IBM SPSS Advanced Statistics

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 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