Dahlia K. Remler & Gregg G. Van Ryzin 
Research Methods in Practice [PDF ebook] 
Strategies for Description and Causation

Thoroughly updated to reflect changes in both research and methods, this
Third Edition of Remler and Van Ryzin’s innovative, standard-setting text is imbued with a deep commitment to making social and policy research methods accessible and meaningful.
Research Methods in Practice: Strategies for Description and Causation motivates readers to examine the logic and limits of social science research from academic journals and government reports. A central theme of causation versus description runs through the text, emphasizing the idea that causal research is essential to understanding the origins of social problems and their potential solutions. Readers will find excitement in the research experience as the best hope for improving the world in which we live, while also acknowledging the trade-offs and uncertainties in real-world research.

Table des matières

Preface

Acknowledgments

About the Authors

PART I: FOUNDATIONS

Chapter 1. Research in the Real World

Learning Objectives

Do Methods Matter?

Research, Policy, and Practice

Evidence Can Mislead

What Is Research?

Descriptive and Causal Research

Epistemology: Ways of Knowing

Approaching Research From Different Angles

Ethics of Research

Conclusion: The Road Ahead

Exercises

Chapter 2. Theory, Models, and Research Questions

Learning Objectives

Community Policing Comes to Portland

What Is a Theory?

What Is a Model?

Logic Models: Mechanisms of Programs

Alternative Perspectives on Theory in Social Research

How to Find and Focus Research Questions

Conclusion: Theories Are Practical

Chapter 3. Qualitative Research

Learning Objectives

Fighting Malaria in Kenya

What Is Qualitative Research?

Existing Qualitative Data

Qualitative Interviews

Focus Groups

Qualitative Observation

Participant Observation and Ethnography

Case Study Research

Qualitative Data Analysis

The Qualitative-Quantitative Debate

Ethics in Qualitative Research

Conclusion: Matching Methods to Questions

Exercises

PART II: STRATEGIES FOR DESCRIPTION

Chapter 4. Measurement

Learning Objectives

The U.S. Poverty Measure

What Is Measurement?

Conceptualization

Operationalization

Validity

Criterion-Related Validity

Measurement Error

Reliability

Validity and Reliability in Qualitative Research

Levels of Measurement

Measurement in the Real World: Trade-offs and Choices

Conclusion: Measurement Matters

Exercises

Chapter 5. Sampling

Learning Objectives

Gauging the Fallout From Hurricane Katrina

Generalizability

Basic Sampling Concepts

Problems and Biases in Sampling

Nonprobability Sampling

Random (Probability) Sampling

Sampling Distributions, Standard Errors, and Confidence Intervals

Sampling in Practice

Sampling and Generalizability: A Summary

Exercises

Chapter 6. Secondary Data

Learning Objectives

Tracking a Global Pandemic

Quantitative Data Forms and Structures

Administrative Records

Aggregate Data Tables

Public Use Microdata

Secondary Qualitative Data

Big Data

Linking Data

Some Limitations of Secondary Data

Conclusion

Exercises

Chapter 7. Surveys and Other Primary Data

Learning Objectives

Taking the Nation’s Economic Pulse

When Should You Do a Survey?

Steps in the Survey Research Process

Modes of Survey Data Collection

Crafting a Questionnaire

Ethics of Survey Research

Other Ways to Collect Primary Data

Conclusion

Exercises

PART III: STATISTICAL TOOLS AND INTERPRETATIONS

Chapter 8. Making Sense of the Numbers

Learning Objectives

“Last Weekend I Walked Eight”

Units, Rates, and Ratios

Statistics Starting Point: Variables in a Data Set

Distributions

Measures of Center: Mean and Median

Measures of Spread and Variation

Relationships Between Categorical Variables

Relationships Between Quantitative Variables: Scatterplots and Correlation

Simple Regression: Best-Fit Straight Line

Practical Significance

Statistical Software

Conclusion: Tools for Description and Causation

Exercises

Chapter 9. Making Sense of Inferential Statistics

Learning Objectives

But Is It Significant?

Statistical Inference: What’s It Good For?

The Sampling Distribution: Foundation of Statistical Inference

Confidence Intervals

Significance Tests

Statistical Significance, Practical Significance, and Power

Issues and Extensions of Statistical Inference

Conclusion

Exercises

Chapter 10. Making Sense of Multivariate Statistics

Learning Objectives

Multiple Regression: The Basics

Inference for Regression

Categorical Independent Variables

Interactions in Regression

Functional Form and Transformations in Regression

Categorical Variables as Dependent Variables in Regression

Which Statistical Methods Can I Use?

Other Multivariate Methods

Conclusion

Exercises

PART IV: STRATEGIES FOR CAUSATION

Chapter 11. Causation

Learning Objectives

Family Dinners and Teenage Substance Abuse

Alternative Explanations of a Correlation

Causal Mechanisms

Evidence of Causation: Some Critical Clues

Self-Selection and Endogeneity

The Counterfactual Definition of Causation

Experimentation and Exogeneity: Making Things Happen

Conclusion: Tools to Probe Causation

Exercises

Chapter 12. Observational Studies

Learning Objectives

Private Versus Public Schools

What Is an Observational Study?

Control Variables

Matching

Control Variables: An Empirical Example

How to Choose Control Variables

Epidemiological Approaches to Observational Studies

Conclusion: Observational Studies in Perspective

Exercises

Chapter 13. Using Regression to Estimate Causal Effects

Learning Objectives

Cigarette Taxes and Smoking

From Stratification to Multiple Regression

Does Greenery Affect Birth Outcomes?

Further Topics in Regression for Estimating Causal Effects

Control Variables With Exogenous Independent Variables: The Gender Earnings Gap

Other Multivariate Techniques for Observational Studies

Conclusion: A Widely Used Strategy, With Drawbacks

Exercises

Chapter 14. Randomized Experiments

Learning Objectives

Time Limits on Welfare

Random Assignment: Creating Statistical Equivalence

The Logic of Randomized Experiments: Exogeneity Revisited

The Settings of Randomized Experiments

Generalizability of Randomized Experiments

Variations on the Design of Experiments

Artifacts in Experiments

Analysis of Randomized Experiments

Ethics of Randomized Experiments

Qualitative Methods and Randomized Experiments

Conclusion: A Gold Standard, With Limitations

Exercises

Chapter 15. Natural and Quasi Experiments

Learning Objectives

A Casino Benefits the Mental Health of Cherokee Children

What Are Natural and Quasi Experiments?

Internal Validity of Natural and Quasi Experiments

Generalizability of Natural and Quasi Experiments

Types of Natural and Quasi Experimental Studies

Difference-in-Differences Strategy

Instrumental Variables and Regression Discontinuity

Regression Discontinuity

Ethics of Quasi and Natural Experiments

Conclusion

Exercises

PART V: CONTEXT AND COMMUNICATION

Chapter 16. The Politics, Production, and Ethics of Research

Learning Objectives

Risking Your Baby’s Health

From Research to Policy

The Production of Research

Making Research Ethical

Making Research Open and Transparent

Conclusion

Exercises

Chapter 17. How to Find, Review, and Present Research

Learning Objectives

Where to Find Research

How to Search for Studies

How to Write a Literature Review

How to Communicate Your Own Research

How to Publish Your Research

Conclusion

Exercises

Glossary

References

Index

A propos de l’auteur

Gregg G. Van Ryzin is Professor at the School of Public Affairs and Administration, Rutgers University—Newark. He received his BA in geography from Columbia University and his Ph D in psychology from the City University of New York. During his doctoral training, he worked as a planner for a nonprofit housing and community development organization in New York City, and he completed his dissertation on low income housing for the elderly in Detroit. He next worked in Washington, D.C., for ICF Inc. and later Westat, Inc. on surveys and program evaluations for the U.S. Department of Housing and Urban Development and other federal agencies. In 1995, he joined the faculty of the School of Public Affairs, Baruch College, where he directed their Survey Research Unit for 8 years. In that role, he helped develop and direct the New York City Community Health Survey, a large-scale behavioral health survey for the city’s health department, and also played a key role in shaping and conducting the city’s survey of satisfaction with government services. He has spent time in Madrid, collaborating with researchers there on the analysis of surveys about public attitudes toward Spanish government policy. Gregg has published many scholarly articles on housing and welfare programs, survey and evaluation methods, and public opinion about government services and institutions. His work has appeared in the International Review of Administrative Sciences, the Journal of Policy Analysis and Management, the Journal of Public Administration Research and Theory, the Journal of Urban Affairs, Nonprofit and Voluntary Sector Quarterly, Public Administration Review, Public Management Review, Public Performance and Management Review, Urban Affairs Review, and other journals.Gregg lives in New York City with his wife, Ada (a history professor at NYU), and their daughters Alina and Lucia. They enjoy life in their Greenwich Village neighborhood, escaping on occasion to Spain, Miami, Maine, Cuba, and other interesting places in the world.