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Stratified Sampling Using Cluster Analysis: A Sample Selection Strategy for Improved Generalizations From Experiments
Elizabeth Tipton
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Corresponding author for this work
Statistics and Data Science
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Article
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peer-review
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Scopus citations
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Dive into the research topics of 'Stratified Sampling Using Cluster Analysis: A Sample Selection Strategy for Improved Generalizations From Experiments'. Together they form a unique fingerprint.
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Keyphrases
Cluster Analysis
100%
Stratified Sampling
100%
Sample Selection Strategy
100%
Design of Experiments
50%
Treatment Effect
50%
Non-response
50%
Developing Method
50%
Sampling Framework
50%
Coverage Error
50%
Misspecification
50%
Educational Experiment
50%
Actual Samples
50%
Balanced Sampling
50%
Distance Ranking
50%
Arts and Humanities
Limitations
100%
Design of Experiments
100%
Experiment Design
100%
Reanalysis
100%
Educational experiment
100%
Random Sampling
100%
Mathematics
Stratified Sampling
100%
Cluster Analysis
100%
Treatment Effect
50%
Generalizability
50%
Selected Sample
50%
Misspecification
50%
Nonresponse
50%
Experiment Design
50%
Social Sciences
Sample selection
100%
Cluster Analysis
100%
Nonresponse
50%
Generalizable
50%