NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 7 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
McNeish, Daniel; Harring, Jeffrey R. – Educational and Psychological Measurement, 2017
To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data-model fit. However, previous studies have demonstrated poor…
Descriptors: Growth Models, Goodness of Fit, Error Correction, Sampling
Peer reviewed Peer reviewed
Direct linkDirect link
Koran, Jennifer – Measurement and Evaluation in Counseling and Development, 2016
Proactive preliminary minimum sample size determination can be useful for the early planning stages of a latent variable modeling study to set a realistic scope, long before the model and population are finalized. This study examined existing methods and proposed a new method for proactive preliminary minimum sample size determination.
Descriptors: Factor Analysis, Sample Size, Models, Sampling
Peer reviewed Peer reviewed
Direct linkDirect link
McNeish, Daniel – Review of Educational Research, 2017
In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…
Descriptors: Models, Statistical Analysis, Sampling, Sample Size
Peer reviewed Peer reviewed
Bentler, Peter M.; Yuan, Ke-Hai – Multivariate Behavioral Research, 1999
Studied the small sample behavior of several test statistics based on the maximum-likelihood estimator but designed to perform better with nonnormal data. Monte Carlo results indicate the satisfactory performance of the "F" statistic recently proposed by K. Yuan and P. Bentler (1997). (SLD)
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods, Sample Size
Peer reviewed Peer reviewed
Anderson, James C.; Gerbing, David W. – Psychometrika, 1984
This study of maximum likelihood confirmatory factor analysis found effects of practical significance due to sample size, the number of indicators per factor, and the number of factors for Joreskog and Sorbom's (1981) goodness-of-fit index (GFI), GFI adjusted for degrees of freedom, and the root mean square residual. (Author/BW)
Descriptors: Factor Analysis, Factor Structure, Goodness of Fit, Mathematical Models
Kolen, Michael J.; Whitney, Douglas R. – 1978
The application of latent trait theory to classroom tests necessitates the use of small sample sizes for parameter estimation. Computer generated data were used to assess the accuracy of estimation of the slope and location parameters in the two parameter logistic model with fixed abilities and varying small sample sizes. The maximum likelihood…
Descriptors: Difficulty Level, Item Analysis, Latent Trait Theory, Mathematical Models
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