Analyzing Quantitative Data : From Description to Explanation

by Norman Blaikie

• ISBN13: 9780761967590
• ISBN10: 0761967591
• Format: Paperback
• Copyright: 2003-03-06
• Publisher: Sage Publications Ltd

Summary

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

Table of Contents

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