Understanding Statistics in Psychology with SPSS, 8th Edition
- Length: 754 pages
- Edition: 8
- Language: English
- Publisher: Pearson
- Publication Date: 2020
- ISBN-10: 1292282304
- ISBN-13: 9781292282305
- Sales Rank: #10775736 (See Top 100 Books)
Understanding Statistics in Psychology with SPSS, eighth edition, offers students a trusted, straightforward, and engaging way of learning to do statistical analyses confidently using SPSS. Comprehensive and practical, the text is organised into 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
- 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:
- Key concept boxes detailing important terms.
- Focus on sections exploring complex topics in greater depth.
- Explaining statistics sections clarify important statistical concepts. .
Dennis Howitt and Duncan Cramer are with Loughborough University.
Front Cover Half Title Page Title Page Copyright Page Brief Contents Contents Guided tour Introduction Acknowledgements 1 Why statistics? Overview 1.1 Introduction 1.2 Research on learning statistics 1.3 Why is learning statistics difficult? 1.4 The importance of understanding research designs 1.5 Positive about statistics 1.6 What statistics can't do 1.7 Easing the way 1.8 What do I need to know to be an effective user of statistics? 1.9 A few words about SPSS 1.10 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 equations? 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: A practical introduction to statistical inference 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 within 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 Basics of 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 Part 5: Assorted advanced techniques 36 Meta-analysis: Combining and exploring statistical findings from previous research Overview 36.1 Introduction 36.2 Pearson correlation coefficient as the effect size 36.3 Other measures of effect size 36.4 Effects of different characteristics of studies 36.5 First steps in meta-analysis 36.6 Illustrative example 36.7 Comparing a study with a previous study 36.8 Reporting the results Key points Computer analysis: Some meta-analysis software Recommended further reading 37 Reliability in scales and measurement: Consistency and agreement Overview 37.1 Introduction 37.2 Item-analysis using item-total correlation 37.3 Split-half reliability 37.4 Alpha reliability 37.5 Agreement among raters Key points Computer analysis: Cronbach's alpha and kappa using SPSS Recommended further reading 38 Influence of moderator variables on relationships between two variables Overview 38.1 Introduction 38.2 Statistical approaches to finding moderator effects 38.3 Hierarchical multiple regression approach to identifying moderator effects (or interactions) 38.4 ANOVA approach to identifying moderator effects (i.e. interactions) Key points Computer analysis: Regression moderator analysis using SPSS Recommended further reading 39 Statistical power analysis: Getting the sample size right Overview 39.1 Introduction 39.2 Types of statistical power analysis and their limitations 39.3 Doing power analysis 39.4 Calculating power 39.5 Reporting the results Key points Computer analysis: Power analysis with G*Power Part 6: Advanced qualitative or nominal techniques 40 Log-linear methods: Analysis of complex contingency tables Overview 40.1 Introduction 40.2 Two-variable example 40.3 Three-variable example 40.4 Reporting the results Key points Computer analysis: Log-linear analysis using SPSS Recommended further reading 41 Multinomial logistic regression: Distinguishing between several different categories or groups Overview 41.1 Introduction 41.2 Dummy variables 41.3 What can multinomial logistic regression do? 41.4 Worked example 41.5 Accuracy of the prediction 41.6 How good are the predictors? 41.7 Prediction 41.8 Interpreting the results 41.9 Reporting the results Key points Computer analysis: Multinomial logistic regression using SPSS 42 Binomial logistic regression Overview 42.1 Introduction 42.2 Typical example 42.3 Applying the logistic regression procedure 42.4 Regression formula 42.5 Reporting the results Key points Computer analysis: Binomial logistic regression using SPSS 43 Data mining and big data Overview 43.1 Introduction 43.2 Adopting a new thinking mode 43.3 Dissatisfactions with traditional psychology 43.4 Web scraping 43.5 Data mining and statistical techniques Key points 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 Back Cover
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