Spss for starters and 2nd levelers / Ton J. Cleophas, Aeilko H. Zwinderman.

Springer eBooks

Preface.- Introduction -- I Continuous outcome data -- One sample continuous data --  Paired continuous outcome data normality assumed -- Paired continuous outcome data nonnormality accounted -- Paired continuous outcome data with predictors -- Unpaired continuous outcome data normality assumed -- Unpaired continuous outcome data nonnormality accounted -- Linear regression for continuous outcome data -- Recoding for categorical predictor data -- Repeated-measures-analysis of variance normality assumed.- Repeated-measures-analysis of variance nonnormality accounted --  Doubly-repeated-measures-analysis of variance -- Multilevel modeling with mixed linear models. Random multilevel modeling with generalized mixed linear models -- One-way-analysis of variance normality assumed -- One-way-analysis of variance nonnormality accounted -- Trend tests of continuous outcome data -- Multistage regression -- Multivariate analysis with path statistics -- Multivariate analysis of variance.- Average-rank-testing for multiple outcome variables and categorical predictors -- Missing data imputation -- Meta-regression -- Poisson regression including a weight variable (time of observation) for rates -- Confounding -- Interaction -- Curvilinear analysis -- Loess and spline modeling for nonlinear data, where curvilinear models lack fit -- Monte Carlo analysis, the easy alternative for continuous outcome data -- Artificial intelligence as a distribution free alternative for nonlinear data -- Robust tests for d ata with large outliers -- Nonnegative outcome data using the gamma distribution -- Nonnegative outcome data with a big spike at zero using the Tweedie distribution -- Polynomial analysis for continuous outcome data with a sinusoidal pattern -- Validating quantitative diagnostic tests -- Reliability assessment of quantitative diagnostic tests -- II Binary outcome data -- One sample binary data -- Unpaired binary data -- Binary logistic regression with a binary predictor --  Binary logistic regression with categorical predictors -- Binary logistic regression with a continuous predictor -- Trend tests of binary data -- Paired binary outcome data without predictors -- Paired binary outcome data with predictors -- Repeated measures binary data -- Multinomial logistic regression for outcome categories -- Multinomial logistic regression with random intercepts for both categorical outcome and predictor data -- Comparing the performance of diagnostic tests -- Poisson regression for binary outcome data -- Loglinear models for the exploration of multidimensional contingency tables -- Probit regression for binary outcome data reported as response rates -- Monte Carlo analysis, the easy alternative for binary outcomes -- Validating qualitative diagnostic tests -- Reliability assessment of qualitative diagnostic tests. III Survival and longitudinal data -- Log rank tests -- Cox regression -- Cox regression with time-dependent variables -- Segmented Cox regression -- Assessing seasonality -- Probability assessment of survival with interval censored data analysis -- Index.

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