Ingo Rohlfing

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

Recently, I came across the open access article [Resolving empirical
controversies with mechanistic
evidence](https://link.springer.com/article/10.1007/s11229-021-03232-2).
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. Depending on the research question at
hand, it may be possible to use quantitative evidence, specification
tests etc. to choose one correct model (leaving aside here what
"correct" means). Let's say this is the stand-alone quantitative
approach toward adjudicating between models. A complementary strategy is
to use evidence about mechanisms that connect the cause to the outcome.

First, the points I agree with:  
1) I concur with the argument that evidence about mechanisms *can*
resolve statistical disagrements. This particularly concerns questions
of what variables to include in a model. Statistical controversies about
whether to estimate fixed effects or random effects are unlikely to be
resolvable through mechanism evidence because this is an estimation
matter.  
2) Economics, which is primarily quantitatively oriented and the focus
of the article, has much potential to benefit from incorporating
mechanism evidence *if* it is qualitative.

Now brief thoughts on the argument: From my peripheral knowledge of
economics, there is discussion of mechanisms, understood as mediators,
that are studied quantitatively. One can question whether mediators are
mechanisms; my point is that there are studies in economics, but also
[in political science](https://doi.org/10.1017/S0003055411000414), that
treat the two synonymously. As far as I understand evidential pluralism
as it was popularized by [Russo and
Williamson](https://doi.org/10.1080/02698590701498084), the analysis of
mediators would qualify as collecting evidence about mechanisms. (On a
slightly unrelated note, this still leaves me struggling with the
distinction between 'difference-making evidence' and 'mechanism
evidence' because [it seems
like](https://doi.org/10.1080/02698595.2019.1630927) the latter can also
be evidence that a mechanism makes a difference.)

The general literature about mixed methods, and the social science
literature about [nested
analysis](https://www.cambridge.org/core/journals/american-political-science-review/article/abs/nested-analysis-as-a-mixedmethod-strategy-for-comparative-research/D4FF59D175D761C20BD6CE440AFD700B)
in particular, have a long tradition of discussing strategies for
integrating different kinds of evidence collected and analyzed with
different methods. As the article on resolving empirical controversies
discusses at the end, evidence about mechanisms *may* help in settling
on one quantitative model, but not necessarily. The evidence about the
presence or absence of mechanisms may be too weak to make strong
inferences about whether and what mechanisms tie the cause to the
outcome. Or there may be good evidence for multiple possible mechanisms,
leaving one uncertain about what mechanism, or pair of mechanisms,
sustains a causal effect. Notwithstanding these issues that one may
confront, it is worth the effort to theorize mechanisms and to collect
evidence about them.


This post summarizes some (late) thoughts on the short article *[The
data revolution in social science needs qualitative
research](https://doi.org/10.1038/s41562-022-01333-7){rel="noreferrer noopener"
target="_blank"}* 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).

From the perspective of political science, this complements calls for
combining qualitative and quantitative research

- in [nested
  analysis](https://doi.org/10.1017/S0003055405051762){rel="noreferrer noopener"
  target="_blank"} and in variants that have been developed later such
  as [pathway
  analysis](https://doi.org/10.1177/0049124114544420){rel="noreferrer noopener"
  target="_blank"}, or
- in [natural experiments](https://doi.org/10.1177/1065912907306470) and
  quasi-experimental designs to validate quantitative identification
  assumptions.

They, in turn, build at least implicitly on a much [longer history of
mixed-methods research](https://doi.org/10.1177%2F1558689815620883) in
other disciplines of the social sciences.

What I wish for is that the value of qualitative research is not
(re)discovered for every (new) quantitative method, but that it becomes
standard to understand that quantitative methods are based on at least
some assumptions that must be assessed with qualitative research. This
is not meant as criticism of the article in Nature Human Behavior
because it hopefully helps in contributing to such a general
understanding.

It is also important that one does qualitative *research* as a
complement to quantitative research that meets the rigor and standards
of whatever qualitative method is used. This might involve standards for
theory-centered case selection, evaluation of source credibility and the
balanced interpretation of evidence. The rising standards of qualitative
and quantitative methods suggest one best works in *mixed-methods
teams*. This point has been made by [George & Bennett in
2005](https://mitpress.mit.edu/books/case-studies-and-theory-development-social-sciences){target="_blank"
rel="noreferrer noopener"} already and is even more valid now than it
has been 17 years ago.


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](http://www.cambridge.org/de/academic/subjects/politics-international-relations/research-methods-politics/multi-method-social-science-combining-qualitative-and-quantitative-tools?format=PB){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"} by [[Jason
Seawright](http://www.polisci.northwestern.edu/people/core-faculty/jason-seawright.html){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}. To my knowledge, it is the
first book on nested analysis/multimethod research in the social
sciences that is not an edited volume. A debate about mixed methods has
been [[going on in adjacent disciplines for
decades](http://dx.doi.org/10.1177/1558689815620883){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, but the political science
development of nested analysis is largely running in parallel to the
mixed-methods debate (and vice versa). Because it is the first book and
there are many interesting and challenging arguments in it, I will
briefly review each chapter on its own terms. (Maybe I will discuss some
chapters in a single post; we will see because I have not finished
reading the book yet.)

In the opening chapter, Seawright distinguishes between *triangulation*
of qualitative and quantitative methods on the one hand, and the
*integration* of both methods on the other. He opposes the idea of
triangulating qualitative and quantitative methods for making a *single*
causal inference and answering the *same* research question because both
methods. In contrast, the integration of qualitative and quantitative
methods allows one to draw on their complementary strengths and to use
one method to check the assumptions underlying the other method (having
read chapter 3, it looks like this is a one-way street where the
qualitative part validates regression assumptions).

This sounds convincing, but it should not come as a big surprise for
those who are familiar with [[nested
analysis](https://doi.org/10.1017/S0003055405051762){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}. The key distinction
underlying nested analysis is one between a causal effect and a causal
mechanism. The inference about a causal effect is different from an
inference about a mechanism, meaning that triangulation as Seawright
defines it is notpossible. It is true that the term 'triangulation' is
used here and there in the multimethod literature, for example by
Lieberman in his seminal APSR article (p. 440). However, it is not meant
in the way Seawright defines triangulation. The idea of [[triangulation
plays a bigger role in the mixed-methods
literature](http://dx.doi.org/10.1177/1558689812437187){style="color:#3366ff;"
target="_blank"}]{style="color:#3366ff;"}, which is, as I said, largely
detached from the nested analysis literature. We do not know, but I
assume that researchers who might feel challenged by Seawright's main
claim are not among his readership, and that the political science
readers of *Multi-Method Social Science* need not be convinced that
integration is central to nested analysis.

Substantively, I think the issue is that the key selling point of
multimethod research has been made in Lieberman's pathbreaking article.
This is not to say that there is no room for methodological work on
nested analysis because some elements still have to be addressed and
refined. However, the one reason why integrated research is valuable,
the combined analysis of causal effects and mechanisms, can only be made
once and has been made already. I should add that the formulation of a
punchline is probably more difficult and less needed for methods books
than substantive books because the value of methods books lies in taking
one through the details of a method.

Stylistically, it is not obvious who is targeted with the rejection of
triangulated causal inference because there is no recent reference cited
in this context (1995 is the most reference that is given). This
reinforces my impression that the central claim is endorsed by everyone
because nobody in the nested analysis community argues for triangulation
and against integration. More generally, my reading of chapter 1 (and
the following chapters) is that it is a little bit too light on the
referencing side. In some cases, a reference jumps to mind that is not
cited, including Lieberman's article at several places, and in some
cases the literature is misrepresented (like the chain of references
supposedly saying that multimethod research is not superior to
single-method research, p. 2). I find this unfortunate because it limits
the value of the first chapter for empirical researchers who want to use
it as a guide to existing discussions of multimethod research and
further readings (the chapters do not have a "Further Readings" section
at the end).

Stay tuned for chapter 2 on the causal foundations of multimethod
analysis.
