Investigating the Construct Validity and Reliability of the Test of Motor Competence Across Iranians’ Lifespan

Author:

Salami Sedigheh1ORCID,Ribeiro Bandeira Paulo Felipe2ORCID,Dehkordi Parvaneh Shamsipour1,Sohrabi Fatemeh1,Martins Clarice3,Duncan Michael J.4ORCID,Hardy Louise L.5ORCID,Shams Amir6ORCID

Affiliation:

1. Department of Motor Behavior, Faculty of Sport Sciences, Alzahra University, Tehran, Iran

2. Departamento de Educação Física, Universidade Regional do Cariri, Crato, Brazil

3. Research Centre in Physical Activity, Health and Leisure, Universidade do Porto, Porto, Portugual

4. Centre for Sport, Exercise and Life Sciences, Coventry University, Coventry, UK

5. Prevention Research Collaboration, School of Public Health, University of Sydney, Sydney, NSW, Australia

6. Motor Behavior Department, Sport Sciences Research Institute (SSRI) of Iran, Tehran, Iran

Abstract

Motor competence (MC) has been extensively examined in children and adolescents, but has not been studied among adults nor across the lifespan. The Test of Motor Competence (TMC) assesses MC in people aged 5–85 years. Among Iranians, aged 5–85 years, we aimed to determine the construct validity and reliability of the TMC and to examine associations between TMC test items and the participants’ age, sex, and body mass index (BMI). We conducted confirmatory factor analysis (CFA) to evaluate the TMC’s factorial structure by age group and for the whole sample. We explored associations between the TMC test items and participant age, sex, and BMI using a network analysis machine learning technique (Rstudio and qgraph). CFA supported the construct validity of a unidimensional model for motor competence for the whole sample (RMSEA = 0.003; CFI = 0.998; TLI = 0.993) and for three age groups (RMSEA <0.08; CFI and TLI >0.95). Network analyses showed fine motor skills to be the most critical centrality skills, reinforcing the importance of fine motor skills for performing and participating in many daily activities across the lifespan. We found the TMC to be a valid and reliable test to measure MC across Iranians’ lifespan. We also demonstrated the advantages of using a machine learning approach via network analysis to evaluate associations between skills in a complex system.

Publisher

SAGE Publications

Subject

Sensory Systems,Experimental and Cognitive Psychology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3