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 1Why 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? 8Notes 9
1 Social Research and DataAnalysis: Demystifying Basic Concepts 10Introduction 10
What is the purpose ofsocial research? 10The 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 measurement23Metric measurement 24
Interval-level measurement25Ratio-level measurement 25
Discrete and continuousmeasurement 26Review 26
Transformations betweenlevels of measurement 27What is data analysis? 28
Types of analysis 29
Univariate descriptiveanalysis 29Bivariate descriptiveanalysis 29Explanatory analysis 30
Inferential analysis 32
Logics of enquiry and dataanalysis 33Summary 34
Notes 36
2 Data Analysis in Context:Working with Two Data Sets 37Introduction 37
Two samples 37
Descriptions of thesamples 39Student 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 47Introduction 47
Basic mathematicallanguage 48Univariate descriptiveanalysis 51Describing distributions52Frequency counts anddistributions 53Nominal categories 53
Ordinal categories 54
Discrete and grouped data55Proportions andpercentages, ratios and rates 59Proportions 59
Percentages 59
Ratios 61
Rates 62
Pictorial representations62Categorical variables 63
Metric variables 64
Shapes of frequencydistributions: symmetrical,skewed and normal 66
Measures of centraltendency 68The three Ms 68
Mode 68
Median 69
Mean 71
Analyzing quantitativedataviii
Mean of means 74
Comparing the mode, medianand mean 75Comparative analysis usingpercentages and means 76Measures 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 84Summary 87
Notes 87
4 Descriptive Analysis –Bivariate: Looking for Patterns 89Introduction 89
Association withnominal-level and ordinal-level variables 91Contingency tables 91
Forms of association 94
Positive and negative 94
Linear and curvilinear 96
Symmetrical andasymmetrical 96Measures of associationfor categorical variables 96Nominal-level variables 97
Contingency coefficient 97
Standardized contingencycoefficient 99Phi 101
Cramér’s V 101
Ordinal-level variables102Gamma 102
Kendall’s tau-b 104
Other methods for rankeddata 105Combinations ofcategorical and metric variables 105Association withinterval-level and ratio-level variables 106Scatter 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 113Summary 114
Notes 114
Detailed chaptercontents5 Explanatory Analysis:Looking for Influences 116
Introduction 116
The use of controlledexperiments 117Explanation incross-sectional research 118Bivariate analysis 120
Influence betweencategorical variables 120Nominal-level variables:lambda 120Ordinal-level variables:Somer’s d 124Influence between metricvariables: bivariate regression 125Two methods of regressionanalysis 128Coefficients 130
An example 132
Points to watch for 133
Influence betweencategorical and metric variables 134Coding to a lower level134Means 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 141An example 141
Other methods 145
Influence between metricvariables 146Partial correlation 146
Multiple regression 146
An example 148
Collinearity 150
Multiple-category dummyvariables 150Other methods 153
Dependence techniques 153
Analysis of variance 154
Multiple analysis ofvariance 154Logistic regression 154
Logit logistic regression154Multiple discriminantanalysis 154Structural equationmodelling 154Interdependence techniques155Factor analysis 155
Cluster analysis 155
Multidimensional scaling155Summary 156
Notes 158
Analyzing quantitativedatax
6 Inferential Analysis:From Sample to Population 159Introduction 159
Sampling 160
Populations and samples160Probability samples 161
Probability theory 163
Sample size 166
Response rate 167
Sampling methods 168
Parametric andnon-parametric tests 171Inference in univariatedescriptive analysis 172Categorical 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 183Some 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 test197Analysis of variance 201
Test of significance forPearson’s r 204Inference in explanatoryanalysis 205Nominal-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 214Introduction 214
Scales and indexes 214
Creating scales 215
Environmental Worldviewscales and subscales 215Pre-testing the items 216
Item-to-item correlations217Detailed chaptercontentsItem-to-total correlations217
Cronbach’s alpha 219
Factor analysis 220
Willingness to Act scale238Indexes 239
Avoidance ofenvironmentally damaging products 240Support for environmentalgroups 240Recycling behaviour 240
Recoding to differentlevels of measurement 241Environmental Worldviewscales and subscales 242Recycling index 243
Age 243
Characteristics of thesamples 244Summary 246
Notes 248
8 Real Data Analysis:Answering Research Questions 249Introduction 249
Univariate descriptiveanalysis 249Environmental Worldview250EnvironmentallyResponsible Behaviour 252Bivariate descriptiveanalysis 257Environmental Worldviewand EnvironmentallyResponsible Behaviour 258
Metric variables 258
Categorical variables 260
Comparing metric andcategorical variables 262Conclusion 263
Age, EnvironmentalWorldview and Environmentally ResponsibleBehaviour 264
Metric variables 264
Categorical variables 266
Gender, EnvironmentalWorldview andEnvironmentallyResponsible Behaviour 268Explanatory analysis 270
Bivariate analysis 273
Categorical variables 274
Categorical and metricvariables: means analysis 276Metric 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 quantitativedataxii
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 339References 344
Index 347
Summary Chart of Methods353