What basic knowledge and skills do novice researchers in social science require? How can students be helped to over-come `symbol phobia' or `figure blindness'?

This generous and constantly insightful book is designed for social researchers who need to know what procedures to use under what circumstances, in practical research projects. It accomplishes this without requiring an in-depth understanding of statistical theory, but also avoids both trivializing procedures or resorting to `cookbook' techniques. Among the key features of the book are:

- Accessibility

- Organization of the wide, often bewildering array of methods of data analysis into a coherent and user-friendly scheme of classification: types of analysis and levels of measurement

- Demystification - the first chapter unpacks commonly taken-for-granted concepts such as `analysis', `data' and `quantitative'

- Location of methods in real research problems

The book is a triumphant introduction to the theory and practice of quantitative methods. It will quickly establish itself as essential reading for students doing social research throughout the social sciences.

`With this book Norman Blaikie retains his reputation as the leading rapporteur and raconteur of social research methodology. With many other introductory texts, data analysis becomes just an exercise unto itself, and students (sometimes) learn to go through the motions without really knowing why. After working with Blaikie's text, novice researchers will know why quantitative inquiry is important' - Ray Pawson, University of Leeds

Detailed Chapter Contents

Introduction: About theBook 1

Why was it written? 1

Who is it for? 3

What makes it different? 4

What are the controversialissues? 6What is the best way toread this book? 7What is needed to copewith it? 8

Notes 9

1 Social Research and DataAnalysis: Demystifying Basic Concepts 10

Introduction 10

What is the purpose ofsocial research? 10

The research problem 11

Research objectives 11

Research questions 13

The role of hypotheses 13

What are data? 15

Data and social reality 16

Types of data 17

Forms of data 20

Concepts and variables 22

Levels of measurement 22

Categorical measurement 23

Nominal-level measurement23Ordinal-level measurement23

Metric measurement 24

Interval-level measurement25

Ratio-level measurement 25

Discrete and continuousmeasurement 26

Review 26

Transformations betweenlevels of measurement 27

What is data analysis? 28

Types of analysis 29

Univariate descriptiveanalysis 29Bivariate descriptiveanalysis 29

Explanatory analysis 30

Inferential analysis 32

Logics of enquiry and dataanalysis 33

Summary 34

Notes 36

2 Data Analysis in Context:Working with Two Data Sets 37

Introduction 37

Two samples 37

Descriptions of thesamples 39

Student sample 39

Resident sample 39

Concepts and variables 40

Formal definitions 40

Operational definitions 40

Levels of measurement 43

Data reduction 44

Notes 45

3 Descriptive Analysis –Univariate: Looking for Characteristics 47

Introduction 47

Basic mathematicallanguage 48Univariate descriptiveanalysis 51Describing distributions52Frequency counts anddistributions 53

Nominal categories 53

Ordinal categories 54

Discrete and grouped data55Proportions andpercentages, ratios and rates 59

Proportions 59

Percentages 59

Ratios 61

Rates 62

Pictorial representations62

Categorical variables 63

Metric variables 64

Shapes of frequencydistributions: symmetrical,

skewed and normal 66

Measures of centraltendency 68

The three Ms 68

Mode 68

Median 69

Mean 71

Analyzing quantitativedata

viii

Mean of means 74

Comparing the mode, medianand mean 75Comparative analysis usingpercentages and means 76

Measures of dispersion 77

Categorical data 78

Interquartile range 78

Percentiles 79

Metric data 79

Range 79

Mean absolute deviation 79

Standard deviation 80

Variance 83

Characteristics of thenormal curve 84

Summary 87

Notes 87

4 Descriptive Analysis –Bivariate: Looking for Patterns 89

Introduction 89

Association withnominal-level and ordinal-level variables 91

Contingency tables 91

Forms of association 94

Positive and negative 94

Linear and curvilinear 96

Symmetrical andasymmetrical 96Measures of associationfor categorical variables 96

Nominal-level variables 97

Contingency coefficient 97

Standardized contingencycoefficient 99

Phi 101

Cramér’s V 101

Ordinal-level variables102

Gamma 102

Kendall’s tau-b 104

Other methods for rankeddata 105Combinations ofcategorical and metric variables 105Association withinterval-level and ratio-level variables 106

Scatter diagrams 106

Covariance 107

Pearson’s r 108

Comparing the measures 111

Association betweencategorical and metric variables 113Code metric variable toordinal categories 113Dichotomize thecategorical variable 113

Summary 114

Notes 114

Detailed chaptercontents

5 Explanatory Analysis:Looking for Influences 116

Introduction 116

The use of controlledexperiments 117Explanation incross-sectional research 118

Bivariate analysis 120

Influence betweencategorical variables 120Nominal-level variables:lambda 120Ordinal-level variables:Somer’s d 124Influence between metricvariables: bivariate regression 125Two methods of regressionanalysis 128

Coefficients 130

An example 132

Points to watch for 133

Influence betweencategorical and metric variables 134Coding to a lower level134

Means analysis 134

Dummy variables 135

Multivariate analysis 136

Trivariate analysis 136

Forms of relationships 136

Interacting variables 137

The logic of trivariateanalysis 138Influence betweencategorical variables 141Three-way contingencytables 141

An example 141

Other methods 145

Influence between metricvariables 146

Partial correlation 146

Multiple regression 146

An example 148

Collinearity 150

Multiple-category dummyvariables 150

Other methods 153

Dependence techniques 153

Analysis of variance 154

Multiple analysis ofvariance 154

Logistic regression 154

Logit logistic regression154Multiple discriminantanalysis 154Structural equationmodelling 154Interdependence techniques155

Factor analysis 155

Cluster analysis 155

Multidimensional scaling155

Summary 156

Notes 158

Analyzing quantitativedata

x

6 Inferential Analysis:From Sample to Population 159

Introduction 159

Sampling 160

Populations and samples160

Probability samples 161

Probability theory 163

Sample size 166

Response rate 167

Sampling methods 168

Parametric andnon-parametric tests 171Inference in univariatedescriptive analysis 172

Categorical variables 173

Metric variables 175

Inference in bivariatedescriptive analysis 177Testing statisticalhypotheses 178Null and alternativehypotheses 179Type I and type II errors180One-tailed and two-tailedtests 181The process of testingstatistical hypotheses 182Testing hypotheses underdifferent conditions 183

Some critical issues 185

Categorical variables 189

Nominal-level data 189

Ordinal-level data 191

Metric variables 192

Comparing means 192

Group t test193Mann–Whitney U test197

Analysis of variance 201

Test of significance forPearson’s r 204Inference in explanatoryanalysis 205

Nominal-level data 205

Ordinal-level data 206

Metric variables 208

Bivariate regression 208

Multiple regression 209

Summary 209

Notes 212

7 Data Reduction: Preparingto Answer Research Questions 214

Introduction 214

Scales and indexes 214

Creating scales 215

Environmental Worldviewscales and subscales 215

Pre-testing the items 216

Item-to-item correlations217Detailed chaptercontents

Item-to-total correlations217

Cronbach’s alpha 219

Factor analysis 220

Willingness to Act scale238

Indexes 239

Avoidance ofenvironmentally damaging products 240Support for environmentalgroups 240

Recycling behaviour 240

Recoding to differentlevels of measurement 241Environmental Worldviewscales and subscales 242

Recycling index 243

Age 243

Characteristics of thesamples 244

Summary 246

Notes 248

8 Real Data Analysis:Answering Research Questions 249

Introduction 249

Univariate descriptiveanalysis 249Environmental Worldview250EnvironmentallyResponsible Behaviour 252Bivariate descriptiveanalysis 257Environmental Worldviewand Environmentally

Responsible Behaviour 258

Metric variables 258

Categorical variables 260

Comparing metric andcategorical variables 262

Conclusion 263

Age, EnvironmentalWorldview and Environmentally Responsible

Behaviour 264

Metric variables 264

Categorical variables 266

Gender, EnvironmentalWorldview andEnvironmentallyResponsible Behaviour 268

Explanatory analysis 270

Bivariate analysis 273

Categorical variables 274

Categorical and metricvariables: means analysis 276

Metric variables 277

Multivariate analysis 277

Categorical variables 278

EWVGSC and WILLACT withERB 279WILLACT, Age and Genderwith ERB 282Categorical and metricvariables: means analysis 285EWVGSC and WILLACT withERB 286WILLACT and Gender withERB 287Analyzing quantitativedata

xii

Metric variables 292

Partial correlation 292

Multiple regression 293

Conclusion 303

Notes 304

Glossary 306

Appendix A: Symbols 324

Appendix B: Equations 326

Appendix C: SPSSProcedures 333Appendix D: StatisticalTables 339

References 344

Index 347

Summary Chart of Methods353