A Power Analysis to Determine Appropriate Sample Size for the Study of Student Design-Team Effectiveness

2020 ◽  
Author(s):  
Shun Takai ◽  
Thomas J. Smith ◽  
Marcos Esterman
Keyword(s):  
Sample Size ◽  
Team Performance ◽  
Power Analysis ◽  
Structural Equation ◽  
Team Effectiveness ◽  
Equation Modeling ◽  
Design Teams ◽  
Effectiveness Model ◽  
Student Teams ◽  
Team Project

Abstract Forming collaborative teams is a critical first step in team-project-based design courses as team composition directly affects not only teamwork processes and outcomes but also teamwork skills and experience. While various approaches have been used to form teams, the best methodology has not been found due to a lack of understanding of how team compositions impact team performance and teamwork learning. We need to establish a team effectiveness model for student design teams that describes relationships between team characteristics and team performance or teamwork learning. One of many challenges in such an effort is to estimate an appropriate sample size to achieve statistically significant results before starting data collection. In this paper, we demonstrate a power analysis for determining an appropriate sample size, i.e., the number of student teams, before we study the effectiveness of student design-teams. We first present a hypothesized team effectiveness model for student design teams that shows possible relationships among team factors. We then illustrate a statistical analysis procedure for studying the team effectiveness model using structural equation modeling (SEM) or path analysis. We finally demonstrate a power analysis of SEM for determining the appropriate sample size for studying the team effectiveness model.

A Conceptual Framework and a Research Roadmap Towards Enhancing Team Working in Engineering Education

2019 ◽  
Author(s):  
Shun Takai ◽  
Marcos Esterman
Keyword(s):  
Engineering Education ◽  
Team Performance ◽  
Team Effectiveness ◽  
Team Member ◽  
Team Building ◽  
Building Design ◽  
Design Team ◽  
Design Teams ◽  
Effectiveness Model ◽  
Form Design

Abstract While design processes have been studied for many years, relationships among design team characteristics, teamwork, and team performance have not yet been fully understood. As such, there is no consensus on how to form design teams or enhance teamwork. In this paper, we propose a conceptual design-team effectiveness model based on team effectiveness theory in which we divide team process into two components: team member collaboration and design process. Built on this model, we then present a six-step research roadmap towards enhancing teamwork in engineering education by 1) improving methodology to form design teams and 2) finding a team-building design exercise to promote team member collaboration. We propose to improve team formation methodology by 1) comprehensively studying associations among team factors and team performance and 2) investigating how associations among team factors and team performance change with team-building design exercises. Together, we expect both team performance and team member collaboration to improve, which should lead to a better teamwork experience in engineering education.

Disseminating the functions of team coaching regarding research and development team effectiveness: Evidence from high-tech industries in Taiwan

2009 ◽  
Vol 37 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Chin-Yun Liu ◽  
Andrew Pirola-Merlo ◽  
Chin-Ann Yang ◽  
Chih Huang
Keyword(s):  
Research And Development ◽  
Team Performance ◽  
Structural Equation ◽  
Team Effectiveness ◽  
Equation Modeling ◽  
High Tech ◽  
Positive Effects ◽  
Team Coaching ◽  
Development Teams ◽  
Sem Analyses

The purpose of this research was to test the predictions of Team Coaching Theory (Hackman & Wageman, 2005) using 137 research and development teams in Taiwan. The results of this study partially supported Hackman and Wageman's theory. Results of the structural equation modeling (SEM) indicated that team coaching functions had positive effects on the team performance processes of effort and skills and knowledge. In addition it was found that the team performance processes of effort and strategy had direct positive impacts on team effectiveness. Further SEM analyses indicated that effort and skills and knowledge both had direct impacts on strategy (which in turn impacted on team effectiveness).

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.

Multilevel influences of team identification and transactive memory on team effectiveness

2018 ◽  
Vol 24 (1/2) ◽  
pp. 106-120 ◽  
Author(s):  
Estelle Michinov ◽  
Jacques Juhel
Keyword(s):  
Team Performance ◽  
Structural Equation ◽  
Team Effectiveness ◽  
Transactive Memory ◽  
Mediating Effect ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Team Identification ◽  
Content Type ◽  
The Relationship

Purpose The purpose of this study was to examine the mediating effect of transactive memory between team identification and two outcomes of team effectiveness (i.e. team member satisfaction and team performance). Design/methodology/approach Data were obtained from a survey among 502 employees working in 53 teams, and analyzed by Multilevel Structural Equation Modeling. Findings Results showed that transactive memory partially mediated the relationship between team identification and team effectiveness at the individual level. Moreover, transactive memory, specifically the coordination component, fully mediated the relationship between team identification and team effectiveness at the team level. Research limitations/implications The study used a cross-sectional design for the questionnaire and no objective measure of team performance. Practical implications Managers who want to develop effective work teams may be advised to organize team-building activities to strengthen both affective and cognitive aspects. Originality value This is the first empirical study to examine the relationships between team identification, transactive memory and team effectiveness from a multilevel perspective.

scholarly journals The effect of organizational companionship on team effectiveness

2018 ◽  
Vol 31 (3) ◽  
pp. 695-714
Author(s):  
Youngshik Kim ◽  
Yongwon Suh
Keyword(s):  
Team Performance ◽  
Structural Equation ◽  
Team Effectiveness ◽  
Moderated Mediation ◽  
Task Conflict ◽  
Mediation Effect ◽  
Relationship Conflict ◽  
Equation Modeling ◽  
The Relationship ◽  
Positive Effect

The present study verified that organizational companionship reduces the effect of task conflict on relationship conflict, which leads to positive effect on team effectiveness indicators - teamwork and team performance. Data were collected from 304 employees using survey questionnaires. The result indicated that relationship conflict mediated the relationship between task conflict and teamwork. Also, the results showed that a moderated mediation effect of organizational companionship was significant. Specifically, the higher organizational companionship, the less mediation effect of relationship conflict. Results of structural equation modeling signified that the moderated mediation effect leads to positive effect on team performance. Lastly, implications and limitations of the results are discussed.

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.

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>

THE RELATIONSHIP OF LEADERSHIP, TEAM TRUST AND TEAM PERFORMANCE: A COMPARISON OF THE SERVICE AND MANUFACTURING INDUSTRIES

2007 ◽  
Vol 35 (5) ◽  
pp. 643-658 ◽  
Author(s):  
Ming-Jian Shen ◽  
Ming-Chia Chen
Keyword(s):  
Team Performance ◽  
Structural Equation ◽  
Leadership Team ◽  
Manufacturing Industries ◽  
Relational Trust ◽  
Equation Modeling ◽  
Manova Analysis ◽  
Relationship Of ◽  
Team Trust ◽  
Positive Effect

The objective of this study was to investigate and compare the relationships and variations among leadership, team trust and team performance in the service and manufacturing industries. The results of using structural equation modeling to conduct hypotheses testing show that leadership has a positive effect on team trust and team performance, and that team trust also has a positive effect on team performance. By using MANOVA analysis to test for significant variances in leadership, team trust and team performance in the service and manufacturing industries, a significant variance was discovered in the testing of instructed leadership, relational trust and institutional trust in both industries.

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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