Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 4 |
Descriptor
| Error of Measurement | 6 |
| Maximum Likelihood Statistics | 6 |
| Test Length | 6 |
| Computation | 4 |
| Sample Size | 4 |
| Statistical Bias | 4 |
| Ability | 2 |
| Bayesian Statistics | 2 |
| Comparative Analysis | 2 |
| Estimation (Mathematics) | 2 |
| Factor Analysis | 2 |
| More ▼ | |
Author
| Kilic, Abdullah Faruk | 2 |
| Atar, Burcu | 1 |
| Ban, Jae-Chun | 1 |
| Dogan, Nuri | 1 |
| Lee, Yi-Hsuan | 1 |
| Lord, Frederic M. | 1 |
| Roberts, James S. | 1 |
| Thompson, Vanessa M. | 1 |
| Uysal, Ibrahim | 1 |
| Wang, Tianyou | 1 |
| Wingersky, Marilyn S. | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 4 |
| Reports - Research | 4 |
| Reports - Evaluative | 2 |
Education Level
Audience
| Researchers | 1 |
Location
| Turkey | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textKilic, Abdullah Faruk; Uysal, Ibrahim; Atar, Burcu – International Journal of Assessment Tools in Education, 2020
This Monte Carlo simulation study aimed to investigate confirmatory factor analysis (CFA) estimation methods under different conditions, such as sample size, distribution of indicators, test length, average factor loading, and factor structure. Binary data were generated to compare the performance of maximum likelihood (ML), mean and variance…
Descriptors: Factor Analysis, Computation, Methods, Sample Size
Kilic, Abdullah Faruk; Dogan, Nuri – International Journal of Assessment Tools in Education, 2021
Weighted least squares (WLS), weighted least squares mean-and-variance-adjusted (WLSMV), unweighted least squares mean-and-variance-adjusted (ULSMV), maximum likelihood (ML), robust maximum likelihood (MLR) and Bayesian estimation methods were compared in mixed item response type data via Monte Carlo simulation. The percentage of polytomous items,…
Descriptors: Factor Analysis, Computation, Least Squares Statistics, Maximum Likelihood Statistics
Direct linkRoberts, James S.; Thompson, Vanessa M. – Applied Psychological Measurement, 2011
A marginal maximum a posteriori (MMAP) procedure was implemented to estimate item parameters in the generalized graded unfolding model (GGUM). Estimates from the MMAP method were compared with those derived from marginal maximum likelihood (MML) and Markov chain Monte Carlo (MCMC) procedures in a recovery simulation that varied sample size,…
Descriptors: Statistical Analysis, Markov Processes, Computation, Monte Carlo Methods
Lee, Yi-Hsuan; Zhang, Jinming – ETS Research Report Series, 2008
The method of maximum-likelihood is typically applied to item response theory (IRT) models when the ability parameter is estimated while conditioning on the true item parameters. In practice, the item parameters are unknown and need to be estimated first from a calibration sample. Lewis (1985) and Zhang and Lu (2007) proposed the expected response…
Descriptors: Item Response Theory, Comparative Analysis, Computation, Ability
Yi, Qing; Wang, Tianyou; Ban, Jae-Chun – 2000
Error indices (bias, standard error of estimation, and root mean square error) obtained on different scales of measurement under different test termination rules in a computerized adaptive test (CAT) context were examined. Four ability estimation methods were studied: (1) maximum likelihood estimation (MLE); (2) weighted likelihood estimation…
Descriptors: Ability, Adaptive Testing, Computer Assisted Testing, Error of Measurement
Wingersky, Marilyn S.; Lord, Frederic M. – 1983
The sampling errors of maximum likelihood estimates of item-response theory parameters are studied in the case where both people and item parameters are estimated simultaneously. A check on the validity of the standard error formulas is carried out. The effect of varying sample size, test length, and the shape of the ability distribution is…
Descriptors: Error of Measurement, Estimation (Mathematics), Item Banks, Latent Trait Theory
