Analyse complex relationships using a high-end modeller's toolkit

Make your analysis more accurate and get more dependable conclusions with procedures designed to fit the inherent characteristics of your data. SPSS Advanced Models^TM provides a powerful set of sophisticated univariate and multivariate analytical techniques for real world problems.

SPSS Advanced Models has the procedures you need to move beyond basic data analysis. These procedures include:

• General Linear Models (GLM): get more flexibility to describe the relationship between a dependent variable and a set of independent variables

• Linear Mixed Models: get more accurate models when working with nested-structure data

• PoLytomous Universal Model (PLUM): easily model your ranked outcomes

• And more.

More statistics for data analysis

Expand SPSS® Base's capabilities for the data analysis stage in the analytical process. Using SPSS Advanced Models^TM with SPSS Base gives you an even wider range of statistics so you can get the most accurate response for specific data types. It easily plugs into SPSS Base so you can seamlessly work in the SPSS environment.

Statistical highlights for SPSS Advanced Models:

Linear Mixed Models: 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. For example, educators can discover how different teaching methods affect student test scores across classrooms.

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 eleven non-spatial covariance types, including First-Order Ante-Dependence, Heterogeneous and First-Order Autoregressive. You'll get 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 or different intervals for different cases or both. For example, a health care organization testing patients recovering from illness might not have uniformly collected data. Ideally, the organization would test individuals at regularly scheduled intervals (for example, every 90 days). But people often miss appointments or are tested earlier or later than the optimal time. More standard procedures will discard data not meeting test conditions. Linear Mixed Models, however, will use all your data -- giving you a more accurate picture of patient recovery.

General Liner Models (GLM) multivariate: work with related multiple dependent variables. GLM multivariate gives you flexible design and contrast options to estimate means and variances and to test and predict means. For example, a financial service company might want to know what predicts account balance for various account types. You can also mix and match categorical and continuous predictors to build models. Because GLM multivariate doesn't limit you to one data type, you have options that give you a wealth of model-building possibilities.

A manufacturer examining the deal sizes between products and customer type can test two types of effects using GLM multivariate:

• Main effects: do public and private sector customers differ, on average, in terms of deal size regardless of whether customer purchases product A or product B?

• Interaction: does the difference between deal sizes for product A and product B differ by customer type? For example, the private sector deal size may be greater for product A than product B; in the public sector, deal size is greater for B than A.

PoLytomous Universal Model (PLUM): make predictions with ordinal responses. Do you want to predict ordinal outcomes? For example, you might want to know customer satisfaction level (very dissatisfied, somewhat dissatisfied, somewhat satisfied, very satisfied) to understand customer loyalty. When you need to predict ordinal-dependent variables, PoLytomous Universal Models (PLUM) gives you a versatile method for model building. When you choose different link functions, you get the flexibility to fit ordinal logistic regression, ordinal probit models and ordinal Cauchit models. You can also model both the location and scale of the underlying distribution. PLUM gives you options to save predicted probabilities for all dependent variable categories back to your data.

SPSS Advanced Models gives you:

• Linear Mixed Models

• General Linear Models (GLM)

  • 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)

• PoLytomous Universal Model (PLUM)

• 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