Expand SPSS Base's capabilities for the data analysis stage in the analytical process. Using SPSS Advanced Models with SPSS Base gives you an even wider range of statistics so you can reach the most accurate response for specific data types. It easily plugs into SPSS Base so you can seamlessly work in the SPSS environment.
Generalized linear models (GENLIN): GENLIN cover 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 linear model (GLM): 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): 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.
Key Products and Services
Resources
Buy
Demos and Downloads
