Abstract
Belief in genetic determinism (BGD) has been associated with increased social stereotyping and prejudicial views and consequently is of significant concern to science educators. The Public Understanding and Attitudes towards Genetics and Genomics (PUGGS) instrument was developed to measure relationships among BGD, genetics knowledge, and demographic variables. PUGGS validity evidence has relied primarily on Classical Test Theory frameworks and Brazilian samples. Using a more advanced psychometric framework (Rasch analysis) and a large North American undergraduate sample (n > 800), we further evaluate validity claims by studying (1) dimensionality and function of PUGGS item sets; (2) magnitudes of item endorsement across human traits (social, biological) and taxonomic (animal, plant) categories; and (3) degree of trait-level genetic overattribution. Similar to Gericke et al. (Sci Educ 26:1223–1259, 2017), we identified a two-dimensional structure for the BGD scale (i.e., social, biological) and the genetics knowledge scale (i.e., gene-environment interactions [GEI], genetics and genomics knowledge [GGK]). However, there were several problems with the functioning of the item sets (e.g., low reliability for GEI, problematic rating scale for BGD biological). We report that the magnitudes of GEI and GGK did not differ by taxonomic context. Finally, genetic over- (and under-) attribution was identified for both biological and social traits, indicating that students harbored considerably diverse and frequently non-normative conceptions about genetic contributions to traits. Importantly, psychometric and theoretical concerns reported here raise questions about the operationalization of the PUGGS BGD construct. Recommendations for PUGGS revisions and educational implications are discussed.
Similar content being viewed by others
Notes
Although we do not directly compare BGD and genetics knowledge in this paper, we discuss important considerations related to this comparison in the Supplementary Materials.
Note that these item sets were structured differently and thus it was not possible to analyze them in a parallel manner (i.e., social vs. biological in the BGD item set, plant vs. animal in the knowledge item sets, see Section 3.2 below).
In contrast, the College Board Standards for College Success (College Board 2009) specify weight, height, and coat color in animals as examples of traits used to illustrate interactions of multiple genes or interactions of genes and environment but do not provide a rationale as to why these traits are used.
References
Adams, R. J., Wu, M. L., & Wilson, M. (2012). The Rasch rating model and the disordered threshold controversy. Educational and Psychological Measurement, 72(4), 547–573.
American Educational Research Association, American Psychological Association, and National Council on Measurement in Education (AERA, APA, and NCME). (2014). Standards for educational and psychological testing. Washington, DC: AERA.
Andreychik, M. R., & Gill, M. J. (2014). Do natural kind beliefs about social groups contribute to prejudice? Distinguishing bio-somatic essentialism from bio-behavioral essentialism, and both of these from entitativity. Group Processes & Intergroup Relations, 18(4), 454–474.
Andrich, D. (2013). An expanded derivation of the threshold structure of the polytomous Rasch model that dispels any “threshold disorder controversy”. Educational and Psychological Measurement, 73(1), 78–124.
Bastian, B., & Haslam, N. (2006). Psychological essentialism and stereotype endorsement. J. Exp. Soc. bastPsychol., 42, 228–235.
Bennett, L., Thirlaway, K., & Murray, A. J. (2008). The stigmatising implications of presenting schizophrenia as a genetic disease. Journal of Genetic Counseling, 17(6), 550–559.
Block, N. (1995). How heritability misleads about race. Cognition, 56(2), 99–128.
Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model: fundamental measurement in the human sciences. Mahwah: Lawrence Erlbaum Associates, Inc..
Boone, W. J. (2016). Rasch analysis for instrument development: why, when, and how? CBE Life Sciences Education, 15(4).
Boone, B., Staver, J. R., & Yale, M. S. (2014). Rasch analysis in the human sciences. Dordrecht: Springer.
Borgerding, L. A., Deniz, H., & Anderson, E. S. (2017). Evolution acceptance and epistemological beliefs of college biology students. Journal of Research in Science Teaching, 54(4), 493–519.
Borsboom, D., Mellenbergh, G. J., & van Heerden, J. (2005). The theoretical status of latent variables. Psychological Review, 110(2), 203–219.
Brescoll, V. L., Uhlmann, E. L., & Newman, G. E. (2013). The effects of system-justifying motives on endorsement of essentialist explanations for gender differences. Journal of Personality and Social Psychology, 105(6), 891.
Brewer, C. A., & Smith, D. (2011). Vision and change in undergraduate biology education: a call to action. American Association for the Advancement of Science, Washington, DC.
Briggs, D. C., & Wilson, M. (2003). An introduction to multidimensional measurement using Rasch models. Journal of Applied Measurement, 4(1), 87–100.
Campbell, C. D., & Nehm, R. H. (2013). A critical analysis of assessment quality in genomics and bioinformatics education research. CBE—Life Sciences Education, 12(3), 530–541.
Carver, R. B., Castéra, J., Gericke, N., Evangelista, N. A. M., & El-Hani, C. N. (2017). Young adults’ BGD, and knowledge and attitudes towards modern genetics and genomics: the PUGGS questionnaire. PLoS One, 12(1), e0169808.
Castellano, K. E., Duckor, B., Wihardini, D., Tellez, K., & Wilson, M. (2016). Assessing academic language in an elementary ́ mathematics teacher licensure exam. Teacher Education Quarterly, 23(1), 3–27.
Castéra, J., & Clément, P. (2014). Teachers’ conceptions about the genetic determinism of human behaviour: a survey in 23 countries. Science & Education, 23(2), 417–443.
Charney, E. (2012). Behavior genetics and postgenomics. Behavioral and Brain Sciences, 35(5), 331–358.
Chou, Y. T., & Wang, W. C. (2010). Checking dimensionality in item response models with principal component analysis on standardized residuals. Educational and Psychological Measurement, 70(5), 717–731.
College Board. (2009). Science College Board Standards for College Success. Available: http://professionals.collegeboard.com/profdownload/cbscs-sciencestandards-2009.pdf
Condit, C. M. (2010). Public understandings of genetics and health. Clinical Genetics, 77(1), 1–9.
Condit, C. M., Gronnvoll, M., Landau, J., Shen, L., Wright, L., & Harris, T. M. (2009). Believing in both genetic determinism and behavioral action: a materialist framework and implications. Public Understanding of Science, 18(6), 730–746.
Dar-Nimrod, I., & Heine, S. J. (2011). Genetic essentialism: on the deceptive determinism of DNA. Psychological Bulletin, 137(5), 800.
de Ayala, R. J. (2010). Item response theory. In G. R. Hancock & R. O. Mueller (Eds.), The Reviewer’s Guide to Quantitative Methods in the Social Sciences (pp. 155–172). New York: Routledge.
de Melo-Martín, I. (2005). Firing up the nature/nurture controversy: bioethics and genetic determinism. Journal of Medical Ethics, 31(9), 526–530.
diSessa, A. A. (2008). A bird’s-eye view of the “pieces” vs. “coherence” controversy. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 35–60). New York: Routledge.
Donovan, B. M. (2014). Playing with fire? The impact of the hidden curriculum in school genetics on essentialist conceptions of race. Journal of Research in Science Teaching, 51(4), 462–496.
Donovan, B. M. (2016). Framing the genetics curriculum for social justice: an experimental exploration of how the biology curriculum influences beliefs about racial difference. Science Education, 100(3), 586–616.
Donovan, B. M., Semmens, R., Keck, P., Brimhal, E., Busch, K. C., Weindling, M., Duncan, A., Stuhlsatz, M., Buck Bracey, Z., Bloom, M., Kowalski, S., & Salazar, B. (2019a). Toward a more humane genetics education: learning about the social and quantitative complexities of human genetic variation research could reduce racial bias in adolescent and adult populations. Science Education., 103(30), 529–560.
Donovan, B. M., Stuhlsatz, M. A., Edelson, D. C., & Buck Bracey, Z. E. (2019b). Gendered genetics: how reading about the genetic basis of sex differences in biology textbooks could affect beliefs associated with science gender disparities. Science Education DOI: https://doi.org/10.1002/sce.21502.
Dougherty, M. J. (2009). Closing the gap: inverting the genetics curriculum to ensure an informed public. The American Journal of Human Genetics, 85(1), 6–12.
Dougherty, M. J., Pleasants, C., Solow, L., Wong, A., & Zhang, H. (2011). A comprehensive analysis of high school genetics standards: are states keeping pace with modern genetics? CBE—Life Sciences Education, 10(3), 318–327.
Duncan, R. G., Castro-Faix, M., & Choi, J. (2016). Informing a learning progression in genetics: which should be taught first, Mendelian inheritance or the central dogma of molecular biology? International Journal of Science and Mathematics Education, 14(3), 445–472.
Fielder, D., Sbeglia, G. C., Nehm, R. H., & Harms, U. (2019). How strongly does statistical reasoning influence knowledge and acceptance of evolution? Journal of Research in Science Teaching (JRST)., 56(9), 1183–1206.
Fischer, H. E., Boone, W. J. Neumann, K. (2014). Quantitative research designs and approaches. In: Lederman, N. G., Abell, S. K. (eds) Handbook of research on science education, Volume 2. Routledge: New York, pg 18–37.
Geller, L., Alper, J. S., Ard, C., Asch, A., & Beckwith, J. (2004). The double-edged helix: social implications of genetics in a diverse society. Baltimore: Johns Hopkins University Press.
Gericke, N. M., Hagberg, M., dos Santos, V. C., Joaquim, L. M., & El-Hani, C. N. (2014). Conceptual variation or incoherence? Textbook discourse on genes in six countries. Science & Education, 23(2), 381–416.
Gericke, N. M., Carver, R., Castéra, J., Evangelista, N. A. M., Marre, C. C., & El-Hani, C. N. (2017). Exploring relationships among belief in genetic determinism, genetics knowledge, and social factors. Science & Education, 26(10), 1223–1259.
Grigg, K., & Manderson, L. (2016). The Australian racism, acceptance, and cultural-ethnocentrism scale (RACES): item response theory findings. International Journal for Equity in Health, 15(1), 49.
Haffie, T. L., Reitmeier, Y. M., & Walden, D. B. (2000). Characterization of university-level introductory genetics courses in Canada. Genome, 43(1), 152–159.
Hambleton, R. K., & Jones, R. W. (1993). An NCME instructional module on comparison of classical test theory and item response theory and their applications to test development. Educational Measurement Issues and Practice, 12, 38–47.
Haskel-Ittah, M., & Yarden, A. (2017). Toward bridging the mechanistic gap between genes and traits by emphasizing the role of proteins in a computational environment. Science & Education, 26(10), 1143–1160.
Haslam, N. (2011). Genetic essentialism, neuroessentialism, and stigma: commentary on Dar-Nimrod and Heine (2011). Psychological Bulletin, 137(5), 819–824.
Haslam, N., & Whelan, J. (2008). Human natures: psychological essentialism in thinking about differences between people. Social and Personality Psychology Compass, 2(3), 1297–1312.
Haslam, N., Rothschild, L., & Ernst, D. (2000). Essentialist beliefs about social categories. The British Journal of Social Psychology, 39, 206–249.
Haslam, N., Rothschild, L., & Ernst, D. (2002). Are essentialist beliefs associated with prejudice? The British Journal of Social Psychology, 41, 87–100.
Haslam, N., Bastian, B., Bain, P., & Kashima, Y. (2006). Psychological essentialism, implicit theories, and intergroup relations. Group Processes & Intergroup Relations, 9, 63–76.
Hoffman, C., & Hurst, N. (1990). Gender stereotypes: perception or rationalization? Journal of Personality and Social Psychology, 58(2), 197.
Horwitz, A. V. (2005). Media portrayals and health inequalities: a case study of characterizations of gene x environment interactions. Journals of Gerontology Series B, 60(2), 48.
Hott, A. M., Huether, C. A., McInerney, J. D., Christianson, C., Fowler, R., Bender, H., et al. (2002). Genetics content in introductory biology courses for non-science majors: Theory and practice. BioScience, 52(11), 1024–1035.
Jamieson, A., & Radick, G. (2013). Putting Mendel in his place: how curriculum reform in genetics and counterfactual history of science can work together. In K. Kampourakis (Ed.), The philosophy of biology: A companion for educators (pp. 577–595). Springer: Netherlands.
Jamieson, A., & Radick, G. (2017). Genetic determinism in the genetics curriculum. Science & Education, 1–30.
Jayaratne, T. E., Ybarra, O., Sheldon, J. P., Brown, T. N., Feldbaum, M., Pfeffer, C. A., & Petty, E. M. (2006). White Americans’ genetic lay theories of race differences and sexual orientation: their relationship with prejudice toward blacks, and gay men and lesbians. Group Processes & Intergroup Relations, 9(1), 77–94.
Jost, J. T., & Banaji, M. R. (1994). The role of stereotyping in system-justification and the production of false consciousness. British Journal of Social Psychology, 33(1), 1–27.
Kampourakis, K. (2017). Making sense of genes. Cambridge: Cambridge University Press.
Kargbo, D. B., Hobbs, E. D., & Erickson, G. L. (1980). Children’s beliefs about inherited characteristics. Journal of Biological Education, 14(2), 137–146.
Keller, J. (2005). In genes we trust: the biological component of psychological essentialism and its relationship to mechanisms of motivated social cognition. Journal of Personality and Social Psychology, 88(4), 686–702.
Krajcik, J. (2015). Three-dimensional instruction: using a new type of teaching in the science classroom. Science Scope, 39(3), 16.
Lanie, A. D., Jayaratne, T. E., Sheldon, J. P., Kardia, S. L. R., Anderson, E. S., Feldbaum, M., et al. (2004). Exploring the public understanding of basic genetic concepts. Journal of Genetic Counseling, 13(4), 305–320.
Lead States, N. G. S. S. (2013). Next generation science standards: for States, by States. Washington, DC: The National Academies Press.
Lewis, J., & Kattmann, U. (2004). Traits, genes, particles and information: re‐visiting students’ understandings of genetics. International Journal of Science Education, 26(2), 195–206.
Linacre J.M. (1997). KR-20 / Cronbach alpha or Rasch person reliability: which tells the “truth”? Rasch Measurement Transactions. 1997, 11:3 p. 580-1. https://www.rasch.org/rmt/rmt113l.htm. Accessed 20 March 2020.
Linacre J.M. (1999). Category disordering (disordered categories) vs. threshold disordering (disordered thresholds). In: Rasch Measurement Transactions. Institute for Objective Measurement. https://www.rasch.org/rmtbooks.htm. Accessed 6 Nov 2018.
Linacre, M., & Wright, B. (1993). Constructing linear measures from counts of qualitative observations. Paper presented at the Fourth International Conference on Bibliometrics, Informetrics and Scientometrics, Berlin.
Liu, X. (2012) Developing measurement instruments for science education research. In: Fraser, B., Tobin, K., McRobbie, C. (eds) Second international handbook of science education. Springer international handbooks of education, vol 24. Springer: Dordrecht. pgs 651–666.
McElhinny, T. L., Dougherty, M. J., Bowling, B. V., & Libarkin, J. C. (2014). The status of genetics curriculum in higher education in the United States: goals and assessment. Science & Education, 23(2), 445–464.
Medin, D. L., & Ortony, A. (1989). Comments on part I: psychological essentialism. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 179–195). New York, NY: Cambridge University Press.
Messick, S (1992). Validity of test interpretation and use. In Encylopedia of educational research (6th ed.), M. C. AIkin (Ed.). New York: MacMillan. Pgs. 1487–1495.
Messick, S. (1993). Foundations of validity: meanings and consequences in psychological assessment. Educational Testing Service. Princeton: New York.
Molster, C., Charles, T., Samanek, A., & O’Leary, P. (2009). Australian study on public knowledge of human genetics and health. Public Health Genomics, 12(2), 84–91.
Monterosso, J., Royzman, E. B., & Schwartz, B. (2005). Explaining away responsibility: effects of scientific explanation on perceived culpability. Ethics & Behavior, 15(2), 139–158.
Moore, D. S. (2015). The developing genome: An introduction to behavioral epigenetics. Oxford: Oxford University Press.
Morange, M. (2001). The misunderstood gene. Cambridge: Harvard University Press.
Morell, L., Collier, T., Black, P., & Wilson, M. (2017). A construct-modeling approach to develop a learning progression of how students understand the structure of matter. Journal of Research in Science Teaching, 54(8), 1024–1048.
Morin-Chassé, A. (2014). Public (mis) understanding of news about behavioral genetics research: a survey experiment. BioScience, 64(12), 1170–1177.
Morton, T. A., & Postmes, T. (2009). When differences become essential: minority essentialism in response to majority treatment. Personality and Social Psychology Bulletin, 35(5), 656–668.
Morton, T. A., Hornsey, M. J., & Postmes, T. (2009a). Shifting ground: the variable use of essentialism in contexts of inclusion and exclusion. British Journal of Social Psychology, 48(1), 35–59.
Morton, T. A., Postmes, T., Haslam, S. A., & Hornsey, M. J. (2009b). Theorizing gender in the face of social change: is there anything essential about essentialism? Journal of Personality and Social Psychology, 96(3), 653.
National Research Council (NRC). (2001). Knowing what students know. Washington, DC: National Academies Press.
National Research Council (NRC). (2012). A framework for K-12 science education: practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press. https://doi.org/10.17226/13165.
Nelkin, D., & Lindee, S. M. (2004). The DNA mystique: the gene as a cultural icon (2nd ed.). New York: Freeman.
Neumann, I., et al. (2011). Evaluating instrument quality in science education: Rasch-based analyses of a nature of science test. International Journal of Science Education, 33(10), 1373–1405.
No, S., Hong, Y. Y., Liao, H. Y., Lee, K., Wood, D., & Chao, M. M. (2008). Lay theory of race affects and moderates Asian Americans’ responses toward American culture. Journal of Personality and Social Psychology, 95, 991–1004. https://doi.org/10.1037/a0012978.
Norenzayan, A., & Heine, S. J. (2005). Psychological universals: What are they and how can we know?. Psychological bulletin, 131(5), 763.
Opfer, J., et al. (2012). Cognitive foundations for science assessment design: knowing what students know about evolution. Journal of Research in Science Teaching, 49(6), 744–777.
Parrott, R., Kahl, M. L., Ndiaye, K., & Traeder, T. (2012). Health communication, genetic determinism, and perceived control: the roles of beliefs about susceptibility and severity versus disease essentialism. Journal of Health Communication, 17(7), 762–778.
Phelan, J. C., Yang, L. H., & Cruz-Rojas, R. (2006). Effects of attributing serious mental illnesses to genetic causes on orientations to treatment. Psychiatric Services, 27, 382–387.
Portin, P., & Wilkins, A. (2017). The evolving definition of the term “gene”. Genetics, 205(4), 1353–1364.
President’s Council of Advisors on Science and Technology (PCAST). (2012). Engage to excel: producing one million additional college graduates with degrees in science, technology, engineering and mathematics. https ://obama white house .archi ves.gov/sites /default/files /micro sites /ostp/pcast -engage-to-excel -final 2–25-12.pdf. Accessed 20 Feb 2018.
Robitzsch A., Kiefer T., Wu M. (2018). Test analysis modules (TAM). v. 2.10–24.
Romine, W. L., Walter, E. M., Bosse, & Todd, A. N. (2017). Understanding patterns of evolution acceptance—a new implementation of the measure of acceptance of the theory of evolution. Journal of Research in Science Teaching, 54(5), 642–671.
Sadler, P. M., & Tai, R. H. (2007). The two high-school pillars supporting college science. Science, 317(5837), 457–458.
Sbeglia, G. C., & Nehm, R. H. (2018). Measuring evolution acceptance using the GAENE: Influences of gender, race, degreeplan, and instruction. Evolution: Education and Outreach. https://doi.org/(10.1186/s12052‐018‐0091‐9).
Sbeglia, G. C., & Nehm, R. H. (2019). Do you see what I-SEA? A Rasch analysis of the psychometric properties of the inventory of student evolution acceptance. Science Education, 103, 287–316.
Schmiemann, P., et al. (2017). Assessment of genetics understanding. Science & Education, 26(10), 1161–1191.
Schwartz, R. and Ayers, E. (2011). Delta dimensional alignment: Comparing performances across dimensions of the learning progression for assessing data modeling and statistical reasoning. Unpublished manuscript, University of California, Berkeley, CA.
Shea, N. A., Duncan, R. G., & Stephenson, C. (2015). A tri-part model for genetics literacy: exploring undergraduate student reasoning about authentic genetics dilemmas. Research in Science Education, 45(4), 485–507.
Shostak, S., Freese, J., Link, B. G., & Phelan, J. C. (2009). The politics of the gene: social status and beliefs about genetics for individual outcomes. Social Psychology Quarterly, 72(1), 77–93.
Singer, E., Antonucci, T. C., Burmeister, M., Couper, M. P., Raghunathan, T. E., & Van Hoewyk, J. (2007). Beliefs about genes and environment as determinants of behavioral characteristics. International Journal of Public Opinion Research, 19(3), 331–353.
Smith, M. U., & Gericke, N. M. (2015). Mendel in the modern classroom. Science & Education, 24(1–2), 151–172.
Smith, M. U., Snyder, S. W., & Devereaux, R. S. (2016). The GAENE—generalized acceptance of evolution evaluation: development of a new measure of evolution acceptance. Journal of Research in Science Teaching, 53(9), 1289–1315.
Smith, M. U., & Siegel, H. (2004). Knowing, believing, and understanding: What goals for science education?. Science & Education, 13(6), 553–582.
Southerland, S. A., Sinatra, G. M., & Matthews, M. R. (2001). Belief, knowledge, and science education. Educational Psychology Review, 13(4), 325–351.
Suhay, E., & Jayaratne, T. (2012). Does biology justify ideology? The politics of genetic attribution. Public Opinion Quarterly, 77(2), 497–521.
Todd, A., & Kenyon, L. (2016). Empirical refinements of a molecular genetics learning progression: the molecular constructs. Journal of Research in Science Teaching, 53(9), 1385–1418.
Todd, A., & Romine, W. L. (2016). Validation of the learning progression-based assessment of modern genetics in a college context. International Journal of Science Education, 38(10), 1673–1698.
Todd, A., Romine, W. L., & Cook Whitt, K. (2017). Development and validation of the learning progression–based assessment of modern genetics in a high school context. Science Education, 101(1), 32–65.
Trumbo, S. (2000). Introducing students to the genetic information age. The American Biology Teacher, 62(4), 259–262.
Ware, E. A., & Gelman, S. A. (2014). You get what you need: an examination of purpose-based inheritance reasoning in undergraduates, preschoolers, and biological experts. Cognitive Science, 38(2), 197–243.
Willoughby, E. A., Love, A. C., McGue, M., Iacono, W. G., Quigley, J., & Lee, J. J. (2019). Free will, determinism, and intuitive judgments about the heritability of behavior. Behavior Genetics, 49(2), 136–153.
Wright, B. D. (1977). Solving measurement problems with the Rasch model. Journal of Educational Measurement, 14(2), 97–116.
Wright, B. D. (2003). Rack and stack: time 1 vs. time 2 or pre-test vs. post-test. Rasch Measurement Transactions, 17(1), 905–906.
Wright, B. D., & Linacre, M. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8(3), 370.
Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. New York: University of Chicago.
Wright, B. D., & Stone, M. (1999). Measurement essentials. Wilmington: Wide Range.
Wyer, R. S. Jr., & Albarracin, D. (2005). Belief formation, organization, and change: Cognitive and motivational influences. The Handbook of Attitudes, 273, 273–322.
Yang, Y., He, P., & Liu, X. (2017). Validation of an instrument for measuring students’ understanding of interdisciplinary science in grades 4-8 over multiple semesters: a Rasch measurement study. International Journal of Sciences and Mathematics Education, 16(4), 639–654.
Yzerbyt, V. Y., Rocher, S., & Schadron, G. (1997). Stereotypes as explanations: a subjective essentialistic view of group perception. In R. Spears, P. Oakes, N. Ellemers, & A. Haslam (Eds.), The psychology of stereotyping and group life (pp. 20–50). London: Basil Blackwell.
Zuk, O., Hechter, E., Sunyaev, S. R., & Lander, E. S. (2012). The mystery of missing heritability: genetic interactions create phantom heritability. Proceedings of the National Academy of Sciences, 109(4), 1193–1198.
Acknowledgments
We thank B. Donovan for sharing his concerns with the PUGGS instrument, which helped to inform our analyses and interpretations. C. El-Hani, N. Gericke, and A. Yarden helped to determine which plant example would be well suited for use in the modified PUGGS instrument. GCS was funded by NSF grant No. 1322872. RET thanks NARST for a Classroom Teachers and Informal Educators Scholarship that facilitated work on this project.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of Interest
The authors declare no conflicts of interest.
Disclaimer
Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation or the National Association for Research in Science Teaching.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
ESM 1
(PDF 352 kb)
Rights and permissions
About this article
Cite this article
Tornabene, R.E., Sbeglia, G.C. & Nehm, R.H. Measuring Belief in Genetic Determinism: A Psychometric Evaluation of the PUGGS Instrument. Sci & Educ 29, 1621–1657 (2020). https://doi.org/10.1007/s11191-020-00146-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11191-020-00146-2