Motivation in science learning is believed to be essential for students’ pursuit of college-level studies and lifelong interest in science. Yet, the trend of low levels of motivation in learning science continued in college can be linked to a national concern about low scientific literacy levels and science career aspirations. To diagnose the current status of motivation of college students, it is important to have an instrument that can assess students’ motivation. The purpose of the present study is to examine the level of motivation of college students and establish the validity and reliability of a motivation questionnaire-the Science Motivation Questionnaire II (SMQ II) developed by Glynn et al. (2011)-using the Rasch-Andrich rating scale model. The original instrument consists of 25 items allocated in five sub-factors. Both person separation reliability and item separation reliability were excellent. The item separation index indicated good variability of the items and the five rating scale functioned well. All Infit and Outfit measures in the Rasch analysis demonstrated a lack of unidimensionality of the science motivation construct in the SMQ II, which supports the deletion of two items to satisfy the unidimensional structure.
REFERENCES(53)
1.
American Educational Research Association (AERA), American Psychological Association (APA), & National Council on Measurement in Education (NCME). (2014). Standards for educational and psychological testing. Washington, DC: AERA.
Awuor, R. A. (2008). Effect of unequal sample sizes on the power of DIF detection: An IRT-based Monte Carlo study with SIBTEST and Mantel-Haenszel procedures (Unpublished doctoral dissertation). Virginia Polytechnic Institute and State University. Blacksburg, VA.
Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117-148. doi:10.1207/s15326985ep2802_3.
Bidwell, A. (2013, December 3). American students fall in international academic tests, Chinese lead the pack. Retrieved from http://www.usnews.com/news/art....
Boone, W. J., Staver, J. R., & Yale, M. S. (2014). Rasch analysis in the human sciences. Dordrecht: Springer Netherlands. doi:10.1007/978-94-007-6857-4.
Bortolotti, S. L. V., Tezza, R., de Andrade, D. F., Bornia, A. C., & de Sousa Júnior, A. F. (2013). Relevance and advantages of using the item response theory. Quality & Quantity, 1-20. doi:10.1007/s11135-012-9684-5.
Brinthaupt, T. M., & Kang, M. (2014). Many-facet Rasch calibration: An example using the self-talk scale. Assessment, 21(2), 241-249. doi:10.1177/1073191112446653.
Britner, S. L. (2008). Motivation in high school science students: A comparison of gender differences in life, physical, and earth science classes. Journal of Research in Science Teaching, 45(8), 955-970. doi:10.1002/tea.20249.
Chowdhury, M. S., & Shahabuddin, A. M. (2007). Self-efficacy, motivation and their relationship to academic performance of Bangladesh college students. College Quarterly, 10(1), 1-9.
Curtin, R., Presser, S., & Singer, E. (2000). The effects of response rate changes on the index of consumer sentiment. Public Opinion Quarterly, 64(4), 413-428. doi:10.1086/318638.
Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109-134. doi:10.1016/0092-6566(85)90023-6.
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227-268. doi:10.1207/S15327965PLI1104_01.
Dike, D. E. (2012). A descriptive study of intrinsic motivation in three California accredited model continuation high schools (Doctoral dissertation). La Verne, CA: University of La Verne.
Eccles, J. S. (2006). A Motivational perspective on school achievement. In R. J. Sternberg & R. F. Subotnik (Eds.), Optimizing student success in school with the other three Rs: Reasoning, resilience, and responsibility (pp. 199-224). Greenwich, Conn: IAP.
Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159-1176. doi:10.1002/tea.20442.
Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2009). Science motivation questionnaire: Construct validation with nonscience majors. Journal of Research in Science Teaching, 46(2), 127-146. doi:10.1002/tea.20267.
Guthrie, J., Wigfield, A., Humenick, N., Perencevich, K., Taboada, A., & Barbosa, P. (2006). Influences of stimulating tasks on reading motivation and comprehension. Journal of Educational Research, 99, 232-245. doi:10.3200/JOER.99.4.232-246.
Hackett, G., & Betz, N.E. (1989). An exploration of the mathematics self-efficacy/mathematics performance correspondence. Journal for Research in Mathematics Education, 20, 261–273. doi:10.2307/749515.
Mantel, N., & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies. Journal of the National Cancer Institute, 22(4), 719-748.
Myers, N. D., Wolfe, E. W., Feltz, D. L., & Penfield, R. D. (2006). Identifying differential item functioning of rating scale items with the Rasch model: An introduction and an application. Measurement in Physical Education and Exercise Science, 10(4), 215-240. doi:10.1207/s15327841mpee1004_1.
National Research Council (NRC). (2012). A framework for K-12 science education: practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020.
Salta, K., & Koulougliotis, D. (2015). Assessing motivation to learn chemistry: Adaptation and validation of Science Motivation Questionnaire II with Greek secondary school students. Chemistry Education Research and Practice, 16(2), 237-250. doi:10.1039/C4RP00196F.
Sheehan, K. B. (2001). E-mail survey response rates: A review. Journal of Computer-Mediated Communication [online], 6(2), 0. doi:10.1111/j.1083-6101.2001.tb00117.x.
Shekhar, C., & Devi, R. (2012). Achievement motivation across gender and different academic majors. Journal of Educational and Developmental Psychology, 2(2), 105-109. doi:10.5539/jedp.v2n2p105.
Singh, K., Chang, M., & Dika, S. (2005). Affective and motivational factors in engagement and achievement in science. International Journal of Learning, 12(6), 207-218.
Trautwein, C., & Stolz, K. (2015). “Press on regardless!”–The role of volitional control in the first year of higher education. Enculturation and Development of Beginning Students, 10(4), 123-143. doi:10.3217/zfhe-10-04/07.
Vallerand, R. J., Fortier, M. S., & Guay, F. (1997). Self-determination and persistence in a real-life setting: Toward a motivational model of high school dropout. Journal of Personality and Social psychology, 72(5), 1161. doi:10.1037/0022-3514.72.5.1161.
Vedder‐Weiss, D., & Fortus, D. (2011). Adolescents’ declining motivation to learn science: Inevitable or not?. Journal of Research in Science Teaching, 48(2), 199-216. doi:10.1002/tea.20398.
Vedder-Weiss, D., & Fortus, D. (2012). Adolescents’ declining motivation to learn science: A follow‐up study. Journal of Research in Science Teaching, 49(9), 1057-1095. doi:10.1002/tea.21049.
Vedder‐Weiss, D., & Fortus, D. (2013). School, teacher, peers, and parents’ goals emphases and adolescents’ motivation to learn science in and out of school. Journal of Research in Science Teaching, 50(8), 952-988. doi:10.1002/tea.21103.
Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics, intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement. Learning and individual differences, 16(1), 1-12. doi:10.1016/j.lindif.2005.06.004.
You, H. S. (2016). Toward interdisciplinary science learning: Development of an assessment for interdisciplinary understanding of ‘carbon cycling’ (Unpublished doctoral dissertation). The University of Texas at Austin, Austin, Texas.
Zhu, W. (1996). Should total scores from a rating scale be used directly? Research Quarterly for Exercise and Sport, 67(3), 363-372. doi:10.1080/02701367.1996.10607966.
Zhu, W., Timm, G., & Ainsworth, B. (2001). Rasch calibration and optimal categorization of an instrument measuring women’s exercise perseverance and barriers. Research Quarterly for Exercise and Sport, 72, 104–116. doi:10.1080/02701367.2001.10608940.
Zwick, R., Thayer, D. T., & Lewis, C. (1999). An empirical Bayes approach to Mantel‐Haenszel DIF analysis. Journal of Educational Measurement, 36(1), 1-28. doi:10.1111/j.1745-3984.1999.tb00543.x.
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.