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#processtracing

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Want more evidence that mathematical and verbal reflection tests could be measuring somewhat distinct psychological processes?

🤓 Stimulating right dorsolateral prefrontal cortex (DLPFC) often impacted performance on the numeric cognitive reflection tests (including a base rate neglect task), but not the verbal cognitive reflection tests (N = 48): doi.org/10.1016/j.heliyon.2024

Finding the needle in the haystack: archival research in European political science
link.springer.com/article/10.1 #methods
Work on #ProcessTracing primarily focused on philosophy of science, design and causal inference. This was all fine, but came at expense of focus on data collection.
It is good to see more and more articles on data collection in qualitatibe like 👆 that are concerned with practical challenges one is likely to confront

SpringerLinkFinding the needle in the haystack: archival research in European political science - European Political ScienceThis short article offers a practical introduction to archival research for political scientists working on European politics. Archival documents are increasingly recognized as a relevant data source for process tracing analyses in small-N or mixed methods studies. Previously classified archival documents are exceptionally trustworthy due to their original confidentiality. Their rich and detailed content facilitates the understanding of causal mechanisms. Still, the hurdles for working with archival sources are high for political scientists. Lack of experience, no special training in handling historic documents, and a shortage of textbooks meeting their demands are a few of the problems political scientists planning archival research face. In the article, I highlight the opportunities of archival research and demonstrate how challenges can be overcome. I emphasize that the archival field trip should only be planned once researchers have gained substantive context knowledge. In their preparation, researchers should use all the resources archives offer and develop measurable expectations from theories.

Can we automate transcript analysis (e.g., from think-aloud recordings, online chats, etc.)?

Huang et al. coded transcripts from med. students who made diagnoses while thinking aloud.

Eight machine learning algorithms seemed to predict most of the variance between correct and incorrect diagnoses from linguistic features of the transcripts!

The future of text analysis may be bright!

doi.org/10.1007/s12528-024-094

How do we know what participants thought when we presented our stimuli?

#ProcessTracing can reveal what people saw (e.g., eye-tracking), consciously thought (e.g., concurrent think-aloud), etc.

Combining those two methods revealed:
(1) thinking aloud didn't impact gaze or word count
(2) retrospective think-aloud left out thoughts that were mentioned concurrently
(3) retrospective think-aloud introduced thoughts unmentioned concurrently

doi.org/10.1007/978-3-319-1495

How can we detect the methods people use to make decisions?

Wanying Jia and colleagues tried asking participants: "May I ask what method you took to choose the answer...?"
- Responses revealed 3 methods
- #EEG patterns differed between them

Authors conclude that this "new" method can be used to study "the interaction between the intuition-based 'fast' ...and the analysis-based 'slow' system[s] in ...decision-making"

doi.org/10.1016/j.compbiomed.2