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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Stratified Sampling Method01:16

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling Plans01:23

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Stratified sampling using cluster analysis: a sample selection strategy for improved generalizations from

Elizabeth Tipton1

  • 1Department of Human Development, Teachers College, Columbia University, NY, USA.

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|March 21, 2014
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Summary
This summary is machine-generated.

This study introduces a balanced-sampling framework to improve experimental generalizability, especially when random sampling is not feasible. The new method enhances covariate balance and reduces errors, even with high nonresponse rates in large-scale studies.

Keywords:
cluster analysisexperimental designexternal validitymodel-based samplingstratified samplingtreatment effect heterogeneity

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Area of Science:

  • Experimental Design
  • Statistical Inference
  • Educational Research

Background:

  • Ensuring experimental findings generalize to larger populations is crucial.
  • Generalizability is challenging with heterogeneous treatment effects and non-random sampling, common in large educational studies.

Purpose of the Study:

  • Introduce a model-based balanced-sampling framework to enhance generalizability.
  • Develop methods robust to model misspecification.
  • Propose a novel sample selection technique.

Main Methods:

  • Utilize cluster analysis to stratify units into homogenous groups.
  • Employ distance rankings for sample selection within strata.
  • Develop a model-based framework for balanced sampling.

Main Results:

  • The new method demonstrated improved covariate balance compared to the original sample.
  • Fewer coverage errors were observed using the proposed sampling technique.
  • The method proved effective even under conditions of high nonresponse.

Conclusions:

  • The balanced-sampling framework offers benefits for improving generalizability in experimental research.
  • Discussion includes the method's advantages and limitations for future application.