NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 8 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Cai, Tianji; Xia, Yiwei; Zhou, Yisu – Sociological Methods & Research, 2021
Analysts of discrete data often face the challenge of managing the tendency of inflation on certain values. When treated improperly, such phenomenon may lead to biased estimates and incorrect inferences. This study extends the existing literature on single-value inflated models and develops a general framework to handle variables with more than…
Descriptors: Statistical Distributions, Probability, Statistical Analysis, Statistical Bias
Peer reviewed Peer reviewed
Direct linkDirect link
Li, Jian; Lomax, Richard G. – Journal of Experimental Education, 2017
Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate,…
Descriptors: Monte Carlo Methods, Structural Equation Models, Evaluation Methods, Measurement Techniques
Peer reviewed Peer reviewed
Direct linkDirect link
Sinharay, Sandip – Journal of Educational and Behavioral Statistics, 2015
Person-fit assessment may help the researcher to obtain additional information regarding the answering behavior of persons. Although several researchers examined person fit, there is a lack of research on person-fit assessment for mixed-format tests. In this article, the lz statistic and the ?2 statistic, both of which have been used for tests…
Descriptors: Test Format, Goodness of Fit, Item Response Theory, Bayesian Statistics
Peer reviewed Peer reviewed
Direct linkDirect link
Paek, Insu; Park, Hyun-Jeong; Cai, Li; Chi, Eunlim – Educational and Psychological Measurement, 2014
Typically a longitudinal growth modeling based on item response theory (IRT) requires repeated measures data from a single group with the same test design. If operational or item exposure problems are present, the same test may not be employed to collect data for longitudinal analyses and tests at multiple time points are constructed with unique…
Descriptors: Item Response Theory, Comparative Analysis, Test Items, Equated Scores
Peer reviewed Peer reviewed
Direct linkDirect link
Seo, Dong Gi; Weiss, David J. – Educational and Psychological Measurement, 2013
The usefulness of the l[subscript z] person-fit index was investigated with achievement test data from 20 exams given to more than 3,200 college students. Results for three methods of estimating ? showed that the distributions of l[subscript z] were not consistent with its theoretical distribution, resulting in general overfit to the item response…
Descriptors: Achievement Tests, College Students, Goodness of Fit, Item Response Theory
Cai, Li; Monroe, Scott – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2014
We propose a new limited-information goodness of fit test statistic C[subscript 2] for ordinal IRT models. The construction of the new statistic lies formally between the M[subscript 2] statistic of Maydeu-Olivares and Joe (2006), which utilizes first and second order marginal probabilities, and the M*[subscript 2] statistic of Cai and Hansen…
Descriptors: Item Response Theory, Models, Goodness of Fit, Probability
Peer reviewed Peer reviewed
Direct linkDirect link
Verkuilen, Jay; Smithson, Michael – Journal of Educational and Behavioral Statistics, 2012
Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…
Descriptors: Responses, Regression (Statistics), Statistical Analysis, Models
Peer reviewed Peer reviewed
Loughner, William – Journal of the American Society for Information Science, 1992
Corrects an error in the calculation of the Kolmogorov-Smirnov (KS) statistic when it is used to empirically confirm or deny the generalized Lotka's law. Examples from the literature are given of both correct and incorrect uses of the KS test and Lotka equations with cumulative distribution functions (CDFs). (six references) (LRW)
Descriptors: Computation, Goodness of Fit, Hypothesis Testing, Literature Reviews