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

About two weeks ago, COMPASSS issued a [Statement on Rejecting Article
Submissions because of QCA Solution
Type](http://www.compasss.org/files/compasss-rejection-policy-statement-20170814.html){target="_blank"
rel="noopener"}. 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. (*Disclosure: I am a member of the COMPASSS
advisory board, but was not involved in this statement in any way*.)
Dimiter Toshkov picked up on the statement and wrote an interesting
[blog post](http://re-design.dimiter.eu/?p=905){target="_blank"
rel="noopener"} on this. Dimiter's blog then hosted a [response by Eva
Thomann](http://re-design.dimiter.eu/?p=915&){target="_blank"
rel="noopener"} that led to a say, heated, exchange of arguments in the
comments section.

With some delay, I want to raise two points about the debate that
largely abstracts from the question of which type of QCA solution
(conservative, intermediate, parsimonious) is the "correct" one and can
be interpreted as causal. I have my viewpoint on this, but my remarks
are more general.

**The bar for public statements should be high**

It is not without precedent that professional associations, which
COMPASSS is for me, publish statements on various matters (such as the
[ASA statement on
p-values](https://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjdn_-H8cfWAhUHZVAKHYNeDd0QFggyMAA&url=http%3A%2F%2Fwww.amstat.org%2Fasa%2Ffiles%2Fpdfs%2FP-ValueStatement.pdf&usg=AFQjCNF8mOLti7RmBKyHaks6dpjqcot8Nw){target="_blank"
rel="noopener"}). I believe that associations have the right to issue
statements and should do so. However, the bar should be set high
because, otherwise, there may be interference with the scientific
process, with the association tending to take sides in a debate (not
necessarily, though). Most of the time, the association should be a
bystander and not an involved party.

The question of how to review QCA submissions to journals certainly is
an important topic that potentially qualifies for a statement. In light
of Eva Thomann's post, however, my impression is that the bar has been
set too low. She writes that the COMPASSS Steering Committee (SC) had no
information about how often papers have been rejected based on arguments
that a specific solution type can(not) be interpreted as causal. One
might read this such that the issue is so important to the SC that the
frequency of its occurrence does not matter. However, I am far from
convinced that frequency should be irrelevant.

If we take this as the precedent of COMPASSS statements, it would mean
that the SC could put out statements at a high rate because I am sure
that reviews of QCA articles often contain arguments the members of the
SC consider dubious or wrong, some of which strike me as being worse
than the issue at stake here. As many QCA researchers surely can tell,
one still has to manage reviews that flatly deny any value of QCA. One
might respond that the SC saw a problem emerging and wanted to intervene
early on, but I do not find this convincing. As a professional
association, COMPASSS includes respected QCA researchers and experts,
but it would still mean that the only professional association in the
field would intervene and, further, would need to frequently intervene
in the future because reviewer arguments that it deems ill-suited will
continue to be made.

**Most studies interpret one solution type as causal**

The second point concerns the internal coherence of the argument, which
dips into the question of which of the three solution types is causal.
The COMPASSS statement does not say anything about a specific type, but
readers with some knowledge of the field of QCA can reasonably guess
that it is about Baumgartner's and Thiem's work and argument that only
the parsimonious solution should be interpreted as
causal\[[1](http://doi.org/10.1007/s11135-014-0026-7){target="_blank"
rel="noopener"},
[2](https://doi.org/10.1177/0049124117701487){target="_blank"
rel="noopener"}\]. If you follow their reasoning, the question is: why
should reviewers not be allowed to criticize a study if it interprets
another type as causal? I agree that the Baumgartner/Thiem article is
"only" one study and that more work is needed on this. However, this is
not a sufficient reason to discount the work that has been done and it
should mean something. If this was the standard, we could pretty much
stop doing research because one could always say "I don't follow this
reasoning because there is not enough research about it." (We would have
to define what "enough" means, but this is probably even more subjective
than the question of QCA solution types.)

Now to the internal consistency of the argument: There seems to be
consensus that only one of the three solution types can be interpreted
as causal (this should be consensus). This means that as soon as an
empirical researcher interprets any solution type as causal (as many
do), they are implicitly saying that the other two types are not causal.
You might, like Eva, say other types are insightful or useful, but they
cannot be causal (another point on which Eva seems to agree). The
Baumgartner/Thiem argument that only one solution type is causal is
therefore *not new at all* (the content is, I'd say, because most
empirical researchers now seem to prefer the intermediate solution).

This means you can criticize reviewers for rejecting papers because they
interpret the wrong type as causal, "wrong" from the reviewer's
perspective. For the sake of consistency, you should then also criticize
empirical researchers if they interpret only one solution type as causal
because they are designating the other solution types as non-causal. The
liberal attitude that shines through the COMPASSS statement, stating
that any solution could be interpreted as causal, is not sustainable
because the causal interpretation of any solution type is exclusive.
Either reviewers should be allowed to settle on the causal
interpretation of one solution type, or reviewers *and authors* should
not interpret any type as causal.

**A way out**

Avoiding causal terminology might not be the worst of ideas, but I think
there is a middle way between rejecting papers because of the wrong
solution type and not using any causal terminology per se. I agree with
Dimiter and Eva that rejecting a paper based solely on the question of
solution types should be discouraged. This has nothing to do with the
topic, but rather the understanding of what constitutes good reviewer
practice. If you think the paper is good, but also that the wrong
solution type is interpreted as causal, then ask the author to clarify
their position on why this solution type is taken as causal. Perhaps the
author has a good answer for why this type and not another and the issue
can be settled. Or the author starts thinking, accepts that this
solution might not be interpreted as causal, and switches to non-causal
terminology. I believe that most QCA researchers want to make causal
claims, but we should keep in mind that good and interesting research
can also be non-causal.


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. The outcome is 'high levels of representation of women in a
parliament' which is not important here. The solution coverage is 0.23,
which leads Stockemer to conclude that "the combined solution only
covers 23 per cent of all cases" (p. 94).

This statement is not true for fuzzy-set QCA (and would neither be true
for crisp-set QCA). For fuzzy sets, solution coverage measures the
percentage of the sum of the outcome membership values that are covered
by the consistent cases' membership in the solution. As most cases have
a membership of less than 1 in the outcome and the solution, this
usually is unrelated to any case-related percentages. If we look at the
data, we see that the analysis includes 54 cases in total. Seven of
these 54 cases are members of the solution. Five cases are typical, one
is a deviant case consistency in degree and one is deviant in kind. If
these cases are meant by "combined solution only covers 23 per of all
cases", the highest percentage we get is 13 (7/54). If we limit the
perspective to the members of the outcome, that is, we speak of coverage
for crisp-set QCA now, we have a coverage value of 0.3 (6/20). (I leave
aside here that one should not simply switch to a crisp-set perspective
based on fuzzy-set results.) If we additionally limit it to the typical
cases, we have a coverage value of 0.26 (5/19, because one member of the
solution is not a typical case). Regardless of how coverage is
calculated if not in the proper way, we do not get a coverage value of
0.23.

It is relatively easy to be on the safe side when interpreting
consistency and coverage scores. Just interpret them without any metric,
for example, by saying "Solution consistency is 0.34" or "Raw coverage
of the first term in the solution is 0.23". This might be less intuitive
than speaking of percentages of cases or percentages more generally, but
it is the proper reading of the scores and avoids misunderstandings.


Qualitative Comparative Analysis (QCA) is the method of choice for the
analysis of set relations and has changed considerably and improved over
the years. The more one delves into the method, however, the more things
you (I, at least) stumble upon that seem a little bit curious, sometimes
also more than a bit (just as with any method, I guess). One issue I
came across relates to the calculation of consistency scores in
fuzzy-set QCA for assigning outcome values to truth table rows.

I used data from [[a study about parliamentary control of EU
affairs](http://dx.doi.org/10.1080/01402382.2012.706412){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, but the specific study and
data are not important because it can happen with any data. I noticed
that there is a truth table row, row 6 by the automatic counting of the
[[QCAGUI package for
R](https://cran.r-project.org/web/packages/QCAGUI/index.html){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, which only has the Czech
Republic as a member. The consistency value of the row is 0.78 and could
be included in the minimization process because it is above the
conventional minimum level of 0.75. The author of the study imposes a
higher threshold than 0.78, but this is not relevant because it only
matters that it legitimately could have been taken as consistent with
the claim that the configuration is sufficient.

Now, when you take a look at the XY-plot for this truth table row, you
see that the Czech Republic is a contradictory case in a qualitative
view (or a "true logical contradiction", as
[[Schneider/Wagemann](http://www.cambridge.org/de/academic/subjects/politics-international-relations/research-methods-politics/set-theoretic-methods-social-sciences-guide-qualitative-comparative-analysis?format=PB){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} call it). This means the
consistency level would allow you to include the row in the minimization
process, but the only member of the configuration is not a member of the
outcome. In other words, the row would be taken as sufficient, but you
could not point to or study a single case representing the sufficient
relationship.

![row6.png](https://ingorohlfing.files.wordpress.com/2016/06/row6.png){.size-full
.wp-image-970 .aligncenter loading="lazy" attachment-id="970"
permalink="https://ingorohlfing.wordpress.com/2016/06/21/curiosities-of-qca-fuzzy-set-consistency/row6/"
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orig-size="480,480" comments-opened="1"
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image-title="row6" image-description="" image-caption=""
medium-file="https://ingorohlfing.files.wordpress.com/2016/06/row6.png?w=300"
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srcset="https://ingorohlfing.files.wordpress.com/2016/06/row6.png 480w, https://ingorohlfing.files.wordpress.com/2016/06/row6.png?w=150&h=150 150w, https://ingorohlfing.files.wordpress.com/2016/06/row6.png?w=300&h=300 300w"
sizes="(max-width: 480px) 100vw, 480px"}

The intricacies of consistency values in fuzzy-set QCA are well-known
and are repeatedly a subject of debate (like here). To a lesser degree,
there is also awareness of the fact that one should watch out for
contradictions in fuzzy-set QCA. In principle, it then should not come
as a surprise that a sufficiently high consistency value masks a
contradiction, but I hadn't linked those two issues until I had a look
at this plot.

The plot conveys two points. First, it should become standard to look at
the XY-plots of all truth table rows having consistency values above the
preferred threshold. Second, in this example, the row should not be
included in the minimization process because the consistency value is
only driven upward by cases that are not members of the truth table row.
In other instances, it will be less clear cut and, say, three cases are
members of the row and consistent and one case is a contradiction. What
do you do then? In a qualitative, crisp-set view, this would be a
consistency value of 0.75 and be minimally acceptable.

I have no definite advice on how to proceed in such situations, but a
two-fold standard would be one way to go: the fuzzy-set consistency
value should be above a given threshold and the crisp-set consistency
value that only effectively relates the members of the row to each other
should be above a selected threshold. If anybody else has other ideas on
how to address this problem, I would be happy to hear about them.

 


Among the recent criticisms of QCA, [[Lucas and Szatrowski's (LS)
critique](http://dx.doi.org/10.1177/0081175014532763){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} 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](http://smx.sagepub.com/content/44/1.toc){style="color:#3366ff;"
target="_blank"} ]{style="color:#3366ff;"}focused on LS's assessment of
QCA, but I find that they make an almost equally interesting and ironic
claim at the end that went largely unnoticed.

Despite all criticism, LS attempt to add a constructive note at the end
of their article by recommending the use of process tracing and
comparative case studies instead of QCA. This is a paradoxical turn
because that is what QCA started with, as the authors themselves note.
The term 'process tracing' was barely in use when [[Ragin introduced QCA
in
1987](http://www.amazon.com/Comparative-Method-Qualitative-Quantitative-Strategies/dp/0520280032/ref=sr_1_1?s=books&ie=UTF8&qid=1427552111&sr=1-1&keywords=ragin+comparative+method#reader_0520280032){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, although it was done in
empirical research and comparative case studies were at the center of
the debate about "[[the comparative
method](http://www.jstor.org/stable/1955513){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}".

Besides that the research goal of process tracing/case studies and QCA
differs, it is well- known that QCA started off as a middle course
between case studies and regression analysis. With respect to case
studies, QCA aimed to overcome the small-n problem in the sense of
getting the bigger picture and determining potentially redundant
conditions. We can argue as to whether or to what extent QCA succeeds in
bridging case knowledge and the formal analysis of a medium or large
number of cases. However, the argument that we can make more solid
cross-case inferences with more rather than fewer cases should be
relatively uncontroversial. At least, this is the take-home message of
Mill's own reflection about his methods and of discussions such as
[[Lieberson's](http://dx.doi.org/10.1093/sf/70.2.307){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} to which I subscribe.

In this perspective, it is ironic to plea for a return to case studies
because this brings us back to the method and associated problems that
QCA took off with. This is not to say that, as it stands, QCA works
fine. However, abandoning QCA because of some [[problems that should not
come as a
surprise](https://wordpress.com/post/6770565/754/){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} overstretches the point. Based
on [[proper
simulations](http://dx.doi.org/10.1007/s11135-015-0251-8){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} and formal assessments of QCA,
the way forward should be to improve QCA and to determine the conditions
under which it works better or worse. The use of QCA and case
studies/process tracing is not an either-or issue because there is a
proper place for both methods in the social sciences.


The ability to reproduce published empirical studies is [central to
progress in
science](http://gking.harvard.edu/files/replication.pdf){target="_blank"}.
Reproducibility requires the proper storage and sharing of data, which
[cannot be taken for
granted](https://orgtheory.wordpress.com/2015/08/11/sociologists-need-to-be-better-at-replication-a-guest-post-by-cristobal-young/){target="_blank"},
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.

The invention of the [R package for
QCA](https://cran.r-project.org/web/packages/QCA/index.html){target="_blank"}
allows QCA users to do the same which is one major advantage of this
package. There are no numbers on the relative use of the package versus
the established [fs/QCA software offered by
Ragin](http://www.socsci.uci.edu/~cragin/fsQCA/software.shtml){target="_blank"},
but my hunch is that fs/QCA is still the dominant software.

Fs/QCA does not allow one to write a script because it is based on a
graphical user interface (GUI), but it nevertheless allows users to
ensure reproducibility, a feature that is largely ignored (based on my
knowledge of the applied QCA literature). Everything you do with your
data is documented in the results window, the window which pops up first
when you open the software, and can be exported via *File* -\> *Save*
-\> *Output*. The file type is .out, but out-files can be edited with
ordinary text editors.

This is basically what you need to document what you did. The only
challenge is to produce a file that exactly captures the analysis. Most
probably, the more cumbersome method is to try to run the analysis from
beginning to end in exactly the same way as is outlined in the
manuscript. Once you forget a step, take one that is unnecessary or
implement it incorrectly, you have to rerun the analysis from scratch.
In the second variant, you simply make sure that all steps required for
reproduction are contained in the out-file and then utilize any text
editor to remove unnecessary entries. Here, you run the risk of
producing an edited file that is missing key information for
reproduction.

In either case, the fact that fs/QCA is GUI-based should not stop one
from properly documenting the empirical analysis. For the sake of living
up to the standards of the social sciences, we hopefully will see more
such documentations of QCA research in the future.
