VALIDATION AND RELIABILITY TESTS OF THE LIFE CIRCUMSTANCES AND MOTIVATIONAL ASPECTS OF STUDENT CONTENT SCALE (CVAME)

Authors

DOI:

https://doi.org/10.47820/recima21.v3i4.1280

Keywords:

Validation, Reliability, Scale, Student, Model

Abstract

The objective of the study is to validate and test the reliability of the scale of content of life circumstances and motivational aspects of the student (CVAME). For this, we use techniques of exploratory factorial analysis (AFE) and confirmatory factorial analysis (CFA). The results obtained from the model represented by Figure 3. Measurement model were considered adequate, because together, the value of 0.782 of composite reliability ("CR") and the value of 0.555 of the mean variance ("AVE") indicate acceptable values of reliability and convergent validity for the measurement model. These values show the quality of the structural model of the instrument. Given the presented results, this instrument seems very useful and allows us to affirm that it is sensitive, valid and reliable for the evaluation of the academic support received by the students, content or changes in the circumstances of the student's life during the training process, the motivational aspects of the learning experiences.

Downloads

Download data is not yet available.

Author Biographies

Oberdan Santos da Costa

Doutor em Ciência da Informação pela Universidade Fernando Pessoa em Porto-Portugal. Mestrado em GESTÃO DE EMPRESAS pela Universidade Lusófona de Humanidades e Tecnologias (2014-2015) Em Lisboa-Portugal. MBA Executivo em Gestão Empresarial pelas Faculdades de Ciências Gerenciais da Bahia (2011-2013). Especialização em Formação de Consultores Organizacionais - FCO pelo ISAN-FGV (2007), Especialização em gestão empresarial pelo ISAN-FGV (2003).

Dr.

Full Professor at the Fernando Pessoa University. He has published 66 articles in specialized magazines and 170 papers in event proceedings, has 57 book chapters and 17 books published. Participated in 65 events abroad and 53 in Portugal. He directed 8 doctoral theses and co-oriented 2, guided 21 master's dissertations and co-oriented 2. He works in the areas of Engineering and Technology with emphasis in Electrical Engineering, Electronics and Computer Science and Exact Sciences with emphasis in Computer Science and Information Sciences

Luis Simões da Cunha

Graduado em Psicologia e em Ciência da Computação. PhD em Sistemas de Informação. Apaixonado por ensinar. Instituto Superior Miguel Torga

References

ANDERSON, J. C.; GERBING, D. W. The Effect of Sampling Error on Convergence, Improper Solutions, and Goodness-of-Fit Indices for Maximum Likelihood Confirma-tory Factor Analysis. Psychometrika, v. 49, n. 2, p. 155-173, 1984. doi: https://doi.org/10.1007/BF02294170

BALABAN-SALI, J. Designing motivational learning systems in distance education. Turkish Online Journal of Distance Education, v. 9, n. 3, p. 149-161, jul., 2008. Disponível em: <https://dergipark.org.tr/download/article-file/156290>. Acesso em: 28 jun. 2019.

BARBOZA, S. I. S. et al. Variações de Mensuração pela Escala de Verificação: uma análise com escalas de 5, 7 e 11 pontos. Teoria e Prática em Administração, n. 3, v. 2, p. 99-120, 2013. Disponível em: <http://www.spell.org.br/documentos/ver/18384/variacoes-de-mensuracao-pela-escala-de-verifica--->. Acesso em: 28 jun. 2019.

BATTALIO, J. Interaction online: A reevaluation. The Quarterly Review of Distance Education, n. 8, v. 4, p. 339-352, 2007.

BENSON, J.; FLEISHMAN, J. A. The robustness of maximum likelihood and distribution-free estimators to non-normality in confirmatory factor analysis. Qual Quant, n. 28, p. 117-136, 1994. Disponível em: <https://link.springer.com/article/10.1007/BF01102757>. Acesso em: 28 jun. 2019.

BERGE, Z.; HUANG, Y. Um modelo para a retenção sustentável de estudantes: Uma perspectiva holística sobre o problema do abandono escolar com atenção especial ao e-learning. DEOSNEWS, n. 13, v. 5, mai., 2004. Disponível em: <http://www.ed.psu.edu/acsde/deos/deosnews/deosnews13_5.pdf>. Acesso em: 09 jul. 2019.

BOLLEN, K. A. Structural equations with latent variables. New York: John Wiley and Sons, 1989.

BYRNE, B. M. Structural equation modeling with AMOS: Basic concepts, applications, and programming. 2. ed. New York: Routledge Taylor & Francis Group, 2010.

COMREY, A. L. Factor-Analytic Methods of Scale Development in Personality and Clinical Psychology. Journal of Consulting and Clinical Psychology, v. 56, p. 754-761, out., 1988. doi: https://doi.org/10.1037/0022-006X.56.5.754

FINKELSTEIN, L. Widely-defined measurement: An analysis of challenges. Measurement, n. 42, p. 1270-1277, 2009.

FINNEY, S.J.; DiSTEFANO, C. Non-normal and Categorical data in structural equation modeling. In: Hancock, G. R.; MUELLER, R. O. (Hrsg.). Structural equation modeling: a second course. Greenwich, Connecticut: Information Age Publishing, 2006, p. 269-314.

GORMLEY, D. K.; COLELLA, C.; SHELL, D. L. Motivating online learners using attention, relevance, confidence, satisfaction motivational theory and distributed scaffolding. Nurse Educator, n. 37, v. 4, p. 177-180, 2012.

HAIR, J. F. et al. Multivariate Data Analysis. 7. ed. Ney Jersey: Prentice Hall, 2010.

HART, C. Factors Associated With Student Persistence in an Online Program of Study: A Review of the Literature. Journal of Interactive Online Learning, v. 11, n. 1, p. 19-42, 2012.

HOE, S. L. Issues and Procedures in Adopting Structural Equation Modeling Technique. Journal of Applied Quantitative Methods, n. 3, p.76-83, 2008.

HUANG, B.; HEW, K. F. Measuring Learners’ Motivation Level in Massive Open Online Courses. International Journal of Information and Education Technology, n. 6, v. 10, p. 759-764, 2016. Disponível em: <http://www.ijiet.org/vol6/788-A001.pdf>. Acesso em: 29 jun. 2019,

HUETT, J. et al. Supporting the distance student: The effect of ARCS-based strategies on confidence and performance. Quarterly Review of Distance Education, n. 9, v. 2, p. 113-126, 2008.

HUNG, I. C. et al. Designing a robot teaching assistant for enhancing and sustaining learning motivation. Interactive Learning Environments, n. 21, v. 2, p. 156-171, 2013. doi: https://doi.org/10.1080/10494820.2012.705855.

JORDAN, K. Massive Open Online Courses Completion Rates Revisited: Assessment, Length and Attrition. In: International Review of Research in Open and Distributed Learning, V. 16, n. 3, jun., 2015. Disponível em: <https://files.eric.ed.gov/fulltext/EJ1067937.pdf>. Acesso em: 28 jun. 2019

JUN, J. Understanding dropout of adult learners in e-learning. Dissertation (Doctoral of Philosophy) - University of Georgia, Athens, Georgia, 2005.

KELLER, K. L. Memory Factors in Advertising: The Effect of Advertising Retrieval Cues on Brand Evaluations. Journal of Consumer Research, v. 14, p. 316-33, december, 1987.

KELLER J. M. Motivational design for learning and performance: the ARCS model approach. New York, NY: Springer, 2010.

KELLOWAY, E. K. Using Mplus for Structural Equation Modeling. Thousand Oaks, CA: Sage, 2015.

KIM, C.; KELLER, J. M. Effects of motivational and volitional email messages (MVEM) with personal messages on undergraduate students' motivation, study habits and achievement. British Journal of Educational Technology, n. 39, v. 1, p. 36-51, 2008. doi: https://doi.org/10.1111/j.1467-8535.2007.00701.x.

KLINE, R. B. Principles and Practice of Structural Equation Modeling. 4. ed., 2016.

KYRIAZOS, T. A. Applied Psychometrics: Sample Size and Sample Power Considerations in Fac-tor Analysis (EFA, CFA) and SEM in General. Psychology, n. 9, p. 2207-2230, 2018. doi: https://doi.org/10.4236/psych.2018.98126.

LEVY, Y. Comparing dropouts and persistence in e-learning courses. Computers and Education, v. 48, p. 185-204, 2007.

MACCALLUM, R. C.; AUSTIN, J. T. Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology, v. 51, p. 201-226, 2010. doi: https://doi.org/10.1146/annurev.psych.51.1.201.

MOLLISON, A. Colleges adjust as more older people seek knowledge [final edition]. Palm Beach Post, August 21, p. 1A, 2000.

MORRIS, L. V.; WU, S.; FINNEGAN, C. Predicting retention in online general education courses. American Journal of Distance Education, n. 19, v. 1, p. 23-36, p. 2005.

NETTO, C.; GUIDOTTI, V.; SANTOS, P. K. A evasão na EAD: Investigando causas, propondo estratégias. In: Conferencia Latinoamericana sobre el Abandono de la Educación Superior, 2., 2012, Porto Alegre. Anais… Porto Alegre: Pontifícia Universidade Católica Rio Grande do Sul, 2012.

NUNNALLY, J. C.. Psychometric theory. 2. ed. New York: McGraw-Hill, 1978.

PARK, J. H.; CHOI, H. J. Factors influencing adult learners decision to drop out or persist in online learning. Educational Technology & Society, Paris, v. 12, n. 4, p. 207-217, 2009.

SAHIN, I. Predicting student satisfaction in distance education and learning environments. Turkish Online Journal of Distance Education-TOJDE, n. 8, v. 2, p. 113-119, 2007.

SALES, G. L. Learning Vectors: Um Modelo de Avaliação da Aprendizagem em EaD Online Aplicando Métricas Não-Lineares. Tese (Doutorado) - Universidade Federal do Ceará (UFC), Programa de Pós-graduação em Teleinformática, Fortaleza, CE, 2010.

SCHUMACKER, R. E.; LOMAX, R. G. A Beginner’s Guide to Structural Equation Modeling. 3. ed. New York: Taylor and Francis Group, 2010.

SINGH, K.; JUNNARKAR, M.,; KAUR, J. Measures of Positive Psychology: Development and Validation. Berlin: Springer, 2016.

TINSLEY, H. E.; TINSLEY, D. J. (1987). Uses of Factor Analysis in Counseling Psychology Research. Journal of Counseling Psychology, n. 34, p. 414-424, 1987. doi: https://doi.org/10.1037/0022-0167.34.4.414

WANG, J.; WANG, X. Structural Equation Modeling: Applications Using Mplus. Chichester, UK: Wiley, 2012.

YUKSELTURK, E.; YILDIRIM, Z. Investigação de interação, suporte online, estrutura do curso e flexibilidade como fatores que contribuem para a satisfação dos alunos em um programa de certificação on-line. Educational Technology & Society, n. 11, v. 4, p. 51-65, 2008.

Published

07/04/2022

How to Cite

Costa, O. S. da, Borges Gouveia, L., & Cunha, L. S. da. (2022). VALIDATION AND RELIABILITY TESTS OF THE LIFE CIRCUMSTANCES AND MOTIVATIONAL ASPECTS OF STUDENT CONTENT SCALE (CVAME). RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 3(4), e341280. https://doi.org/10.47820/recima21.v3i4.1280