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

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Ha M, Haury DL, Nehm RH. Feeling of certainty: uncovering a missing link between knowledge and acceptance of evolution. J Res Sci Teach. 2012;49:95–121. Simpson, J. A., & Gangestad, S. W. (1991). Individual differences in sociosexuality: Evidence for convergent and discriminant validity. Journal of Personality and Social Psychology, 60(6), 870–883. https://doi.org/10.1037/0022-3514.60.6.870. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03. 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). 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.

Andrich D. Controversy and the Rasch model: a characteristic of incompatible paradigms? Med Care. 2004;42(1):1–7. Strike KA, Posner GJ. A revisionist theory of conceptual change. In: Duschl R, Hamilton R, editors. Philosophy of science, cognitive psychology, and educational theory and practice. Albany: Suny; 1992. p. 147–76.The Inventory of Student Evolution Acceptance (I-SEA) and the Evolution Attitudes and Literacy Survey (EALS) are also constructed multidimensionally, meaning that multiple quantitative measures are used to account for students’ observed responses (Kline 2014). The I-SEA (Nadelson and Southerland 2012) was designed to improve on the MATE by disentangling microevolution, macroevolution, and human evolution contexts. This is an important contribution given that microevolutionary events may be easier for students to accept than those related to macro- and human evolution (Alters and Alters 2001; Scott 2005). Given these goals, the I-SEA consists of 24 total items, 9 of which are negatively worded, with 8 items assigned to one of three subscales for constructs defining acceptance of microevolution, macroevolution, and human evolution, respectively. The authors hypothesized that the items related to acceptance of microevolution would be easier than the items measuring acceptance of macroevolution and human evolution, and their analysis bears this out (Nadelson and Southerland 2012). Here, it may be useful to draw a distinction between Nadelson and Southerland’s ( 2012) decision to use 3 dimensions to explain the differences in items, including their difficulty, versus letting the items take a difficulty hierarchy along a single dimension as is often done in Rasch studies (Boone et al. 2013). Nadelson and Southerland show that the 3-dimensional model explains students’ responses adequately; however, the efficacy of the comparatively parsimonious approach of defining the items hierarchically along a single unidimensional Rasch scale remains unexplored. 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. Sabini, J., & Green, M. C. (2004). Emotional responses to sexual and emotional infidelity: Constants and differences across genders, samples, and methods. Personality and Social Psychology Bulletin, 30(11), 1375–1388. https://doi.org/10.1177/0146167204264012. Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift’s electric factor analysis machine. Understanding Statistics, 2(1), 13–43. https://doi.org/10.1207/S15328031US0201_02.

Boxer, C. F., Noonan, M. C., & Whelan, C. B. (2013). Measuring mate preferences: a replication and extension. Journal of Family Issues, 36(2), 163–187. doi: 10.1177/0192513X13490404. Smith MU. Current status of research in teaching and learning evolution: I. Philosophical/epistemological issues. Sci Educ. 2010;19(6–8):523–38.Significant work has been done towards exploring the relationship between knowledge of evolution and acceptance of evolutionary theory (Nadelson and Southerland 2010; Romine et al. 2017). However, it is currently difficult to tease out how much of this relationship is due to the fact that evolution acceptance as it is currently being measured reflects knowledge of evolution. The MATE has been criticized on the grounds that it possibly conflates knowledge of evolution with acceptance of evolutionary theory (Hogan 2000; Smith 2010; Wagler and Wagler 2013), and both the MATE and the I-SEA are criticized on the grounds that they are not tied to a theoretical foundation for evolution acceptance (Smith et al. 2016). Purpose of the Research