A01=Diana C. Mutz
Accuracy and precision
Americans
Analysis of variance
Author_Diana C. Mutz
Average treatment effect
Behavior
Career
Category=JHBC
Causal inference
Causality
Consideration
Covariate
Criticism
Cyberball
Debriefing
Demography
Disadvantage
Effect size
Effectiveness
Employment
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Estimation
Experiment
External validity
Extrapolation
Factorial experiment
Field experiment
Finding
Health care
Heuristic
Human subject research
Inference
Infidelity
Institutional review board
Internal validity
Interview
Likelihood function
Manipulation checks
Measurement
Methodology
National Science Foundation
Norm (social)
Observational error
Observational study
Operationalization
Opportunism
Participant
Probability
Psychiatry
Quasi-experiment
Random assignment
Randomization
Respondent
Result
Sample Size
Sampling (statistics)
Science
Self-report study
Social desirability bias
Social science
Spurious relationship
Statistical inference
Statistical population
Statistical power
Statistics
Technology
Telephone interview
Textbook
Trade-off
Variable (mathematics)
Virtual world
Visser (novel)
Weighting
Product details
- ISBN 9780691144528
- Weight: 312g
- Dimensions: 152 x 235mm
- Publication Date: 25 Jul 2011
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Paperback
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Population-based survey experiments have become an invaluable tool for social scientists struggling to generalize laboratory-based results, and for survey researchers besieged by uncertainties about causality. Thanks to technological advances in recent years, experiments can now be administered to random samples of the population to which a theory applies. Yet until now, there was no self-contained resource for social scientists seeking a concise and accessible overview of this methodology, its strengths and weaknesses, and the unique challenges it poses for implementation and analysis. Drawing on examples from across the social sciences, this book covers everything you need to know to plan, implement, and analyze the results of population-based survey experiments. But it is more than just a "how to" manual. This lively book challenges conventional wisdom about internal and external validity, showing why strong causal claims need not come at the expense of external validity, and how it is now possible to execute experiments remotely using large-scale population samples.
Designed for social scientists across the disciplines, Population-Based Survey Experiments provides the first complete introduction to this methodology. * Offers the most comprehensive treatment of the subject * Features a wealth of examples and practical advice * Reexamines issues of internal and external validity * Can be used in conjunction with downloadable data from ExperimentCentral.org for design and analysis exercises in the classroom
Diana C. Mutz is the Samuel A. Stouffer Professor of Political Science and Communication at the University of Pennsylvania.
