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Dunn, Peter K.; Marshman, Margaret – Australian Mathematics Education Journal, 2021
This is the fourth in a series of statistical articles for mathematics teachers. In this article, the authors discuss topics in General Mathematics in Unit 2 Topic 1 (Univariate data analysis and the statistical investigation process) and topics in Essential Mathematics, Unit 2 Topic 1 (Representing and comparing data).
Descriptors: Mathematics Education, Mathematics Instruction, Data Analysis, Graphs
Kastberg, David; Murray, Gordon; Ferraro, David; Arieira, Carlos; Roey, Shep; Mamedova, Saida; Liao, Yuqi – National Center for Education Statistics, 2021
The Program for International Student Assessment Young Adult Follow-up Study (PISA YAFS) is a follow-up study with students who participated in PISA 2012 in the United States. The study is designed to measure how performance on PISA 2012 relates to subsequent measures of outcomes and skills of young adults on an online assessment, Education and…
Descriptors: Foreign Countries, Achievement Tests, Secondary School Students, Young Adults
Dunlap, Mickey; Studstill, Sharyn – Teaching Statistics: An International Journal for Teachers, 2014
The number of increases a particular stock makes over a fixed period follows a Poisson distribution. This article discusses using this easily-found data as an opportunity to let students become involved in the data collection and analysis process.
Descriptors: Experiential Learning, Learning Activities, Statistical Distributions, Probability
Aksut, Ann Ahu – ProQuest LLC, 2013
Numerous organizations collect and distribute non-aggregate personal data for a variety of different purposes, including demographic and public health research. In these situations, the data distributor is responsible with the protection of the anonymity and personal information of individuals. Microaggregation is one of the most commonly used…
Descriptors: Data Collection, Data Analysis, Multivariate Analysis, Disclosure
Anyieni, Abel G.; Areri, Damaris K. – Journal of Education and Practice, 2016
Past research have pointed out that excellent strategies have been written but extremely small have been accomplished in their implementation. It has additionally been proposed that only 10% of formulated strategies are successfully implemented. However, crafting the best strategy is not the end in itself but the ultimate result will only be…
Descriptors: Strategic Planning, Program Implementation, Secondary Schools, Foreign Countries
Sarkar, Saurabh – ProQuest LLC, 2013
In the modern world information has become the new power. An increasing amount of efforts are being made to gather data, resources being allocated, time being invested and tools being developed. Data collection is no longer a myth; however, it remains a great challenge to create value out of the enormous data that is being collected. Data modeling…
Descriptors: Data Analysis, Data Collection, Error of Measurement, Research Problems
Brophy, Caroline; Hahn, Lukas – Journal of Statistics Education, 2014
In this paper, we describe an in-class experiment that is easy to implement with large groups of students. The experiment takes approximately 15-20 minutes to run and involves each student completing one of four types of Sudoku puzzles and recording the time it takes to completion. The resulting data set can be used as a teaching tool at an…
Descriptors: Large Group Instruction, Lecture Method, Educational Experiments, Puzzles
Kemerer, Richard W.; Schmid, Richard F. – Performance and Instruction, 1984
Describes the Adjusted Agreement Index, a strategy for analyzing collected data from questionnaires utilizing Likert-type scales. Steps involved in using this index and its strengths and weaknesses are outlined, and a case study illustrating how it was used to summarize data on a distance education course package is presented. (MBR)
Descriptors: Banking, Case Studies, Course Evaluation, Data Analysis
Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss – Journal of Statistics Education, 2004
In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…
Descriptors: Introductory Courses, Statistics, Class Activities, Data Collection
Morrisson, Christian; Murtin, Fabrice – Centre for the Economics of Education (NJ1), 2009
Global economic transformations have never been as dramatic as in the twentieth century. Most countries have experienced radical changes in the standards of income per capita, technology, fertility, mortality, income inequality and the extent of democracy in the course of the past century. It is the goal of many disciplines--economics, history,…
Descriptors: Economic Development, Educational Attainment, Demography, Global Approach
Santmire, Toni E. – 1984
The purpose of this paper is to discuss ways in which developmental psychology suffers from the lack of an appropriate technology of measurement and statistical analysis. The paper begins by noting that developmental psychology is the study of change; that individuals develop through a succession of "stages" which are separated by…
Descriptors: Data Analysis, Data Collection, Developmental Psychology, Developmental Stages
Peer reviewedKelly, Ivan; Ryan, Alan – Science Teacher, 1983
Explains the use of contingency tables as a tool in assessing variables to determine whether a relationship exists. Develops an example hypothesis step-by-step, noting the scientific processes and attitudes being addressed. Cautions that a large difference, which suggests a relationship, is not explanation since correlation does not guarantee…
Descriptors: Classification, Data Analysis, Data Collection, High Schools
Peer reviewedBryan, Elizabeth H. – Mathematics Teacher, 1988
This set of activity sheets requires students to organize and display data with a box-and-whiskers graph. Students use the plot as a summary display to detect patterns and to highlight the important features of the data for purposes of comparison. (PK)
Descriptors: Class Activities, Comparative Analysis, Data, Data Analysis

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