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Causal Analyses: Structural Equation Models and (Quasi-)Experimental Designs

  • Wicher BergsmaEmail author
  • Marcel Croon
  • Jacques A. Hagenaars
Chapter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

Marginal modeling and causal analysis are often seen as two opposite and mutually exclusive approaches. This is understandable because marginal modeling is frequently defined in contrast to conditional analyses, and the outcomes of conditional analyses are typically interpreted in causal terms. However, this opposition of marginal and causal analyses is wrong or at least way too simple. For instance, most, if not all, marginal models discussed in the previous chapters might have been derived from causal theories. The tests on the validity of these marginal models could then be regarded as tests on the validity of these underlying causal theories. Further, marginalmodeling is often an indispensable part of approaches that are explicitly intended to investigate causal relationships, such as (quasi-)experimental designs and causal models in the form of structural equation models (SEMs). In this chapter, the focus will be on the role of marginal modeling in these last two approaches. Although this chapter is about ‘causal modeling,’ the term ‘causality’ itself will not be used very often (unless it leads to cumbersome and awkward phrases). Unfortunately, it is still common social science practice to use ‘causality’ in a rather weak and general sense, more pointing towards some assumed asymmetry in the relationships rather than necessarily implying proper causal connections. The disadvantages and dangers of this careless use of the term are first of all, that when drawing the final conclusions researchers tend to forget the caveats surrounding their causal analyses, evenwith research designs and research questions that hardly permit inferences about causal connections; and second, that researchers do not think hard enough before the start of their investigations about the appropriate research design for answering their causal questions. Therefore, even when the term causality is used below, the reader should keep these reservations in mind. For just a few treatments of causality with different emphases, see Rubin (1974), Cartwright (1989), Sobel (1995),McKim and Turner (1997), Sobel (2000), Pearl (2000), Snijders and Hagenaars (2001), Winship and Sobel (2004),Heckman (2005) (with discussion), and Morgan andWinship (2007), and the references cited in these publications.

Keywords

Structural Equation Model Causal Analysis Marginal Modeling Dependent Observation Ordinal Location 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  • Wicher Bergsma
    • 1
    Email author
  • Marcel Croon
    • 2
  • Jacques A. Hagenaars
    • 2
  1. 1.Department of StatisticsLondon School of EconomicsLondonUnited Kingdom
  2. 2.Department of MethodologyTilburg University Fac. of Social & Behavioural SciencesTilburgThe Netherlands

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