Bulletin of the International Statistical Institute.

Vol. 31 bound with its Proceedings, v. 3 (310.6 In745 v.3).

Research in statistical education — planning of an international survey

As announced in the November 2000 issue of MathStats&OR [1], one of the projects supported by the Maths, Stats & OR Network funds is an international survey of research into pedagogic issues in statistics and OR. I am taking the lead on this and report here on the progress that has been made during the first year. A paper giving some background to the project and describing initial thinking on how it might be implemented was presented at the 53rd session of the International Statistical Institute in Seoul, Korea, in August 2001 in a session on The future of statistics education research [2]. It sounded easy. I considered that I was something of an expert on surveys having lectured on the topic for many years and having helped students and others who were doing surveys, particularly with the design of their questionnaires. Surely all I had to do was to draft a few questions, send them electronically to colleagues in statistical education who would be only to happy to respond, and summarise their responses? I should have learnt from my experience of advising all those students who thought that doing a survey was easy and to whom I had to explain that their ideas were too ambitious. There are several inter-related stages in survey research and it is important to think about these before rushing into the collection of data. In the case of the survey in question, this planning stage revealed several challenges. Surveys are usually done for a purpose so even before planning how to do them, it is advisable to think about the final product and the dissemination of results. This is the route I followed.

Report on the coordination of international statistical activities in the area of the environment

Includes bibliography.

Establishing statistical foundations early : data modeling with young learners

This paper addresses research from a three-year longitudinal study that engaged children in data modeling experiences from the beginning school year through to third year (6-8 years). A data modeling approach to statistical development differs in several ways from what is typically done in early classroom experiences with data. In particular, data modeling immerses children in problems that evolve from their own questions and reasoning, with core statistical foundations established early. These foundations include a focus on posing and refining statistical questions within and across contexts, structuring and representing data, making informal inferences, and developing conceptual, representational, and metarepresentational competence. Examples are presented of how young learners developed and sustained informal inferential reasoning and metarepresentational competence across the study to become “sophisticated statisticians”.

Numeracy counts in the statistical reasoning equation

The relationship between mathematics and statistical reasoning frequently receives comment (Vere-Jones 1995, Moore 1997); however most of the research into the area tends to focus on maths anxiety. Gnaldi (Gnaldi 2003) showed that in a statistics course for psychologists, the statistical understanding of students at the end of the course depended on students’ basic numeracy, rather than the number or level of previous mathematics courses the student had undertaken. As part of a study into the development of statistical thinking at the interface between secondary and tertiary education, students enrolled in an introductory data analysis subject were assessed regarding their statistical reasoning ability, basic numeracy skills and attitudes towards statistics. This work reports on the relationships between these factors and in particular the importance of numeracy to statistical reasoning.

Paradigms in Statistical Inference for Finite Populations; Up to the 1950s

Modern sample surveys started to spread after statistician at the U.S. Bureau of the Census in the 1940s had developed a sampling design for the Current Population Survey (CPS). A significant factor was also that digital computers became available for statisticians. In the beginning of 1950s, the theory was documented in textbooks on survey sampling. This thesis is about the development of the statistical inference for sample surveys. For the first time the idea of statistical inference was enunciated by a French scientist, P. S. Laplace. In 1781, he published a plan for a partial investigation in which he determined the sample size needed to reach the desired accuracy in estimation. The plan was based on Laplace s Principle of Inverse Probability and on his derivation of the Central Limit Theorem. They were published in a memoir in 1774 which is one of the origins of statistical inference. Laplace s inference model was based on Bernoulli trials and binominal probabilities. He assumed that populations were changing constantly. It was depicted by assuming a priori distributions for parameters. Laplace s inference model dominated statistical thinking for a century. Sample selection in Laplace s investigations was purposive. In 1894 in the International Statistical Institute meeting, Norwegian Anders Kiaer presented the idea of the Representative Method to draw samples. Its idea was that the sample would be a miniature of the population. It is still prevailing. The virtues of random sampling were known but practical problems of sample selection and data collection hindered its use. Arhtur Bowley realized the potentials of Kiaer s method and in the beginning of the 20th century carried out several surveys in the UK. He also developed the theory of statistical inference for finite populations. It was based on Laplace s inference model. R. A. Fisher contributions in the 1920 s constitute a watershed in the statistical science He revolutionized the theory of statistics. In addition, he introduced a new statistical inference model which is still the prevailing paradigm. The essential idea is to draw repeatedly samples from the same population and the assumption that population parameters are constants. Fisher s theory did not include a priori probabilities. Jerzy Neyman adopted Fisher s inference model and applied it to finite populations with the difference that Neyman s inference model does not include any assumptions of the distributions of the study variables. Applying Fisher s fiducial argument he developed the theory for confidence intervals. Neyman s last contribution to survey sampling presented a theory for double sampling. This gave the central idea for statisticians at the U.S. Census Bureau to develop the complex survey design for the CPS. Important criterion was to have a method in which the costs of data collection were acceptable, and which provided approximately equal interviewer workloads, besides sufficient accuracy in estimation.

Programme of international statistical work for Latin America and the Caribbean, June 2001-June 2003

Includes bibliography

Preliminary proposal for an integrated presentation of international statistical work in Latin America and the Caribbean

Includes bibliography

Progress report on the activities of the programme of international statistical work for Latin America and the Caribbean, June 2001-June 2003

Second meeting of the Statistical Conference of the Americas of the Economic Commission for Latin America and the Caribbean Santiago, Chile, 18-20 June 2003.

Programme of international statistical work for Latin America and the Caribbean, July 2003-June 2005

Second Meeting of the Statistical Conference of the Americas of the Economic Commission for Latin America and the Caribbean Santiago, Chile, 18-20 June 2003.

Progress report on the activities of the Programme of International Statistical Work for Latin America and the Caribbean, July 2003-June 2005

Includes bibliography