دانلود کتاب SPSS for psychologists

فهرست مطالب :

Contents
Preface
How to use this book
Chapter 1
Chapters 2–4
Chapters 5–9
Chapters 10–13
Chapter 14
Differences between versions of SPSS
Acknowledgements
1: Introduction
Section 1: PSYCHOLOGICAL RESEARCH AND SPSS
But I am studying psychology, not statistics – why do I need to learn to use SPSS?
Asking questions and collecting data
Levels of measurement
Hypotheses
Operationalisation
Types of study design
Correlational designs
Experimental designs
Quasi-experimental designs
Related and unrelated designs in psychological research
Populations and samples
Parameters and statistics
Descriptive statistics
Confidence intervals and point estimates
Bootstrapping
Inferential statistics and probability
Adjusting p values for one- and two-tailed hypotheses
Exact and asymptotic significance
Confidence intervals and statistical inference
Effect size
Statistical power
Practical equivalence of two samples
Statistical power and SPSS
Section 2: GUIDE TO THE STATISTICAL TESTS COVERED
Choosing the correct statistical procedures
Section 3: WORKING WITH SPSS
Data analysis using SPSS
The different types of window used in SPSS
The Data Editor window
The Viewer window
Some other windows used in SPSS
Section 4: STARTING SPSS
The menu and toolbars of the Data Editor window
Section 5: HOW TO EXIT FROM SPSS
2: Data entry in SPSS
Section 1: THE DATA EDITOR WINDOW
What is the Data Editor window?
The arrangement of the data in the Data Editor window
Section 2: DEFINING A VARIABLE IN SPSS
The Data View and Variable View
Setting up your variables
Variable name
Variable type
Variable width and decimal places
Variable label
Value labels
Missing values
Column format
Column alignment
Measure
Role
Check your settings
Copying variable settings
Section 3: ENTERING DATA
A first data entry exercise
Moving around the Data Editor window
The value labels button
Section 4: SAVING A DATA FILE
To save the data to a file
Section 5: OPENING A DATA FILE
Section 6: DATA ENTRY EXERCISES
Data from an unrelated (independent groups) design
Data from a related (repeated measures) design
Section 7: ANSWERS TO DATA ENTRY EXERCISES
A data file for an unrelated (independent groups) design
The data file for a related (repeated measures) design
Section 8: CHECKING AND CLEANING DATA FILES
3: Exploring, cleaning and graphing data in SPSS
Section 1: DESCRIPTIVE STATISTICS
Section 2: THE DESCRIPTIVES COMMAND
To obtain output from the Descriptives command
Section 3: THE VIEWER WINDOW
Section 4: THE FREQUENCIES COMMAND
To obtain a Frequencies output
The output produced by the Frequencies command
Section 5: THE EXPLORE COMMAND
Using the Explore command to analyse data from an independent groups design
The output produced by the Explore command for an independent groups design
Using the Explore command to analyse data from a repeated measures design
The boxplots produced by the Explore command for a repeated measures design
Section 6: USING DESCRIPTIVE STATISTICS TO CHECK YOUR DATA
Checking variables in Variable View
Using descriptive statistics to check the data
Checking scale variables
Finish cleaning the file
Section 7: INTRODUCING GRAPHING IN SPSS
Producing graphs in SPSS
Graph types
Boxplots
Histogram
Bar charts
Error bar chart
Line charts
Scatterplot
Section 8: CHART BUILDER
To use Chart Builder
Editing charts in the Chart Editor window
Section 9: GRAPHBOARD TEMPLATE CHOOSER
To use Graphboard Template Chooser
The Graphboard Editor window
4: Data handling
Section 1: AN INTRODUCTION TO DATA HANDLING
An example data file
Section 2: SORTING A FILE
The Sort Cases command
Section 3: SPLITTING A FILE
Options
Output
Unsplitting a file
Section 4: SELECTING CASES
Comparing the Select Cases and Split File commands
The Select Cases command
Selection rules
Selection methods
Reselecting all cases
Section 5: RECODING VALUES
Recode into Different Variables
Specifying the values to be recoded
Recode into Same Variables
Conditional recode
Section 6: COMPUTING NEW VARIABLES
Compute and Missing Values
Section 7: COUNTING VALUES
Conditional Count
Section 8: RANKING CASES
Ranking tied values
Ranking within categories
Section 9: DATA TRANSFORMATION
Log transformation of decision latency data
Section 10: DATA FILE FOR SCALES OR QUESTIONNAIRES
A simple check on data entry
Reversals
5: Tests of difference for  one- and two-sample designs
Section 1: AN INTRODUCTION TO THE t-TEST
Section 2: THE ONE-SAMPLE t-TEST
Example study: assessing memory
To perform a one-sample t-test
SPSS output for one-sample t-test
Obtained using menu items: Compare Means > One-Sample T Test
Reporting the results
Section 3: THE INDEPENDENT t-TEST
Example study: the memory experiment
To perform an independent t-test
SPSS output for independent groups t-test
Obtained using menu items: Compare Means > Independent-Samples T Test
Measure of effect size
Reporting the results
Creating an error bar graph: independent groups design
To obtain an error bar graph using Chart Builder
SPSS output for bar chart
Section 4: THE PAIRED t-TEST
Example study: the mental imagery experiment
To perform a paired t-test
SPSS output for paired (or related) t-test
Obtained using menu items: > Compare Means > Paired-Samples T Test
Footnotes
Measure of effect size
R eporting the results
Section 5: AN INTRODUCTION TO NONPARAMETRIC TESTS OF DIFFERENCE
Section 6: THE MANN–WHITNEY TEST
Example study: sex differences and emphasis on physical attractiveness
How to do it
SPSS output for Mann–Whitney U test
Obtained using menu items: Nonparametric Tests > Legacy Dialogs > 2 Independent Samples
Reporting the results
Section 7: THE WILCOXON TEST
Example study: quality of E-FIT images
How to do it
SPSS output for Wilcoxon matched-pairs signed-ranks test
Obtained using menu items: Nonparametric Tests > Legacy Dialogs > 2 Related Samples
Reporting the results
6: Tests of correlation and bivariate regression
Section 1: AN INTRODUCTION TO TESTS OF CORRELATION
Section 2: PRODUCING A SCATTERPLOT
Example study: relationship between age and CFF
How to obtain a scatterplot using Legacy Dialogs
Producing a scatterplot with a regression line using Chart Builder
How to add a regression equation to the scatterplot
How to obtain a scatterplot using Graphboard Template Chooser
Section 3: PEARSON’S r: PARAMETRIC TEST OF CORRELATION
Example study: critical flicker frequency and age
How to perform a Pearson’s r
SPSS output for Pearson’s r
Obtained using menu item: Correlate > Bivariate
Effect sizes in correlation
Re porting the results
Section 4: SPEARMAN’S rS: NONPARAMETRIC TEST OF CORRELATION
Example study: the relationships between attractiveness, believability and confidence
How to perform Spearman’s rs
SPSS output for Spearman’s rs
Obtained using menu item: Correlate > Bivariate
Reporting the results
How to perform Kendall’s tau-b
Section 5: PARTIAL CORRELATIONS
How to perform a partial correlation
SPSS output
Obtained using: Correlate > Partial
Reporting the results
Section 6: COMPARING THE STRENGTH OF CORRELATION COEFFICIENTS
Using equations
Reporting the results
Section 7: BRIEF INTRODUCTION TO REGRESSION
Regression as a model
Section 8: BIVARIATE REGRESSION
From bivariate correlation to bivariate regression
The bivariate regression equation
Residuals
Proportion of variance explained
How to perform bivariate regression in SPSS
SPSS output
Obtained using: Analyze, Regression, Curve Estimation
Using the procedure to predict Y for new cases
7: Tests for nominal data
Section 1: NOMINAL DATA AND DICHOTOMOUS VARIABLES
Descriptives for nominal data
Entering nominal data into SPSS
Section 2: CHI-SQUARE TEST VERSUS THE CHI-SQUARE DISTRIBUTION
Section 3: THE GOODNESS OF FIT CHI-SQUARE
To perform the goodness of fit chi-square test
Section 4: THE MULTIDIMENSIONAL CHI-SQUARE
General issues for chi-square
Causal relationships
Type of data
The N*N contingency table
Rationale for chi-square test
Example study: investigating tendency towards anorexia
To perform the multidimensional chi-square test
SPSS output for chi-square without using Exact option
Obtained using menu items: Descriptive Statistics > Crosstabs
Output for first chi-square: tendency towards anorexia * employment (a variable with two levels against a variable with three levels)
Reporting the results
Output for second chi-square: tendency towards anorexia* education (two variables each with two levels)
Interpreting and reporting results from chi-square
R eporting the results
Use of exact tests in chi-square
Output for third chi-square: tendency towards anorexia* cultural background (a variable with two levels against a variable with three levels)
Using the Exact option for chi-square
Output for third chi-square (2*3) with Exact option
Reporting the results
Output for a 2*2 chi-square with Exact option
Performing a chi-square using the Weight Cases option
Section 5: THE MCNEMAR TEST FOR REPEATED MEASURES
Example study: gender and handwriting
How to perform the McNemar test
McNemar test and Crosstabs command
SPSS output for the McNemar test
Obtained using menu items: Descriptive Statistics > Crosstabs
How to obtain a bar chart using Chart Builder
R eporting the results
How to obtain a bar chart using Graphboard Template Chooser
Note about causation and the McNemar test
8: One-way analysis of variance
Section 1: AN INTRODUCTION TO ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
When can we use One-way ANOVA?
How does it work?
How do we find out if the F-ratio is significant?
What about degrees of freedom?
What terms are used with One-way ANOVA?
Factors
Levels of factors
Between-subjects factors
Within-subjects factors
Main effect
How is the F-ratio calculated?
Between-subjects One-way ANOVA design
Within-subjects One-way ANOVA design
Using SPSS to calculate the F-ratio
Effect size and ANOVA
Planned and unplanned comparisons
Section 2: ONE-WAY BETWEEN-SUBJECTS ANOVA, PLANNED AND UNPLANNED COMPARISONS, AND NONPARAMETRIC EQUIVALENT
Example study: the effects of witness masking
How to do it: using General Linear Model command
SPSS output for One-way between-subjects ANOVA
Obtained using menu items: General Linear Model > Univariate
Calculating eta squared: one measure of effect size
Reporting the results
How to do it: using One-way ANOVA command
SPSS output for One-way between-subjects ANOVA
Obtained using menu items: Compare Means > One-way ANOVA
Calculating eta squared: one measure of effect size
Reporting the results
Planned and unplanned comparisons
Unplanned (post-hoc) comparisons in SPSS
Using General Linear Model command
Using One-way ANOVA command
SPSS output for post-hoc tests
Reporting the results
Planned comparisons in SPSS
Using One-way ANOVA command
SPSS output for contrasts
Obtained using menu items: Compare Means > One-way ANOVA
R eporting the results
Using General Linear Model command
The Kruskal–Wallis test
How to do it
SPSS output for Kruskal–Wallis test
Obtained by using menu items: Nonparametric Tests > K Independent Samples
Reporting the results
Section 3: ONE-WAY WITHIN-SUBJECTS ANOVA, PLANNED AND UNPLANNED COMPARISONS AND NONPARAMETRIC EQUIVALENT
Example study: the Stroop effect
Understanding the output
How to do it
SPSS output for One-way within-subjects ANOVA
Obtained using menu items: General Linear Model > Repeated Measures
Calculating eta squared: one measure of effect size
Reporting the results
Planned comparisons: more contrasts for  within-subjects factor
Unplanned comparisons for within-subjects factor ANOVA
The Friedman test
How to do it
SPSS output for Friedman test
Obtained by using menu items: Nonparametric Tests > K Related Samples
R eporting the results
9: Factorial analysis of variance
Section 1: AN INTRODUCTION TO FACTORIAL ANALYSIS OF VARIANCE (ANOVA)
Different types of Factorial ANOVA?
Between-subjects ANOVA
Within-subjects ANOVA
Mixed ANOVA
Factorial ANOVAs
Main effects and interactions
Interactions and moderation
Understanding ANOVA output
Section 2: TWO-WAY BETWEEN-SUBJECTS ANOVA
Example study: the effect of defendant’s attractiveness and sex on sentencing
How to do it
SPSS output for two-way between-subjects ANOVA
Obtained using menu items: General Linear Model > Univariate
Calculating eta squared: one measure of effect size
Rep orting the results
Section 3: TWO-WAY WITHIN-SUBJECTS ANOVA
Example study: the effects of two memory tasks on finger tapping performance
Labelling within-subjects factors
How to do it
How to obtain an interaction graph
SPSS output for two-way within-subjects ANOVA
Obtained using menu items: General Linear Model > Repeated Measures
Calculating eta squared: one measure of effect size
Reporting the results
Section 4: MIXED ANOVA
Example study: perceptual expertise in the own-age bias
How to do it
SPSS output for three-way mixed ANOVA
Obtained using menu items: General Linear Model > Repeated Measures
Report ing the results
10: Multiple regression
Section 1: AN INTRODUCTION TO MULTIPLE REGRESSION
From bivariate to multiple
An example
How does multiple regression relate to analysis of variance?
Causation
When should I use multiple regression?
The multiple regression equation
Regression coefficients: B (unstandardised) and beta (standardised)
R, R-squared and adjusted R-squared
Regression methods
Unique and shared variance
Simultaneous or standard method
Sequential or hierarchical method
Statistical (or stepwise) methods
Validating results from statistical regression methods
Section 2: STANDARD OR SIMULTANEOUS METHOD OF MULTIPLE REGRESSION
Example study: state anxiety
SPSS output for standard multiple regression
Obtained using menu items: Regression > Linear (method = enter)
R eporting the results
Section 3: SEQUENTIAL OR HIERARCHICAL METHOD OF MULTIPLE REGRESSION
SPSS output for sequential multiple regression
Obtained using menu items: Regression > Linear (method = enter; three blocks have been entered)
A note on sequential method and regression coefficients
Reporting the results
Section 4: STATISTICAL METHODS OF MULTIPLE REGRESSION
How to perform multiple regression using the stepwise method
SPSS output for a statistical multiple regression
Obtained using menu items: Regression > Linear (method = stepwise)
Reporting the results
11: Analysis of covariance and multivariate analysis of variance
Section 1: AN INTRODUCTION TO ANALYSIS OF COVARIANCE
What does ANCOVA do?
When should I use ANCOVA?
An example
Checklist for choosing one or more covariates
Section 2: PERFORMING ANALYSIS OF COVARIANCE iN SPSS
Example study: exposure to low levels of organophosphates
How to check for homogeneity of regression slopes
SPSS output from procedure to check for homogeneity of regression slopes
How to check for linear relationship between covariate and dependent variable
SPSS output for graph
How to perform ANCOVA
SPSS output for ANCOVA
Rep orting the results
Section 3: AN INTRODUCTION TO MULTIVARIATE ANALYSIS OF VARIANCE
An example
What does MANOVA do?
Following up a significant result
When should I use MANOVA?
Checklist for using MANOVA
Section 4: PERFORMING MULTIVARIATE ANALYSIS OF VARIANCE iN SPSS
Example study: exposure to low levels of organophosphates
How to perform MANOVA
SPSS output for MANOVA
Reporting the results
A note on within-subjects designs
12: Discriminant analysis and logistic regression
Section 1: DISCRIMINANT ANALYSIS AND LOGISTIC REGRESSION
An example
Similarities and differences between discriminant analysis and logistic regression
Section 2: AN INTRODUCTION TO DISCRIMINANT ANALYSIS
An example
Two steps in discriminant analysis
Assumptions
Methods in discriminant analysis
Choosing a method to adopt
What does each method tell us?
How does each method work?
Section 3: PERFORMING DISCRIMINANT ANALYSIS iN SPSS
Example study: reconviction among offenders
To perform a simultaneous (enter method) discriminant analysis
SPSS output for discriminant analysis using enter method
Obtained using menu items: Classify > Discriminant (enter independents together)
R eporting the results
To perform a stepwise (or statistical) discriminant analysis
Using discriminant function analysis to predict group membership
Section 4: AN INTRODUCTION TO LOGISTIC REGRESSION
Section 5: PERFORMING LOGISTIC REGRESSION ON SPSS
Example study: reconviction among offenders
To perform a binary logistic regression
SPSS output for logistic regression using Enter method
Obtained using menu items: Regression > Binary Logistic
Reporting the results
Using logistic regression to predict group membership
13: Factor analysis, and reliability and dimensionality of scales
Section 1: AN INTRODUCTION TO FACTOR ANALYSIS
How this chapter is organised
How factor analysis relates to other statistical tests
Correlation and covariance
Multiple regression
Analysis of variance
Discriminant analysis and logistic regression
Correlation matrix and other matrices in factor analysis
Pearson’s r output
Correlation matrix from factor analysis output
Partial correlations from factor analysis output
Reproduced correlations and residuals from factor analysis output
When should I use factor analysis?
Usefulness/validity of a factor analysis
Terminology
Component and factor
Extraction
Communality
Eigenvalue
Scree plot
Factor loadings
Rotation
Section 2: PERFORMING A BASIC FACTOR ANALYSIS USING SPSS
Hypothetical study
How to perform the analysis
Output from factor analysis using principal component extraction and direct oblimin rotation
Obtained using menu items: Analyze > Dimension Reduction > Factor
Considering the results
Reporting the results
Section 3: OTHER ASPECTS OF FACTOR ANALYSIS
Other options from the Factor Analysis: Descriptives dialogue box
Determinant
Inverse
Other options from the Factor Analysis: Extraction dialogue box
Method
Extract options
Other options from the Factor Analysis: Rotation dialogue box
Method
Display
Factor Analysis: Options dialogue box
Coefficient Display Format
Negative and positive factor loadings
R factor analysis
Section 4: RELIABILITY ANALYSIS FOR SCALES AND QUESTIONNAIRES
Internal consistency
How to perform a reliability analysis
SPSS output for reliability analysis with Cronbach’s alpha
Obtained using menu item: Scale > Reliability Analysis (model = alpha)
Acting on the results
SPSS output for reliability analysis with split-half
Obtained using menu item: Scale > Reliability Analysis (model = split-half)
Section 5: DIMENSIONALITY OF SCALES AND QUESTIONNAIRES
To identify those items that load on a single component
Acting on the results
To assess the structure of items within a scale
Acting on the results
Important
14: Using syntax and other useful features of SPSS
Section 1: THE SYNTAX WINDOW
An example of a syntax command
The Paste button and the Syntax window
1. Duplicating actions
2. Keeping a record of your analysis and repeating an analysis
3. Describing the analysis you have undertaken
4. Tweaking the parameters of a command
The Syntax window
Basic rules of syntax
Saving syntax files
Executing syntax commands
Syntax errors
Selecting the correct data file
Section 2: SYNTAX EXAMPLES
Comparing correlation coefficients
System variables
Section 3: GETTING HELP IN SPSS
The Help button in dialogue boxes
The Help menu
What’s this?
Section 4: OPTION SETTINGS IN SPSS
Changing option settings
Some useful option settings
General tab
Syntax Editor tab
Viewer tab
Data tab
Output tab
File Locations tab
Section 5: PRINTING FROM SPSS
Printing output from the Output viewer window
Printing data and syntax files
Special output options for pivot tables
Section 6: INCORPORATING SPSS OUTPUT INTO OTHER DOCUMENTS
Exporting SPSS output
Section 7: SPSS AND EXCEL: IMPORTING AND EXPORTING DATA FILES
Import: opening an Excel file in SPSS
Export: saving an SPSS file to Excel
Appendix
Data for data handling exercises: Chapter 4, Sections 1–9
Data for data handling exercises: Chapter 4, Section 10
Data for one-sample t-test: Chapter 5, Section 2
Data for independent t-test: Chapter 5, Section 3
Data for paired t-test: Chapter 5, Section 4
Data for Mann–Whitney U test: Chapter 5, Section 6
Data for Wilcoxon matched-pairs signed-ranks test: Chapter 5, Section 7
Data for Pearson’s r correlation: Chapter 6, Section 3
Data for Spearman’s rho correlation: Chapter 6, Section 4
Data for chi-square test: Chapter 7, Section 4
Data for McNemar test: Chapter 7, Section 5
Glossary
References
Index