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SPSS Statistical Data Analysis
   
Statistical methods *General linear model: A widely used model on which various methods are based (e.g. t test, ANOVA, ANCOVA, MANOVA). Usable for assessing the effect of several predictors on one or more continuous dependent variables. * Generalized linear model: An extension of the general linear model for discrete dependent variables. * Structural equation modelling: Usable for assessing latent structures from measured manifest variables. * Item response theory: Models for (mostly) assessing one latent variable from several binary measured variables (e.g. an exam). Statistics included in the SPSS * Descriptive statistics: Cross tabulation, Frequencies, Descriptives, Explore, Descriptive Ratio Statistics * Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), Nonparametric tests * Prediction for numerical outcomes: Linear regression • Prediction for identifying groups: Factor analysis, cluster analysis (two-step, K-means, hierarchical), Discriminant Analysis * Univariate statistics (single variable) * Bivariate associations (correlations) * Graphical techniques (scatter plots) * Nominal and ordinal variables * Frequency counts (numbers and percentages) * Associations * circumambulations (crosstabulations) * hierarchical loglinear analysis (restricted to a maximum of 8 variables) * loglinear analysis (to identify relevant/important variables and possible confounders) * Exact tests or bootstrapping (in case subgroups are small) * Computation of new variables * Continuous variables * Distribution * Statistics (M, SD, variance, skewness, kurtosis) * Stem-and-leaf displays * Box plots
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