Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 6 |
Since 2016 (last 10 years) | 15 |
Since 2006 (last 20 years) | 18 |
Descriptor
Data | 18 |
Statistical Analysis | 10 |
Simulation | 8 |
Factor Analysis | 7 |
Item Response Theory | 7 |
Computation | 6 |
Models | 6 |
Sample Size | 5 |
Accuracy | 4 |
Error of Measurement | 4 |
Correlation | 3 |
More ▼ |
Source
Educational and Psychological… | 18 |
Author
Publication Type
Journal Articles | 18 |
Reports - Research | 18 |
Education Level
Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
Program for International… | 1 |
Raven Advanced Progressive… | 1 |
What Works Clearinghouse Rating
Schweizer, Karl; Gold, Andreas; Krampen, Dorothea – Educational and Psychological Measurement, 2023
In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of…
Descriptors: Data, Models, Factor Analysis, Correlation
Ziying Li; A. Corinne Huggins-Manley; Walter L. Leite; M. David Miller; Eric A. Wright – Educational and Psychological Measurement, 2022
The unstructured multiple-attempt (MA) item response data in virtual learning environments (VLEs) are often from student-selected assessment data sets, which include missing data, single-attempt responses, multiple-attempt responses, and unknown growth ability across attempts, leading to a complex and complicated scenario for using this kind of…
Descriptors: Sequential Approach, Item Response Theory, Data, Simulation
Foster, Robert C. – Educational and Psychological Measurement, 2021
This article presents some equivalent forms of the common Kuder-Richardson Formula 21 and 20 estimators for nondichotomous data belonging to certain other exponential families, such as Poisson count data, exponential data, or geometric counts of trials until failure. Using the generalized framework of Foster (2020), an equation for the reliability…
Descriptors: Test Reliability, Data, Computation, Mathematical Formulas
Wind, Stefanie A.; Schumacker, Randall E. – Educational and Psychological Measurement, 2021
Researchers frequently use Rasch models to analyze survey responses because these models provide accurate parameter estimates for items and examinees when there are missing data. However, researchers have not fully considered how missing data affect the accuracy of dimensionality assessment in Rasch analyses such as principal components analysis…
Descriptors: Item Response Theory, Data, Factor Analysis, Accuracy
Goretzko, David – Educational and Psychological Measurement, 2022
Determining the number of factors in exploratory factor analysis is arguably the most crucial decision a researcher faces when conducting the analysis. While several simulation studies exist that compare various so-called factor retention criteria under different data conditions, little is known about the impact of missing data on this process.…
Descriptors: Factor Analysis, Research Problems, Data, Prediction
Shi, Dexin; Lee, Taehun; Fairchild, Amanda J.; Maydeu-Olivares, Alberto – Educational and Psychological Measurement, 2020
This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a…
Descriptors: Factor Analysis, Statistical Analysis, Computation, Goodness of Fit
Gilholm, Patricia; Mengersen, Kerrie; Thompson, Helen – Educational and Psychological Measurement, 2021
Developmental surveillance tools are used to closely monitor the early development of infants and young children. This study provides a novel implementation of a multidimensional item response model, using Bayesian hierarchical priors, to construct developmental profiles for a small sample of children (N = 115) with sparse data collected through…
Descriptors: Bayesian Statistics, Item Response Theory, Sample Size, Child Development
De Raadt, Alexandra; Warrens, Matthijs J.; Bosker, Roel J.; Kiers, Henk A. L. – Educational and Psychological Measurement, 2019
Cohen's kappa coefficient is commonly used for assessing agreement between classifications of two raters on a nominal scale. Three variants of Cohen's kappa that can handle missing data are presented. Data are considered missing if one or both ratings of a unit are missing. We study how well the variants estimate the kappa value for complete data…
Descriptors: Interrater Reliability, Data, Statistical Analysis, Statistical Bias
Xiao, Jiaying; Bulut, Okan – Educational and Psychological Measurement, 2020
Large amounts of missing data could distort item parameter estimation and lead to biased ability estimates in educational assessments. Therefore, missing responses should be handled properly before estimating any parameters. In this study, two Monte Carlo simulation studies were conducted to compare the performance of four methods in handling…
Descriptors: Data, Computation, Ability, Maximum Likelihood Statistics
Fujimoto, Ken A. – Educational and Psychological Measurement, 2019
Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the…
Descriptors: Bayesian Statistics, Item Response Theory, Psychometrics, Models
Wind, Stefanie A.; Patil, Yogendra J. – Educational and Psychological Measurement, 2018
Recent research has explored the use of models adapted from Mokken scale analysis as a nonparametric approach to evaluating rating quality in educational performance assessments. A potential limiting factor to the widespread use of these techniques is the requirement for complete data, as practical constraints in operational assessment systems…
Descriptors: Scaling, Data, Interrater Reliability, Writing Tests
Sachse, Karoline A.; Mahler, Nicole; Pohl, Steffi – Educational and Psychological Measurement, 2019
Mechanisms causing item nonresponses in large-scale assessments are often said to be nonignorable. Parameter estimates can be biased if nonignorable missing data mechanisms are not adequately modeled. In trend analyses, it is plausible for the missing data mechanism and the percentage of missing values to change over time. In this article, we…
Descriptors: International Assessment, Response Style (Tests), Achievement Tests, Foreign Countries
Is the Factor Observed in Investigations on the Item-Position Effect Actually the Difficulty Factor?
Schweizer, Karl; Troche, Stefan – Educational and Psychological Measurement, 2018
In confirmatory factor analysis quite similar models of measurement serve the detection of the difficulty factor and the factor due to the item-position effect. The item-position effect refers to the increasing dependency among the responses to successively presented items of a test whereas the difficulty factor is ascribed to the wide range of…
Descriptors: Investigations, Difficulty Level, Factor Analysis, Models
DiStefano, Christine; McDaniel, Heather L.; Zhang, Liyun; Shi, Dexin; Jiang, Zhehan – Educational and Psychological Measurement, 2019
A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and…
Descriptors: Factor Analysis, Effect Size, Data, Sample Size
Wind, Stefanie A.; Engelhard, George, Jr. – Educational and Psychological Measurement, 2016
Mokken scale analysis is a probabilistic nonparametric approach that offers statistical and graphical tools for evaluating the quality of social science measurement without placing potentially inappropriate restrictions on the structure of a data set. In particular, Mokken scaling provides a useful method for evaluating important measurement…
Descriptors: Nonparametric Statistics, Statistical Analysis, Measurement, Psychometrics
Previous Page | Next Page ยป
Pages: 1 | 2