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Tipton, Elizabeth – American Journal of Evaluation, 2022
Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to…
Descriptors: Sampling, Sample Size, Selection, Randomized Controlled Trials
Riley, Richard D.; Collins, Gary S.; Hattle, Miriam; Whittle, Rebecca; Ensor, Joie – Research Synthesis Methods, 2023
Before embarking on an individual participant data meta-analysis (IPDMA) project, researchers should consider the power of their planned IPDMA conditional on the studies promising their IPD and their characteristics. Such power estimates help inform whether the IPDMA project is worth the time and funding investment, before IPD are collected. Here,…
Descriptors: Computation, Meta Analysis, Participant Characteristics, Data
Lee, Hollylynne S.; Starling, Tina T.; Gonzalez, Marggie D. – Mathematics Teacher, 2014
Research shows that students often struggle with understanding empirical sampling distributions. Using hands-on and technology models and simulations of problems generated by real data help students begin to make connections between repeated sampling, sample size, distribution, variation, and center. A task to assist teachers in implementing…
Descriptors: Sampling, Sample Size, Statistical Distributions, Simulation
Jozwiak, Katarzyna; Moerbeek, Mirjam – Journal of Educational and Behavioral Statistics, 2012
Studies on event occurrence aim to investigate if and when subjects experience a particular event. The timing of events may be measured continuously using thin precise units or discretely using time periods. The latter metric of time is often used in social science research and the generalized linear model (GLM) is an appropriate model for data…
Descriptors: Statistical Analysis, Time, Sample Size, Social Science Research
Stack, Sue; Watson, Jane – Australian Mathematics Teacher, 2013
There is considerable research on the difficulties students have in conceptualising individual concepts of probability and statistics (see for example, Bryant & Nunes, 2012; Jones, 2005). The unit of work developed for the action research project described in this article is specifically designed to address some of these in order to help…
Descriptors: Secondary School Mathematics, Grade 10, Mathematical Concepts, Probability
Derryberry, DeWayne R.; Schou, Sue B.; Conover, W. J. – Journal of Statistics Education, 2010
Students learn to examine the distributional assumptions implicit in the usual t-tests and associated confidence intervals, but are rarely shown what to do when those assumptions are grossly violated. Three data sets are presented. Each data set involves a different distributional anomaly and each illustrates the use of a different nonparametric…
Descriptors: Nonparametric Statistics, Hypothesis Testing, Instruction, Statistical Distributions
Dawson, Robert – Journal of Statistics Education, 2011
It is common to consider Tukey's schematic ("full") boxplot as an informal test for the existence of outliers. While the procedure is useful, it should be used with caution, as at least 30% of samples from a normally-distributed population of any size will be flagged as containing an outlier, while for small samples (N less than 10) even extreme…
Descriptors: Spreadsheets, Educational Technology, Simulation, Mathematics Activities
Lewis, Charla P. – 1999
The sampling distribution is a common source of misuse and misunderstanding in the study of statistics. The sampling distribution, underlying distribution, and the Central Limit Theorem are all interconnected in defining and explaining the proper use of the sampling distribution of various statistics. The sampling distribution of a statistic is…
Descriptors: Estimation (Mathematics), Probability, Sample Size, Sampling
Rennie, Kimberly M. – 1997
This paper explains the underlying assumptions of the sampling distribution and its role in significance testing. To compute statistical significance, estimates of population parameters must be obtained so that only one sampling distribution is defined. A sampling distribution is the underlying distribution of a statistic. Sampling distributions…
Descriptors: Analysis of Variance, Estimation (Mathematics), Sample Size, Sampling

Headrick, Todd C.; Sawilosky, Shlomo S. – Psychometrika, 1999
Proposes a procedure for generating multivariate nonnormal distributions. The procedure, an extension of the Fleishman power method (A. Fleishman, 1978), generates the average value of intercorrelations much closer to population parameters than competing procedures for skewed and heavy tailed distributions and small sample sizes. Reports Monte…
Descriptors: Correlation, Equations (Mathematics), Monte Carlo Methods, Multivariate Analysis
von Davier, Alina A.; Kong, Nan – Journal of Educational and Behavioral Statistics, 2005
This article describes a new, unified framework for linear equating in a non-equivalent groups anchor test (NEAT) design. The authors focus on three methods for linear equating in the NEAT design--Tucker, Levine observed-score, and chain--and develop a common parameterization that shows that each particular equating method is a special case of the…
Descriptors: Equations (Mathematics), Sample Size, Statistical Distributions, Error of Measurement
Cook, Linda L.; Petersen, Nancy S. – 1986
This paper examines how various equating methods are affected by: (1) sampling error; (2) sample characteristics; and (3) characteristics of anchor test items. It reviews empirical studies that investigated the invariance of equating transformations, and it discusses empirical and simulation studies that focus on how the properties of anchor tests…
Descriptors: Educational Research, Equated Scores, Error of Measurement, Evaluation Methods