Exploratory and Confirmatory Factor Analysis for Disposition Levels of Computational Thinking Instrument Among Secondary School Students
Saralah Sovey , Kamisah Osman , Mohd Effendi Ewan Mohd-Matore
Computational thinking (CT) is a method for solving complex problems, but also gives people an inventive inspiration to adapt to our smart and changin.
- Pub. date: April 15, 2022
- Pages: 639-652
- 873 Downloads
- 1406 Views
- 10 Citations
Computational thinking (CT) is a method for solving complex problems, but also gives people an inventive inspiration to adapt to our smart and changing society. Globally it has been considered as vital abilities for solving genuine issues successfully and efficiently in the 21st century. Recent studies have revealed that the nurture of CT mainly centered on measuring the technical skill. There is a lack of conceptualization and instruments that cogitate on CT disposition and attitudes. This study attends to these limitations by developing an instrument to measure CT concerning dispositions and attitudes. The instruments' validity and reliability testing were performed with the participation from secondary school students in Malaysia. The internal consistency reliability, standardized residual variance, construct validity and composite reliability were examined. The result revealed that the instrument validity was confirmed after removing items. The reliability and validity of the instrument have been verified. The findings established that all constructs are useful for assessing the disposition of computer science students. The implications for psychometric assessment were evident in terms of giving empirical evidence to corroborate theory-based constructs and also validating items' quality to appropriately represent the measurement.
Keywords: Attitudes, computational thinking, disposition, factor analysis.
References
Abas, A. (2016). Computational thinking skills to be introduced in school curriculum next year. New Straits Times. https://bit.ly/3em43TZ
Ahmad, M. F. (2017). Application of structural equation modelling (SEM) in quantitative research (1st ed.). UTHM Publication.
Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Educational Technology & Society, 19(3), 47–57. http://www.jstor.org/stable/jeductechsoci.19.3.47
Association for Computing Machinery & IEEE Computer Society. (2013). Computer science curricula 2013. ACM-IEEE. https://doi.org/10.1145/2534860
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Sciences, 16, 74-94. https://doi.org/10.1007/BF02723327
Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: A digital age skill for everyone. Learning and Leading With Technology, 38(6), 20–23. https://eric.ed.gov/?id=EJ918910
Bartlett, M. S. (1954). A note on the multiplying factors for various chi-square approximations in factor analysis. Journal of the Royal Statistical Society, 16(2), 296-298. https://doi.org/10.1111/j.2517-6161.1954.tb00174.x
Belanger, C., Christenson, H., & Lopac, K. (2018). Confidence and common challenges: The effects of teaching computational thinking to students ages 10-16 [Masters’ thesis, St. Catherine University]. St.Catherine University Repository. https://sophia.stkate.edu/maed/267
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238
Bers, M. U. (2020). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge. https://doi.org/10.4324/9781003022602
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
Beyer, B. K. (1995). Critical thinking. Phi Delta Kappa Educational Foundations.
Brennan, K., & Resnick, M. (2012, April13-17). New frameworks for studying and assessing the development of computational thinking [Paper presentation]. Annual Meeting of the American Educational Research Association (AERA), Vancouver, BC, Canada. http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf
Brown, T. A. (2006). Confirmatory factor analysis for applied research. Guilford Press.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A Bollen., & J. S. Long (Eds.), Testing structural equation models. SAGE Publications Inc.
Byrne, B. M. (2010). Structural equation modeling with AMOS. Basic concept, applications and programming (2nd ed.). Routledge.
Cabrera-Nguyen, E. P. (2010). Author guidelines for reporting scale development and validation results in the journal of the society for social work and research. Journal of the Society for Social Work and Research, 1(2), 99-103. https://bit.ly/3qWVRiV
Chalmers, C. (2018). Robotics and computational thinking in primary school. International Journal of Child-Computer Interaction, 17, 93–100. https://doi.org/10.1016/j.ijcci.2018.06.005
Chua, Y. P. (Ed.). (2014). Ujian regresi, analisis faktor dan analisis SEM [Regression, factor analysis and structural equation modelling]. Mcgraw-Hill Education.
Computer Science Teachers Association. (2017). CSTA K–12 computer science standards. Revised 2017. http://www.csteachers.org/standards
Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98
Costello, A., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(7), 1-9. https://doi.org/10.7275/jyj1-4868
Cristobal, E., Flavián, C., & Guinalíu, M. (2007). Perceived e‐service quality (PeSQ). Managing Service Quality. An International Journal, 17(3), 317–340. https://doi.org/10.1108/09604520710744326
Curriculum Development Division. (2015). Kurikulum standard sekolah menengah (KSSM): Dokumen standard kurikulum dan pentaksiran (dskp) sains komputer tingkatan empat [Secondary school standard curriculum (KSSM): Form four computer science curriculum and assessment standard document (DSKP)]. https://bit.ly/3EWRBpy
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Sage Publications.
DeVon, H. A., Block, M. E., Moyle-Wright, P., Ernst, D. M., Hayden, S. J., Lazzara, D. J., Savoy, S. M., & Kostas-Polston, E. (2007). A psychometric toolbox for testing validity and reliability. Journal of Nursing Scholarship, 39(2), 155–164. https://doi.org/10.1111/j.1547-5069.2007.00161.x
Ennis, R. H. (1996). Critical Thinking Dispositions: Their nature and assessability. Informal Logic, 18(2), 165-182. https://doi.org/10.22329/il.v18i2.2378
Facione, N. C., Facione, P. A., & Sanchez, C. A. (1994). Critical thinking disposition as a measure of competent clinical judgment: The development of the California critical thinking disposition inventory. Nursing Education, 33(8), 345–350. https://doi.org/10.3928/0148-4834-19941001-05
Facione, P. A. (2000). The disposition toward critical thinking: its character, measurement, and relationship to critical thinking skill. Informal Logic, 20(1), 61-84. https://doi.org/10.22329/il.v20i1.2254
Falloon, G. (2015). Building computational thinking through programming in K-6 education: A New Zealand experience. In L. Gomez Chova, A. Lopez Martinez, & I. Chandel Torres (Eds.), EDULearn Proceedings (pp. 882–892). https://hdl.handle.net/10289/9455.
Field, A. (2009). Discovering statistics using SPSS (3rd ed.). SAGE Publications.
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). SAGE Publications.
García-Peñalvo, F. J., & Mendes, A. J. (2018). Exploring the computational thinking effects in pre-university education. Computers in Human Behavior, 80, 407–411. https://doi.org/10.1016/j.chb.2017.12.005.
García-Valcárcel-Muñoz-Repiso, A., & Caballero-González, Y.-A. (2019). Robótica para desarrollar el pensamiento computacional en Educación Infantil [Robotics to develop computational thinking in early childhood education]. Media Education Research Journal/ Comunicar, 27(59), 63–72. https://doi.org/10.3916/c59-2019-06
Gouws, L. A., Bradshaw, K., & Wentworth, P. (2013). Computational thinking in educational activities. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education - ITiCSE ’13, (pp. 10-15). ACM. https://doi.org/10.1145/2462476.2466518
Grover, S., & Pea, R. (2013). Computational thinking in K–12. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189x12463051
Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2018). Multivariate data analysis (8th ed.). CENGAGE.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice-Hall.
Hair, J. F., Celsi, M. W., Oritinau, D. J., & Bush, R. P. (2017). Essentials of marketing research (4th ed.). McGraw Hill.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modelling in marketing research. Journal of The Academy of Marketing Science, 40(3), 414–433. https://doi.org/10.1007/s11747-011-0261-6
Haseski, H. I., Ilic, U., & Tugtekin, U. (2018). Defining a new 21st century skill-computational thinking: Concepts and Trends. International Education Studies, 11(4), 29-42. https://doi.org/10.5539/ies.v11n4p29.
Hilgard, E. R. (1980). The trilogy of mind: Cognition, affection, and conation. Journal of the History of the Behavioral Sciences, 16(2), 107–117. https://doi.org/10.1002/1520-6696(198004)16:2<107::AID-JHBS2300160202>3.0.CO;2-Y
Holmes-Smith, P., Coote, L., & Cunningham, E. (2006). Structural equation modeling: From the fundamentals to advanced topics. School Research, Evaluation and Measurement Services.
Izquierdo, I., Díaz, J., & Abad, F. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26(3), 395-400. https://doi.org/10.7334/psicothema2013.349
Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/bf02300500
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36. https://doi.org/10.1007/BF02291575
Kenny, D. A. (2016, April 9). Multiple factor models: Confirmatory factor analysis. https://davidakenny.net/cm/mfactor.htm
Kim, B., Kim, T., & Kim, J. (2013). Paper-and-pencil programming strategy toward computational thinking for non-majors: Design your solution. Educational Computing Research, 49(4), 437–459. https://doi.org/10.2190/ec.49.4.b
Kim, H. Y. (2012). Statistical notes for clinical researchers: Assessing normal distribution (1). Restorative Dentistry. Endodontics, 37(4), 245–24. https://doi.org/10.5395%2Frde.2012.37.4.245
Kim, H. Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry. Endodontics 38(1), 4–52. https://doi.org/10.5395%2Frde.2013.38.1.52
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in Human Behavior, 72, 558–569. https://doi.org/10.1016/j.chb.2017.01.005
Little, T. D., Lindenberger, U., & Nesselroade, J. R. (1999). On selecting indicators for multivariate measurement and modeling with latent variables: When ‘good’ indicators are bad and ‘bad’ indicators are good. Psychological Methods, 4(2), 192–211. https://doi.org/10.1037/1082-989X.4.2.192
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84–99. https://doi.org/10.1037/1082-989x.4.1.84
Maltby, J., Day, L., & Williams, G. (2007). Introduction to statistics for nurses (1st ed.) Routledge. https://doi.org/10.4324/9781315847597
Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014). Computational thinking in K-9 education. In L. Mannila., V. Dagiene., B. Demo., N. Grgurina., C. Mirolo., L. Rolandsson., & A. Settle (Eds.), Proceedings of the 2014 conference on Innovation & Technology in Computer Science Education Conference - ITiCSE '14 (pp. 1-29). ACM Press Digital Library. https://doi.org/10.1145/2713609.2713610
Mills, G. E., & Gay, L. R. (2018). Educational research: Competencies for analysis and applications (12th ed.). Pearson.
Missiroli, M., Russo, D., & Ciancarini, P. (2017). Cooperative thinking, or: computational thinking meets agile. In M. Missiroli., D. Russo., & P. Ciancarini (Eds.), IEEE 30th Conference on Software Engineering Education and Training (CSEE&T) (pp. 187-191). IEEE. https://doi.org/10.1109/CSEET.2017.37
Mvududu, N. H., & Sink, C. A. (2013). Factor analysis in counseling research and practice. Counseling Outcome Research and Evaluation, 4(2), 75–98. https://doi.org/10.1177/2150137813494766
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw Hill.
Pallant, J. F., & Tennant, A. (2007). An introduction to the Rasch measurement model: An example using the Hospital Anxiety and Depression Scale (HADS). British Journal of Clinical Psychology, 46(1), 1–18. https://doi.org/10.1348/014466506x96931
Perkins, D. N., & Tishman, S. (2001). Dispositional aspects of intelligence. In D. N. Perkins., & S. Tishman (Eds.), Intelligence and personality: Bridging the gap in theory and measurement (pp. 233-257). Psychology Press.
Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. SAGE Publications. https://doi.org/10.4135/9781412984898
Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM’s SPSS (6th ed.). Routledge. https://doi.org/10.1017/CBO9781107415324.004
Qin, H. (2009). Teaching computational thinking through bioinformatics to biology students. ACM SIGCSE Bulletin, 41(1), 188–191. https://doi.org/10.1145/1539024.1508932
Radhakrishna, R. B. (2007). Tips for developing and testing questionnaires/instruments. Journal of Extension, 45(1), 1-4. http://www.joe.org/joe/2007february/tt2.php
Rantz, M. J., Zwygart-Stauffacher, M., Mehr, D. R., Petroski, G. F., Owen, S. V., Madsen, R. W., Flesner, M., Conn, V., Bostick, J., Smith, R., & Maas, M. (2006). Field testing, refinement, and psychometric evaluation of a new measure of nursing home care quality. Journal of Nursing Measurement, 14(2), 129–148. https://doi.org/10.1891/jnm-v14i2a005
Raubenheimer, J. (2004). An item selection procedure to maximize scale reliability and validity. SA Journal of Industrial Psychology, 30(4), 59–64. https://doi.org/10.4102/sajip.v30i4.168
Robinson, J. P., Shaver, P. R., & Wrightsman, L. S. (1991). Criteria for scale selection and evaluation. In J. P. Robinson., P. R. Shaver., & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes (pp. 1-15). Academic Press. https://doi.org/10.1016/B978-0-12-590241-0.50005-8
Rode, J. A., Weibert, A., Marshall, A., Aal, K., von Rekowski, T., El Mimouni, H., & Booker, J. (2015). From computational thinking to computational making. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp ’15 (pp. 239-250). ACM. .https://doi.org/10.1145/2750858.2804261
Rust, J., & Golombok, S. (1989). Modern psychometrics. Routledge.
Sanford, J. F., & Naidu, J. T. (2016). Computational thinking concepts for grade school. Contemporary Issues in Education Research, 9(1), 23–32. https://doi.org/10.19030/cier.v9i1.9547
Schiffman, L. G., Kanuk, L. L., & Hansen, H. (2012). Consumer behavior: A European outlook (2nd ed.). Pearson.
Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition. University of Southampton Institutional Repository. https://eprints.soton.ac.uk/id/eprint/356481
Settle, A., Franke, B., Hansen, R., Spaltro, F., Jurisson, C., Rennert-May, C., & Wildeman, B. (2012). Infusing computational thinking into the middle- and high-school curriculum. In A. Settle., B. Franke., R. Hansen., F. Spaltro., C. Jurisson., C. Rennert-May., & B. Wildeman (Eds.), Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education - ITiCSE ’12 (pp. 22-27). ACM. https://doi.org/10.1145/2325296.2325306
Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22(1), 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
Sondakh, D. E., Osman, K., & Zainudin, S. (2020). A proposal for holistic assessment of computational thinking for undergraduate: Content validity. European Journal of Educational Research, 9(1), 33-50. https://doi.org/10.12973/eu-jer.9.1.33
Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5th ed.). Taylor & Francis Group.
Streiner, D. L., Norman, G. R., & Cairney, J. (2015). Health measurement scales: A practical guide to their development and use (5th ed.). Oxford University Press. https://doi.org/10.1093/med/9780199685219.001.0001
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Allyn and Bacon.
Towhidnejad, M., Kestler, C., Jafer, S., & Nicholas, V. (2014). Introducing computational thinking through stealth teaching. In Proceedings of 2014 IEEE Frontiers in Education Conference (FIE) (pp. 1-7). IEEE. https://doi.org/10.1109/FIE.2014.7044407
Velicer, W. F., & Fava, J. L. (1998). Effects of variable and subject sampling on factor pattern recovery. Psychological Methods, 3(2), 231–251. https://doi.org/10.1037/1082-989X.3.2.231
Walden, J., Doyle, M., Garns, R., & Hart, Z. (2013). An informatics perspective on computational thinking. In J. Walden., M. Doyle., R. Garns., & Z. Hart (Eds.), Proceedings of the 18th ACM conference on innovation and technology in computer science education (pp. 4-9). Association for Computing Machinery. https://doi.org/10.1145/2462476.2483797
Weese, J. L. (2016). Mixed methods for the assessment and incorporation of computational thinking in k-12 and higher education. In J. L. Weese (Ed.), Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 279-280). Association for Computing Machinery. https://doi.org/10.1145/2960310.2960347
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1–16. https://doi.org/10.1145/2576872
Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. In A. Yadav., N. Zhou., C. Mayfield., S. Hambrusch., & J. T. Korb (Eds.), Proceedings of the 42nd ACM technical symposium on Computer science education (pp. 465-470). ACM. https://doi.org/10.1145/1953163.1953297
Yeşil, R. (2017). Validity and reliability study of the scale for determining the civic-mindedness levels of teaching staff. Education and Training Studies, 5(4), 44-53. https://doi.org/10.11114/jets.v5i4.2116
Zainuddin, A. (2012). Structural equation modeling using AMOS graphic (1st ed.). Teknologi MARA University Press.
Zainuddin, A. (2013). Structural equation modeling using AMOS graphic (2nd ed.). Teknologi MARA University Press.
Zainuddin, A. (2014). A handbook on SEM for academicians and practitioners (1st ed.). MPWS Rich Resources.
Zainuddin, A. (2015). SEM Made Simple: A Gentle Approach to Learning Structural Equation Modelling (1st ed.). MPWS Rich Resources.