Jeremy Freese

Deborah Carr

Elizabeth Heger Boyle

Benjamin Cornwell

Shelley Correll

Robert Crosnoe

Mary C Waters

Written by a team of internationally renowned sociologists with experience in both the field and the classroom, The Art and Science of Social Research offers authoritative and balanced coverage of the full range of methods used to study the social world. The authors highlight the challenges of investigating the unpredictable topic of human lives while providing insights into what really happens in the field, the laboratory, and the survey call center.

First Edition

The Art and Science of Social Research    

Edward Miguel

Garret Christensen

Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research.

How to Do Open Science

Transparent and Reproducible Social Science Research

J. Scott Long

After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.

Regression Models for Categorical Dependent Variables Using Stata, Second Edition

Jeremy Freese

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