Introduction to SPSS in psychology for version 22 and earlier 6th Edition by Duncan Cramer, Dennis Howitt 1292000694 9781292000695
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ISBN 10: 1292000694
ISBN 13: 9781292000695
Author: Duncan Cramer, Dennis Howitt
Introduction to SPSS Statistics in Psychology gives you a straight-forward way of learning to carry out statistical analyses and use SPSS with confidence. This edition is fully updated to include the latest version of SPSS Statistics, and covers the same wide range of statistical tests that made the previous edition such an trusted guide. Clear diagrams and screenshots from SPSS version 22 make the text suitable for beginners while the broad coverage of topics ensures that you can continue to use it as you progress to more advanced techniques.
Introduction to SPSS in psychology for version 22 and earlier 6th Table of contents:
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
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