Literature Index

This article shows how the CensusAtSchool project can be implemented in the classroom to make data handling much more real and relevant. It includes full lesson plans and notes on what the pupils achieved. Editor's Note The lesson plans for this article are provided in figure 4 , which has been placed at the end of the article for convenience. The plans are based directly on CensusAtSchool material.

This article describes a model for an interactive, introductory secondary- or tertiary-level statistics course that is designed to develop students’ statistical reasoning. This model is called a ‘Statistical Reasoning Learning Environment’ and is built on the constructivist theory of learning.test_363 72..77

This article described three heuristics that are employed in making judgments under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.

In this paper, two examples of multilevel modeling as part of the analysis of data from HIV evaluation studies are presented. Strategies for teaching multilevel models for each type of data are discussed. The first, a panel study, uses multiple linear regression models to show how a hierarchical linear model can be developed. The second, a repeated cross-sectional design, uses simple analysis of variance models to show how a random coefficients model can be fit to the data. Complex multilevel models may be easier to understand and apply when broken down into these more familiar strategies. Analyses are presented using the HLM program and SAS.