The Development and Validation of the Elementary Activity Interest Measure

References

• American Educational Research Association (AERA), American Psychological Association (APA), & National Council on Measurement in Education (NCME) (2014). Standards for educational and psychological testing. American Educational Research Association.
   Google Scholar
• Ainley, M. (2012). Students’ interest and engagement in classroom activities. In S. L. Christenson, A. L. Reschly & C. Wylie (Eds.), Handbook of research on student engagement (pp. 283–302). Springer Science + Business Media.
   Google Scholar
• Ainley, M. (2019). Curiosity and interest: Emergence and divergence. Educational Psychology Review, 31(4), 789–806. https://doi.org/10.1007/s10648-019-09495-z
   Web of Science ®Google Scholar
• Ainley, M., Hidi, S., & Berndorff, D. (2002). Interest, learning, and the psychological processes that mediate their relationship. Journal of Educational Psychology, 94(3), 545–561. https://doi.org/10.1037//0022-0663.94.3.545
   Web of Science ®Google Scholar
• Alexander, P. A., & Murphy, P. K. (1998). Profiling the differences in students’ knowledge, interest, and strategic processing. Journal of Educational Psychology, 90(3), 435–447. https://doi.org/10.1037//0022-0663.90.3.435
   Web of Science ®Google Scholar
• Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford publications.
   Google Scholar
• Bybee, R. W., Taylor, J. A., Gardner, A., Scotter, P. V., Powell, J. C., Westbrook, A., & Landes, N. (2006). The BSCS 5e instructional model: Origins and effectiveness (pp. 1–80). Office of Science Education National Institutes of Health.
   Google Scholar
• Byrne, B. M., Shavelson, R. J., & Muthén, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105(3), 456–466. https://doi.org/10.1037/0033-2909.105.3.456
   Web of Science ®Google Scholar
• Byrne, B. M, (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.) Taylor & Francis.
   Google Scholar
• Calabrese Barton, A., & Tan, E. (2018). A longitudinal study of equity-oriented STEM-rich making among youth from historically marginalized communities. American Educational Research Journal, 55(4), 761–800. https://doi.org/10.3102/0002831218758668
   Web of Science ®Google Scholar
• Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10
   PubMed Web of Science ®Google Scholar
• Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834
   Web of Science ®Google Scholar
• Chen, A., Darst, P. W., & Pangrazi, R. P. (1999). What constitutes situational interest? Validating a construct in physical education. Measurement in Physical Education and Exercise Science, 3(3), 157–XXX. https://doi.org/10.1207/s15327841mpee0303_3
   Google Scholar
• Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5
   Web of Science ®Google Scholar
• Crouch, C. H., Wisittanawat, P., Cai, M., & Renninger, K. A. (2018). Life science students’ attitudes, interest, and performance in introductory physics for life sciences: An exploratory study. Physical Review Physics Education Research, 14(1), 010111. https://doi.org/10.1103/PhysRevPhysEducRes.14.010111
   Web of Science ®Google Scholar
• Danielson, R. W., Grace, E., White, A. J., Kelton, M. K., Martinez, A., Fallon, M., Owen, J. P., & Butterfield, P. (2020). Assessing science learning and systems thinking through arts. Washington Educational Research Journal, 13(1), 36–45.
   Google Scholar
• Danielson, R. W., *Grace, E., White, A. J., Kelton, M. K., Owen, J. P., Saba Fisher, K., Diaz Martinez, A+., & Mozo, M. (2022). Facilitating Systems Thinking Through Arts-Based ST EM Integration. Frontiers in Education, 7 https://doi.org/10.3389/feduc.2022.915333
   Google Scholar
• Deng, L., & Chan, W. (2017). Testing the difference between reliability coefficients alpha and omega. Educational and Psychological Measurement, 77(2), 185–203. https://doi.org/10.1177/0013164416658325
   PubMed Web of Science ®Google Scholar
• Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology (London, England: 1953), 105(3), 399–412. https://doi.org/10.1111/bjop.12046
   PubMed Web of Science ®Google Scholar
• Durik, A. M., & Harackiewicz, J. M. (2007). Different strokes for different folks: How individual interest moderates the effects of situational factors on task interest. Journal of Educational Psychology, 99(3), 597–610. https://doi.org/10.1037/0022-0663.99.3.597
   Web of Science ®Google Scholar
• Eid, M., Geiser, C., Koch, T., & Heene, M. (2017). Anomalous results in G-factor models: Explanations and alternatives. Psychological Methods, 22(3), 541–562. https://doi.org/10.1037/met0000083
   PubMed Web of Science ®Google Scholar
• Fabrigar, L. R., & Wegener, D. T. (2011). Exploratory factor analysis. Oxford University Press.
   Google Scholar
• Falk, J. H., & Needham, M. D. (2011). Measuring the impact of a science center on its community. Journal of Research in Science Teaching, 48(1), 1–12. https://doi.org/10.1002/tea.20394
   Web of Science ®Google Scholar
• Fang, Z. (1996). Illustrations, text, and the child reader: What are pictures in children’s storybooks for? Reading Horizons: A Journal of Literacy and Language Arts, 37(2), 130–142, Retrieved from https://scholarworks.wmich.edu/reading_horizons/vol37/iss2/3
   Google Scholar
• Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. Structural Equation Modeling: A Second Course, 10(6), 269–314.
   Google Scholar
• Finney, S. J., & DiStefano, C. (2013). Nonnormal and categorical data in structural equation models. In G. R. Hancock & R. O. Mueller (Eds.), A second course in structural equation modeling (2nd Edition; pp. 439–492) Information Age.
   Google Scholar
• Finney, S. J., DiStefano, C., & Kopp, J. P. (2013). Overview of estimation methods and preconditions for their application with structural equation modeling. In Principles and Methods of Test Construction: Standards and Recent Advances (pp. 135–165). Hogrefe Publishing GmbH.
   Google Scholar
• Flowerday, T., & Shell, D. F. (2015). Disentangling the effects of interest and choice on learning, engagement, and attitude. Learning and Individual Differences, 40, 134–140. https://doi.org/10.1016/j.lindif.2015.05.003
   Web of Science ®Google Scholar
• Frenzel, A. C., Goetz, T., Pekrun, R., & Watt, H. M. (2010). Development of mathematics interest in adolescence: Influences of gender, family, and school context. Journal of Research on Adolescence, 20(2), 507–537. https://doi.org/10.1111/j.1532-7795.2010.00645.x
   Web of Science ®Google Scholar
• Gillespie, A. (2021). What do the data say about the current state of K-12 STEM education in the US?. Science Matters, National Science Foundation.
   Google Scholar
• Harackiewicz, J. M., Durik, A. M., Barron, K. E., Linnenbrink-Garcia, L., & Tauer, J. M. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100(1), 105–122. https://doi.org/10.1037/0022-0663.100.1.105
   Web of Science ®Google Scholar
• Heddy, B. C., Sinatra, G. M., Seli, H., Taasoobshirazi, G., & Mukhopadhyay, A. (2017). Making learning meaningful: Facilitating interest development and transfer in at-risk college students. Educational Psychology, 37(5), 565–581. https://doi.org/10.1080/01443410.2016.1150420
   Web of Science ®Google Scholar
• Heddy, B. C., & Sinatra, G. M. (2017). Transformative parents: Facilitating transformative experiences and interest with a parent involvement intervention. Science Education, 101(5), 765–786. https://doi.org/10.1002/sce.21292
   Web of Science ®Google Scholar
• Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and Evaluation in Counseling and Development, 34(3), 177–189. https://doi.org/10.1080/07481756.2002.12069034
   Web of Science ®Google Scholar
• Hidi, S. E., & McLaren, J. A. (1991). Motivational factors and writing: The role of topic interestingness. European Journal of Psychology of Education, 6(2), 187–197. https://doi.org/10.1007/BF03191937
   Google Scholar
• Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep4102_4
   Web of Science ®Google Scholar
• Hidi, S., & Renninger, K. A. (2015). The power of interest for motivation and engagement (1st ed.). Routledge.
   Google Scholar
• Hillman, S. J., Zeeman, S. I., Tilburg, C. E., & List, H. E. (2016). My attitudes toward science (MATS): The development of a multidimensional instrument measuring students’ science attitudes. Learning Environments Research, 19(2), 203–219. https://doi.org/10.1007/s10984-016-9205-x
   Google Scholar
• Honey, M., Alberts, B., Bass, H., Castillo, C., Lee, O., Strutches, M. M., & Rodriguez, F. (2020). STEM education for the future: A visioning report. National Science Foundation. Retrieved from https://www.nsf.gov/ehr/Materials/STEM%20Education%20for%20the%20Future%20-%202020%20Visioning%20Report.pdf
   Google Scholar
• Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447
   PubMed Web of Science ®Google Scholar
• Houts, P. S., Doak, C. C., Doak, L. G., & Loscalzo, M. J. (2006). The role of pictures in improving health communication: A review of research on attention, comprehension, recall, and adherence. Patient Education and Counseling, 61(2), 173–190. https://doi.org/10.1016/j.pec.2005.05.004
   PubMed Web of Science ®Google Scholar
• Jansen, M., Lüdtke, O., & Schroeders, U. (2016). Evidence for a positive relation between interest and achievement: Examining between-person and within-person variation in five domains. Contemporary Educational Psychology, 46, 116–127. https://doi.org/10.1016/j.cedpsych.2016.05.004
   Web of Science ®Google Scholar
• Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. https://doi.org/10.1177/001316446002000116
   Web of Science ®Google Scholar
• Kim, A. Y., Sinatra, G. M., & Seyranian, V. (2018). Developing a STEM identity among young women: A social identity perspective. Review of Educational Research, 88(4), 589–625. https://doi.org/10.3102/0034654318779957
   Web of Science ®Google Scholar
• Krapp, A. (1999). Interest, motivation and learning: An educational-psychological perspective. European Journal of Psychology of Education, 14(1), 23–40. https://doi.org/10.1007/BF03173109
   Web of Science ®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(3), 643–668. https://doi.org/10.1007/s10763-011-9314-z
   Web of Science ®Google Scholar
• Lance, C. E., Butts, M. M., & Michels, L. C. (2006). The sources of four commonly reported cutoff criteria: What did they really say? Organizational Research Methods, 9(2), 202–220. https://doi.org/10.1177/1094428105284919
   Web of Science ®Google Scholar
• Leibham, M. B., Alexander, J. M., & Johnson, K. E. (2013). Science interests in preschool boys and girls: Relations to later self‐concept and science achievement. Science Education, 97(4), 574–593. https://doi.org/10.1002/sce.21066
   Web of Science ®Google Scholar
• Lima, J., & Timm-Bottos, J. (2018). This is not a Pipe: Incorporating Art in the Science Curriculum. Journal of Teaching and Learning, 11(2), 43–60. https://doi.org/10.22329/jtl.v11i2.5063
   Web of Science ®Google Scholar
• Linnenbrink-Garcia, L., Durik, A. M., Conley, A. M., Barron, K. E., Tauer, J. M., Karabenick, S. A., & Harackiewicz, J. M. (2010). Measuring situational interest in academic domains. Educational and Psychological Measurement, 70(4), 647–671. https://doi.org/10.1177/0013164409355699
   Web of Science ®Google Scholar
• Mayer, R. E. (2001). Multimedia learning. Cambridge University Press.
   Google Scholar
• Mayer, R. E. (2002). Cognitive theory and the design of multimedia instruction: An example of the two-way street between cognition and instruction. New Directions for Teaching and Learning, 2002(89), 55–71. https://doi.org/10.1002/tl.47
   Google Scholar
• Mayer, R. E. (2003). Elements of a science of e-learning. Journal of Educational Computing Research, 29(3), 297–313. Retrieved from https://doi.org/10.2190/YJLG-09F9-XKAX-753D
   Google Scholar
• Mayer, R. E. (2019a). Thirty years of research on online learning. Applied Cognitive Psychology, 33(2), 152–159. https://doi.org/10.1002/acp.3482
   Web of Science ®Google Scholar
• Mayer, R. E. (2019b). Illustrations that instruct. Advances in Instructional Psychology, 10, 253–284.
   Google Scholar
• McDonald, R. P. (1999). Test theory: A unified treatment. LEA.
   Google Scholar
• McMurrer, J. (2008). Instructional time in elementary schools: A closer look at changes for specific subjects. Arts Education Policy Review, 109(6), 23–28. https://doi.org/10.3200/aepr.109.6.23-28
   Google Scholar
• Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. The Journal of Applied Psychology, 93(3), 568–592. https://doi.org/10.1037/0021-9010.93.3.568
   PubMed Web of Science ®Google Scholar
• Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749. https://doi.org/10.1037/0003-066X.50.9.741
   Web of Science ®Google Scholar
• Mitchell, M. B. (1993). Situational interest: Its multifaceted structure in the secondary school mathematics classroom. Journal of Educational Psychology, 85(3), 424–436. https://doi.org/10.1037//0022-0663.85.3.424
   Web of Science ®Google Scholar
• Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358–368. https://doi.org/10.1037//0022-0663.91.2.358
   Web of Science ®Google Scholar
• Murphy, P. K., & Alexander, P. A. (2002). What counts? The predictive powers of subject-matter knowledge, strategic processing, and interest in domain-specific performance. The Journal of Experimental Education, 70(3), 197–214. https://doi.org/10.1080/00220970209599506
   Web of Science ®Google Scholar
• Muthén, L. (2005). Mplus Discussion ≫ Negative Residual Variance. http://www.statmodel.com/discussion/messages/11/555.html?1358188287
   Google Scholar
• Muthén, B., & Muthén, L. (2017). Mplus. In W. J. van der Linden (Ed.), Handbook of item response theory (pp. 507–518). Chapman and Hall/CRC.
   Google Scholar
• National Center for Science and Engineering Statistics (NCES) (2018). Science and Engineering Degrees, by Race and Ethnicity of Recipients. https://www.nsf.gov/statistics/degreerecipients/
   Google Scholar
• Nolen, S. B. (2007). Young children’s motivation to read and write: Development in social contexts. Cognition and Instruction, 25(2-3), 219–270. https://doi.org/10.1080/07370000701301174
   PubMed Web of Science ®Google Scholar
• Nunnally, J. C. (1978). An overview of psychological measurement. In Clinical Diagnosis of Mental Disorders (pp. 97–146). Springer Science & Business Media.
   Google Scholar
• Palmer, D., Dixon, J., & Archer, J. (2017). Using situational interest to enhance individual interest and science related behaviours. Research in Science Education, 47(4), 731–753. https://doi.org/10.1007/s11165-016-9526-x
   Web of Science ®Google Scholar
• Patrick, M. D., Carter, G., & Wiebe, E. N. (2005). Visual representations of DNA replication: Middle grades students’ perceptions and interpretations. Journal of Science Education and Technology, 14(3), 353–365. https://doi.org/10.1007/s10956-005-7200-6
   Google Scholar
• Peeck, J. (1993). Increasing picture effects in learning from illustrated text. Learning and Instruction, 3(3), 227–238. https://doi.org/10.1016/0959-4752(93)90006-l
   Google Scholar
• Pekrun, R. (2019). The murky distinction between curiosity and interest: State of the art and future prospects. Educational Psychology Review, 31(4), 905–914. https://doi.org/10.1007/s10648-019-09512-1
   Web of Science ®Google Scholar
• Polikoff, M., Le, Q. T., Danielson, R. W., Sinatra, G. M., & Marsh, J. A. (2018). The impact of speedometry on student knowledge, interest, and emotions. Journal of Research on Educational Effectiveness, 11(2), 217–239. https://doi.org/10.1080/19345747.2017.1390025
   Web of Science ®Google Scholar
• Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement invariance. Psychological Bulletin, 114(3), 552–566. https://doi.org/10.1037/0033-2909.114.3.552
   PubMed Web of Science ®Google Scholar
• Renninger, K. A. (2010). Working with and cultivating the development of interest, self-efficacy, and self-regulation. In D. D. Preiss & R. J. Sternberg (Eds.), Perspectives on learning, teaching, and human development (pp. 107–138). Springer Publishing Company.
   Google Scholar
• Renninger, K. A., & Bachrach, J. E. (2015). Studying triggers for interest and engagement using observational methods. Educational Psychologist, 50(1), 58–69. https://doi.org/10.1080/00461520.2014.999920
   Web of Science ®Google Scholar
• Renninger, K. A., Bachrach, J. E., & Hidi, S. E. (2019). Triggering and maintaining interest in early phases of interest development. Learning, Culture and Social Interaction, 23, 100260. https://doi.org/10.1016/j.lcsi.2018.11.007
   Web of Science ®Google Scholar
• Renninger, K. A., & Hidi, S. E. (2002). Student interest and achievement: Developmental issues raised by a case study. In A. Wigfield & J. S. Eccles (Eds.), A Vol. in the educational psychology series. Development of achievement motivation (pp. 173–195). Academic Press. https://doi.org/10.1016/B978-012750053-9/50009-7
   Google Scholar
• Renninger, K. A., & Hidi, S. E. (2022). Interest: A unique affective and cognitive motivational variable that develops. In A. Elliot (Ed.), Advances in motivation science (Vol. 8). Elsevier.
   Google Scholar
• Renninger, K. A., & Su, S. (2012). Interest and its development. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 167–187) Oxford University Press.
   Google Scholar
• Renninger, K. A., & Su, S. (2019). Interest and its development, revisited. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 205–225). Oxford Academic.
   Google Scholar
• Rutkowski, L., & Svetina, D. (2014). Assessing the hypothesis of measurement invariance in the context of large-scale international surveys. Educational and Psychological Measurement, 74(1), 31–57. https://doi.org/10.1177/0013164413498257
   Web of Science ®Google Scholar
• Saß, S., Wittwer, J., Senkbeil, M., & Köller, O. (2012). Pictures in test items: Effects on response time and response correctness. Applied Cognitive Psychology, 26(1), 70–81. https://doi.org/10.1002/acp.1798
   Web of Science ®Google Scholar
• Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120. https://doi.org/10.1007/s11336-008-9101-0
   PubMed Web of Science ®Google Scholar
• Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), 1–13. https://doi.org/10.1080/00461520.2014.1002924
   Web of Science ®Google Scholar
• Sinatra, G. M., Mukhopadhyay, A., Allbright, T. N., Marsh, J. A., & Polikoff, M. S. (2017). Speedometry: A vehicle for promoting interest and engagement through integrated STEM instruction. The Journal of Educational Research, 110(3), 308–316. https://doi.org/10.1080/00220671.2016.1273178
   Web of Science ®Google Scholar
• The Jamovi project (2021). Jamovi (Version 1.6) [Computer Software]. https://www.jamovi.org
   Google Scholar
• Tyler-Wood, T., Knezek, G., & Christensen, R. (2010). Instruments for assessing interest in STEM content and careers. Journal of Technology and Teacher Education, 18(2), 341–363.
   Google Scholar
• Vooren, M., Haelermans, C., Groot, W., & van den Brink, H. M. (2022). Comparing success of female students to their male counterparts in the STEM fields: An empirical analysis from enrollment until graduation using longitudinal register data. International Journal of STEM Education, 9(1), 1–17. https://doi.org/10.1186/s40594-021-00318-8
   Web of Science ®Google Scholar
• Wigfield, A., Eccles, J. S., Yoon, K. S., Harold, R. D., Arbreton, A. J., Freedman-Doan, C., & Blumenfeld, P. C. (1997). Change in children’s competence beliefs and subjective task values across the elementary school years: A 3-year study. Journal of Educational Psychology, 89(3), 451–469. https://doi.org/10.1037//0022-0663.89.3.451
   Web of Science ®Google Scholar
• Xie, K., Debacker, T. K., & Ferguson, C. (2006). Extending the Traditional Classroom Through Online Discussion: The Role of Student Motivation. Journal of Educational Computing Research, 34(1), 67–89. https://doi.org/10.2190/7BAK-EGAH-3MH1-K7C6
   Google Scholar
• Yang, Y., & Green, S. B. (2011). Coefficient alpha: A reliability coefficient for the 21st century? Journal of Psychoeducational Assessment, 29(4), 377–392. https://doi.org/10.1177/0734282911406668
   Web of Science ®Google Scholar
• Young, A. M., Wendel, P. J., Esson, J. M., & Plank, K. M. (2018). Motivational decline and recovery in higher education STEM courses. International Journal of Science Education, 40(9), 1016–1033. https://doi.org/10.1080/09500693.2018.1460773
   Web of Science ®Google Scholar