Causality and Generalization in Qualitative and Quantitative Methods

19 Nov

Science deals with the fundamental epistemological question of how we can claim to know something. The quality of the system, forever open to challenge, determines any claims of epistemic superiority that the scientific method may make over other competing claims of gleaning knowledge from data.

The extent to which claims are solely arbitrated on scientific merit is limited by a variety of factors, as outlined by Lakatos, Kuhn, and Feyerabend, resulting in at best an inefficient process, and at worst, something far more pernicious. I ignore such issues and focus narrowly on methodological questions around causality and generalizability in qualitative methods.

In science, the inquiry into generalizable causal processes is greatly privileged. There is a good reason for that. Causality and generalizability can provide the basis for intervention. However, not all kinds of data make themselves readily accessible to imputing causality, or even to making generalizable descriptive statements. For example, causal inference in most historical research remains out of bounds. Keeping this in mind, I analyze how qualitative methods within Social Sciences (can) interrogate causality and generalizability.

Causality

Hume thought that there was no place for causality within empiricism. He argued that the most we can find is that “the one [event] does actually, in fact, follow the other.” Causality is nothing but an illusion occasioned when events follow each other with regularity. That formulation, however, didn’t prevent Hume from believing in scientific theories. He felt that regularly occurring constant conjunctions were sufficient basis for scientific laws. Theoretical advances in the 200 or so years since Hume have been able to provide a deeper understanding of causality, including a process-based understanding and an experimental understanding.

Donald Rubin defines causal effect as follows: “Intuitively, the causal effect of one treatment, E, over another, C, for a particular unit and an interval of time from t1 to t2 is the difference between what would have happened at time t2 if the unit had been exposed to E initiated at t1 and what would have happened at t2 if the unit had been exposed to C initiated at t1: ‘If an hour ago I had taken two aspirins instead of just a glass of water, my headache would now be gone,’ or because an hour ago I took two aspirins instead of just a glass of water, my headache is now gone.’ Our definition of the causal effect of the E versus C treatment will reflect this intuitive meaning.”

Note that the Rubin Causal Model (RCM), as presented above, depicts an elementary causal connection between two Boolean variables: one explanatory variable (two aspirins) with a single effect (eliminates headaches). Often, the variables take multiple values. And to estimate the effect of each change, we need a separate experiment. To estimate the effect of a treatment to a particular degree of precision in different subgroups, for example, the effect of aspirin on women and men, the sample size for each group needs to be increased.

RCM formulation can be expanded to include a probabilistic understanding of causation. A probabilistic understanding of causality means accepting that certain parts of the explanation are still missing. Hence, a necessary and sufficient condition is absent. Though attempts have been made to include necessary and sufficient clauses in probabilistic statements. David Papineau (Probabilities and Causes, 1985, Journal of Philosophy) writes, “Factor A is a cause of some B just in case it is one of a set of conditions that are jointly and minimally sufficient for B. In such a case we can write A&X ->B. In general, there will also be other sets of conditions minimally sufficient for B. Suppose we write their disjunction as Y. If now we suppose further that B is always determined when it occurs, that it never occurs unless one of these sufficient sets (let’s call them B’s full causes) occurs first, then we have, A and X condition conjugated with Y is equivalent with B. Given this equivalence, it is not difficult to see why A’s causing B should be related to A’s being correlated with B. If A is indeed a cause of B, then there is a natural inference to Prob(B/A) > Prob(B/-A): for, given A, one will have B if either X or Y occurs, whereas without A one will get B only with Y. And conversely it seems that if we do find that Prob(B/A) > Prob(B/-A), then we can conclude that A is a cause of B: for if A didn’t appear in the disjunction of full causes which are necessary and sufficient for B, then it wouldn’t affect the chance of B occurring.”

Papineau’s definition is a bit archaic and doesn’t entirely cover the set of cases we define as probabilistically causal. John Gerring (Social Science Methodology: A Criterial Framework, 2001: 127,138; emphasis in original), provides a definition of probabilistic causality: “[c]auses are factors that raise the (prior) probabilities of an event occurring. [Hence] a sensible and minimal definition: X may be considered a cause of Y if (and only if) it raises the probability of Y occurring.”

A still more sensible yet minimal definition of causality, can be found in Gary King et al. (Designing Social Inquiry: Scientific Inference in Qualitative Research, 1994: 81-82), “the causal effect is the difference between the systematic component of observations made when the explanatory variable takes one value and the systematic component of comparable observations when the explanatory variable takes on another value.”

Causal Inference in Qualitative and Quantitative Methods

While the above formulations of causality—the Rubin Causal Model, Gerring, and King—seem more quantitative, they can be applied to qualitative methods. We discuss how below.

A parallel understanding of causality, one that is used much more frquently in qualitative social science, is a process-based understanding of causality wherein you trace the causal process to construct a theory. Simplistically, in quantitative methods in the Social Sciences, one often deduces the causal process, while in qualitative methods the understanding of the causal process is learned from deep and close interaction with data.

Both deduction and induction, however, are rife with problems. Deduction privileges formal rules (statistics) that straightjacket the systematic deductive process so that the deductions are systematic and conditional on the veracity of assumptions like normal distribution of data, the linearity of the effect, lack of measurement error, etc. The formal deductive process bestows a host of appealing qualities like generalizability, when an adequate random sample of the population is taken, or even systematic handle on causal inference. In quantitative methods, the methodological assumptions for deduction are cleanly separated from data. The same separation, between the formal deductive process with a rather arbitrarily chosen statistical model and data, however, makes the discovery process less than optimal, and sometimes deeply problematic. Recent research by Ho and King (Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference, Political Analysis, 2007), and methods like Bayesian Model Averaging (Volinsky), have gone some ways in providing ways to mitigate problems with model selection.

No such sharp delineation between method and data exists in qualitative research, where data is collected iteratively—[in studies using iterative abstraction (Sayer 1981, 1992; Lawson 1989, 1995) or grounded theory (Glaser 1978; Strauss 1987; Strauss and Corbin 1990)]—till it explains the phenomenon singled out for explanation. Grounded data-driven qualitative methods often run the risk of modeling particularistic aspects of data, which reduces the reliability with which they can come up with a generalizable causal model. This is indeed only one kind of qualitative research. There are others who do qualitative analysis in the vein of experiments, for example, with a 2×2 model, and yet others who will test apriori assumptions by analytically controlling for variables in a verbal regression equation to get at the systematic effect of the explanatory variable on the explanandum. Perhaps more than grounded theory method, the pseudo-quantitative style qualitative analysis runs the risk of coming to deeply problematic conclusions based on the cases used.

King et al. (1994: 75, note 1): “[a]t its core, real explanation is always based on causal inferences.”

Limiting Discussion to Positivist Qualitative Methods

Qualitative methods can be roughly divided into positivist methods, e.g., case-studies, and interpretive methods. I will limit my comments to positivist qualitative methods.

The differences between positivist qualitative and quantitative methods “are only stylistic and are methodologically and substantively unimportant” (King et al., 1994:4). Both methods share “an epistemological logic of inference: they all agree on the importance of testing theories empirically, generating an inclusive list of alternative explanations and their observable implications, and specifying what evidence might infirm or affirm a theory” (King et al. 1994: 3).

Empirical Causal Inference

To impute causality, we either need evidence about the process or an experiment that obviates the need to know the process, though researchers are often encouraged to have a story to explain the process, and test variables implicated in the story.

Experimentation provides one of the best ways to reliably impute causality. However, for experiments to have value outside the lab, the treatment must be ecological—it should reflect the typical values that the variables take in the world. For instance, the effect of televised news is best measured with real-life news clips shown in a realistic setting where the participant has the control of the remote. Ideally, we also want to elicit our measures in a realistic way, in the form of their votes, or campaign contributions, or expressions online. The problem is that most problems in social science cannot be studied experimentally. Brady et al. (2001:8) write, “A central reason why both qualitative and quantitative research are hard to do well is that any study based on observational (i.e., non-experimental) data faces the fundamental inferential challenge of eliminating rival explanations.” I would phrase this differently. It doesn’t make social science hard to do. It just means that we have to be ok with the fact that we cannot know certain things. Science is an exercise in humility, not denial.

Learning from Quantitative Methods

  1. Making Assumptions Clear: Quantitative methods often make a variety of assumptions including to make inferences. For instance, empiricists often use ceteris paribus—all other things being equal, which may mean assigning away everything ‘else’ to randomization—to make inferences. Others use the assumption that the error term is uncorrelated with other independent variables to infer the correlation between an explanatory variable x and dependent variable y can only be explained as x’s effect on y. There are a variety of assumptions in regression models and the penalty for violation of each of these assumptions. For example, we can analytically think through how education will affect (or not affect) racist attitudes. Analytical claims are based on deductive logic and a priori assumptions or knowledge. Hence the success of analytical claims is contingent upon the accuracy of the knowledge and the correctness of the logic.
  2. Controlling for things: Quantitative methods often ‘control’ for stuff. It is a way to eliminate an explanation. If gender is a ‘confounder,’ check for variation within men and women. In Qualitative Methods, one can either analytically (or where possible empirically) control for variables, or trace the process.
  3. Sampling: Traditional probability sampling theories are built on the highly conservative assumption that we that we know nothing about the world. And the only systematic way to go about knowing it is through random sampling, a process that delivers ‘representative’ data on average. Newer sampling theories, however, acknowledge what we know some things about the world and uses that knowledge by selectively over-sampling things (or people) we are truly clueless about, and under-sampling where we have a good idea. For example, polling organizations under-sample self-described partisans and over-sample non-partisans. This provides a window for positivist qualitative methods to make generalizable claims. Qualitative methods can overcome their limitations and make legitimate generalizable claims if their sampling reflects the extent of prior knowledge about the world.
  4. Moderators: Getting a handle on the variables that ‘moderate’ the effect of a particular variable that we may be interested in studying.
  5. Sample: One of the problems in qualitative research that has been pointed out is the habit of selecting on the dependent variable. Selection on dependent variable deviously leaves out cases where, for example, the dependent variable doesn’t take extreme values. Selection bias can not only lead to misleading conclusions about causal effects but also about causal processes. It is essential hence not to use a truncated dependent variable to do one’s analysis. One of the ways one can systematically drill down to causal processes in qualitative research is by starting off with the broadest palette, either in prior research or elsewhere, to grasp the macro-processes and other variables that may affect the case. Then cognizant of the particularistic aspects of a particular case, analyze the microfoundations or microprocesses present in the system.
  6. Reproducible: One central point of the empirical method is that there is no privileged observed. What you observe, another should to be able to reproduce. So, whatever data you collect, however you draw your inferences, all need to be clearly stated and explained.

How Are Academic Disciplines Divided?

18 Jul

The social sciences are split into disciplines like Psychology, Political Science, Sociology, Anthropology, Economics, etc. There is a certain anarchy to the way they are split. For example, while Psychology is devoted to understanding how the individual mind works, and sociology to the study of groups, Political science is devoted merely to an aspect of groups—group decision making.

One of the primary reasons the social sciences are divided so is because of the history of how social sciences developed. As major figures postulated important variables that constrain the social world, fields took shape around them. The other pertinent variables that explain some of the new disciplines in social sciences are changes in technology, and more broadly changing social problems. For example, the discipline of Communication took shape around the time mass media became popular.

The way the social sciences are currently divided has left them with a host of inefficiencies which leave them largely inefficacious in a variety of scenarios where they can offer substantive help. Firstly, The containerized way of understanding the social world provide inadequate ways of understanding complex social systems that are imposed upon by a variety of variables that range from the individual to the institutional. And secondly, the largely discipline-specific theoretical motivations lead academic to concoct elaborate theories that often misstate their applicability in complex ecosystems. We all know how economics never met common sense till of recently. It isn’t that disciplines haven’t tried to bridge the inter-disciplinary divide, they certainly have by creating sub-disciplines ranging from social-psychology (in psychology) to political psychology (in Political Science), and in fact that is exactly where some of the most exciting research is taking place right now, the problem is that we have been slow to question the larger restructuring of the social sciences. The question then arises as to what should we put at the center of our focus of our disciplines? The answer is by no means clear to me though I think it would be useful to develop competencies around primary organizing social structures/institutions.

Role of Social Science

Let me assume away the fact that most social science knowledge will end up in the society either through Capitalism or selective uptake by policymakers. Next, we need to evaluate how social science can meaningfully contribute to society. One intuitive way would be to create social engineering departments that are focused on specific social problems. The advice is by no means radical— certainly Education as a discipline has been around for some time, and relatively recently departments (or schools) devoted to Public Health, Environmental Policy have opened up across college campuses. Secondly, social science should create social engineering departments that help offer solutions for real-life problems, much the same way engineering departments affiliated with natural sciences do and try experimenting with how for example different institutional structures would affect decision making. Lastly, social scientists have a lot more to offer to third world countries which have yet to be overrun by brute Capitalism. What social science departments need to do is lead more data collection efforts in third world countries and offer solutions.

Qualitative Vs. Quantitative Methods

9 Jun

Epistemology of Causality

How do we know that something is the ’cause’ of something and how do we impute ‘causality’ through data?

To impute causality in quantitative models, we rely on the argument that it is unlikely that the change in Y could be explained by anything else other than X since we have ‘statistically controlled for other variables’. We ‘control’ for variables via experiments or we can do it via regression equations. This allows us to isolate the effect of say variable x on y. There are of course some caveats and some assumptions that go along with using these methods but robust experimental designs still allow us to impute causality in a fairly robust way. Generally, the causal claim is buffeted with a description of a plausible causal pathway. All of the analysis and the resulting benefits of reliably imputing causality are predicated on our ability to ‘correctly’ assign numbers to ‘constructs’ (the real variables of interest).

Let’s analyze now how qualitative methods can impute causality. While it seems reasonable to assume that ‘systematic’ ‘qualitative’ analysis of a problem can provide us with a variety of causal explanations and under most circumstances provide us with a reasonably good idea of how much each of the explanatory variables affects the dependent variable, there are crucial problems and limitations that may induce bias in the analyses. Additionally, we must define what constitutes as ‘systematic’ analysis.

Another thing to keep in mind is that ethics and rigor are not enough to impute causality. What one needs are the right epistemic tools.

A lot of qualitative research is marred by the fact that it ‘selects on the dependent variable’. In other words, it sees a dependent variable and then goes sleuthing for the possible causal mechanisms. It is hard in that case to impute wider causality between variables because the relationship hasn’t been tested for varying levels of X and Y. It is useful to keep in mind that sometimes it is all that we can hope to achieve. Additional problems can emerge from things like “selection bias” and logical fallacies like “Post hoc ergo propter hoc”. Partly the way qualitative research is written can also impose its own demands and biases including demands for narrative consistency.

It is unclear to me whether a system exists to impute causality reliably using qualitative methods. There are however some techniques that qualitative methods can borrow from quantitative methods to improve any causal claims that they may be inclined to make – one is to use a representative set of variables, the other is to look for ‘natural experiments’, and pay attention to larger sociological issues and iterate through why alternative explanations don’t apply as well here – a sort of a verbal regression equation.

There are of course instances where deeper more in-depth analysis of few cases allows one to get a deeper understanding of the issue but that shouldn’t be mistaken as coming up with causes.

Epistemology of generalization in empirical methods

There is very little space that we get edgeways when we think about a systematic theory of generalization for empirical theories unless. To generalize we must either ‘know’ fundamental causal mechanisms and how they work under a variety of contextual factors or use probability sampling. Probability sampling theories are built on the belief that we know nothing about the world. Hence we need to take care to collect data (which ideally transposes to the constructs) in a way that makes it generalizable to the entire population of interest.

Causal arguments in Qualitative research

For making ‘well grounded’ causal arguments in qualitative research – say with a small n – the case must be made for generalizability of the selected cases, use deduction to articulate possible causal pathways, and then bring them together in a ‘verbal regression equation’ and analyze which of the causal pathways are important – as in likely or have a large effect size- and which are not.

Epistemic standards in interpretation and methodology

Quantitative methods share a broad repertoire of skills that is shared across the disciplines while comparatively no such common epistemic standards exist across a variety of qualitative sub-streams that differ radically in terms of what data to look at and how to interpret the data. Common epistemic standards allow for research to be challenged in a variety of ways. From Gay and Lesbian studies to Feminist Scholarship to others – there is little in common in terms of epistemic standards and how best to interpret things. What we then have is merely incommensurability. Partly, of course, different questions are being asked but even when same questions are being asked – there appears to be little consensus as to what explanation is preferred over the other. While each new way to “interpret” facts in some ways does expand our understanding of the social phenomena, given the incommensurability in epistemic standards –we cannot bring all of them to a qualitative ‘verbal regression equation’ (my term) through which we can reliably infer the size of the effect of each.

Caveat Lector
The above article deals with the debate between qualitative methods and quantitative methods on a small select sample of issues – generalizability and causality – that are explicitly more tractable through quantitative models. It would be unwise to construe larger points about the relevance of qualitative methods from the article.

Social Science and the Theory of All

22 Apr

Social phenomenon, unlike natural phenomenon, is bound and morphed not only by nature (evolution, etc.) but also history, institutions (religious, governance, etc.), and technology, among others. Before I go any further, I would like to issue a caveat: the categories that I mention above are not orthogonal and in fact, do trespass into each other regularly. We can study particular social phenomena in aggregate through disciplines like political science, which study everything from study of psychology to institutions to history, or study them by focusing on one particular aspect – psychology or genetics – and investigating how each effect multiple social phenomena like politics, communication, etc.

Given the disparate range of fields that try to understand the social phenomenon, often the field is straddled with multiple competing paradigms and multiple theories within or across those paradigms with little or no objective criteria on which the theories can be judged. This is not to say that theories are always mutually irreconcilable for often they are not (though they may be seen as such – which is an artifact of how they are sold), or that favoring one theory automatically implies rejecting others. The success of a theory, hence, often depend on how well it is sold and the historical proclivities of the age.

Proclivities of an age; theories of an age

Popular paradigms emerge over time and then are discarded for entirely new ones. It is not that the old don’t hold but just that the new ones hold the imagination of the age. Take for example variables that people have chosen to describe culture over the ages – Weber argued religion was culture, Marx argued that political economy was culture, Freud proposed a psycho-analytical take on culture (puritan, liberated, etc.), Carey proposed communication as culture, political theorists have argued institutions as culture, bio-evolutionists argue that cognition and bio-rootedness are primary determinants of culture, Tech. evangelists have argued technology is culture, while others have argued that infrastructure dictates culture.

It is useful to acknowledge that the popularity of the paradigms that were used to define culture had something to do with the most important forces shaping culture at that particular time. For example, it is quite reasonable to imagine that Marx’s paradigm was a useful one for explaining the industrial society (in fact it continues to be useful), while Carey’s paradigm was useful to explain the results of rapid multiplication (and accessibility) of communication (mass-) media. I would like to reissue this caveat that adopting new paradigms doesn’t automatically imply rejecting the prior ones. In fact intersection of old and new paradigms provide fecund breeding grounds for interesting arguments and theories – for example political economy of mass media and its impact. Let me illuminate the point with another example from Political Science which a decade or so ago saw a resurgence of cultural theory at the back of Huntington’s theory of ‘Clash of civilizations’. Huntington’s theory didn’t mean an end to traditional paradigms like economic competition; it just postulated that there was another significant variable that needed to be factored in the discourse.

The structure of scientific revolutions

Drawing extensively from historical evidence from the natural sciences, Thomas Kuhn, a Harvard physicist, argued in his seminal book, The Structure of scientific revolutions, that science progressed through “paradigm shifts.” While natural sciences paid scant attention to the book, the book provoked an existentialist crisis within the social sciences. To arrive at that crisis point, social scientists made a number of significant leaps (not empirically based) from what Kuhn said – they argued that growth of social science was anarchic, its judgments historically situated and never objective, and hence the social sciences were pointless – or more correctly had a point but were misguided. This self-flagellation is typical in social sciences that have always been more introspective about their role and value in society as compared to the natural sciences, which have always proceeded with the implicit assumption that ‘progress’ cannot be checked and eventually what they produce are merely tools in service of humanity. Of course, that is quite bunk and has been exposed as such without making even the slightest dent in the research in science and technology. Criticizing natural sciences, especially the majority of it that is in service of ‘value-free’ economics, doesn’t take away from the questions that Kuhn posed for the social sciences. Social scientists, in my estimation, put disproportionate emphasis on Kuhn’s work. Social science is admittedly much behind in terms of coming up with generalizable theories, but they have been quite successful in identifying macro-variables and phenomena.

The most intractable problem that social scientists need to deal with is answering what is the purpose of their discipline. Is it to describe reality or to critique it or engineer alternative realities? If indeed it is all of above, and I believe it is, then social science must think about melding its often disparate traditions – theory and practice.

Rorty and the structure of philosophical revolutions

Richard Rorty in his book, Philosophy and the Mirror of Nature, launches a devastating attack on philosophy – especially its claims to any foundational insights. Rorty traces the history of philosophy and finds that the discipline is embedded, much more deeply than social science, in the milieu of paradigm shifts – philosophers from different ages not only offer different “foundational” insights but often deal with different problems altogether.

Battling at the margins

Those who argue that the singular purpose of social science should be to normatively critique it and offer alternative paradigms are delusional. Understanding how a society works (or how institutions work, people work) is important to craft interventions – be it drug policy or engineering new governance systems. Normative debates often times are nothing but frivolous debates at the margins. The broad overarching problems of today don’t need normative theorists devoted to analysis – though I don’t dispute their contribution – they are evident and abundantly clear. When we take out the vast middle of what needs to be decided, normative theory becomes a battle at the margins.

Post-positivist theorizing; and the sociology of research

The most significant challenges for social science as discipline lie within the realm of how the discipline aggregates research and moves forward and how that process is muzzled by a variety of factors.
Imre Lakatos sees “history of science in terms of a continuous competition between alternative research programs rather than of successive conjectures and refutations on the one hand, or of total paradigm-switches on the other.” Lakatos argues that any research program possess a kernel of theoretical principles which are taken as fixed and hence create a ‘negative heuristic’ that forbids release of anomalous results, and instead scientists are directed to create a “protective belt” of auxiliary assumptions intended to secure correctness of theoretical principles at the core. Finally, ‘positive heuristic’ is at work to “Defend and extend!” (Little, 1981)

Post-positivist scientific philosophy, like the ones forwarded by Kuhn and Lakatos, raise larger questions about the nature (and viability) of the scientific enterprise. While we may have a firmer grasp of what we mean by a good scientific theory, we are still floundering when it comes to creating an ecosystem that foments good social science and creates a rational and progressive research agenda. (Little, 1981) We must analyze the sociology, and political economy of journal publication as the whole venture is increasingly institutionalized and as careerism, etc. become more pronounced.

Social Science, Epistemology, and Future Directions in Research

4 Oct

There is a schism that runs right in the middle of social science divvying up the field between the critical theorists and the positivists. Positivists aspire to model the success in natural science and hence tend to focus on the causality and statistical proof, according to Dr. Tang at the National Taiwan University. Critical school, on the other hand, starts from some starts from some philosophical axioms, for example, “social good” or “justice for all” or “maximizing social good.”

The approach by critical theorists is riven with difficulties due to the multiplicity of the philosophical starting points around which one can build theories. As one would expect, critical theory today has myriad “schools,” each based on different philosophical assumptions and each with, if one may say, their own geometry and calculus which works only in their own universe. Positivists circumvent the epistemological and other philosophical issues that dog the critical theorists by relying on the natural science model of doing research that stresses on coming up with a falsifiable hypothesis that can then tested either via experimentation or observation (within a representative randomized sample). Given the difficulty of defining and measuring useful variables in social science, positivists rely upon their own methodological axioms though a lot of research is currently underway to help refine the methodology.

The dichotomy in the field brings one to question how social science should ideally be conducted. The answer depends on what one expects from social science. One may argue that social science has ceded its primary responsibility of trying to resolve the dispute between different philosophical paradigms and wrestling with issues pertaining to the nature and future of society. Without the moral or philosophical grounding, a lot of research may seem like monkey work – repetitive and commercial applications aside utterly aimless. On the other hand, one may argue that quantitative work often illuminates how humans and society works and it is first important to understand both of them before we move on to the task of circumscribing their behavior in philosophy. I would argue that social science’s aims need to be a hybrid of both of the strands. Social science needs to continue to grasp with the important epistemological and philosophical questions that underpin our existence and provide direction in a way to where research is headed. At the same time, social science needs to be more pro-active in understanding humans and society.