Chapter
At What Point Does Coding Occur in the Course of Your Research Project?
Codes and the Phenomena We Study
A Graphic Depiction of the Relation of Coding to Analysis
Examples of Coding and Analysis
Part I. Coding Data—by Design
Suggestions for Further Reading
Chapter 1. Coding Survey Data
An Example: Pitfalls When Constructing a Survey
What Methods to Use to Construct an Effective Questionnaire
Coding and Measuring Respondents’ Answers to the Questions
Conclusion: Where to Find Analysis Guidelines for Surveys in This Book
Suggestions for Further Reading
Chapter 2. Coding Interview Data
Goals: What Do You Seek When Asking Questions?
Your Role: What Should Your Part Be in the Dialogue?
Samples: How Many Interviews and with Whom?
Questions: When Do You Ask What Kinds of Questions?
Modes: How Do You Communicate with Interviewees?
Observations: What Is Important That Isn’t Said?
Records: What Methods Do You Use to Preserve the Dialogue?
Tools: When Should You Use Computers to Code Your Data?
Getting Help: When to Use Member Checks and Multiple Coders
Suggestions for Further Reading
Chapter 3. Coding Experimental Data
Coding and Measurement Issues for All Experimental Designs
Coding and Measurement Issues That Vary by Type of Experimental Design
Conclusion: Where in This Book to Find Guidelines for Analyzing Experimental Data
Suggestions for Further Reading
Chapter 4. Coding Data from Naturalistic and Participant Observations
Introduction to Observational Research
Conclusions and Tips for Completing an Observational Study
Appendix 4.1. Example of a Site Visit Protocol
Suggestions for Further Reading
Chapter 5. Coding Archival Data: Literature Reviews, Big Data, and New Media
Reviews of the Research Literature
Coding Data from the Web, Including New Media
Conclusion: Coding Data from Archival, Web,and New Media Sources
Suggestions for Further Reading
Part II. Analysis and Interpretation of Quantitative Data
Conceptual and Terminological Housekeeping: Theory, Model, Hypothesis, Concept, Variable
Suggestions for Further Reading and a Note on Software
Chapter 6. Describing, Exploring, and Visualizing Your Data
What Is Meant by Descriptive Statistics?
Overview of the Main Types of Descriptive Statistics and Their Uses
What Descriptive Statistics to Use to Prepare for Further Analyses
When to Use Correlations as Descriptive Statistics
When and Why to Make the Normal Curve Your Point of Reference
When Can You Use Descriptive Statistics Substantively?
When to Use Descriptive Statistics Preparatory to Applying Missing Data Procedures
Suggestions for Further Reading
Chapter 7. What Methods of Statistical Inference to Use When
Null Hypothesis Significance Testing
Which Statistical Tests to Use for What
When to Use Confidence Intervals
When to Report Power and Precision of Your Estimates
When Should You Use Distribution-Free, Nonparametric Significance Tests?
When to Use the Bootstrap and Other Resampling Methods
When to Use Bayesian Methods
Which Approach to Statistical Inference Should You Take?
The “Silent Killer” of Valid Inferences: Missing Data
Appendix 7.1. Examples of Output of Significance Tests
Suggestions for Further Reading
Chapter 8. What Associational Statistics to Use When
When to Use Correlations to Analyze Data
When to Use Regression Analysis
What to Do When Your Dependent Variables Are Categorical
Summary: Which Associational Methods Work Best for What Sorts of Data and Problems?
The Most Important Question: When to Include Which Variables
Conclusion: Relations among Variables to Investigate Using Regression Analysis
Suggestions for Further Reading
Chapter 9. Advanced Associational Methods
Factor Analysis—Exploratory and Confirmatory
Structural Equation Modeling
Suggestions for Further Reading
Chapter 10. Model Building and Selection
When Can You Benefit from Building a Model or Constructing a Theory?
When to Use a Multimodel Approach
Conclusion: A Research Agenda
Suggestions for Further Reading
Part III. Analysis and Interpretation of Qualitative and Combined/Mixed Data
Chapter 11. Inductive Analysis of Qualitative Data: Ethnographic Approaches and Grounded Theory
The Foundations of Inductive Social Research in Ethnographic Fieldwork
Grounded Theory: An Inductive Approach to Theory Building
Suggestions for Further Reading
Chapter 12. Deductive Analyses of Qualitative Data: Comparative Case Studies and Qualitative Comparative Analysis
Case Studies and Deductive Analyses
When to Do a Single-Case Analysis: Discovering, Describing, and Explaining Causal Links
When to Conduct Small-N Comparative Case Studies
When to Conduct Analyses with an Intermediate N of Cases
Suggestions for Further Reading
Chapter 13. Coding and Analyzing Data from Combined and Mixed Designs
Coding and Analysis Considerations for Deductive and Inductive Designs
Coding Considerations for Sequential Analysis Approaches
Data Transformation/Data Merging in Combined Designs
Suggestions for Further Reading
Chapter 14. Conclusion: Common Themes and Diverse Choices
Selecting the Right Analyses for Your Data
：Quantitative, Qualitative, and Mixed Methods
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