Learning Resources LSP0339-UK 5-in-1 Outdoor Measure-Mate

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Current direct Likert measures for evolution acceptance include the MATE, GAENE, and I-SEA. Pros and cons of each of these instruments have been debated, and yet there is a dearth of research teasing out their similarities and differences when they are used together in a single context beyond the fact that their measures tend to be highly correlated. We administered these to 452 college students in non-major biology classes at two research-intensive universities from the Midwestern and Western United States to investigate the measurement properties of the items within these instruments when combined as a single corpus. Results

Cherlin, A. J. (2004). The deinstitutionalization of American marriage. Journal of Marriage and Family, 66(4), 848–861. doi: 10.1111/j.0022-2445.2004.00058.x. Moore, F. R., & Cassidy, C. (2007). Female status predicts female mate preferences across nonindustrial societies. Cross-Cultural Research, 41(1), 66–74. doi: 10.1177/1069397106294860. Rusbult, C. E. (1980). Commitment and satisfaction in romantic associations: A test of the investment model. Journal of Experimental Social Psychology, 16(2), 172–186. https://doi.org/10.1016/0022-1031(80)90007-4. Before proceeding to discuss the use of Rasch methods in modeling construct validity of the items, we would like to outline some limitations of EFA, and factor analysis in general. From the perspective of test validation, EFA is criticized based on its treatment of ordinal observations as linear measures and its tendency to select for items which are similar in difficulty (Wright 1996), which is contrary to the objective of more contemporary methodologies such as Rasch modeling (Wright 1996; Boone and Scantlebury 2006). Further, factor analysis is a data-fitting approach which can be dubious when validity of the assessment is in question—rather, a data-independent validity standard is needed (Boone and Scantlebury 2006). Given these criticisms, and its discord with the Rasch validation philosophy in particular, we do not use factor analysis as an end to validation, but instead as a starting point to inform us about how to proceed with Rasch modeling. Rasch analysisBrown, T. A. (2003). Confirmatory factor analysis of the Penn State Worry Questionnaire: Multiple factors or method effects? Behaviour Research and Therapy, 41(12), 1411–1426. https://doi.org/10.1016/S0005-7967(03)00059-7. Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. https://doi.org/10.1177/109442810031002. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. https://doi.org/10.1037//0033-2909.107.2.238. Romine WL, Barrow LH, Folk WR. Exploring secondary students’ knowledge and misconceptions about influenza: development, validation, and implementation of a multiple-choice influenza knowledge scale. Int J Sci Educ. 2013;35(11):1874–901. Previous literature supports the idea that acceptance of evolution varies depending on context (i.e. Scott 2005; Nadelson and Hardy 2015). Figures 3 and 4 suggest that this comprises a progression of acceptance similar in structure to what we have seen in research applying Rasch models to learning progressions (Romine et al. 2016; Todd et al. 2017; Todd and Romine 2016). From Figs. 3 and 4, it appears that undergraduate students look at evolution acceptance as the act of accepting that evolution is true and rejecting incorrect ideas, and that within these constructs, students tend to progress from accepting evolution on short time scales, then on long time scales, and then ultimately that evolution happens in humans.

Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29. https://doi.org/10.1038/466029a. When the 57 items are broken into two separate sub-constructs consisting of the 38 positively worded and 19 negatively worded items, respectively, excellent reliability is maintained and the measures become unidimensional. The 38 positively-worded items provide a measure for acceptance of the truth of evolution with a Rasch person reliability of 0.97 (separation = 5.95). The first eigenvalue from PCA on residuals from the Rasch rating scale model fitting these items consists of 1.51 items of variance, which is well below 2. The 19 negatively worded items generate measures for rejection of incredible ideas about evolution with a Rasch person reliability of 0.94 (separation = 4.09). The Rasch model also shows that this scale is unidimensional, with a first eigenvalue of 1.15 items of variance from PCA on the residuals with respect to the model. The brightly coloured Measure-Mate™ is perfect for non-standard measuring or the introduction of standard units Wright BD, Linacre JM, Gustafson JE, Martin-Loff P. Reasonable mean square fit values. Rasch Meas Trans. 1994;8(3):370. Miller, G. F. (1998). How mate choice shaped human nature: a review of sexual selection and human evolution. In C. Crawford & D. L. Krebs (Eds.), Handbook of evolutionary psychology: ideas, issues, and applications (pp. 87–129). Mahwah: Lawrence Erlbaum Associates.Rosengren KS, Brem SK, Evans EM, Sinatra GM, editors. Evolution challenges: Integrating research and practice in teaching and learning about evolution. Oxford: Oxford University Press; 2012. Harris S, Kemmerling RL, North MM. Brief virtual reality therapy for public speaking anxiety. Cyberpsychol Behav. 2002;5(6):543–50. We have been working on perimeter and area recently, in our maths studies so using a tool like a measure mate is a really helpful way for us to get outside and put our learning into practical action and measure REAL things. Brewer C, Smith D. Vision and change in undergraduate biology education: a call to action. Washington, DC: AAAS; 2011.