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Examination of Longitudinal Invariance on a Framework for Observing and Categorizing Instructional Strategies

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Abstract

In longitudinal studies, measurement invariance is required to conduct substantive comparisons over time or across groups. In this study, we examined measurement invariance on a recently developed instrument capturing student preferences for seven instructional strategies related to science learning and career interest. We have labeled these seven instructional strategies as Collaborating, Competing, Caretaking, Creating/Making, Discovering, Performing, and Teaching. A better understanding of student preferences for particular instructional strategies can help educators, researchers, and policy makers deliberately tailor programmatic instructional structure to increase student persistence in the STEM pipeline. However, simply confirming the relationship between student preferences for science instructional strategies and their future career choices at a single time point is not sufficient to clarify our understanding of the relationship between instructional strategies and student persistence in the STEM pipeline, especially since preferences for instructional strategies are understood to vary over time. As such, we sought to develop a measure that invariantly captures student preference over a period of time: the Framework for Observing and Categorizing Instructional Strategies (FOCIS). We administered the FOCIS instrument over four semesters over two middle school grades to 1009 6th graders and 1021 7th graders and confirmed the longitudinal invariance of the FOCIS measure. This confirmation of longitudinal invariance will allow researchers to examine the relationship between student preference for certain instructional strategies and student persistence in the STEM pipeline.

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References

  • Aschbacher, P. R., Li, E., & Roth, E. J. (2010). Is science me? High school students’ identities, participation and aspiration in science, engineering and medicine. Journal of Research in Science Teaching, 47(5), 564–582.

    Google Scholar 

  • Bhattacharyya, S., & Mead, T. P. (2011). The influence of science summer camp on African-American high school students’ career choices. School Science & Mathematics, 111(7), 345–353.

    Article  Google Scholar 

  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: The Guildford Press.

    Google Scholar 

  • Carnevale, A. P., Smith, N., & Strohl, J. (2010). Help wanted: Projections of jobs and education requirements through 2018. Washington DC: Georgetown University, Center on Education and the Workforce.

    Google Scholar 

  • Chen, F., Curran, P.J., Bollen, K.A., Kirby, J., & Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models, 36(4), 462–494.

  • Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255.

    Article  Google Scholar 

  • Coertjens, L., Donche, V., de Maeyer, S., Vanthournout, G., & van Petegem, P. (2012). Longitudinal measurement invariance of likert-type learning strategy scales: are we using the same ruler at each wave? Journal of Psychoeducational Assessment, 30(6), 577–587.

    Article  Google Scholar 

  • Dabney, K. P., Tai, R. H., Almarode, J. T., Miller-Friedmann, J. L., Sonnert, G., Sadler, P. M., & Hazari, Z. (2012). Out-of-school time science activities and their association with career interest in STEM. International Journal of Science Education, Part B, 2(1), 63–79.

    Article  Google Scholar 

  • De Beurs, D. P., Fokkema, M., de Groot, M. H., de Keijser, J., & Kerkhof, J. F. M. (2015). Longitudinal measurement invariance of the Beck scale for suicide ideation. Psychiatry Research, 225(3), 368–373.

    Article  Google Scholar 

  • Elam, M. E., Donham, B. L., & Solomon, S. R. (2012). An engineering summer program for underrepresented students from rural school districts. Journal of STEM Education: Innovations and Research, 13(2), 35–44.

    Google Scholar 

  • Erkut, S., & Marx, F. (2005). 4 schools for WIE (evaluation report). Wellesley, MA: Wellesley College, Center for Research on Women. Retrieved from.

    Google Scholar 

  • Ferreira, M. M., & Trudel, A. R. (2012). The impact of problem-based learning on student attitudes toward science, problem-solving skills, and sense of community in the classroom. Journal of Classroom Interaction, 47(1), 23–30.

    Google Scholar 

  • Häussler, P., & Hoffmann, L. (2000). A curricular frame for physics education: development, comparison with students’ interests, and impact on students’ achievement and self-concept. Science Education, 84(6), 689–705.

    Article  Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

    Article  Google Scholar 

  • Klein, R. B. (2016). Principles and Practice of Structural Equation Modeling (4thed.). New York. NY: The Guilford Press.

    Google Scholar 

  • Lamb, R. L., Annetta, L., Meldrum, J., & Vallett, D. (2012). Measuring science interest: Rasch validation of the science interest survey. International Journal of Science and Mathematics Education, 10, 643–668.

    Article  Google Scholar 

  • Little, T. D. (2013). Longitudinal structural equation modeling. New York, NY: The Guilford Press.

    Google Scholar 

  • Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: sources of early interest in science. International Journal of Science Education, 32(5), 669–685.

    Article  Google Scholar 

  • Marsh, H. W., & Grayson, D. (1994). Longitudinal stability of latent means and individual differences: a unified approach. Structural Equation Modeling, 1(2), 317–359.

    Article  Google Scholar 

  • Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93, 568–592.

    Article  Google Scholar 

  • Minner, D. D., Levy, A. J., & Century, J. (2009). Inquiry-based science instruction—what is it and does it matter? Results from a research synthesis years 1984-2002. Journal of Research in Science Teaching, 47(4), 474–496.

    Article  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998-2015). Mplus User’s Guide (Seventh ed.). Los Angeles, CA: Muthén & Muthén.

  • Newell, A. D., Zientek, L. R., Tharp, B. Z., Vogt, G. L., & Moreno, N. P. (2015). Students’ attitudes toward science as predictors of gains on student content knowledge: Benefits of an after-school program. School Science and Mathematics, 115(5), 216–225.

    Article  Google Scholar 

  • Robbins, M. E., Schoenfisch, M. H., Moore, J. T., & Tolar, D. (2005). An interactive analytical chemistry summer camp for middle school girls. Journal of Chemical Education, 82(10), 1486–1488.

    Article  Google Scholar 

  • Romine, W., Sadler, T., Presley, M., & Klosterman, M. (2013). Student interest in technology and science (SITS) survey, development, validation, and use of a new instrument. International Journal of Science and Mathematics Education, 12, 261–283.

    Article  Google Scholar 

  • Swarat, S., Ortony, A., & Revelle, W. (2012). Activity matters: understanding student interest in school science. Journal of Research in Science Teaching, 49(4), 515–537.

    Article  Google Scholar 

  • Unfried, A., Faber, M., Stanhope, D. S., & Wiebe, E. (2015). The development and validation of a measure of science attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622–639.

    Article  Google Scholar 

  • Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. https://doi.org/10.1111/j.1750-8606.2009.00110.x

    Article  Google Scholar 

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Correspondence to Ji Hoon Ryoo.

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Ryoo, J.H., Tai, R.H. & Skeeles-Worley, A.D. Examination of Longitudinal Invariance on a Framework for Observing and Categorizing Instructional Strategies. Res Sci Educ 50, 489–504 (2020). https://doi.org/10.1007/s11165-018-9698-7

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