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Zhou, Todd; Jiao, Hong – Educational and Psychological Measurement, 2023
Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study…
Descriptors: Cheating, Measurement, Artificial Intelligence, Algorithms
Gonzalez, Oscar – Educational and Psychological Measurement, 2023
When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the…
Descriptors: Classification, Accuracy, Intervals, Probability
Franco-Martínez, Alicia; Alvarado, Jesús M.; Sorrel, Miguel A. – Educational and Psychological Measurement, 2023
A sample suffers range restriction (RR) when its variance is reduced comparing with its population variance and, in turn, it fails representing such population. If the RR occurs over the latent factor, not directly over the observed variable, the researcher deals with an indirect RR, common when using convenience samples. This work explores how…
Descriptors: Factor Analysis, Factor Structure, Scores, Sampling
Lee, Sooyong; Han, Suhwa; Choi, Seung W. – Educational and Psychological Measurement, 2022
Response data containing an excessive number of zeros are referred to as zero-inflated data. When differential item functioning (DIF) detection is of interest, zero-inflation can attenuate DIF effects in the total sample and lead to underdetection of DIF items. The current study presents a DIF detection procedure for response data with excess…
Descriptors: Test Bias, Monte Carlo Methods, Simulation, Models
Pavlov, Goran; Maydeu-Olivares, Alberto; Shi, Dexin – Educational and Psychological Measurement, 2021
We examine the accuracy of p values obtained using the asymptotic mean and variance (MV) correction to the distribution of the sample standardized root mean squared residual (SRMR) proposed by Maydeu-Olivares to assess the exact fit of SEM models. In a simulation study, we found that under normality, the MV-corrected SRMR statistic provides…
Descriptors: Structural Equation Models, Goodness of Fit, Simulation, Error of Measurement
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
Trafimow, David; Wang, Tonghui; Wang, Cong – Educational and Psychological Measurement, 2019
Two recent publications in "Educational and Psychological Measurement" advocated that researchers consider using the a priori procedure. According to this procedure, the researcher specifies, prior to data collection, how close she wishes her sample mean(s) to be to the corresponding population mean(s), and the desired probability of…
Descriptors: Statistical Distributions, Sample Size, Equations (Mathematics), Statistical 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
Johnson, Wendy; Deary, Ian J.; Bouchard, Thomas J., Jr. – Educational and Psychological Measurement, 2018
Most study samples show less variability in key variables than do their source populations due most often to indirect selection into study participation associated with a wide range of personal and circumstantial characteristics. Formulas exist to correct the distortions of population-level correlations created. Formula accuracy has been tested…
Descriptors: Correlation, Sampling, Statistical Distributions, Accuracy
McNeish, Daniel; Harring, Jeffrey R. – Educational and Psychological Measurement, 2017
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
Descriptors: Growth Models, Goodness of Fit, Error Correction, Sampling
Valente, Matthew J.; Gonzalez, Oscar; Miocevic, Milica; MacKinnon, David P. – Educational and Psychological Measurement, 2016
Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method…
Descriptors: Structural Equation Models, Sampling, Statistical Inference, Statistical Bias
Guerra-Peña, Kiero; Steinley, Douglas – Educational and Psychological Measurement, 2016
Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This…
Descriptors: Growth Models, Bayesian Statistics, Sampling, Statistical Inference
Padilla, Miguel A.; Divers, Jasmin – Educational and Psychological Measurement, 2016
Coefficient omega and alpha are both measures of the composite reliability for a set of items. Unlike coefficient alpha, coefficient omega remains unbiased with congeneric items with uncorrelated errors. Despite this ability, coefficient omega is not as widely used and cited in the literature as coefficient alpha. Reasons for coefficient omega's…
Descriptors: Reliability, Computation, Statistical Analysis, Comparative Analysis
Chalmers, R. Philip; Counsell, Alyssa; Flora, David B. – Educational and Psychological Measurement, 2016
Differential test functioning, or DTF, occurs when one or more items in a test demonstrate differential item functioning (DIF) and the aggregate of these effects are witnessed at the test level. In many applications, DTF can be more important than DIF when the overall effects of DIF at the test level can be quantified. However, optimal statistical…
Descriptors: Test Bias, Sampling, Test Items, Statistical Analysis
Leth-Steensen, Craig; Gallitto, Elena – Educational and Psychological Measurement, 2016
A large number of approaches have been proposed for estimating and testing the significance of indirect effects in mediation models. In this study, four sets of Monte Carlo simulations involving full latent variable structural equation models were run in order to contrast the effectiveness of the currently popular bias-corrected bootstrapping…
Descriptors: Mediation Theory, Structural Equation Models, Monte Carlo Methods, Simulation