scholarly journals The Effect of Weighting Data on the Goodness of Fit Indicators of the Six Sigma Structural Equation Modeling

2021 ◽  
Vol 2 (2) ◽  
pp. 36-49
Author(s):  
Mohammed Al-Ghmadi ◽  
Ezz Abdelfattah ◽  
Ahmed Ezz
Keyword(s):  
Structural Equation Modeling ◽  
Covariance Matrix ◽  
Six Sigma ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Estimation Method ◽  
Equation Modeling ◽  
Rank Test ◽  
Data Set ◽  
Variance Covariance Matrix

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators.  A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea.   The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.

Childhood Intelligence Predicts Adult Trait Openness

2016 ◽  
Vol 37 (2) ◽  
pp. 105-111 ◽  
Author(s):  
Adrian Furnham ◽  
Helen Cheng
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Social Background ◽  
Study Data ◽  
Equation Modeling ◽  
Direct Effects ◽  
Data Set ◽  
National Child Development Study ◽  
Nationally Representative ◽  
The Uk

Abstract. This study used a longitudinal data set of 5,672 adults followed for 50 years to determine the factors that influence adult trait Openness-to-Experience. In a large, nationally representative sample in the UK (the National Child Development Study), data were collected at birth, in childhood (age 11), adolescence (age 16), and adulthood (ages 33, 42, and 50) to examine the effects of family social background, childhood intelligence, school motivation during adolescence, education, and occupation on the personality trait Openness assessed at age 50 years. Structural equation modeling showed that parental social status, childhood intelligence, school motivation, education, and occupation all had modest, but direct, effects on trait Openness, among which childhood intelligence was the strongest predictor. Gender was not significantly associated with trait Openness. Limitations and implications of the study are discussed.

scholarly journals Explanation of Children’s Health through Parents’ Mental Health and Job Satisfaction by Structural Equation Modeling

2016 ◽  
Vol 9 (2) ◽  
pp. 166
Author(s):  
Majid Golzarpour ◽  
Meroe Vameghi ◽  
Homeira Sajjadi ◽  
Gholamreza Ghaedamini Harouni
Keyword(s):  
Mental Health ◽  
Job Satisfaction ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
General Health Questionnaire ◽  
Children's Health ◽  
Children’S Health ◽  
Equation Modeling ◽  
Health Questionnaire

<p><strong>BACKGROUND:</strong> Worldwide, much evidence exists on the influence of parents’ socioeconomic conditions, including employment, on children’s health. However, the mechanisms for this affect are still being investigated. Few studies have been conducted in Iran to investigate this issue. This study investigated working conditions, job satisfaction, and mental health of employed people and the association between these variables and their children’s health.<strong></strong></p><p><strong>MATERIALS &amp; METHODS:</strong> In this correlational work, 200 male and female staff of the official part of Educational Organization and the schools of Mashhad with children aged 5-18 years was randomly selected. The data were gathered using a demographic questionnaire, the 20-item Minnesota Job Satisfaction Questionnaire, the 28-item General Health Questionnaire, and the 28-item Child Health Questionnaire. The data were then analyzed using SPSS. The associations under study were investigated by structural equation modeling in AMOS.<strong></strong></p><p><strong>RESULTS:</strong> Approximately 17% of the variation in the parents’ job satisfaction could be explained by the parents’ insurance, income, and work hours; 6% of the variation in their mental health was explained by job satisfaction, and 26% of the variation in children’s health was directly explained by the parents’ job satisfaction and mental health. However, approximately 32.2% of the variation in children’s health could be explained in the light of the direct effect of the parents’ mental health and direct and indirect effects of the parents’ job satisfaction. The goodness of fit index was 0.94.</p><p><strong>CONCLUSION:</strong> Parents’ job satisfaction was associated with and considerably explained children’s health. Although this finding may be partially related to the job satisfaction effect on mental health, the reasons for the affect of job satisfaction on children’s health and the potential mechanisms of this association require further studies.<strong></strong></p>

scholarly journals The reasoned action approach applied to health behavior: Role of past behavior and test of some key moderators using meta-analytic structural equation modeling

2018 ◽  
Author(s):  
Martin S Hagger ◽  
Juho Polet ◽  
Taru Lintunen
Keyword(s):  
Structural Equation Modeling ◽  
Health Behavior ◽  
Indirect Effects ◽  
Structural Equation ◽  
Social Cognitive ◽  
Reasoned Action ◽  
Equation Modeling ◽  
Reasoned Action Approach ◽  
Data Set ◽  
Past Behavior

Rationale: The reasoned action approach (RAA) is a social cognitive model that outlines the determinants of intentional behavior. Primary and meta-analytic studies support RAA predictions in multiple health behaviors. However, including past behavior as a predictor in the RAA may attenuate model effects. Direct effects of past behavior on behavior may reflect non-conscious processes while indirect effects of past behavior through social cognitive variables may represent reasoned processes. Objective: The present study extended a previous meta-analysis of the RAA by including effects of past behavior. The analysis also tested effects of candidate moderators of model predictions: behavioral frequency, behavior type, and measurement lag.Method: We augmented a previous meta-analytic data set with correlations between model constructs and past behavior. We tested RAA models that included and excluded past behavior using meta-analytic structural equation modeling and compared the effects. Separate models were estimated in studies on high and low frequency behaviors, studies on different types of behavior, and studies with longer and shorter measurement lag.Results: Including past behavior attenuated model effects, particularly the direct effect of intentions on behavior, and indirect effects of experiential attitudes, descriptive norms, and capacity on behavior through intentions. Moderator analyses revealed larger intention-behavior and past behavior-behavior effects in high frequency studies, but the differences were not significant. No other notable moderator effects were observed.Conclusion: Findings indicate a prominent role for habitual processes in determining health behavior and inclusion of past behavior in RAA tests is important to yield precise estimates of model effects.

scholarly journals MODEL PENGUKURAN TINGKAT KEPERCAYAAN PELANGGAN (ONLINE TRUST) TERHADAP SITUS E-COMMERCE (Studi Kasus pada Pelanggan E-Commerce di Provinsi Bali)

2018 ◽  
Vol 1 (3) ◽  
pp. 100
Author(s):  
I Made Endra Wiartika Putra ◽  
Gede Rasben Dantes ◽  
I Made Candiasa
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Equation Modeling ◽  
Snowball Sampling ◽  
Cronbach’S Alpha ◽  
Purposive Sampling ◽  
Online Trust ◽  
Cronbach's Alpha ◽  
Fit Index

Penelitian ini bertujuan untuk mengetahui model pengukuran tingkat kepercayaan pelanggan terhadap situs e-commerce. Langkah awal yang dilakukan yaitu identifikasi faktor-faktor yang mempengaruhi kepercayaan pelanggan melalui studi literatur dan studi empirik untuk menentukan model analisis terhadap kepuasan pelanggan. Faktor yang mempengaruhi kepercayaan pelanggan untuk bertransaksi secara online yaitu pengetahuan konsumen terhadap e-commerce, reputasi penjual, resiko dalam transaksi, kemudahan penggunaan e-commerce, jaminan sistem, sikap/perilaku terhadap sistem dan sistem keamanan. Populasi dalam penelitian ini adalah masyarakat Provinsi Bali menggunakan metode purposive sampling dan snowball sampling dengan kriteria responden pernah berkunjung dan melakukan transaksi di e-commerce yang ada di Indonesia lebih dari 3 kali. Instrumen penelitian berupa kuesioner dengan data interval berskala 5 Likert. Instrumen terlebih dahulu diuji validitas isi dengan metode Robert Gregory, validitas empiris menggunakan rumus product moment, reliabilitas instrument menggunakan Cronbach’s Alpha, dan menghasilkan 59 pernyataan yang dapat digunakan untuk pengambilan data. Jumlah responden yang digunakan dalam penelitian ini adalah sebanyak 126 responden. Teknik analisis data, pengujian hipotesis dan pengujian model menggunakan metode Structural Equation Modeling dengan bantuan aplikasi SPSS AMOS 21. Hasil penelitian ini melalui pengujian hipotesis menunjukkan bahwa pengetahuan tentang situs e-commerce dan perlindungan keamanan berpengaruh negatif dan tidak signifikan terhadap kepercayaan pelanggan. Resiko, kemudahan e-commerce, jaminan sistem dan sistem keamanan bukan menjadi sesuatu yang penting untuk dipertimbangkan dalam meningkatkan kepercayaan pelanggan karena pengaruhnya tidak signifikan. Reputasi yang dirasakan dan sikap merupakan hal yang perlu diperhatikan dan paling berpengaruh terhadap kepercayaan pelanggan pelanggan. Hasil penelitian ini kemudian diuji menggunakan goodness of fit index dan menghasilkan bahwa model penelitian tersebut dapat diterima dan dapat digunakan untuk meningkatkan keinginan pelanggan untuk bertransaksi online

scholarly journals The Influence of Leader Encouragement of Creativity on Innovation Speed: Findings from SEM and fsQCA

2019 ◽  
Vol 11 (9) ◽  
pp. 2693 ◽  
Author(s):  
Cong Cheng ◽  
Liebing Cao ◽  
Huihui Zhong ◽  
Yining He ◽  
Jiahong Qian
Keyword(s):  
Structural Equation Modeling ◽  
Qualitative Analysis ◽  
Fuzzy Set ◽  
Creative Process ◽  
Structural Equation ◽  
Sem Analysis ◽  
Equation Modeling ◽  
Organizational Ambidexterity ◽  
Data Set ◽  
The Impact

Adopting the empowerment perspective of leadership, this study proposes and examines the mediating model that leader encouragement of creativity affects innovation speed through strengthening employees’ engagement in the creative process. Using a sample of 245 participants in China, the results from structural equation modeling (SEM) suggest that the impact of leader encouragement of creativity on innovation speed is significantly mediated by creative process engagement, and positively moderated by organizational ambidexterity at the same time. Additionally, the results from fuzzy-set comparative qualitative analysis (fsQCA) with the same data set reveal that the aforementioned factors have a holistic effect on enhancing innovation speed. The results of fsQCA reinforce and refine the findings of the SEM analysis concerning the limits and conditions for how leader encouragement of creativity affects innovation speed.

A Comparison of Accuracy Based on Data Imputation between Unconstrained Structural Equation Modeling and Weighted Least Squares Regression

2011 ◽  
Vol 130-134 ◽  
pp. 730-733
Author(s):  
Narong Phothi ◽  
Somchai Prakancharoen
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Structural Equation ◽  
Weighted Least Squares ◽  
Equation Modeling ◽  
Data Imputation ◽  
Least Squares Regression ◽  
Waveform Generator ◽  
Data Set ◽  
Testing Group

This research proposed a comparison of accuracy based on data imputation between unconstrained structural equation modeling (Uncon-SEM) and weighted least squares (WLS) regression. This model is developed by University of California, Irvine (UCI) and measured using the mean magnitude of relative error (MMRE). Experimental data set is created using the waveform generator that contained 21 indicators (1,200 samples) and divided into two groups (1,000 for training and 200 for testing groups). In fact, training group was analyzed by three main factors (F1, F2, and F3) for creating the models. The result of the experiment show MMRE of Uncon-SEM method based on the testing group is 34.29% (accuracy is 65.71%). In contrast, WLS method produces MMRE for testing group is 55.54% (accuracy is 44.46%). So, Uncon-SEM is high accuracy and MMRE than WLS method that is 21.25%.

scholarly journals Cutoff criteria for overall model fit indexes in generalized structured component analysis

2020 ◽  
Vol 8 (4) ◽  
pp. 189-202
Author(s):  
Gyeongcheol Cho ◽  
Heungsun Hwang ◽  
Marko Sarstedt ◽  
Christian M. Ringle
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Model Fit ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
Root Mean Square Residual ◽  
Fit Indexes ◽  
Cutoff Criteria

AbstractGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

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