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Lohmann, Julian F.; Zitzmann, Steffen; Voelkle, Manuel C.; Hecht, Martin – Large-scale Assessments in Education, 2022
One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that "continuous-time models" are well suited to study the continuously developing constructs of primary interest in the education…
Descriptors: Longitudinal Studies, Structural Equation Models, Time, Achievement Tests
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Wook, Muslihah; Yusof, Zawiyah M.; Nazri, Mohd Zakree Ahmad – Education and Information Technologies, 2017
The acceptance of Educational Data Mining (EDM) technology is on the rise due to, its ability to extract new knowledge from large amounts of students' data. This knowledge is important for educational stakeholders, such as policy makers, educators, and students themselves to enhance efficiency and achievements. However, previous studies on EDM…
Descriptors: Educational Research, Information Retrieval, Data Analysis, Educational Technology
Nese, Joseph F. T.; Lai, Cheng-Fei; Anderson, Daniel – Behavioral Research and Teaching, 2013
Longitudinal data analysis in education is the study growth over time. A longitudinal study is one in which repeated observations of the same variables are recorded for the same individuals over a period of time. This type of research is known by many names (e.g., time series analysis or repeated measures design), each of which can imply subtle…
Descriptors: Longitudinal Studies, Data Analysis, Educational Research, Hierarchical Linear Modeling
Blanchard, Rebecca D.; Konold, Timothy R. – Online Submission, 2011
This paper introduces latent growth modeling (LGM) as a statistical method for analyzing change over time in latent, or unobserved, variables, with particular emphasis of the application of this method in higher education research. While increasingly popular in other areas of education research and despite a wealth of publicly-available datasets…
Descriptors: Data Analysis, Statistical Analysis, Structural Equation Models, Higher Education
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Lee, In Heok – Career and Technical Education Research, 2012
Researchers in career and technical education often ignore more effective ways of reporting and treating missing data and instead implement traditional, but ineffective, missing data methods (Gemici, Rojewski, & Lee, 2012). The recent methodological, and even the non-methodological, literature has increasingly emphasized the importance of…
Descriptors: Vocational Education, Data Collection, Maximum Likelihood Statistics, Educational Research
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Mayhew, Fred – Journal of MultiDisciplinary Evaluation, 2011
Background: Evaluation is a tool that can promote accountability and enhance organizational improvement. For these reasons funding entities from government to foundations are increasingly relying on program evaluation as a key instrument to determine effectiveness and hold recipient organizations accountable. What has ensued is an environment of…
Descriptors: Philanthropic Foundations, Financial Support, Colleges, Educational Research
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Varela, Otmar E.; Cater, John James, III; Michel, Norbert – Human Resource Development Quarterly, 2011
This study tests a process model of learning in which trainer and trainee traits are simultaneously considered as endogenous variables of learning outcomes. The article builds on a social view of training and similarity-attraction paradigms. In this context, the authors hypothesize that trainer-trainee similarity in personality (agreeableness)…
Descriptors: Evidence, Undergraduate Students, Personality Traits, Interpersonal Attraction
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Moore, Alan D. – Remedial and Special Education, 1995
This article suggests the use of structural equation modeling in special education research, to analyze multivariate data from both nonexperimental and experimental research. It combines a structural model linking latent variables and a measurement model linking observed variables with latent variables. (Author/DB)
Descriptors: Data Analysis, Disabilities, Educational Research, Elementary Secondary Education
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Keith, Timothy Z. – Remedial and Special Education (RASE), 1993
This overview of nonexperimental causal research methods focuses on latent variable structural equation modeling using the LISREL computer program. An extended example in special education is used to present LISREL as an extension of structural equations analysis (path analysis) and as a method of reducing the effects of error in research.…
Descriptors: Causal Models, Computer Oriented Programs, Computer Software, Data Analysis