Topic
Cluster sampling
About: Cluster sampling is a research topic. Over the lifetime, 2232 publications have been published within this topic receiving 88552 citations.
Papers published on a yearly basis
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Papers
TL;DR: Seven major types of sampling for observational studies of social behavior have been found in the literature and the major strengths and weaknesses of each method are pointed out.
Abstract: Seven major types of sampling for observational studies of social behavior have been found in the literature. These methods differ considerably in their suitability for providing unbiased data of various kinds. Below is a summary of the major recommended uses of each technique: In this paper, I have tried to point out the major strengths and weaknesses of each sampling method. Some methods are intrinsically biased with respect to many variables, others to fewer. In choosing a sampling method the main question is whether the procedure results in a biased sample of the variables under study. A method can produce a biased sample directly, as a result of intrinsic bias with respect to a study variable, or secondarily due to some degree of dependence (correlation) between the study variable and a directly-biased variable. In order to choose a sampling technique, the observer needs to consider carefully the characteristics of behavior and social interactions that are relevant to the study population and the research questions at hand. In most studies one will not have adequate empirical knowledge of the dependencies between relevant variables. Under the circumstances, the observer should avoid intrinsic biases to whatever extent possible, in particular those that direcly affect the variables under study. Finally, it will often be possible to use more than one sampling method in a study. Such samples can be taken successively or, under favorable conditions, even concurrently. For example, we have found it possible to take Instantaneous Samples of the identities and distances of nearest neighbors of a focal individual at five or ten minute intervals during Focal-Animal (behavior) Samples on that individual. Often during Focal-Animal Sampling one can also record All Occurrences of Some Behaviors, for the whole social group, for categories of conspicuous behavior, such as predation, intergroup contact, drinking, and so on. The extent to which concurrent multiple sampling is feasible will depend very much on the behavior categories and rate of occurrence, the observational conditions, etc. Where feasible, such multiple sampling can greatly aid in the efficient use of research time.
13,789 citations
TL;DR: It is concluded that the choice of the techniques (Convenience Sampling and Purposive Sampling) depends on the nature and type of the research.
Abstract: This article studied and compared the two nonprobability sampling techniques namely, Convenience Sampling and Purposive Sampling. Convenience Sampling and Purposive Sampling are Nonprobability Sampling Techniques that a researcher uses to choose a sample of subjects/units from a population. Although, Nonprobability sampling has a lot of limitations due to the subjective nature in choosing the sample and thus it is not good representative of the population, but it is useful especially when randomization is impossible like when the population is very large. It can be useful when the researcher has limited resources, time and workforce. It can also be used when the research does not aim to generate results that will be used to create generalizations pertaining to the entire population. Therefore, there is a need to use nonprobability sampling techniques. The aim of this study is to compare among the two nonrandom sampling techniques in order to know whether one technique is better or useful than the other. Different articles were reviewed to compare between Convenience Sampling and Purposive Sampling and it is concluded that the choice of the techniques (Convenience Sampling and Purposive Sampling) depends on the nature and type of the research.
8,712 citations
TL;DR: A new variant of chain-referral sampling, respondent-driven sampling, is introduced that employs a dual system of structured incentives to overcome some of the deficiencies of such samples and discusses how respondent- driven sampling can improve both network sampling and ethnographic investigation.
Abstract: A population is “hidden” when no sampling frame exists and public acknowledgment of membership in the population is potentially threatening. Accessing such populations is difficult because standard probability sampling methods produce low response rates and responses that lack candor. Existing procedures for sampling these populations, including snowball and other chain-referral samples, the key-informant approach, and targeted sampling, introduce well-documented biases into their samples. This paper introduces a new variant of chain-referral sampling, respondent-driven sampling, that employs a dual system of structured incentives to overcome some of the deficiencies of such samples. A theoretic analysis, drawing on both Markov-chain theory and the theory of biased networks, shows that this procedure can reduce the biases generally associated with chain-referral methods. The analysis includes a proof showing that even though sampling begins with an arbitrarily chosen set of initial subjects, as do most chain-referral samples, the composition of the ultimate sample is wholly independent of those initial subjects. The analysis also includes a theoretic specification of the conditions under which the procedure yields unbiased samples. Empirical results, based on surveys of 277 active drug injectors in Connecticut, support these conclusions. Finally, the conclusion discusses how respondent- driven sampling can improve both network sampling and ethnographic 44 investigation.
4,473 citations
Book•
22 May 1997TL;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.
3,748 citations
Posted Content•
TL;DR: In this paper, the residuals are sorted and the observation is located in the residual corresponding to the quantile in question, taking into account weights if they are applied, and the square root of the sum of the weights is calculated.
Abstract: We first sort the residuals and locate the observation in the residuals corresponding to the quantile in question, taking into account weights if they are applied. We then calculate wn, the square root of the sum of the weights. Unweighted data is equivalent to weighted data where each observation has weight 1, resulting in wn p n. For analytically weighted data, the weights are rescaled so that the sum of the weights is the sum of the observations, resulting in p n again. For frequency weighted data, wn literally is the square of the sum of the weights.
3,406 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 126 |
| 2024 | 204 |
| 2023 | 531 |
| 2022 | 961 |
| 2021 | 71 |
| 2020 | 93 |