By Dennis Howitt
Descripción: Understanding Statistics in Psychology with SPSS 7th edition, offers students a trusted, straightforward, and engaging way of learning how to carry out statistical analyses and use SPSS with confidence.
Comprehensive and practical, the text is organised by short, accessible chapters, making it the ideal text for undergraduate psychology students needing to get to grips with Statistics in class or independently.
Clear diagrams and full colour screenshots from SPSS make the text suitable for beginners while the broad coverage of topics ensures that students can continue to use it as they progress to more advanced techniques.
Key features
· Now combines coverage of statistics with full guidance on how to use SPSS to analyse data
· Suitable for use with all versions of SPSS
· Examples from a wide range of real psychological studies illustrate how statistical techniques are used in practice
· Includes clear and detailed guidance on choosing tests, interpreting findings and reporting and writing up research
· Student focused pedagogical approach including
o Key concept boxes detailing important terms
o Focus on sections exploring complex topics in greater depth
o ‘Explaining statistics sections clarify important statistical concepts’.
Contenido: 1 Why statistics?
Overview
1.1 Introduction
1.2 Research on learning statistics
1.3 What makes learning statistics difficult?
1.4 Positive about statistics
1.5 What statistics doesn’t do
1.6 Easing the way
1.7 What do I need to know to be an effective user of statistics?
1.8 A few words about SPSS
1.9 Quick guide to the book’s procedures and statistical tests
Key points
Computer analysis: SPSS Analyze Graphs and Transform drop-down menus
Part 1 Descriptive statistics
2 Some basics: Variability and measurement
Overview
2.1 Introduction
2.2 Variables and measurement
2.3 Major types of measurement
Key points
Computer analysis: Some basics of data entry using SPSS
3 Describing variables: Tables and diagrams
Overview
3.1 Introduction
3.2 Choosing tables and diagrams
3.3 Errors to avoid
Key points
Computer analysis: Tables, diagrams and recoding using SPSS
4 Describing variables numerically: Averages, variation and spread
Overview
4.1 Introduction
4.2 Typical scores: mean, median and mode
4.3 Comparison of mean, median and mode
4.4 Spread of scores: range and interquartile range
4.5 Spread of scores: variance
Key points
Computer analysis: Descriptive statistics using SPSS
5 Shapes of distributions of scores
Overview
5.1 Introduction
5.2 Histograms and frequency curves
5.3 Normal curve
5.4 Distorted curves
5.5 Other frequency curves
Key points
Computer analysis: Frequencies using SPSS
6 Standard deviation and z-scores: Standard unit of measurement in statistics
Overview
6.1 Introduction
6.2 Theoretical background
6.3 Measuring the number of standard deviations – the z-score
6.4 Use of z-scores
6.5 Standard normal distribution
6.6 Important feature of z-scores
Key points
Computer analysis: Standard deviation and z-scores using SPSS
7 Relationships between two or more variables: Diagrams and tables
Overview
7.1 Introduction
7.2 Principles of diagrammatic and tabular presentation
7.3 Type A: both variables numerical scores
7.4 Type B: both variables nominal categories
7.5 Type C: one variable nominal categories, the other numerical scores
Key points
Computer analysis: Crosstabulation and compound bar charts using SPSS
8 Correlation coefficients: Pearson’s correlation and Spearman’s rho
Overview
8.1 Introduction
8.2 Principles of the correlation coefficient
8.3 Some rules to check out
8.4 Coefficient of determination
8.5 Significance testing
8.6 Spearman’s rho – another correlation coefficient
8.7 Example from the literature
Key points
Computer analysis: Correlation coefficients using SPSS
Computer analysis: Scattergram using SPSS
9 Regression: Prediction with precision
Overview
9.1 Introduction
9.2 Theoretical background and regression equations
9.3 Confidence intervals and standard error: how accurate are the predicted score and the regression
Key points
Computer analysis: Simple regression using SPSS
Part 2 Significance testing
10 Samples from populations
Overview
10.1 Introduction
10.2 Theoretical considerations
10.3 Characteristics of random samples
10.4 Confidence intervals
Key points
Computer analysis: Selecting a random sample using SPSS
11 Statistical significance for the correlation coefficient: Practical introduction to statistical
Overview
11.1 Introduction
11.2 Theoretical considerations
11.3 Back to the real world: null hypothesis
11.4 Pearson’s correlation coefficient again
11.5 Spearman’s rho correlation coefficient
Key points
Computer analysis: Correlation coefficients using SPSS
12 Standard error: Standard deviation of the means of samples
Overview
12.1 Introduction
12.2 Theoretical considerations
12.3 Estimated standard deviation and standard error
Key points
Computer analysis: Standard error using SPSS
13 Related t-test: Comparing two samples of related/correlated/paired scores
Overview
13.1 Introduction
13.2 Dependent and independent variables
13.3 Some basic revision
13.4 Theoretical considerations underlying the computer analysis
13.5 Cautionary note
Key points
Computer analysis: Related/correlated/paired t-test using SPSS
14 Unrelated t-test: Comparing two samples of unrelated/uncorrelated/independent scores
Overview
14.1 Introduction
14.2 Theoretical considerations
14.3 Standard deviation and standard error
14.4 Cautionary note
Key points
Computer analysis: Unrelated/uncorrelated/independent t-test using SPSS
15 What you need to write about your statistical analysis
Overview
15.1 Introduction
15.2 Reporting statistical significance
15.3 Shortened forms
15.4 APA (American Psychological Association) style
Key points
16 Confidence intervals
Overview
16.1 Introduction
16.2 Relationship between significance and confidence intervals
16.3 Regression
16.4 Writing up a confidence interval using APA style
16.5 Other confidence intervals
Key points
Computer analysis: Examples of SPSS output containing confidence intervals
17 Effect size in statistical analysis: Do my findings matter?
Overview
17.1 Introduction
17.2 Statistical significance and effect size
17.3 Size of the effect in studies
17.4 Approximation for nonparametric tests
17.5 Analysis of variance (ANOVA)
17.6 Writing up effect sizes using APA style
17.7 Have I got a large, medium or small effect size?
17.8 Method and statistical efficiency
Key points
18 Chi-square: Differences between samples of frequency data
Overview
18.1 Introduction
18.2 Theoretical issues
18.3 Partitioning chi-square
18.4 Important warnings
18.5 Alternatives to chi-square
18.6 Chi-square and known populations
18.7 Chi-square for related samples – the McNemar test
18.8 Example from the literature
Key points
Computer analysis: Chi-square using SPSS
Recommended further reading
19 Probability
Overview
19.1 Introduction
19.2 Principles of probability
19.3 Implications
Key points
20 One-tailed versus two-tailed significance testing
Overview
20.1 Introduction
20.2 Theoretical considerations
20.3 Further requirements
Key points
Computer analysis: One- and two-tailed statistical significance using SPSS
21 Ranking tests: Nonparametric statistics
Overview
21.1 Introduction
21.2 Theoretical considerations
21.3 Nonparametric statistical tests
21.4 Three or more groups of scores
Key points
Computer analysis: Two-group ranking tests using SPSS
Recommended further reading
Part 3 Introduction to analysis of variance
22 Variance ratio test: F-ratio to compare two variances
Overview
22.1 Introduction
22.2 Theoretical issues and application
Key points
Computer analysis: F-ratio test using SPSS
23 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA
Overview
23.1 Introduction
23.2 Some revision and some new material
23.3 Theoretical considerations
23.4 Degrees of freedom
23.5 Analysis of variance summary table
Key points
Computer analysis: Unrelated one-way analysis of variance using SPSS
24 ANOVA for correlated scores or repeated measures
Overview
24.1 Introduction
24.2 Theoretical considerations underlying the computer analysis
24.3 Examples
Key points
Computer analysis: Related analysis of variance using SPSS
25 Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?
Overview
25.1 Introduction
25.2 Theoretical considerations
25.3 Steps in the analysis
25.4 More on interactions
25.5 Three or more independent variables
Key points
Computer analysis: Unrelated two-way analysis of variance using SPSS
26 Multiple comparisons with in ANOVA: A priori and post hoc tests
Overview
26.1 Introduction
26.2 Planned (a priori) versus unplanned (post hoc) comparisons
26.3 Methods of multiple comparisons testing
26.4 Multiple comparisons for multifactorial ANOVA
26.5 Contrasts
26.6 Trends
Key points
Computer analysis: Multiple comparison tests using SPSS
Recommended further reading
27 Mixed-design ANOVA: Related and unrelated variables together
Overview
27.1 Introduction
27.2 Mixed designs and repeated measures
Key points
Computer analysis: Mixed design analysis of variance using SPSS
Recommended further reading
28 Analysis of covariance (ANCOVA): Controlling for additional variables
Overview
28.1 Introduction
28.2 Analysis of covariance
Key points
Computer analysis: Analysis of covariance using SPSS
Recommended further reading
29 Multivariate analysis of variance (MANOVA)
Overview
29.1 Introduction
29.2 MANOVA’s two stages
29.3 Doing MANOVA
29.4 Reporting your findings
Key points
Computer analysis: Multivariate analysis of variance using SPSS
Recommended further reading
30 Discriminant (function) analysis – especially in MANOVA
Overview
30.1 Introduction
30.2 Doing the discriminant function analysis
30.3 Reporting your findings
Key points
Computer analysis: Discriminant function analysis using SPSS
Recommended further reading
31 Statistics and analysis of experiments
Overview
31.1 Introduction
31.2 The Patent Stats Pack
31.3 Checklist
31.4 Special cases
Key points
Computer analysis: Selecting subsamples of your data using SPSS
Computer analysis: Recoding groups for multiple comparison tests using SPSS
Part 4 More advanced correlational statistics
32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables
Overview
32.1 Introduction
32.2 Theoretical considerations
32.3 Doing partial correlation
32.4 Interpretation
32.5 Multiple control variables
32.6 Suppressor variables
32.7 Example from the research literature
32.8 Example from a student’s work
Key points
Computer analysis: Partial correlation using SPSS
33 Factor analysis: Simplifying complex data
Overview
33.1 Introduction
33.2 A bit of history
33.3 Concepts in factor analysis
33.4 Decisions, decisions, decisions
33.5 Exploratory and confirmatory factor analysis
33.6 Example of factor analysis from the literature
33.7 Reporting the results
Key points
Computer analysis: Principal components analysis using SPSS
Recommended further reading
34 Multiple regression and multiple correlation
Overview
34.1 Introduction
34.2 Theoretical considerations
34.3 Assumptions of multiple regression
34.4 Stepwise multiple regression example
34.5 Reporting the results
34.6 Example from the published literature
Key points
Computer analysis: Stepwise multiple regression using SPSS
Recommended further reading
35 Path analysis
Overview
35.1 Introduction
35.2 Theoretical considerations
35.3 Example from published research
35.4 Reporting the results
Key points
Computer analysis: Hierarchical multiple regression using SPSS
Recommended further reading
36 Analysis of a questionnaire/survey project
Overview
36.1 Introduction
36.2 Research project
36.3 Research hypothesis
36.4 Initial variable classification
36.5 Further coding of data
36.6 Data cleaning
36.7 Data analysis
Key points
Computer analysis: Adding and averaging components of a measure using SPSS
Part 5 Assorted advanced techniques
37 Meta-analysis: Combining and exploring statistical findings from previous research
Overview
37.1 Introduction
37.2 Pearson correlation coefficient as the effect size
37.3 Other measures of effect size
37.4 Effects of different characteristics of studies
37.5 First steps in meta-analysis
37.6 Illustrative example
37.7 Comparing a study with a previous study
37.8 Reporting the results
Key points
Computer analysis: Some meta-analysis software
Recommended further reading
38 Reliability in scales and measurement: Consistency and agreement
Overview
38.1 Introduction
38.2 Item-analysis using item–total correlation
38.3 Split-half reliability
38.4 Alpha reliability
38.5 Agreement among raters
Key points
Computer analysis: Cronbach’s alpha and kappa using SPSS
Recommended further reading
39 Influence of moderator variables on relationships between two variables
Overview
39.1 Introduction
39.2 Statistical approaches to finding moderator effects
39.3 Hierarchical multiple regression approach to identifying moderator effects (or interactions)
39.4 ANOVA approach to identifying moderator effects (i.e. interactions)
Key points
Computer analysis: Regression moderator analysis using SPSS
Recommended further reading
40 Statistical power analysis: Getting the sample size right
Overview
40.1 Introduction
40.2 Types of statistical power analysis and their limitations
40.3 Doing power analysis
40.4 Calculating power
40.5 Reporting the results
Key points
Computer analysis: Power analysis with G*Power
Part 6 Advanced qualitative or nominal techniques
41 Log-linear methods: Analysis of complex contingency tables
Overview
41.1 Introduction
41.2 Two-variable example
41.3 Three-variable example
41.4 Reporting the results
Key points
Computer analysis: Log-linear analysis using SPSS
Recommended further reading
42 Multinomial logistic regression: Distinguishing between several different categories or groups
Overview
42.1 Introduction
42.2 Dummy variables
42.3 What can multinomial logistic regression do?
42.4 Worked example
42.5 Accuracy of the prediction
42.6 How good are the predictors?
42.7 Prediction
42.8 Interpreting the results
42.9 Reporting the results
Key points
Computer analysis: Multinomial logistic regression using SPSS
43 Binomial logistic regression
Overview
43.1 Introduction
43.2 Typical example
43.3 Applying the logistic regression procedure
43.4 Regression formula
43.5 Reporting the results
Key points
Computer analysis: Binomial logistic regression using SPSS
Appendices
Appendix A Testing for excessively skewed distributions
Appendix B1 Large-sample formulae for the nonparametric tests
Appendix B2 Nonparametric tests for three or more groups
Computer analysis: Kruskal–Wallis and Friedman nonparametric tests using SPSS
Appendix C Extended table of significance for the Pearson correlation coefficient
Appendix D Table of significance for the Spearman correlation coefficient
Appendix E Extended table of significance for the t-test
Appendix F Table of significance for chi-square
Appendix G Extended table of significance for the sign test
Appendix H Table of significance for the Wilcoxon matched pairs test
Appendix I Tables of significance for the Mann–Whitney U-test
Appendix J Tables of significance values for the F-distribution
Appendix K Table of significance values for t when making multiple t-tests
Glossary
References
Index
Material: