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Introduction to Survey Sampling (Quantitative Applications in the Social Sciences)
A concise, practical guide to designing and analyzing probability sample surveys, balancing sampling theory with the real-world problems of frames, nonresponse, and complex designs.
A profile of this book is on the way.
What it’s about
Graham Kalton's Introduction to Survey Sampling distills the essential techniques of probability sampling into a highly readable text for researchers who use surveys but are not statisticians. Beginning with simple random sampling, it builds systematically through systematic sampling, stratification, clustering, multistage and probability-proportional-to-size designs, then confronts the messy realities of imperfect sampling frames, nonresponse, weighting, and the estimation of sampling errors from complex designs. Two worked examples (a national face-to-face survey and a telephone RDD survey) and a discussion of nonprobability and quota sampling show how the pieces combine in practice. The book teaches the reader to weigh precision against cost, to recognize when standard formulas mislead, and to anticipate the practical pitfalls that can ruin an otherwise well-conceived study.
The through-line
- Who it’s for
- A social-science researcher or survey practitioner who wants to draw valid, efficient samples and produce trustworthy population estimates.
- The problem
- Designing a sample that yields precise, unbiased estimates within budget while coping with imperfect frames and nonresponse. Feeling that sampling is an intimidating technical black box best left to statisticians.
- The plan
- Define the target and survey populations carefully.
- Choose an appropriate probability design (SRS, systematic, stratified, cluster, multistage, PPS).
- Build and assess the sampling frame, handling missing, clustered, blank, and duplicate listings.
- Minimize and compensate for nonresponse.
- Apply weights and compute sampling errors appropriate to the design.
- The payoff
- Surveys produce defensible, precise estimates with quantified uncertainty. · The researcher confidently navigates frame and nonresponse problems and complex designs. · Resources are used efficiently, matching precision to need and budget.
See our guide
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Additional reading
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences · Cohen, J., & Cohen, P.
The book recommends this text for its excellent, accessible narrative discussions on regression, particularly for explanatory purposes in the social sciences.
- Classical and Modern Regression with Applications · Myers, R.
Cited as an excellent resource for its modern approach to regression analysis, especially its strong treatment of regression diagnostics for checking assumptions and identifying influential data points.
- Multivariate Statistical Methods in Behavioral Research · Bock, R. D.
This text is frequently cited by the author for more advanced or technical explanations of concepts in MANOVA, repeated measures, and step-down analysis.
- The Analysis of Covariance and Alternatives · Huitema, B.
Recommended as a very comprehensive and thorough text for readers wishing to gain a deeper understanding of Analysis of Covariance (ANCOVA).
- Structural Equations with Latent Variables · Bollen, K. A.
The guest-authored chapter on SEM heavily references this book as a key source for understanding fundamental concepts like model identification.
- Hierarchical Linear Models: Applications and Data Analysis Methods · Raudenbush, S., & Bryk, A.
The guest-authored chapter on Hierarchical Linear Modeling (HLM) cites this as the seminal text on the topic and the basis for the HLM software.
- Applied Discriminant Analysis · Huberty, C.
The chapter on Discriminant Analysis introduces this book as an excellent, current, and very thorough resource on the topic.
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan · John Kruschke
Recommended for reading 'During this book' to get additional information and a different perspective on Bayesian statistics and modeling.
- Regression and Other Stories · Andrew Gelman, Jennifer Hill, & Aki Vehtari
Recommended for reading 'During this book' as a supplementary text for a broader understanding of regression and Bayesian modeling.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan · Richard McElreath
Recommended for reading 'After this book' as a next step for readers who have mastered the concepts and wish to deepen their understanding of Bayesian modeling.