The debate between Yann LeCun and Elon Musk, reported in Nature, questions whether science necessitates publishing results. My position is that science depends on how you produce your knowledge, not necessarily requiring publication. Only if you face the public, you need to publish about your work.

Ingo Rohlfing

Nature reports about a debate on X that started between Yann LeCun and Elon Musk. In short, the question is whether you can only call ‘science’ science when you publish your results. The Nature article is paywalled, but you can, as of now, still read the original post by LeCun that initiated the discussion.

Is it science when you don’t publish it?

Is Open Science passé?

is the question asked by Xenia Schmalz in this blogpost. I recommend reading it before I share brief thoughts on some points that are raised. I wish an open science movement was not needed anymore, but I agree this is most likely not the answer to the leading question. Neither has the open science movement failed; progress toward more transparent and credible science is simply slow.

Open Science is neither passé nor is it there yet

The LSE Impact blog has a post from May 2021 raising some reservations about the idea of ‘Slow Science’. The ‘Slow Science’ idea hasn’t really picked up in academia, as far as I can tell. The post presents some good thoughts about why the “slowness-idea” is problematic in general. I agree that slowness is not a value in itself. Sometimes, developments and events like a pandemic demand it to do research faster than one would do it otherwise.

Science should be as fast as possible and as slow as necessary to do good research

This post summarizes some (late) thoughts on the short article

 The data revolution in social science needs qualitative research

by Grigoropoulou and Small, published in Nature Human Behavior. This is an excellent article that systemizes the ways in which qualitative research should complement big data/computational social science (CSS) and gives example of work that has done this already (I understand big data/CSS to be the focus here).

Qualitative Methods in Computational Social Science

Some ideas about how the peer review process in academic publishing can be used as a device for preregistering parts of an empirical analysis.

Preregistration of an analysis requires the prior unobservability of data. One publicly declares what one intends to do before one can access data. One goal of preregistration is to diffuse potential concerns that a publication misrepresents the order in which an empirical analysis was implemented.

The peer review process as a preregistration device

Why the filling of gaps in the research literature is similar to replication and probably better than its reputation.

A common justification of empirical research is that a gap in the literature needs to be filled.

Gap-filling, puzzle-solving and replication in empirical research

On September 27, the German Science Foundation (DFG) announced its decision to award the status of a research cluster of excellence (Exzellenzcluster) to 57 cluster proposals from all disciplines.

Correlation vs causation: The case of competitive funding and research quality

About two weeks ago, COMPASSS issued a Statement on Rejecting Article Submissions because of QCA Solution Type. In short, the reasoning was that methodological work on QCA is developing and that reviewers and editors should not judge empirical work based on whether one particular solution type is interpreted as causal.

The COMPASSS statement and QCA solution types

The charge that Political Science (or other non-STEM disciplines) is lacking relevance and does not produce interesting research is made then and again, with two new pieces published these days. One is written by a political economist, stating that most research is boring;

The relevance of Political Science: Some thoughts on the recent critique

For what it’s worth, I wanted to see how often ‘process tracing’ is mentioned in the offerings on Google Books.

The rise of process tracing (in Google Books)

For some time now, a discussion has been raging about the pros and cons of set theory and the use of set-theoretic methods (STM) in the social sciences (e.g., in Sociological Methodology and the APSA Newsletter). Following up on a critical discussion by Paine and a constructive, comparative discussion of STM and regression analysis by Thiem, Baumgartner and Bol (TBB),

 Comparative Political Studies

organized a symposium on STM.

Another mistaken criticism of set theory and set-theoretic methods

Recently, I came across an interesting study from 2009, the content of which is not relevant here.

Two proposals for incentivizing data archiving

Let’s imagine you are attending a presentation and the presenter does not follow the standard intro-literature-theory-design-results-conclusion structure. Instead, she only presents the results of her research. What would be the first question you would ask? I assume at least 90 percent of the audience would ask about how she arrived at her findings.

Different tools, shared standards: The debate about DA-RT

At the end of last week, a two-day conference,

 Qualitative Comparative Analysis – Social Science Applications and Methodological Challenges

, took place in Tilburg, the Netherlands. Needless to say, the recent and ongoing wave of criticism of QCA was a key topic on the agenda and in discussions among participants.

QCA is developing… apart?

Open access and gated publishing have been discussed at length by researchers, publishers, and the media. Still, the debate got more intense when Elsevier sent takedown notices to Academia.edu. Yesterday, it got even more intense when it became public that the University of Calgary is also confronted with takedown notices from Elsevier. Elsevier responded to the critiques, but it is unlikely that this will take the heat out of the debate.

You may not like what Elsevier is doing, but…

Schlimm? 470000 entscheiden über die Koalition

Der Mitgliederentscheid wurde kritisiert, weil etwa 470000 SPD-Mitglieder über die große Koalition entscheiden.

Mit dem Quatsch aufhören III: Parteien entscheiden in eigener Sache

Der Mitgliederentscheid der SPD über den Koalitionsvertrag ist angelaufen. Je mehr Zeit seit der Ankündigung verstrichen ist, umso stärker und wilder scheint die Kritik zu werden, so dass ich nun doch einen Blogeintrag dazu schreibe (was eh wieder Zeit wurde). Aufgrund der Anhäufung von Kritik werde ich die über die nächsten Tage mehrere Einträge daraus machen.

 Alle Staatsgewalt geht vom Volke aus.

Mit dem Quatsch aufhören: Über die andauernde Kritik am SPD-Mitgliederentscheid und der Großen Koalition

Under the rubric of “Reader’s view”, I will post short reviews of books, mostly from the field of social science methods.

Reader’s view: Goertz/Mahoney (2012): A Tale of Two Cultures

The bi-annual publication of the APSA Section on Qualitative Methods and Multi-Method Research has a highly interesting and controversial symposium on confirmation bias in process tracing in its current issue.

Confirmation bias in causal qualitative research

How to properly interpret consistency and coverage values in fuzzy-set QCA.

In 2000, Ragin added the idea of fuzzy sets to Qualitative Comparative Analysis (QCA) and they have been widely used since then. However, one sometimes still finds misunderstandings about what fuzzy sets are, in particular when it comes to the interpretation of consistency and coverage scores. In a criticism of QCA by Stockemer (which is criticized for a variety of reasons and to which Stockemer again replied), he runs a fuzzy-set analysis.

Don’t conflate fuzzy set membership with cases in QCA

Alt-Bundespräsident Roman Herzog wird von Focus mit den Worten zitiert: „Im Prinzip ist die Fünf-Prozent-Hürde nicht mehr zeitgemäß. Eigentlich müssten wir die Hürde nach oben setzen.“ Die Begründung: Vor dem Hintergrund von immer mehr kleineren Parteien werde der Bundeskanzler/die Bundeskanzlerin ansonsten „nicht mehr von einer großen Mehrheit der Bevölkerung getragen“. Dies sieht Herzog als eine Gefährdung der parlamentarischen Demokratie.

Roman Herzog und die Hürde

Research on causal mechanisms is a growth industry, with the largest percentage of studies falling into the camp of qualitative research. The reason for this is the admonition that correlation is not causation, implying the claim that valid causal inference requires evidence on causal mechanisms.

Mechanisms in qualitative and quantitative research

In

 Finding Pathways: Mixed-Method Research for Studying Causal Mechanisms

, Weller and Barnes seek to explain “how the small-N component of multi-method research can meaningfully contribute and add value to the study of causal mechanisms” (quote from blurb). The book contains nine chapters, including the introduction and concluding chapter.

Review of “Finding Pathways”, by Weller/Barnes

Live and let live, or: unity and diversity in the social sciences

As an approach to and method for the analysis of set relations,

 Qualitative Comparative Analysis

(QCA) belongs in the toolbox of many social scientists (and organizational and management researchers alike). Although it has proven to be very popular, a question that one still often confronts is: what is QCA? Even those who understand the technicalities of QCA wonder what the qualitative part in QCA represents in practice.

What is QCA? Simple question, many answers

As is well known, QCA has been under intense scrutiny in recent years and subject to criticism (sometimes quite strong). I am not going to review studies on the validity of QCA that entail criticism;

Surprised, anyone? Putting the debate about QCA into context

Qualitative Comparative Analysis (QCA) is a method utilized by different disciplines in the social sciences and beyond, e.g., business economics and management. However, QCA users must still justify their choice of method more frequently than the users of other methods. Whatever the reason, it is actually not a bad thing to reflect upon the choice of a method because it should be suitable for answering our research question.

How (not) to justify running a QCA

If this was a blog post about

 the

#APSA2014, I would have to write about Friday night’s fire emergency at the Marriott (i.e., #APSAonfire) as the non-academic event that left a definite imprint (and affected me as one of the many people who had a room at the Marriott). But uncomfortable as that night had been, I will focus instead on the ongoing debate about set theory and Qualitative Comparative Analysis (QCA) which are

 the

Impressions from the APSA 2014, or: QCA under fire?

People familiar with the development of qualitative methods know that process tracing has developed rapidly over the last years. As the discussion about a method becomes broader and deeper, it becomes more important to systematize and sort the field in order to understand what it can and cannot achieve.

Three varieties of process tracing? It’s four

The choice of cases for empirical analysis is a central topic in the methods literature. The argument that quantitative and qualitative research studies different templates, which is probably most forcefully described in

 A Tale of Two Cultures

, does not come as a surprise. What I want to focus on here are the arguments on case selection in qualitative research.

Theory and substance in case selection, or: Give random selection a chance

The methods literature on process tracing took a Bayesian turn in recent years. Bayesian inference, whereby you condition on evidence in order to update your prior confidence in a hypothesis, is presented as a mode of inference one should follow in process tracing (Bennett, Andrew (2008): Process-Tracing: A Bayesian Perspective. Box-Steffensmeier, Janet M., Henry Brady and David Collier (ed.):

Frequentist inference and process tracing: an oxymoron?

Bekanntermaßen wurde der SPD-Mitgliederentscheid von einigen Verfassungsrechtlern in Frage gestellt. Mittlerweile hat das Verfassungsgericht in seiner Ablehnung eines Eilantrags auch deutlich gemacht, dass der Entscheid verfassungsrechtlich nicht zu beanstanden ist. Sigmar Gabriel hat die Verfassungsfrage des Mitgliederentscheid forsch beiseite gewischt, allerdings ist die Begründung irritierend (zumindest die, die er bei 3 nach 9 gegeben hat).

Die Verfassung und der Mitgliederentscheid: Wo Sigmar Gabriel irrt

Qualitative Methods (i.e., process tracing, set theoretic methods, informal Bayesian inference etc.) and multi-method research, in particular the combination of regression analysis or QCA with case studies, are certainly a growth industry in political science and sociology. In light of some methods panels held at the APSA Annual Meeting in Chicago and the ECPR General Conference in Bordeaux, this might actually come as a surprise.

Qualitative Methods: A Field in Search of Common Ground

Recently, Frontal 21 – a German newscast – reported about the handling of error in treatments under German health care law (about which I am not an expert). A lawyer specializing in health care law explained that it is very difficult to win lawsuits against medical doctors (MD’s) because the patient claiming to experience treatment error has to substantiate this claim (except for grave treatment error). Particularly problematic are cases where

Error in medical treatment and causation in German health care law

The current APSA Comparative Politics Newsletter is dedicated to “Doing Comparative Politics Elsewhere” (i.e. , outside of the US). Thomas Plümper contributes a discussion on Comparative Politics in Europe. In brief, Plümper argues that, until recently, the field of Comparative Politics (CP) has been dominated by qualitative methods.

Comparative politics and the choice of methods

Sourcely, an AI company, promises to streamline research by finding, summarizing, and adding credible sources in minutes. While this sounds appealing, skepticism arises as using such a tool may prioritize citing over genuine research. Initial tests revealed limited functionality, leaving doubts about its practical value in the research process.

I came across the AI company Sourcely these days. It promises to “

 Finish Your Research in Minutes. Save Your Sleep. Paste your essay to find, summarize, and add credible sources. (That’s something Google Scholar can’t do!)

” Saving time for sleep is good and if it would work, it would outperform GS, or any traditional search for citations and references, for that matter. However, I have some thoughts and concerns.

Citation-infusion of research papers with AI?

The article “Resolving empirical controversies with mechanistic evidence” discusses the potential of using evidence about mechanisms to resolve statistical disagreements and aid in choosing the correct quantitative model. While there are challenges and uncertainties in this approach, it emphasizes the value of theorizing about mechanisms and collecting evidence about them, especially in disciplines like economics.

Recently, I came across the open access article Resolving empirical controversies with mechanistic evidence. I have some thoughts on the arguments made in the article, but first a disclaimer: This is not meant to be a disciplinary beauty contest. The article raises a valuable point: Two or more quantitative models may produce contradictory results.

“Resolving empirical controversies with mechanistic evidence” – Some thoughts

In place of a generic blog post, I am reposting a short Twitter thread here. The thread is a response to an opinion piece on the Times Higher Education website titled Pay researchers for results, not plans. (Posts on the THE website require registration of an account that includes a couple of free reads.)  I copy-paste thread into this post. If you prefer to read it on Threadreader, you find it here.

Is payment for publications and paying for citations a better to way to fund research? Most likely not

When we use the Quine-McCluskey algorithm to derive a QCA solution, we can choose between the conservative, intermediate or parsimonious solution. While I do not have any figures about which solution has been produced how frequently in empirical research, it is safe to say that the conservative solution is quite popular.

The conservative QCA solution isn’t based on counterfactuals? Not so fast

When making causal (or descriptive) inferences, it is important to think about the context within which the causal relationship is expected to hold because it probably does not hold universally and, possibly, only in a limited setting. Falleti and Lynch have written an excellent article about “context” (I drop the ““ now) in relation with causal mechanisms.

“Context” is important, but (almost) useless if used as a causal category

One of the recent big and, in my view, underappreciated innovations in the field of Qualitative Comparative Analysis (QCA) is Baumgartner’s formulation of the Coincidence Analysis algorithm (CNA). Baumgartner presents it as an alternative to QCA, which I do not find convincing because I do not see QCA married to a specific algorithm.

You are a regularity theorist when using the Coincidence Analysis algorithm in QCA

More often than one might expect, television series and films offer excellent illustrations of methodological and methods-related arguments (which is worth a blog post of its own). When I was working on my paper on comparative hypothesis testing in process tracing, I was watching the first season of the terrific TV series, Homeland.

What Homeland has to do with causal inference in process tracing

Continuing the chapter-by-chapter review of Seawright’s book on Multi-Method Social Science took me longer than I imagined and it should have, but here we go again.

Review of ‘Multi-Method Social Science’ (Seawright, CUP) –  Chapter 2: Causation as A Shared Standard

Review of the first chapter of the book “Multi-Method Social Science” by Jason Seawright, published with Cambridge University Press in 2016.

Over the next weeks, I will give a chapter-by-chapter review of the new book Multi-Method Social Science: Combining Qualitative and Quantitative Tools by Jason Seawright. To my knowledge, it is the first book on nested analysis/multimethod research in the social sciences that is not an edited volume.

Review of “Multi-Method Social Science” (Seawright, CUP): Chapter 1

Das 2014er Gutachten der Expertenkommission Forschung und Innovation (EFI) schlug diese Woche einige Wellen, weil es nach wie vor eine Abwanderung der besten Wissenschaftler aus Deutschland konstatiert (

 brain drain

, wenn man den Begriff mag). Nach einem Blick in das Gutachten (Abschnitt B2, ab S. 85) sind dazu einige Anmerkungen angebracht, die die Schlagzeilen in den Kontext rücken. Erstens, die Ergebnisse sind wenig überraschend.

Das EFI-Gutachten und die Ab- und Zuwanderung deutscher Wissenschaftler: Ein paar kritische Anmerkungen

Inspired by an email exchange I had with someone on theoretically non-exclusive hypotheses in Bayesian process tracing, I believed it might be useful to write down some thoughts in a blog post. It ended up as a PDF on Github because it has a minor R element and formal notation.

Theoretically non-exclusive hypotheses in Bayesian process tracing

Many APSA 2016 panels and discussions in the Section on Qualitative and Multimethod Research and the Political Methodology Section were centered on the Data Access and Research Transparency (DART) Initiative (probably worth a blog post of its own). Even panels not explicitly dedicated to DART have digressed into this topic, which includes a short exchange between Tasha Fairfield and me in a panel on causal inference in qualitative research.

People require preregistration, not methods

Qualitative Comparative Analysis (QCA) is the method of choice for the analysis of set relations and has changed considerably and improved over the years.

Curiosities of QCA: Fuzzy-set consistency

The idea of most-likely and least-likely cases dates back to Eckstein and was one of the few remaining things in qualitative research there seemed to be no disagreement about because they are considered an asset in causal analysis. In a paper that is advance access, Beach and Pedersen (BP) now argue that process tracing and the analysis of mechanisms does not make sense with most-likely and least-likely cases.

Process tracing is possible with most-likely and least-likely cases

Among the recent criticisms of QCA, Lucas and Szatrowski’s (LS) critique stands out in multiple respects, including its scope, its tone (QCA is “a nonanalytic means to identify asymmetric causal illusions”, p. 66), and the responses it provoked (and the notorious mistake in their first simulation). Naturally, the critical replies focused on LS’s assessment of QCA, but I find that they make an almost equally interesting and ironic claim at the

The irony of Lucas/Szatrowski’s QCA criticism

How to enhance reproducibility when using fs/QCA software

The ability to reproduce published empirical studies is central to progress in science. Reproducibility requires the proper storage and sharing of data, which cannot be taken for granted, and a detailed, step-by-step description of the empirical analysis. Statistical research has no problems with the latter, in principle, because a script can be compiled (do-file etc.) that can be easily shared.

Reproducible QCA studies with fs/QCA

The social science literature is full of discussions about causation and what the best method for causal inference might be. However, a relatively small percentage of them draw on the philosophical debate about causation. Certainly, there is a great deal of talk about philosophy of science on the ontological and metaphysical level, including, for example, engagement with the relation between neo-positivism and realism.

Review of “Causation: A Very Short Introduction” by Mumford/Anjum

Most multi-method research (MMR) studies with which I am familiar start with regression analysis (or, in recent years, QCA) and perform the case studies afterward. This is the order recommended by Lieberman in his nested analysis article which, in turn, most probably reflects the widely held view that case studies are only worth doing in MMR when something is going on at the cross-case level.

What do we learn about case studies from follow-up regression analysis?

In einem Kommentar auf SPIEGEL ONLINE wird unter anderem der Mitgliederentscheid dafür verantwortlich gemacht, dass der Koalitionsvertrag wenig zukunftsorientiert sei und das Durchsetzen von Reformen zu Beginn der Legislaturperiode verhindere. Führt die Ausübung der innerparteilichen Demokratie also zu einer mutlosen Politik im hier und jetzt? Aller Wahrscheinlichkeit nach nicht.

Verhindert der Mitgliederentscheid zukunftsorientierte Politik?

Das Zögern und Zaudern der SPD vor, während und nach den Koalitionsverhandlungen wird gerne beiseite gewischt mit dem Hinweis, „der Wähler“ (und die Wählerin) wolle die große Koalition.

Mit dem Quatsch aufhören (II): “Der Wähler” will die Große Koalition

At the end of last week, I came across a blog entry by Olaf Storbeck, reporting that a rising star from German business economics, Prof. Ulrich Lichtenthaler, is faced with numerous inquiries concerning his publishing record. Two journals have already retracted three of his articles and additional articles are under scrutiny.

Quality and quantity in publishing

In the case study literature, one new pair of terms has been established by the field that concerns the distinction between

 data set observations

and

 causal process observations

. In short, Collier, Brady, and Seawright (2004: Sources of Leverage in Causal Inference: Toward an Alternative View of Methodology. Brady, Henry E. and David Collier (eds.):

 Rethinking Social Inquiry.

Don’t call it causal when it is an observation

Politics, Science, Political Science

Nature [reports about a debate on
X](https://www.nature.com/articles/d41586-024-01626-z) that started
between Yann LeCun and Elon Musk. In short, the question is whether you
can only call 'science' science when you publish your results. The
Nature article is paywalled, but you can, as of now, still read [the
original post by LeCun](https://x.com/ylecun/status/1795589846771147018)
that initiated the discussion.

If I remember correctly, my methods professor also said it's only
science when you publish it. I wasn't convinced then (long time ago) and
am still not convinced today. For me, science is about the process of
producing knowledge, which does not require publication. (a position
also addressed in the Nature article) To illustrate my position, imagine
you stranded on an island and run your own small experiments to figure
out how to survive. The experiments could cover different techniques of
growing vegetables and fruit; different techniques of storing food and
water; etc. Wouldn't this be scientific and science?

I think one needs to distinguish what one does with scientifically
generated insights. If you face the public with your findings, you have
to publish them to allow others to evaluate your research. If you don't
claim anything publicly based on your research, there is no need for
publishing. Of course, privately done research is also likely to benefit
from public scrutiny and feedback. If you get a second or third opinion
of how to grow vegetables and store them, your chances of surviving are
likely to increase. However, if you don't want anything from the public
because of your research (promotion, money, fame, whatever), you can
keep your science for yourself and still call it science because it is
in the process.


The social science literature is full of discussions about causation and
what the best method for causal inference might be. However, a
relatively small percentage of them draw on the philosophical debate
about causation. Certainly, there is a great deal of talk about
philosophy of science on the ontological and metaphysical level,
including, for example, engagement with the relation between
neo-positivism and realism. What I have in mind are what are called
theories of causation in philosophy of science that ask what causation
is, what makes a causal statement true and how to detect a causal
relationship in practice.

The book, [[*Causation: A Very Short
Introduction*](http://ukcatalogue.oup.com/product/9780199684434.do){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, by [[Stephen
Mumford](https://nottingham.ac.uk/philosophy/people/stephen.mumford){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} and [[Rani Lill
Anjum](https://sites.google.com/site/ranilillanjum/home){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, is a useful entry point for
those who want to learn more about the theories that are on offer. The
book has ten chapters that are presented under the headings of "The
Problem", "Regularity", "Time and Space", "Necessity", "Counterfactual
Dependence", "Physicalism", "Pluralism", "Primitivism",
"Dispositionalism" and "Finding Causes". Chapters 2 through 9 are
conceptualizations of what causation is, while the last chapter
discusses strategies for finding causal relationships.

Those who are familiar with the debate in social science will notice
that the theories discussed in this book are more comprehensive, with
regularity and counterfactual dependence (supposedly) capturing
quantitative methods and physicalism (supposedly) covering process
tracing (e.g., Beach/Pedersen in their [[process tracing
book](http://www.press.umich.edu/2556282/process_tracing_methods){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}). Therefore, *Causation* is
certainly appropriate for extending one's horizon about theories of
causation.

Readers who expect in-depth introductions to a specific theory are not
well-served by the book, but this is not the goal of the [["Very Short
Introduction" series by Oxford
UP](http://ukcatalogue.oup.com/category/academic/series/general/vsi.do){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}. Beyond that, I had three
concerns when reading the book. First, the authors could have been more
generous with references to more specialized literature. At the end of
the book, the authors discuss a few books for each theory that they
discussed and offer their opinions on which is helpful, but for someone
who is accustomed to in-text citations referring to specific aspects,
this is not ideal.

Second, stylistically, the authors overstretch the use of questions
throughout the book. This is particularly problematic because I lost
sight of what questions were answered and which remained unaddressed.
Fewer questions and the use of summarizing tables would have been
beneficial in structuring the discussion.

Third, it was not always clear to me whether a theory was supposed to be
ontological or methodological, i.e., useful for the detection of causal
relationships. It became clearer when I reached the last chapter, which
is on causal inference (not a term that is widely used in philosophy of
science) and which mainly features [[Pearl's
account](http://www.cambridge.org/de/academic/subjects/philosophy/philosophy-science/causality?format=HB){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} and [[Woodward's
interventionist
theory](https://global.oup.com/academic/product/making-things-happen-9780195189537?cc=de&lang=en&){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}. (Although the interventionist
theory is close to the idea of an experiment, in my reading, Woodward
conceives of it as a semantic, conceptual theory, which makes it a bit
surprising that the authors present it as a methodological theory, a
problem that underscores the importance of clarifying what type of
theory one is talking about). Although the interventionist theory has
some appeal, I asked myself whether all metaphysical and ontological
theories are compatible with Woodward's and Pearl's account located on
the type level that has much affinity with quantitative, large-n
research. From a social science perspective, it would have been
interesting to read more about the link between metaphysical,
ontological and semantic theories on the one hand and methodological
theories on the other but that might have exceeded the goal for a *A
Very Short Introduction*.


The current version (vol. 10, issue 1) of the newsletter of the A[PSA
Section on Qualitative Methods and Multi-Methods
Research](http://www.maxwell.syr.edu/moynihan/cqrm/APSA_s_Qualitative_and_Multi-Method_Research_Section/ "APSA Section"){target="_blank"}
includes a symposium on John Gerring's 2^nd^ edition of [Social Science
Methodology](http://www.cambridge.org/us/knowledge/isbn/item6566290/?site_locale=en_US "Gerring: SSM2"){target="_blank"}
(SSM2, for short). The various contributors to the symposium deal with
different aspects of SSM2 such as concept formation (Ariel Ahram) or the
compatibility of set theory with the potential outcomes framework (by
Carsten Schneider and me, available on
[SSRN](http://ssrn.com/abstract=2096834 "Schneider/Rohlfing: Newsletter"){target="_blank"}).
Some of the contributors provided very strong criticism of Gerring's
book.  What I want to focus on here is Patrick T. Jackson's discussion
of Gerring's take on philosophy of science. To a degree, I have to admit
that it was a fun read for me. Jackson starts out by saying that he
wholeheartedly agrees with the last paragraph of the book, but disagrees
with most of what is written on all the previous pages. Why is that?

At the risk of oversimplification, Gerring argues that social science
researchers should be able to communicate with each other. Communication
is hampered by the pursuit of different ontologies and understandings of
science (Gerring frequently speaks of methodology, but I think he mostly
means ontology and sometimes epistemology). According to Gerring, this
implies that the adoption of one methodology and an understanding of
science is necessary for effective communication, and this should be the
(neo-)positivist take on science. Science then should be "systematic,
rigorous, evidence-based, falsifiable, replicable, generalizable,
non-subjective, transparent, skeptical, rational, frequently causal, and
cumulative" (p. 2). Jackson correctly asserts that, for one reason or
another, many social scientists do not subscribe to this view and are
likely to be offended at calling these the characteristics of true
science. He criticizes Gerring's plea for unity from various sides: the
definition of causal mechanisms; the reference to falsifiability, an
idea that one usually shies away; and the need for unity in the first
place, just to mention three points.

However, there is a more fundamental problem with Gerring's plea for
unity that is not addressed by Jackson. As Gerring notes (and what is
not criticized by Jackson), ontological disputes cannot be settled
because they are beyond the reach of empirical analysis (see alsoHay,
Colin (2006): Political Ontology. Goodin, Robert E. and Charles Tilly
(eds.): *The Oxford Handbook of Contextual Political Analysis*. Oxford:
Oxford University Press: 78-96). This is what Gerring identifies as a
problem because exchange among scholars is hampered by different
ontological and, as a consequence, methodological positions. That's all
fine, but Gerring then draws an unsustainable conclusion from this: that
all researchers should follow one ontology and that this should be his
pet ontology of the neo-positivist sort. I think the problem with this
claim is apparent: if there is no right and wrong ontology and we cannot
adjudicate between them, how do we justify all researchers settling on
one of these ontologies? Regardless of whether one likes Gerring's view
of science and ontology or not (Jackson clearly is on the do-not-like
side), one should eschew claiming that one has "found" an ontology that
everyone should follow. For the same reason, there is little to be
gained by criticizing Gerring's view on science (or anyone's view on
science) by censuring it on substantive ground. One might disagree with
Gerring's conception of science and ontology, but these criticisms are
not any more right or wrong than Gerring's position itself.

Having said that, we are still left with Gerring's correct diagnosis
that the pursuit of different ontologies hampers communication (also
partly because many scholars treat ontology as a matter that is
decidable, therefore trying to convince the "disbelievers" of the
"truth" that they own). What then is the basis for judging empirical
insights gathered on the basis of a specific ontology? In my view, the
answer is *consistency*, which has two elements. First, one's personal
take on ontology, methodology, and epistemology should form a coherent
whole (see Petter Hall's (2003: Aligning Ontology and Methodology in
Comparative Research. Mahoney, James and Dietrich Rueschemeyer (eds.):
*Comparative Historical Analysis in the Social Sciences*. Cambridge:
Cambridge University Press: 373-404) intriguing piece on aligning
ontology and methodology). Second, each of the three "-ologies" should
be internally consistent. The rationale for consistency as the guiding
principle is that if we cannot judge the adequacy of an ontology (and
the other --ologies), we should focus on the extent to which a
researcher does justice to his/her ontology and follows a methodology
and epistemology that is consistent with it.

Here is a simple example of what consistency means. When you run a
regression, you automatically make the assumption that the cases are
causally homogenous (the assumption can be right or wrong, but it is the
default assumption with which one starts). Now, suppose that you do
post-regression process tracing in order to learn something about the
underlying causal mechanisms. One sometimes finds the opinion that the
generalization of insights derived from process tracing should follow
the idea of contingent generalization (Nome, Martin Austvoll (2008):
Getting the Pathway Case Off the Drawing Board. *APSA Qualitative
Methods Newsletter* 6 (1): 20-23). Contingent generalization means that
causal inferences are only generalized to cases that share certain
conditions. For instance, when the regression analysis includes all
countries in the world, contingent generalization would mean that
process tracing insights gathered in a European country are only
generalized to other countries from Europe. This practice is
inconsistent because it is in discord with the causal homogeneity
assumption embodied in the regression analysis. One either has to limit
the regression analysis to European countries, or go for a more sweeping
generalization of the process tracing inferences. One can think of more
severe consequences of inconsistency than this one, but it illustrates
the importance of thinking coherently when linking ontology,
methodology, and epistemology to each other.

I acknowledge that it can be painful to take ontologies for granted,
ontologies with which one wholeheartedly disagrees, and to limit oneself
to an assessment of their internal coherence and consistency with
methodology and epistemology. But ontology wars based on the flat
rejection of any ontology other than the one we prefer yield little
benefit. At the least, the engagement with consistency hopefully ensures
that researchers produce the best research *given* their ontological
commitments.
