2019
DOI: 10.1016/j.knosys.2018.09.007
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A stratified sampling based clustering algorithm for large-scale data

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Cited by 50 publications
(29 citation statements)
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“…It overcomes the shortcomings of the partial split-half method and is the most commonly used reliability analysis method in the social science research field. If the answer to the question in the questionnaire is not comprehensive and does not conform to the convention, it will be invalid (Zhao et al, 2019;Liu and Chen, 2021).…”
Section: Survey Designmentioning
confidence: 99%
“…It overcomes the shortcomings of the partial split-half method and is the most commonly used reliability analysis method in the social science research field. If the answer to the question in the questionnaire is not comprehensive and does not conform to the convention, it will be invalid (Zhao et al, 2019;Liu and Chen, 2021).…”
Section: Survey Designmentioning
confidence: 99%
“…Research has identified the impact of housing and job choices on the urban structure in surveys using a representative sample (Acheampong, 2018). A cluster sampling (Zhao et al, 2019) is used to classify large-scale data and the ability of each sample category to access the sample.…”
Section: Methodology For Evaluation Of Residential Plot Selection In mentioning
confidence: 99%
“…Our manual study is composed of the following phases: Phase I: We use stratified random sampling, with a 95% confidence level and 5% confidence interval, to acquire 368 samples from the test annotation changes identified by our RefactoringMiner extension. We adopted stratified random sampling to sample each studied system independently to reduce sampling error when a sub-population within the overall population varies [30]. Phase II: To create the taxonomy for the test annotations, we first classified the changes at a high level based on the annotation type (e.g., @Ignore).…”
Section: Rq3: Why Do Developers Change Test Annotations?mentioning
confidence: 99%