PASW Advanced Statistics
More Accurately Analyze Complex Relationships Using Powerful Univariate and Multivariate Analysis
Make your analysis more accurate and reach more dependable conclusions with procedures designed to fit the inherent characteristics of data describing
complex relationships. PASW Advanced Statistics (formerly called SPSS Advanced Statistics), provides a powerful set of sophisticated univariate and multivariate analysis techniques for real-world problems, such as:
- Medical research: Analyze patient survival rates
- Manufacturing: Assess production processes
- Pharmaceutical: Report test results to the FDA
- Market research: Determine product interest levels
Access a Range of Powerful Models
In addition to the general linear models (GLM) and mixed models procedures, PASW Advanced Statistics now offers the generalized linear models (GENLIN) and generalized estimating equations (GEE) procedures.
More Statistics for Data Analysis
Using PASW Advanced Statistics with PASW 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.
Highlights for PASW Advanced Statistics
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.
PASW Advanced Statistics includes:
- Generalized linear models (GENLIN)
- Generalized estimating equations (GEE)
- General linear models (GLM)
- Linear mixed models
- 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)
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
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