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Stratified sampling

About: Stratified sampling is a research topic. Over the lifetime, 5476 publications have been published within this topic receiving 97043 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Abstract: Two types of sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies. These plans are shown to be improvements over simple random sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.

8,328 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a discussion of mixed methods sampling techniques, which combines well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs.
Abstract: This article presents a discussion of mixed methods (MM) sampling techniques. MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs. Several issues germane to MM sampling are presented including the differences between probability and purposive sampling and the probability-mixed-purposive sampling continuum. Four MM sampling prototypes are introduced: basic MM sampling strategies, sequential MM sampling, concurrent MM sampling, and multilevel MM sampling. Examples of each of these techniques are given as illustrations of how researchers actually generate MM samples. Finally, eight guidelines for MM sampling are presented.

3,256 citations

Book
22 May 1997
TL;DR: This book presents the principles of Estimation for Finite Populations and Important Sampling Designs and a Broader View of Errors in Surveys: Nonsampling Errors and Extensions of Probability Sampling Theory.
Abstract: PART I: Principles of Estimation for Finite Populations and Important Sampling Designs: Survey Sampling in Theory and Practice. Basic Ideas in Estimation from Probability Samples. Unbiased Estimation for Element Sampling Designs. Unbiased Estimation for Cluster Sampling and Sampling in Two or More Stages. Introduction to More Complex Estimation Problems.- PART II: Estimation through Linear Modeling, Using Auxiliary Variables: The Regression Estimator. Regression Estimators for Element Sampling Designs. Regression Estimators for Cluster Sampling and Two-Stage Sampling.- PART III: Further Questions in Design and Analysis of Surveys: Two-Phase Sampling. Estimation for Domains. Variance Estimation. Searching for Optimal Sampling Designs. Further Statistical Techniques for Survey Data.- PART IV: A Broader View of Errors in Surveys: Nonsampling Errors and Extensions of Probability Sampling Theory. Nonresponse. Measurement Errors. Quality Declarations for Survey Data.- Appendix A - D.- References.

3,197 citations

Journal ArticleDOI
TL;DR: A four-point approach to sampling in qualitative interview-based research is presented and critically discussed in this article, which integrates theory and process for the following: (1) defining a sample universe, by way of specifying inclusion and exclusion criteria for potential participation; (2) deciding upon a sample size, through the conjoint consideration of epistemological and practical concerns; (3) selecting a sampling strategy, such as random sampling, convenience sampling, stratified sampling, cell sampling, quota sampling or a single-case selection strategy; and (4) sample sourcing, which includes
Abstract: Sampling is central to the practice of qualitative methods, but compared with data collection and analysis its processes have been discussed relatively little. A four-point approach to sampling in qualitative interview-based research is presented and critically discussed in this article, which integrates theory and process for the following: (1) defining a sample universe, by way of specifying inclusion and exclusion criteria for potential participation; (2) deciding upon a sample size, through the conjoint consideration of epistemological and practical concerns; (3) selecting a sampling strategy, such as random sampling, convenience sampling, stratified sampling, cell sampling, quota sampling or a single-case selection strategy; and (4) sample sourcing, which includes matters of advertising, incentivising, avoidance of bias, and ethical concerns pertaining to informed consent. The extent to which these four concerns are met and made explicit in a qualitative study has implications for its coherence, tran...

2,286 citations

Book
01 Jan 1987
TL;DR: In this paper, the authors proposed three-stage sampling: simple random sampling, two stage sampling and three stage sampling, and two-stage and double sampling, respectively, to estimate the mean and variance from censored data sets.
Abstract: Sampling Environmental Populations. Environmental Sampling Design. Simple Random Sampling. Stratified Random Sampling. Two-Stage Sampling. Compositing and Three-Stage Sampling. Systematic Sampling. Double Sampling. Locating Hot Spots. Quantiles, Proportions, and Means. Skewed Distributions and Goodness-of-Fit Tests. Characterizing Lognormal Populations. Estimating the Mean and Variance from Censored Data Sets. Outlier Detection and Control Charts. Detecting and Estimating Trends. Trends and Seasonality. Comparing Populations. Appendices. Symbols. Glossary. Bibliography. Index.

2,253 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023818
20221,873
2021215
2020300
2019306
2018317