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Direct linkSchweizer, Karl; Gold, Andreas; Krampen, Dorothea – Educational and Psychological Measurement, 2023
In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at exploring the consequences of…
Descriptors: Data, Models, Factor Analysis, Correlation
Chanaa, Abdessamad; El Faddouli, Nour-eddine – International Journal of Information and Communication Technology Education, 2022
Massive open online courses (MOOCs) have evolved rapidly in recent years due to their open and massive nature. However, MOOCs suffer from a high dropout rate, since learners struggle to stay cognitively and emotionally engaged. Learner feedback is an excellent way to understand learner behaviour and model early decision making. In the presented…
Descriptors: MOOCs, Student Attitudes, Data Analysis, Electronic Learning
Zhao, Xin; Coxe, Stefany; Sibley, Margaret H.; Zulauf-McCurdy, Courtney; Pettit, Jeremy W. – Prevention Science, 2023
There has been increasing interest in applying integrative data analysis (IDA) to analyze data across multiple studies to increase sample size and statistical power. Measures of a construct are frequently not consistent across studies. This article provides a tutorial on the complex decisions that occur when conducting harmonization of measures…
Descriptors: Data Analysis, Sample Size, Decision Making, Test Items
Levy, Michal; Gumpel, Thomas P. – Journal of School Violence, 2018
This study explores correlations between bystanders' intervention styles by means of the bullying circle model. Three aims were examined in this study. First, we reevaluated the number and type of bystander intervention styles in aggressive school incidents. Second, we examined the association between reports of relational aggression and…
Descriptors: Audiences, Intervention, Bullying, Prevention
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
Jia, Fan; Moore, E. Whitney G.; Kinai, Richard; Crowe, Kelly S.; Schoemann, Alexander M.; Little, Todd D. – International Journal of Behavioral Development, 2014
Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This article explores this question by using…
Descriptors: Data Analysis, Statistical Inference, Error of Measurement, Computation
Download full textLiu, Ran; Koedinger, Kenneth R. – Journal of Educational Data Mining, 2017
As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis
Li, Lan; Gao, Fei – Assessment & Evaluation in Higher Education, 2016
Peer assessment has been increasingly integrated in educational settings as a strategy to foster student learning. Yet little has been studied about how students at different learning levels may benefit from peer assessment. This study examined how peer-assessment and students' learning levels influenced students' project performance using a…
Descriptors: Peer Evaluation, Performance Based Assessment, Factor Analysis, Statistical Analysis
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
Waters, Andrew; Studer, Christoph; Baraniuk, Richard – Journal of Educational Data Mining, 2014
Identifying collaboration between learners in a course is an important challenge in education for two reasons: First, depending on the courses rules, collaboration can be considered a form of cheating. Second, it helps one to more accurately evaluate each learners competence. While such collaboration identification is already challenging in…
Descriptors: Cooperation, Large Group Instruction, Online Courses, Probability
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
Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian – Multivariate Behavioral Research, 2011
Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor…
Descriptors: Simulation, Factor Analysis, Item Response Theory, Models
Nygaard, Egil; Jensen, Tine K.; Dyb, Grete – Journal of Clinical Child and Adolescent Psychology, 2012
The aim of this study was to evaluate the structure of posttraumatic stress reaction factors and their relation to general mental health problems in Norwegian children exposed to the tsunami on December 26, 2004. A total of 133 children and adolescents (ages 6-17) were interviewed 10 months posttsunami using the UCLA PTSD Reaction Index, and 104…
Descriptors: Posttraumatic Stress Disorder, Mental Health, Longitudinal Studies, Foreign Countries
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
Brydges, Christopher R.; Reid, Corinne L.; Fox, Allison M.; Anderson, Mike – Intelligence, 2012
Executive functions (EF) and intelligence are of critical importance to success in many everyday tasks. Working memory, or updating, which is one latent variable identified in confirmatory factor analytic models of executive functions, predicts intelligence (both fluid and crystallised) in adults, but inhibition and shifting do not (Friedman et…
Descriptors: Intelligence, Learning Disabilities, Inhibition, Task Analysis
