Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 4 |
Since 2006 (last 20 years) | 7 |
Descriptor
Error of Measurement | 7 |
Models | 7 |
Computation | 3 |
Correlation | 3 |
Measurement Techniques | 3 |
Hypothesis Testing | 2 |
Intervals | 2 |
Item Response Theory | 2 |
Statistical Analysis | 2 |
Comparative Analysis | 1 |
Computer Software | 1 |
More ▼ |
Author
Raykov, Tenko | 7 |
Marcoulides, George A. | 3 |
Calvocoressi, Lisa | 1 |
DiStefano, Christine | 1 |
Dimitrov, Dimiter M. | 1 |
Li, Tatyana | 1 |
Menold, Natalja | 1 |
Penev, Spiridon | 1 |
Volker, Martin | 1 |
Publication Type
Journal Articles | 7 |
Reports - Descriptive | 5 |
Reports - Evaluative | 1 |
Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Raykov, Tenko; Marcoulides, George A. – Measurement: Interdisciplinary Research and Perspectives, 2023
This article outlines a readily applicable procedure for point and interval estimation of the population discrepancy between reliability and the popular Cronbach's coefficient alpha for unidimensional multi-component measuring instruments with uncorrelated errors, which are widely used in behavioral and social research. The method is developed…
Descriptors: Measurement, Test Reliability, Measurement Techniques, Error of Measurement
Raykov, Tenko; DiStefano, Christine; Calvocoressi, Lisa; Volker, Martin – Educational and Psychological Measurement, 2022
A class of effect size indices are discussed that evaluate the degree to which two nested confirmatory factor analysis models differ from each other in terms of fit to a set of observed variables. These descriptive effect measures can be used to quantify the impact of parameter restrictions imposed in an initially considered model and are free…
Descriptors: Effect Size, Models, Measurement Techniques, Factor Analysis
Raykov, Tenko; Dimitrov, Dimiter M.; Marcoulides, George A.; Li, Tatyana; Menold, Natalja – Educational and Psychological Measurement, 2018
A latent variable modeling method for studying measurement invariance when evaluating latent constructs with multiple binary or binary scored items with no guessing is outlined. The approach extends the continuous indicator procedure described by Raykov and colleagues, utilizes similarly the false discovery rate approach to multiple testing, and…
Descriptors: Models, Statistical Analysis, Error of Measurement, Test Bias
Raykov, Tenko; Marcoulides, George A. – Educational and Psychological Measurement, 2016
The frequently neglected and often misunderstood relationship between classical test theory and item response theory is discussed for the unidimensional case with binary measures and no guessing. It is pointed out that popular item response models can be directly obtained from classical test theory-based models by accounting for the discrete…
Descriptors: Test Theory, Item Response Theory, Models, Correlation
Raykov, Tenko – Educational and Psychological Measurement, 2012
A latent variable modeling approach that permits estimation of propensity scores in observational studies containing fallible independent variables is outlined, with subsequent examination of treatment effect. When at least one covariate is measured with error, it is indicated that the conventional propensity score need not possess the desirable…
Descriptors: Computation, Probability, Error of Measurement, Observation
Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…
Descriptors: Correlation, Models, Vertical Organization, Predictor Variables
Raykov, Tenko; Penev, Spiridon – Structural Equation Modeling: A Multidisciplinary Journal, 2010
A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…
Descriptors: Reliability, Evaluation, Models, Intervals