Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 8 |
Since 2016 (last 10 years) | 52 |
Since 2006 (last 20 years) | 139 |
Descriptor
Correlation | 191 |
Factor Structure | 191 |
Factor Analysis | 133 |
Models | 89 |
Structural Equation Models | 76 |
Foreign Countries | 62 |
Statistical Analysis | 49 |
Measures (Individuals) | 47 |
Goodness of Fit | 46 |
Questionnaires | 37 |
Mathematical Models | 29 |
More ▼ |
Source
Author
Publication Type
Education Level
Audience
Researchers | 4 |
Teachers | 1 |
Location
Germany | 8 |
Australia | 7 |
Canada | 5 |
Turkey | 5 |
China | 4 |
Norway | 4 |
United States | 4 |
Hong Kong | 3 |
Italy | 3 |
Japan | 3 |
Taiwan | 3 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Liu, Xiaoling; Cao, Pei; Lai, Xinzhen; Wen, Jianbing; Yang, Yanyun – Educational and Psychological Measurement, 2023
Percentage of uncontaminated correlations (PUC), explained common variance (ECV), and omega hierarchical ([omega]H) have been used to assess the degree to which a scale is essentially unidimensional and to predict structural coefficient bias when a unidimensional measurement model is fit to multidimensional data. The usefulness of these indices…
Descriptors: Correlation, Measurement Techniques, Prediction, Regression (Statistics)
Strauss, Christian L. L. – ProQuest LLC, 2022
In many psychological and educational applications, it is imperative to obtain valid and reliable score estimates of multilevel processes. For example, in order to assess the quality and characteristics of high impact learning processes, one must compute accurate scores representative of student- and classroom-level constructs. Currently, there…
Descriptors: Scores, Factor Analysis, Models, True Scores
Kartal, Seval Kula – International Journal of Progressive Education, 2020
One of the aims of the current study is to specify the model providing the best fit to the data among the exploratory, the bifactor exploratory and the confirmatory structural equation models. The study compares the three models based on the model data fit statistics and item parameter estimations (factor loadings, cross-loadings, factor…
Descriptors: Learning Motivation, Measures (Individuals), Undergraduate Students, Foreign Countries
Lee, Bitna; Sohn, Wonsook – Educational and Psychological Measurement, 2022
A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition,…
Descriptors: Goodness of Fit, Factor Structure, Monte Carlo Methods, Factor Analysis
Liang, Xinya – Educational and Psychological Measurement, 2020
Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both…
Descriptors: Factor Structure, Bayesian Statistics, Structural Equation Models, Goodness of Fit
Kush, Joseph M.; Konold, Timothy R.; Bradshaw, Catherine P. – Grantee Submission, 2021
Multilevel structural equation (MSEM) models allow researchers to model latent factor structures at multiple levels simultaneously by decomposing within- and between-group variation. Yet the extent to which the sampling ratio (i.e., proportion of cases sampled from each group) influences the results of MSEM models remains unknown. This paper…
Descriptors: Sampling, Structural Equation Models, Factor Structure, Monte Carlo Methods
Alamer, Abdullah; Marsh, Herbert – Studies in Second Language Acquisition, 2022
This study offers methodological synergy in the examination of factorial structure in second language (L2) research. It illustrates the effectiveness and flexibility of the recently developed exploratory structural equation modeling (ESEM) method, which integrates the advantages of exploratory factor analysis (EFA) and confirmatory factor analysis…
Descriptors: Factor Structure, Factor Analysis, Structural Equation Models, Second Language Learning
Arens, A. Katrin; Jansen, Malte; Preckel, Franzis; Schmidt, Isabelle; Brunner, Martin – Review of Educational Research, 2021
The structure of academic self-concept (ASC) is assumed to be multidimensional and hierarchical. This methodological review considers the most central models depicting the structure of ASC: a higher-order factor model, the Marsh/Shavelson model, the nested Marsh/Shavelson model, a bifactor representation based on exploratory structural equation…
Descriptors: Foreign Countries, High School Students, Self Concept, Models
Fathalla, Mohammed Mohammed; Ibrahim, Fatima Midhat – International Journal of Psycho-Educational Sciences, 2020
The aim of this study was to assess the reliability and validity of ERQ in a group of Egyptian adolescents. 648 adolescents from middle schools in Nasr city, Egypt were recruited. These adolescents aged 14-15 years old (M=14.4, SD=2.22). Of which,400 were females (61.72%) while 248 were males (38.27%). Exploratory Factor Analysis, with CFA and…
Descriptors: Self Control, Validity, Reliability, Middle School Students
Lo, Lawrence L.; Molenaar, Peter C. M.; Rovine, Michael – Applied Developmental Science, 2017
Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…
Descriptors: Factor Analysis, Structural Equation Models, Correlation, Sample Size
Clark, D. Angus; Bowles, Ryan P. – Grantee Submission, 2018
In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present…
Descriptors: Factor Analysis, Goodness of Fit, Factor Structure, Monte Carlo Methods
Horwood, Marcus; Marsh, Herbert W.; Parker, Philip D.; Riley, Philip; Guo, Jiesi; Dicke, Theresa – Journal of Educational Psychology, 2021
Paradoxically, school leaders as a group report high levels of burnout but also high job satisfaction and passion for their work. School principals are passionate about their job, but this passion can be a double-edged sword leading to good (job satisfaction) and bad (burnout) outcomes. We extend the dualistic model of passion (DMP) in a study of…
Descriptors: Instructional Leadership, Principals, Burnout, Job Satisfaction
Mohammed, Saif Husam; Kinyó, László – Research and Practice in Technology Enhanced Learning, 2022
This study's primary aim is to validate a research instrument in Iraqi Kurdistan middle and secondary schools to explore learners' perspectives concerning social constructivist learning environments and e-learning outcomes. The research instrument was updated and devised based on Aldridge, Fraser, Taylor, and Chen's (Aldridge et al., International…
Descriptors: Foreign Countries, Constructivism (Learning), Outcomes of Education, Secondary School Students
Johnson, Keith; Willoughby, Shannon D. – Physical Review Physics Education Research, 2018
The reliability and validity of inventories should be verified in multiple ways. Although the epistemological beliefs about the physical science survey (EBAPS) has been deemed to be reliable and valid by the authors, the axes or factor structure proposed by the authors has not been independently checked. Using data from a study sample we discussed…
Descriptors: Epistemology, Beliefs, Introductory Courses, Astronomy
Quinn, Jamie M.; Wagner, Richard K. – Child Development, 2018
The purpose of this review was to introduce readers of "Child Development" to the meta-analytic structural equation modeling (MASEM) technique. Provided are a background to the MASEM approach, a discussion of its utility in the study of child development, and an application of this technique in the study of reading comprehension (RC)…
Descriptors: Meta Analysis, Reading Comprehension, Structural Equation Models, Factor Structure