Make your analysis more accurate and reach more dependable conclusions with procedures designed to fit the inherent characteristics of data describing complex relationships. IBM SPSS Advanced Statistics (formerly called SPSS Advanced Statistics), provides a powerful set of sophisticated univariate and multivariate analysis techniques for real-world problems, such as:
In addition to the general linear models (GLM) and mixed models procedures, IBM SPSS Advanced Statistics now offers the generalized linear models (GENLIN) and generalized estimating equations (GEE) procedures.
IBM SPSS Advanced Statistics continues to offer the following procedures:
Read more about these procedures and the others included in IBM SPSS Advanced Statistics.
IBM SPSS Advanced Statistics is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian. Contact your local office to find out more.
Bootstrapping in IBM SPSS 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 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.
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