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.

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

Executive Functions, Episodic Autobiographical Memory, Problem-Solving Capacity, and Depression Proposal for a Structural Equations Model

2019 ◽  
Vol 32 (2) ◽  
pp. 81-89
Author(s):  
Juan C. Meléndez ◽  
Rita Redondo ◽  
Joaquin Escudero ◽  
Encarna Satorres ◽  
Alfonso Pitarque
Keyword(s):  
Problem Solving ◽  
Executive Functions ◽  
Autobiographical Memory ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Depressive Symptomatology ◽  
Mood State ◽  
Structural Equations ◽  
Healthy Elderly ◽  
Structural Equations Model

The executive functions play an important role in storing and recovering autobiographical memories, especially episodic memories. These types of memories provide information about solutions and experiences from the past that can be utilized as examples in the present when seeking solutions to any problem. In addition, a close relationship between depression and the executive functions has been widely recognized. This study aims to elaborate a structural equations model that empirically supports the relationships among the executive functions, episodic autobiographical memory, and the adaptive capacity to solve problems, taking into account the depressed mood state. In all, 32 healthy elderly people, 32 patients with Parkinson disease, 32 with amnestic mild cognitive impairment, and 32 with Alzheimer disease were evaluated. Structural equation models were estimated to test the effects among the constructs. The final model shows adequate fit indexes, thus revealing that an individual’s problem-solving capacity will depend on the capacity to access the episodic autobiographical memory, which in turn will depend on the maintenance of executive functioning. In a parallel way, the mood state, and specifically depression, will play a modulator role because when there is depressive symptomatology, some capacities that depend on executive control can be diminished.

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.

scholarly journals Variables determining the growth of micro and small businesses

2019 ◽  
pp. 9-17
Author(s):  
Yara Landazuri-Aguilera
Keyword(s):  
Linear Regression ◽  
Small Businesses ◽  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Multiple Linear Regression Model ◽  
Structural Equations ◽  
Small Companies ◽  
Business Growth ◽  
Emerging Nations

Both developed and emerging nations share a high proportion of SMEs in their economic structure. For developing countries like Mexico, this situation can represent development opportunities. In this context, the objective of this study is to statistically analyze the relationship between the variables finance, technology, competition and human resources, with the variable dependent on business growth in the micro and small companies of southern Sonora. To do this, we propose a structural model, tested through the model of structural equations with estimated panel data and a contrast of said results through a multiple linear regression model. The sample size reached was 65 companies and the information was collected through interviews. The results through structural equation models and linear regression show that there is a positive and statistically significant relationship between viable technology and competition with business growth, not being so with the variables finances and human resource, when not being significant, concluding that the variables technology and competition explain the behavior of the dependent variable (business growth). The findings are consistent with those found in other research conducted in other countries.

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