Module A
Advanced Stata programming
Most students at ISS use Stata. This module introduces advanced topics in Stata programming. These will be highly valuable skills when conducting own research or thesis work. Examples include progammatical manipulation of analysis results, automation of tasks, and producing publication-ready tables and graphs for your thesis.
Literature:
- Baum, Christopher F. 2009/2011. An Introduction to Stata Programming. Stata Press: College Station, Texas.
- Jann, Benn 2014. Plotting regression coefficients and other estimates. Stata Journal 14(4): 708-737. (https://journals.sagepub.com/doi/pdf/10.1177/1536867X1401400402)
- Various Stata Reference manuals
Module B
Quantile regression using Stata
This module introduces quantile regression models. These techniques allow researchers to examine statistical relationships effects that vary in strength over the outcome distribution. There are many instances where social scientists expect that effects vary across the outcome distribution. For example, may one ask whether effects of homework assignements on school grades are strongest for the best-performing students. Quantile regression is a tool that allow one to depart from models of the mean only.
Literature:
- Hao, Lingxin, and Daniel Q. Naiman. 2007. Quantile regression. Sage. Pages 1-42 (https://methods.sagepub.com/book/quantile-regression)
- Firpo, Sergio, Nicole M Fortin, and Thomas Lemieux. 2009. “Unconditional quantile regressions.” Econometrica 77(3):953-973. (https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6822)
- Firpo, Sergio. 2007 “Efficient semiparametric estimation of quantile treatment effects.” Econometrica 75.1: 259-276. (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0262.2007.00738.x)
- Powell, David 2016. “Quantile treatment effects in the presence of covariates.” Review of Economics and Statistics : 1-39. (https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00858)
- Fr?lich, Markus, and Blaise Melly. 2010. “Estimation of quantile treatment effects with Stata.” Stata Journal 10(3):423-457. (https://journals.sagepub.com/doi/10.1177/1536867X1001000309)
- Borgen, Nicolai T. 2016. “Fixed effects in unconditional quantile regression.” Stata Journal 16(2):403-415. (https://journals.sagepub.com/doi/10.1177/1536867X1601600208)
- Killewald, Alexandra, and Jonathan Bearak. 2014. “Is the Motherhood Penalty Larger for Low-Wage Women? A Comment on Quantile Regression.” American Sociological Review 79(2):350-357. (https://journals.sagepub.com/doi/full/10.1177/0003122414524574)
- Cooke, Lynn Prince 2014. “Gendered Parenthood Penalties and Premiums across the Earnings Distribution in Australia, the United Kingdom, and the United States.” European Sociological Review 30(3):360-372. (https://academic.oup.com/esr/article/30/3/360/2763423)
Supplementary literature:
- Porter, Stephen R. 2015. “Quantile regression: analyzing changes in distributions instead of means.” Pp. 335-381 In Higher Education: Handbook of Theory and Research. Springer. (https://link.springer.com/chapter/10.1007/978-3-319-12835-1_8)
- Borah, Bijan J, and Anirban Basu. 2013. “Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence.” Health economics 22(9):1052-1070. (https://www.ncbi.nlm.nih.gov/pubmed/23616446)
- Koenker, Roger and Kevin Hallock. 2001. “Quantile regression: An introduction.” Journal of Economic Perspectives 15(4):43-56. (https://www.aeaweb.org/articles?id=10.1257/jep.15.4.143)
- Firpo, Sergio, Nicole Fortin, and Thomas Lemieux. 2007. “Unconditional Quantile Regressions. Technical working paper, 339.” National Bureau of Economic Research.
(https://www.nber.org/papers/t0339)
Module C
Biometric models
This module introduces biometric models (“twin models”, and other related models) from a sociological standpoint. What can we use these models for, and how do we use them? The module provides some introductory lecture material, and practical exercises in estimating and interpreting biometric models. It also offers a simple introduction to molecular genetics approaches in social sciences.
Literature:
- Neale, M. C. C. L., & Cardon, L. R. (2013). Methodology for genetic studies of twins and families (Vol. 67). Springer Science & Business Media. Available at http://delta.colorado.edu/workshop2004/cdrom/HTML/book2004a.pdf
Supplementary literature:
- Polderman, T. J., Benyamin, B., De Leeuw, C. A., Sullivan, P. F., Van Bochoven, A., Visscher, P. M., & Posthuma, D. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature genetics, 47(7), 702.
- Heath, A. C., Berg, K., Eaves, L. J., Solaas, M. H., Corey, L. A., Sundet, J., … & Nance, W. E. (1985). Education policy and the heritability of educational attainment. Nature, 314(6013), 734-736.
- D’Onofrio, B. M., Turkheimer, E. N., Eaves, L. J., Corey, L. A., Berg, K., Solaas, M. H., & Emery, R. E. (2003). The role of the children of twins design in elucidating causal relations between parent characteristics and child outcomes. Journal of Child Psychology and Psychiatry, 44(8), 1130-1144.
- Baier, T., & Lang, V. (2019). The social stratification of environmental and genetic influences on education: New evidence using a register-based twin sample. Sociological Science, 6, 143-171.
- Figlio, D. N., Freese, J., Karbownik, K., & Roth, J. (2017). Socioeconomic status and genetic influences on cognitive development. Proceedings of the National Academy of Sciences, 114(51), 13441-13446.
- Nielsen, F., & Roos, J. M. (2015). Genetics of Educational Attainment and the Persistence of Privilege at the Turn of the 21st Century. Social Forces, 94(2), 535-561.
Module D
Fixed and Random Effects Regression Models using sibling and panel data
This module is a workshop in one of the workhorses of empirical social science, panel data analysis. Emphasis are on fixed effects approaches, but also cover random effects to some extent. Examples are taken from the broader sociological literature on social stratification.
Literature:
- Allison, P. (2009). Fixed Effects Regression Models. SAGE Publications, Inc. Quantitative Applications in the Social Sciences. Vol 160. Sage. Chp. 1-4, 70 pages https://doi.org/10.4135/9781412993869 E-LINK: http://methods.sagepub.com/Book/fixed-effects-regression-models
- Petersen, T. (2004). Analyzing Panel Data: Fixed- and Random-Effects Models. In M. Hardy & A. Bryman, Handbook of Data Analysis (pp. 332–345). SAGE Publications, Ltd. https://doi.org/10.4135/9781848608184.n14 E-LINK: https://methods.sagepub.com/book/handbook-of-data-analysis/n14.xml
Supplementary literature:
- Cameron, A. C., and Trivedi, P. K. (2010). Microeconometrics Using Stata: Revised Edition (2 edition). Stata Press.
- Rabe-Hesketh, Sophia & Skrondal, Anders 2008. Multilevel and longitudinal modelling using Stata. Second edition. Stata Press.
Some examples of applications in sociological stratification research:
- Andersen, P. L., and Hansen, M. N. (2012). Class and Cultural Capital—The Case of Class Inequality in Educational Performance. European Sociological Review, 28(5), 607–621. https://doi.org/10.1093/esr/jcr029
- Elstad, J. I., and Bakken, A. (2015). The effects of parental income on Norwegian adolescents’ school grades: A sibling analysis. Acta Sociologica, 58(3), 265–282. https://doi.org/10.1177/0001699315594411
- Erola, J., Jalonen, S., and Lehti, H. (2016). Parental education, class and income over early life course and children’s achievement. Research in Social Stratification and Mobility, 44, 33–43. https://doi.org/10.1016/j.rssm.2016.01.003
- H?llsten, M., and Pfeffer, F. T. (2017). Grand Advantage: Family Wealth and Grandchildren’s Educational Achievement in Sweden. American Sociological Review. https://doi.org/10.1177/0003122417695791
- J?ger, M. M. (2011). Does Cultural Capital Really Affect Academic Achievement? New Evidence from Combined Sibling and Panel Data. Sociology of Education, 84(4), 281–298. https://doi.org/10.1177/0038040711417010
- Wiborg, ?. N., and Hansen, M. N. (2018). The Scandinavian model during increasing inequality: Recent trends in educational attainment, earnings and wealth among Norwegian siblings. Research in Social Stratification and Mobility, 56, 53–63. https://doi.org/10.1016/j.rssm.2018.06.006
Supplementary literature on empirical applications of FE and RE:
- Conley, D., Domingue, B. W., Cesarini, D., Dawes, C., Rietveld, C. A., and Boardman, J. D. (2015). Is the Effect of Parental Education on Offspring Biased or Moderated by Genotype? Sociological Science, 2, 82–105. https://doi.org/10.15195/v2.a6
- Dahl, S.-?., Hansen, H.-T., and Olsen, K. M. (2010). Sickness Absence among Immigrants in Norway, 1992—2003. Acta Sociologica, 53(1), 35–52. https://doi.org/10.1177/0001699309357841
- Gr?tz, M., Barclay, K. J., Wiborg, ?., Lyngstad, T., Karhula, A., Erola, J., Pr?g, P., Laidley, T., and Conley, D. (2019). Universal family background effects on education across and within societies (WP-2019-007; MPIDR Working Papers). Max Planck Institute for Demographic Research, Rostock, Germany. https://ideas.repec.org/p/dem/wpaper/wp-2019-007.html
- Gr?tz, M., and Torche, F. (2016). Compensation or Reinforcement? The Stratification of Parental Responses to Children’s Early Ability. Demography, 53(6), 1883–1904. https://doi.org/10.1007/s13524-016-0527-1
- Hermansen, A. S., and Birkelund, G. E. (2015). The Impact of Immigrant Classmates on Educational Outcomes. Social Forces, 94(2), 615–646. https://doi.org/10.1093/sf/sov073
- J?ger, M. M., and M?llegaard, S. (2017). Cultural capital, teacher bias, and educational success: New evidence from monozygotic twins. Social Science Research, 65, 130–144. https://doi.org/10.1016/j.ssresearch.2017.04.003
- Laidley, T., Vinneau, J., and Boardman, J. D. (2019). Individual and Social Genomic Contributions to Educational and Neighborhood Attainments: Geography, Selection, and Stratification in the United States. Sociological Science, 6, 580–608. https://doi.org/10.15195/v6.a22
- Petersen, T., Penner, A. M., and H?gsnes, G. (2014). From Motherhood Penalties to Husband Premia: The New Challenge for Gender Equality and Family Policy, Lessons from Norway. American Journal of Sociology, 119(5), 1434–1472. https://doi.org/10.1086/674571
- Petersen, T., Penner, A. M., and H?gsnes, G. (2014). From Motherhood Penalties to Husband Premia: The New Challenge for Gender Equality and Family Policy, Lessons from Norway. American Journal of Sociology, 119(5), 1434–1472. https://doi.org/10.1086/674571
- Vauhkonen, T., Kallio, J., Kauppinen, T. M., and Erola, J. (2017). Intergenerational accumulation of social disadvantages across generations in young adulthood. Research in Social Stratification and Mobility, 48, 42–52. https://doi.org/10.1016/j.rssm.2017.02.001
Supplementary literature on advances within the RE and FE-design:
- Allison, P. D., Williams, R., and Moral-Benito, E. (2017). Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects. Socius, 3, 2378023117710578. https://doi.org/10.1177/2378023117710578
- Bollen, K. A., and Brand, J. E. (2010). A General Panel Model with Random and Fixed Effects: A Structural Equations Approach. Social Forces, 89(1), 1–34. https://doi.org/10.1353/sof.2010.0072
- Leszczensky, L., and Wolbring, T. (2019). How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study. Sociological Methods & Research, 0049124119882473. https://doi.org/10.1177/0049124119882473
- Schunck, R. (2013). Within and between Estimates in Random-Effects Models: Advantages and Drawbacks of Correlated Random Effects and Hybrid ModelsRetrieved February 13, 2020, from https://journals.sagepub.com/doi/abs/10.1177/1536867X1301300105