A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t‐tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within‐subject and between‐group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed‐effects model that are designed to handle autocorrelated data, such as within‐subject repeated‐measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.     

图式化程度对解决代数文字题的影响

本研究探讨了图式化程度即例子的概括化程度对解决代数文字题的影响。结果表明 :(1 )例子的表层类似性与目标问题具有一种格式塔的同型效应 ,容易解决“属性”一一对应的问题。 (2 )由例子表层类似性构成的图式化程度低 ,迁移的范围比较窄 ;由例子结构类似性构成的图式化程度高 ,适用的范围比较宽。 (3 )尽管增加了例子与目标问题的难度 ,由结构类似性形成的代数图式仍然可以比较容易地解决目标问题     

Representation of Game Algebras

We prove that every abstractly defined game algebra can be represented as an algebra of consistent pairs of monotone outcome relations over a game board. As a corollary we obtain Goranko's result that van Benthem's conjectured axiomatization for equivalent game terms is indeed complete.