scholarly journals Simultaneous estimation of indirect and interaction effects using structural equation models

2004 ◽  
Vol 1 (1) ◽  
pp. 163-184
Author(s):  
Joan Manuel Batista-Foguet ◽  
Germà Coenders ◽  
Willem Saris ◽  
Josep Bisbe
Keyword(s):  
Regression Analysis ◽  
Measurement Error ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Interaction Effects ◽  
Linear Constraints ◽  
Simultaneous Estimation ◽  
Equation Approach ◽  
Direct Effects ◽  
And Performance

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting for measurement error. From the various specifications, Jöreskog and Yang's (1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance.

scholarly journals Behavioral engagement and disaffection in school activities: Exploring a model of motivational facilitators and performance outcomes

2015 ◽  
Vol 31 (3) ◽  
pp. 869 ◽  
Author(s):  
Antonio González ◽  
Paola Verónica Paoloni
Keyword(s):  
Perceived Control ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Performance Outcomes ◽  
Theory And Practice ◽  
Behavioral Engagement ◽  
School Activities ◽  
Structural Relationships ◽  
And Performance

Previous research has shown that perceived control, task value, behavioral engagement and disaffection are personal determinants of academic performance. However, little research has simultaneously examined these constructs in secondary education. The present study analyzed the structural relationships between these variables and the role of engagement and disaffection as mediators of control and value on performance. Participants were 446 students (51.3% girls) ranging in age from 12 to 16 years attending six Spanish compulsory secondary schools (from 7th to 10th grades). The variables were assessed over a nine-month period. Structural equation models results confirmed the hypotheses: control and value significantly predicted engagement, disaffection, and performance; engagement and disaffection predicted performance and partially mediated the effects from control and value on performance. Implications for psycho-educational theory and practice are discussed.

scholarly journals Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics

1981 ◽  
Vol 18 (3) ◽  
pp. 382-388 ◽  
Author(s):  
Claes Fornell ◽  
David F. Larcker
Keyword(s):  
Statistical Analysis ◽  
Measurement Error ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Structural Equations ◽  
Testing System

Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.

The Impact of Inertia as Mediator and Antecedent on Consumer Loyalty and Continuance Intention

2018 ◽  
pp. 960-981 ◽  
Author(s):  
Donald Louis Amoroso ◽  
Pajaree Ackaradejruangsri ◽  
Ricardo A. Lim
Keyword(s):  
Information Systems ◽  
Relationship Marketing ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Explanatory Power ◽  
Consumer Attitudes ◽  
Consumer Loyalty ◽  
Direct Effects ◽  
Continuance Intention ◽  
The Impact

This study builds on existing loyalty literature and theories, and extends to include consumer attitudes impact on continuous intention and loyalty based on relationship marketing and information systems. Three structural equation models built from a survey of 458 mobile Thai consumers revealed that inertia was the strongest factor among all constructs in predicting consumer loyalty and continuance intention, either as mediator or antecedent. Support was found for all of the hypothesized relationships for consumers using mobile wallet apps, except for the path between loyalty and continuance intention. Though the direct effects of consumer attitudes were more or less constant, satisfaction became insignificant when inertia acted as a mediator. As an antecedent to both consumer attitudes and satisfaction, inertia significantly increased the explanatory power of continuance intention and loyalty. This study provides new insights into factors that influence loyalty and continuance intention in the context of mobile wallet applications.

Using structural equation models to evaluate the magnitude of measurement error in blood pressure

2001 ◽  
Vol 20 (15) ◽  
pp. 2351-2368 ◽  
Author(s):  
J. M. Batista-Foguet ◽  
G. Coenders ◽  
M. Artés Ferragud
Keyword(s):  
Blood Pressure ◽  
Measurement Error ◽  
Structural Equation ◽  
Structural Equation Models

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

1981 ◽  
Vol 18 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Claes Fornell ◽  
David F. Larcker
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Statistical Tests ◽  
Explanatory Power ◽  
Measurement Model ◽  
Measurement Properties ◽  
Chi Square

The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.

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