Analyzing Social Science Data: 50 Key Problems in Data Analysis

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Overview

In this novel and refreshing textbook, David de Vaus directs students to the core of data analysis. The book is an authoritative guide to the problems facing beginners in the field. Analyzing Social Science Data guides students in: problems with the initial data; problems with the initial variables; how to handle too much data; how to generalize; problems of analyzing single variables; problems examining bivariate relationships; and problems examining multivariate relationships

The book is a tour de force in making data analysis manageable and rewarding for today's undergraduate studying research methods.

'I'm full of admiration for this book. Once again, David de Vaus has come up with a superb book that is well written and organized and which will be a boon to a wide range of students. He has taken a vast array of problems that users of quantitative data analysis procedures are likely to encounter. The selection of issues and problems ... reflects the experience of a true practitioner with a grasp of his field and of the intricacies of the research process. The selection of issues clearly derives also from experience of teaching students how to do research and analyse data....A large number of practitioners will want the book. I was surprised at how much I learned from this. This will be a vital book for the bookshelves of practitioners of the craft of quantitative data analysis' - Alan Bryman, Professor of Social Research, Loughborough University

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Product Details

  • ISBN-13: 9780761959373
  • Publisher: SAGE Publications
  • Publication date: 9/17/2002
  • Pages: 402
  • Product dimensions: 7.10 (w) x 9.26 (h) x 1.18 (d)

Table of Contents

List of figures
List of tables
Preface
Pt. 1 How to Prepare Data for Analysis 1
1 How to Code Data 1
2 How to Code Questions with Multiple Answers 10
3 Can the Respondent's Answers be Relied on? 17
4 How to Check that the Right Thing is being Measured 25
Pt. 2 How to Prepare Variables for Analysis 33
5 How to Deal with Variables with Lots of Categories 33
6 How to Identify and Change the Level of Measurement of Variables 40
7 How to Deal with Questions that Fail to Identify Real Differences Between Cases 48
8 How to Rearrange the Categories of a Variable 54
9 What to do with Gaps in the Data 64
10 What to do with People who 'Don't Know', 'Have no Opinion' or 'Can't Decide' 72
11 How to Tell if the Distribution is Normal 75
12 How to Tell if the Relationship is Linear 82
13 How to Tell if Outlier Cases are a Problem 92
14 What to do if the Required Variable is not Available 99
15 How to Compare Apples with Oranges: Comparing Scores on Different Variables 107
Pt. 3 How to Reduce the Amount to Data to Analyse 113
16 How to Work Out which Variables to Use 113
17 How to Combine Information from a Set of Variables into a Single Measure 117
18 How to Build a Good Likert Scale 124
19 How to Build a Scale Using Factor Analysis 134
Pt. 4 How and When to Generalize 147
20 What does it Mean to Generalize? 147
21 How to Judge the Extent and Effect of Sample Bias 152
22 How to Weight Samples to Adjust for Bias 160
23 What are Tests of Significance? 166
24 Should Tests of Significance be Used? 171
25 What Factors Affect Significance Levels? 175
26 Is the Sample Large Enough to Achieve Statistical Significance? 180
27 Should Confidence Intervals be Used? 187
Pt. 5 How to Analyse a Single Variable 195
28 How to Use Tables Effectively to Display the Distribution of a Single Variable 195
29 How to Use Graphs for Single Variables 203
30 Which Summary Statistics to Use to Describe a Single Variable 218
31 Which Statistic to Use to Generalize about a Single Variable? 228
Pt. 6 How to Analyse Two Variables 237
32 How and When to Use Crosstabulations 237
33 Which Graph to Use 246
34 How to Narrow down the Choice When Selecting Summary Statistics 265
35 How to Interpret a Correlation Coefficient 267
36 Which Correlation? 274
37 How much Impact does a Variable have? 279
38 How to Tell if Groups are Different 288
39 Which Test of Significance? 293
40 How are Confidence Intervals used in Bivariate Analysis? 306
Pt. 7 How to Carry out Multivariate Analysis 315
41 Understanding Bivariate Relationships: The Logic of Elaboration Analysis 315
42 Using Conditional Tables as a Method of Elaboration Analysis 321
43 Using Conditional Correlations for Elaboration Analysis 328
44 Using Partial Tables as a Method of Elaboration Analysis 333
45 Using Partial Correlations for Elaboration Analysis 337
46 What Type of Data are Needed for Multiple Regression? 343
47 How to do a Multiple Regression 353
48 How to Use Non-interval Variables in Multiple Regression 368
49 What does the Multiple Regression Output Mean? 374
50 What Other Multivariate Methods are Available? 383
References 391
Index 393
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