Way ANOVA' to apply contrast coefficients that involve special contrasts in a 'factorial' design. The logic is developed in ZumaStat's extensive Users Manual.

Matrix Operations: Using matrix manipulations in SPSS requires knowledge of the syntax language. ZumaStat uses the SPSS matrix language to make it easy to perform basic matrix operations through point and click. The operations include absolute value, Cholesky decomposition, determinant, eigenvalues, exponentiation, generalized inverse, inverse, rank, scalar multiply, square root, trace and transpose. ZumaStat also uses SPSS matrix language to make it easy for you to add, subtract, multiply or divide two matrices, as well as to compute Kronecker products, element-wise multiplication and element-wise division.

Create Simulation Data:  ZumaStat has a complete package for generating data for simulations. It allows you to create a set of random variables for a user selected number of cases such that the variables come from either a normal distribution, a chi square distribution, a log normal distribution, a uniform distribution or a mixed normal distribution.  This is useful for conducting simulations.  You can also create data for variables with a specified population correlation matrix and format data in such a way that simulation studies are relatively easy to do. 

Create Input for LISREL:  Although LISREL now imports SPSS data files, there are times when it will be more convenient to output a correlation matrix with means and standard deviations in ascii files for input into LISREL.  ZumaStat offers this possibility with a few mouse clicks as well as the ability to output ascii raw data in free field format that is compatible with LISREL.

Analysis of Change:  ZumaStat offers a suite of programs that provides useful perspectives on the analysis of change between two time points. Within Z Plus, it has a utility that will calculate six types of change scores, (1) raw change, (2) the reliable change index (RCI) that is popular in clinical psychology, (3) an ordinal version of the RCI, (4) a residualized change score, (5) a backward residualized change score, and (6) the Lord-McNemar true change score. ZumaStat interfaces with Excel to generate Galton squeeze diagrams, a useful graphic for visualizing regression to the mean. It also offers Pair-Link plots to better see individual change. ZumaStat offers utilities to determine the reliability of change scores from the reliability of its component parts, inherent correlations between pretest and change scores by virtue of part-whole dynamics, and the biasing effects of regression to the mean in intervention studies where there are pre-existing differences on the pretest in the treatment and control groups.

Simple SPSS Integration:  ZumaStat integrates conveniently with SPSS, becoming part of the SPSS menu bar so that you may easily and conveniently access it as you work in SPSS.