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

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Gallup G. Evolution, creationism and intelligent design. 2010. Retrieved from http://www.gallup.com/poll/21814/Evolution-Creationism-Intelligent-Design.aspx. Retrieved 18 Dec 2018. Rusbult, C. E., & Buunk, B. P. (1993). Commitment processes in close relationships: An interdependence analysis. Journal of Social and Personal Relationships, 10(2), 175–204. https://doi.org/10.1177/026540759301000202. Wang, S., Chen, C. C., Dai, C. L., & Richardson, G. B. (2018). A call for, and beginner’s guide to, measurement invariance testing in evolutionary psychology. Evolutionary Psychological Science, 4(2), 166–178. https://doi.org/10.1007/s40806-017-0125-5).

Boone WJ. Rasch analysis for instrument development: why, when, and how? CBE Life Sci Educ. 2016;15(4):4. Todd A, Romine WL, Cook Whitt K. Development and validation of the learning progression-based assessment of modern genetics in a high school context. Sci Educ. 2017;101(1):32–65. In addition to understanding how our current measures of evolution acceptance fit with conceptual work, this study also yields insight into the empirical implications for parametrizing evolution acceptance. Various parametrizations have been explored. Nadelson and Southerland ( 2012) utilized a three-dimensional model for evolution acceptance in their construction of the I-SEA, suggesting that the dimensions of evolution acceptance should be delineated by the type of evolution: microevolution, microevolution, and human evolution. What this study suggests is that, from a quantitative perspective, topic is a determinant of the difficulty of an item along the same sub-construct(Figs. 3 and 4), but it does not seem to serve as the key delimiter in terms of the unique sub-constructs. In other words, while acceptance of macroevolution, human evolution, and microevolution may be distinct in their difficulty, it may not be necessary to treat them asdistinct sub-constructs. Rather, the data show that differences between students’ responses on items across contexts are accounted for by the expected difficulty hierarchy imposed by the Rasch model (Boone 2016), making it unnecessary to define new sub-constructs to account for the different response patterns across contexts.Todd A, Romine WL. Validation of the learning progression-based assessment of modern genetics in a college context. Int J Sci Educ. 2016;38(10):1673–98. Kashyap, R., Esteve, A., & García-Román, J. (2015). Potential (mis)match? Marriage markets amidst sociodemographic change in India, 2005–2050. Demography, 52(1), 183–208. doi: 10.1007/s13524-014-0366-x.

Webster, G. D. (2007). Evolutionary theory’s increasing role in personality and social psychology. Evolutionary Psychology, 5(1), 84–91.We used exploratory factor analysis (EFA) in SPSS 21.0 to reconcile how the items on these three instruments define latent dimensions related to evolution acceptance, and then followed Nadelson and Southerland ( 2012) in proceeding to use confirmatory factor analysis (CFA) to evaluate how well the hypothesizedfactor solution derived from EFA reproduces the relationships between the item responses. While the dimensionality of these individual instruments has been discussed and explored in previous work, we initially took the position in this study that we did not know the dimensionality of this collection of items when they are administered together, thereby warranting an a posteori approach for exploring dimensionality. This can be contrasted with the a priori, or hypothesis-driven, approach that involves study of the itemsthrough apre-specified model.EFA is a variable-centered clustering technique where latent factors among observed variables are extracted mathematically from the data (Collins and Lanza 2013). EFA has been used extensively in science education research, particularly in the context of instrument validation (i.e. Romine et al. 2013; Corwin et al. 2015), and is a technique that is oftenassociated with classical test validation methods and other situations where the researcher wishes to reduce a larger feature set to relatively few latent constructsor dimensions (Henson and Roberts 2006). Gangestad, S. W., & Simpson, J. A. (1990). Toward an evolutionary history of female sociosexual variation. Journal of Personality, 58(1), 69–96. https://doi.org/10.1111/j.1467-6494.1990.tb00908.x. Taking into account the dimensionality, which items tend to provide the most useful measures of evolution acceptance, and which items could be considered problematic? Sugiyama, L. S. (2015). Physical attractiveness: An adaptationist perspective. In D. M. Buss (Ed.), The handbook of evolutionary psychology (pp. 1–68). Hoboken, NJ: Wiley. Given the two parametrizations, a big-picture question that comes forth immediately is: should evolution acceptance be treated unidimensionally as it has in the vast majority of quantitative work on evolution acceptance, or should it be treated as a two-dimensional construct which is apparently delineated by the positive or negative orientation of the wording of the items (also found in a recent study by Romine et al. 2017)? Our position is that a unidimensional usage of these items may provide a useful measure for evolution acceptance. Evidence for this includes: (1) the high variance of the first dimension in the EFA (28.9 items of variance out of 57 total items) in comparison to the other dimensions (Fig. 1), (2) the closeness of the first eigenvalue from PCA on Rasch residuals to 2 for a unidimensional construction (2.11 items of variance), and (3) the moderate relationship between acceptance of truth and rejection of incredible ideas (r = 0.73). In fact Metzger et al. ( 2018) found that much of the apparent two-dimensionality of the MATE found by Romine et al. ( 2017) disappears in more advanced science students. However, deciding not to recognize evolution acceptance as a two-dimensional construct may also result in a missed opportunity—CFA and Rasch analysis demonstrate that a unidimensional construction does not capture the entire story regarding how these three instruments measure evolution acceptance.