How (not) to study suicide terrorism

Today is the 20 year anniversary for 9/11. That made me look into one of the most salient methodological discussions on how to study suicide terrorism within political science.

Suicide terrorism is a difficult topic to study. Why? Because we cannot learn about the causes (or correlates) of suicide terrorism from only studying cases of terrorism. Pape (2003) studies 188 suicide attacks in the period 1980-2001. He concludes that there is a strategic logic to these attacks, namely that they pay off for the organisations and groups pursuing such attacks.

Ashworth et al. (2008) use simple statistics such as conditional probabilities to show that there are problems with the paper in question, namely that the original paper “samples on the dependent variable.” I especially liked this formulation in the conclusion: “It is important to note that our critique of Pape’s (2003) analysis does not make the well-known point that association does not imply causation. Rather, because Pape collects only instances of suicide terrorism, his data do not even let him calculate the needed associations.”

Pape (2008) provides a reply to the critique raised by Ashworth and colleagues. He first brings a long excerpt from his book not taking the critique of Ashworth et al. into account. Then, he writes: “One might still wonder whether the article is flawed by sample bias because it considered systematically only actual instances of suicide terrorism. The answer is no, for two reasons. First, the article did not sample suicide terrorism, but collected the universe of suicide terrorist attacks worldwide from 1980 through 2001. […] There is no such thing as sample bias in collecting a universe. Second, although it is true that the universe systematically studied did not include suicide terrorist campaigns that did not happen, and that this limits the claims that my article could make, this does not mean that my analysis could not support any claims or that it could not support the claims I actually made.”

Importantly, just because you might have the universe of suicide terrorist attacks, you should still treat it as a sample (especially if you want to make policy recommendations about future cases we have not seen yet). In other words, this is a weird way of defending your flawed analysis. In an unpublished rejoinder, Ashworth (2008) provide some additional arguments to why the response to the criticism is flawed. Also, Horowitz (2010) shows that when you increase the universe of cases, Pape’s findings do not hold.

The debate is more than ten years old but reminiscent of similar contemporary debates on data and causality. Accordingly, I find it to be a good read for people interested in research design, data and inference — and it’s a good case to discuss what can (not) be learned from ‘selecting on the dependent variable’. Last, and most importantly, if you want to understand this amazing tweet, it is good to be familiar with the debate.

10 method books you should read before you die

In this post you will find my 10 recommendations for method books you should read (or at least buy to impress your so-called friends). I have tried my best to put some order into the list so you can begin from the beginning. However, you should be able to read the books in any order you prefer.

Before we begin, I should note a few things. First, the list is ‘biased’ towards quantitative approaches. This is not to say that such books are more important or better (they are); the list is simply a reflection of my personally biased and professional interests. Second, while I can recommend books such as Data Analysis Using Regression and Multilevel/Hierarchical Models, Mostly Harmless Econometrics and Quantitative Social Science etc., I decided to go with 10 recommendations instead of 15 or 20.

1. The Seven Deadly Sins of Psychology: A Manifesto for Reforming the Culture of Scientific Practice
Science is broken. We all know that, but Chris Chambers knows it better than anyone else. He has been part of the open science movement for a long time and provides a tour de force through how “bad” science (i.e. most science) is conducted. From confirmation bias to p-hacking and everything else you need to be aware of when you read the endnotes in PNAS (i.e. the method section).

I suggest that this is the first book you should read. The book reminds you that science is done by humans and no specific method or no amount of statistics can remove the human element in doing scientific research. The book is about the procedures we don’t think about but should. Most importantly, I find the book optimistic in so far that it is pragmatic in terms of what we can do in order to conduct better science.

Related to this, I can also recommend this article: Five ways to fix statistics

2. Bit by Bit: Social Research in the Digital Age
This is a great book by Matthew J. Salganik. The book is introductory in its material and provides a lot of interesting and relevant examples. For that reason, I have used this book in my teaching.

The book provides a good introduction to the basics of social science research with a focus on contemporary data sources, e.g. social media data, and the different methods we can use. In addition, I also find the ‘Ethics‘ chapter much more relevant compared to what you often find in similar books.

Interestingly, and another reason why I can definitely recommend this book, the book is available for free online. If you do like the book, consider buying a copy.

3. Understanding Psychology as a Science: An Introduction to Scientific and Statistical Inference
Multiple books deal with philosophy of science and research methods, but no book is better than Understanding Psychology as a Science to give a solid introduction to the philosophy of (social) science.

What I find great about this book is that it fills a gap between philosophy of science and research methods compared to how most books cover both topics. Specifically, the book connects the work of Karl Popper and Imre Lakatos on scientific inference to the foundations of statistics (in particular hypothesis testing and significance testing).

4. Designing Social Inquiry: Scientific Inference in Qualitative Research
There is no way around this political science classic. Whether you like it or not, you cannot engage with the literature on research design in political science without having read KKV (an abbreviation of the three authors, King, Keohane and Verba).

The book is now over 25 years old (published in 1994) but still worth reading.

I have read the book from A to Z a few times (it is an easy read),

5-7. Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction, Experimental and Quasi-Experimental Designs for Generalized Causal Inference and Counterfactuals and Causal Inference: Methods and Principles for Social Research

There are different causal models. Each of these models have their advantages and disadvantages. The three most important causal models to know about are Rubin’s causal model, Campbell’s causal model and Pearl’s causal model (see Shadish and Sullivan 2012 for a comparison).

In my view, the most important causal model to be familiar with is the potential outcome framework. In Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction, Guido W. Imbens and Donald B. Rubin provide an introduction to Rubin’s causal model and several topics related to experimental and observational research.

Next, the classic book on the validity model to causality (Campbell’s causal model) is Experimental and Quasi-experimental Designs for Generalised Causal Inference. This book is written with psychology research in mind but is relevant for most of the social sciences. What I like about this book is that it devotes a lot of attention to the threats to validity that researchers will often encounter but might not even consider.

For an introduction to Pearl’s causal model, I recommend Counterfactuals and Causal Inference: Methods And Principles For Social Research. This book provides a very good introduction to the directed acyclic graph (DAG) framework to causality.

Some might ask why I don’t recommend any of the work by Judea Pearl himself. In short, while I do like his work I am not a great fan of his writing. His book Causality: Models, Reasoning and Inference is not a good introduction (especially not for most social scientists) and The Book of Why: The New Science of Cause and Effect is not doing a good job positioning the framework within the broader literature (in other words, I agree with Peter M. Aronow and Fredrik Sävje that the book is selective and narrow in its introduction to the history of causality).

I recommend to read the three books and compare the different approaches to causality. Not for the purpose of finding your ‘causality tribe’, but – on the contrary – to understand the strengths and limitations of different approaches.

8. Field Experiments – Design, Analysis, and Interpretation
Field Experiments – Design, Analysis, and Interpretation is a solid book on how to design, analyse and interpret experiments. In other words, the subtitle of the book is very much correct. If you have very limited experience with experiments, this book is a must read.

The book is great at introducing the logic of the experimental method and connect this to statistical topics such as different estimators, how to calculate standard errors etc.

Also, while Don Green, one of the co-authors, was involved in some problematic “empirical” research (to say the least), this book is definitely still worth your time.

9. Design of Observational Studies

Design of Observational Studies by Paul Rosenbaum is one of the best books to understand the design of observational studies (not to be compared with Observational Studies by the same author).

The book deals with statistical approaches to observational studies (including matching) and is not too difficult to get into (even for social science students). I have also included it on this list as it covers various elements of observational studies that I didn’t find in any other books.

10. Experimental Political Science and the Study of Causality: From Nature to the Lab
If you are into experiments this book is the primer on all aspects of experiments. What is great about this book is that it covers a lot of topics and how different experimental traditions within economics and psychology look at these topics. For example, what is the role of deception in experiments and what can we learn from experiments when deception is involved?

This is, in other words, the go-to reference for people who wants to conduct experimental political science. And even if you are not a political scientist, I can highly recommend this book.

These are my ten recommendations. Have fun! Last, my apologies for the clickbait title. These books will not sell themselves. Also, if you made it this far I am sure you wouldn’t need an apology in any case.