scholarly journals Mediation Analysis With Intermediate Confounding: Structural Equation Modeling Viewed Through the Causal Inference Lens

2014 ◽  
Vol 181 (1) ◽  
pp. 64-80 ◽  
Author(s):  
Bianca L. De Stavola ◽  
Rhian M. Daniel ◽  
George B. Ploubidis ◽  
Nadia Micali
Keyword(s):  
Structural Equation Modeling ◽  
Causal Inference ◽  
Mediation Analysis ◽  
Structural Equation ◽  
Equation Modeling

scholarly journals A Primer on the Conditional Mediation Analysis in PLS-SEM

2021 ◽  
Vol 52 (SI) ◽  
pp. 43-100
Author(s):  
Jun-Hwa Cheah ◽  
Christian Nitzl ◽  
José L. Roldán ◽  
Gabriel Cepeda-Carrion ◽  
Siegfried P. Gudergan
Keyword(s):  
Structural Equation Modeling ◽  
Information Systems ◽  
Individual Differences ◽  
Least Squares ◽  
Mediation Analysis ◽  
Structural Equation ◽  
Empirical Studies ◽  
Equation Modeling ◽  
Advance Theory ◽  
Do So

Conditional mediation (CoMe) analysis integrates mediation and moderation analyses to examine and test hypotheses about how mediated relationships vary as a function of context, boundaries, or individual differences. Although CoMe analysis can be a crucial element of empirical studies that seek to advance theory in information systems, applications of such analysis are scarce, in general, and in partial least squares structural equation modeling (PLS-SEM), in particular. This paper clarifies conceptual fundamentals of and develops guidelines for CoMe analysis within the PLS-SEM context. Furthermore, the paper outlines the illustrative use of CoMe analysis in PLS-SEM and presents detailed step-by-step procedures to do so in the PLS-SEM setting. Overall, this paper provides researchers and practitioners with the required knowledge to properly carry out, report, and interpret CoMe analysis in PLS-SEM.

scholarly journals Mediation Using Covariance Based-Structural Equation Modeling (CB-SEM): The Why and How?

2018 ◽  
Vol 8 (12) ◽  
pp. 434-442
Author(s):  
Mohammad Mizanur Rahman ◽  
Samuel Babatunji Adedeji ◽  
Md Abdul Bashir ◽  
Jahirul Islam ◽  
Md Reaz ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Knowledge Base ◽  
Mediation Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Mediation Model ◽  
Informed Decisions ◽  
Proper Analysis

This paper summaries and provides answers to some of the challenging issues in relation to mediation analysis. Specifically, emphasis was on four key burning aspects with reference to up-to-date literature on mediation analysis. For a better appreciation of the efforts put in here, scholars and practitioners, have some guidelines suggested to conceptualise, test and interpret a simple mediation model. Researchers have the benefits of updating their knowledge base from the current citations provided to carry out proper analysis based on informed decisions.

scholarly journals Structural equation modeling to detect correlates of childhood vaccination: A moderated mediation analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0240749
Author(s):  
Abraham Degarege ◽  
Karl Krupp ◽  
Vijaya Srinivas ◽  
Boubakari Ibrahimou ◽  
Purnima Madhivanan
Keyword(s):  
Structural Equation Modeling ◽  
Mediation Analysis ◽  
Structural Equation ◽  
Moderated Mediation ◽  
Childhood Vaccination ◽  
Equation Modeling

scholarly journals Innovations in Human Resources Management: Mediating Role of Intrinsic Motivation

2020 ◽  
pp. 110-120 ◽  
Author(s):  
Muhammad Sajjad ◽  
Anam Riaz ◽  
Orangzab ◽  
Muhammad Chani ◽  
Rai Hussain
Keyword(s):  
Structural Equation Modeling ◽  
Data Collection ◽  
Intrinsic Motivation ◽  
Mediation Analysis ◽  
Random Sampling ◽  
Structural Equation ◽  
Simple Random Sampling ◽  
Equation Modeling ◽  
Survey Method

This study determines the impact of loneliness on creativity through a powerful and essential mediator of intrinsic motivation. The ability to motivate oneself leads towards the accomplishment of tasks and creative work. In recent years, workplace loneliness has become a more critical issue in both academic and practical debates. So, this study highlighted the significant cause of creativity which could help the organization to handle the problem of loneliness. The study assumes that when isolation at the workplace is high, employees are less approachable towards their tasks. So, their performance and creativity reduced. Because loneliness leads to stress, depression and anger, it reduces the creative skills of employees. To enhance creativity, lonely employees must be motivated to minimise their adverse outcomes. So, the research question arises: Does intrinsic motivation (IM) intervene in between the workplace loneliness (WL) and employees' creativity? This study is also significant because employees and employers' relations are the backbone of their respective organizations and directly affect the performance and growth of their respective organizations. A data collection survey method was held on employees of Banking Sector currently working in Vehari district of Pakistan. Sample space was the employees of Pakistani banks working on officer grades. There is a total of 124 bank branches in Vehari District of Pakistan. In these 124 branches, a total of 726 employees were working on managerial positions which were targeted for data collection. Simple random sampling technique was employed to collect the information from the respondents. Simple random sampling techniques were used to avoid from the busyness and undesirable unknown effects from the target population. Data were obtained from 400 banking officers. For data collection, validated and adapted questionnaires were used. Each variable of the survey was measured through a Likert scale of 5 points. For statistics evaluation, the statistical tools such as reliability of data, collinearity of data, the association of variable's, mediation and Regression were tested. For data reliability and validation, Statistical Package for Social Sciences (SPSS) was used while to measure the direct, indirect or mediation effect was measured through smart pls 3. Smart PLS3 is a landmark in latent variable modelling. It combines state of the art methods with an easy to use and in-built graphical user interface. Structural Equation Modeling (SEM) is utilized to quantify the power of essential philosophies with spotted data. Results showed Workplace Loneliness has a definite and meaningful relationship with employee's creativity. Results also supported the intervening role of Intrinsic motivation (IM) between the relationship Workplace Loneliness (WL) on Employees’ creativity (EC). The study provides significant implications in literature and for practitioners who are interested in measuring the profound effect of loneliness on creativity. Hypotheses are tested through mediation and for mediation analysis through the most commonly used approach of baron and Kenny. From results, it is stated that workplace loneliness is predicting the intrinsic motivation and creativity. Intrinsic motivation is the mediator in the current study, which partially intermediates the relation of WL and EC. So, it can be stated that employees can only be creative and able to utilize their cognition abilities for creativity when they are socially isolated from other works. The findings authenticate all hypotheses and their correspondence between the workplace loneliness, creativity and intrinsic motivation. In a nutshell, intrinsic motivation is the crucial element for creativity because when employees are internally satisfied with their work, they become energized and motivated and perform their work in a creative way. Keywords employees creativity, intrinsic motivation, mediation analysis, structural equation modeling, workplace loneliness.

scholarly journals A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data: Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems

2021 ◽  
Author(s):  
David Huh ◽  
Xiaoyin Li ◽  
Zhengyang Zhou ◽  
Scott T. Walters ◽  
Scott A. Baldwin ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Mediation Analysis ◽  
Structural Equation ◽  
Equation Modeling ◽  
Individual Participant Data ◽  
Behavioral Strategies ◽  
Brief Motivational Intervention ◽  
Protective Behavioral Strategies ◽  
Individual Participant ◽  
Alcohol Related Problems

AbstractThis paper introduces a meta-analytic mediation analysis approach for individual participant data (IPD) from multiple studies. Mediation analysis evaluates whether the effectiveness of an intervention on health outcomes occurs because of change in a key behavior targeted by the intervention. However, individual trials are often statistically underpowered to test mediation hypotheses. Existing approaches for evaluating mediation in the meta-analytic context are limited by their reliance on aggregate data; thus, findings may be confounded with study-level differences unrelated to the pathway of interest. To overcome the limitations of existing meta-analytic mediation approaches, we used a one-stage estimation approach using structural equation modeling (SEM) to combine IPD from multiple studies for mediation analysis. This approach (1) accounts for the clustering of participants within studies, (2) accommodates missing data via multiple imputation, and (3) allows valid inferences about the indirect (i.e., mediated) effects via bootstrapped confidence intervals. We used data (N = 3691 from 10 studies) from Project INTEGRATE (Mun et al. Psychology of Addictive Behaviors, 29, 34–48, 2015) to illustrate the SEM approach to meta-analytic mediation analysis by testing whether improvements in the use of protective behavioral strategies mediate the effectiveness of brief motivational interventions for alcohol-related problems among college students. To facilitate the application of the methodology, we provide annotated computer code in R and data for replication. At a substantive level, stand-alone personalized feedback interventions reduced alcohol-related problems via greater use of protective behavioral strategies; however, the net-mediated effect across strategies was small in size, on average.

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