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Download full textXu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei – Journal of Educational Data Mining, 2013
This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…
Descriptors: Electronic Libraries, Use Studies, Multivariate Analysis, Data Analysis
Direct linkWu, Amery D.; Zumbo, Bruno D.; Marshall, Sheila K. – International Journal of Behavioral Development, 2014
This article describes a method based on Pratt's measures and demonstrates its use in exploratory factor analyses. The article discusses the interpretational complexities due to factor correlations and how Pratt's measures resolve these interpretational problems. Two real data examples demonstrate the calculation of what we call the…
Descriptors: Factor Analysis, Correlation, Comparative Analysis, Multiple Regression Analysis
Hao, Jiangang; Shu, Zhan; von Davier, Alina – Journal of Educational Data Mining, 2015
Students' activities in game/scenario-based tasks (G/SBTs) can be characterized by a sequence of time-stamped actions of different types with different attributes. For a subset of G/SBTs in which only the order of the actions is of great interest, the process data can be well characterized as a string of characters (i.e., action string) if we…
Descriptors: Task Analysis, Data Analysis, Vignettes, Correlation
Jennrich, Robert I.; Bentler, Peter M. – Psychometrika, 2012
Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…
Descriptors: Factor Structure, Factor Analysis, Models, Comparative Analysis
Ernst, Jeremy V.; Bowen, Bradley D.; Williams, Thomas O. – American Journal of Engineering Education, 2016
Students identified as at-risk of non-academic continuation have a propensity toward lower academic self-efficacy than their peers (Lent, 2005). Within engineering, self-efficacy and confidence are major markers of university continuation and success (Lourens, 2014 Raelin, et al., 2014). This study explored academic learning self-efficacy specific…
Descriptors: Engineering, Engineering Education, College Freshmen, Academic Achievement
Redlinger, Lawrence J.; Wiorkowski, John J.; Moses, Anna I. – New Directions for Institutional Research, 2012
The purpose of this chapter is to discuss the conceptual, methodological, and statistical challenges in selecting appropriate peer institutions for comparative purposes. The authors' approach embraces a Western scientific tradition that physical things and phenomena can be "reduced into a set of key variables--identifiable parts--that make key…
Descriptors: Factor Analysis, Colleges, Benchmarking, Institutional Research
Green, Samuel B.; Levy, Roy; Thompson, Marilyn S.; Lu, Min; Lo, Wen-Juo – Educational and Psychological Measurement, 2012
A number of psychometricians have argued for the use of parallel analysis to determine the number of factors. However, parallel analysis must be viewed at best as a heuristic approach rather than a mathematically rigorous one. The authors suggest a revision to parallel analysis that could improve its accuracy. A Monte Carlo study is conducted to…
Descriptors: Monte Carlo Methods, Factor Structure, Data Analysis, Psychometrics
Geiser, Christian; Lockhart, Ginger – Psychological Methods, 2012
Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…
Descriptors: Psychological Studies, Simulation, Measurement, Error of Measurement
Toplak, Maggie E.; Sorge, Geoff B.; Flora, David B.; Chen, Wai; Banaschewski, Tobias; Buitelaar, Jan; Ebstein, Richard; Eisenberg, Jacques; Franke, Barbara; Gill, Michael; Miranda, Ana; Oades, Robert D.; Roeyers, Herbert; Rothenberger, Aribert; Sergeant, Joseph; Sonuga-Barke, Edmund; Steinhausen, Hans-Christoph; Thompson, Margaret; Tannock, Rosemary; Asherson, Philip; Faraone, Stephen V. – Journal of Child Psychology and Psychiatry, 2012
Objective: To examine the factor structure of attention-deficit/hyperactivity disorder (ADHD) in a clinical sample of 1,373 children and adolescents with ADHD and their 1,772 unselected siblings recruited from different countries across a large age range. Hierarchical and correlated factor analytic models were compared separately in the ADHD and…
Descriptors: Attention Deficit Hyperactivity Disorder, Siblings, Factor Structure, Adolescents
Svetina, Dubravka – ProQuest LLC, 2011
The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in compensatory and noncompensatory multidimensional item response models (MIRT) of assessment data using dimensionality assessment procedures based on conditional covariances (i.e., DETECT) and a factor analytical approach (i.e., NOHARM). …
Descriptors: Item Response Theory, Factor Analysis, Correlation, Sample Size
Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Gunnell, Katie E.; Wilson, Philip M.; Zumbo, Bruno D.; Mack, Diane E.; Crocker, Peter R. E. – Measurement in Physical Education and Exercise Science, 2012
The researchers examined if scores from the original Psychological Need Satisfaction in Exercise Scale (Wilson, Rogers, Rodgers, & Wild, 2006) were invariant from a modified version specific to physical activity and then examined measurement invariance of scores across groups on the modified scale. Three groups were examined: (a) Students/staff…
Descriptors: Psychological Needs, Physical Activities, Structural Equation Models, Factor Structure
Nye, Christopher D.; Drasgow, Fritz – Journal of Applied Psychology, 2011
Because of the practical, theoretical, and legal implications of differential item functioning (DIF) for organizational assessments, studies of measurement equivalence are a necessary first step before scores can be compared across individuals from different groups. However, commonly recommended criteria for evaluating results from these analyses…
Descriptors: Effect Size, North American English, Comparative Analysis, Factor Analysis
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
Ruscio, John; Walters, Glenn D. – Psychological Assessment, 2009
Factor-analytic research is common in the study of constructs and measures in psychological assessment. Latent factors can represent traits as continuous underlying dimensions or as discrete categories. When examining the distributions of estimated scores on latent factors, one would expect unimodal distributions for dimensional data and bimodal…
Descriptors: Factor Analysis, Comparative Analysis, Data Analysis, Monte Carlo Methods
