Analysis of Structural Equation Modeling as a Measuring Tool for Educational Management Research

In management education and psychology there are certain concepts that cannot bewell defined and then various discussions arise about the true meaning of the concept.Concepts such as management intelligence, personality, attitudes, interests, ambitions, socialprejudice and social status are hypothetical constructs that are not available in operationalmethods that can directly measure them. Concepts such as intelligence, personality, attitudes,interests, ambitions, social prejudice, social status are hypothetical constructs that are notavailable operational methods that can directly measure them. This study aims to determine thebackground of the use of Structural Equation Modeling (SEM), understanding SEM, basicconcepts of SEM: constructs, manifest variables, validity, reliability, factor analysis, polycoriccorrelation, causal relationships, LISREL: Linear Structural Relationship, SEM procedures :definition of variance and covariance, model specifications, model identification, modelestimation, model formation, model compatibility test, model specification, LISREL programoutput. Symbols in SEM and SEM mathematical equations. The method used during this studytook place, namely using a literature study method which functions so that in research,researchers continue to add insight

Structural equation modeling of political discussion networks

This study conducts structural equation modeling (SEM) of political discussion networks. It examines multiple relationships between political discussion networks—network size and non-kin composition, political efficacy, and neighborhood conversation. Based on a two-step approach, it first analyzes and revises the measurement model and then analyzes and revises the structural model given the revised measurement model. The proposed SEM model includes ordered categorical variables as factor indicators in the confirmatory analysis and outcome variables in the structural regressions. Traditional estimation and regression methods need to be adjusted accordingly. This study uses WLS estimation and adopts a latent variable approach to study the categorical outcome variables in the SEM. The results show that the hypothesized SEM model is fully supported. Neighborhood conversation positively and directly contributes to political discussion network size as well as the non-kin composition of the networks. It also indirectly affects network size through political efficacy. Political efficacy also has a direct effect on network size.Statistic

Modeling alcohol use disorder severity: An integrative structural equation modeling approach

Background: Alcohol dependence is a complex psychological disorder whose phenomenology changes as the disorder progresses. Neuroscience has provided a variety of theories and evidence for the development, maintenance, and severity of addiction; however, clinically, it has been difficult to evaluate alcohol use disorder (AUD) severity.Objective: This study seeks to evaluate and validate a data-driven approach to capturing alcohol severity in a community sample.Method: Participants were non-treatment seeking problem drinkers (n = 283). A structural equation modeling approach was used to (a) verify the latent factor structure of the indices of AUD severity; and (b) test the relationship between the AUD severity factor and measures of alcohol use, affective symptoms, and motivation to change drinking.Results: The model was found to fit well, with all chosen indices of AUD severity loading significantly and positively onto the severity factor. In addition, the paths from the alcohol use, motivation, and affective factors accounted for 68% of the variance in AUD severity. Greater AUD severity was associated with greater alcohol use, increased affective symptoms, and higher motivation to change.Conclusion: Unlike the categorical diagnostic criteria, the AUD severity factor is comprised of multiple quantitative dimensions of impairment observed across the progression of the disorder. The AUD severity factor was validated by testing it in relation to other outcomes such as alcohol use, affective symptoms, and motivation for change. Clinically, this approach to AUD severity can be used to inform treatment planning and ultimately to improve outcomes. © 2013 Moallem, Courtney, Bacio and Ray

Penerapan Metode Structural Equation Modeling (Sem) dalam Menentukan Pengaruh Kepuasan, Kepercayaan dan Mutu terhadap Kesetiaan Pasien Rawat Jalan dalam Memanfaatkan Pelayanan Rumah Sakit di RSUD Dr. Pirngadi Medan Tahun 2012

Implementation of Structural Equation Modeling (SEM) Method in Determining the Effects of Satisfaction, Trust, and Quality towards Out Patients' Loyalty Using Hospital Services at Dr.Pirngadi Local General Hospital Medan in 2012. SEM (Structural Equation Modeling) is a statistical technique that can analyze a relationship pattern between a latent construct and its indicators, another latent construct with the other latent constructs, and a direct incorrect measurement. SEM is a statistical techniques that can test a series of relationships simultaneously. In this research a series of simultaneous relationship - satisfaction, trust, quality, and patient's loyalty is conducted. Patients are the most important persons in the hospital, both as the consumers of the medical services and as the hospital products. The patient's loyalty is not formed in a short time, but it is based on the patient's own experience from the consistent and repeated uses of hospital. The purposes of this research are to analyze the effects of satisfaction, trust, and the quality of services towards the patients by using the SEM at Dr. Pirngadi Hospital Medan in 2012. The results of the research show that the variable of service quality influences the patient's satisfaction (p=0,001), quality influences the trust (p=0,001), quality influences the loyalty (p=0,032), the satisfaction influences the loyalty (p=0,014) and trust influences the loyalty (p=0,004). The data with Fit model based on Goodness-of Fit Index criteria result in the Chi-Square evaluation 190,011, the significant level 0,073 and the value of RMSEA (0,026), GFI (0,935), AGFI (0,899), CMIN/df (1,166), TLI (0,984), and CFI (0,989). There is an indirect effect of quality services on loyalty through the satisfaction and trust variables that they are both called interference variables

Predicting Students’ Physical Activity and Health-Related Well-Being: A Prospective Cross-Domain Investigation of Motivation Across School Physical Education and Exercise Settings \ud

A three-wave prospective design was used to assess a model of motivation guided by self-determination theory (Ryan & Deci, 2008) spanning the contexts of school physical education (PE) and exercise. The outcome variables examined were health-related quality of life (HRQoL), physical self-concept (PSC), and 4 days of objectively assessed estimates of activity. Secondary school students (n = 494) completed questionnaires at three separate time points and were familiarized with how to use a sealed pedometer. Results of structural equation modeling supported a model in which perceptions of autonomy support from a PE teacher positively predicted PE-related need satisfaction (autonomy, competence, and relatedness). Competence predicted PSC, whereas relatedness predicted HRQoL. Autonomy and competence positively predicted autonomous motivation toward PE, which in turn positively predicted autonomous motivation toward exercise (i.e., 4-day pedometer step count). Autonomous motivation toward exercise positively predicted step count, HRQoL, and PSC. Results of multisample structural equation modeling supported gender invariance. Suggestions for future work are discussed.\ud \u