

IBM SPSS Statistics 19 Statistical Procedures Companion: Chapters
 Introduction. An overview of the statistical procedures described in the book.
 Getting to Know IBM SPSS Statistics. Tutorials; windows; dialog boxes; the online help system; Pivot Table Editor; Chart Editor; using command syntax.
 Introducing Data. Planning the data file; getting data into IBM SPSS Statistics; the Text Wizard; creating new variables; selecting cases.
 Preparing Your Data. Checking variable definitions; case counts; data values.
 Transforming Your Data. Computing new variables; changing coding schemes; ranking.
 Describing Your Data. Taking a first look at your data with tables, charts, and descriptive statistics.
 Testing Hypotheses. Samples and populations; missing values; steps in testing a hypothesis; calculating confidence intervals; reporting your results correctly; commonly used tests for popular hypotheses.
 Ttests. Onesample, pairedsamples, and independentsamples ttests; data setups for the different ttests; interpreting the output.
 Oneway Analysis of Variance. Preparing the data file; examining the data; checking assumptions; interpreting the output; atoning for multiple comparisons; setting up contrasts.
 Crosstabulation. Chisquare tests; McNemar's test; measures of association and agreement; measures of risk; testing hypotheses about odds ratios.
 Correlation. Plotting the data; scatterplot matrices; correlation coefficients based on ranks; partial correlation coefficients; identifying points in a scatterplot.
 Bivariate Linear Regression. Least squares regression line; measures of fit; assumptions and transformations; looking for unusual points.
 Multiple Linear Regression. Formulating the problems; interpreting the coefficients; including categorical variables; comparing models; automated model building; checking for violations of assumptions; residuals; unusual observations.
 Automatic Linear Modeling. All possible subsets regression models; specifying the model; model building summary; comparing observed and expected values; examining the coefficients; examining assumptions; looking for influential points; ensemble models; automatic data preparation; model summary; evaluating the predictors.
 Discriminant Analysis. Calculating the functions; testing hypotheses; classifying cases into groups; automated model building; analyzing more than two groups; classification function coefficients.
 Logistic Regression Analysis. Basics of the model; predicted probabilities; coefficients; testing hypotheses; categorical variables; interaction terms; evaluating linearity; automated model building; diagnostics; model calibration; model discrimination; diagnostics for individual cases.
 Cluster Analysis. Hierarchical clustering; kmeans clustering; twostep clustering; distance and similarity measures; interpreting the results.
 Factor Analysis. Basics of the model; determining the number of factors; goodnessoffit tests; methods for factor extraction and rotation; computing factor scores.
 Reliability Analysis. Reliability coefficients: Cronbach's alpha, splithalf reliability, Guttman's lower bounds; testing hypotheses about scales: parallel and strictly parallel models; Cochran's Q; intraclass correlation coefficients.
 Nonparametric Tests. Onesample tests: chisquare, binomial, runs; two related groups: sign test, Wilcoxon test; two independent groups: Wilcoxon, WaldWolfowitz runs test; three or more groups: KruskalWallis, median, Friedman, Kendall's W, Cochran's Q.
 General Loglinear Analysis. Basic model; fitting a saturated model; fitting an unsaturated model; goodnessoffit tests; models for ordinal data; incomplete tables; tests for square tables; Poisson regression; standardizing tables.
 Univariate General Linear Model. Regression; twoway ANOVA; randomized complete block design; randomized complete block design with empty cells; analysis of covariance; mixed effects nested designs; splitplot designs.
 Multivariate General Linear Model. Multivariate twoway fixedeffects model with interaction; profile analysis; setting up custom linear hypotheses.
 Repeated Measures Designs. Checking assumptions; testing hypotheses; doubly multivariate repeated measures analysis of variance.


