scholarly journals Statistically Validated Component- and Indicator-Level Requirements for Sustainable Thai Homestay Businesses

2021 ◽  
Vol 13 (2) ◽  
pp. 936
Author(s):  
Nawhath Thanvisitthpon
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Sem Analysis ◽  
Equation Modeling ◽  
Component Level ◽  
Factor Loadings ◽  
Statistical Reliability ◽  
Confirmatory Factor

This research proposes an updated version of homestay indicators for 10 existing component-level homestay requirements. The updated indicators were aimed to replace the decade-old original indicators for Thai homestay businesses. Besides, the 31 original homestay indictors were not statistically validated. In this study, structural equation modeling (SEM) was used to determine the statistical relevancy between the updated homestay indicators and the components. The SEM analysis involved two steps: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The EFA statistically regrouped the 31 updated indictors by six new homestay components. The six components and 31 updated indicators were further validated by CFA to determine the factor loadings and statistical reliability of the components and indicators. The factor loadings indicate the levels of importance that homestay guests attach to different homestay components and indicators. Therefore, Thai homestay operators should give priority to the components and indicators with high factor loadings.

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

scholarly journals Modeling and Analysis of the Motivations of Fast Fashion Consumers in Relation to Innovativeness

2016 ◽  
Vol 16 (4) ◽  
pp. 205-213
Author(s):  
Canan Saricam ◽  
Nazan Erdumlu
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Motivational Factors ◽  
Equation Modeling ◽  
Fast Fashion ◽  
Modeling And Analysis ◽  
Confirmatory Factor ◽  
The Relationship

Abstract In this study, fast fashion concept is investigated in order to understand the motivations of the consumers that make them adopt these products because of their willingness for the innovativeness. The relationship between the motivational factors which were named as “Social or status image” and “Uniqueness” as expressions of individuality, “Conformity” and the willingness for “Innovativeness” is analyzed using a conceptual model. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling were used to analyze and validate the model. The data used for the study was obtained from 244 people living in Turkey. The findings showed that the motivational factors “Social or status image” and “Uniqueness” as expressions of individuality are influential on the consumers’ willingness for “Innovativeness”.

Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review

2006 ◽  
Vol 99 (6) ◽  
pp. 323-338 ◽  
Author(s):  
James B. Schreiber ◽  
Amaury Nora ◽  
Frances K. Stage ◽  
Elizabeth A. Barlow ◽  
Jamie King
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Confirmatory Factor

Investigating the Structure of the Pediatric Symptoms Checklist in the Preschool Setting

2016 ◽  
Vol 35 (5) ◽  
pp. 494-505 ◽  
Author(s):  
Christine DiStefano ◽  
Jin Liu ◽  
Yin Burgess
Keyword(s):  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Short Form ◽  
School Setting ◽  
Equation Modeling ◽  
Confirmatory Factor ◽  
Psychological Instruments ◽  
Preschool Setting

When using educational/psychological instruments, psychometric investigations should be conducted before adopting to new environments to ensure that an instrument measures the same constructs. Exploratory structural equation modeling and confirmatory factor analysis methods were used to examine the utility of the short form of the Pediatric Symptoms Checklist (PSC-17) in the school setting. Using a sample of 836 preschool children rated by teachers, three factors were identified across both techniques, with factors matching the hypothesized structure of the instrument. The PSC-17 may be an option for use in preschool settings when conducting behavioral and emotional screening.

scholarly journals Student Burnout in Higher Education: A Demand-Resource Model Approach

2022 ◽  
Author(s):  
Balázs Jagodics ◽  
Éva Szabó
Keyword(s):  
Higher Education ◽  
Factor Analysis ◽  
Structural Equation Modeling ◽  
Confirmatory Factor Analysis ◽  
Correlation Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Resource Model ◽  
Confirmatory Factor ◽  
Student Burnout

AbstractStudent burnout is a serious problem in higher education. It is associated with harmful consequences, such as decreased engagement, performance, and motivation, which can lead to dropout. The job demand-resource model of burnout is a comprehensive framework to grasp the factors related to the emergence of burnout. Although numerous studies claim its suitability in explaining burnout in work environments, its applicability in the educational context is less explored. The study aimed to analyze the structure and reliability of the newly developed University Demand-Resource Questionnaire (UDRQ) and to explore the links between its subscales and symptoms of student burnout. Using the online survey method, 743 Hungarian undergraduate students participated in the data collection. The student version of the Maslach Burnout Inventory was used in addition to the UDRQ. In the data analysis procedure, confirmatory factor analysis, correlation analysis, and structural equation modeling were utilized. The confirmatory factor analysis identified a five-factor structure related to both demands and resources. Correlation analysis revealed burnout to be associated positively to the subscales of demands and negatively to resources. Structural equation modeling analysis indicated that all five demands and two resources subscales can be used to build a model that predicts a significant proportion of the variance of student burnout scores. The findings suggest the demand-resource theory is an appropriate framework to predict burnout in higher education. The newly developed UDRQ has stable structure and good reliability and can be a useful tool in subsequent research related to student burnout.

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