Literature Index

No general theory has been formulated to show interrelations among a collection of variables that are related to statistics anxiety. The present study made an attempt to develop a comprehensive model that would predict statistics anxiety from several dispositional, situational, and environmental antecedents derived from the current literature. Two hundred forty-six college students who were enrolled in introductory statistics courses completed a survey packet that included a set of questions and five standardized assessment instruments that measured statistics anxiety, mathematics anxiety, attitudes toward statistics, test anxiety, and general anxiety. Extensive preliminary data screening assured the appropriateness of the data for parametric statistics. Independent - test results showed significant differences between low-and-high anxious students in terms of attitudes toward statistics, test anxiety, mathematics anxiety, general anxiety, previous mathematics experience, satisfaction, and pace. A direct discriminant function analysis was used to discriminate between low-and-high statistics-anxious students. A significant discriminant function, based on the attitudes toward statistics and test anxiety, classified the groups accurately approximately 80% of the time. Five measurement models and one structural model were specified, identified, estimated, and tested. Results showed that the modified structural model did not fit the data well. However, the dispositional and situational antecedents models fit the data well. The original environmental antecedents model was modified to fit the data. In the final model, the dispositional and situational antecedents models contributed significantly to statistics anxiety. The environmental antecedents model was not a significant contributor; however, it was significantly related to the other variables in the model. The dispositional antecedents model alone accounted for 58% and the situational antecedents model alone accounted for 23% of the variance in statistics anxiety scores. The present study showed that statistics anxiety is a complicated construct that is difficult to measure and investigate. Findings of the present study also suggest that personality-related factors may be one of the most important effects of statistics anxiety. More studies are needed to clarify the construct of statistics anxiety and its relationships with other variables.

This paper proposes an argument framework for the teaching of null hypothesis statistical testing and its application in support of research. Elements of the Toulmin (1958) model of argument are used to illustrate the use of p values and Type I and Type II error rates in support of claims about statistical parameters and subject matter research constructs. By viewing the application of null hypothesis statistical testing within this framework, the language and intent of statistical support for research can be more precisely understood and taught.

Describes the development of an artificial intelligence system called GIDE that analyzes student errors in statistics problems by inferring the students' intentions. Learning strategies involved in problem solving are discussed and the inclusion of goal structures is explained. (LRW)

Probability and Statistics are increasingly being given an important place in the K-12 mathematics curriculum. According to the NCTM Curriculum and Evaluation Standards (1989), students should learn to apply probability and statistics concepts to solve problems and evaluate information in the world around them. The statistics standards suggest using hands-on activities involving collecting and organizing data, representing and modeling data including the use of technology, and communicating ideas verbally and in written reports. Teachers are encouraged to help students develop important ideas (for example, about distributions, randomness, and bias) and gain experience in choosing appropriate techniques to use in analyzing data. Many teachers are currently using materials from recent projects or projects in development that have developed curricula and software to implement the NCTM Standards (e.g., the Quantitative Literacy Project, the Reasoning under Uncertainty Project, and the ChancePlus Project). These new materials encourage teachers to have students work on statistical projects: formulate research questions, collect and analyze data, and write up the results. Working on statistical projects individually or in groups engages students in learning about statistics and helps them to integrate the knowledge they have learned.