Analyzing Social Science Data: 50 Key Problems in Data Analysis

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SAGE, Jun 1, 2002 - 402 pages
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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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Contents

List of Figures
2
How to Code Questions with Multiple Answers
10
Can the Respondents Answers be Relied on?
17
How to Check that the Right Thing is being Measured
25
How to Prepare Variables for Analysis
33
variable has too many categories
34
How to Identify and Change the Level
40
How to Deal with Questions that Fail to Identify Real
48
Should Confidence Intervals be Used?
187
How to Analyse a Single Variable
195
How to Use Graphs for Single Variables
203
Which Summary Statistics to Use to Describe
218
Which Statistic to Use to Generalize about
228
How to Analyse Two Variables
237
Which Graph to Use
246
exnuptial children by age group percentages
250

How to Rearrange the Categories of a Variable
54
What to do with Gaps in the Data
64
What to do with People who Dont Know Have
72
How to Tell if the Relationship is Linear
82
two variables
83
How to Tell if Outlier Cases are a Problem
92
What to do if the Required Variable is not Available
99
Comparing
107
How to Reduce the Amount of Data to Analyse
113
How to Build a Good Likert Scale
124
How to Build a Scale Using Factor Analysis
134
How and When to Generalize
147
How to Weight Samples to Adjust for Bias
160
What are Tests of Significance?
166
What Factors Affect Significance Levels?
175
Is the Sample Large Enough to Achieve Statistical
180
same parameters as Table 26 1
184
How to Narrow down the Choice When
265
Which Correlation?
274
How to Tell if Groups are Different
288
Which Test of Significance?
293
How are Confidence Intervals used in Bivariate Analysis?
306
How to Carry out Multivariate Analysis
315
Using Conditional Tables as a Method
321
Using Conditional Correlations for Elaboration Analysis
328
Using Partial Correlations for Elaboration Analysis
337
What Type of Data are Needed for Multiple Regression?
343
forms of heteroscedasticity
349
How to do a Multiple Regression
353
How to Use Noninterval Variables in Multiple Regression
368
What does the Multiple Regression Output Mean?
374
What Other Multivariate Methods are Available?
383
References
391
Copyright

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About the author (2002)

De Vaus is with the La Trobe University.

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