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Liang, Xinya; Kamata, Akihito; Li, Ji – Educational and Psychological Measurement, 2020

One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance…

Descriptors: Bayesian Statistics, Randomized Controlled Trials, Effect Size, Sampling

Shi, Dexin; Maydeu-Olivares, Alberto – Educational and Psychological Measurement, 2020

We examined the effect of estimation methods, maximum likelihood (ML), unweighted least squares (ULS), and diagonally weighted least squares (DWLS), on three population SEM (structural equation modeling) fit indices: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the standardized root mean square residual…

Descriptors: Structural Equation Models, Computation, Maximum Likelihood Statistics, Least Squares Statistics

Yang, Yanyun; Xia, Yan – Educational and Psychological Measurement, 2019

When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a…

Descriptors: Scores, Sample Size, Bayesian Statistics, Item Analysis

Konstantopoulos, Spyros; Li, Wei; Miller, Shazia; van der Ploeg, Arie – Educational and Psychological Measurement, 2019

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the…

Descriptors: Regression (Statistics), Statistical Analysis, Educational Research, Social Science Research

DiStefano, Christine; McDaniel, Heather L.; Zhang, Liyun; Shi, Dexin; Jiang, Zhehan – Educational and Psychological Measurement, 2019

A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and…

Descriptors: Factor Analysis, Effect Size, Data, Sample Size

Koziol, Natalie A.; Bovaird, James A. – Educational and Psychological Measurement, 2018

Evaluations of measurement invariance provide essential construct validity evidence--a prerequisite for seeking meaning in psychological and educational research and ensuring fair testing procedures in high-stakes settings. However, the quality of such evidence is partly dependent on the validity of the resulting statistical conclusions. Type I or…

Descriptors: Computation, Tests, Error of Measurement, Comparative Analysis

Huang, Francis L. – Educational and Psychological Measurement, 2018

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials…

Descriptors: Multivariate Analysis, Sampling, Statistical Inference, Data Analysis

Cho, Sun-Joo; Preacher, Kristopher J. – Educational and Psychological Measurement, 2016

Multilevel modeling (MLM) is frequently used to detect cluster-level group differences in cluster randomized trial and observational studies. Group differences on the outcomes (posttest scores) are detected by controlling for the covariate (pretest scores) as a proxy variable for unobserved factors that predict future attributes. The pretest and…

Descriptors: Error of Measurement, Error Correction, Multivariate Analysis, Hierarchical Linear Modeling

Stanley, Leanne M.; Edwards, Michael C. – Educational and Psychological Measurement, 2016

The purpose of this article is to highlight the distinction between the reliability of test scores and the fit of psychometric measurement models, reminding readers why it is important to consider both when evaluating whether test scores are valid for a proposed interpretation and/or use. It is often the case that an investigator judges both the…

Descriptors: Test Reliability, Goodness of Fit, Scores, Patients

Harring, Jeffrey R. – Educational and Psychological Measurement, 2014

Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…

Descriptors: Regression (Statistics), Models, Statistical Analysis, Maximum Likelihood Statistics

Dimitrov, Dimiter M.; Atanasov, Dimitar V. – Educational and Psychological Measurement, 2012

Many models of cognitive diagnosis, including the "least squares distance model" (LSDM), work under the "conjunctive" assumption that a correct item response occurs when all latent attributes required by the item are correctly performed. This article proposes a "disjunctive" version of the LSDM under which the correct item response occurs when "at…

Descriptors: Least Squares Statistics, Models, Item Response Theory, Cognitive Measurement

Hardin, Andrew M.; Chang, Jerry Cha-Jan; Fuller, Mark A.; Torkzadeh, Gholamreza – Educational and Psychological Measurement, 2011

The use of causal indicators to formatively measure latent constructs appears to be on the rise, despite what appears to be a troubling lack of consistency in their application. Scholars in any discipline are responsible not only for advancing theoretical knowledge in their domain of study but also for addressing methodological issues that…

Descriptors: Structural Equation Models, Measurement, Statistical Data, Meta Analysis

Ding, Cody S.; Davison, Mark L. – Educational and Psychological Measurement, 2010

Akaike's information criterion is suggested as a tool for evaluating fit and dimensionality in metric multidimensional scaling that uses least squares methods of estimation. This criterion combines the least squares loss function with the number of estimated parameters. Numerical examples are presented. The results from analyses of both simulation…

Descriptors: Multidimensional Scaling, Least Squares Statistics, Criteria, Computation

Granberg-Rademacker, J. Scott – Educational and Psychological Measurement, 2010

The extensive use of survey instruments in the social sciences has long created debate and concern about validity of outcomes, especially among instruments that gather ordinal-level data. Ordinal-level survey measurement of concepts that could be measured at the interval or ratio level produce errors because respondents are forced to truncate or…

Descriptors: Intervals, Rating Scales, Surveys, Markov Processes

Weitzman, R. A. – Educational and Psychological Measurement, 2009

Building on the Kelley and Gulliksen versions of classical test theory, this article shows that a logistic model having only a single item parameter can account for varying item discrimination, as well as difficulty, by using item-test correlations to adjust incorrect-correct (0-1) item responses prior to an initial model fit. The fit occurs…

Descriptors: Item Response Theory, Test Items, Difficulty Level, Test Bias