In this novel and refreshing textbook David de Vaus directs students to the core of data analysis. The book is a lean and authoritative guide to the problems facing beginner's in the field.
In this novel and refreshing textbook David de Vaus directs students to the core of data analysis. The book is a lean and authoritative guide to the problems facing beginner's in the field.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
David De Vaus is Associate Professor of Sociology at La Trobe University, Melbourne. He is the author of Surveys in Social Research and Research Design in Social Research. He is an international authority in the field of social research.
Inhaltsangabe
PART ONE: HOW TO PREPARE DATA FOR ANALYSIS How to Code Data How to Code Questions with Multiple Answers Can the Respondent s Answers be Relied on? How to Check that the Right Thing is Being Measured TWO: HOW TO PREPARE VARIABLES FOR ANALYSIS How to Deal with Variables with Lots of Categories How to Identify and Change the Level of Measurement of Variables How to Deal with Questions that Fail to Identify Real Differences Between Cases How to Rearrange the Categories of a Variable What to do with Gaps in the Data What to do with People who Don t Know , Have no Opinion or Can t Decide How to Tell if the Distribution is Normal How to Tell if the Relationship is Linear How to Tell if Outlier Cases are a Problem What to do if the Required Variable is not Available How to Compare Apples with Oranges Comparing Scores on Different Variables PART THREE: HOW TO REDUCE THE AMOUNT OF DATA TO ANALYSE How to Work Out Which Variables to Use How to Combine Information from a Set of Variables into a Single Measure How to Build a Good Likert Scale How to Build a Scale Using Factor Analysis PART FOUR: HOW AND WHEN TO GENERALISE What Does it Mean to Generalize? How to Judge the Extent and Effect of Sample Bias How to Weight Samples to Adjust for Bias What are Tests of Significance? Should Tests of Significance be Used? What Factors Affect Significance Levels? Is the Sample Large Enough to Achieve Statistical Significance? Should Confidence Intervals be Used? PART FIVE: HOW TO ANALYSE A SINGLE VARIABLE How to Use Tables Effectively to Display the Distribution of a Single Variable How to Use Graphs for Single Variables Which Summary Statistics to Use to Describe a Single Variable Which Statistics to Use to Generalise about a Single Variable PART SIX: HOW TO ANALYSE TWO VARIABLES How and When to Use Crosstabulations Which Graph to Use How to Narrow down the Choice When Selecting Summary Statistics How to Interpret a Correlation Coefficient Which Correlation? How much Impact Does a Variable Have? How to Tell if Groups are Different Which Test of Significance? How are Confidence Intervals used in Bivariate Analysis? PART SEVEN: HOW TO CARRY OUT MULTIVARIATE ANALYSIS Understanding Bivariate Relationships The Logic of Elaboration Analysis Using Conditional Tables as a Method of Elaboration Analysis Using Conditional Correlations for Elaboration Analysis Using Partial Tables as a Method of Elaboration Analysis Using Partial Correlations for Elaboration Analysis What Type of Data are Needed for Multiple Regression? How to do a Multiple Regression How to Use Non-interval Variables in Multiple Regression What Does the Multiple Regression Output Mean? What Other Multivariate Methods are Availabe?
PART ONE: HOW TO PREPARE DATA FOR ANALYSIS How to Code Data How to Code Questions with Multiple Answers Can the Respondent s Answers be Relied on? How to Check that the Right Thing is Being Measured TWO: HOW TO PREPARE VARIABLES FOR ANALYSIS How to Deal with Variables with Lots of Categories How to Identify and Change the Level of Measurement of Variables How to Deal with Questions that Fail to Identify Real Differences Between Cases How to Rearrange the Categories of a Variable What to do with Gaps in the Data What to do with People who Don t Know , Have no Opinion or Can t Decide How to Tell if the Distribution is Normal How to Tell if the Relationship is Linear How to Tell if Outlier Cases are a Problem What to do if the Required Variable is not Available How to Compare Apples with Oranges Comparing Scores on Different Variables PART THREE: HOW TO REDUCE THE AMOUNT OF DATA TO ANALYSE How to Work Out Which Variables to Use How to Combine Information from a Set of Variables into a Single Measure How to Build a Good Likert Scale How to Build a Scale Using Factor Analysis PART FOUR: HOW AND WHEN TO GENERALISE What Does it Mean to Generalize? How to Judge the Extent and Effect of Sample Bias How to Weight Samples to Adjust for Bias What are Tests of Significance? Should Tests of Significance be Used? What Factors Affect Significance Levels? Is the Sample Large Enough to Achieve Statistical Significance? Should Confidence Intervals be Used? PART FIVE: HOW TO ANALYSE A SINGLE VARIABLE How to Use Tables Effectively to Display the Distribution of a Single Variable How to Use Graphs for Single Variables Which Summary Statistics to Use to Describe a Single Variable Which Statistics to Use to Generalise about a Single Variable PART SIX: HOW TO ANALYSE TWO VARIABLES How and When to Use Crosstabulations Which Graph to Use How to Narrow down the Choice When Selecting Summary Statistics How to Interpret a Correlation Coefficient Which Correlation? How much Impact Does a Variable Have? How to Tell if Groups are Different Which Test of Significance? How are Confidence Intervals used in Bivariate Analysis? PART SEVEN: HOW TO CARRY OUT MULTIVARIATE ANALYSIS Understanding Bivariate Relationships The Logic of Elaboration Analysis Using Conditional Tables as a Method of Elaboration Analysis Using Conditional Correlations for Elaboration Analysis Using Partial Tables as a Method of Elaboration Analysis Using Partial Correlations for Elaboration Analysis What Type of Data are Needed for Multiple Regression? How to do a Multiple Regression How to Use Non-interval Variables in Multiple Regression What Does the Multiple Regression Output Mean? What Other Multivariate Methods are Availabe?
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