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Fujimoto, Ken A.; Neugebauer, Sabina R. – Educational and Psychological Measurement, 2020
Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are…
Descriptors: Bayesian Statistics, Item Response Theory, Sample Size, Factor Structure
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Obrecht, Natalie A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2019
Previous research is mixed regarding whether laypeople are sensitive to sample size. Here the author argues that this is in part because sample size sensitivity follows a curvilinear function with decreasing sensitivity as sample size become larger. This functional form reconciles apparent discrepancies in the literature, accounting for results…
Descriptors: Sample Size, Statistical Inference, Numeracy, Cognitive Processes
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Chen, Li-Ting; Andrade, Alejandro; Hanauer, Matthew James – AERA Online Paper Repository, 2017
Single-case design is a repeated-measures research approach for the study of the effect of an intervention, and its importance is increasingly being recognized in education and psychology. We propose a Bayesian approach for estimating intervention effects in SCD. A Bayesian inference does not rely on large sample theories and thus is particularly…
Descriptors: Bayesian Statistics, Research Design, Case Studies, Intervention
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Wainer, Howard – Journal of Educational and Behavioral Statistics, 2016
The usual role of a discussant is to clarify and correct the paper being discussed, but in this case, the author, Howard Wainer, generally agrees with everything David Thissen says in his essay, "Bad Questions: An Essay Involving Item Response Theory." This essay expands on David Thissen's statement that there are typically two principal…
Descriptors: Item Response Theory, Educational Assessment, Sample Size, Statistical Inference
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García-Pérez, Miguel A. – Educational and Psychological Measurement, 2017
Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…
Descriptors: Hypothesis Testing, Statistical Inference, Effect Size, Bayesian Statistics
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Ishak, Noriah Mohd; Abu Bakar, Abu Yazid – World Journal of Education, 2014
Due to statistical analysis, the issue of random sampling is pertinent to any quantitative study. Unlike quantitative study, the elimination of inferential statistical analysis, allows qualitative researchers to be more creative in dealing with sampling issue. Since results from qualitative study cannot be generalized to the bigger population,…
Descriptors: Case Studies, Statistical Analysis, Sampling, Qualitative Research
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Algina, James; Keselman, H. J.; Penfield, Randall D. – Educational and Psychological Measurement, 2010
The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…
Descriptors: Computation, Statistical Analysis, Correlation, Statistical Inference
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Bonett, Douglas G. – Psychological Methods, 2009
L. Wilkinson and the Task Force on Statistical Inference (1999) recommended reporting confidence intervals for measures of effect sizes. If the sample size is too small, the confidence interval may be too wide to provide meaningful information. Recently, K. Kelley and J. R. Rausch (2006) used an iterative approach to computer-generate tables of…
Descriptors: Intervals, Sample Size, Effect Size, Statistical Inference
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Finstuen, Kenn; And Others – Educational and Psychological Measurement, 1994
Computation of a one-way analysis of variance (ANOVA) "F" ratio from descriptive statistics in the absence of raw data is corrected from two sources. Means associated with inferential statistical hypotheses are identified as estimable population parameters. (Author)
Descriptors: Analysis of Variance, Computation, Estimation (Mathematics), Hypothesis Testing
Chou, Tungshan; Wang, Lih-Shing – 1992
P. O. Johnson and J. Neyman (1936) proposed a general linear hypothesis testing procedure for testing the null hypothesis of no treatment difference in the presence of some covariates. This is generally known as the Johnson-Neyman (JN) technique. The need for the hypothesis testing step (often omitted) as originally presented and the…
Descriptors: Computer Simulation, Equations (Mathematics), Foreign Countries, Hypothesis Testing
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Kiger, Jack E.; Wise, Kenneth – College and Research Libraries, 1993
Describes the of attribute sampling to estimate characteristics of library collections and operations. The nature of statistical sampling and making a statistical inference are covered, and examples from library situations are given. Tables of determination of sample size and evaluation of results are included. (Contains six references.) (EAM)
Descriptors: Expectancy Tables, Library Administration, Library Collections, Methods