PRUEBAS DE VALIDACIÓN Y FIABILIDAD DE LA ESCALA DE CONTENIDOS DE LAS CIRCUNSTANCIAS DE LA VIDA Y ASPECTOS MOTIVACIONALES DEL ALUMNO (CVAME)
DOI:
https://doi.org/10.47820/recima21.v3i4.1280Palabras clave:
validar y probar la fiabilidad de la escalaResumen
El objetivo del estudio es validar y probar la fiabilidad de la escala de contenido Circunstancias de la Vida y Aspectos Motivacionales del Alumno (CVAME). Para ello se utilizan el Análisis Factorial Exploratorio (AFE) y el Análisis Factorial Confirmatorio (CFA). Los resultados obtenidos del modelo representado por la Figura 3. Modelo de medición se consideraron adecuados, porque en conjunto, el valor de 0.782 de la confiabilidad compuesta (“CR”) y el valor de 0.555 de la varianza promedio extraída (“varianza promedio extraída”, o “AVE”) indican valores aceptables de confiabilidad y validez convergente para el modelo de medición. Estos valores muestran la calidad del modelo estructural del instrumento. A la vista de los resultados presentados, este instrumento nos parece de gran utilidad y nos permite afirmar que es sensible, válido y fiable para la evaluación del apoyo académico que reciben los estudiantes, el contenido o cambios en las circunstancias de la vida del estudiante. vida durante el proceso de formación, a los aspectos motivacionales de las experiencias de aprendizaje.
Descargas
Citas
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.
Descargas
Publicado
Cómo citar
Número
Sección
Categorías
Licencia
Derechos de autor 2022 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.
