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Tipton, Elizabeth; Sullivan, Kate; Hedges, Larry; Vaden-Kiernan, Michael; Borman, Geoffrey; Caverly, Sarah – Society for Research on Educational Effectiveness, 2011
In this paper the authors present a new method for sample selection for scale-up experiments. This method uses propensity score matching methods to create a sample that is similar in composition to a well-defined generalization population. The method they present is flexible and practical in the sense that it identifies units to be targeted for…
Descriptors: Sampling, Selection, Research Methodology, Reading Programs
Valliant, Richard; Dever, Jill A.; Kreuter, Frauke – Springer, 2013
Survey sampling is fundamentally an applied field. The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least…
Descriptors: Sampling, Surveys, Computer Software, College Students
Meyer, Ilan H.; Wilson, Patrick A. – Journal of Counseling Psychology, 2009
Sampling has been the single most influential component of conducting research with lesbian, gay, and bisexual (LGB) populations. Poor sampling designs can result in biased results that will mislead other researchers, policymakers, and practitioners. Investigators wishing to study LGB populations must therefore devote significant energy and…
Descriptors: Research Design, Sampling, Homosexuality, Probability
Sanchez-Meca, Julio; Marin-Martinez, Fulgencio – Psychological Methods, 2008
One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account…
Descriptors: Intervals, Monte Carlo Methods, Meta Analysis, Effect Size
Bartlett, James E., II; Bartlett, Michelle E.; Reio, Thomas G., Jr. – Delta Pi Epsilon Journal, 2008
This research examined the issue of nonresponse bias and how it was reported in nonexperimental quantitative research published in the "Delta Pi Epsilon Journal" between 1995 and 2004. Through content analysis, 85 articles consisting of 91 separate samples were examined. In 72.5% of the cases, possible nonresponse bias was not examined in the…
Descriptors: Content Analysis, Probability, Response Rates (Questionnaires), Business Education
Teddlie, Charles; Yu, Fen – Journal of Mixed Methods Research, 2007
This article presents a discussion of mixed methods (MM) sampling techniques. MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs. Several issues germane to MM sampling are presented including the differences between probability and…
Descriptors: Sampling, Probability, Research Methodology, Research Design

Cox, Caryl; Mouw, John T. – Educational Studies in Mathematics, 1992
The explicit, experimental introduction of a series of logical inconsistencies is described and recommended as a means of disrupting the faulty logic and, thereby, enhancing the use of more appropriate probabilistic reasoning by graduate students enrolled in an introductory inferential statistics course. (14 references) (JJK)
Descriptors: Heuristics, Higher Education, Logical Thinking, Mathematics Education
Royeen, Charlotte Brasic; Fortune, Jim Carlton – 1987
This paper identifies typical sampling problems, including improper application of the Central Limit Theorem, that are associated with the probability-based sampling procedures currently used in clinical psychology research. It then presents two alternative research designs, the theory validation model and the extended case study model, which…
Descriptors: Case Studies, Clinical Psychology, Medical Research, Models

Nichols, James D. – Bioscience, 1992
Describes advances in capture-recapture modeling, including the biologically motivated emphasis on model-based hypothesis testing and the development of models for spatially stratified populations. Discusses the severe problems associated with count statistics reflecting unknown sampling fractions. (67 references) (KR)
Descriptors: Ecological Factors, Estimation (Mathematics), Higher Education, Mathematical Models