IBM Support

Release notes: IBM SPSS Statistics Subscription

Release Notes


Abstract

Release notes: IBM SPSS Statistics Subscription

Content

IBM® SPSS® Statistics Subscription is now available. Compatibility and other issues are addressed.

For information on issues that were resolved, see the Statistics fix list.

Contents

Description

IBM SPSS Statistics Subscription empowers clients with pay-as-you-go pricing and easy software management in order to make better data-driven decisions. IBM® SPSS® Statistics Subscription offers the self-service advanced statistical capabilities that are required to gain insights from data and make better decisions. SPSS Statistics is the world's leading statistical software used to solve business and research problems by means of ad hoc analysis, hypothesis testing, geospatial analysis, and predictive analytics.

  • Quickly understand large and complex data sets by using advanced statistical procedures that ensure high accuracy to drive quality decision making.
  • Reveal deeper insights and provide better confidence intervals by using visualizations and geographic spatial analysis.
  • Process and deploy analytics faster with flexible deployment options.
  • Build a predictive enterprise, making the business more agile and maximizing return on investment.

With the introduction of SPSS Statistics Subscription (SPSS Statistics), organizations, groups, and individual users are now able to leverage its predictive analytics capabilities to deliver the maximum amount of value to the user.

  • Intelligent add-ons extend the capabilities of the license to meet the needs of users of all abilities.
  • Easily consumable by the user: SPSS Statistics simplifies the experience for everything from downloading software, to managing licensing, to updating your software.
  • Flexible billing options: A monthly payment option offers the flexibility to leverage SPSS Statistics when needed.
  • Highly secured and easily scalable with a simplified renewal process.
September 2022 updates and enhancements

Analyze procedures

Linear OLS Alternatives

Elastic Net

Click Analyze > Regression > Linear OLS Alternatives > Elastic Net to obtain a Linear Elastic Net Regression analysis. The new Linear Elastic Net extension procedure uses the Python sklearn.linear_model.ElasticNet class to estimate regularized linear regression models for a dependent variable on one or more independent variables. Regularization combines L1 (Lasso) and L2 (Ridge) penalties. The extension includes optional modes to display trace plots for different values of alpha for a given L1 ratio, and to select the L1 ratio and alpha hyperparameter values based on crossvalidation. When a single model is fitted or crossvalidation is used to select the penalty ratio and/or alpha, a partition of holdout data can be used to estimate out-of-sample performance.

Lasso

Click Analyze > Regression > Linear OLS Alternatives > Lasso to obtain a Linear Lasso Regression analysis. The new Linear Lasso extension procedure uses the Python sklearn.linear_model.Lasso class to estimate L1 loss regularized linear regression models for a dependent variable on one or more independent variables, and includes optional modes to display trace plots and to select the alpha hyperparameter value based on crossvalidation. When a single model is fitted or crossvalidation is used to select alpha, a partition of holdout data can be used to estimate out-of-sample performance.

Ridge

Click Analyze > Regression > Linear OLS Alternatives > Ridge to obtain a Linear Ridge Regression analysis. The new Linear Ridge extension procedure uses the Python sklearn.linear_model.Ridge class to estimate L2 or squared loss regularized linear regression models for a dependent variable on one or more independent variables, and includes optional modes to display trace plots and to select the alpha hyperparameter value based on crossvalidation. When a single model is fitted or crossvalidation is used to select alpha, a partition of holdout data can be used to estimate out-of-sample performance.

Parametric Accelerated Failure Time (AFT) Models

Click Analyze > Survival > Parametric Accelerated Failure Time (AFT) Models to obtain a Parametric Accelerated Failure Time (AFT) Model analysis, which invokes the parametric survival models procedure with non-recurrent life time data. Parametric survival models assume that survival time follow a known distribution, and this analysis fits accelerated failure time models with their model effects proportional with respect to survival time.

Pseudo-R2 measures in Linear Mixed Models and Generalized Linear Mixed Models

Pseudo-R2 measures and the intra-class correlation coefficient are now included in Linear Mixed Models and Generalized Linear Mixed Models output (when appropriate). The coefficient of determination R2 is a commonly reported statistic, because it represents the proportion of variance explained by a linear model. The intra-class correlation coefficient (ICC) is a related statistic that quantifies the proportion of variance explained by a grouping (random) factor in multilevel/ hierarchical data.

Command syntax

GENLINMIXED

The output now includes pseudo-R2 measures and the intra-class correlation coefficient (when appropriate).

LINEAR_ELASTIC_NET

The new extension command uses the Python sklearn.linear_model.ElasticNet class to estimate regularized linear regression models for a dependent variable on one or more independent variables.

LINEAR_LASSO

The new extension command uses the Python sklearn.linear_model.Lasso class to estimate L1 loss regularized linear regression models for a dependent variable on one or more independent variables. The command includes optional modes to display trace plots and to select the alpha hyper-parameter value that is based on cross-validation.

LINEAR_RIDGE

The new extension command uses the Python sklearn.linear_model.Ridge class to estimate L2 or squared loss regularized linear regression models for a dependent variable on one or more independent variables. The command includes optional modes to display trace plots and to select the alpha hyper-parameter value that is based on cross-validation.

MIXED

The output now includes pseudo-R2 measures and the intra-class correlation coefficient (when appropriate).

SURVREG AFT

The new extension command invokes the parametric survival models procedure with non-recurrent life time data.

Python and R upgrades

Python 3.10.4 and R 4.2.0 are installed with IBM® SPSS® Statistics Subscription.

Select Cases - hidden cases

Unselected cases are no longer hidden in the Data Editor when a subset of cases is selected, and the unselected cases are not discarded. This represents a return to the behavior of the November 2020 update and earlier updates.

Violin plots

The Graphboard Template Chooser includes a new violin plot, which is a hybrid of the box and kernel density plots. Violin plots show peaks in the data and are used to visualize the distribution of numerical data. Unlike a box plot that can only show summary statistics, violin plots depict summary statistics and the density of each variable.

Workbook mode enhancements

  • Two new workbook toolbar items: Show/Hide all syntax windows and Clear all output.
  • New button on the Status bar to switch between Classic (Output and Syntax) and Workbook modes.

Search enhancements

The Search feature now provides options for entering terms directly in a toolbar field and for viewing results in a drop-down pane.

November 2021 updates and enhancements
 

Analyze procedures

Kernel Ridge Regression

The new extension-based procedure uses the Python sklearn.kernel_ridge.KernelRidge class to estimate a kernel ridge regression of a dependent variable on one or more independent variables. The independent variables include model hyperparameters, or a selection of hyperparameter values, over a specified grid of values. Cross validation is achieved by using the sklearn.model_selection.GridSearchCV class.

Linear Mixed Models

A new output table for the procedure provides the marginal and conditional pseudo-R2 measures. The table displays only in instances where appropriate.

Power Analysis procedures

The new precision feature computes the sample sizes that are required to estimate the population parameter with precision that is determined by user-specified confidence interval half-widths. The expected result produces the minimum sample size to ensure that the actual confidence interval half-widths do not exceed the desired values.

      Note: The new feature is available for all Power Analysis procedures except Univariate Linear Regression.

Effect size as an input to the estimation of the power or sample size is now supported. The defined effect size value is passed to the intermediate step in the procedure and calculates the desired power or sample size. The following Power Analysis procedures support effect size as an input to the estimation of the power or sample:

  • Power Analysis of One-Sample t Test
  • Power Analysis of Paired-Samples t Test
  • Power Analysis of Independent-Samples t Test
  • Power Analysis of One-Way ANOVA
  • Power Analysis of Univariate Linear Regression Test

Command syntax

OUTPUT CREATE

The new command provides options for creating custom tables, charts, and other output items from user-entered JSON or from an external *.json file.

POWER ONEWAY ANOVA

The CONTRAST subcommand's new HALFWIDTH keyword estimates the sample size based on specified confidence interval half-widths.

The PARAMETERS subcommand's new ES and keyword specifies the effect size of the overall test, which is measured by either f or η2.

The PLOT subcommand's new ES, ES_YAXIS, and ES_XAXIS keywords control the two-dimensional power by effect size chart, the three-dimensional power by total sample size (x-axis) and effect size (y-axis) chart, and the three-dimensional power by total sample size (y-axis) and effect size (x-axis) chart.

POWER MEANS INDEPENDENT

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

The PARAMETERS subcommand's new ES keyword specifies the effect size as an input to the estimation of the power or sample size. When the two independent groups for comparison are assumed to have unequal variances, the effect size of the independent-sample analysis is measured by the mean difference.

POWER MEANS ONESAMPLE

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

The PARAMETERS subcommand's new ES keyword specifies the effect size as an input to the estimation of the power or sample size.

POWER MEANS RELATED

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

The PARAMETERS subcommand's new ES keyword specifies the effect size as an input to the estimation of the power or sample size.

POWER PARTIALCORR

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

POWER PEARSON ONESAMPLE

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

POWER PROPORTIONS INDEPENDENT

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

POWER PROPORTIONS ONESAMPLE

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

POWER PROPORTIONS RELATED

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

POWER SPEARMAN ONESAMPLE

The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths.

POWER UNIVARIATE LINEAR

The PARAMETERS subcommand's new ES keyword specifies the effect size value that is measured by f2.

SAVE DATA COLLECTION

The command has been deprecated.

Pivot table enhancements

Most tables contain a mix of values, and applying a heat map to an entire table typically produces tables with wildly varying ranges. The pivot table editor now includes the menu option Color Scales, which provides heat map style settings that display selected table cells in varying colors based on the cell values.

Workbook mode enhancements

Context menu

The right-click context menu now provides options for cutting, copying, and pasting content, and for displaying the Style Output dialog, which specifies changes to make to selected output objects in the Workbook.

Syntax paragraph error pane

Syntax-related error information now displays under the syntax paragraph.

Proxy settings

A proxy.ini configuration file is now installed with the product and provides options for manually configuring proxy settings.

Documentation

The "Exporting to Data Collection" topic has been removed as IBM SPSS Statistics no longer supports UNICOM Intelligence (formerly IBM SPSS Data Collection).

May 2021 updates and enhancements
Analyze procedures
Meta Analysis
Meta analysis is the analysis of the data obtained from a collection of studies that answer similar research questions. These studies are known as primary studies. Meta analysis uses statistical methods to produce an overall estimate of an effect, explore between-study heterogeneity, and investigate the impact of publication bias or, more generally, small-study effects on the final result. The following Meta Analysis procedures are new in IBM® SPSS® Statistics Subscription.

Meta Analysis Continuous. Performs meta-analysis with continuous outcomes on raw data that are provided in the active data set for the estimation of the effect size.

Meta Analysis Continuous Effect Size. Performs meta-analysis with continuous outcomes when the pre-calculated effect size data are provided in the active data set.

Meta Analysis Binary. Performs meta-analysis with binary outcomes on raw data that are provided in the active data set for the estimation of the effect size.

Meta Analysis Binary Effect Size. Performs meta-analysis with binary outcomes when the pre-calculated effect size data are provided in the active data set.

Meta Analysis Regression. Performs meta-analysis regression.
General Linear Model (GLM) procedures
The General Linear Model (GLM) procedure user interfaces now provide a Compare simple main effects setting on their EM Means dialogs. The setting is enabled whenever the target list contains one or more product or interaction effects (for example, A*B, A*B*C). The setting supports the specification of comparisons among simple main effects, which are main effects nested within the levels of other factors.
Linear Regression
Interaction variable terms for categorical variables are now supported.
One-Way ANOVA
The procedure now supports non-numeric categorical variables.
Power Analysis
The new Grid Values dialog provides options for specifying a range of POWER values for the purpose of viewing projected sample sizes in a grid format for every specified POWER range value.
The Grid Values dialog is available for every Power Analysis procedure when the Estimate sample size and Grid power values options are selected (click the Grid control to display the dialog).
Ratio Statistics
Price-Related Bias (PRB). The procedure now supports the Price-Related Bias (PRB) dispersion method. PRB is an index of whether assessment to price ratios are systematically higher or lower for higher-priced properties. PRB regresses percentage differences in assessment ratios. The differences are drawn from the median ratio on the base 2 logarithms of value proxy measures. The proxy measures are calculated as the "averages of sales prices" and the "ratios of assessed values to the median ratio". The method also gives the percentage change in assessment ratios for a 100 percent change in value.

Coefficient of Variation (COV). The new COV dispersion method includes the median and mean-centered coefficients of variation and effectively replaces the Median Centered COV and Mean Centered COV dispersion methods. The median-centered coefficient of variation is the result of expressing the root mean squares of deviation from the median as a percentage of the median. The mean-centered coefficient of variation is the result of expressing the standard deviation as a percentage of the mean.
Command syntax enhancements
COXREG command
The CONTRAST subcommand's DEVIATION keyword now defaults the refcat to the first category.
LOGISTIC REGRESSION command
The CONTRAST subcommand's DEVIATION keyword now defaults the refcat to the first category.
META BINARY command
The new command represents the meta-analysis procedure for binary outcomes when the raw data are provided in the active data set for the estimation of the effect size.
META ES BINARY command
The new command represents the meta-analysis procedure for binary outcomes when the pre-calculated effect size data are provided in the active data set.
META CONTINUOUS command
The new command represents the meta-analysis procedure for continuous outcomes when the raw data are provided in the active data set for the estimation of the effect size.
META ES CONTINUOUS command
The new command represents the meta-analysis procedure for continuous outcomes when the pre-calculated effect size data are provided in the active data set.
META REGRESSION command
The new command represents the meta-regression procedure.
RATIO STATISTICS
COV and PRB keywords added to the OUTFILE subcommand. COV, PRB, and N keywords added to the PRINT subcommand.

Relationship Maps

Relationship maps are useful for determining how variables relate to each other by providing a visual representation of the connections and influences that each node and link has over each other. Relationship maps visually represent connections and influences through nodes and links. Nodes represent variables and variable categories; links represent the strength of influence between nodes. Larger nodes and thicker link lines represent stronger connections and influence. Smaller nodes and thinner link lines represent weaker connections and influence. The Relationship maps feature is accessed through Graphs > Relationship Map...

R and Essentials for R

R 4.0.x and IBM SPSS Statistics Essentials for R are now installed with IBM® SPSS® Statistics. The R environment settings are defined in Edit > Options... > File Locations > R Location.

Python 3 and R programmability

Support for Python 3 and R is enhanced by enabling an easily configurable virtual runtime environment.

The Python runtime environment is accessed by clicking the Python 3 IDLE (PythonGUI) (Windows) or Python 3 for SPSS Statistics (macOS) option in the product folder.

Note: Python 2 is no longer officially supported. If you still need to run Python 2, refer to the Programmability SDK.

The R runtime environment is accessed by clicking the R x64 4.0.5 (Windows) or R for SPSS Statistics (macOS) option in the product folder.

Installation and licensing

Subscription. The product installer is updated to provide the option registering either the subscription or licensed version of IBM® SPSS® Statistics. IBM SPSS Statistics Subscription requires an IBMid to activate and install the subscription-based version of the software. You must purchase IBM SPSS Statistics Subscription in order to activate the product through the subscription method.

Licensed. Requires an authorized user license or concurrent user license to activate the software. You must purchase an on-premises license for IBM SPSS Statistics in order to activate the product through a user license or concurrent user license.

For more information on the differences between the subscription and licensed versions, see Which IBM SPSS Statistics version is right for you?

Output enhancements

Workbooks. Viewing output in Workbook mode bridges the IBM SPSS Statistics syntax editing ability with a Notebook approach that provides an interactive method for running syntax and viewing the corresponding output. Workbook documents (*spwb) consist of individual paragraphs. The paragraphs contain the output elements (syntax, tables, charts, and so on). Syntax paragraphs provide full syntax edit and run capabilities. Rich text paragraphs provide full rich-text editing capabilities.

Pivot table editor. The pivot table editor user interface now includes edit options slide-out pane on the right side of the dialog. The pane provides options for handling rows and columns, specifying text attributes, defining border parameters, specifying cells formats, and defining footnotes and table comments.

Installed extensions

Commonly used extensions are now automatically installed with the product. The installed extensions can be identified by the plus symbol next to their menu entry.

Search enhancements

The search feature now provides results for procedures, help topics, syntax reference, and case studies. The Search feature now searches all words and terms in each user interface dialog and help topic.

Command syntax help provides tooltips that provide syntax examples when hovering the cursor over commands and subcommands in the Syntax Editor.

Export output enhancements

  • Word Document (*.docx). You can now export output to Microsoft Word (*.docx) format.

  • Text - Plain (*txt), Text - UTF8 (*txt), and Text - UTF16 (*txt). The text export settings are now divided into three distinct options that provide different encoding methods.

  • Excel output. The Microsoft Excel export settings now provide options for creating both workbooks and worksheets.

  • Print preview. The File > Print Preview provides a PDF formatted preview version of the output.

Hidden rows

Rows that are hidden in the Data Editor through the Select Cases feature are no longer picked up when rows are copied from the Data Editor.

Chart Builder usability enhancements

The template controls in the Chart Appearance tab have been redesigned to streamline template selection options.

Accessibility

The user interface now supports high contrast mode, which adjusts the background and text colors to make the application easier to read.

November 2020 updates and enhancements
 
Analyze procedures
Bivariate Correlations
The procedure provides the option of suppressing the correlations table from the output. The procedure also now provides options for controlling the estimation of the confidence intervals.
Independent-Samples Proportions
The new procedure provides tests and confidence intervals for the difference in two independent binomial proportions. Output includes observed proportions, estimates of differences in population proportions, asymptotic standard errors of population differences under null and alternative hypotheses, specified test statistics with two-sided probabilities, and specified confidence intervals for differences in proportions.
One-Sample Proportions
The new procedure provides tests and confidence intervals for individual binomial proportions. Output includes the observed proportion, the estimate of the difference between the population proportion and the hypothesized population proportion, asymptotic standard errors under null and alternative hypotheses, specified test statistics with two-sided probabilities, and specified confidence intervals for proportions.
Paired-Samples Proportions
The new procedure provides tests and confidence intervals for the difference in two related or paired binomial proportions. Output includes observed proportions, estimates of differences in population proportions, asymptotic standard errors of population differences under null and alternative hypotheses, specified test statistics with two-sided probabilities, and specified confidence intervals for differences in proportions.
Reliability Analysis
The procedure provides the Omega (McDonald’s Omega) model option. This model assumes that the model is uni-dimensional including a single factor with no local item dependence in the form of error covariances. The model implies that the covariance of the two different items is the product of their loadings.
Command syntax enhancements
CORRELATIONS command
Added support for the NOMATRIX keyword in the PRINT subcommand. The keyword suppresses the correlations table from the output.
Added support for the CI subcommand. The subcommand controls the estimation of the confidence intervals.
MULTIPLE IMPUTATION command
Added support for specifying a single numeric parameter in the IMPUTE subcommand's SCALEMODEL keyword PMM method. The imputed value is based on the value defined for the closest randomly selected complete case from the closest (k) predictions, where (k) is a positive integer with a default value of 5.
PORPORTIONS command
The new PROPORTIONS command computes tests and confidence intervals for binomial proportions or differences of proportions. Statistics are available for one-sample proportions (tested against a specified value), paired samples (different variables), or independent samples (different groups of cases).
RELIABILITY command
Added support for the OMEGA keyword in the MODEL subcommand. The keyword provides an estimation of McDonald’s Omega to evaluate reliability.
Restore points
Restore points save data from active sessions that either quit unexpectedly (automatic recovery) or that you explicitly save. Each restore point is an SPSS® Statistics session snapshot. Each restore point contains Data Editor, syntax, and output file information that was active at the time the session either quit unexpectedly or that you explicitly saved. Saved restore points remain in a backed-up state until you either restore or delete them.
Output enhancements
Export SVG charts
You can now export charts to Scalar Vector Graphics (*.svg) format.
Chart and table editor usability enhancements
  • A Reset button was added to both the chart and table editors. The button resets the chart/table to its original configuration.
  • The table editor toolbar has been split into Edit and Format toolbars.
  • Increase Decimals and Decrease Decimals toolbar controls are now available. The controls allow you to increase or decrease the decimal place settings in tables.
APA style enhancements
  • Footnotes and captions can now be double-spaced.
  • Footnote alignment issues have been fixed.
  • Table footnotes and captions can now be disabled.
  • Chart spacing and alignment issue have been addressed.
  • Small significance values can now be represented with "<0.001".
June 2020 updates and enhancements
 
Packaging
The Bootstrapping and Data Preparation features are now included in the IBM® SPSS® Statistics Base edition (Bootstrapping was previously included in Custom Tables and Advanced Statistics; Data Preparation was previously included in Sampling and Testing).
Auto-Recovery
Automatic recovery is designed to recover unsaved files and content in instances where the application quits unexpectedly. You can select to enable/disable the automatic recovery feature (the feature is enabled by default), select a time interval (in minutes) between saving files, and view or change the auto-recovery file location. For more information, see General options.
Upon relaunching SPSS Statistics after an unexpected exit, you are presented with an IBM SPSS Statistics error report, which allows you to enter information about your session before the unexpected exit. After leaving the exit report, you are presented with the Auto-Recovery dialog, which provides options for recovering previous session data or deleting the saved session data. For more information, see Auto-Recovery.
Privacy settings
The Options dialog now includes a Privacy tab the provides options for:
  • Allow the SPSS Statistics application share information with IBM.
  • Enable or disable SPSS Statistics from retrieving Welcome dialog content updates.
  • Enable or disable SPSS Statistics from sending error reports to IBM.
For more information, see Privacy options.
Issue reporter
The Help menu now provides a Report Issue link that launches the IBM SPSS Statistics Issue Reporter dialog. The dialog allows you to enter information regarding any issues you may encounter when using the product. The information you enter is sent to IBM for use in improving the product.
Analyze procedures
Bivariate Correlations
The Show only the lower triangle setting was added to main dialog. When the setting is enabled only the correlation matrix table's lower triangle is presented in the output. When not selected, the full correlation matrix table is presented in the output. The setting was introduced to allow table output to adhere to APA style guidelines. For more information, see Bivariate Correlations.
Crosstabs
The Create APA style table settings was added to Cell Display dialog. The setting produces a table that adheres to APA style guidelines. For more information, see Crosstabs cell display.
Frequencies
The Create APA style tables settings was added to main dialog. The setting produces tables that adhere to APA style guidelines. For more information, see Frequencies.
Power Analysis
Power analysis plays a pivotal role in a study plan, design, and conduction. The calculation of power is usually before any sample data is collected, except possibly from a small pilot study. The precise estimation of the power can tell investigators how likely it is that a statistically significant difference is detected based on a finite sample size under a true alternative hypothesis. If the power is too low, there is little chance of detecting a significant difference, and non-significant results are likely even if real differences truly exist. The new procedures are grouped as follows.
Means
One-Sample T-Test
In one-sample analysis, the observed data are collected as a single random sample. It is assumed that the sample data independently and identically follow a normal distribution with a fixed mean and variance, and draws statistical inference about the mean parameter. For more information, see Power Analysis of One-Sample T Test.
Independent-Samples T-Test
In independent-samples analysis, the observed data contains two independent samples. It is assumed that the data in each sample independently and identically follow a normal distribution with a fixed mean and variance, and draws statistical inference about the difference of the two means. For more information, see Power Analysis of Independent-Samples T Test.
Paired-Samples T-Test
In paired-samples analysis, the observed data contains two paired and correlated samples, and each case has two measurements. It is assumed that the data in each sample independently and identically follow a normal distribution with a fixed mean and variance, and draws statistical inference about the difference of the two means. For more information, see Power Analysis of Paired-Samples T Test.
One-Way ANOVA
Analysis of variance (ANOVA) is a statistical method of estimating the means of several populations that are assumed to be normally distributed. The One-way ANOVA, a common type of ANOVA, is an extension of the two-sample t-test. The procedure provides approaches for estimating the power for two types of hypothesis to compare the multiple group means, the overall test, and the test with specified contrasts. The over test focuses on the null hypothesis that all group means are equal. The test with specified contrasts breaks down the overall ANOVA hypotheses into smaller but more describable and useful pieces of the means. For more information, see Power Analysis of One-Way ANOVA.
Proportions
One-Sample Binomial Test
The binomial distribution is based on a sequence of Bernoulli trials. It can be used to model those experiments including a fixed number of total trials that are assumed to be independent of each other. Each trial leads to a dichotomous result, with the same probability for a "successful" outcome.
The one-sample binomial test makes statistical inference about the proportion parameter by comparing it with a hypothesized value. The methods for estimating the power for such a test are either the normal approximation or the binomial enumeration. For more information, see Power Analysis of One-Sample Binomial Test.
Related-Samples Binomial Test
The binomial distribution is based on a sequence of Bernoulli trials. It can be used to model those experiments including a fixed number of total trials that are assumed to be independent of each other. Each trial leads to a dichotomous result, with the same probability for a "successful" outcome.
The related-sample binomial estimates the power of McNemar's test to compare two proportion parameters based on the matched pair subjects sampled from two related binomial populations. For more information, see Power Analysis of Related-Sample Binomial Test.
Independent-Samples Binomial Test
The binomial distribution is based on a sequence of Bernoulli trials. It can be used to model those experiments including a fixed number of total trials that are assumed to be independent of each other. Each trial leads to a dichotomous result, with the same probability for a "successful" outcome.
The independent-sample binomial test compares two independent proportion parameters. For more information, see Power Analysis of Independent-Sample Binomial Test.
Correlations
Pearson Product-Moment
Pearson's product-moment correlation coefficient measures the strength of linear association between two scale random variables that are assumed to follow a bivariate normal distribution. By convention, it is a dimensionless quantity and obtained by standardizing the covariance between two continuous variables, ranging between -1 and 1.
The test uses Fisher's asymptotic method to estimate the power for the one-sample Pearson correlation. For more information, see Power Analysis of One-Sample Pearson Correlation Test.
Spearman Rank-Order
Spearman rank-order correlation coefficient is a rank-based nonparametric statistic to measure the monotonic relationship between two variables that are usually censored and not normally distributed. The Spearman rank-order correlation is equal to the Pearson correlation between the rank values of the two variables, also ranging between -1 and 1. Detecting the power of the Spearman rank correlation test is an important topic in the analysis of hydrological time series data.
The test uses Fisher's asymptotic method to estimate the power for the one-sample Spearman rank-order correlation. For more information, see Power Analysis of One-Sample Spearman Correlation Test.
Partial
Partial correlation can be explained as the association between two random variables after the effect of another or several other variables is eliminated. It is a useful measurement in the presence of confounding. Similar to the Pearson correlation coefficient, partial correlation coefficient is also a dimensionless quantity ranging between -1 and 1.
The test uses Fisher's asymptotic method to estimate the power for the one-sample Pearson correlation. For more information, see Power Analysis of Partial Pearson Correlation Test.
Regression
Univariate Linear
Univariate linear regression is a basic and standard statistical approach in which researchers use the values of several variables to explain or predict values of a scale outcome.
The test invokes power analysis for the type III F-test in univariate linear regression. For more information, see Power Analysis of Univariate Linear Regression Test.
Command enhancements
CORRELATIONS command
Added support for the FULL, LOWER, and LNODIAG keywords in the PRINT subcommand. The keywords control the display of the correlation matrix table's lower triangle or the full correlation matrix table. The keywords were introduced to allow table output to adhere to APA style guidelines. For more information, see PRINT Subcommand (Correlations command).
MATRIX-END MATRIX command
  • The NCDF.BETA cumulative distribution function is now supported.
  • Probability density functions are now supported (they were previously only supported by the COMPUTE command).
  • Tail probability functions are now supported (they were previously only supported by the COMPUTE command).
  • Random variable functions are now supported (they were previously only supported by the COMPUTE command).
For more information, see MATRIX-END MATRIX.
NONPAR CORR command
Added support for the FULL, LOWER, and LNODIAG keywords in the PRINT subcommand. The keywords control the display of the correlation matrix table's lower triangle or the full correlation matrix table. The keywords were introduced to allow table output to adhere to APA style guidelines. For more information, see PRINT Subcommand (NONPAR CORR command).
NPTESTS command
CRITERIA subcommand
The SEED keyword is now supported. The keyword resets the random seed used for the Monte Carlo sampling.
ONESAMPLE subcommand
The KOLMOGOROV_SMIRNOV keyword now supports the following Lilliefors test for Monte Carlo sampling settings:
NSAMPLES keyword
Resets the number of replicates used by the Lilliefors test for Monte Carlo sampling.
MC_CILEVEL keyword
Resets the confidence interval level that is estimated by the Kolmogorov-Smirnov test.
SIMULATION keyword
Controls whether the Monte Carlo simulation is used to conduct the Lilliefors test for Normal distribution when the parameters are not specified.
POISSON keyword
The SAMPLE setting has been removed from the POISSON keyword.
For more information, see NPTESTS.
NPAR TESTS command
KS_SIM subcommand
The KS_SIM subcommand is now supported. KS_SIM (KOLMOGOROV-SMIRNOV simulation) controls the parameters for the Monte Carlo simulation for Normal, Uniform, and Exponential distributions. The new subcommand supports the following Lilliefors test for Monte Carlo sampling keywords:
CIN keyword
Resets the estimated confidence interval level used by the Kolmogorov-Smirnov test (using the Monte Carlo simulations).
SAMPLES keyword
Resets the number of replicates used by the Lilliefors test for Monte Carlo sampling.
NONORMAL keyword
When specified, results will not include the Monte Carlo sampling for Normal distribution.
K-S subcommand
POISSON=varlist is no longer supported.
For more information, see NPAR TESTS.
OMS
  • FORMAT=REPORTHTML and FORMAT=REPORTMHT deprecated from the DESTINATION subcommand. The subcommand syntax has been mapped to the HTML subcommand.
  • REPORTTITLE keyword deprecated from the DESTINATION subcommand.
For more information, see OMS.
ONEWAY command
The CRITERIA and ES subcommands are now supported by the ONEWAY command:
CRITERIA subcommand
The optional subcommand controls the significance level to estimate the confidence intervals.
ES subcommand
The optional subcommand controls the effect size estimation by providing keywords for controlling the effect size calculation for the overall test, and controlling the calculation of the contrast test effect size.
For more information, see ONEWAY.
OUTPUT EXPORT
Support for the REPORT subcommand has been deprecated. The REPORT subcommand syntax has been mapped to the HTML subcommand. For more information, see OUTPUT EXPORT.
OUTPUT MODIFY
  • Added support for the PIVOT keyword in the TABLES subcommand. The keyword pivots the specified row dimension to the specified column dimension. Any existing column dimensions are incremented outwards. The keyword was introduced to allow table output to adhere to APA style guidelines.
  • Added support for the HIDE and UNGROUP keywords in the TABLECELLS subcommand. HIDE suppresses the selected row or column; UNGROUP deletes the selected row or column group header. The keywords were introduced to allow table output to adhere to APA style guidelines.
  • Added support for the PARENT and CHILD options for the SELECTCONDITION keyword in the TABLECELLS subcommand. Both options specify primary and secondary string conditions to apply changes within the area of the table specified by the SELECT keyword.
  • Added support for the VALID, TOTAL, MISSING, CUMULATIVEPERCENT, and VALIDPERCENT options for the SELECTCONDITION keyword in the TABLECELLS subcommand.
For more information, see OUTPUT MODIFY.
OUTPUT SAVE
The TYPE subcommand's SPW option has been deprecated. For more information, see OUTPUT SAVE.
POWER ONEWAY ANOVA command
The new command estimates the power for two types of hypothesis to compare the multiple group means, the overall test, and the test with specified contrasts. The over test focuses on the null hypothesis that all group means are equal. The test with specified contrasts breaks down the overall ANOVA hypotheses into smaller but more describable and useful pieces of the means. For more information, see POWER ONEWAY ANOVA.
POWER MEANS INDEPENDENT command
The new command invokes power analysis for the independent sample t-test to draw statistical inference about the difference of the two means. For more information, see POWER MEANS INDEPENDENT.
POWER MEANS ONESAMPLE command
The new command invokes power analysis for the one sample t-test to draw statistical inference about the mean parameter. For more information, see POWER MEANS ONESAMPLE.
POWER MEANS RELATED command
The new command invokes power analysis for the related sample t-test to draw statistical inference about the difference of the two means. For more information, see POWER MEANS RELATED.
POWER PARTIALCORR command
The new command invokes the power analysis for the one-sample partial correlation test. Partial correlation can be explained as the association between two random variables after eliminating the effect of another or several other variables. It is a useful measurement in the presence of confounding. For more information, see POWER PARTIALCORR.
POWER PEARSON ONESAMPLE command
The new command invokes the power analysis for the one-sample Pearson correlation test. Pearson product-moment correlation coefficient measures the strength of linear association between two scale random variables, which are assumed to follow a bivariate normal distribution. For more information, see POWER PEARSON ONESAMPLE.
POWER PROPORTIONS INDEPENDENT command
The new command invokes the power analysis for the independent-sample binomial test to compare two independent proportion parameters. For more information, see POWER PROPORTIONS INDEPENDENT.
POWER PROPORTIONS ONESAMPLE command
The new command invokes power analysis for the one-sample binomial test to make statistical inference about the proportion parameter by comparing it with a hypothesized value. For more information, see POWER PROPORTIONS ONESAMPLE.
POWER PROPORTIONS RELATED command
The new command invokes power analysis for the related-sample binomial test (or McNemar's test) to compare two proportion parameters based on the matched pair subjects sampled from two related binomial populations. For more information, see POWER PROPORTIONS RELATED.
POWER SPEARMAN ONESAMPLE command
The new command invokes the power analysis for the one-sample Spearman rank-order correlation test. Spearman rank-order correlation coefficient is a rank-based nonparametric statistic to measure the monotonic relationship between two variables that are usually censored and not normally distributed. For more information, see POWER SPEARMAN ONESAMPLE.
POWER UNIVARIATE LINEAR command
The new command invokes power analysis for the type III F-test in univariate linear regression. Univariate linear regression is a basic and standard statistical approach in which researchers use the values of several variables to explain or predict values of a scale outcome. For more information, see POWER UNIVARIATE LINEAR.
QUANTILE REGRESSION command
CRITERIA subcommand
The QUANTILE keyword now provides support for a grid of quantiles (connected by the keywords TO and BY). The quantile grid can be mixed with other quantiles, and can be placed anywhere. For more information, see CRITERIA Subcommand (QUANTILE REGRESSION command).
T-TEST command
The ES subcommand is now supported:
ES subcommand
The optional subcommand controls the effect size estimation by providing keywords for controlling the printing of the effect size calculation for the overall test, and controlling how the standardizer is computed in estimating the Cohen's d and Hedges' correction for each variable pair (only for Paired-Samples T Test). For more information, see ES Subcommand (T_TEST command).
WEIGHTED KAPPA command
Cohen’s kappa statistic is broadly used in cross-classification as a measure of agreement between two observed raters. It is an appropriate index of agreement when ratings are nominal scales with no order structure. The new WEIGHTED KAPPA command is an important generalization of the kappa statistic that measures the agreement of two ordinal subjects with identical categories. For more information, see WEIGHTED KAPPA.
Charting enhancements
Chart Builder includes the following features/enhancements.
Bubble charts
Bubble charts display categories in groups as non-hierarchical packed circles. The size of each circle (bubble) is proportional to its value. Bubble charts are useful for comparing relationships in data.
High-resolution chart export options
When the None (Graphics only) option is selected as the document type in the Export Output dialog, the default file type is now set to Production Ready PostScript (*.eps), which is a high-resolution image format.
When the None (Graphics only) option is selected as the document type in the Export Output dialog,you can now select the Scalable Vector Graphics (*.svg) format, which is a high-resolution image format.
Chart templates
  • The Edit > Options > Charts dialog now includes a Samples Settings section that provides preview settings for any selected chart template. The dialog dynamically updates the preview chart images based on the specified settings.
  • Chart Builder's Chart Appearance tab now provides options for selecting chart templates. You can choose to use settings that are defined in Edit > Options > Charts, select a chart template that is installed with IBM SPSS Statistics, or select a chart template from another location. For more information, see Chart Appearance Settings.
Default chart colors
The default chart colors have been changed to a blue theme.
Chart Builder > Chart Appearance tab
The tab now allows you to directly select different chart template files.
Chart Editor
You can now increase/decrease font sizes directly in the editor.
Legends and titles
You can now move chart images and titles directly in the output.
Native macOS file selection dialogs
The file selection dialogs in the macOS version of SPSS Statistics have historically been heavily customized to accommodate specific SPSS Statistics file features. You now have the option of enabling native macOS file selection dialogs (via Edit > Options... > General > Windows > Display native macOS file dialogs). Native macOS file dialogs provide the following benefits:
  • All of the benefits of native macOS file selection dialogs are available (for example, search, sidebar shortcuts, keyboard shortcuts, and so on).
  • The SPSS Statistics file selection dialogs are consistent with other macOS file selection dialogs.
SPSS Web Reports and Cognos Active Reports
Support for both SPSS Web Reports and Cognos Active Reports have been deprecated.
Font size selection
You can now manually change the font size in the following locations:
  • Edit > Options... > Viewer
  • File > Page Attributes... > Font
  • Pivot Table Editor (via the Formatting toolbar)
The font Size lists provide a set of predefined sizes, but you can manually enter other, supported size values.
Search enhancements
The Search feature provides results that include:
  • Menu dialogs
  • Help topics
  • Case studies
  • Syntax reference
Clicking a search result will take you directly to the relevant procedure dialog, help topic, case study, or syntax reference topic.
November 2019 updates and enhancements
 
  • Application startup
  • macOS updates
    • A new Oracle JRE version resolves a macOS JRE crash that affected many applications in addition to SPSS Statistics.
    • The application was updated to address stricter macOS Catalina security standards. Refer to the SPSS Statistics on Catalina (macOS 10.15) article for more information.
  • ROC Analysis - Minor enhancements to the ROC Analysis procedure shows how well a classification model fits the data compared to a random assignment. The addition of the CLASSIFIER keyword to the PRINT subcommand controls the display of the Classifier Evaluation Metrics table in the output.
  • Custom Dialog Builder enhancements - The Custom Dialog Builder underwent changes to fix issues pertaining to UI generation and deployment.
  • Data acquisition - The application improves support for importing Cognos BI data. A fix was also made to support Microsoft Access database with the Office 2016 drivers.
  • Improved Microsoft Office clipboard support - Fixes and enhancements were made to support pasting data into Excel and pasting charts as Microsoft Office Drawing Objects.
  • Chart Editor enhancements - Various fixes were made to the Chart Editor to better support histograms and fit lines on scatter plots. Work was also done to improve legend handling during chart transposition.
  • R and Python extensions - Several customer-reported issues were addressed related to R-Essential and Python-Essentials.
  • Double-byte characters sets - There were several fixes made related to the handling of Chinese and Japanese character sets.
     
June 2019 updates and enhancements
 
  • User Interface
    • Welcome screen - The Welcome screen layout was enhanced and URLs were updated.
    • Data submission - Added opt-in feature to allow users to help improve SPSS Statistics with usage reports.
    • Give feedback prompt - The 'Give Feedback' prompt was optimized to not trigger as often.
    • Startup screen - The startup splash screen includes improved hover text and displays consistent product names.
    • About dialog - The About dialog now identifies, and easily allows copying of, the product version.
  • Licensing
    • Validation and response time - License validation performance and response time have been improved.
  • Bug Fixes
    • Fixed Custom Dialog installation issue caused when help file is not installed properly.
    • Fixed Fleiss' Kappa confidence interval computation issue.
    • Fixed save syntax document issue caused by highlighted syntax in the syntax editor.
    • Fixed an issue where the machine temporarily stops responding during application startup.
    • Fixed a system resource consumption issue during application startup.
    • Fixed Quantile Regression Estimated Parameters issue caused when plots are too wide to display.
    • Various fixes to improve security and reliability.

April 2019 updates and enhancements

  • Analyze procedures
    • Quantile Regression - Models the relationship between a set of predictor (independent) variables and specific percentiles (or "quantiles") of a target (dependent) variable, most often the median. For more information, see Quantile Regression. Quantile regression makes no assumptions about the distribution of the target variable, tends to resist the influence of outlying observations, and is widely used for researching in industries such as ecology, healthcare, and financial economics.
    • ROC Analysis - Assesses the accuracy of model predictions by plotting sensitivity versus (1-specificity) of a classification test (as the threshold varies over an entire range of diagnostic test results). ROC Analysis supports the inference regarding a single AUC, precision-recall (PR) curves, and provides options for comparing two ROC curves that are generated from either independent groups or paired subjects.
    • Bayesian Statistics
      • One-way Repeated Measures ANOVA - This new procedure measures one factor from the same subject at each distinct time point or condition, and allows subjects to be crossed within the levels. It is assumed that each subject has a single observation for each time point or condition (as such, the subject-treatment interaction is not accounted for).
      • One Sample Binomial enhancements - The procedure provides options for executing Bayesian one-sample inference on Binomial distribution. The parameter of interest is π, which denotes the probability of success in a fixed number of trials that can lead to either success or failure. Note that each trial is independent of each other, and the probability π remains the same in each trial. A binomial random variable can be seen as the sum of a fixed number of independent Bernoulli trials.
      • One Sample Poisson enhancements - The procedure provides options for executing Bayesian one-sample inference on Poisson distribution. Poisson distribution, a useful model for rare events, assumes that within small time intervals, the probability of an event to occur is proportional to the length of waiting time. A conjugate prior within the Gamma distribution family is used when drawing Bayesian statistical inference on Poisson distribution.
    • Reliability Analysis - The procedure had been updated to provide options for Fleiss' Multiple Rater Kappa statistics that assess the interrater agreement to determine the reliability among the various raters. A higher agreement provides more confidence in the ratings reflecting the true circumstance. The Fleiss' Multiple Rater Kappa options are available in the Reliability Analysis: Statistics dialog.
  • Command enhancements
    • GENLINMIXED command - New Covariance Type structures ARH1 & CSH, Random Effects. The CSH and ARH1 options were added to the /RANDOM subcommand (keyword COVARIANCE_TYPE).
      New Covariance Type structures ARH1 & CSH, Repeated Effects. The CSH and ARH1 options were added to the /DATA_STRUCTURE subcommand (keyword COVARIANCE_TYPE).
      Kenward - Roger Degree of Freedom method. The KENWARD_ROGER option was added to the /BUILD_OPTIONS subcommand (keyword DF_METHOD).
      Kronecker Covariance types. The options UN_AR1, UN_CS, UN_UN were added to the /DATA_STRUCTURE subcommand (keyword COVARIANCE_TYPE).
      New KRONECKER_MEASURES keyword. The keyword is used for specifying a list of variables for the /DATA_STRUCTURE subcommand. The keyword should be used only when COVARIANCE_TYPE is one of three Kronecker types. The rules for KRONECKER_MEASURES are the same as for REPEATED_MEASURES. When both specifications are in effect, they may or may not have common fields, but cannot be exactly the same (regardless of whether they are in the same order).
    • MIXED command - DFMETHOD keyword introduced on the CRITERIA subcommand.
      KRONECKER keyword added to the REPEATED subcommand. The keyword should be used only when COVTYPE is one of three following Kronecker types.
      UN_AR1, UN_CS, and UN_UN options added to the COVTYPE keyword on the REPEATED subcommand.

January 2019 updates and enhancements

  • Fixed a value label display issue for Windows when toggling the value labels tool button multiple times or when inputting data using predefined value labels.
  • Added a usability improvement for the 'Give Feedback' dialog Clicking the close button now resets the timer so there won’t be multiple dialogs in one feedback cycle.
  • Fixed an issue where numbers display incorrectly in pivot tables for scientific notations.
  • Fixed an issue where external Python configurations cannot run with Statistics.

December 2018 updates and enhancements

  • New "Give Feedback" option in the Help menu allows users to submit product feedback.
  • Updated SPSS support link and SPSS Statistics Subscription services terms.
  • Deprecated support for the .NET plug-in.
  • The following issues have been addressed:
    • Fixed Set Customer Table Look as default and other Table Look issues.
    • Fixed a MIXED procedure issue that caused it to hang with certain excel data types.
    • Fixed a Statistics engine crash issue when executing Explore Analysis on mm:ss timestamp variable.
    • Fixed an error when adding values to a password protected file.
    • Fixed a Pivot Table issue that caused incorrect rounded decimal digital values to be displayed.
    • Fixed an issue where Hiragana characters could not be entered in the Syntax Editor.
    • Fixed the silent installation error when using the -log flag.
    • Fixed Python application bundle startup failure caused by spaces in the installation path.
    • Fixed various issues to improve security and reliability.

September 2018 updates and enhancements

  • New user feedback options provide a convenient method for submitting product feedback.
  • New silent installation parameter is available for enterprise system administrators. The parameter allows administrators to toggle the application’s auto-update functionality. This enhancement provides enhanced security for enterprises by allowing them to control when product updates are made available (and to which users). For more information, see the silent installation sections below.
  • Exporting to Microsoft PowerPoint no longer fails when Microsoft Office 2013 or Office 365 is installed.
  • The Generalopen extension, that is available via the Extension Hub, prevents generating the “NameError: global name 'newdatasetname' is not defined” error. 
  • Exporting pivot tables to Microsoft Word now provides correct results when the row labels dimension is hidden.
  • The logistic regression confidence interval is now correctly calculated for multiple imputation pooled results.

System requirements and installation

Note: The following steps assume you have already purchased IBM SPSS Statistics Subscription.

To download and install IBM SPSS Statistics Subscription, go to the IBM Marketplace and then:

  1. Sign in with your IBM account (also known as IBMid). You must register for an IBMid if you do not already have an active IBM account.
  2. After you are logged in, your account profile provides a Product and Services section that displays all of the IBM products and services to which you are entitled.
  3. Click Download next to IBM SPSS Statistics Subscription.
  4. On the Product and Services page, click the Download link underneath IBM SPSS Statistics Subscription.
  5. Select the appropriate download option (for example, 64-bit or Mac OS) and then click Download.
  6. Click Save File if prompted.
  7. Navigate to the save file location on your workstation and double-click the installer file. On the Mac OS you must double-click the installer file after mounting the disk image.
  8. Follow the installation steps (including accepting the license agreement), until the product installation is complete.
  9. IBM SPSS Statistics Subscription is now ready for use.

Windows silent installation instructions

Pushing an installation is a method for remotely distributing software to any number of users without any user intervention. You can push the full installation of IBM SPSS Statistics to the user's desktop computers running Windows. The technology that you are using for pushing the installation must support the MSI 3.0 engine or higher.

Properties for silent installations

The following properties can be used for silent installations. All properties are case-sensitive. Values must be quoted if they contain spaces.

Properties for silent installations
Property Description Valid Values Default
INSTALLDIR The directory where IBM SPSS Statistics should be installed on the user's desktop computer. This property is optional. If it is excluded, the default is C:\Program Files\IBM\SPSS\Statistics . A valid path such as C:\Statistics. C:\Program Files\IBM\SPSS\Statistics
PROXY_USERID The user ID for the proxy. This parameter is necessary when your site is using a proxy that requires a user ID and password to connect to the Internet. This parameter works only if the local area network (LAN) settings in the Internet Settings control panel reference a specific proxy server address and port. A valid proxy user ID.
PROXY_PASSWORD The password for the proxy user. Refer to the discussion of PROXY_USERID for more information. A password associated with the proxy user ID.
ENABLE_CONNECTIONS Enables and disables Internet connectivity features (information sharing, error reporting, and welcome screen updates). YES or NO

MSI files

The IBM SPSS Statistics.msi file is located under the Windows\SPSSStatistics\ directory in the extracted contents of the downloaded eImage.

Command line example

The following command line can be used to push a product installation. Enter all of the text on one line.

MsiExec.exe /i "IBM SPSS Statistics.msi" /qn /L*v logfile.txt
INSTALLDIR="C:\Statistics"

Using SMS to push the installation

The basic steps for using Systems Management Servers (SMS) to push IBM SPSS Statistics are:

  1. If you downloaded the software, you must first extract the contents of the eImage, then copy the appropriate subdirectory under the Windows\SPSSStatistics\ directory to a directory on a network computer.
  2. Edit the IBM SPSS Statistics.sms file located in the copied directory. Using a text editor, modify the value of CommandLine by adding the appropriate properties. For a list of the available properties, refer to the preceding Properties for silent installations section. Make sure to specify the correct MSI file in the command line.
  3. Create a package from the IBM SPSS Statistics.sms file and distribute the package to the user's desktop machines.

Pushing an uninstallation

Note: When you push the uninstall command, the user loses customizations. If specific users require customizations, you can exclude those users from the distribution and ask them to install the product manually.

If you push an installation of a later version of IBM SPSS Statistics, you may want to uninstall first. You can do this silently by using the push_uninstall.bat file that is included in the extracted eImage files.

Mac OS silent installation instructions

A silent, unattended installation does not display a graphical interface and does not require any user intervention.

Use the following command to run a silent installation. On macOS you must run as root or with the sudo command.

sudo installer -pkg IBM\ SPSS\ Statistics.pkg -target /
Note: If you want to push a silent installation on MacOS with an activated license, you must write a script that executes the silent installation command and calls the licenseactivator tools after the installation completes. For example:
sudo installer -pkg IBM\ SPSS\ Statistics.pkg -target /
cd /Applications/IBM\ SPSS\ Statistics/Resources/Activation
Disabling Internet connectivity features
 
After installation, you can use the following command to disable Internet connectivity features (information sharing, error reporting, and welcome screen updates). The command must be run from the /Applications/IBM SPSS Statistics/Resources/Configuration directory (this is the default installation location):
  
./Configure NO
 

Mac OS uninstall instructions

  1. Drag the installation folder (by default, /Applications/IBM SPSS Statistics) to the Trash.
  2. In the Home folder, browse to Library/Preferences.
  3. Drag com.ibm.spss.plist to the Trash. The file is also used by IBM SPSS Statistics on premises, IBM SPSS Statistics and the Student Version. Do not remove the file if any of these applications are still installed.
  4. In the Home folder, drag Library/Application Support/IBM/SPSS/Statistics to the Trash.
  5. Empty the Trash.

For information regarding system requirements, see the IBM detailed system requirements site.

Known issues

  • The new Workbook feature does not support exporting to Microsoft PowerPoint at this time.
  • The new Workbook feature does not support COM or Python scripting at this time.

[{"Product":{"code":"SSLVMB","label":"IBM SPSS Statistics"},"Business Unit":{"code":"BU059","label":"IBM Software w\/o TPS"},"Component":"--","Platform":[{"code":"PF025","label":"Platform Independent"}],"Version":"Not Applicable","Edition":"","Line of Business":{"code":"LOB10","label":"Data and AI"}}]

Document Information

Modified date:
13 September 2022

UID

swg27049540

Page Feedback