Observational studies – Page 2

Models in Political Science

Published by demetriodor on April 9, 2012

Inside Higher Ed has a good interview with David Primo and Kevin Clarke on their new book A Model Discipline: Political Science and the Logic of Representations. The book and the interview criticize the hypothetico-deductive tradition in social science: The actual research was prompted by a student who asked, “Why test deductive models?” The essence of a deductive model is that if the assumptions of the model are true, then the conclusions must be true. If the assumptions are false, then the conclusions may be true or false, and the logical connection to the model is broken. The point is that social scientists work with assumptions that are known to be false. Thus, whether a model’s conclusions are true or not has nothing to do with the model itself, and “testing” cannot tell us anything that we did not already know. My thoughts exactly. Unfortunately, I don’t see the new book changing the practice of political science research (Primo and Clarke are also pessimistic about the short term impact of the book).

Explanation and the quest for ‘significant’ relationships. Part II

Published by demetriodor on February 22, 2012

In Part I I argue that the search and discovery of statistically significant relationships does not amount to explanation and is often misplaced in the social sciences because the variables which are purported to have effects on the outcome cannot be manipulated. Just to make sure that my message is not misinterpreted – I am not arguing for a fixation on maximizing R-squared and other measures of model fit in statistical work, instead of the current focus on the size and significance of individual coefficients. R-squared has been rightly criticized as a standard of how good a model is** (see for example here). But I am not aware of any other measure or standard that can convincingly compare the explanatory potential of different models in different contexts. Predictive success might be one way to go, but prediction is altogether something else than explanation. I don’t expect much to change in the future with regard to the problem I outlined. In practice, all one could hope for is some clarity on the part of the researchers whether their objective is to explain (account for) or find significant effects. The standards for evaluating progress towards the former objective (model fit, predictive success, ‘coverage’ in the QCA sense) should be different than the standards for the latter (statistical & practical significance and the practical possibility to manipulate the exogenous variables). Take the so-called garbage-can regressions, for example. These are models with tens of variables all of which are interpreted causally if they reach the magic…

Explanation and the quest for ‘significant’ relationships. Part I

Published by demetriodor on February 17, 2012

The ultimate goal of social science is causal explanation*. The actual goal of most academic research is to discover significant relationships between variables. The two goals are supposed to be strongly related – by discovering (the) significant effects of exogenous (independent) variables, one accounts for the outcome of interest. In fact, the working assumption of the empiricist paradigm of social science research is that the two goals are essentially the same – explanation is the sum of the significant effects that we have discovered. Just look at what all the academic articles with ‘explanation’, ‘determinants’, and ’causes’ in their titles do – they report significant effects, or associations, between variables. The problem is that explanation and collecting significant associations are not the same. Of course they are not. The point is obvious to all uninitiated into the quantitative empiricist tradition of doing research, but seems to be lost to many of its practitioners. We could have discovered a significant determinant of X, and still be miles (or even light-years) away from a convincing explanation of why and when X occurs. This is not because of the difficulties of causal identification – we could have satisfied all conditions for causal inference from observational data, but the problem still stays. And it would not go away after we pay attention (as we should) to the fact that statistical significance is not the same as practical significance. Even the discovery of convincingly-identified causal effects, large enough to be of practical rather than only statistical significance, does not amount to explanation. A successful explanation needs to account for…

Writing with the rear-view mirror

Published by demetriodor on February 2, 2012

Social science research is supposed to work like this: 1) You want to explain a certain case or a class of phenomena; 2) You develop a theory and derive a set of hypotheses; 3) You test the hypotheses with data; 4) You conclude about the plausibility of the theory; 5) You write a paper with a structure (research question, theory, empirical analysis, conclusions) that mirrors the steps above. But in practice, social science research often works like this: 1) You want to explain a certain case or a class of phenomena; 2) You test a number hypotheses with data; 3) You pick the hypotheses that matched the data best and combine them in a theory; 4) You conclude that this theory is plausible and relevant; 5) You write a paper with a structure (research question, theory, empirical analysis, conclusions) that does not reflect the steps above. In short, an inductive quest for a plausible explanation is masked and reported as deductive theory-testing. This fallacy is both well-known and rather common (at least in the fields of political science and public administration). And, in my experience, it turns out to be tacitly supported by the policies of some journals and reviewers. For one of my previous research projects, I studied the relationship between public support and policy output in the EU. Since the state of the economy can influence both, I included levels of unemployment as a potential omitted variable in the empirical analysis. It turned out that lagged unemployment is positively related to the volume of policy output. In the paper, I mentioned this result in passing…

Unit of analysis vs. Unit of observation

Published by demetriodor on January 27, 2012

Having graded another batch of 40 student research proposals, the distinction between ‘unit of analysis’ and ‘unit of observation’ proves to be, yet again, one of the trickiest for the students to master. After several years of experience, I think I have a good grasp of the difference between the two, but it obviously remains a challenge to explain it to students. King, Keohane and Verba (1994) [KKV] introduce the difference in the context of descriptive inference where it serves the argument that what often goes under the heading of a ‘case study’ often actually has many observations (p.52, see also 116-117). But, admittedly the book is somewhat unclear about the distinction and unambiguous definitions are not provided. In my understanding, the unit of analysis (a case) is at the level at which you pitch the conclusions. The unit of observation is at the level at which you collect the data. So, the unit of observation and the unit of analysis can be the same but they need not be. In the context of quantitative research, units of observation could be students and units of analysis classes, if classes are compared. Or students can be both the units of observation and analysis if students are compared. Or students can be the units of analyses and grades the unit of observations if several observations (grades) are available per student. So it all depends on the design. Simply put, the unit of observation is the row in the data table but the unit of analysis can…

Slavery, ethnic diversity and economic development

Published by demetriodor on December 14, 2011

What is the impact of the slave trades on economic progress in Africa? Are the modern African states which ‘exported’ a higher number of slaves more likely to be underdeveloped several centuries afterwards? Harvard economist Nathan Nunn addresses these questions in his chapter for the “Natural experiments of history” collection. The edited volume is supposed to showcase a number of innovative methods for doing empirical research to a broader audience, and historians in particular. But what Nunn’s study actually illustrates is the difficulty of making causal inferences based on observational data. He claims that slave exports contributed to economic underdevelopment, partly through impeding ethnic consolidation. But his data is entirely consistent with a very different interpretation: ethnic diversity in a region led to a higher volume of slave exports and is contributing to economic underdevelopment today. If this interpretation is correct, it could render the correlation between slave exports and the lack of economic progress in different African states spurious – a possibility that is not addressed in the chapter. The major argument of Nunn’s piece is summarized in the following scatterplot. Modern African states from which more slaves were captured and exported (correcting for the size of the country) between the XVth and the XIXth centuries are associated with lower incomes per capita in 2000 (see Figure 5.1 on p.162, the plot reproduced below is actually from an article in the Quarterly Journal of Economics which looks essentially the same): The link grows only stronger after we take into…

Category: Observational studies