Created by David Howell of the University of Vermont, this lesson describes bootstrapping in the context of a statistics class for psychology students. The site integrates screen shots from various software programs with interesting, readable explanations and examples of bootstrapping.
The site is structured in such a way that the author introduces the concept of bootstrapping, follows up with the purpose, provides examples, and draws conclusions about the findings.
Type of Material:
Tutorial/presentation
Recommended Uses:
Overall, this learning material will inform students' understanding of how we can look at populations that are not normal or whose normality cannot be assumed. It would be assigned most effectively as homework reading.
Technical Requirements:
The site works well with any browser.
Identify Major Learning Goals:
1. Students will understand the importance of and the logic behind bootstrapping, which does not assume our sample’s population is normal. Rather, it assumes instead that the sample data reflects the population.
2. Students will learn how bootstrapping can be used to come up with a confidence interval for various parameters.
Target Student Population:
The target population is students in research methods courses. The material is particularly appropriate for students interested in advanced experiment techniques.
Prerequisite Knowledge or Skills:
The following prerequisite knowledge would be beneficial.
• A basic understanding of experimentation with random samples.
• Elementary statistics
Content Quality
Rating:
Strengths:
• This lesson contains a clear description of the method of bootstrapping to estimate a confidence interval for a parameter without needing any assumptions. The author includes graphics, texts and samples of code that will assist students who are learning this concept. The author’s choice of examples works well to demonstrate the point.
• The site clearly explains bootstrapping by utilizing examples and screen shots and also explains why this is important to understand.
• Computer programs allow us to run the data, but this site adds value by clearly explaining why we need to consider bootstrapping as well as the logic behind it.
Concerns:
• Although the article clearly explains the step by step process involved in bootstrapping, the author left out the important point that the sample size for each of the simulations must be the same as the sample size for that which was sampled originally. The author also noted that an error would be fixed and the associated remark would be deleted when the correction was done. This error is still present today.]
Potential Effectiveness as a Teaching Tool
Rating:
Strengths:
• This is a lesson that is very well organized and contains examples that will assist the students learn the idea and process of bootstrapping. It compares the traditional methods to the bootstrapping method, and the graphics clearly show the students what the outcome of bootstrapping will look like.
• While most classes on statistics do not cover this topic, everything one would need to introduce the topic to a class or understand the purpose of bootstrapping on one's own is included in this site.
Concerns:
• The lesson clearly caters to the author’s students (for a particular course). Students who are not specifically guided by their own instructor may find this tailoring somewhat difficult or awkward.
Ease of Use for Both Students and Faculty
Rating:
Strengths:
• The lesson is well organized with clear headings and well-structured writing. Any student will be able to open the site and read it without needing any assistance.
• The site is easy to follow and includes clearly entitled descriptions and explanations. No special equipment or tools are required.
Concerns:
• It would be nice if the site included an embedded bootstrapping app or at least a link to one that is easy to access on the web. That would increase interactivity.
Other Issues and Comments:
Professor Howell's online lesson provides a great explanation of bootstrapping and why we should care about it.
Creative Commons:
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