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Year 2025, Volume: 14 Issue: 2, 531 - 545, 30.04.2025
https://doi.org/10.14686/buefad.1408446

Abstract

References

  • Achtziger, A., & Gollwitzer, P. M. (2018). Motivation and volition in the course of action. In J. Heckhausen, & H. Heckhausen (Eds.), Motivation and Action (pp. 485-527). (3rd ed.). Switzerland, Cham: Springer Publishing.
  • Akbaba, S. (2006). Eğitimde motivasyon [ Motivation in education ]. Atatürk University Journal of Kazım Karabekir Education Faculty, 13, 343-361. https://dergipark.org.tr/tr/pub/ataunikkefd/issue/2774/3717
  • Bacanlı, H. (2004). Gelişim ve Öğrenme (10th ed.). Ankara: Nobel Yayın Dağıtım.
  • Bayındır, N. (2021). Motivation Factor in the Online Teaching Process. Gaziantep Üniversitesi Eğitim Bilimleri Dergisi, 5 (2), 291-303. Retrieved from https://dergipark.org.tr/en/pub/guebd/issue/67489/960254
  • Bayrakçeken, S., Samancı, O., Canpolat, N. & Doymuş, K. (2021). Motivasyon ve Başari: Öz Belirleme Kurami Temelinde Öğrenciler Öğrenmeye Nasil Motive Edilebilir? [Motivation and Success: How Can Students Be Motivated to Learn on the Basis of Self-Determination Theory?]. Atatürk Üniversitesi Edebiyat Fakültesi Dergisi, (66), 482-505. Retrieved from https://dergipark.org.tr/en/pub/atauniefd/issue/62743/951311
  • Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral Research: A primer. Frontiers in Public Health, 6, Article 149. https://doi.org/10.3389/fpubh.2018.00149
  • Brophy, J. E. (2010). Motivating Students to Learn (3rd ed.). New York: Routledge.
  • Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York: Guilford Press.
  • Büyüköztürk, Ş. (2013). Sosyal Bilimler için Veri Analizi El Kitabı (22.bs.) [Data Analysis Handbook for Social Sciences (22nd ed.]. Pegem Akademi.
  • Cengiz, E. (2009). The Effects of Arcs Motivation Model on the Students' Success and Retention of Learning in Science and Technology Lessons (Master's Thesis). Ataturk University Graduate School of Natural and Applied Sciences, Erzurum.
  • Chiu, T.K.F., Lin, TJ. & Lonka, K. (2021). Motivating Online Learning: The Challenges of COVID-19 and Beyond. Asia-Pacific Edu Res 30, 187–190. https://doi.org/10.1007/s40299-021-00566-w
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cline, T., Gulliford, A., & Birch, S. (2023). Educational Psychology (3rd ed.). Topics in Applied Psychology. Routledge.
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). New York: Routledge.
  • Dattalo, P. (2013). Analysis of multiple dependent variables. New York: Oxford University Press.
  • Deimann, M., & Bastiaens, T. (2010). The role of volition in distance education: An exploration of its capacities. International Review of Research in Open and Distributed Learning, 11(1), 1-16. https://doi.org/10.19173/irrodl.v11i1.778.
  • DeVellis, R. F., & Thorpe, C. T. (2021). Scale Development: Theory and Applications (5th ed.). Los Angeles: SAGE.
  • Ferrando Piera, P. J., & Lorenzo Seva, U. (2017). Program FACTOR at 10: Origins, development and future directions. Psicothema.
  • Finch, W. H. (2020). Using fit statistic differences to determine the optimal number of factors to retain in an exploratory factor analysis. Educational and psychological measurement, 80(2), 217-241.
  • Gana, K. & Broc, G. (2019). Structural equation modeling with lavaan. New Jersey: Wiley.
  • George, D., & Mallery, P. (2022). IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference (17th ed.). New York: Routledge.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Harlow: Cengage Learning.
  • Heckhausen, H., & Gollwitzer, P. M. (1987). Thought contents and cognitive functioning in motivational versus volitional states of mind. Motivation and Emotion, 11(2), 101–120.
  • JASP Team. (2023). JASP. https://jasp-stats.org
  • Jung, S. (2013). Exploratory factor analysis with small sample sizes: A comparison of three approaches. Behavioural processes, 97, 90-95.
  • Keith, T. Z. (2019). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling (3. bs.). New York: Routledge.
  • Keller, J. M. (2008). An integrative theory of motivation, volition, and performance. Technology, Instruction, Cognition, and Learning, 6(2), 79-104.
  • Keller, J. M. (2010). Motivational Design for Learning and Performance: The ARCS Model Approach (1st ed.). New York, NY: Springer.
  • Keller, J. M. (2016). Motivation, Learning, and Technology: Applying the ARCS-V Motivation Model. Participatory Educational Research, 3 (2) , 1-15 . DOI: 10.17275/per.16.06.3.2
  • Keller, J. M., Ucar, H., & Kumtepe, A. T. (2020). Development and validation of a scale to measure volition for learning. Open Praxis, 12(2), 161-174.
  • Kılıç, S. (2013). Örnekleme yöntemleri [Sampling methods]. Journal of Mood Disorders, 3(1), 44-6. DOI: 10.5455/jmood.20130325011730
  • Koç, M. (2004). Gelişim psikolojisi açısından ergenlik dönemi ve genel özellikleri [Adolescence from the point of view of developmental psychology and its general characteristics]. Erciyes University Journal of Social Sciences Institute, 1(17), 231-238.
  • Kuhl, J. (1984). Volitional Aspects of Achievement Motivation and Learned Helplessness: Toward a Comprehensive Theory of Action Control. Progress in Experimental Personality Research, 13, 99-171.
  • Kuhl, J. (2000). A Functional-Design Approach to Motivation and Self-Regulation: The Dynamics of Personality Systems Interactions. Öz Düzenleme (pp. 111-169). Akademik Yayıncılık.
  • Maydeu-Olivares, A. (2017). Maximum likelihood estimation of structural equation models for continuous data: Standard errors and goodness of fit. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 383-394. doi:10.1080/10705511.2016.1269606
  • Mcgraw, K. O. & Wong, S. P. (1996). Forming Inferences About Some Intraclass Correlation Coefficients. Psychological Methods, 1(1), 30-46.
  • Odabaşı, H. F., Kurt, A. A., Akbulut, Y., Dönmez, O., Ceylan, B., Şahin-izmirli, Ö., Kuzu, E. B. & Karakoyun, F. (2011). Bilgi ve İletişim Teknolojileri (BİT) Eylem Yeterliliği [Information and Communication Technologies (ICT) Action Competence]. Anadolu Journal of Educational Sciences International, 1 (1), 36-48. Retrieved from https://dergipark.org.tr/en/pub/ajesi/issue/1525/18729
  • Roshanpour, R. & Nikroo, M. H. (2022). Investigating the Impact of Virtual Reality and Gamification on Improving Physical Activities in Children. Journal of Depression and Anxiety Science, 1(1), 01-08.
  • Senemoğlu, N. (2012). Gelişim, Öğrenme ve Öğretim: Kuramdan Uygulamaya (21. bs.) [Information and Communication Technologies (ICT) Action Competence (21st ed.)]. Pegem Akademi.
  • Tabachnick, B. G. & Fidell, L. S. (2012). Using multivariate statistics (6. bs.). Boston: Pearson.
  • Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.
  • TDK (Türk Dil Kurumu). (2023). https://sozluk.gov.tr
  • Uçar, H., Bozkurt, A., Öztürk, A. & Kumtepe T. A. (2020). Uzaktan Öğrenenlerin Eylem Yetkinliklerinin İncelenmesi [Investigation of Action Competencies of Distance Learners]. Anadolu Journal of Educational Sciences International, 10(1), 303-323. doi: 10.18039/ajesi.682037
  • Ucar, H. & Kumtepe, A. T. (2016, Mart). Use of ARCS-V Motivational Design Model in Online Distance Education. In Society for Information Technology & Teacher Education International Conference (pp. 55-60). Association for the Advancement of Computing in Education (AACE).
  • Yuan, L. I. U., & Wen-Zhi, H. U. A. N. G. (2016). From wishes to action: The explanatory power of the Rubicon model and its application. Journal of Psychological Science, 39(3), 754.
  • Yurdugül, H. & Sırakaya Alsancak, D. (2013). Çevrimiçi Öğrenme Hazır Bulunuşluluk Ölçeği: geçerlik ve güvenirlik çalışması [Online Learning Readiness Scale: a validity and reliability study]. Eğitim ve Bilim, 38(169), 391-405
  • Wang, J. & Wang, X. (2020). Structural equation modeling: Applications using Mplus (2. bs.). New Jersey: Wiley.
  • Wheaton, B., Muthen, B., Alwin, D. F. & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological methodology, 8, 84-136.
  • Watkins, M. W. (2021). A Step-by-Step Guide to Exploratory Factor Analysis with SPSS (1st ed.). Routledge.

Adaptation of Volitional Competency Scale for Middle School Students

Year 2025, Volume: 14 Issue: 2, 531 - 545, 30.04.2025
https://doi.org/10.14686/buefad.1408446

Abstract

This study aims to adapt the Volitional Competency Scale (VCS), developed by Keller, Uçar, and Kumtepe (2020) based on the ARCS-V model, for use with middle school students. After consulting with experts with doctoral degrees in distance education, Turkish language teachers, and middle school students, the scale was revised. In the second phase, an exploratory factor analysis was conducted with a sample of 156 middle school students to determine the factor structure of the scale. In the third phase, the factor structure obtained as a result of the exploratory factor analysis was examined with a confirmatory factor analysis in a new sample of 272 middle school students. In the final stage, the test-retest reliability of the scale was examined in a sample of 20 students. The exploratory factor analysis revealed that the scale has a single-factor structure for middle school students. The internal consistency reliability analysis of the scale in two different samples showed that the Cronbach's alpha reliability coefficient varied between .83 and .93. Lastly, the test-retest reliability, calculated two weeks apart, was found to be .91. These results indicate that the VCS is a relevant and reliable measurement tool for use with middle school students.

References

  • Achtziger, A., & Gollwitzer, P. M. (2018). Motivation and volition in the course of action. In J. Heckhausen, & H. Heckhausen (Eds.), Motivation and Action (pp. 485-527). (3rd ed.). Switzerland, Cham: Springer Publishing.
  • Akbaba, S. (2006). Eğitimde motivasyon [ Motivation in education ]. Atatürk University Journal of Kazım Karabekir Education Faculty, 13, 343-361. https://dergipark.org.tr/tr/pub/ataunikkefd/issue/2774/3717
  • Bacanlı, H. (2004). Gelişim ve Öğrenme (10th ed.). Ankara: Nobel Yayın Dağıtım.
  • Bayındır, N. (2021). Motivation Factor in the Online Teaching Process. Gaziantep Üniversitesi Eğitim Bilimleri Dergisi, 5 (2), 291-303. Retrieved from https://dergipark.org.tr/en/pub/guebd/issue/67489/960254
  • Bayrakçeken, S., Samancı, O., Canpolat, N. & Doymuş, K. (2021). Motivasyon ve Başari: Öz Belirleme Kurami Temelinde Öğrenciler Öğrenmeye Nasil Motive Edilebilir? [Motivation and Success: How Can Students Be Motivated to Learn on the Basis of Self-Determination Theory?]. Atatürk Üniversitesi Edebiyat Fakültesi Dergisi, (66), 482-505. Retrieved from https://dergipark.org.tr/en/pub/atauniefd/issue/62743/951311
  • Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral Research: A primer. Frontiers in Public Health, 6, Article 149. https://doi.org/10.3389/fpubh.2018.00149
  • Brophy, J. E. (2010). Motivating Students to Learn (3rd ed.). New York: Routledge.
  • Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). New York: Guilford Press.
  • Büyüköztürk, Ş. (2013). Sosyal Bilimler için Veri Analizi El Kitabı (22.bs.) [Data Analysis Handbook for Social Sciences (22nd ed.]. Pegem Akademi.
  • Cengiz, E. (2009). The Effects of Arcs Motivation Model on the Students' Success and Retention of Learning in Science and Technology Lessons (Master's Thesis). Ataturk University Graduate School of Natural and Applied Sciences, Erzurum.
  • Chiu, T.K.F., Lin, TJ. & Lonka, K. (2021). Motivating Online Learning: The Challenges of COVID-19 and Beyond. Asia-Pacific Edu Res 30, 187–190. https://doi.org/10.1007/s40299-021-00566-w
  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319. https://doi.org/10.1037/1040-3590.7.3.309
  • Cline, T., Gulliford, A., & Birch, S. (2023). Educational Psychology (3rd ed.). Topics in Applied Psychology. Routledge.
  • Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). New York: Routledge.
  • Dattalo, P. (2013). Analysis of multiple dependent variables. New York: Oxford University Press.
  • Deimann, M., & Bastiaens, T. (2010). The role of volition in distance education: An exploration of its capacities. International Review of Research in Open and Distributed Learning, 11(1), 1-16. https://doi.org/10.19173/irrodl.v11i1.778.
  • DeVellis, R. F., & Thorpe, C. T. (2021). Scale Development: Theory and Applications (5th ed.). Los Angeles: SAGE.
  • Ferrando Piera, P. J., & Lorenzo Seva, U. (2017). Program FACTOR at 10: Origins, development and future directions. Psicothema.
  • Finch, W. H. (2020). Using fit statistic differences to determine the optimal number of factors to retain in an exploratory factor analysis. Educational and psychological measurement, 80(2), 217-241.
  • Gana, K. & Broc, G. (2019). Structural equation modeling with lavaan. New Jersey: Wiley.
  • George, D., & Mallery, P. (2022). IBM SPSS Statistics 27 Step by Step: A Simple Guide and Reference (17th ed.). New York: Routledge.
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate Data Analysis (8th ed.). Harlow: Cengage Learning.
  • Heckhausen, H., & Gollwitzer, P. M. (1987). Thought contents and cognitive functioning in motivational versus volitional states of mind. Motivation and Emotion, 11(2), 101–120.
  • JASP Team. (2023). JASP. https://jasp-stats.org
  • Jung, S. (2013). Exploratory factor analysis with small sample sizes: A comparison of three approaches. Behavioural processes, 97, 90-95.
  • Keith, T. Z. (2019). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling (3. bs.). New York: Routledge.
  • Keller, J. M. (2008). An integrative theory of motivation, volition, and performance. Technology, Instruction, Cognition, and Learning, 6(2), 79-104.
  • Keller, J. M. (2010). Motivational Design for Learning and Performance: The ARCS Model Approach (1st ed.). New York, NY: Springer.
  • Keller, J. M. (2016). Motivation, Learning, and Technology: Applying the ARCS-V Motivation Model. Participatory Educational Research, 3 (2) , 1-15 . DOI: 10.17275/per.16.06.3.2
  • Keller, J. M., Ucar, H., & Kumtepe, A. T. (2020). Development and validation of a scale to measure volition for learning. Open Praxis, 12(2), 161-174.
  • Kılıç, S. (2013). Örnekleme yöntemleri [Sampling methods]. Journal of Mood Disorders, 3(1), 44-6. DOI: 10.5455/jmood.20130325011730
  • Koç, M. (2004). Gelişim psikolojisi açısından ergenlik dönemi ve genel özellikleri [Adolescence from the point of view of developmental psychology and its general characteristics]. Erciyes University Journal of Social Sciences Institute, 1(17), 231-238.
  • Kuhl, J. (1984). Volitional Aspects of Achievement Motivation and Learned Helplessness: Toward a Comprehensive Theory of Action Control. Progress in Experimental Personality Research, 13, 99-171.
  • Kuhl, J. (2000). A Functional-Design Approach to Motivation and Self-Regulation: The Dynamics of Personality Systems Interactions. Öz Düzenleme (pp. 111-169). Akademik Yayıncılık.
  • Maydeu-Olivares, A. (2017). Maximum likelihood estimation of structural equation models for continuous data: Standard errors and goodness of fit. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 383-394. doi:10.1080/10705511.2016.1269606
  • Mcgraw, K. O. & Wong, S. P. (1996). Forming Inferences About Some Intraclass Correlation Coefficients. Psychological Methods, 1(1), 30-46.
  • Odabaşı, H. F., Kurt, A. A., Akbulut, Y., Dönmez, O., Ceylan, B., Şahin-izmirli, Ö., Kuzu, E. B. & Karakoyun, F. (2011). Bilgi ve İletişim Teknolojileri (BİT) Eylem Yeterliliği [Information and Communication Technologies (ICT) Action Competence]. Anadolu Journal of Educational Sciences International, 1 (1), 36-48. Retrieved from https://dergipark.org.tr/en/pub/ajesi/issue/1525/18729
  • Roshanpour, R. & Nikroo, M. H. (2022). Investigating the Impact of Virtual Reality and Gamification on Improving Physical Activities in Children. Journal of Depression and Anxiety Science, 1(1), 01-08.
  • Senemoğlu, N. (2012). Gelişim, Öğrenme ve Öğretim: Kuramdan Uygulamaya (21. bs.) [Information and Communication Technologies (ICT) Action Competence (21st ed.)]. Pegem Akademi.
  • Tabachnick, B. G. & Fidell, L. S. (2012). Using multivariate statistics (6. bs.). Boston: Pearson.
  • Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson.
  • TDK (Türk Dil Kurumu). (2023). https://sozluk.gov.tr
  • Uçar, H., Bozkurt, A., Öztürk, A. & Kumtepe T. A. (2020). Uzaktan Öğrenenlerin Eylem Yetkinliklerinin İncelenmesi [Investigation of Action Competencies of Distance Learners]. Anadolu Journal of Educational Sciences International, 10(1), 303-323. doi: 10.18039/ajesi.682037
  • Ucar, H. & Kumtepe, A. T. (2016, Mart). Use of ARCS-V Motivational Design Model in Online Distance Education. In Society for Information Technology & Teacher Education International Conference (pp. 55-60). Association for the Advancement of Computing in Education (AACE).
  • Yuan, L. I. U., & Wen-Zhi, H. U. A. N. G. (2016). From wishes to action: The explanatory power of the Rubicon model and its application. Journal of Psychological Science, 39(3), 754.
  • Yurdugül, H. & Sırakaya Alsancak, D. (2013). Çevrimiçi Öğrenme Hazır Bulunuşluluk Ölçeği: geçerlik ve güvenirlik çalışması [Online Learning Readiness Scale: a validity and reliability study]. Eğitim ve Bilim, 38(169), 391-405
  • Wang, J. & Wang, X. (2020). Structural equation modeling: Applications using Mplus (2. bs.). New Jersey: Wiley.
  • Wheaton, B., Muthen, B., Alwin, D. F. & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological methodology, 8, 84-136.
  • Watkins, M. W. (2021). A Step-by-Step Guide to Exploratory Factor Analysis with SPSS (1st ed.). Routledge.
There are 50 citations in total.

Details

Primary Language English
Subjects Cross-Cultural Scale Adaptation
Journal Section Articles
Authors

Abdurrahman Yıldırım 0000-0002-2520-0530

Serçin Karataş 0000-0002-1731-0676

Early Pub Date April 30, 2025
Publication Date April 30, 2025
Submission Date December 22, 2023
Acceptance Date July 19, 2024
Published in Issue Year 2025 Volume: 14 Issue: 2

Cite

APA Yıldırım, A., & Karataş, S. (2025). Adaptation of Volitional Competency Scale for Middle School Students. Bartın University Journal of Faculty of Education, 14(2), 531-545. https://doi.org/10.14686/buefad.1408446

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