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Beath, Ken J. – Research Synthesis Methods, 2014
When performing a meta-analysis unexplained variation above that predicted by within study variation is usually modeled by a random effect. However, in some cases, this is not sufficient to explain all the variation because of outlier or unusual studies. A previously described method is to define an outlier as a study requiring a higher random…
Descriptors: Mixed Methods Research, Robustness (Statistics), Meta Analysis, Prediction
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Manolov, Rumen; Solanas, Antonio – Psychological Methods, 2012
There is currently a considerable diversity of quantitative measures available for summarizing the results in single-case studies. Given that the interpretation of some of them is difficult due to the lack of established benchmarks, the current article proposes an approach for obtaining further numerical evidence on the importance of the results,…
Descriptors: Sampling, Probability, Statistical Significance, Case Studies
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Phillips, Gary W. – Applied Measurement in Education, 2015
This article proposes that sampling design effects have potentially huge unrecognized impacts on the results reported by large-scale district and state assessments in the United States. When design effects are unrecognized and unaccounted for they lead to underestimating the sampling error in item and test statistics. Underestimating the sampling…
Descriptors: State Programs, Sampling, Research Design, Error of Measurement
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Qian, Jiahe – ETS Research Report Series, 2006
Weighting and variance estimation are two statistical issues involved in survey data analysis for large-scale assessment programs such as the Higher Education Information and Communication Technology (ICT) Literacy Assessment. Because survey data are always acquired by probability sampling, to draw unbiased or almost unbiased inferences for the…
Descriptors: Weighted Scores, Sampling, Statistical Analysis, Higher Education
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Rust, Keith F.; Johnson, Eugene G. – Journal of Educational Statistics, 1992
Procedures for obtaining student samples for the National Assessment of Educational Progress (NAEP) and deriving survey weights for analysis of survey data are described. Sample designs are economically and operationally feasible, and weighting procedures result in increased precision of estimates as they account for the probabilities of student…
Descriptors: Data Analysis, Educational Assessment, Elementary Secondary Education, Estimation (Mathematics)
Folsom, Ralph E., Jr. – 1975
This memorandum demonstrates a variance components methodology for partitioning the overall design effect (D) for a ratio mean into stratification (S), unequal weighting (W), and clustering (C) effects, so that D = WSC. In section 2, a sample selection scheme modeled after the National Longitudinal Study of the High School Class of 1972 (NKS)…
Descriptors: Analysis of Variance, Cluster Analysis, Followup Studies, Graduate Surveys
Moore, R. Paul – 1975
The report traces the activities which led to the development of the adjusted weights--beginning with the basic National Longitudinal Study design, the base-year weight calculations, and the resurvey procedures. Next, the report describes the first follow-up weight calculations (including unadjusted student weights), nonresponse adjustment…
Descriptors: Attrition (Research Studies), Followup Studies, Graduate Surveys, High School Graduates