Validity Of Structural Equation Modeling Research Articles

Effects of Reducing the Length of the Questionnaire by Multiple Matrix Sampling on the Validity of Structural Equation Modeling for Factors Affecting Job Morale

The research was designed to examine the effects of question setting using different conditions into 10 sets on the validity of structural equation modeling for factors affecting job morale. The data was collected from 690 personnel working in regional Statistical Offices around Thailand by using cluster random sampling. The tool used in collecting data was the questionnaire with 95 items and 5 levels of rating scale. The discrimination value was between 0.244 and 0.860 and the reliability: α was from 0.699 to 0.900. Data analysis was conducted by the use of descriptive statistics and structural equation modeling by Mplus 7.4 program. The findings showed that the structural equation modeling with question setting in 3 sets of sub-questionnaires, cooperated with item sampling without replacement and non-fixing core items, conformed to the empirical data the most and that overall relationship between the structural model parameter and the model parameter from the complete questionnaire was high in a positive way.

Validity of the Turkish version of the European Heart Failure Self-Care Behavior Scale

The specific purpose of this study was to investigate the validity of the Turkish version of the European Heart Failure Self-care Behavior Scale by applying to 494 Turkish Heart Failure Patients. The study was conducted as a cross-sectional sample survey, between October 2012 and January 2013. The 494 patients who applied with a diagnosis of heart failure were included in the study after determining the clinical diagnosis and type of treatment. Cronbach's alpha coefficient was used to evaluate the validity and reliability of the scale. Exploratory and confirmatory factor analyses were applied for the determination of the sub-factors of the scale in Turkish adaptation; content, item and factor adaption. Structural Equation Modeling was used for the purpose of creation and supervision of the structural models of the scale. Cronbach's alpha coefficient of internal consistency of Turkish version of the Scale was found to be 0.69. According to the results of the factor analysis, it was determined that the data is in a form suitable for factor analysis and the data have the assumption of multivariate normal distribution. The goodness of fit measures used for the validity of Structural Equation Modeling were obtained to be RMSEA=0.047 (CI=0.00-0.079), AGFI=0.83, GFI=0.91 respectively. The scale is divided into 4 sub-factors according to the Structural Equation Modeling. The European Heart Failure Self-care Behavior Scale is a scale that easily applied to measure the behavior of self-care in heart failure patients. In addition, the scale reaches the conclusion as soon as possible and does not require additional training for researchers. Further, patients can apply themselves the scale easily.

Reliability and validity of structural equation modeling applied to neuroimaging data: A simulation study

Structural equation modeling aims at quantifying the strength of causal relationships within a set of interacting variables. Although the literature emphasizes that large sample sizes are required, this method is increasingly used with neuroimaging data of a limited number of subjects to study the relationships between cerebral structures. Here, we use a simulation approach to evaluate its ability to provide accurate information under the constraints of neuroimaging. Artificial samples representing the activity of a virtual set of structures were generated under both recursive and non-recursive connectivity models. Structural equation modeling was performed on these samples, and the quality of the analyses was evaluated by directly comparing the estimated path coefficients with the original ones. The validity and the reliability are shown to decrease with sample size, but the estimated models respect the relative strength of path coefficients in a large percentage of cases. The “smoothing method” appears to be the most appropriate to prevent improper solutions. Both the experimental error and the external structures influencing the network have a weak influence. Accordingly, structural equation modeling can be applied to neuroimaging data, but confidence intervals should be presented together with the path coefficient estimation.