scholarly journals Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Sample Size Determination ◽  
Equation Modeling ◽  
Minimum Sample Size ◽  
Research Project ◽  
Rules Of Thumb ◽  
Minimum Sample

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.

scholarly journals The Role of Innovation in the Tourist Attitude and Improving the Destination Performance(Case study: visitors of historical places in Tehran)

2021 ◽  
Vol 04 (01) ◽  
Author(s):  
Roya Sadat Alavi Pour ◽  
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Simple Random Sampling ◽  
Sample Size Determination ◽  
Equation Modeling ◽  
Statistical Population ◽  
Mediating Role ◽  
The Impact

This study aims to investigate the role of innovation in the tourist attitude and improve the performance of the destination. In this regard, while reviewing the concepts of innovation, tourist attitude, and destination performance, using the structural equation modeling method, we examined the effect of innovation on tourist attitude and promotion of destination performance. The statistical population of this study includes tourists from historical places in Tehran, whose number is unlimited. The sample size was determined using the Krejcie-Morgan 384 sample size determination table, which was selected using simple random sampling. In order to collect data in this research, a questionnaire was used, the validity of which was confirmed as content validity by experts, and the validity of construction and structure by confirmatory factor analysis in Smart-pls software and its reliability using Cronbach's alpha coefficient, approved by a factor of 0.954. In order to analyze the data in this study, the Chlomogroff-Smirnov test was used for regular testing, and structural equation modeling was used to test the hypotheses. The results showed that innovation in services and marketing has a significant impact on tourist attitudes, marketing promotion, and destination performance. The results also show that the mediating role of marketing promotion on the impact of innovation on destination performance has not been confirmed.

scholarly journals A Step-by-Step Process on Sample Size Determination for Medical Research

2021 ◽  
Vol 28 (2) ◽  
pp. 15-27
Author(s):  
Mohamad Adam Bujang
Keyword(s):  
Sample Size ◽  
Type I Error ◽  
Early Stage ◽  
Sample Size Determination ◽  
Type I ◽  
Minimum Sample Size ◽  
Size Determination ◽  
Prerequisite Knowledge ◽  
Minimum Sample

Determination of a minimum sample size required for a study is a major consideration which all researchers are confronted with at the early stage of developing a research protocol. This is because the researcher will need to have a sound prerequisite knowledge of inferential statistics in order to enable him/her to acquire a thorough understanding of the overall concept of a minimum sample size requirement and its estimation. Besides type I error and power of the study, some estimates for effect sizes will also need to be determined in the process to calculate or estimate the sample size. The appropriateness in calculating or estimating the sample size will enable the researchers to better plan their study especially pertaining to recruitment of subjects. To facilitate a researcher in estimating the appropriate sample size for their study, this article provides some recommendations for researchers on how to determine the appropriate sample size for their studies. In addition, several issues related to sample size determination were also discussed.

Perceived effectiveness of in-service training in improving Teacher's Performance: Evidence from Private Schools in Karachi-Pakistan

2020 ◽  
Author(s):  
Seema Chandani ◽  
Dr. afaq kazi ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Private Schools ◽  
Structural Equation ◽  
School Management ◽  
Partial Least Square ◽  
Equation Modeling ◽  
Perceived Effectiveness ◽  
Purposive Sampling ◽  
Service Training

<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>

Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

2019 ◽  
Vol 2673 (3) ◽  
pp. 24-43 ◽  
Author(s):  
Meiping Yun ◽  
Wenwen Qin
Keyword(s):  
Sample Size ◽  
Travel Time ◽  
Real Time ◽  
Time Distribution ◽  
Ground Truth ◽  
Sample Size Determination ◽  
Minimum Sample Size ◽  
Travel Time Distribution ◽  
Minimum Sample ◽  
Link Travel Time

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

scholarly journals SMES’ E-COMMERCE ADOPTION TOWARDS CONSUMER EXPERIENCE

2020 ◽  
Vol 21 (2) ◽  
pp. 144-155
Author(s):  
Nuryanti Taufik ◽  
Faizal Haris Eko Prabowo ◽  
Allicia Deana Santosa ◽  
Andina Eka Mandasari
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Quantitative Study ◽  
Structural Equation ◽  
Analytical Tool ◽  
Equation Modeling ◽  
Survey Method ◽  
Consumer Experience ◽  
Consumer Responses

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.

scholarly journals Analisis Peningkatan Kinerja Karyawan Di UP PTSP Kecamatan Makasar dengan Metode Structural Equation Modelling (SEM)

2020 ◽  
Vol 5 (1) ◽  
pp. 53
Author(s):  
Arif Rahman ◽  
Surya Perdana
Keyword(s):  
Structural Equation Modeling ◽  
Work Environment ◽  
Structural Equation ◽  
Employee Performance ◽  
Work Conditions ◽  
Equation Modeling ◽  
Level Of Service ◽  
Equation Modelling ◽  
Time Standard

<p><em>The decline in employee performance at UP PTSP Makasar District is characterized by a decreased level of service time efficiency in the area of KRK and IMB licensing. In addition, the cause of the decline in employee performance is due to the behavior of employees who like to postpone work and come late to work intentionally, and also employees lack the determination of high morale at work. The achievement of the duties of UP PTSP employees in Makasar Sub-District was not in accordance with the determined time standard. The purpose of this study was to determine the effect of the work environment and workload on the performance of UP PTSP Makasar District employees and to know the factors that had to be prioritized to become better. The data used for research is questionnaire data which is processed by the Structural Equation Modeling (SEM) method. This study analyzes the factors of work environment and workload using Structural Equation Modeling (SEM) with AMOS applications. Based on SEM output regression weights value states that there is no significant effect between work environment on employee performance. While there is a significant effect between workload on employee performance. The main priority in improving employee performance through the work environment is cleanliness in the workplace. The main priority in improving employee performance through better workloads is with work conditions at the workplace.</em></p>

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