Publication Date
| In 2026 | 0 |
| Since 2025 | 13 |
| Since 2022 (last 5 years) | 67 |
| Since 2017 (last 10 years) | 403 |
| Since 2007 (last 20 years) | 1223 |
Descriptor
| Factor Analysis | 1340 |
| Structural Equation Models | 1340 |
| Foreign Countries | 681 |
| Correlation | 445 |
| Statistical Analysis | 382 |
| Questionnaires | 340 |
| Student Attitudes | 240 |
| Measures (Individuals) | 223 |
| Goodness of Fit | 217 |
| Predictor Variables | 204 |
| Factor Structure | 181 |
| More ▼ | |
Source
Author
Publication Type
Education Level
Audience
| Researchers | 8 |
| Teachers | 3 |
| Students | 2 |
| Practitioners | 1 |
Location
| Turkey | 75 |
| Taiwan | 56 |
| China | 54 |
| Australia | 48 |
| Germany | 40 |
| Malaysia | 38 |
| South Korea | 29 |
| Hong Kong | 28 |
| Spain | 24 |
| Norway | 23 |
| Canada | 21 |
| More ▼ | |
Laws, Policies, & Programs
| Americans with Disabilities… | 2 |
| No Child Left Behind Act 2001 | 2 |
| Individuals with Disabilities… | 1 |
| Rehabilitation Act 1973… | 1 |
Assessments and Surveys
What Works Clearinghouse Rating
| Does not meet standards | 1 |
Myers, Nicholas D.; Feltz, Deborah L.; Chase, Melissa A.; Reckase, Mark D.; Hancock, Gregory R. – Educational and Psychological Measurement, 2008
The purpose of this validity study was to improve measurement of coaching efficacy, an important variable in models of coaching effectiveness. A revised version of the coaching efficacy scale (CES) was developed for head coaches of high school teams (CES II-HST). Data were collected from head coaches of 14 relevant high school sports (N = 799).…
Descriptors: Factor Structure, Measures (Individuals), Factor Analysis, Athletic Coaches
Peer reviewedThompson, Bruce – Educational and Psychological Measurement, 1997
A general linear model framework is used to suggest that structure coefficients ought to be interpreted in structural equation modeling confirmatory factor analysis (CFA) studies in which factors are correlated. Two heuristic data sets make the discussion concrete, and two additional studies illustrate the benefits of CFA structure coefficients.…
Descriptors: Factor Analysis, Mathematical Models, Structural Equation Models
Peer reviewedMcArdle, J. J.; Cattell, Raymond B. – Multivariate Behavioral Research, 1994
Some problems of multiple-group factor rotation based on the parallel proportional profiles and confactor rotation of R. B. Cattell are described, and several alternative modeling solutions are proposed. Benefits and limitations of the structural-modeling approach to oblique confactor resolution are examined, and opportunities for research are…
Descriptors: Factor Analysis, Factor Structure, Structural Equation Models
Maydeu-Olivares, Albert; Coffman, Donna L. – Psychological Methods, 2006
The common factor model assumes that the linear coefficients (intercepts and factor loadings) linking the observed variables to the latent factors are fixed coefficients (i.e., common for all participants). When the observed variables are participants' observed responses to stimuli, such as their responses to the items of a questionnaire, the …
Descriptors: Factor Analysis, Structural Equation Models, Item Analysis
Ruchkin, Vladislav; Jones, Stephanie; Vermeiren, Robert; Schwab-Stone, Mary – Psychological Assessment, 2008
This study examined the factor structure of the Strengths and Difficulties Questionnaire (SDQ) in urban inner-city and suburban general population samples of American youth. The SDQ was administered to 4,661 predominantly minority urban youth (mean age = 13.0 years, SD = 2.02) and 937 predominantly Caucasian suburban youth (mean age = 14.0 years,…
Descriptors: Emotional Problems, Structural Equation Models, Factor Structure, Measures (Individuals)
Hawk, Skyler T.; Hale, William W., III; Raaijmakers, Quinten A. W.; Meeus, Wim – Journal of Early Adolescence, 2008
Following suggestions from prior research, this 2-wave longitudinal study examined whether parental solicitation and control influenced adolescents' later perceptions of privacy invasion. Data from 307 Dutch adolescents were tested with structural equation modeling (SEM). Differences in adolescents' invasion perceptions were examined in terms of…
Descriptors: Structural Equation Models, Parent Child Relationship, Adolescents, Adolescent Attitudes
van Daal, John; Verhoeven, Ludo; van Leeuwe, Jan; van Balkom, Hans – Journal of Communication Disorders, 2008
In the present study, the relations of various aspects of working memory to various aspects of language problems in a clinical sample of 97 Dutch speaking 5-year-old children with severe language problems were studied. The working memory and language abilities of the children were examined using an extensive battery of tests. Working memory was…
Descriptors: Semantics, Language Impairments, Memory, Severe Disabilities
Martin, Andrew J.; Marsh, Herbert W. – Journal of School Psychology, 2008
Academic buoyancy is developed as a construct reflecting everyday academic resilience within a positive psychology context and is defined as students' ability to successfully deal with academic setbacks and challenges that are typical of the ordinary course of school life (e.g., poor grades, competing deadlines, exam pressure, difficult…
Descriptors: Structural Equation Models, Self Efficacy, Factor Analysis, Teacher Student Relationship
Sun, Jun; Willson, Victor L. – Educational and Psychological Measurement, 2008
This article proposes a multilevel modeling approach to study the general and specific attitudes formed in human learning behavior. Based on the premises of activity theory, it conceptualizes the unit of analysis for attitude measurement as a scalable and evolving activity system rather than a single action. Measurement issues related to this…
Descriptors: Structural Equation Models, Psychometrics, Multitrait Multimethod Techniques, Attitude Measures
Olsen, Joseph A.; Kenny, David A. – Psychological Methods, 2006
Structural equation modeling (SEM) can be adapted in a relatively straightforward fashion to analyze data from interchangeable dyads (i.e., dyads in which the 2 members cannot be differentiated). The authors describe a general strategy for SEM model estimation, comparison, and fit assessment that can be used with either dyad-level or pairwise…
Descriptors: Structural Equation Models, Data Analysis, Models, Factor Analysis
Selim, Hassan M. – Computers & Education, 2007
E-learning, one of the tools emerged from information technology, has been integrated in many university programs. There are several factors that need to be considered while developing or implementing university curriculums that offer e-learning based courses. This paper is intended to specify e-learning critical success factors (CSFs) as…
Descriptors: Information Technology, College Students, Online Courses, Higher Education
Kung, Hsin-Yi – Policy Futures in Education, 2009
The Third International Mathematics and Science Study research of the International Association for the Evaluation of Educational Achievement reported in 2003 that Taiwanese fourth- and eighth-graders' mathematics performance exceeded the international average; both groups ranked fourth from among all the participant countries. However, the Index…
Descriptors: Mathematics Education, Self Efficacy, Mathematics Achievement, Foreign Countries
Gardner, Robert D. – Arts Education Policy Review, 2010
The purpose of this study was to construct a profile of K-12 music teachers in the United States and develop a model to predict their retention, turnover, and attrition. Responses to the "Schools and Staffing Survey" from 47,857 K-12 public and private school teachers, including 1,903 music teachers, were analyzed using comparative…
Descriptors: Teaching (Occupation), Private Schools, Elementary Secondary Education, Structural Equation Models
Peer reviewedMarkus, Keith A. – Structural Equation Modeling, 2000
Explores the four-step procedure for testing structural equation models and outlines some problems with the approach advocated by L. Hayduk and D. Glaser (2000) and S. Mulaik and R. Milsap (2000). Questions the idea that there is a "correct" number of constructs for a given phenomenon. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Structural Equation Models
Peer reviewedGreen, Samuel B.; Thompson, Marilyn S.; Babyak, Michael A. – Multivariate Behavioral Research, 1998
Simulated data for factor analytic models is used in the evaluation of three methods for controlling Type I errors: (1) the standard approach that involves testing each parameter at the 0.05 level; (2) the Bonferroni approach; and (3) a simultaneous test procedure (STP). Advantages offered by the Bonferroni approach are discussed. (SLD)
Descriptors: Factor Analysis, Monte Carlo Methods, Simulation, Structural Equation Models

Direct link
