scholarly journals Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial

2020 ◽  
Author(s):  
Yilin Andre Wang ◽  
Mijke Rhemtulla
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Latent Variables ◽  
Power Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Regression Coefficient ◽  
Equation Modeling ◽  
Shiny App ◽  
Target Effect

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample size rules of thumb that are not backed up by research, and power analyses for detecting model misfit. Missing from most current practices is power analysis to detect a target effect (e.g., a regression coefficient between latent variables). In this paper we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a 3-predictor latent regression model, and (c) introduce a Shiny app, pwrSEM, for user-friendly power analysis for detecting target effects in structural equation models.

scholarly journals Power Analysis for Parameter Estimation in Structural Equation Modeling: A Discussion and Tutorial

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592091825
Author(s):  
Y. Andre Wang ◽  
Mijke Rhemtulla
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Latent Variables ◽  
Power Analysis ◽  
Structural Equation ◽  
Regression Coefficient ◽  
Model Misspecification ◽  
Equation Modeling ◽  
Shiny App ◽  
Target Effect

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample-size rules of thumb that are not backed up by research and power analyses for detecting model misspecification. Missing from most current practices is power analysis for detecting a target effect (e.g., a regression coefficient between latent variables). In this article, we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a three-predictor latent regression model, and (c) introduce a user-friendly Shiny app, pwrSEM, for conducting power analysis for detecting target effects in structural equation models.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

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>

Building a Model of Family Caregiving for Children with Emotional Disorders

1997 ◽  
Vol 5 (3) ◽  
pp. 138-148 ◽  
Author(s):  
Thomas P. Mcdonald ◽  
Thomas K. Gregoire ◽  
John Poertner ◽  
Theresa J. Early
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Disorders ◽  
Problem Behaviors ◽  
Family Caregiving ◽  
Latent Variables ◽  
Structural Equation ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Special Challenge ◽  
And Coping

In this article we describe the results of an ongoing effort to better understand the caregiving process in families of children with severe emotional problems. We make two assumptions. First, we assume that these families are essentially like other families but are faced with a special challenge in raising and caring for their special children while at the same time performing the multiple tasks and demands faced by all families. Second, we assume that public policy and programs must be supportive of the care of these children in their own homes and communities whenever possible. The purpose of this article is to present a model of family caregiving that draws broadly from available theory and empirical literature in multiple fields and to subject this model to empirical testing. We use structural equation modeling with latent variables to estimate an empirical model based on the theoretical model. Results of the model testing point to the importance of the child's external problem behaviors and the family's socioeconomic status and coping strategies as determinants of caregiver stress. Other findings highlight difficulties in measuring and modeling the complex mediating process, which includes formal and informal supports, perceptions, and coping behaviors. The use of structural equation modeling can benefit our efforts to support families by making explicit our theories about the important dimensions of this process and the relationship between these dimensions, which can then be subjected to measurement and validation.

scholarly journals PENGARUH KEPEMIMPINAN, KEPRIBADIAN, UMUR, DAN PENDIDIKAN TERHADAP KINERJA PEGAWAI MELALUI MOTIVASI PEGAWAI BALAI PELAKSANA TEKNIS BINA MARGA WILAYAH TEGAL

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Mr. Wasmo ◽  
Mr. Basuki
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Regression Coefficient ◽  
Employee Performance ◽  
Equation Modeling ◽  
P Value ◽  
Research Use ◽  
Leadership Motivation ◽  
And Performance

The purpose of this research is 1) to describe leadership, personality, age, education, motivation and the employee performance 2) to know the influence of leadership, personality, age, and education motivation employees to 3) to know the influence of leadership, personality, age, and education against the employee performance 4) to know the motivation to performance employees and 5) to know the influence of leadership, personality, age, and education on performance through motivation. Respondents in this study by the 107. These respondents are civil of technical execution Bina Marga Region Tegal. The methods of this research use Structural Equation Modeling (SEM) who run through AMOS as a means of the analysis. Is the between leadership, personality, age and education for employees at the motivation bina marga the tegal. This is evidenced of the value of the terstandar regression (beta) leadership motivation to obtain value of 0,11, to obtain personality motivation value of 0,32, my motivation to obtain value of -0,19 and education motivation to obtain value of 0,23 and more tender testing obtained value p-value very small (< 0,001). There are relations between leadership, personality, age and education of the performance of employees in city bina marga areas tegal. This is evidenced of the value of the regression coefficient terstandar (beta) leadership of the performance of have value of 0,06, personality of the performance of have value of 0,48, age of the performance of get value 0.01 and education on performance have value of 0.09 and from the testing obtained value pvalue very small (< 0,001). the incentives on performance in city bina marga areas tegal. This is evidenced of the value of the regression coefficient terstandar (beta) motivation on performance have value of 0,36 and from the testing obtained value pvalue very small (& lt; 0,001). Is the between leadership, personality, age and education on performance through motivation in city bina marga areas tegal. This is evidenced value the regression coefficient terstandar (beta) leadership, personality, of and education on performance through motivation have value of. 30 and from the testing obtained value p-value very small (< 0,001). There are relations between leadership, personality, age and education through motivation to performance in the region tegal bina marga.This is evidenced value regression coefficient terstandar (beta) leadership, personality, age) and education on performance through motivation for value of 0,30 and more tender testing obtained value p-value very small (< 0,001). Keywords: leadership, personality, age, education, motivation and performance.

scholarly journals Structural Equation Modeling for the Effect of Main Factors on Abortion Issue

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Aras Jalal Mhamad ◽  
Renas Abubaker Ahmed
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Close Contact ◽  
Maternity Hospital ◽  
Equation Modeling ◽  
Research Model ◽  
Statistical Tools ◽  
Main Factors

       Based on medical exchange and medical information processing theories with statistical tools, our study proposes and tests a research model that investigates main factors behind abortion issue. Data were collected from the survey of Maternity hospital in Sulaimani, Kurdistan-Iraq. Structural Equation Modelling (SEM) is a powerful technique as it estimates the causal relationship between more than one dependent variable and many independent variables, which is ability to incorporate quantitative and qualitative data, and it shows how all latent variables are related to each other. The dependent latent variable in SEM which have one-way arrows pointing to them is called endogenous variable while others are exogenous variables. The structural equation modeling results reveal is underlying mechanism through which statistical tools, as relationship between factors; previous disease information, food and drug information, patient address, mother’s information, abortion information, which are caused abortion problem. Simply stated, the empirical data support the study hypothesis and the research model we have proposed is viable. The data of the study were obtained from a survey of Maternity hospital in Sulaimani, Kurdistan-Iraq, which is in close contact with patients for long periods, and it is number one area for pregnant women to obtain information about the abortion issue. The results shows arrangement about factors effectiveness as mentioned at section five of the study. This gives the conclusion that abortion problem must be more concern than the other pregnancy problem.

scholarly journals MODEL PERSAMAAN STRUKTURAL UNTUK MENGETAHUI PERSEPSI KONSUMEN TERHADAP BUAH IMPOR

2020 ◽  
Vol 9 (3) ◽  
pp. 149
Author(s):  
I KADEK TEGUH PRADANA ◽  
NI KETUT TARI TASTRAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Sem Analysis ◽  
Consumer Perception ◽  
Equation Modeling ◽  
Consumer Knowledge

This research is aimed to determine the factors that significantly influence consumer perception in buying imported fruits using structural equation modeling (SEM) analysis. The study use 164 data obtained from questionnaire, which respondents were aged 18 years old or above, from Gianyar Regency, had bought and had felt imported fruits. The study use 4 latent variables (perception, product, personal, culture) with 19 measured variables. The results showed that consumer knowledge about imported fruits (product) and culture about the use of imported fruits in traditional ceremonies (culture) were significantly influence consumer perception about consumption of imported fruit (perception).

scholarly journals COVID-19 Risk Factors, Economic Factors, and Epidemiological Factors nexus on Economic Impact: Machine Learning and Structural Equation Modelling Approaches

2021 ◽  
pp. 395-405
Author(s):  
David Opeoluwa Oyewola ◽  
Emmanuel Gbenga Dada ◽  
Juliana Ngozi Ndunagu ◽  
Terrang Abubakar Umar ◽  
Akinwunmi S.A
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Equation Modeling ◽  
Support Vector ◽  
Economic Factors ◽  
Negative Effects ◽  
The Impact

Since the declaration of COVID-19 as a global pandemic, it has been transmitted to more than 200 nations of the world. The harmful impact of the pandemic on the economy of nations is far greater than anything suffered in almost a century. The main objective of this paper is to apply Structural Equation Modeling (SEM) and Machine Learning (ML) to determine the relationships among COVID-19 risk factors, epidemiology factors and economic factors. Structural equation modeling is a statistical technique for calculating and evaluating the relationships of manifest and latent variables. It explores the causal relationship between variables and at the same time taking measurement error into account. Bagging (BAG), Boosting (BST), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) Machine Learning techniques was applied to predict the impact of COVID-19 risk factors. Data from patients who came into contact with coronavirus disease were collected from Kaggle database between 23 January 2020 and 24 June 2020. Results indicate that COVID-19 risk factors have negative effects on epidemiology factors. It also has negative effects on economic factors.

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.

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