Causal effects of academic self-concept on academic achievement: Structural equation models of longitudinal data.

1997 ◽  
Vol 89 (1) ◽  
pp. 41-54 ◽  
Author(s):  
Herbert W. Marsh ◽  
Alexander Seeshing Yeung
Keyword(s):  
Academic Achievement ◽  
Longitudinal Data ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Causal Effects ◽  
Self Concept

Parents' expectations and students' achievement in two western nations

2007 ◽  
Vol 31 (6) ◽  
pp. 594-602 ◽  
Author(s):  
Markus P. Neuenschwander ◽  
Mina Vida ◽  
Jessica L. Garrett ◽  
Jacquelynne S. Eccles
Keyword(s):  
Academic Achievement ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Educational Expectations ◽  
Sixth Graders ◽  
Life Transitions ◽  
Self Concept ◽  
High Predictability ◽  
Role Of Parents

The present study compares the relations of family SES and parents' educational expectations during early adolescence with students' self-concept of ability and academic achievement in mathematics and language in two western countries, Switzerland and USA Participants were drawn from two US longitudinal samples, The Michigan Study of Adolescent Life Transitions (1983) and the Childhood and Beyond study (1990) and a representative sample of Swiss sixth graders (2002). Results from a series of structural equation models indicate a high predictability and stability across nations indicating the broad usefulness of the model for understanding the role of parents' expectations on student's self-concepts and achievement.

scholarly journals Science Self-Concept – More Than the Sum of its Parts? A Comparison of Different Assessment and Modeling Approaches

2019 ◽  
Author(s):  
Ulrich Schroeders ◽  
Malte Jansen
Keyword(s):  
Structural Equation ◽  
Large Scale ◽  
Structural Equation Models ◽  
Factor Model ◽  
Self Concept ◽  
General Science ◽  
Theoretical Status ◽  
Subject Specific ◽  
Mean Structure ◽  
The Subject

Academic self-concept is understood as a multidimensional, hierarchical construct. Multidimensionality refers to the subject-specific differentiation of academic self-concepts, whereas hierarchy refers to the aggregation of more specific facets of self-concepts into more general ones. Previous research demonstrated that students distinguish between their self-concepts in biology, chemistry, and physics if taught as separate school subjects, as is done in Germany. However, large-scale international educational studies, such as PISA, often use a monolithic science self-concept measure. It is yet unclear whether an aggregate of subject-specific self-concepts is equivalent to a directly measured science self-concept. We assessed the subject-specific and and a general science self-concept of 1,232 German grade 10 students. A higher-order factor model and a bifactor model demonstrated a very high correlation between the “inferred” and the explicitly assessed general science self-concept. Despite the high empirical overlap, we argue for a more nuanced view of the science self-concept, because statistical unity is not to be confused with causal unity. Moreover, from a methodological perspective, we used multi-group confirmatory factor analysis to examine the mean structure and local weighted structural equation models to study measurement invariance across science ability. Implications for the theoretical status of self-concept as a hierarchical construct are discussed.

Analyzing Individual and Average Causal Effects via Structural Equation Models

Methodology ◽  
2005 ◽  
Vol 1 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Rolf Steyer
Keyword(s):  
Measurement Error ◽  
Untreated Control ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Causal Effect ◽  
Latent Trait ◽  
Causal Effects ◽  
Control Group ◽  
Expected Outcome ◽  
The Individual

Abstract. Although both individual and average causal effects are defined in Rubin's approach to causality, in this tradition almost all papers center around learning about the average causal effects. Almost no efforts deal with developing designs and models to learn about individual effects. This paper takes a first step in this direction. In the first and general part, Rubin's concepts of individual and average causal effects are extended replacing Rubin's deterministic potential-outcome variables by the stochastic expected-outcome variables. Based on this extension, in the second and main part specific designs, assumptions and models are introduced which allow identification of (1) the variance of the individual causal effects, (2) the regression of the individual causal effects on the true scores of the pretests, (3) the regression of the individual causal effects on other explanatory variables, and (4) the individual causal effects themselves. Although random assignment of the observational unit to one of the treatment conditions is useful and yields stronger results, much can be achieved with a nonequivalent control group. The simplest design requires two pretests measuring a pretest latent trait that can be interpreted as the expected outcome under control, and two posttests measuring a posttest latent trait: The expected outcome under treatment. The difference between these two latent trait variables is the individual-causal-effect variable, provided some assumptions can be made. These assumptions - which rule out alternative explanations in the Campbellian tradition - imply a single-trait model (a one-factor model) for the untreated control condition in which no treatment takes place, except for change due to measurement error. These assumptions define a testable model. More complex designs and models require four occasions of measurement, two pretest occasions and two posttest occasions. The no-change model for the untreated control condition is then a single-trait-multistate model allowing for measurement error and occasion-specific effects.

Construct Validation of Hebrew Versions of Three Physical Self-Concept Measures: An Extended Multitrait-Multimethod Analysis

2006 ◽  
Vol 28 (3) ◽  
pp. 310-343 ◽  
Author(s):  
Herbert W. Marsh ◽  
Michael Bar-Eli ◽  
Sima Zach ◽  
Garry E. Richards
Keyword(s):  
Structural Equation ◽  
Discriminant Validity ◽  
Structural Equation Models ◽  
A Priori ◽  
Self Esteem ◽  
Self Concept ◽  
Education Majors ◽  
Convergent And Discriminant Validity ◽  
Physical Education Majors ◽  
Validity Criteria

This study extends support for the construct validity of the three strongest physical self-concept measures for 395 Israeli university students (60% women) aged 18 to 54, demonstrating a new extension of the multitrait-multimethod (MTMM) design that incorporates external validity criteria and a test of jingle-jangle fallacies. Structural equation models of this MTMM data confirmed the a priori 23-factor structure of the three instruments, and the convergent and discriminant validity of factors from each instrument in relation to those from the other instruments. There were few age effects, whereas gender differences were smaller than expected and stable over age. In support of the known-group-difference approach, physical education majors had systematically higher physical self-concepts than management majors. Relations of body image to self-concept factors supported the convergent and discriminant validity of the physical self-concept factors and the separation of body fat from physical appearance self-concepts, but having a more obese body was not significantly related to health self-concept or global self-esteem factors.

scholarly journals The Use of Mixed Models for the Analysis of Mediated Data with Time-Dependent Predictors

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Emily A. Blood ◽  
Debbie M. Cheng
Keyword(s):  
Longitudinal Data ◽  
Mixed Models ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Type I Error ◽  
Time Dependent ◽  
Type I ◽  
Delayed Effects ◽  
Causal Pathways ◽  
Observational Cohort

Linear mixed models (LMMs) are frequently used to analyze longitudinal data. Although these models can be used to evaluate mediation, they do not directly model causal pathways. Structural equation models (SEMs) are an alternative technique that allows explicit modeling of mediation. The goal of this paper is to evaluate the performance of LMMs relative to SEMs in the analysis of mediated longitudinal data with time-dependent predictors and mediators. We simulated mediated longitudinal data from an SEM and specified delayed effects of the predictor. A variety of model specifications were assessed, and the LMMs and SEMs were evaluated with respect to bias, coverage probability, power, and Type I error. Models evaluated in the simulation were also applied to data from an observational cohort of HIV-infected individuals. We found that when carefully constructed, the LMM adequately models mediated exposure effects that change over time in the presence of mediation, even when the data arise from an SEM.

scholarly journals The dyspnoea–inactivity vicious circle in COPD: development and external validation of a conceptual model

2018 ◽  
Vol 52 (3) ◽  
pp. 1800079 ◽  
Author(s):  
Maria A. Ramon ◽  
Gerben Ter Riet ◽  
Anne-Elie Carsin ◽  
Elena Gimeno-Santos ◽  
Alvar Agustí ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Exercise Capacity ◽  
Structural Equation ◽  
Structural Equation Models ◽  
External Validation ◽  
Model Fit ◽  
Vicious Circle ◽  
Chronic Obstructive ◽  
New Model ◽  
Copd Exacerbations

The vicious circle of dyspnoea–inactivity has been proposed, but never validated empirically, to explain the clinical course of chronic obstructive pulmonary disease (COPD). We aimed to develop and validate externally a comprehensive vicious circle model.We utilised two methods. 1) Identification and validation of all published vicious circle models by a systematic literature search and fitting structural equation models to longitudinal data from the Spanish PAC-COPD (Phenotype and Course of COPD) cohort (n=210, mean age 68 years, mean forced expiratory volume in 1 s (FEV1) 54% predicted), testing both the hypothesised relationships between variables in the model (“paths”) and model fit. 2) Development of a new model and external validation using longitudinal data from the Swiss and Dutch ICE COLD ERIC (International Collaborative Effort on Chronic Obstructive Lung Disease: Exacerbation Risk Index Cohorts) cohort (n=226, mean age 66 years, mean FEV157% predicted).We identified nine vicious circle models for which structural equation models confirmed most hypothesised paths but showed inappropriate fit. In the new model, airflow limitation, hyperinflation, dyspnoea, physical activity, exercise capacity and COPD exacerbations remained related to other variables and model fit was appropriate. Fitting it to ICE COLD ERIC, all paths were replicated and model fit was appropriate.Previously published vicious circle models do not fully explain the vicious circle concept. We developed and externally validated a new comprehensive model that gives a more relevant role to exercise capacity and COPD exacerbations.

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