A Guide to Teaching Statistics: Innovations and Best by Michael R Hulsizer

By Michael R Hulsizer

A advisor to educating facts: recommendations and most sensible Practices addresses the serious facets of training facts to undergraduate scholars, performing as a useful software for either amateur and pro lecturers of facts.

  • Guidance on textbook choice, syllabus building, and path outline
  • Classroom routines, machine purposes, and net assets designed to advertise lively learning
  • Tips for incorporating genuine facts into path content
  • Recommendations on integrating ethics and variety issues into statistics education
  • Strategies to evaluate student's statistical literacy, pondering, and reasoning skills
  • Additional fabric on-line at www.teachstats.org

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Additional info for A Guide to Teaching Statistics: Innovations and Best Practices

Example text

A small handful of authors elected to go beyond simple regression and include a chapter on multiple regression (13%). Finally, only a few authors included a substantive discussion of analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA), logistic regression, factor analysis, and structural equation modeling. Despite the popularity of meta-analysis in psychological research, not one book covered the topic in any detail. , Grimm & Yarnold, 1995, 2000; Tabachnick & Fidell, 2007).

The lack of mathematical ability among many incoming students may haunt them in future statistics courses given the reported positive correlations between highest mathematical grade level completed, mathematical achievement, and performance in an introductory statistics course (Lalonde & Gardner, 1993). Unfortunately, the situation may be worsening. Mulhern and Wylie (2004) argued that mathematical competencies are uniformly decreasing at the college level. In a comparison of two psychology undergraduate cohorts, 1992 and 2002, they found significant reductions in mathematical competencies for all six of the components that they measured (calculation, graphical interpretation, algebraic reasoning, probability and sampling, proportionality and ratio, and estimation).

As a result, these textbooks are a nice fit for most introductory statistics courses at community and four year colleges 34 Nuts and Bolts of Teaching Statistics and universities. Unfortunately, a high proportion of intermediate level texts focus on computation versus the theoretical underpinnings of statistical reasoning. According to Griggs (2006), the decision to adopt an intermediate versus a more advanced text hinges on the extent to which you wish to challenge students. In addition, instructors who add supplemental readings may wish to use an intermediate text given the fact that the text will not be the sole focus of the course.

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