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Advanced Statistics provides univariate and multivariate modeling techniques to help users reach the most accurate conclusions when working with data describing complex relationships. These sophisticated analytical techniques are frequently applied to gain deeper insights from data used in disciplines such as medical research, manufacturing, pharmaceuticals and market research.

SPSS Advanced Statistics provides the following capabilities:

 

  • 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.

  • Linear mixed models, also known as hierarchical linear models (HLM), which expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability.

  • Generalized estimating equations (GEE) procedures that extend generalized linear models to accommodate correlated longitudinal data and clustered data.

  • Generalized linear mixed models (GLMM) for use with hierarchical data and a wide range of outcomes, including ordinal values.

  • Survival analysis procedures for examining lifetime or duration data.

General linear models (GLM) multivariate:

Gain more flexibility to describe the relationship between a dependent variable and a set of independent variables. GLM doesn’t limit you to a single data type, but offers a wealth of model-building possibilities.

Linear mixed models (Mixed):

Expand on the GLM technique with the Mixed procedure and achieve more accurate models. Use the Mixed procedure to model means, variances, and covariances in your data when working with nested-structure data. Or use Mixed when working with repeated measures data, including situations in which there are different numbers of repeated measurements or different intervals for different cases, or both.

Loglinear analysis:

Fit loglinear and logit models to count data so you can easily model and predict your outcomes.

Survival analysis:

Analyze event history and duration data to better understand events. SPSS Advanced Models includes state-of-the-art survival procedures such as Kaplan-Meier and Cox Regression.

Variance component estimation (VARCOMP):

Choose from a number of methods to estimate the variance component for each random effect in a mixed model. Follow up your GLM analysis with variance component estimation analysis to estimate the variance. It’s easy to determine where to focus
your attention when reducing the variance.