IBM SPSS Advanced Statistics

More Accurately Analyze Complex Relationships Using Powerful Univariate and Multivariate Analysis

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  • Overview
  • Features and Benefits

IBM® SPSS® Advanced Statistics (formerly PASW® Advanced Statistics) makes analysis more accurate and conclusions more dependable when working with complex relationships.

IBM SPSS Advanced Statistics provides powerful techniques for real-world problems in a variety of disciplines, including medical research, manufacturing, pharmaceutics, and market research.

IBM SPSS Advanced Statistics offers powerful and sophisticated univariate and multivariate analysis techniques, including:

  • Generalized linear mixed models (GLMM) for use with hierarchical data
  • General linear models (GLM) and mixed models procedures
  • Generalized linear models (GENLIN), including widely used statistical models such as linear regression for normally distributed responses, logistic models for binary data, and loglinear models for count data.
  • GENLIN also offers many useful statistical models through its very general model formulation
  • Generalized estimating equations (GEE) procedures extend generalized linear models to accommodate correlated longitudinal data and clustered data

Using IBM SPSS Advanced Statistics with IBM SPSS Statistics Base gives you an even wider range of statistics so you can reach the most accurate response for specific data types. You can seamlessly work in the IBM SPSS Statistics environment.

IBM SPSS Advanced Statistics Procedures

IBM SPSS Advanced Statistics continues to offer the following procedures:

  • General linear models (GLM) – Provides you with more flexibility to describe the relationship between a dependent variable and a set of independent variables. The GLM gives you flexible design and contrast options to estimate means and variances and to test and predict means. You can also mix and match categorical and continuous predictors to build models. Because GLM doesn't limit you to one data type, you have options that provide you with a wealth of model-building possibilities.
  • Linear mixed models, also known as hierarchical linear models (HLM)
    • Fixed effect analysis of variance (ANOVA), analysis of covariance (ANOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA)
    • Random or mixed ANOVA and ANCOVA
    • Repeated measures ANOVA and MANOVA
    • Variance component estimation (VARCOMP)
    The linear mixed models procedure expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability. If you work with data that display correlation and non-constant variability, such as data that represent students nested within classrooms or consumers nested within families, use the linear mixed models procedure to model means, variances and covariances in your data.

    Its flexibility means you can formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design. You can also select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive. You'll reach more accurate predictive models because it takes the hierarchical structure of your data into account.

    You can also use linear mixed models if you're working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both. Unlike standard methods, linear mixed models use all your data and give you a more accurate analysis.
  • Generalized linear models (GENLIN): GENLIN covers not only widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear model for count data, but also many useful statistical models via its very general model formulation. The independence assumption, however, prohibits generalized linear models from being applied to correlated data.
  • Generalized estimating equations (GEE): GEE extend generalized linear models to accommodate correlated longitudinal data and clustered data.
  • General models of multiway contingency tables (LOGLINEAR)
  • Hierarchical loglinear models for multiway contingency tables (HILOLINEAR)
  • Loglinear and logit models to count data by means of a generalized linear models approach (GENLOG)
  • Survival analysis procedures:
    • Cox regression with time-dependent covariates
    • Kaplan-Meier
    • Life Tables

What’s New in IBM SPSS Statistics 19?

  • Generalized Linear Mixed Models (GLMM) – Allows more accurate models when predicting nonlinear outcomes (for example, what product a customer is likely to buy) by taking into account hierarchical data structures (customer nested with an organization).

    Mixed-effects models provide a powerful and flexible tool for the analysis of hierarchical/nested data, whether that data is continuous or categorical. Some examples of analyses are: repeated measures data, longitudinal studies and nested designs. This procedure can produce a variety of outcome types.
  • Interactive and improved visualizations enable a more intuitive explanation of model predictors and outcomes.