Erratum to “Lower bounds on sample size in structural equation modeling” [Electron. Commerce Res. Appl. 9 (6) (2010) 476–487]

2012 ◽  
Vol 11 (4) ◽  
pp. 445 ◽  
Author(s):  
J. Christopher Westland
2020 ◽  
Author(s):  
Seema Chandani ◽  
Dr. afaq kazi ahmed

<p><b>Research Paradigm:</b> The approach for this study is based on positivism theory with an intention to obtain evidence through focused observations and identify its likeliness with the actual data collected so that it can be generalized with the findings of other scholars who have worked on the topic (Remenyi et al., 1998). As elaborated by (Gill & Johnson, 2002), the crux of positivism philosophy revolves around the relationship of cause and its effect generated by different constructs, and the best possible exploration of these variables.</p><p><b>Research Design:</b> Primary data would be collected through a structured close-ended questionnaire which use to gather the responses from the stakeholders including teachers, coordinators, and school management and human resource personnel in the private schools. Therefore, the research questionnaire adapted from Dogan (2009) and Asiyai (2016) as a research instrument.</p> <p><b>Sample Size and Sampling technique:</b> The target population for this research study consists of different stakeholders for instance: teachers, coordinators, and school management and HR personnel working in private schools of Karachi -Pakistan. Non-probability purposive sampling is being used for this study. The sample size of 400 different stakeholders from private schools would be adequate to figure out the results (Loehlin, 2004). Since the actual size of the population is not exactly known and neither accessible to conduct random sampling, therefore non probability purposive sampling is chosen. Since the sample consists of different stakeholders for instance: teachers, coordinators, and school management and HR personnel working in private schools of Karachi -Pakistan. Therefore, it is a purposive sampling.</p><p><b>Data Collection Tool:</b> In this study we used the quantitative research to measure the perceived effectiveness of in-service training in improving teacher’s performance with one independent variables: in-service training (Training need assessment and training methodology), one dependent variables teachers performance (Task performance and contextual performance), 03 mediators (professional skills, knowledge enhancement and work engagement) and one moderator (reward). Questionnaire has distributed in the several schools by hand or through email.</p><p><b>Statistical Technique:</b> Since the model consists of multiple variables with mediation and moderation model, therefore the Structural Equation Modeling (SEM) is used. The interface terms integrated with the model is measured for statistical significance via bootstrapping method. The structural equation modeling has executed by the partial least square approach.</p><p></p>


2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.


2020 ◽  
Vol 21 (2) ◽  
pp. 144-155
Author(s):  
Nuryanti Taufik ◽  
Faizal Haris Eko Prabowo ◽  
Allicia Deana Santosa ◽  
Andina Eka Mandasari

This study aims to determine how the effect of e-commerce adoption on SMEs towards consumer experience in shopping online and its impact on repurchases. This research is a quantitative study with a survey method. The analytical tool used is Structural Equation Modeling. The sample size in this study is 205 respondents who have made transactions on the fashion SME e-commerce websites. The results showed that the better the adoption of e-commerce carried out by SMEs fashion, the better it is in providing a good experience for consumers, which ultimately made consumers repurchase on the website. This study provides new measurements of consumer responses in the form of experience after using SME e-commerce websites.


2021 ◽  
pp. 312-315

Introduction: Disaster preparedness is one of the most important components of reducing vulnerability, which is poorly considered in Tehran, Iran. Several factors and personality traits are involved in this negligence. This study aimed to investigate the procrastination trait and its relationship with disaster preparedness levels in Tehran, Iran. Method: This descriptive-correlational study was conducted between 2018 and 2021 using structural equation modeling. The statistical population of this study was citizens in Tehran, Iran. A sample size of 419 cases was included in this study. Standard tests have also been utilized to investigate the variables. Findings: Based on the obtained results, according to the coefficients reported in the model, it can be observed that procrastination has no significant effect on disaster preparedness Conclusion: Procrastination and experiences gained affect people's preparedness for disaster. According to the results, the preparedness of the people of Tehran for accidents is very low


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