Topic
Multistage sampling
About: Multistage sampling is a research topic. Over the lifetime, 741 publications have been published within this topic receiving 26441 citations.
Papers published on a yearly basis
1 / 2
Papers
Book•
29 Jun 2009
TL;DR: In this paper, the authors discuss the use of sample surveys in estimating the proportion of the population to the sample population in a complex sample survey, and present several sample survey design and estimation methods.
Abstract: Tables. Boxes. Figures. Getting Files from the Wiley ftp and Internet Sites. List of Data Sites Provides on Web Site. Preface to the Fourth Edition. Part 1: Basic Concepts. 1. Use of Sample Surveys. 2. The Population and the Sample. Part 2: Major Sampling Designs and Estimation Procedures. 3. Simple Random Sampling. 4. Systematic Sampling. 5. Stratification and Stratified Random Sampling. 6. Stratified Random Sampling: Further Issues. 7. Ratio Estimation. 8. Cluster Sampling: Introduction and Overview. 9. Simple One-Stage Cluster Sampling. 10. Two-Stage Cluster Sampling: Clusters Sampled with Equal Probability. 11. Cluster Sampling in Which Clusters Are Sampled with Unequal Probability: Probability Proportional to Size Sampling. 12. Variance Estimation in Complex Sample Surveys. Part 3: Selected Topics in Sample Survey Methodology. 13. Nonresponse and Missing Data in Sample Surveys. 14. Selected Topics in Sample Design and Estimation Methodology. 15. Telephone Survey Sampling (Michael W. Link and Mansour Fahimi). 16. Constructing the Survey Weights (Paul P. Biemer and Sharon L. Christ). 17. Strategies for Design-Based Analysis of Sample Survey Data. Appendix. Answers to Selected Exercises. Index.
1,687 citations
TL;DR: It is shown that Monte Carlo confidence intervals and Bayesian credible intervals closely reflect the sampling distribution of reliability estimates under most conditions and that small cluster size can lead to overestimates of reliability at the between level of analysis.
Abstract: Scales with varying degrees of measurement reliability are often used in the context of multistage sampling, where variance exists at multiple levels of analysis (e.g., individual and group). Because methodological guidance on assessing and reporting reliability at multiple levels of analysis is currently lacking, we discuss the importance of examining level-specific reliability. We present a simulation study and an applied example showing different methods for estimating multilevel reliability using multilevel confirmatory factor analysis and provide supporting Mplus program code. We conclude that (a) single-level estimates will not reflect a scale's actual reliability unless reliability is identical at each level of analysis, (b) 2-level alpha and composite reliability (omega) perform relatively well in most settings, (c) estimates of maximal reliability (H) were more biased when estimated using multilevel data than either alpha or omega, and (d) small cluster size can lead to overestimates of reliability at the between level of analysis. We also show that Monte Carlo confidence intervals and Bayesian credible intervals closely reflect the sampling distribution of reliability estimates under most conditions. We discuss the estimation of credible intervals using Mplus and provide R code for computing Monte Carlo confidence intervals.
1,278 citations
1,062 citations
Book•
1 Jan 1983
TL;DR: Introduction Simple Random Sampling Systematic Sampling Stratification Cluster and Multistage Sampling Probability Proportional to Size Sampling Other Probability Designs Sampling Frames Nonresponse Survey Analysis Sample Size Two Examples Nonprobability Sampling Concluding Remarks
Abstract: Introduction Simple Random Sampling Systematic Sampling Stratification Cluster and Multistage Sampling Probability Proportional to Size Sampling Other Probability Designs Sampling Frames Nonresponse Survey Analysis Sample Size Two Examples Nonprobability Sampling Concluding Remarks
1,047 citations
TL;DR: A typology of sampling designs for qualitative researchers is provided, which represents a body of sampling strategies that facilitate credible comparisons of two or more different subgroups that are extracted from the same levels of study.
Abstract: The purpose of this paper is to provide a typology of sampling designs for qualitative researchers. We introduce the following sampling strategies: (a) parallel sampling designs, which represent a body of sampling strategies that facilitate credible comparisons of two or more different subgroups that are extracted from the same levels of study; (b) nested sampling designs, which are sampling strategies that facilitate credible comparisons of two or more members of the same subgroup, wherein one or more members of the subgroup represent a sub-sample of the full sample; and (c) multilevel sampling designs, which represent sampling strategies that facilitate credible comparisons of two or more subgroups that are extracted from different levels of study. Key Words: Qualitative Research, Sampling Designs, Random Sampling, Purposive Sampling, and Sample Size
894 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 34 |
| 2024 | 55 |
| 2023 | 87 |
| 2022 | 175 |
| 2021 | 24 |
| 2020 | 23 |