Introduction to Statistics with SPSS for Social Science

Transcription

1 New Introduction to Statistics with SPSS for Social Science Gareth Norris Faiza Qureshi Dennis Howitt Duncan Cramer Aberystwyth University City University London University of Loughborough University of Loughborough PEARSON Harlow, England London York Boston San Francisco Toronto Sydney Auckland Singapore Hong Kong Tokyo Seoul Taipei New Delhi Cape Town Sao Paulo Mexico City Madrid Amsterdam Munich Paris Milan

2 Contents J Guided tour Introduction List of figures List of tables List of boxes List of calculation boxes Acknowledgements XVI xviii xxi xxiii xxviii xxix xxx Part 1 Descriptive statistics 1 Why you need statistics: types of data Overview 1.1 Introduction 1.2 Variables and measurement 1.3 Statistical significance 1.4 SPSS guide: an introduction 2 Describing variables: tables and diagrams Overview 2.1 Introduction 2.2 Choosing tables and diagrams 2.3 Errors to avoid 2.4 SPSS analysis 2.5 Pie diagram of category data 2.6 Bar chart of category data 2.7 Histograms 3 Describing variables numerically: averages, variation and spread Overview 3.1 Introduction: mean, median and mode

3 . viii CONTENTS V 3.2 Comparison of mean, median and mode The spread of scores: variability Probability 3.5 Confidence intervals SPSS analysis /Cey po/nts 4 Shapes of distributions of scores 55 Ouerv/eiv Introduction Histograms and frequency curves The normal curve Distorted curves Other frequency curves SPSS analysis 5 Standard deviation, z-scores and standard error: the standard unit of measurement in statistics 67 Overview Introduction What is standard deviation? When to use standard deviation When not to use standard deviation Data requirements for standard deviation Problems in the use of standard deviation SPSS analysis 5.8 Standard error: the standard deviation of the means of samples When to use standard error When not to use standard error SPSS analysis for standard error Relationships between two or more variables: diagrams and tables 81 Overview Introduction The principles of diagrammatic and tabular presentation Type A: both variables numerical scores Type B: both variables nominal categories Type C: one variable nominal categories, the other numerical scores SPSS analysis J

4 another CONTENTS ix 7 Correlation coefficients: the Pearson correlation and Spearman's rho 99 Overview Introduction Principles of the correlation coefficient Some rules to check out Coefficient of determination Data requirements for correlation coefficients SPSS analysis Spearman's rho - correlation coefficient SPSS analysis for Spearman's rho Scatter diagram using SPSS Problems in the use of correlation coefficients Regression and standard error 118 Overview Introduction Theoretical background and regression equations When and when not to use simple regression Data requirements for simple regression Problems in the use of simple regression SPSS analysis Regression scatterplot Standard error: how accurate are the predicted score and the regression equations? V. Part 2 Inferential statistics The analysis of a questionnaire/survey project 137 Overview Introduction The research project The research hypothesis Initial variable classification Further coding of data Data cleaning Data analysis SPSS analysis

5 CONTENTS 10 The related f-test: comparing two samples of correlated/ related scores 145 Overview Introduction Dependent and independent variables Theoretical considerations SPSS analysis A cautionary note The unrelated f-test: comparing two samples of unrelated/ uncorrelated scores 158 Overview Introduction Theoretical considerations Standard deviation and standard error A cautionary note Data requirements for the unrelated f-test When not to use the unrelated f-test Problems in the use of the unrelated f-test SPSS analysis Chi-square: differences between samples of frequency data 176 Overview Introduction Theoretical considerations When to use chi-square When not to use chi-square Data requirements for chi-square Problems in the use of chi-square SPSS analysis The Fisher exact probability test SPSS analysis for the Fisher exact test Partitioning chi-square Important warnings Alternatives to chi-square Chi-square and known populations Recommended further reading 196

6 CONTENTS xi Part 3 Introduction to analysis of variance 197; 13 Analysis of variance (ANOVA): introduction to one-way unrelated or uncorretated ANOVA 199 Overview Introduction Theoretical considerations Degrees of freedom When to use one-way ANOVA When not to use one-way ANOVA Data requirements for one-way ANOVA Problems in the use of one-way ANOVA SPSS analysis Computer analysis for one-way unrelated ANOVA Two-way analysis of variance for unrelated/uncorrelated scores: two studies for the price of one? 212 Overview Introduction Theoretical considerations Steps in the analysis When to use two-way ANOVA When not to use two-way ANOVA Data requirements for two-way ANOVA Problems in the use of two-way ANOVA SPSS analysis Computer analysis for two-way unrelated ANOVA Three or more independent variables Multiple-comparisons testing in ANOVA Analysis of covariance (ANCOVA): controlling for additional variables 240 Overview Introduction Example of the analysis of covariance When to use ANCOVA When not to use ANCOVA Data requirements for ANCOVA 250

7 CONTENTS 15.6 SPSS analysis Recommended further reading Multivariate analysis of variance (MANOVA) 258 Overview Introduction Questions for MANOVA MANOVA's two stages Doing MANOVA When to use MANOVA When not to use MANOVA Data requirements for MANOVA Problems in the use of MANOVA SPSS analysis Recommended further reading 273 Part 4 More advanced statistics and techniques 275 J 17 Partial correlation: spurious correlation, third or confounding variables (control variables), suppressor variables 277 Overview Introduction Theoretical considerations The calculation Multiple control variables Suppressor variables An example from the research literature When to use partial correlation When not to use partial correlation Data requirements for partial correlation Problems in the use of partial correlation SPSS analysis Factor analysis: simplifying complex data 288 Overview Introduction Data issues in factor analysis 290

8 CONTENTS xiii / 18.3 Concepts in factor analysis Decisions, decisions, decisions When to use factor analysis When not to use factor analysis Data requirements for factor analysis Problems in the use of factor analysis SPSS analysis Recommended further reading Multiple regression and multiple correlation 308 Overview Introduction Theoretical considerations Stepwise multiple regression example Reporting the results What is stepwise multiple regression? When to use stepwise multiple regression When not to use stepwise multiple regression Data requirements for stepwise multiple regression Problems in the use of stepwise multiple regression SPSS analysis What is hierarchical multiple regression? When to use hierarchical multiple regression When not to use hierarchical multiple regression Data requirements for hierarchical multiple regression Problems in the use of hierarchical multiple regression SPSS analysis Recommended further reading Multinomial logistic regression: distinguishing between several different categories or groups 332 Overview Introduction Dummy variables What can multinomial logistic regression do? Worked example Accuracy of the prediction How good are the predictors? The prediction What have we found? 344 V

9 Xiv CONTENTS 20.9 Reporting the results When to use multinomial logistic regression When not to use multinomial logistic regression Data requirements for multinomial logistic regression Problems in the use of multinomial logistic regression SPSS analysis Binomial logistic regression 357 Overview Introduction Simple logistic regression Typical example Applying the logistic regression procedure The regression formula Reporting the results When to use binomial logistic regression When not to use binomial logistic regression Data requirements for binomial logistic regression Problems in the use of binomial logistic regression SPSS analysis Log-linear methods: the analysis of complex contingency tables 378 Overview Introduction A two-variable example A three-variable example Reporting the results When to use log-linear analysis When not to use log-linear analysis Data requirements for log-linear analysis Problems in the use of log-linear analysis SPSS analysis Recommended further reading 405 V Appendices 407 A Testing for excessively skewed distributions 409 A.l Skewness 409 A.2 Standard error of skewness 410 J

10 CONTENTS XV B Extended table of significance for the Pearson correlation coefficient 412 C Table of significance for the Spearman correlation coefficient 416 D Extended table of significance for the f-test 420 E Table of significance for chi-square 424 F Extended table of significance for the sign test 425 G Table of significance for the Wilcoxon matched pairs test 429 H Tables of significance for the Mann-Whitney L/-test 433 I Tables of significant values for the F-distribution 436 J Table of significant values of f when making multiple f-tests 439 K Some other statistics in SPSS Statistics 443 Glossary 445 References 453 Index 454 V. J