SPSS 14.0 Advanced Statistical Procedures Companion

SPSS 14.0 Advanced Statistical Procedures Companion: Chapters 1. Model Selection in Loglinear Analysis. Model formulation parameters in saturated models hypothesis testing convergence goodness-of-fit tests hierarchical models generating classes model selection with backward elimination. 2. Logit Loglinear Analysis. Dichotomous logit model loglinear representation parameter estimates goodness-of-fit statistics measures of dispersion and association polychotomous logit model interpreting parameters examining residuals introducing covariates. 3. Multinomial Logistic Regression. Baseline logits likelihood-ratio tests for models and individual effects evaluating the model calculating predicted probabilities the classification table goodness-of-fit tests residuals pseudo R-square measures overdispersion model selection matched case-control studies. 4. Ordinal Regression. Modeling cumulative counts parameter estimates testing for parallel lines model fit observed and expected counts measures of strength of association classifying cases link functions fitting a heteroscedastic probit model fitting location and scale parameters. 5. Probit Regression. Probit and logit response models confidence intervals for effective dosages comparing groups comparing relative potencies estimating the natural response rate multiple stimuli. 6. Kaplan-Meier Survival Analysis. Calculating survival time estimating the survival function, the conditional probability of survival, and the cumulative probability of survival plotting survival functions comparing survival functions stratified comparisons. 7. Life Tables. Calculating survival probabilities assumptions observations lost to follow-up plotting survival functions comparing survival functions. 8. Cox Regression. The model proportional hazards assumption coding categorical variables interpreting the regression coefficients baseline hazard and cumulative survival rates global tests of the model checking the proportional hazards assumption stratification log-minus-log survival plot identifying influential cases examining residuals partial (Schoenfeld) residuals martingale residuals variable-selection methods time-dependent covariates specifying a time-dependent covariate calculating segmented time-dependent covariates testing the proportional hazards assumption with a time-dependent covariate fitting a conditional logistic regression model. 9. Variance Components. Factors, effects, and models model for one-way classification estimation methods negative variance estimates nested design model for two-way classification univariate repeated measures analysis using a Mixed Models Approach distribution assumptions estimation methods. 10. Linear Mixed Models. Background Unconditional random-effects models hierarchical models random-coefficient model model with school-level and individual-level covariates three-level hierarchical model repeated measurements selecting a residual covariance structure. 11. Nonlinear Regression. The nonlinear model transforming nonlinear models intrinsically nonlinear models fitting a logistic population growth model finding starting values approximate confidence intervals for the parameters bootstrapped estimates starting values from previous analysis linear approximation computational issues common models for nonlinear regression specifying a segmented model. 12. Two-Stage Least-Squares Regression. Demand-price-income economic model estimation with ordinary least squares feedback and correlated errors estimation with two-stage least squares. 13. Weighted Least-Squares Regression. Diagnosing the problem estimating weights examining the log-likelihood function the WLS solution estimating weights from replicates diagnostics from the linear regression procedure. 14. Multidimensional Scaling. Data, models, and multidimensional scaling analysis nature of data analyzed in MDS measurement level of data shape of data conditionality of data missing data multivariate data classical MDS Euclidean model details of CMDS Replicated MDS Weighted MDS geometry of the weighted Euclidean model algebra of the weighted Euclidean model matrix algebra of the weighted Euclidean model Weirdness index flattened weights.