What's new in IBM SPSS Statistics Subscription
September 2022 update
- Analyze procedures
-
- Linear OLS Alternatives
-
- Elastic Net
- Click
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 cross validation. When a single model is fitted or cross validation is used to select the penalty ratio and/or alpha, a partition of holdout data can be used to estimate out-of-sample performance.
to obtain a Linear Elastic Net Regression analysis. The new Linear
Elastic Net extension procedure uses the Python - Lasso
- Click
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 cross validation. When a single model is fitted or cross validation is used to select alpha, a partition of holdout data can be used to estimate out-of-sample performance.
to obtain a Linear Lasso
Regression analysis. The new Linear Lasso extension procedure uses the Python
- Ridge
- Click
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 cross validation. When a single model is fitted or cross validation is used to select alpha, a partition of holdout data can be used to estimate out-of-sample performance.
to obtain a Linear Ridge
Regression analysis. The new Linear Ridge extension procedure uses the Python
- Parametric Accelerated Failure Time (AFT) Models
- Click to obtain a Parametric Accelerated Failure Time (AFT) Model analysis, which invokes the parametric survival models procedure with nonrecurrent life time data. Parametric survival models assume that survival time follows 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 that is explained by a linear model. The intra-class correlation coefficient (ICC) is a related statistic that quantifies the proportion of variance that is 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 command invokes the parametric survival models procedure with nonrecurrent life-time data.
- Python and R upgrades
- Python 3.10.4 and R 4.2.0 are a part of the 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 Graph board 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 show only 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 update
- 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.
- 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. For more information, see OUTPUT CREATE.
- POWER ONEWAY ANOVA
- The CONTRAST subcommand's new HALFWIDTH keyword estimates the sample size based on specified confidence interval half-widths. For more information, see CONTRAST Subcommand (POWER ONEWAY ANOVA command).
- POWER MEANS INDEPENDENT
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER MEANS INDEPENDENT command).
- POWER MEANS ONESAMPLE
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER MEANS ONESAMPLE command).
- POWER MEANS RELATED
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER MEANS RELATED command).
- POWER PARTIALCORR
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER PARTIALCORR command).
- POWER PEARSON ONESAMPLE
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER PEARSON ONESAMPLE command).
- POWER PROPORTIONS INDEPENDENT
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER PROPORTIONS INDEPENDENT command).
- POWER PROPORTIONS ONESAMPLE
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER PROPORTIONS ONESAMPLE command).
- POWER PROPORTIONS RELATED
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER PROPORTIONS RELATED command).
- POWER SPEARMAN ONESAMPLE
- The new PRECISION subcommand estimates the sample size based on specified confidence interval half-widths. For more information, see PRECISION Subcommand (POWER SPEARMAN ONESAMPLE command).
- POWER UNIVARIATE LINEAR
- The PARAMETERS subcommand's new ES keyword specifies the effect size value that is measured by f2. For more information, see PARAMETERS Subcommand (POWER UNIVARIATE LINEAR command).
- 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. For more information, see Color scales.
- 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. For more information, see Style Output: Select.
- 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. For more information, see Proxy configuration file.
- 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 update
- 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 results.
- 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. - 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.
- 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
-
- COXREG
- The CONTRAST subcommand's DEVIATION keyword now defaults the refcat to the first category. For more information, see CONTRAST Subcommand (COXREG command).
- LOGISTIC REGRESSION
- The CONTRAST subcommand's DEVIATION keyword now defaults the refcat to the first category. For more information, see CONTRAST Subcommand (LOGISTIC REGRESSION command).
- META BINARY command
- The new command represents the meta-analysis procedure for binary outcomes when the raw data are provided in the active dataset for the estimation of the effect size. For more information, see META BINARY.
- 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. For more information, see META ES BINARY.
- META CONTINUOUS command
- The new command represents the meta-analysis procedure for continuous outcomes when the raw data are provided in the active dataset for the estimation of the effect size. For more information, see META CONTINUOUS.
- 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. For more information, see META ES CONTINUOUS.
- META REGRESSION command
- The new command represents the meta-regression procedure. For more information, see META REGRESSION.
- 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.
- R
- R 4.2.0 is now part of the IBM SPSS Statistics. The R environment settings are defined in .
- Python 3 and R programmability
- Support for Python 3 and R has been enhanced by enabling an easily configurable virtual runtime environment.
- Installation and licensing
- The product installer has been updated to provide the option registering
either the subscription or licensed version of 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 via the subscription method.
- Licensed
- Requires an authorized user license or concurrent user license to activate the software. You must purchase an on-premise license for IBM SPSS Statistics in order to activate the product via a user license or concurrent user license.
- Output enhancements
-
- Workbooks
- Viewing output in Workbook mode bridges the 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.
- Chart and table editor usability enhancements
-
- 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
- Additional 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 (for example, ).
- Search enhancements
- The search feature has been updated to now provide results for procedures,
help topics, syntax reference, and case studies. The Search feature now searches all words/terms in
each user interface dialog and help topic.
Refer to the following introductory video for a brief overview on the search enhancements:
- 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 provides a PDF formatted preview version of the output.
- Select Cases - hidden cases
- By default, unselected cases are now hidden in the Data Editor when a subset of cases is selected, and the unselected cases are not discarded. Hidden cases are not 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 update
- Analyze procedures
-
- Bivariate Correlations
- The procedure has been updated to provide 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 has been updated to provide 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 enhancements
-
- CORRELATIONS command
- Added support for the NOMATRIX keyword in the PRINT subcommand. The keyword suppresses the correlations table from the output. For more information, see PRINT Subcommand (Correlations command).
- 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. For more information, see IMPUTE Subcommand (MULTIPLE IMPUTATION command).
- 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).For more information, see PROPORTIONS, ONESAMPLE subcommand, PAIREDSAMPLES subcommand, and INDEPENDENTSAMPLES subcommand.
- RELIABILITY command
- Added support for the OMEGA keyword in the MODEL subcommand. The keyword provides an estimation of McDonald’s Omega to evaluate reliability. For more information, see MODEL Subcommand (RELIABILITY command).
- 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 update
- 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.
- 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.
- 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.
- 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
). 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.
- 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 have been collected, except possibly from
a small pilot study. The precise estimation of the power may tell investigators how likely it is
that a statistically significant difference will be 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 contain 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 contain 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 which are often 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.
- 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.
- 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.
- 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, thereby ranging between -1 and 1.
- 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, thereby 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.
- Partial
- 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. Similar to the Pearson correlation coefficient, partial correlation coefficient is also a dimensionless quantity ranging between -1 and 1.
- 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.
- 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).
- 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 will be 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.
- 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.
- OMS
-
FORMAT=REPORTHTML
andFORMAT=REPORTMHT
deprecated from theDESTINATION
subcommand. The subcommand syntax has been mapped to the HTML subcommand.REPORTTITLE
keyword deprecated from theDESTINATION
subcommand.
- 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.
- 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.
- 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
andBY
). 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 has been updated to include 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.
- Chart templates
-
- The 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 , 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.
- 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.
- 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:
- Pivot Table Editor (via the Formatting Toolbar)
- Search enhancements
- The Search feature has been updated to provide results that include:
- Menu dialogs
- Help topics
- Case studies
- Syntax reference
November 2019 update
- Analyze procedures
-
- ROC Analysis
- The CLASSIFIER keyword was added to the PRINT subcommand. The keyword controls the display of the Classifier Evaluation Metrics table in the output. The table shows how well a classification model fits the data compared to a random assignment. For more information, see ROC Analysis: Display.
- Performance enhancements
-
- Memory consumption has been improved when performing transformations.
- Application start time is now improved on Microsoft Windows machines.
- Improved support for importing Cognos BI data into the application.
- Support for the Microsoft Access database with the Office 2016 drivers.
June 2019 update
- 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.
April 2019 update
- 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.
- 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 may 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.
December 2018 update
- 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 plugin
- 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.
November 2017 update
- MATRIX-END MATRIX command
- SPSS Statistics now
supports to following MATRIX command enhancements:
- Long variable names (up to 64 bytes) can be used to name a matrix or vector name (such as COMPUTE, CALL, PRINT, READ, WRITE, GET, SAVE, MGET, MSAVE, DISPLAY, RELEASE, and so on).
- Variable names that are included in a vector or matrix object are truncated to 8 bytes. This is because the matrix/vector structure is an array of numbers, and each number can match a string only up to 8 bytes. Long names (up to 64 bytes) are supported only when explicitly specified.
- Long variable names are supported in GET and SAVE commands when explicitly specified on the /VARIABLES subcommand (and when specified on the /STRINGS subcommand for the SAVE command). Variable names for GET and SAVE commands are truncated to 8 bytes when they are referenced through a vector in the /NAMES subcommand.
- The GET, SAVE, MGET, or MSAVE statements support both dataset references and physical file specifications.
- MATRIX-END MATRIX now supports statistical functions that were previously only supported by the COMPUTE command (for example IDF.CHISQ, CDF.NORMAL, NCDF.F, and so on).
August 2017 update
Bayesian statistics
SPSS Statistics now supports Bayesian statistics. Bayesian inference is a method of statistical inference in which Baye's theorem is used to update the probability for a hypothesis as more information becomes available. The following Bayesian statistics are supported:
- One Sample and Pair Sample t-tests
- One Sample Binomial Proportion tests
- One Sample Poisson Distribution Analysis
- Related Samples
- Independent Samples t-tests
- Pairwise Correlation (Pearson)
- Linear Regression
- One-way ANOVA
- Loglinear Regression
Output Viewer "Copy as" enhancement
You can now right-click a selected object in the Output Viewer and select All, Image, or Microsoft Office Graphic Object). Selecting copies All.
to copy to the most popular formats (for example,Licensing
The SPSS Statistics licensing process has been replaced by your IBM account, also known as IBMid. An IBMid provides access to all IBM's applications (to which you are licensed), communities, and support channels. For more information, see Logging on and downloading updates.
When you first open IBM SPSS Statistics Subscription, you're prompted to log on with your IBMid. If you don't yet have an IBMid, follow the on-screen instructions.
The SPSS Statistics licensing options have been simplified. Previous versions provided 14 separate licensing options; those options have been consolidated down to 4 options.
SPSS Statistics 24 option | SPSS Statistics Subscription option |
---|---|
IBM SPSS Statistics Base Option | IBM SPSS Statistics Base Edition |
Bootstrapping Option | IBM SPSS Statistics Base Edition |
Data Preparation Option | IBM SPSS Statistics Base Edition |
Advanced Statistics Option | IBM SPSS Statistics Custom Tables and Advanced Statistics |
Custom Tables Option | IBM SPSS Statistics Custom Tables and Advanced Statistics |
Regression Option | IBM SPSS Statistics Custom Tables and Advanced Statistics |
Decision Trees Option | IBM SPSS Statistics Forecasting and Decision Trees |
Direct Marketing Option | IBM SPSS Statistics Forecasting and Decision Trees |
Neural Network Option | IBM SPSS Statistics Forecasting and Decision Trees |
Forecasting Option | IBM SPSS Statistics Forecasting and Decision Trees |
Categories Option | IBM SPSS Statistics Sampling and Testing |
Complex Samples Option | IBM SPSS Statistics Sampling &Testing |
Conjoint Option | IBM SPSS Statistics Sampling and Testing |
Exact Tests Option | IBM SPSS Statistics Sampling and Testing |
Missing Values Option | IBM SPSS Statistics Sampling and Testing |