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Abdul Haq; Muhammad Usman; Manzoor Khan – Measurement: Interdisciplinary Research and Perspectives, 2024
Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined…
Descriptors: Sampling, Error of Measurement, Evaluation Methods, Statistical Bias
Hsin-Yun Lee; You-Lin Chen; Li-Jen Weng – Journal of Experimental Education, 2024
The second version of Kaiser's Measure of Sampling Adequacy (MSA[subscript 2]) has been widely applied to assess the factorability of data in psychological research. The MSA[subscript 2] is developed in the population and little is known about its behavior in finite samples. If estimated MSA[subscript 2]s are biased due to sampling errors,…
Descriptors: Error of Measurement, Reliability, Sampling, Statistical Bias
Jiaying Xiao; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Accurate item parameters and standard errors (SEs) are crucial for many multidimensional item response theory (MIRT) applications. A recent study proposed the Gaussian Variational Expectation Maximization (GVEM) algorithm to improve computational efficiency and estimation accuracy (Cho et al., 2021). However, the SE estimation procedure has yet to…
Descriptors: Error of Measurement, Models, Evaluation Methods, Item Analysis
Ting Dai; Yang Du; Jennifer Cromley; Tia Fechter; Frank Nelson – Journal of Experimental Education, 2024
Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle…
Descriptors: Research Problems, Research Design, Data, Matrices
Weicong Lyu; Chun Wang; Gongjun Xu – Grantee Submission, 2024
Data harmonization is an emerging approach to strategically combining data from multiple independent studies, enabling addressing new research questions that are not answerable by a single contributing study. A fundamental psychometric challenge for data harmonization is to create commensurate measures for the constructs of interest across…
Descriptors: Data Analysis, Test Items, Psychometrics, Item Response Theory
Pere J. Ferrando; David Navarro-González; Fabia Morales-Vives – Educational and Psychological Measurement, 2025
The problem of local item dependencies (LIDs) is very common in personality and attitude measures, particularly in those that measure narrow-bandwidth dimensions. At the structural level, these dependencies can be modeled by using extended factor analytic (FA) solutions that include correlated residuals. However, the effects that LIDs have on the…
Descriptors: Scores, Accuracy, Evaluation Methods, Factor Analysis
Kim, Hyung Jin; Brennan, Robert L.; Lee, Won-Chan – Journal of Educational Measurement, 2020
In equating, smoothing techniques are frequently used to diminish sampling error. There are typically two types of smoothing: presmoothing and postsmoothing. For polynomial log-linear presmoothing, an optimum smoothing degree can be determined statistically based on the Akaike information criterion or Chi-square difference criterion. For…
Descriptors: Equated Scores, Sampling, Error of Measurement, Statistical Analysis
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
Qian, Jiahe; Li, Shuhong – ETS Research Report Series, 2021
In recent years, harmonic regression models have been applied to implement quality control for educational assessment data consisting of multiple administrations and displaying seasonality. As with other types of regression models, it is imperative that model adequacy checking and model fit be appropriately conducted. However, there has been no…
Descriptors: Models, Regression (Statistics), Language Tests, Quality Control
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Multilevel structural equation (MSEM) models allow researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This paper…
Descriptors: Sampling, Structural Equation Models, Factor Structure, Monte Carlo Methods
Goodman, Joshua T.; Dallas, Andrew D.; Fan, Fen – Applied Measurement in Education, 2020
Recent research has suggested that re-setting the standard for each administration of a small sample examination, in addition to the high cost, does not adequately maintain similar performance expectations year after year. Small-sample equating methods have shown promise with samples between 20 and 30. For groups that have fewer than 20 students,…
Descriptors: Equated Scores, Sample Size, Sampling, Weighted Scores
Jia, Yuane; Konold, Timothy – Journal of Experimental Education, 2021
Traditional observed variable multilevel models for evaluating indirect effects are limited by their inability to quantify measurement and sampling error. They are further restricted by being unable to fully separate within- and between-level effects without bias. Doubly latent models reduce these biases by decomposing the observed within-level…
Descriptors: Hierarchical Linear Modeling, Educational Environment, Aggression, Bullying
Ellison, George T. H. – Journal of Statistics and Data Science Education, 2021
Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the…
Descriptors: Statistics Education, Medical Education, Undergraduate Students, Graphs
Qian, Jiahe – ETS Research Report Series, 2020
The finite population correction (FPC) factor is often used to adjust variance estimators for survey data sampled from a finite population without replacement. As a replicated resampling approach, the jackknife approach is usually implemented without the FPC factor incorporated in its variance estimates. A paradigm is proposed to compare the…
Descriptors: Computation, Sampling, Data, Statistical Analysis
Shear, Benjamin R.; Nordstokke, David W.; Zumbo, Bruno D. – Practical Assessment, Research & Evaluation, 2018
This computer simulation study evaluates the robustness of the nonparametric Levene test of equal variances (Nordstokke & Zumbo, 2010) when sampling from populations with unequal (and unknown) means. Testing for population mean differences when population variances are unknown and possibly unequal is often referred to as the Behrens-Fisher…
Descriptors: Nonparametric Statistics, Computer Simulation, Monte Carlo Methods, Sampling