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All Probabilities Are Equal, but Some Probabilities are More Equal than Others
Edmond Malinvaud and the Problem of Statistical Induction
The Effectiveness of Propositional Manipulation as a Lecturing Method in the Statistics Knowledge Domain The current experiment examined the potential effects of the method of propositional manipulation (MPM) as a lecturing method on motivation to learn and conceptual understanding of statistics. MPM aims to help students develop conceptual understanding by guiding them into self-explanation at two different stages: First, at the stage of propositions (statements referring to single statistical concepts and ideas), and subsequently, at the stage of more complex problems that comprise a set of relevant propositions. A total of 71 bachelor students in psychology who were preparing for the re-sit of their inferential statistics exam participated in one of two possible lectures. Topic, content, lecturer, and duration of both lectures were the same, and in both lectures five true/false hypotheses were presented. Students in the first lecture (control group) discussed interactively the truth or falsity of each hypothesis. In the second lecture (MPM group), this interactive discussion was structured by presenting a number of short open-ended questions along with each hypothesis. Conceptual understanding was measured by means of a twelve items multiple choice test. Further, the intrinsic motivation inventory was administered to examine motivation to learn. The results indicate that MPM does not lead to enhanced motivation to learn but can facilitate conceptual understanding development among students.
Engaging Diverse Community Members to Enhance Analysis and Interpretation: Processing Qualitative Interview Data
Establishing Normative Data for Repeated Cognitive Assessment: A Comparison of Different Statistical Methods Serial cognitive assessment is conducted to monitor changes in the cognitive abilities of patients over time. At present, mainly the regression-based change and the ANCOVA approaches are used to establish normative data for serial cognitive assessment. These methods are straight-forward, but they have some severe drawbacks. For example, they can only consider the data of two measurement occasions. In this article, we propose three alternative normative methods that are not hampered by these problems-that is, multivariate regression, the standard linear mixed model (LMM), and the linear mixed model combined with multiple imputation (LMM with MI) approaches. The multivariate regression method is primarily useful when a small number of repeated measurements are taken at fixed time points. When the data are more unbalanced, the standard LMM and the LMM with MI methods are more appropriate because they allow for a more adequate modeling of the covariance structure. The standard LMM has the advantage that it is easier to conduct and that it does not require a Monte Carlo component. The LMM with MI, on the other hand, has the advantage that it can flexibly deal with missing responses and missing covariate values at the same time. The different normative methods are illustrated on the basis of the data of a large longitudinal study in which a cognitive test (the Stroop Color Word Test) was administered at four measurement occasions (i.e., at baseline and 3, 6, and 12 years later). The results are discussed and suggestions for future research are provided.
Is the Effect Larger in Group A or B? It Depends: Understanding Results From Nonlinear Probability Models
Low Complexity, Low Probability Patterns and Consequences for Algorithmic Probability Applications
Probabilistic Forecasting of Maximum Human Lifespan by 2100 Using Bayesian Population Projections