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Solomon, Benjamin G.; Forsberg, Ole J. – School Psychology Quarterly, 2017
Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading…
Descriptors: Bayesian Statistics, Regression (Statistics), Least Squares Statistics, Evaluation Methods
Apaloo, Francis – Online Submission, 2013
A key issue in quasi-experimental studies and also with many evaluations which required a treatment effects (i.e. a control or experimental group) design is selection bias (Shadish el at 2002). Selection bias refers to the selection of individuals, groups or data for analysis such that proper randomization is not achieved, thereby ensuring that…
Descriptors: Quasiexperimental Design, Probability, Scores, Least Squares Statistics
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Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying – Research Quarterly for Exercise and Sport, 2011
Monte Carlo methods can be used in data analytic situations (e.g., validity studies) to make decisions about sample size and to estimate power. The purpose of using Monte Carlo methods in a validity study is to improve the methodological approach within a study where the primary focus is on construct validity issues and not on advancing…
Descriptors: Sample Size, Monte Carlo Methods, Construct Validity, Validity
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Chun, So Yeon; Shapiro, Alexander – Multivariate Behavioral Research, 2009
The noncentral chi-square approximation of the distribution of the likelihood ratio (LR) test statistic is a critical part of the methodology in structural equation modeling. Recently, it was argued by some authors that in certain situations normal distributions may give a better approximation of the distribution of the LR test statistic. The main…
Descriptors: Statistical Analysis, Structural Equation Models, Validity, Monte Carlo Methods
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Shieh, Gwowen – Multivariate Behavioral Research, 2009
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…
Descriptors: Social Science Research, Sample Size, Monte Carlo Methods, Validity
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Bang, Jung W.; Schumacker, Randall E.; Schlieve, Paul L. – Educational and Psychological Measurement, 1998
The normality of number distributions generated by various random-number generators were studied, focusing on when the random-number generator reached a normal distribution and at what sample size. Findings suggest the steps that should be followed when using a random-number generator in a Monte Carlo simulation. (SLD)
Descriptors: Monte Carlo Methods, Sample Size, Simulation, Statistical Distributions
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Rasmussen, Jeffrey Lee – Applied Psychological Measurement, 1988
The performance was studied of five small-sample statistics--by F. M. Lord, W. Kristof, Q. McNemar, R. A. Forsyth and L. S. Feldt, and J. P. Braden--that test whether two variables measure the same trait except for measurement error. Effects of non-normality were investigated. The McNemar statistic was most powerful. (TJH)
Descriptors: Error of Measurement, Monte Carlo Methods, Psychometrics, Sample Size
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Zientek, Linda Reichwein; Capraro, Mary Margaret; Capraro, Robert M. – Educational Researcher, 2008
The authors of this article examine the analytic and reporting features of research articles cited in "Studying Teacher Education: The Report of the AERA Panel on Research and Teacher Education" (Cochran-Smith & Zeichner, 2005b) that used quantitative reporting practices. Their purpose was to help to identify reporting practices that can be…
Descriptors: Preservice Teacher Education, Social Science Research, Intervals, Social Sciences
Fan, Xitao – 1994
This paper empirically and systematically assessed the performance of bootstrap resampling procedure as it was applied to a regression model. Parameter estimates from Monte Carlo experiments (repeated sampling from population) and bootstrap experiments (repeated resampling from one original bootstrap sample) were generated and compared. Sample…
Descriptors: Estimation (Mathematics), Monte Carlo Methods, Regression (Statistics), Sample Size