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Michael Kane – ETS Research Report Series, 2023
Linear functional relationships are intended to be symmetric and therefore cannot generally be accurately estimated using ordinary least squares regression equations. Orthogonal regression (OR) models allow for errors in both "Y" and "X" and therefore can provide symmetric estimates of these relationships. The most…
Descriptors: Factor Analysis, Regression (Statistics), Mathematical Models, Relationship
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
Kane, Michael T.; Mroch, Andrew A. – ETS Research Report Series, 2020
Ordinary least squares (OLS) regression and orthogonal regression (OR) address different questions and make different assumptions about errors. The OLS regression of Y on X yields predictions of a dependent variable (Y) contingent on an independent variable (X) and minimizes the sum of squared errors of prediction. It assumes that the independent…
Descriptors: Regression (Statistics), Least Squares Statistics, Test Bias, Error of Measurement
Oranje, Andreas; Li, Deping; Kandathil, Mathew – ETS Research Report Series, 2009
Several complex sample standard error estimators based on linearization and resampling for the latent regression model of the National Assessment of Educational Progress (NAEP) are studied with respect to design choices such as number of items, number of regressors, and the efficiency of the sample. This paper provides an evaluation of the extent…
Descriptors: Error of Measurement, Computation, Regression (Statistics), National Competency Tests
Antal, Tamás – ETS Research Report Series, 2007
Full account of the latent regression model for the National Assessment of Educational Progress is given. The treatment includes derivation of the EM algorithm, Newton-Raphson method, and the asymptotic standard errors. The paper also features the use of the adaptive Gauss-Hermite numerical integration method as a basic tool to evaluate…
Descriptors: Regression (Statistics), Item Response Theory, National Competency Tests, Evaluation Methods
Li, Deping; Oranje, Andreas – ETS Research Report Series, 2007
Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…
Descriptors: Error of Measurement, Regression (Statistics), Trend Analysis, National Competency Tests
Rotou, Ourania; Patsula, Liane; Steffen, Manfred; Rizavi, Saba – ETS Research Report Series, 2007
Traditionally, the fixed-length linear paper-and-pencil (P&P) mode of administration has been the standard method of test delivery. With the advancement of technology, however, the popularity of administering tests using adaptive methods like computerized adaptive testing (CAT) and multistage testing (MST) has grown in the field of measurement…
Descriptors: Comparative Analysis, Test Format, Computer Assisted Testing, Models

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