Blaikie, Norman W.

Analyzing Quantitative Data : From Description to Explanation

List of Figures xiv List of Tables xvi Acknowledgements xx Introduction: About the Book 1(1) Why was it written? 1(2) Who is it for? 3(1) What makes it different? 4(2) What are the controversial issues? 6(1) What is the best way to read this book? 7(1) What is needed to cope with it? 8(1) Notes 9(1) Social Research and Data Analysis: Demystifying Basic Concepts 10(27) Introduction 10(1) What is the purpose of social research? 10(5) The research problem 11(1) Research objectives 11(2) Research questions 13(1) The role of hypotheses 13(2) What are data? 15(13) Data and social reality 16(1) Types of data 17(3) Forms of data 20(2) Concepts and variables 22(1) Levels of measurement 22(1) Categorical measurement 23(1) Nominal-level measurement 23(1) Ordinal-level measurement 23(1) Metric measurement 24(1) Interval-level measurement 25(1) Ratio-level measurement 25(1) Discrete and continuous measurement 26(1) Review 26(1) Transformations between levels of measurement 27(1) What is data analysis? 28(5) Types of analysis 29(1) Univariate descriptive analysis 29(1) Bivariate descriptive analysis 29(1) Explanatory analysis 30(2) Inferential analysis 32(1) Logics of enquiry and data analysis 33(1) Summary 34(2) Notes 36(1) Data Analysis in Context: Working with Two Data Sets 37(10) Introduction 37(1) Two samples 37(2) Descriptions of the samples 39(1) Student sample 39(1) Resident sample 39(1) Concepts and variables 40(3) Formal definitions 40(1) Operational definitions 40(3) Levels of measurement 43(1) Data reduction 44(1) Notes 45(2) Descriptive Analysis -- Univariate: Looking for Characteristics 47(42) Introduction 47(1) Basic mathematical language 48(3) Univariate descriptive analysis 51(33) Describing distributions 52(1) Frequency counts and distributions 53(1) Nominal categories 53(1) Ordinal categories 54(1) Discrete and grouped data 55(4) Proportions and percentages, ratios and rates 59(1) Proportions 59(1) Percentages 59(2) Ratios 61(1) Rates 62(1) Pictorial representations 62(1) Categorical variables 63(1) Metric variables 64(2) Shapes of frequency distributions: symmetrical, skewed and normal 66(2) Measures of central tendency 68(1) The three Ms 68(1) Mode 68(1) Median 69(2) Mean 71(3) Mean of means 74(1) Comparing the mode, median and mean 75(1) Comparative analysis using percentages and means 76(1) Measures of dispersion 77(1) Categorical data 78(1) Interquartile range 78(1) Percentiles 79(1) Metric data 79(1) Range 79(1) Mean absolute deviation 79(1) Standard deviation 80(3) Variance 83(1) Characteristics of the normal curve 84(3) Summary 87(1) Notes 87(2) Descriptive Analysis -- Bivariate: Looking for Patterns 89(27) Introduction 89(2) Association with nominal-level and ordinal-level variables 91(15) Contingency tables 91(3) Forms of association 94(1) Positive and negative 94(2) Linear and curvilinear 96(1) Symmetrical and asymmetrical 96(1) Measures of association for categorical variables 96(1) Nominal-level variables 97(1) Contingency coefficient 97(2) Standardized contingency coefficient 99(2) Phi 101(1) Cramer's V 101(1) Ordinal-level variables 102(1) Gamma 102(2) Kendall's tau-b 104(1) Other methods for ranked data 105(1) Combinations of categorical and metric variables 105(1) Association with interval-level and ratio-level variables 106(5) Scatter diagrams 106(1) Covariance 107(1) Pearson's r 108(3) Comparing the measures 111(2) Association between categorical and metric variables 113(1) Code metric variable to ordinal categories 113(1) Dichotomize the categorical variable 113(1) Summary 114(1) Notes 114(2) Explanatory Analysis: Looking for Influences 116(43) Introduction 116(1) The use of controlled experiments 117(1) Explanation in cross-sectional research 118(2) Bivariate analysis 120(16) Influence between categorical variables 120(1) Nominal-level variables: lambda 120(4) Ordinal-level variables: Somer's d 124(1) Influence between metric variables: bivariate regression 125(3) Two methods of regression analysis 128(2) Coefficients 130(2) An example 132(1) Points to watch for 133(1) Influence between categorical and metric variables 134(1) Coding to a lower level 134(1) Means analysis 134(1) Dummy variables 135(1) Multivariate analysis 136(20) Trivariate analysis 136(1) Forms of relationships 136(1) Interacting variables 137(1) The logic of trivariate analysis 138(3) Influence between categorical variables 141(1) Three-way contingency tables 141(1) An example 141(4) Other methods 145(1) Influence between metric variables 146(1) Partial correlation 146(1) Multiple regression 146(2) An example 148(2) Collinearity 150(1) Multiple-category dummy variables 150(3) Other methods 153(1) Dependence techniques 153(1) Analysis of variance 154(1) Multiple analysis of variance 154(1) Logistic regression 154(1) Logit logistic regression 154(1) Multiple discriminant analysis 154(1) Structural equation modelling 154(1) Interdependence techniques 155(1) Factor analysis 155(1) Cluster analysis 155(1) Multidimensional scaling 155(1) Summary 156(2) Notes 158(1) Inferential Analysis: From Sample to Population 159(55) Introduction 159(1) Sampling 160(11) Populations and samples 160(1) Probability samples 161(2) Probability theory 163(3) Sample size 166(1) Response rate 167(1) Sampling methods 168(3) Parametric and non-parametric tests 171(1) Inference in univariate descriptive analysis 172(5) Categorical variables 173(2) Metric variables 175(2) Inference in bivariate descriptive analysis 177(28) Testing statistical hypotheses 178(1) Null and alternative hypotheses 179(1) Type I and type II errors 180(1) One-tailed and two-tailed tests 181(1) The process of testing statistical hypotheses 182(1) Testing hypotheses under different conditions 183(2) Some critical issues 185(4) Categorical variables 189(1) Nominal-level data 189(2) Ordinal-level data 191(1) Metric variables 192(1) Comparing means 192(1) Group t test 193(4) Mann--Whitney U test 197(4) Analysis of variance 201(3) Test of significance for Pearson's r 204(1) Inference in explanatory analysis 205(4) Nominal-level data 205(1) Ordinal-level data 206(2) Metric variables 208(1) Bivariate regression 208(1) Multiple regression 209(1) Summary 209(3) Notes 212(2) Data Reduction: Preparing to Answer Research Questions 214(35) Introduction 214(1) Scales and indexes 214(25) Creating scales 215(1) Environmental Worldview scales and subscales 215(1) Pre-testing the items 216(1) Item-to-item correlations 217(1) Item-to-total correlations 217(2) Cronbach's alpha 219(1) Factor analysis 220(18) Willingness to Act scale 238(1) Indexes 239(2) Avoidance of environmentally damaging products 240(1) Support for environmental groups 240(1) Recycling behaviour 240(1) Recoding to different levels of measurement 241(3) Environmental Worldview scales and subscales 242(1) Recycling index 243(1) Age 243(1) Characteristics of the samples 244(2) Summary 246(2) Notes 248(1) Real Data Analysis: Answering Research Questions 249(57) Introduction 249(1) Univariate descriptive analysis 249(8) Environmental Worldview 250(2) Environmentally Responsible Behaviour 252(5) Bivariate descriptive analysis 257(13) Environmental Worldview and Environmentally Responsible Behaviour 258(1) Metric variables 258(2) Categorical variables 260(2) Comparing metric and categorical variables 262(1) Conclusion 263(1) Age, Environmental Worldview and Environmentally Responsible Behaviour 264(1) Metric variables 264(2) Categorical variables 266(2) Gender, Environmental Worldview and Environmentally Responsible Behaviour 268(2) Explanatory analysis 270(33) Bivariate analysis 273(1) Categorical variables 274(2) Categorical and metric variables: means analysis 276(1) Metric variables 277(1) Multivariate analysis 277(1) Categorical variables 278(1) EWVGSC and WILLACT with ERB 279(3) WILLACT, Age and Gender with ERB 282(3) Categorical and metric variables: means analysis 285(1) EWVGSC and WILLACT with ERB 286(1) WILLACT and Gender with ERB 287(5) Metric variables 292(1) Partial correlation 292(1) Multiple regression 293(10) Conclusion 303(1) Notes 304(2) Glossary 306(18) Appendix A: Symbols 324(2) Appendix B: Equations 326(7) Appendix C: SPSS Procedures 333(6) Appendix D: Statistical Tables 339(5) References 344(3) Index 347(6) Summary Chart of Methods 353