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Zhichao Jiang; Peng Ding – Grantee Submission, 2018
Frequently, empirical studies are plagued with missing data. When the data are missing not at random, the parameter of interest is not identifiable in general. Without additional assumptions, we can derive bounds of the parameters of interest, which, unfortunately, are often too wide to be informative. Therefore, it is of great importance to…
Descriptors: Foreign Countries, Acquired Immunodeficiency Syndrome (AIDS), Public Health, Data
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
Schoen, Robert C.; Yang, Xiaotong; Liu, Sicong; Paek, Insu – Grantee Submission, 2017
The Early Fractions Test v2.2 is a paper-pencil test designed to measure mathematics achievement of third- and fourth-grade students in the domain of fractions. The purpose, or intended use, of the Early Fractions Test v2.2 is to serve as a measure of student outcomes in a randomized trial designed to estimate the effect of an educational…
Descriptors: Psychometrics, Mathematics Tests, Mathematics Achievement, Fractions
Zu, Jiyun; Yuan, Ke-Hai – Journal of Educational Measurement, 2012
In the nonequivalent groups with anchor test (NEAT) design, the standard error of linear observed-score equating is commonly estimated by an estimator derived assuming multivariate normality. However, real data are seldom normally distributed, causing this normal estimator to be inconsistent. A general estimator, which does not rely on the…
Descriptors: Sample Size, Equated Scores, Test Items, Error of Measurement