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Showing 1 to 15 of 16 results Save | Export
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Abdolvahab Khademi; Craig S. Wells; Maria Elena Oliveri; Ester Villalonga-Olives – SAGE Open, 2023
The most common effect size when using a multiple-group confirmatory factor analysis approach to measurement invariance is [delta]CFI and [delta]TLI with a cutoff value of 0.01. However, this recommended cutoff value may not be ubiquitously appropriate and may be of limited application for some tests (e.g., measures using dichotomous items or…
Descriptors: Factor Analysis, Factor Structure, Error of Measurement, Test Items
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Hyunjung Lee; Heining Cham – Educational and Psychological Measurement, 2024
Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Goodness of Fit
Emily A. Brown – ProQuest LLC, 2024
Previous research has been limited regarding the measurement of computational thinking, particularly as a learning progression in K-12. This study proposes to apply a multidimensional item response theory (IRT) model to a newly developed measure of computational thinking utilizing both selected response and open-ended polytomous items to establish…
Descriptors: Models, Computation, Thinking Skills, Item Response Theory
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Karina Mostert; Clarisse van Rensburg; Reitumetse Machaba – Journal of Applied Research in Higher Education, 2024
Purpose: This study examined the psychometric properties of intention to drop out and study satisfaction measures for first-year South African students. The factorial validity, item bias, measurement invariance and reliability were tested. Design/methodology/approach: A cross-sectional design was used. For the study on intention to drop out, 1,820…
Descriptors: Intention, Potential Dropouts, Student Satisfaction, Test Items
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Zhong Jian Chee; Anke M. Scheeren; Marieke de Vries – Autism: The International Journal of Research and Practice, 2024
Despite several psychometric advantages over the 50-item Autism Spectrum Quotient, an instrument used to measure autistic traits, the abridged AQ-28 and its cross-cultural validity have not been examined as extensively. Therefore, this study aimed to examine the factor structure and measurement invariance of the AQ-28 in 818 Dutch (M[subscript…
Descriptors: Autism Spectrum Disorders, Questionnaires, Factor Structure, Factor Analysis
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Rujun Xu; James Soland – International Journal of Testing, 2024
International surveys are increasingly being used to understand nonacademic outcomes like math and science motivation, and to inform education policy changes within countries. Such instruments assume that the measure works consistently across countries, ethnicities, and languages--that is, they assume measurement invariance. While studies have…
Descriptors: Surveys, Statistical Bias, Achievement Tests, Foreign Countries
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Maïano, Christophe; Thibault, Isabelle; Dreiskämper, Dennis; Henning, Lena; Tietjens, Maike; Aimé, Annie – Measurement in Physical Education and Exercise Science, 2023
The present study sought to examine the psychometric properties of the French and German versions of the Physical Self-Concept Questionnaire for Elementary School Children-Revised (PSCQ-C-R). A sample of 519 children participated in this study. Of those, 197 were French-Canadian and 322 were German. Results support the factor validity and…
Descriptors: Elementary School Students, Self Concept, Human Body, Questionnaires
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Alhija, Fadia Nasser-Abu; Wisenbaker, Joseph – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A simulation study was conducted to examine the effect of item parceling on confirmatory factor analysis parameter estimates and their standard errors at different levels of sample size, number of indicators per factor, size of factor structure/pattern coefficients, magnitude of interfactor correlations, and variations in item-level data…
Descriptors: Monte Carlo Methods, Computation, Factor Analysis, Sample Size
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Seddon, G. M.; And Others – Journal of Educational Measurement, 1981
In a Monte Carlo simulation, a methodology was developed to investigate the existence of radex properties among objective test items. In an experiment with items covering four categories of Bloom's cognitive domain taxonomy, the items did not have the factorial properties of a radex with four levels of complexity. (Author/BW)
Descriptors: Correlation, Error of Measurement, Factor Analysis, Factor Structure
Divgi, D. R. – 1980
Because it is difficult to ascertain the dimensionality of a test composed of binary items through the use of factor analysis alone, a method is proposed that combines item characteristic curve (ICC) theory with factor analysis. Factor structure of tetrachoric correlations is distorted by non-normal distribution of ability. Item characteristics…
Descriptors: Achievement Tests, Error of Measurement, Factor Analysis, Factor Structure
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Schurr, K. Terry; Henriksen, L. W. – Journal of Educational Measurement, 1983
Using three different forms of a 61-item administrator/supervisor questionnaire, the effects of both order and grouping were examined in separate analyses. For low-inference type items, factor structures obtained from survey data appear to be affected by item order, and, to a lesser extent, by grouping items. (Author/PN)
Descriptors: Administrator Attitudes, Elementary Secondary Education, Error of Measurement, Factor Structure
Thompson, Bruce; Borrello, Gloria M. – 1987
Attitude measures frequently produce distributions of item scores that attenuate interitem correlations and thus also distort findings regarding the factor structure underlying the items. An actual data set involving 260 adult subjects' responses to 55 items on the Love Relationships Scale is employed to illustrate empirical methods for…
Descriptors: Adults, Analysis of Covariance, Attitude Measures, Correlation
Pilotte, William J.; Gable, Robert K. – 1989
Confirmatory factor analysis (LISREL VI) is the method best suited to the comparison of measurement models when those models are based on a priori assumptions. Traditionally, positive and negative item stems were mixed on affective scales to reduce response set bias since the item pairs were considered to be parallel. Recent studies indicate that…
Descriptors: Affective Measures, Computer Science Education, Error of Measurement, Factor Analysis
Marsh, Herbert W.; Hocevar, Dennis – 1986
The advantages of applying confirmatory factor analysis (CFA) to multitrait-multimethod (MTMM) data are widely recognized. However, because CFA as traditionally applied to MTMM data incorporates single indicators of each scale (i.e., each trait/method combination), important weaknesses are the failure to: (1) correct appropriately for measurement…
Descriptors: Computer Software, Construct Validity, Correlation, Error of Measurement
Morgan, Rick; Mazzeo, John – 1988
The dimensional structure of the 1987 Advanced Placement (AP) French language examination was tested in four populations using a series of confirmatory linear factor analysis models. To mitigate problems with the linear factor analysis of multiple choice items, the linear factor analysis of item parcel scores, made of small mutually exclusive…
Descriptors: Advanced Placement Programs, College Students, Comparative Analysis, Error of Measurement
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