Literature Index

This paper is concerned with a general education course in elementary statistics, a course which is open to students of all disciplines. This provides an interesting mix of student interests. Some of the students are very familiar with the use of technology, having personal computers in their homes which they use on a frequent basis. Other students have never touched a computer and suffer a great deal of anxiety at the mere thought of using this technology. The curriculum must be designed to appeal to both types of students.

The purpose of this study is to examine our efforts to help preservice teachers develop statistical knowledge and experience through integrating reasoning with data into existing K-6 curriculum via a professional field-based practicum.

2 experiments on the development of the understanding of random phenomena are reported. Of interest was whether children understand the characteristic uncertainty in the physical nature of random phenomena as well as the unpredictability of outcomes. Children were asked, for both a random and a determined phenomenon, whether they knew what its next outcome would be and why. In Experiment 1, 4-, 5-, and 7-year-olds correctly differentiated their responses to the question of outcome predictability; the 2 older groups also mentioned appropriate characteristics of the random mechanism in explaining why they did not know what its outcome would be. Although 3-year-olds did not differentiate the random and determined phenomena, neither did they treat both phenomena as predictable. This latter result is inconsistent with Piaget and Inhelder's characterization of an early stage of development. Experiment 2 was designed to control for the possibility that children in Experiment 1 learned how to respond on the basis of pretest experience with the 2 different phenomena. 5- and 7-year-olds performed at a comparable level to the same-aged children in Experiment 1. Results suggest an earlier understanding of random phenomena than previously has been reported and support results on the literature indicating an early understanding of causality.

The purpose of the current research was to investigate the relationship between preference for numerical information (PNI), math self-concept, and six types of statistics anxiety in an attempt to establish support for the nomological validity of the PNI. Correlations indicate that four types of statistics anxiety were strongly related to PNI, and two were not related. Math self-concept was also strongly related to PNI. Results suggest that higher PNI is associated with higher math self-concept and lower statistics anxiety in graduate students, and indicate support for the nomological validity of the PNI within the context of graduate statistics classes.