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Philipp Sterner; Florian Pargent; Dominik Deffner; David Goretzko – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Measurement invariance (MI) describes the equivalence of measurement models of a construct across groups or time. When comparing latent means, MI is often stated as a prerequisite of meaningful group comparisons. The most common way to investigate MI is multi-group confirmatory factor analysis (MG-CFA). Although numerous guides exist, a recent…
Descriptors: Structural Equation Models, Causal Models, Measurement, Predictor Variables
Vivian Chau; Valsamma Eapen; Erinn Hawkins; Jane Kohlhoff – Child & Youth Care Forum, 2025
Background: There is growing interest in research understanding the individual-specific predictors of child callous-unemotional (CU) traits, particularly in early childhood. Objective: This study reviewed evidence from studies that investigated the relationship between early child temperament factors (between 0 and 3 years) and CU traits in…
Descriptors: Children, Child Behavior, Student Behavior, Personality Traits
Thao-Trang Huynh-Cam; Long-Sheng Chen; Tzu-Chuen Lu – Journal of Applied Research in Higher Education, 2025
Purpose: This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability. Design/methodology/approach: The real-world…
Descriptors: Foreign Countries, Undergraduate Students, At Risk Students, Dropout Characteristics
Jaylin Lowe; Charlotte Z. Mann; Jiaying Wang; Adam Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2024
Recent methods have sought to improve precision in randomized controlled trials (RCTs) by utilizing data from large observational datasets for covariate adjustment. For example, consider an RCT aimed at evaluating a new algebra curriculum, in which a few dozen schools are randomly assigned to treatment (new curriculum) or control (standard…
Descriptors: Randomized Controlled Trials, Middle School Mathematics, Middle School Students, Middle Schools
Ting, Choo-Yee; Sam, Yok-Cheng; Wong, Chee-Onn – Computers & Education, 2013
Constructing a computational model of conceptual change for a computer-based scientific inquiry learning environment is difficult due to two challenges: (i) externalizing the variables of conceptual change and its related variables is difficult. In addition, defining the causal dependencies among the variables is also not trivial. Such difficulty…
Descriptors: Concept Formation, Bayesian Statistics, Inquiry, Science Instruction
Davey, Carla Mae – ProQuest LLC, 2010
According to generational theorists, the interests and experiences of incoming students have fluctuated over time, with Millennial students being more engaged and accomplished than their predecessors. This project explored data from 1974-2007 to determine the actual trends in engagement and accomplishments for three generations of students. Over…
Descriptors: Learner Engagement, School Activities, Grade Point Average, School Holding Power
Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M. – Learning and Motivation, 2006
Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…
Descriptors: Time, Retention (Psychology), Intervals, Stimuli
Sun, Xiaogeng; Hoffman, Sharon C.; Grady, Marilyn L. – International Journal of Educational Advancement, 2007
Despite readily available alumni survey data warehoused at many alumni associations and foundations across colleges and universities, researchers have underutilized the abundant available data to identify key predictors of alumni donation, including factors that trigger alumni donation behavior. Utilizing the data from a two-year alumni survey…
Descriptors: Causal Models, Alumni, Gender Differences, Age Differences