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Lingbo Tong; Wen Qu; Zhiyong Zhang – Grantee Submission, 2025
Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…
Descriptors: Factor Analysis, Monte Carlo Methods, Statistical Analysis, Sample Size
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Aitana González-Ortiz de Zárate; Helena Roig-Ester; Paulina E. Robalino Guerra; Anja Garone; Carla Quesada-Pallarès – International Journal of Training and Development, 2025
Transfer beliefs are understudied in the training transfer field, whereas structural equation modelling (SEM) has been a widely used technique to study transfer models. New methodologies are needed to study training transfer and network analysis (NA) has emerged as a new approach that provides a visual representation of a given network. We…
Descriptors: Trainees, Student Attitudes, Beliefs, Transfer of Training
Bastürk, Savas – Online Submission, 2017
Selecting and applying appropriate research techniques, analysing data using information and communication technologies, transferring the obtained results of the analysis into tables and interpreting them are the performance indicators evaluated by the Ministry of National Education under teacher competencies. At the beginning of the courses that…
Descriptors: Scientific Research, Research Methodology, Courses, Research Projects
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Stapleton, Laura M.; McNeish, Daniel M.; Yang, Ji Seung – Educational Psychologist, 2016
Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003; Sterba, 2009). The overarching goal of this article is to suggest when it is appropriate…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Statistical Data, Multivariate Analysis
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Canivez, Gary L.; Kush, Joseph C. – Journal of Psychoeducational Assessment, 2013
Weiss, Keith, Zhu, and Chen (2013a) and Weiss, Keith, Zhu, and Chen (2013b), this issue, report examinations of the factor structure of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) and Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV), respectively; comparing Wechsler Hierarchical Model (W-HM) and…
Descriptors: Intelligence Tests, Factor Structure, Comparative Analysis, Arithmetic
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Liu, Yan; Zumbo, Bruno D. – Educational and Psychological Measurement, 2012
There is a lack of research on the effects of outliers on the decisions about the number of factors to retain in an exploratory factor analysis, especially for outliers arising from unintended and unknowingly included subpopulations. The purpose of the present research was to investigate how outliers from an unintended and unknowingly included…
Descriptors: Factor Analysis, Factor Structure, Evaluation Research, Evaluation Methods
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Martin, Andrew J.; Yu, Kai; Papworth, Brad; Ginns, Paul; Collie, Rebecca J. – Journal of Psychoeducational Assessment, 2015
This study explored motivation and engagement among North American (the United States and Canada; n = 1,540), U.K. (n = 1,558), Australian (n = 2,283), and Chinese (n = 3,753) secondary school students. Motivation and engagement were assessed via students' responses to the Motivation and Engagement Scale-High School (MES-HS). Confirmatory factor…
Descriptors: Foreign Countries, Motivation, Learner Engagement, Secondary School Students
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Beausaert, Simon; Segers, Mien; Gijselaers, Wim – International Journal of Training and Development, 2011
Confronted with the speed of technological advancements and increasing global competition, organizations have come to realize that their employees' continuous learning drives business success. A popular tool to support and enhance continuous learning is the personal development plan (PDP). Despite its popularity, empirical evidence of the…
Descriptors: Portfolio Assessment, Research Methodology, Factor Structure, Lifelong Learning
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Lee, Soon-Mook – International Journal of Testing, 2010
CEFA 3.02(Browne, Cudeck, Tateneni, & Mels, 2008) is a factor analysis computer program designed to perform exploratory factor analysis. It provides the main properties that are needed for exploratory factor analysis, namely a variety of factoring methods employing eight different discrepancy functions to be minimized to yield initial…
Descriptors: Factor Structure, Computer Software, Factor Analysis, Research Methodology
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Ozturk, Mehmet Ali – Educational Sciences: Theory and Practice, 2011
This article reports results of a confirmatory factor analysis performed to cross-validate the factor structure of the Educators' Attitudes Toward Educational Research Scale. The original scale had been developed by the author and revised based on the results of an exploratory factor analysis. In the present study, the revised scale was given to…
Descriptors: Methods Courses, Educational Research, Research Methodology, Factor Structure
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Jones-Farmer, L. Allison – Structural Equation Modeling: A Multidisciplinary Journal, 2010
When comparing latent variables among groups, it is important to first establish the equivalence or invariance of the measurement model across groups. Confirmatory factor analysis (CFA) is a commonly used methodological approach to examine measurement equivalence/invariance (ME/I). Within the CFA framework, the chi-square goodness-of-fit test and…
Descriptors: Factor Structure, Factor Analysis, Evaluation Research, Goodness of Fit
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Nagy, Gabriel; Trautwein, Ulrich; Ludtke, Oliver – Journal of Vocational Behavior, 2010
The cross-cultural generalizability of vocational interest structures has received significant attention in recent years. This article adds to this research in four respects. First, data from a context that has not previously been investigated (Germany) was analyzed. Second, students at different stages of their educational career were examined.…
Descriptors: Vocational Interests, Factor Analysis, Foreign Countries, Hypothesis Testing
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de Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods
Stellefson, Michael; Hanik, Bruce – Online Submission, 2008
When conducting an exploratory factor analysis, the decision regarding the number of factors to retain following factor extraction is one that the researcher should consider very carefully, as the decision can have a dramatic effect on results. Although there are numerous strategies that can and should be utilized when making this decision,…
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Evaluation Methods
Atkins-Burnett, Sally; Xue, Yange; Kopack, Ashley; Induni, Marta; Moiduddin, Emily – Mathematica Policy Research, Inc., 2010
As part of Phase 3 of the Universal Preschool Child Outcomes Study (UPCOS-3), Mathematica Policy Research worked with the First 5 LA Children and Families Commission and Los Angeles Universal Preschool (LAUP) to conduct a descriptive study of the characteristics of classrooms in LAUP programs during winter 2010. This study has a particular focus…
Descriptors: Second Language Learning, English (Second Language), Teaching Methods, Educational Practices
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