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Xiaohui Luo; Yueqin Hu – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Intensive longitudinal data has been widely used to examine reciprocal or causal relations between variables. However, these variables may not be temporally aligned. This study examined the consequences and solutions of the problem of temporal misalignment in intensive longitudinal data based on dynamic structural equation models. First the impact…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Causal Models
Shi, Dingjing; Tong, Xin – Sociological Methods & Research, 2022
This study proposes a two-stage causal modeling with instrumental variables to mitigate selection bias, provide correct standard error estimates, and address nonnormal and missing data issues simultaneously. Bayesian methods are used for model estimation. Robust methods with Student's "t" distributions are used to account for nonnormal…
Descriptors: Bayesian Statistics, Monte Carlo Methods, Computer Software, Causal Models
Leiber, Theodor; Stensaker, Bjørn; Harvey, Lee – Quality in Higher Education, 2015
In this paper, the theoretical perspectives and general methodological elements of impact evaluation of quality assurance in higher education institutions are discussed, which should be a cornerstone of quality development in higher education and contribute to improving the knowledge about the effectiveness (or ineffectiveness) of quality…
Descriptors: Quality Assurance, Outcome Measures, Higher Education, Evaluation Methods

Campbell, Donald T. – Evaluation and Program Planning, 1996
Regression artifacts are a source of mistaken causal inference in inferences based on time-series data and from longitudinal studies. These artifacts are illustrated, and it is noted that their magnitude is computable (and distinguishable from genuine effects) if the autocorrelation patterns for various lags is known. (SLD)
Descriptors: Causal Models, Evaluation Methods, Longitudinal Studies, Regression (Statistics)

Haynes, Stephen N.; And Others – Psychological Assessment, 1995
Implications of phase space functions for psychological assessment are examined in this third article of the special section. The ability to predict the future time course of variables and the strength of causal relationships can be enhanced if temporal, dynamic, and nonlinear characteristics of variables are considered. (SLD)
Descriptors: Causal Models, Evaluation Methods, Longitudinal Studies, Prediction
Shanahan, Michael J.; Erickson, Lance D.; Bauer, Daniel J. – Journal of Research on Adolescence, 2005
The practice of science as a mode of discovery is subject to change. This paper examines the "sciences" practiced by G. Stanley Hall in his "Adolescence" of 1904 and by contemporary researchers who study youth in 2004. After briefly reviewing the nature of Hall's empiricism, we draw on a representative sample of articles (n=182) published between…
Descriptors: Longitudinal Studies, Evaluation Research, Research Methodology, Meta Analysis
Barth, Joan M.; Dunlap, Sarah T.; Dane, Heather; Lochman, John E.; Wells, Karen C. – Journal of School Psychology, 2004
Peers serve as reinforcers and models of behavior, and consequently classrooms containing high numbers of students with poor academic skills or behavior problems are likely to promote these behaviors in individual students. This study examined how variations in social and academic classroom composition as well as the larger school context affected…
Descriptors: Student Behavior, Classroom Environment, Peer Relationship, Children