Introduction to SPSS in psychology :for version 16 and earlier
Harlow, England ; New York
Pearson/Prentice Hall
2008
xxxiv, 377 p. : ill. ; 25 cm. +
Includes bibliographical references and index
Dennis Howitt, Duncan Cramer
Introduction to SPSS -- Basics of SPSS data entry and statistical analysis -- Descriptive statistics -- Describing variables : tables and diagrams -- Describing variables numerically : averages, variation and spread -- Shapes of distributions of scores -- Standard deviation : the standard unit of measurement in statistics -- Relationships between two or more variables : diagrams and tables -- Correlation coefficients : Pearson's correlation and Spearman's rho -- Regression : prediction with precision -- Significance testing and basic inferential tests -- Standard error -- The t-test : comparing two samples of correlated/related scores -- The t-test : comparing two groups of unrelated/uncorrelated scores -- Confidence intervals -- Chi-square : differences between samples of frequency data -- Ranking tests for two groups : non-parametric statistics -- Ranking tests for three or more groups : non-parametric statistics -- Analysis of variance -- The variance ratio test : using the F-ratio to compare two variances -- Analysis of variance )ANOVA( : introduction to the one-way unrelated or uncorrelated ANOVA -- Analysis of variance for correlated scores or repeated measures -- Two-way analysis of variance for unrelated/uncorrelated scores -- Multiple comparisons in ANOVA -- Two-way mixed analysis of variance )ANOVA( -- Analysis of covariance )ANCOVA( -- Multivariate analysis of variance )MANOVA( -- Discriminant function analysis )for MANOVA( -- More advanced correlational statistics -- Partial correlation -- Factor analysis -- Item reliability and inter-rater agreement -- Stepwise multiple regression -- Hierarchical multiple regression -- Advanced qualitative or nominal techniques -- Log-linear analysis -- Multinomial logistic regression -- Binomial logistic regression -- Data handling procedures -- Reading ASCII or text files into the data editor -- Missing values -- Recoding values -- Computing new variables with no values missing -- Computing new variables with some values missing -- Selecting cases -- Samples and populations : generating a random sample -- Inputting a correlation matrix -- Checking the accuracy of data input