I read an interesting new article about the moral attitudes of terrorists published in Nature Human Behavior and written by Sandra Baez, Eduar Herrera, Adolfo M. García, Facundo Manes, Liane Young, and Agustín Ibáñez.
The main result shows that moral judgement in terrorists is abnormally guided by outcomes rather than by the integration of intentions and outcomes. The participants were 66 incarcerated Colombian paramilitary fighters (terrorist)** and 66 matched non-criminals (control).
The experiment varied the intention (neutral vs. negative) and the outcome (neutral vs. negative) of a hypothetical scenario. The idea being that moral attitudes incorporate both the outcome and intention. Consider the text a below, describing an 'accidental harm' scenario. The intention was neutral but the outcome was negative. The second image b shows the 2 x 2 design altering intention and outcome, but changing the words in bold. Accidental Harm is in the top right, while (unsuccessful) attempted harm is in the bottom left.
After reading the four stories above, the participants rated the scenario on a Likert scale ranging from totally forbidden (1) to totally permissible (7). Below is a the average moral judgment for each scenario, separated by group (terrorist vs. control).
Terrorists judged accidental harm as less permissible (p < 0.01) and attempted harm as more permissible (p < 0.01) than non-criminals. The differences between groups for non-harm and successful attempted harm were not statistically significant. These results point to terrorist moral judgement being largely driven by outcome rather than some combination of both outcome and intention. The authors discuss the possible implications of this in the conclusion of the paper:
In legal and cognitive settings, intentions are assessed and often used to evaluate others’ actions. The capacity to represent and reason about intentions is crucial in judging whether others’ actions are right or wrong, harmless or harmful, punishable or unpunishable. However, our results reveal that terrorists judge others’ actions by focusing on the outcomes, suggesting that their moral code prioritizes ends over means. Thus, impairments in processing intentions and in integrating them with actions’ outcomes may be one of the key social cognitive factors underlying the cruel acts committed by terrorist paramilitary groups.
** The subject pool was very unique, consisting of 66 incarcerated paramilitary terrorists who participated in a collective demobilization from 2003 to 2006 as part of a Colombian statutory Justice and Peace Law. All 66 subjects declared having participated in illegal armed right-wing paramilitary groups and gave a full, voluntary deposition and confession of crimes involving terrorist acts. This unique sample is characterized by high levels of terrorism and insurgency as well as aggressive and disruptive behaviors. All participants in this group were convicted of murder, with a mean of 33 victims per subject (most of them were accountable for several massacres, with death tolls sometimes exceeding 600 victims). They had also engaged in other crimes, such as theft, kidnapping and fraud. (paraphrased from Baez et al 2017)
I thought this was a very interesting article by Chelsea Howe comparing "gameful design" to gamification.
Gamification is the integration of game elements into services or applications with the purpose of incentivizing certain behaviors. It seems to me that gamification is an offshoot of nudges- the designer links some minor reward to the desired behavior making it more attractive to the agent. As Howe's post points out, if the goal is lasting behavioral change, these short term nudges will not be effective because they are not intrinsically motivated, they do not treat people as people, and they do not give players agency.
You don’t actually play games for points or badges– those are just progress indicators that help you contextualize your improvements/skill (which is exciting). People love games because they are in control and can affect the world (this is called agency), because they can make meaningful choices and interesting decisions. They play because games are delightful, challenging, and filled with clear goals. Operant conditioning ignores all of those things, and tries to motivate using our most basic human instincts instead of the complex depth that makes us human.
There’s a difference between celebrating accomplishment (“award”) and incentivizing actions (“reward”)....Getting an award is a great feeling – when you’ve worked for it. When it feels relevant and special to you. When it represents success at something appropriately challenging. There’s nothing wrong about celebrating accomplishment; it feels great to be recognized for what you’ve done, as long as what you’ve done is actually something worthwhile. If you go to certain sites you’ll find yourself with random badges for seemingly no reason at all, after just clicking through a few pages (and of course, you have to sign up to keep them). Is that satisfying? (No.)
Another great resource for the analyst is What Statistical Analysis Should I Use? courtesy of the Institute for Digital Research and Education (IDRE) at UCLA. The linked document outlines different tests very clearly, with working examples in Stata. They also have examples for SAS and SPSS which you can look up in this handy table if you are so inclined.
Each statistical test gets a short paragraph describing what it tests, for which types of variables it's appropriate, and how the test relates to other methods. I especially like that important assumptions, which can easily be overlooked, are pointed out explicitly.
Take for example the two-sample t-test above. For any range-based variable you can calculate the t-statistic and then use the limiting distribution to estimate the confidence interval/p-value. However, the limiting distribution is only applicable for range-based variables that are normally distributed. So if your variable of interest cannot be assumed to be normal, a t-test is absolutely inappropriate.
As with most things Stata, the document is geared towards causal analysis. This means that terms like "dependent" and "independent" variable are thrown around. In a two-sample example, the "dependent" variable is the variable of interest and the "independent" variable is an indicator of which sample a given observation belongs to.
I came across this document when trying to answer the question: how do I test if two non-normal samples arise from the same distribution? In my case, the Wilcoxon-Mann-Whitney test (or the Kruskal Wallis test on two samples) would be more appropriate than a two-sample t-test.
The list of methods provided by "What Stat...?" is far from exhaustive. Other tests that I have found and believe would also be appropriate are Kolmogorov-Smirnov and Anderson-Darling. Which yields a new question: how can the analyst reconcile contradictory results from different non-parametric tests? The subject of a later post I believe.
I recently came across The R Inferno written by Patrick Burns. In a satirical style following Dante's Inferno, he discusses pretty much every stack exchange question I've ever looked up.
Among the many new things I learned and immediately implemented:
"NOTE: Failing to use drop=FALSE inside functions is a major source of bugs. "
Suppose you have a matrix my.Matrix and you want to take some subset my.subset of the rows in M.
> M[ my.subset, ]
If my.subset has length 1, then the above code will return a single vector.
> M[ my.subset, , drop = FALSE]
Even if my.subset has length 1, the above will return a matrix with a single row.