Meaningful Individual Differences in Statistics Cognition

Meaningful Individual Differences in Statistics Cognition

DOI: 10.4018/978-1-5225-2420-5.ch006
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This chapter focuses on the understanding and use of individual differences in statistics cognition. We argue that individual differences can be classified along a continuum ranging from within an individual (internally derived) to an outside source (externally prescribed), and that where an individual differences falls on the continuum may have important implications for how individual differences are used to describe, control for, predict, or explain findings in scholarly research. We argue that individual differences are more useful when they meaningfully pertain to cognitive development, and outline how motivation (using goal orientation and self-determination theory) can be used as an individual difference. We conclude with a discussion of aligning motivational goals and how online courses could adapt themselves to student motivational profiles.
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We already know that even modest amounts of statistical training can have a big impact... and we have little basis for predicting how much more improvement is feasible. - Ziva Kunda and Richard E. Nisbett, 1986, p. 222


2. Individual Differences For Statistics Cognition

In their simplest form individual differences may be thought of as classification schemes that can be used to explain or controls for differential findings among groups. Yet, as will be discussed, there is more to this process than simply selecting variables that offer a high degree of explanatory power on any particular dataset. To understand the use of individual differences in practice and research, it is first necessary to consider how the theoretical sources of individual differences can influence its application. Once the origin has been examined, a discussion of how individual differences can be used to meet the objectives of modern social sciences is laid out, followed by a review of what types of individuals are most common in statistics education, and are thus likely to be of interest for statistics educators developing courses in online environments.

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