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Video lectures: https://meehl.umn.edu/video
Faust, D., & Meehl, P. E. (1992). Using scientific methods to resolve questions in the history and philosophy of science: Some illustrations. Behavior Therapy, 23(2), 195–211. https://doi.org/10.1016/S0005-7894(05)80381-8
Serlin, R. C., & Lapsley, D. K. (1985). Rationality in psychological research: The good-enough principle. American Psychologist, 40(1), 73–83. https://doi.org/10.1037/0003-066X.40.1.73
Meehl, P. E. (1990). Appraising and amending theories: The strategy of Lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108–141. https://doi.org/10.1207/s15327965pli0102_1
Meehl, P. E. (1992). Cliometric metatheory: The actuarial approach to empirical, history-based philosophy of science. Psychological Reports, 71, 339–467.
In advance of the next three episodes discussing the Philosophical Psychology lectures by Paul E. Meehl, we present a brief reading from his autobiography in A history of psychology in autobiography.
In this episode, we discuss objectivity and disinterestedness in science. We talk about norms, values, interests, and objectivity in research practice, peer review, and hiring decisions. Is it possible to be completely objective? Is objectivity a feature of epistemic products or epistemic processes? And most importantly, how would you objectively rate this podcast?
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In this episode, we discuss the role of criticism in science. When is criticism constructive as opposed to obsessive? What are the features of fair and useful scientific criticism? And should we explicitly teach junior researchers to both give and accept criticism?
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In this episode, we continue discussing Dubin’s 8-step method for theory building. We discuss the measurement of theoretical constructs, using logical propositions to make falsifiable predictions from theories, and the importance of specifying boundary conditions.
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In this episode we discussed the 8-step method of theory building proposed by Robin Dubin in his classic 1969 book Theory Building.
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In this episode, we discuss the barriers to cumulative science, including inconsistent measurement tools, overreliance on single studies, and the large volume of research publications. Can replications, interdisciplinary collaborations, and prospective meta-analyses help us solve this issue? Can AI solve all our problems? And do most scientists treat their theories like toothbrushes?
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In today’s episode, we continue our conversation about preregistration. How flexible can we be when we preregister, without increasing flexibility in our analysis? How well do people preregister, and what does a good preregistration look like? And how do we deal with deviations from preregistrations?
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