Empirical bayes point estimates of latent trait scores without knowledge of the trait distribution

Psychometrika ◽  
1973 ◽  
Vol 38 (4) ◽  
pp. 533-554 ◽  
Author(s):  
William Meredith ◽  
Jack Kearns
Keyword(s):  
Empirical Bayes ◽  
Latent Trait ◽  
Point Estimates ◽  
Trait Distribution

Methods for evaluating empirical bayes point estimates of latent trait scores

Psychometrika ◽  
1975 ◽  
Vol 40 (3) ◽  
pp. 373-394 ◽  
Author(s):  
Jack Kearns ◽  
William Meredith
Keyword(s):  
Empirical Bayes ◽  
Latent Trait ◽  
Point Estimates

scholarly journals The Effect of Small Sample Size on Measurement Equivalence of Psychometric Questionnaires in MIMIC Model: A Simulation Study

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Jamshid Jamali ◽  
Seyyed Mohammad Taghi Ayatollahi ◽  
Peyman Jafari
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
Measurement Equivalence ◽  
Latent Trait ◽  
Type I ◽  
Mimic Model ◽  
Latent Construct ◽  
Trait Distribution ◽  
Latent Trait Distribution ◽  
Scale Length

Evaluating measurement equivalence (also known as differential item functioning (DIF)) is an important part of the process of validating psychometric questionnaires. This study aimed at evaluating the multiple indicators multiple causes (MIMIC) model for DIF detection when latent construct distribution is nonnormal and the focal group sample size is small. In this simulation-based study, Type I error rates and power of MIMIC model for detecting uniform-DIF were investigated under different combinations of reference to focal group sample size ratio, magnitude of the uniform-DIF effect, scale length, the number of response categories, and latent trait distribution. Moderate and high skewness in the latent trait distribution led to a decrease of 0.33% and 0.47% power of MIMIC model for detecting uniform-DIF, respectively. The findings indicated that, by increasing the scale length, the number of response categories and magnitude DIF improved the power of MIMIC model, by 3.47%, 4.83%, and 20.35%, respectively; it also decreased Type I error of MIMIC approach by 2.81%, 5.66%, and 0.04%, respectively. This study revealed that power of MIMIC model was at an acceptable level when latent trait distributions were skewed. However, empirical Type I error rate was slightly greater than nominal significance level. Consequently, the MIMIC was recommended for detection of uniform-DIF when latent construct distribution is nonnormal and the focal group sample size is small.

Approximation for Bayesian Ability Estimation

1988 ◽  
Vol 13 (2) ◽  
pp. 117-130 ◽  
Author(s):  
Robert K. Tsutakawa ◽  
Michael J. Soltys
Keyword(s):  
Standard Deviation ◽  
Empirical Bayes ◽  
Numerical Comparison ◽  
Item Response Model ◽  
Posterior Mean ◽  
Point Estimates ◽  
Item Parameters ◽  
Using Data ◽  
Math Test ◽  
Ability Parameter

An approximation is proposed for the posterior mean and standard deviation of the ability parameter in an item response model. The procedure assumes that approximations to the posterior mean and covariance matrix of item parameters are available. It is based on the posterior mean of a Taylor series approximation to the posterior mean conditional on the item parameters. The method is illustrated for the two-parameter logistic model using data from an ACT math test with 39 items. A numerical comparison with the empirical Bayes method using n = 400 examinees shows that the point estimates are very similar but the standard deviations under empirical Bayes are about 2% smaller than those under Bayes. Moreover, when the sample size is decreased to n = 100, the standard deviation under Bayes is shown to increase by 14% in some cases.

Item response theory and the measurement of psychiatric constructs: some empirical and conceptual issues and challenges

2016 ◽  
Vol 46 (10) ◽  
pp. 2025-2039 ◽  
Author(s):  
S. P. Reise ◽  
A. Rodriguez
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Latent Trait ◽  
Response Theory ◽  
Outcomes Measurement ◽  
Measurement Models ◽  
Irt Models ◽  
Trait Distribution ◽  
Item Pools ◽  
Latent Trait Distribution

Item response theory (IRT) measurement models are now commonly used in educational, psychological, and health-outcomes measurement, but their impact in the evaluation of measures of psychiatric constructs remains limited. Herein we present two, somewhat contradictory, theses. The first is that, when skillfully applied, IRT has much to offer psychiatric measurement in terms of scale development, psychometric analysis, and scoring. The second argument, however, is that psychiatric measurement presents some unique challenges to the application of IRT – challenges that may not be easily addressed by application of conventional IRT models and methods. These challenges include, but are not limited to, the modeling of conceptually narrow constructs and their associated limited item pools, and unipolar constructs where the expected latent trait distribution is highly skewed.

An IRT Investigation of the Validity of Non-Patient Analogue Research Using the Beck Depression Inventory

1995 ◽  
Vol 11 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Sean M. Hammond
Keyword(s):  
Differential Item Functioning ◽  
Beck Depression Inventory ◽  
Rating Scale ◽  
Latent Trait ◽  
Scale Model ◽  
Item Functioning ◽  
Two Samples ◽  
Patient Groups ◽  
Analogue Research ◽  
Rating Scale Model

This paper presents an IRT analysis of the Beck Depression Inventory which was carried out to assess the assumption of an underlying latent trait common to non-clinical and patient samples. A one parameter rating scale model was fitted to data drawn from a patient and non-patient sample. Findings suggest that while the BDI fits the model reasonably well for the two samples separately there is sufficient differential item functioning to raise serious duobts of the viability of using it analogously with patient and non-patient groups.

Sign in / Sign up

Export Citation Format

Share Document