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Effects of Single-Sex Schooling in the Final Years of High School: A Comparison of Analysis of Covariance and Propensity Score Matching

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Abstract

Typically, the effects of single-sex schooling are small at best, and tend to be statistically non-significant once pre-existing differences are taken into account. However, researchers often have had to rely on observational studies based on small non-representative samples and have not used more advanced propensity score methods to control the potentially confounding effects of covariates. Here, we apply optimal full matching to the large historical longitudinal dataset best suited to evaluating this issue in US high schools: the nationally representative High School and Beyond study. We compare the effects of single-sex education in the final 2 years of high school on Grade 12 and post-secondary outcomes using the subsample of students attending Catholic schools (N = 2379 students, 29 girls’ schools, 22 boys’ schools, 33 coeducational schools) focusing on achievement-related, motivational and social outcomes. We contrast conventional Analysis of Covariance (ANCOVA) with optimal full matching based on the propensity score that provides a principled way of controlling for selection bias. Results from the two approaches converged: When background and Year 10 covariates were controlled, uncorrected apparent differences between the school types disappeared and the pattern of effects was very similar across the two methods. Overall, there was little evidence for positive effects of single-sex schooling for a broad set of outcomes in the final 2 years of high school and 2 years after graduation. We conclude with a discussion of the advantages of propensity score methods compared to ANCOVA.

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References

  • Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.

    Google Scholar 

  • Austin, P. C., Grootendorst, P., & Anderson, G. M. (2007). A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: A Monte Carlo study. Statistics in Medicine, 26, 734–753. doi:10.1002/sim.2580.

    Article  PubMed  Google Scholar 

  • Baker, D. B., Riordan, C., & Schaub, M. (1995). The effects of sex-grouped schooling on achievement: The role of national context. Comparative Education Review, 39, 468–482.

    Article  Google Scholar 

  • Bigler, R. S., & Signorella, M. L. (2011). Single-sex education: New perspectives and evidence on a continuing controversy. Sex Roles, 65, 659–669. doi:10.1007/s11199-011-0046-x.

    Article  Google Scholar 

  • Billger, S. M. (2009). On reconstructing school segregation: The efficacy and equity of single-sex schooling. Economics of Education Review, 28, 393–402. doi:10.1016/j.econedurev.2007.08.005.

    Article  Google Scholar 

  • Burgess, S., Greaves, E., Vignoles, A., & Wilson, D. (2009). Parental choice of primary school in England: what ‘type’ of school do parents choose? (Working Paper No. 09/224). Bristol, UK: The centre for market and public organisation. Retrieved from www.bristol.ac.uk/cmpo/publications/papers/2009/wp224.pdf.

  • Carpenter, P., & Hayden, M. (1987). Girls’ academic achievements: Single-sex versus coeducational schools in Australia. Sociology of Education, 60, 156–167. doi:10.2307/2112273.

    Article  Google Scholar 

  • Caspi, A. (1995). Puberty and the gender organization of schools: How biology and social context shape the adolescent experience. In L. J. Crockett & A. C. Crouter (Eds.), Pathways through adolescence: Individual development in relation to social contexts (pp. 57–74). Mahwah: Erlbaum.

    Google Scholar 

  • Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankhya-A, 35, 417–446.

    Google Scholar 

  • Daly, P. (1996). The effects of single-sex and coeducational secondary schooling on girls’ achievement. Research Papers in Education, 11, 289–306. doi:10.1080/0267152960110306.

    Article  Google Scholar 

  • Daly, P., & Shuttleworth, I. (1997). Determinants of public examination entry and attainment in mathematics: Evidence on gender and gender-type of school from the 1980s and 1990s in Northern Ireland. Evaluation and Research in Education, 11, 91–101. doi:10.1080/09500799708666919.

    Article  Google Scholar 

  • Dehejia, R. H., & Wahba, S. (1999). Propensity score matching methods for nonexperimental causal studies: Re-evaluating the evaluation of training programs. Journal of the American Statistical Association, 94, 1053–1062. doi:10.1162/003465302317331982.

    Article  Google Scholar 

  • R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org.

  • Diamond, A., & Sekhon, J. S. (2006). Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies. (Working paper). University of California, Berkeley. Retrieved from http://sekhon.berkeley.edu/papers/GenMatch.pdf.

  • Eliot, L. (2011). Single-sex education and the brain. Sex Roles, this issue. doi:10.1007/s11199-011-0037-y.

  • Foster, E. M. (2010). Causal inference and developmental psychology. Developmental Psychology, 46, 1454–1480. doi:10.1037/a0020204.

    Article  PubMed  Google Scholar 

  • Gitelman, A. I. (2005). Estimating causal effects from multilevel group-allocation data. Journal of Educational and Behavioral Statistics, 30, 397–412. doi:10.3102/10769986030004397.

    Article  Google Scholar 

  • Hansen, B. B. (2004). Full matching in an observational study of coaching for the SAT. Journal of the American Statistical Association, 99, 609–618. doi:10.1198/016214504000000647.

    Article  Google Scholar 

  • Hansen, B. B., & Klopfer, S. O. (2006). Optimal full matching and related designs via network flows. Journal of Computational and Graphical Statistics, 15, 609–627. doi:10.1198/106186006X137047.

    Article  Google Scholar 

  • Harder, V. S., Stuart, E. A., & Anthony, J. C. (2010). Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research. Psychological Methods, 15, 234–249. doi:10.1037/a0019623.

    Article  PubMed  Google Scholar 

  • Harker, R. (2000). Achievement, gender, and the single-sex/coed debate. British Journal of Sociology of Education, 21, 203–218. doi:10.1080/713655349.

    Article  Google Scholar 

  • Hattie, J. (2008). Visible learning. A synthesis of over 800 meta-analyses relating to achievement. London: Routledge.

    Google Scholar 

  • Heckman, J. J., & Vytlacil, E. J. (2007a). Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation. In J. J. Heckman & E. E. Leamer (Eds.), Handbook of econometrics (Vol. 6, pt. 2, pp. 4779–4874). Amsterdam, the Netherlands: North-Holland.

  • Hill, J. L., Weiss, C., & Zhai, F. (2011). Challenges with propensity score strategies in a high-dimensional setting and a potential alternative. Multivariate Behavioral Research, 46, 477–513. doi:10.1080/00273171.2011.570161.

    Article  Google Scholar 

  • Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15, 199–236. doi:10.1093/pan/mpl013.

    Article  Google Scholar 

  • Ho, D. E., Imai, K., King, G. & Stuart, E. A. (2011). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42, (8). Retrieved from http://www.jstatsoft.org/v42/i08/paper.

  • Hoffnung, M. (2011). Career and family outcomes for women graduates of single-sex versus coed colleges. Sex Roles, 65, 680–692. doi:10.1007/s11199-010-9914-z.

    Article  Google Scholar 

  • Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960. doi:10.1080/01621459.1986.10478354.

    Article  Google Scholar 

  • Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multilevel observational data. Journal of the American Statistical Association, 101, 901–910. doi:10.1198/016214506000000447.

    Article  Google Scholar 

  • Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142, 615–635. doi:10.1016/j.jeconom.2007.05.001.

    Article  Google Scholar 

  • Jencks, C. (1985). How much do high school students learn? Sociology of Education, 58, 128–135. doi:10.2307/2112252.

    Article  Google Scholar 

  • Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double-robustness: A comparison of alternative strategies for estimating a population mean from incompleted data. Statistical Science, 22, 523–539. doi:10.1214/07-STS227.

    Article  Google Scholar 

  • LaLonde, R. J. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review, 76, 604–620. doi:10.2307/1806062.

    Google Scholar 

  • Lee, V. E. (1998). Is single-sex secondary schooling a solution to the problem of gender inequity? In American Association for University Women Educational Foundation (Ed.). Separated by sex. A critical look at single-sex education for girls (pp. 41–52). Washington, D.C.: American Association for University Women Educational Foundation.

  • Lee, V. E., & Bryk, A. S. (1986). Effects of single-sex schools on student achievement and attitudes. Journal of Educational Psychology, 78, 381–395. doi:10.1037/0022-0663.78.5.381.

    Article  Google Scholar 

  • Lee, V. E., & Bryk, A. S. (1989). Effects of single-sex schools: Response to Marsh. Journal of Educational Psychology, 81, 647–650. doi:10.1037/0022-0663.81.4.647.

    Article  Google Scholar 

  • Lee, V. E., & Lockheed, M. E. (1990). The effects of single-sex schooling on achievement and attitudes in Nigeria. Comparative Education Review, 34, 209–231.

    Article  Google Scholar 

  • Lee, V. E., & Marks, H. M. (1990). Sustained effects of the single-sex secondary school experience on attitudes, behaviors, and values in college. Journal of Educational Psychology, 82, 578–592. doi:10.1037/0022-0663.82.3.578.

    Article  Google Scholar 

  • LePore, P. C., & Warren, J. R. (1997). A comparison of single-sex and coeducational Catholic secondary schooling: Evidence from the National Educational Longitudinal Study of 1988. American Educational Research Journal, 34, 485–511. doi:10.3102/00028312034003485.

    Article  Google Scholar 

  • Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). New York: John Wiley.

    Google Scholar 

  • Lumley, T. (2010). Survey: analysis of complex survey samples [Computer software]. R package version 3.22–4.

  • Mael, F., Alonso, A., Gibson, D., Rogers, K., & Smith, M. (2005). Single-sex versus coeducational schooling: A systematic review. Doc# 2005-01. Washington: Department of Education. Office of Planning, Evaluation and Policy Development.

    Google Scholar 

  • Manski, C. F. (2010). Identification of treatment response with social interactions.Working paper. Evanston: Northwestern University, Department of Economics and Institute for Policy Research.

    Google Scholar 

  • Marsh, H. W. (1989a). Effects of attending single-sex and coeducational high schools on achievement, attitudes, behaviors, and sex differences. Journal of Educational Psychology, 81, 70–85. doi:10.1037/0022-0663.81.1.70.

    Article  Google Scholar 

  • Marsh, H. W. (1989b). Effects of single-sex and coeducational schools. A reponse to Lee and Bryk. Journal of Educational Psychology, 81, 651–653. doi:10.1037/0022-0663.81.4.651.

    Article  Google Scholar 

  • Marsh, H. W. (1991). Public, Catholic single-sex and Catholic coeducational high schools: Their effect on achievement, affect, and behaviors. American Journal of Education, 99, 320–356.

    Article  Google Scholar 

  • Marsh, H. W., & Hau, K.-T. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32, 151–170. doi:10.1016/j.cedpsych.2006.10.008.

    Article  Google Scholar 

  • Marsh, H. W., Smith, I. D., Marsh, M. R., & Owens, L. (1988). The transition from single-sex to coeducational high schools: Effects on multiple dimensions of self-concept and on academic achievement. American Educational Research Journal, 25, 237–269. doi:10.3102/00028312025002237.

    Article  Google Scholar 

  • Marsh, H. W., Owens, L., Marsh, M. R., & Smith, I. D. (1989). The transition from single-sex to coeducational high schools: Teacher perceptions, academic achievement, and self-concept. British Journal of Educational Psychology, 59, 155–173. doi:10.1111/j.2044-8279.1989.tb03088.x.

    Article  Google Scholar 

  • Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Muthén, L. K. & Muthén, B. O. (1998–2010). Mplus User’s Guide. Sixth Edition. Los Angeles, CA: Muthén & Muthén

  • Nagengast, B. (2009). Causal inference in multilevel designs. Unpublished doctoral dissertation. School of Social and Behavioural Sciences. Germany: Friedrich-Schiller-Universität Jena.

    Google Scholar 

  • National Center for Educational Statistics. (1986). High school and beyond, 1980: sophomore cohort second follow-up (1984). Data file user’s manual. Ann Arbor: Inter-university Consortium for Political and Social Research.

    Google Scholar 

  • Neyman, J. (1923/1990). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science, 5, 465–480.

  • OECD. (2010). PISA 2009 results: Overcoming social background. Equity in learning opportunities and outcomes. Volume 2. Paris: Author.

    Book  Google Scholar 

  • Park, H., Behrman, J. R., & Choi, J. (2010). Causal effects of single-sex schools on college attendance: Random assignment in Korean high schools. PSC Working Paper Series, 15.

  • Pohl, S., Steiner, P. M., Eisermann, J., Soellner, R., & Cook, T. D. (2009). Unbiased causal inference from an observational study: Results of a within-study comparison. Educational Evaluation and Policy Analysis, 31, 463–479. doi:10.3102/0162373709343964.

    Article  Google Scholar 

  • Raudenbush, S. W. (2004). What are value-added models estimating and what does this imply for statistical practice? Journal of Educational and Behavioral Statistics, 29, 121–129. doi:10.3102/10769986029001121.

    Article  Google Scholar 

  • Raudenbush, S. W., & Willms, J. D. (1995). The estimation of school effects. Journal of Educational and Behavioral Statistics, 20, 307–335. doi:10.3102/10769986020004307.

    Google Scholar 

  • Riordan, C. (1990). Girls and boys in school: Together or separate? New York: Teachers College Press.

    Google Scholar 

  • Riordan, C. (1994). Single-gender schools: Outcomes for African and Hispanic Americans. Research in Sociology of Education and Socialization, 10, 177–205.

    Google Scholar 

  • Riordan, C. (1998). The future of single-sex schools. In AAUW Educational Foundation (Ed.), Separated by sex (pp. 53–62). Washington, DC: American Association for University Women Educational Foundation. Retrieved from http://www.aauw.org/research/upload/SeparatedBySex.pdf.

  • Rosenbaum, P. R. (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society—Series A, 147, 656–666. doi:10.2307/2981697.

    Article  Google Scholar 

  • Rosenbaum, P. R. (1991). A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society—Series B, 53, 597–610.

    Google Scholar 

  • Rosenbaum, P. R. (2002). Covariance adjustment in randomized experiments and observational studies. Statistical Science, 17, 286–304. doi:10.1214/ss/1042727942.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983a). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55. doi:10.1093/biomet/70.1.41.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983b). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society—Series B, 45, 212–218. doi:10.2307/2345524.

    Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38. doi:10.1080/00031305.1985.10479383.

    Google Scholar 

  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology, 66, 688–701. doi:10.1037/h0037350.

    Article  Google Scholar 

  • Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2, 1–26. doi:10.3102/10769986002001001.

    Article  Google Scholar 

  • Rubin, D. B. (1978). Bayesian-inference for causal effects: The role of randomization. The Annals of Statistics, 6, 34–58. doi:10.2307/2958688.

    Article  Google Scholar 

  • Rubin, D. B. (1980). Bias reduction using Mahalanobis metric matching. Biometrics, 36, 293–298. doi:10.2307/2529981.

    Article  Google Scholar 

  • Rubin, D. B. (1986). Statistics and causal inference—which ifs have causal answers. Journal of the American Statistical Association, 81, 961–962. doi:10.1080/01621459.1986.10478355.

    Google Scholar 

  • Rubin, D. B. (1990a). Formal modes of statistical-inference for causal effects. Journal of Statistical Planning and Inference, 25, 279–292. doi:10.1016/0378-3758(90)90077-8.

    Article  Google Scholar 

  • Rubin, D. B. (1990b). Neyman (1923) and causal inference in experiments and observational studies. Statistical Science, 5, 472–480.

    Google Scholar 

  • Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services & Outcomes Research Methodology, 2, 169–188. doi:10.1023/A:1020363010465.

    Article  Google Scholar 

  • Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331. doi:10.1198/016214504000001880.

    Article  Google Scholar 

  • Rubin, D. B. (2008). Comment: The design and analysis of gold standard randomized experiments. Journal of the American Statistical Association, 103, 1350–1353. doi:10.1198/016214508000001011.

    Article  Google Scholar 

  • Rubin, D. B., & Thomas, N. (1996). Matching using estimated propensity scores, relating theory to practice. Biometrics, 52, 249–264. doi:10.2307/2533160.

    Article  PubMed  Google Scholar 

  • Rubin, D. B., & Thomas, N. (2000). Combining propensity score matching with additional adjustments for prognostic covariates. Journal of the American Statistical Association, 95, 573–585. doi:10.1080/01621459.2000.10474233.

    Article  Google Scholar 

  • Schafer, J. L., & Kang, J. D. Y. (2008). Average causal effects from nonrandomized studies: a practical guide and simulated example. Psychological Methods, 13, 279–313. doi:10.1037/a0014268.

    Article  PubMed  Google Scholar 

  • Senn, S., Graf, E., & Caputo, A. (2007). Stratification for the propensity score compared with linear regression techniques to assess the effect of treatment or exposure. Statistics in Medicine, 26, 5529–5544. doi:10.1002/sim.3133.

    Article  PubMed  Google Scholar 

  • Shadish, W. R., & Cook, T. D. (2009). The renaissance of field experimentation in evaluating interventions. Annual Review of Psychology, 60, 607–629. doi:10.1146/annurev.psych.60.110707.163544.

    Article  PubMed  Google Scholar 

  • Shadish, W. R., Clark, M. H., & Steiner, P. M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random to nonrandom assignment. Journal of the American Statistical Association, 103, 1334–1343. doi:10.1198/016214508000000733.

    Article  Google Scholar 

  • Smithers, A., & Robinson, P. (2006). The paradox of single-sex and coeducational schooling. Buckingham: Carmichael Press.

    Google Scholar 

  • Sobel, M. E. (2006). What do randomized studies of housing mobility demonstrate?: Causal inference in the face of interference. Journal of the American Statistical Association, 101, 1398–1407. doi:10.1198/016214506000000636.

    Article  Google Scholar 

  • Spielhofer, T., Benton, T., & Schagen, S. (2004). A study of the effects of school size and single-sex education in English schools. Research Papers in Education, 19, 133–159. doi:10.1080/02671520410001695407.

    Article  Google Scholar 

  • Steyer, R., von Davier, A. A., Nachtigall, C., & Buhl, T. (2000). Causal regression models I: Individual and average causal effects. Methods of Psychological Research Online, 5, 39–71.

    Google Scholar 

  • Steyer, R., Nachtigall, C., Wüthrich-Martone, O., & Kraus, K. (2002). Causal regression models III: Covariates, conditional, and unconditional average causal effects. Methods of Psychological Research Online, 7, 41–68.

    Article  Google Scholar 

  • Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25, 1–21. doi:10.1214/09-STS313.

    Article  PubMed  Google Scholar 

  • Stuart, E. A., & Green, K. M. (2008). Using full matching to estimate causal effects in non-experimental studies: Examining the relationship between adolescent marijuana use and adult outcomes. Developmental Psychology, 44, 395–406. doi:10.1037/0012-1649.44.2.395.

    Article  PubMed  Google Scholar 

  • Sullivan, A. (2009). Academic self-concept, gender and single-sex schooling. British Educational Research Journal, 35, 259–288. doi:10.1080/01411920802042960.

    Article  Google Scholar 

  • Sullivan, A., Joshi, H., & Leonhard, D. (2010). Single-sex schooling and academic attainment at school and through the lifecourse. American Educational Research Journal, 47, 6–36. doi:10.3102/0002831209350106.

    Article  Google Scholar 

  • Thoemmes, F., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46, 90–118. doi:10.1080/00273171.2011.540475.

    Article  Google Scholar 

  • U.S. Department of Education (2006). Nondiscrimination on the basis of sex in education programs or activities receiving financial assistance: final rule, Federal Register, 34 CFR Part 106, 25 October.

  • VanderWeele, T. J. (2008). Ignorability and stability assumptions in neighborhood effects research. Statistics in Medicine, 27, 1934–1943. doi:10.1002/sim.3139.

    Article  PubMed  Google Scholar 

  • Watson, C. M., Quatman, T., & Edler, E. (2002). Career aspirations of adolescent girls: Effects of achievement level, grade, and single-sex school environment. Sex Roles, 46, 323–335. doi:10.1023/A:1020228613796.

    Article  Google Scholar 

  • West, S. G., & Thoemmes, F. (2010). Campbell’s and Rubin’s perspectives on causal inference. Psychological Methods, 15, 18–37. doi:10.1037/a0015917.

    Article  PubMed  Google Scholar 

  • Woodward, L. J., Fergusson, D. M., & Horwood, L. J. (1999). Effects of single-sex and coeducational secondary schooling on children’s academic achievement. Australian Journal of Education, 43, 142–156.

    Article  Google Scholar 

  • Wooldridge, J. (2005). Fixed-effects and related estimators for correlated random-coefficient and treatment-effect panel data models. The Review of Economics and Statistics, 87, 385–390. doi:10.1162/0034653053970320.

    Article  Google Scholar 

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Author note

This research was supported in part by grants to the second author from the UK Economic and Social Research Council and the King Saud University in Saudi Arabia. Requests for further information about this investigation should be sent to Benjamin Nagengast, Department of Education, Center for Educational Science and Psychology, University of Tübingen, Europastr. 6, 72072 Tübingen, Germany; E-mail: benjamin.nagengast@uni-tuebingen.de

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Nagengast, B., Marsh, H.W. & Hau, KT. Effects of Single-Sex Schooling in the Final Years of High School: A Comparison of Analysis of Covariance and Propensity Score Matching. Sex Roles 69, 404–422 (2013). https://doi.org/10.1007/s11199-013-0261-8

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