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Causality in Mixed-Methods Projects That Use Regression

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Systematic Mixed-Methods Research for Social Scientists

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Abstract

In this chapter I expand on a method of developing a structural-institutional regression. Mechanisms and events are also represented in most regression models. Some language from realism, including a depth ontology and multilevel models, is presented along with definitions to make these concepts easier to apply. A regression using structural-institutional-mechanisms-events (S I M E) will typically achieve an explanation that represents the world as changing, complex, and open. There are alternative variants of S I M E which all contrast with the notion of a ‘flat’ regression. ‘Flat’ here refers to atomistic modelling, that is a simpler form in which all units or cases are homogeneous. If the mind is trained for multilevel conceptual thinking, innovative regression formats can be developed. To illustrate, the chapter provides path diagrams for child labour and a multi-sectoral model of poverty and work outcomes. These illustrate a working approach to causal explorations. The notions of ‘depth ontology’ and Context+Mechanism➔Outcome (CMO) are presented as helpful tools for expanding the linkages between variables, real objects, and actual events.

In this chapter, the logical process of doing ‘retroduction’ is explained and illustrated in detail with empirical examples.

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References

  • Beach, Derek (2018) “Achieving Methodological Alignment When Combining QCA and Process Tracing in Practice”, Sociological Methods & Research, 47(1) 64-99. https://doi.org/10.1177/0049124117701475.

  • Blaikie, Norman W.H. (2000) Designing Social Research: The logic of anticipation. Cambridge, UK; Malden, MA: Polity Press: Blackwell.

    Google Scholar 

  • Blaikie, Norman, and Jan Priest (2013) Designing Social Research. 3rd Edition, Cambridge: Polity (2nd ed. 2009).

    Google Scholar 

  • Bollen, Kenneth, and Judea Pearl (2013) “Eight Myths About Causality and Structural Models”, pages 301–328 in S.L. Morgan (Ed.), Handbook of Causal Analysis for Social Research, London: Springer.

    Google Scholar 

  • Bonell, Chris, G.J. Melendez-Torres, and Stephen Quilley (2018), “The Potential Role For Sociologists in Designing RCTs and of RCTs in Refining Sociological Theory: A commentary on Deaton and Cartwright”, Social Science and Medicine, 210, 29-31.

    Google Scholar 

  • Bridges, Sarah, David Lawson, and Sharifa Begum (2011) “Labour Market Outcomes in Bangladesh: The Role of Poverty and Gender Norms”, European Journal of Development Research, 23:3, 459-487.

    Google Scholar 

  • Brown, Timothy A. (2015) 2nd ed. Confirmatory Factor Analysis for Applied Research, NY and London: Guilford Press.

    Google Scholar 

  • Bunge, M. (1997) “Mechanism and Explanation”. Philosophy of the Social Sciences. 27(4), 410-465.

    Google Scholar 

  • Byrne, Barbara (1994) “Burnout: Testing for the Validity, Replication, and Invariance of Causal Structure Across Elementary, Intermediate, and Secondary Teachers”, American Educational Research Journal, 31, 645-673.

    Google Scholar 

  • Byrne, D. (2009) “Using Cluster Analysis, Qualitative Comparative Analysis and NVivo in Relation to the Establishment of Causal Configurations with Pre-existing Large N Datasets: Machining Hermeneutics”. Pp. 260–68 in The Sage Handbook of Case-Based Methods, edited by D. Byrne and C. Ragin, London: Sage Publications.

    Google Scholar 

  • Cook, S. (1999) “Methodological aspects of the encompassing principle.” Journal of Economic Methodology 6: 61-78.

    Google Scholar 

  • Cross, Beth, and Helen Cheyne (2018) “Strength-based approaches: a realist evaluation of implementation in maternity services in Scotland”, Journal of Public Health (2018) 26:4, 425–436, doi.org/10.1007/s10389-017-0882-4.

    Google Scholar 

  • Danermark, Berth, Mats Ekstrom, Liselotte Jakobsen, and Jan Ch. Karlsson, (2002; 1st published 1997 in Swedish language) Explaining Society: Critical Realism in the Social Sciences, London: Routledge.

    Google Scholar 

  • De Vaus, D. A. (2001) Research Design in Social Research. London: Sage.

    Google Scholar 

  • Deaton, Angus, and Nancy Cartwright (2018), Understanding And Misunderstanding Randomized Controlled Trials, Social Science & Medicine 210, 2–21, https://doi.org/10.1016/j.socscimed.2017.12.005.

    Google Scholar 

  • Dow, S. (2004) Structured Pluralism, Journal of Economic Methodology, 11: 3.

    Google Scholar 

  • Downward, P. and A. Mearman (2007) “Retroduction as mixed-methods triangulation in economic research: reorienting economics into social science.” Cambridge Journal of Economics, 31(1): 77-99.

    Google Scholar 

  • Downward, P., John H. Finch, and John Ramsay (2002) “Critical Realism, Empirical Methods and Inference: A critical discussion.” Cambridge Journal of Economics 26(4): 481.

    Google Scholar 

  • Dubey, A., W. Olsen and K. Sen (2017), “The Decline in the Labour Force Participation of Rural Women in India: Taking a Long-Run View”, Indian Journal of Labour Economics, 60:4, 589–612. URL https://link.springer.com/article/10.1007/s41027-017-0085-0

  • Elster, J (1998) “A Plea for Mechanisms” in Hedström, P and Swedberg, R (eds.) Social Mechanisms: An Analytical Approach to Social Theory. 45–73.

    Google Scholar 

  • Field, A. (2009) Discovering Statistics Using SPSS for Windows: Advanced techniques for the beginner, London: Sage.

    Google Scholar 

  • Field, A. (2013) Discovering Statistics Using IBM SPSS Statistics, London: Sage, 4th ed.

    Google Scholar 

  • Gelman, Andrew, and Jennifer Hill (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models, Analytical Methods for Social Research Series, Cambridge: Cambridge University Press.

    Google Scholar 

  • Goldthorpe, John H. (2001) “Causation, Statistics, and Sociology”, European Sociological Review, 17:1, 1–20, https://doi.org/10.1093/esr/17.1.1.

  • Hamilton, Lawrence C. (2006) Statistics with Stata, London: Brooks.

    Google Scholar 

  • Hox, Joop, Mirjam Moerbeek, and Rens Van De Schoot (2017) Multilevel Analysis: Techniques and Applications, Third Edition (Quantitative Methodology Series), London: Routledge.

    Google Scholar 

  • Kabeer, Naila, Lopita Huq, and Simeen Mahmud (2013) “Diverging stories of “missing women” in South Asia: is son preference weakening in Bangladesh?” Feminist Economics, 20:4, 1–26, https://doi.org/10.1080/13545701.2013.857423

  • Lawson, T. (2003) Reorienting Economics, London and New York: Routledge.

    Google Scholar 

  • Morgan, Jamie (2013) “Forward-looking Contrast Explanation, Illustrated using the Great Moderation”, Cambridge Journal of Economics, 37:4, 737–58.

    Google Scholar 

  • Olsen, Wendy (2012) Data Collection: Key Trends and Methods in Social Research, London: Sage.

    Google Scholar 

  • Olsen, Wendy (2019) “Social Statistics Using Strategic Structuralism and Pluralism”, in Contemporary Philosophy and Social Science: An Interdisciplinary Dialogue, edited by Michiru Nagatsu and Attilia Ruzzene. London: Bloomsbury Publishing.

    Google Scholar 

  • Olsen, Wendy, and Jamie Morgan (2005) “A Critical Epistemology Of Analytical Statistics: Addressing the sceptical realist”, Journal for the Theory of Social Behaviour, 35:3, 255–284.

    Google Scholar 

  • Rabe-Hesketh, Sophia, and Brian Everitt (2009) A Handbook of Statistical Analyses Using STATA, London: Chapman and Hall/CRC.

    Google Scholar 

  • Rosenbaum, Paul R., and Donald B. Rubin (1983), “The Central Role of the Propensity Score in Observational Studies for Causal Effects”, Biometrika, 70:1, pp. 41–55. URL http://links.jstor.org/sici?sici=0006-3444%28198304%2970%3A1%3C41%3ATCROTP%3E2.0.CO%3B2-Q

  • Rossi Borges, João Augusto, Alfons G. J. M. Oude Lansink, Claudio Marques Ribeiro, and Vanessa Lutke (2014), “Understanding Farmers’ Intention to Adopt Improved Natural Grassland Using the Theory of Planned Behavior”, Livestock Science 169, 163–174. 10.1016/j.livsci.2014.09.014 1871-1413.

    Google Scholar 

  • Smithson, M. and J. Verkuilen (2006) Fuzzy Set Theory: Applications in the social sciences. Thousand Oaks, London: Sage Publications.

    Google Scholar 

  • Snijders, Tom A.B., and Roel Bosker (2011) Multilevel Analysis: An Introduction To Basic And Advanced Multilevel Modeling, 2nd rev. ed., London: Sage.

    Google Scholar 

  • Ullman, J. (2006), ch. 17 of Tabachnick, B.G. & Fidell, L. S., eds., Using Multivariate Statistics (4th Ed). Boston: Allyn and Bacon. See also Ullman (2006) “Structural Equation Modeling: Reviewing the basics and moving forward”, in Statistical Developments And Applications section, Journal Of Personality Assessment, 87(1), 35–50, URL https://pubmed.ncbi.nlm.nih.gov/16856785/

    Google Scholar 

  • Watson, Samantha K., and Mark Elliot (2015), “Entropy balancing: a maximum-entropy reweighting”, Quality and Quantity 50(4):1–17. https://doi.org/10.1007/s11135-015-0235-8

    Article  Google Scholar 

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  1. Department of Social Statistics, University of Manchester, Manchester, UK

    Wendy Olsen

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Correspondence to Wendy Olsen .

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Olsen, W. (2022). Causality in Mixed-Methods Projects That Use Regression. In: Systematic Mixed-Methods Research for Social Scientists. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-93148-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-93148-3_3

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-93147-6

  • Online ISBN: 978-3-030-93148-3

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