Introduction to SPSS in psychology : for version 22 and earlier

Cover
Contents
Guided tour
Introduction
Acknowledgements
Key differences between IBM SPSS Statistics 22 and earlier versions
Part 1 Introduction to SPSS Statistics
	1 A brief introduction to statistics
		Overview
		1.1 Basic statistical concepts essential in SPSS Statistics analyses
		1.2 Basic research designs: comparative versus correlational designs
		1.3 The different types of variables in statistics
		1.4 Descriptive and inferential statistics compared
		1.5 Related versus unrelated designs
		1.6 Quick summaries of statistical analyses
		1.7 Which procedure or test to use
	2 Basics of SPSS Statistics data entry and statistical analysis
		Overview
		2.1 What is SPSS Statistics?
		2.2 Accessing SPSS Statistics
		2.3 Entering data
		2.4 Moving within a window with the mouse
		2.5 Moving within a window using the keyboard keys with the mouse
		2.6 Saving data to disk
		2.7 Opening up a data file
		2.8 Using ‘Variable View’ to create and label variables
		2.9 More on ‘Data View’
		2.10 A simple statistical calculation with SPSS
		2.11 The SPSS Statistics output
		Summary of SPSS Statistics steps for a statistical analysis
Part 2 Descriptive statistics
	3 Describing variables: Tables
		Overview
		3.1 What are tables?
		3.2 When to use tables
		3.3 When not to use tables
		3.4 Data requirements for tables
		3.5 Problems in the use of tables
		3.6 The data to be analysed
		3.7 Entering summarised categorical or frequency data by weighting
		3.8 Percentage frequencies
		3.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for frequency tables
	4 Describing variables: Diagrams
		Overview
		4.1 What are diagrams?
		4.2 When to use diagrams
		4.3 When not to use diagrams
		4.4 Data requirements for diagrams
		4.5 Problems in the use of diagrams
		4.6 The data to be analysed
		4.7 Entering summarised categorical or frequency data by weighting
		4.8 Pie diagram of category data
		4.9 Adding labels to the pie diagram and removing the legend and label
		4.10 Changing the colour of a pie diagram slice to a black and white pattern
		4.11 Bar chart of category data
		4.12 Histograms
		Summary of SPSS steps for charts
	5 Describing variables numerically: Averages, variation and spread
		Overview
		5.1 What are averages, variation and spread?
		5.2 When to use averages, variation and spread
		5.3 When not to use averages, variation and spread
		5.4 Data requirements for averages, variation and spread
		5.5 Problems in the use of averages, variation and spread
		5.6 The data to be analysed
		5.7 Entering the data
		5.8 Mean, median, mode, standard deviation, variance and range
		5.9 Interpreting the output
		5.10 Other features
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for descriptive statistics
	6 Shapes of distributions of scores
		Overview
		6.1 What are the different shapes of scores?
		6.2 When to use histograms and frequency tables of scores
		6.3 When not to use histograms and frequency tables of scores
		6.4 Data requirements for using histograms and frequency tables of scores
		6.5 Problems in using histograms and frequency tables of scores
		6.6 The data to be analysed
		6.7 Entering the data
		6.8 Frequency tables
		6.9 Interpreting the output
		REPORTING THE OUTPUT
		6.10 Histograms
		6.11 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for frequency distributions
	7 Standard deviation: The standard unit of measurement in statistics
		Overview
		7.1 What is standard deviation?
		7.2 When to use standard deviation
		7.3 When not to use standard deviation
		7.4 Data requirements for standard deviation
		7.5 Problems in the use of standard deviation
		7.6 The data to be analysed
		7.7 Entering the data
		7.8 Standard deviation
		7.9 Interpreting the output
		7.10 Z -scores
		7.11 Other features
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for standard deviation
	8 Relationships between two or more variables: Tables
		Overview
		8.1 What tables are used to show relationships between variables?
		8.2 When to use tables to show relationships between variables
		8.3 When not to use tables to show relationships between variables
		8.4 Data requirements for tables to show relationships between variables
		8.5 Problems in the use of tables to show relationships between variables
		8.6 The data to be analysed
		8.7 Entering the data
		8.8 Weighting the data
		8.9 Cross-tabulation with frequencies
		8.10 Displaying frequencies as a percentage of the total number
		8.11 Displaying frequencies as a percentage of the column total
		Summary of SPSS Statistics steps for contingency tables
	9 Relationships between two or more variables: Diagrams
		Overview
		9.1 What diagrams are used to show relationships between variables?
		9.2 When to use diagrams to show relationships between variables
		9.3 When not to use diagrams to show relationships between variables
		9.4 Data requirements for diagrams to show relationships between variables
		9.5 Problems in the use of diagrams to show relationships between variables
		9.6 The data to be analysed
		9.7 Entering the data
		9.8 Weighting the data
		9.9 Compound (stacked) percentage bar chart
		9.10 Compound (clustered) bar chart
		Summary of SPSS Statistics steps for bar charts
	10 Correlation coefficients: Pearson’s correlation and Spearman’s rho
		Overview
		10.1 What is a correlation coefficient?
		10.2 When to use Pearson and Spearman rho correlation coefficients
		10.3 When not to use Pearson and Spearman rho correlation coefficients
		10.4 Data requirements for Pearson and Spearman rho correlation coefficients
		10.5 Problems in the use of correlation coefficients
		10.6 The data to be analysed
		10.7 Entering the data
		10.8 Pearson’s correlation
		10.9 Interpreting the output
		REPORTING THE OUTPUT
		10.10 Spearman’s rho
		10.11 Interpreting the output
		REPORTING THE OUTPUT
		10.12 Scatter diagram
		10.13 Interpreting the output
		REPORTING THE OUTPUT
		10.14 Scattergram with more than one case with the same two values
		Summary of SPSS Statistics steps for correlation
	11 Regression: Prediction with precision
		Overview
		11.1 What is simple regression?
		11.2 When to use simple regression
		11.3 When not to use simple regression
		11.4 Data requirements for simple regression
		11.5 Problems in the use of simple regression
		11.6 The data to be analysed
		11.7 Entering the data
		11.8 Simple regression
		11.9 Interpreting the output
		11.10 Regression scatterplot
		11.11 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for simple regression
Part 3 Significance testing and basic inferential tests
	12 Standard error
		Overview
		12.1 What is standard error?
		12.2 When to use standard error
		12.3 When not to use standard error
		12.4 Data requirements for standard error
		12.5 Problems in the use of standard error
		12.6 The data to be analysed
		12.7 Entering the data
		12.8 Estimated standard error of the mean
		12.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for standard error
	13 The t-test: Comparing two samples of correlated/related/paired scores
		Overview
		13.1 What is the related t-test?
		13.2 When to use the related t-test
		13.3 When not to use the related t-test
		13.4 Data requirements for the related t-test
		13.5 Problems in the use of the related t-test
		13.6 The data to be analysed
		13.7 Entering the data
		13.8 Related t-test
		13.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for related t-test
	14 The t-test: Comparing two groups of unrelated/uncorrelated scores
		Overview
		14.1 What is the unrelated t-test?
		14.2 When to use the unrelated t-test
		14.3 When not to use the unrelated t-test
		14.4 Data requirements for the unrelated t-test
		14.5 Problems in the use of the unrelated t-test
		14.6 The data to be analysed
		14.7 Entering the data
		14.8 Unrelated t-test
		14.9 Interpreting the output
		REPORTING THE RESULTS
		Summary of SPSS Statistics steps for unrelated t -test
	15 Confidence intervals
		Overview
		15.1 What are confidence intervals?
		15.2 The relationship between significance and confidence intervals
		15.3 Confidence intervals and limits in SPSS Statistics
	16 Chi-square: Differences between unrelated samples of frequency data
		Overview
		16.1 What is chi-square?
		16.2 When to use chi-square
		16.3 When not to use chi-square
		16.4 Data requirements for chi-square
		16.5 Problems in the use of chi-square
		16.6 The data to be analysed
		16.7 Entering the data using the ‘Weighting Cases’ procedure
		16.8 Entering the data case by case
		16.9 Chi-square
		16.10 Interpreting the output for chi-square
		REPORTING THE OUTPUT
		16.11 Fisher’s exact test
		16.12 Interpreting the output for Fisher’s exact test
		REPORTING THE OUTPUT
		16.13 One-sample chi-square
		16.14 Interpreting the output for a one-sample chi-square
		REPORTING THE OUTPUT
		16.15 Chi-square without ready-made tables
		Summary of SPSS Statistics steps for chi-square
	17 McNemar’s test: Differences between related samples of frequency data
		Overview
		17.1 What is McNemar’s test?
		17.2 When to use McNemar’s test
		17.3 When not to use McNemar’s test
		17.4 Data requirements for McNemar’s test
		17.5 Problems in the use of McNemar’s test
		17.6 The data to be analysed
		17.7 Entering the data using the ‘Weighting Cases’ procedure
		17.8 Entering the data case by case
		17.9 McNemar’s test
		17.10 Interpreting the output for McNemar’s test
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for McNemar’s test
	18 Ranking tests for two groups: Non-parametric statistics
		Overview
		18.1 What are non-parametric tests?
		18.2 When to use non-parametric tests
		18.3 When not to use non-parametric tests
		18.4 Data requirements for non-parametric tests
		18.5 Problems in the use of non-parametric tests
		18.6 The data to be analysed
		18.7 Entering the data
		18.8 Related scores:sign test
		18.9 Interpreting the output for the sign test
		REPORTING THE OUTPUT FOR THE SIGN TEST
		18.10 Related scores: Wilcoxon test
		18.11 Interpreting the output for the Wilcoxon test
		REPORTING THE OUTPUT FOR THE WILCOXON TEST
		18.12 Unrelated scores: Mann–Whitney U test
		18.13 Entering the data
		18.14 Mann–Whitney U test
		18.15 Interpreting the output for the Mann–Whitney U test
		REPORTING THE OUTPUT FOR THE MANN–WHITNEY U TEST
		Summary of SPSS Statistics steps for non-parametric tests for two groups
	19 Ranking tests for three or more groups: Non-parametric statistics
		Overview
		19.1 What are ranking tests?
		19.2 When to use ranking tests
		19.3 When not to use ranking tests
		19.4 Data requirements for ranking tests
		19.5 Problems in the use of ranking tests
		19.6 The data to be analysed
		19.7 Friedman three or more related samples test
		19.8 Entering the data for the Friedman test
		19.9 Friedman test
		19.10 Interpreting the output for the Friedman test
		REPORTING THE OUTPUT FOR THE FRIEDMAN TEST
		19.11 Kruskal–Wallis three or more unrelated samples test
		19.12 Entering the data for the Kruskal–Wallis test
		19.13 Kruskal–Wallis test
		19.14 Interpreting the output for the Kruskal–Wallis test
		REPORTING THE OUTPUT FOR THE KRUSKAL–WALLIS TEST
		Summary of SPSS Statistics steps for non-parametric tests for three or more groups
Part 4 Analysis of variance
	20 The variance ratio test: Using the F-ratio to compare two variances
		Overview
		20.1 What is the variance ratio test?
		20.2 When to use the variance ratio test
		20.3 When not to use the variance ratio test
		20.4 Data requirements for the variance ratio test
		20.5 Problems in the use of the variance ratio test
		20.6 The data to be analysed
		20.7 Entering the data
		20.8 Variance estimate
		20.9 Calculating the variance ratio from the output
		REPORTING THE VARIANCE RATIO
		Summary of SPSS Statistics steps for the variance ratio test
	21 Analysis of variance (ANOVA): Introduction to the one-way unrelated or uncorrelated ANOVA
		Overview
		21.1 What is one-way ANOVA?
		21.2 When to use one-way ANOVA
		21.3 When not to use one-way ANOVA
		21.4 Data requirements for one-way ANOVA
		21.5 Problems in the use of one-way ANOVA
		21.6 The data to be analysed
		21.7 Entering the data
		21.8 One-way unrelated ANOVA
		21.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for one-way ANOVA
	22 Analysis of variance for correlated scores or repeated measures
		Overview
		22.1 What is repeated-measures ANOVA?
		22.2 When to use repeated-measures ANOVA
		22.3 When not to use repeated-measures ANOVA
		22.4 Data requirements for repeated-measures ANOVA
		22.5 Problems in the use of repeated-measures ANOVA
		22.6 The data to be analysed
		22.7 Entering the data
		22.8 One-way repeated-measures ANOVA
		22.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for repeated-measures ANOVA
	23 Two-way analysis of variance for unrelated/uncorrelated scores
		Overview
		23.1 What is two-way ANOVA?
		23.2 When to use two-way ANOVA
		23.3 When not to use two-way ANOVA
		23.4 Data requirements for two-way ANOVA
		23.5 Problems in the use of two-way ANOVA
		23.6 The data to be analysed
		23.7 Entering the data
		23.8 Two-way unrelated ANOVA
		23.9 Interpreting the output
		23.10 Editing the graph
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for two-way ANOVA
	24 Multiple comparisons in ANOVA
		Overview
		24.1 What is multiple comparison testing?
		24.2 When to use multiple comparison tests
		24.3 When not to use multiple comparison tests
		24.4 Data requirements for multiple comparison tests
		24.5 Problems in the use of multiple comparison tests
		24.6 The data to be analysed
		24.7 Entering the data
		24.8 Multiple comparison tests
		24.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for multiple comparison tests
	25 Analysis of variance for two-way correlated scores or repeated measures
		Overview
		25.1 What is two-way repeated-measures ANOVA?
		25.2 When to use two-way repeated-measures ANOVA
		25.3 When not to use two-way repeated-measures ANOVA
		25.4 Data requirements for two-way related-measures ANOVA
		25.5 Problems in the use of two-way repeated-measures ANOVA
		25.6 The data to be analysed
		25.7 Entering the data
		25.8 Two-way repeated-measures ANOVA
		25.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for two-way repeated-measures ANOVA
	26 Two-way mixed analysis of variance (ANOVA)
		Overview
		26.1 What is two-way mixed ANOVA?
		26.2 When to use two-way mixed ANOVA
		26.3 When not to use two-way mixed ANOVA
		26.4 Data requirements for two-way mixed ANOVA
		26.5 Problems in the use of two-way mixed ANOVA
		26.6 The data to be analysed
		26.7 Entering the data
		26.8 Two-way mixed ANOVA
		26.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for mixed ANOVA
	27 Analysis of covariance (ANCOVA)
		Overview
		27.1 What is analysis of covariance (ANCOVA)?
		27.2 When to use ANCOVA
		27.3 When not to use ANCOVA
		27.4 Data requirements for ANCOVA
		27.5 Problems in the use of ANCOVA
		27.6 The data to be analysed
		27.7 Entering the data
		27.8 One-way ANCOVA
		27.9 Testing that the slope of the regression line within cells is similar
		27.10 Interpreting the output
		27.11 Testing the full model
		27.12 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for ANCOVA
	28 Multivariate analysis of variance (MANOVA)
		Overview
		28.1 What is multivariate analysis of variance (MANOVA)?
		28.2 When to use MANOVA
		28.3 When not to use MANOVA
		28.4 Data requirements for MANOVA
		28.5 Problems in the use of MANOVA
		28.6 The data to be analysed
		28.7 Entering the data
		28.8 MANOVA
		28.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for MANOVA
	29 Discriminant function analysis (for MANOVA)
		Overview
		29.1 What is discriminant function analysis?
		29.2 When to use discriminant function analysis
		29.3 When not to use discriminant function analysis
		29.4 Data requirements for discriminant function analysis
		29.5 Problems in the use of discriminant function analysis
		29.6 The data to be analysed
		29.7 Entering the data
		29.8 Discriminant function analysis
		29.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for discriminant function analysis
Part 5 More advanced correlational statistics
	30 Partial correlation
		Overview
		30.1 What is partial correlation?
		30.2 When to use partial correlation
		30.3 When not to use partial correlation
		30.4 Data requirements for partial correlation
		30.5 Problems in the use of partial correlation
		30.6 The data to be analysed
		30.7 Entering the data
		30.8 Partial correlation
		30.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for partial correlation
	31 Factor analysis
		Overview
		31.1 What is factor analysis?
		31.2 When to use factor analysis
		31.3 When not to use factor analysis
		31.4 Data requirements for factor analysis
		31.5 Problems in the use of factor analysis
		31.6 The data to be analysed
		31.7 Entering the data
		31.8 Principal components analysis with orthogonal rotation
		31.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps factor analysis
	32 Item reliability and inter-rater agreement
		Overview
		32.1 What are item reliability and inter-rater agreement?
		32.2 When to use item reliability and inter-rater agreement
		32.3 When not to use item reliability and inter-rater agreement
		32.4 Data requirements for item reliability and inter-rater agreement
		32.5 Problems in the use of item reliability and inter-rater agreement
		32.6 The data to be analysed for item alpha reliability
		32.7 Entering the data
		32.8 Alpha reliability
		32.9 Interpreting the output
		REPORTING THE OUTPUT
		32.10 Split-half reliability
		32.11 Interpreting the output
		REPORTING THE OUTPUT
		32.12 The data to be analysed for inter-rater agreement (kappa)
		32.13 Entering the data
		32.14 Kappa
		32.15 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for reliability
	33 Stepwise multiple regression
		Overview
		33.1 What is stepwise multiple regression?
		33.2 When to use stepwise multiple regression
		33.3 When not to use stepwise multiple regression
		33.4 Data requirements for stepwise multiple regression
		33.5 Problems in the use of stepwise multiple regression
		33.6 The data to be analysed
		33.7 Entering the data
		33.8 Stepwise multiple regression analysis
		33.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for stepwise multiple regression
	34 Simultaneous or standard multiple regression
		Overview
		34.1 What is simultaneous or standard multiple regression?
		34.2 When to use simultaneous or standard multiple regression
		34.3 When not to use simultaneous or standard multiple regression
		34.4 Data requirements for simultaneous or standard multiple regression
		34.5 Problems in the use of simultaneous or standard multiple regression
		34.6 The data to be analysed
		34.7 Entering the data
		34.8 Simultaneous or standard multiple regression analysis
		34.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for simultaneous or standard multiple regression
	35 Simple mediational analysis
		Overview
		35.1 What is simple mediational analysis?
		35.2 When to use simple mediational analysis
		35.3 When not to use simple mediational analysis
		35.4 Data requirements for a simple mediational analysis
		35.5 Problems in the use of simple mediational analysis
		35.6 The data to be analysed
		35.7 Entering the data
		35.8 Simultaneous or standard multiple regression analysis
		35.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for simultaneous or standard multiple regression
	36 Hierarchical multiple regression
		Overview
		36.1 What is hierarchical multiple regression?
		36.2 When to use hierarchical multiple regression
		36.3 When not to use hierarchical multiple regression
		36.4 Data requirements for hierarchical multiple regression
		36.5 Problems in the use of hierarchical multiple regression
		36.6 The data to be analysed
		36.7 Entering the data
		36.8 Hierarchical multiple regression analysis
		36.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for hierarchical multiple regression
	37 Moderator analysis with continuous predictor variables
		Overview
		37.1 What is a moderator variable analysis?
		37.2 When to use moderator variable analysis
		37.3 When not to use moderator variable analysis
		37.4 Data requirements for moderator variable analysis
		37.5 Problems in the use of moderator variable analysis
		37.6 The data to be analysed
		37.7 Entering the data
		37.8 Standardising the variables
		37.9 Computing the interaction term
		37.10 Hierarchical multiple-regression analysis
		37.11 Interpreting the output
		37.12 Entering the data for predicting the criterion values
		37.13 Computing the predicted criterion values
		37.14 Plotting the predicted criterion values
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for moderator analysis with hierarchical multiple regression
Part 6 Advanced qualitative or nominal techniques
	38 Log-linear analysis
		Overview
		38.1 What is log-linear analysis?
		38.2 When to use log-linear analysis
		38.3 When not to use log-linear analysis
		38.4 Data requirements for log-linear analysis
		38.5 Problems in the use of log-linear analysis
		38.6 The data to be analysed
		38.7 Entering the data
		38.8 Log-linear analysis
		38.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for log-linear analysis
	39 Multinomial logistic regression
		Overview
		39.1 What is multinomial logistic regression?
		39.2 When to use multinomial logistic regression
		39.3 When not to use multinomial logistic regression
		39.4 Data requirements for multinomial logistic regression
		39.5 Problems in the use of multinomial logistic regression
		39.6 The data to be analysed
		39.7 Entering the data
		39.8 Stepwise multinomial logistic regression
		39.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS steps for multinomial logistic regression
	40 Binomial logistic regression
		Overview
		40.1 What is binomial logistic regression?
		40.2 When to use binomial logistic regression
		40.3 When not to use binomial logistic regression
		40.4 Data requirements for binomial logistic regression
		40.5 Problems in the use of binomial logistic regression
		40.6 The data to be analysed
		40.7 Entering the data
		40.8 Binomial logistic regression
		40.9 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for binomial logistic regression
Part 7 Data handling procedures
	41 Reading ASCII or text files into the Data Editor
		Overview
		41.1 What is an ASCII or text data file?
		41.2 Entering data into an ASCII or text data file
		41.3 Reading an ASCII or text data file
		Summary of SPSS Statistics steps for inputting an ASCII or text data file
	42 Missing values
		Overview
		42.1 What are missing values?
		42.2 Entering the data
		42.3 Defining missing values
		42.4 Pairwise and listwise options
		42.5 Sample output for pairwise deletion
		42.6 Sample output for listwise deletion
		42.7 Interpreting the output
		REPORTING THE OUTPUT
		Summary of SPSS Statistics steps for handling missing values
	43 Recoding values
		Overview
		43.1 What is recoding values?
		43.2 Entering the data
		43.3 Recoding values
		43.4 Recoding missing values
		43.5 Saving the Recode procedure as a syntax file
		43.6 Adding some extra cases to Table 43.1
		43.7 Running the Recode syntax command
		Summary of SPSS Statistics steps for recoding values
	44 Computing a scale score with no missing values
		Overview
		44.1 What is computing a scale score?
		44.2 Entering the data
		44.3 Displaying variable labels in dialog boxes
		44.4 Computing a scale score
		44.5 Saving the Compute procedure as a syntax file
		44.6 Adding some extra cases to Table 44.1
		44.7 Running the Compute syntax command
		Summary of SPSS Statistics steps for computing a scale score with no missing values
	45 Computing a scale score with some values missing
		Overview
		45.1 What is computing a scale score with some values missing?
		45.2 Entering the data
		45.3 Computing a scale score with some values missing
		45.4 Saving the Compute procedure as a syntax file
		45.5 Adding some extra cases to Table 45.1
		45.6 Running the Compute syntax command
		Summary of SPSS Statistics steps for computing a scale score with some missing values
	46 Computing a new group variable from existing group variables
		Overview
		46.1 What is computing a new group variable from existing group variables?
		46.2 Entering the data
		46.3 Syntax file for computing a new group variable from existing group variables
		46.4 Running the Compute syntax commands
		46.5 Computing a new group using menus and dialog boxes
		Summary of SPSS Statistics steps for computing a new group variable from existing group variables
	47 Selecting cases
		Overview
		47.1 What is selecting cases?
		47.2 Entering the data
		47.3 Selecting cases
		Summary of SPSS Statistics steps for selecting cases
	48 Samples and populations: Generating a random sample
		Overview
		48.1 What is generating random samples?
		48.2 Entering the data
		48.3 Selecting a random sample
		48.4 Interpreting the results
		48.5 Statistical analysis on random sample
		Summary of SPSS Statistics steps for generating a random sample
	49 Inputting a correlation matrix
		Overview
		49.1 What is inputting a correlation matrix?
		49.2 Syntax file for inputting a correlation matrix and running a stepwise multiple regression
		49.3 Running the syntax file
		49.4 Part of the output
		Summary of SPSS Statistics steps for inputting a correlation matrix
	50 Checking the accuracy of data inputting
		Overview
		50.1 What is checking the accuracy of data inputting?
		50.2 Creating two data files
		50.3 Combining the two data files
		50.4 Creating a syntax file for computing the difference between the two entries for the same variables
		Summary of SPSS Statistics steps for checking the accuracy of data inputting
Part 8 Other statistical procedures
	51 Statistical power analysis: Sample size estimation
		Overview
		51.1 What is statistical power analysis?
		51.2 When to use statistical power analysis
		51.3 When not to use statistical power analysis
		51.4 Data requirements for statistical power analysis
		51.5 Problems in the use of statistical power analysis
		51.6 The data to be analysed
		51.7 Power analysis
		51.8 Interpreting the output
		REPORTING THE OUTPUT
	52 Meta-analysis
		Overview
		52.1 What is meta-analysis?
		52.2 When to use meta-analysis
		52.3 When not to use meta-analysis
		52.4 Data requirements for meta-analysis
		52.5 Problems in the use of meta-analysis
		52.6 The data to be analysed
		52.7 Meta-analysis
		52.8 Interpreting the output
		REPORTING THE OUTPUT
APPENDIX: Some other statistics in SPSS Statistics
GLOSSARY
INDEX