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Particular considerations for correlation analysis using data from the Teacher Education and Development Study in Mathematics (TEDS-M)

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Abstract:

The Teacher Education and Development Study in Mathematics (TEDS-M), focusing on how teachers are prepared to teach mathematics in primary and lower secondary schools in 17 countries, is the first large scale assessment in higher education that uses national representative samples. Its results will be released in spring 2011, and the public-use database will be released shortly afterwards, providing a precious basis for secondary analysis of the collected data.
The specific design of the study calls for analysis methods that consider the particular structure of the data, originating from complex cluster samples.
In this paper, the focus is on particularities that need to be considered when conducting correlation analysis. In many instances correlation analysis may produce misleading results when performed on raw data. For example, a researcher might be interested in whether there is a relationship between opportunities offered to connect classroom learning to practice (comprised in one index variable) and the pedagogical content knowledge score. Obviously, the opportunities to learn or practice specific contents are very similar (if not identical) to future teachers attending the same program. The variation in the respective variable is therefore small if not zero, within a program. We believe it is therefore more appropriate to use data that was aggregated in a first step using the program as a break variable and to conduct the correlation analysis using the aggregated data afterwards. Also, the choice of the correlation method must be considered carefully since in many instances, the data will not fulfill the preconditions for a Pearson correlation.
Further, balanced repeated replication needs to be applied for variance estimation and the correct weights must be used in order to get unbiased estimates of the correlation coefficient and its sampling variance. The proper procedures to perform this kind of analysis will be introduced.
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Association:
Name: 55th Annual Conference of the Comparative and International Education Society
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http://www.cies.us


Citation:
URL: http://citation.allacademic.com/meta/p492570_index.html
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MLA Citation:

Meinck, Sabine. and Rodriguez, Michael. "Particular considerations for correlation analysis using data from the Teacher Education and Development Study in Mathematics (TEDS-M)" Paper presented at the annual meeting of the 55th Annual Conference of the Comparative and International Education Society, Fairmont Le Reine Elizabeth, Montreal, Quebec, Canada, May 01, 2011 <Not Available>. 2014-11-26 <http://citation.allacademic.com/meta/p492570_index.html>

APA Citation:

Meinck, S. and Rodriguez, M. , 2011-05-01 "Particular considerations for correlation analysis using data from the Teacher Education and Development Study in Mathematics (TEDS-M)" Paper presented at the annual meeting of the 55th Annual Conference of the Comparative and International Education Society, Fairmont Le Reine Elizabeth, Montreal, Quebec, Canada <Not Available>. 2014-11-26 from http://citation.allacademic.com/meta/p492570_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: The Teacher Education and Development Study in Mathematics (TEDS-M), focusing on how teachers are prepared to teach mathematics in primary and lower secondary schools in 17 countries, is the first large scale assessment in higher education that uses national representative samples. Its results will be released in spring 2011, and the public-use database will be released shortly afterwards, providing a precious basis for secondary analysis of the collected data.
The specific design of the study calls for analysis methods that consider the particular structure of the data, originating from complex cluster samples.
In this paper, the focus is on particularities that need to be considered when conducting correlation analysis. In many instances correlation analysis may produce misleading results when performed on raw data. For example, a researcher might be interested in whether there is a relationship between opportunities offered to connect classroom learning to practice (comprised in one index variable) and the pedagogical content knowledge score. Obviously, the opportunities to learn or practice specific contents are very similar (if not identical) to future teachers attending the same program. The variation in the respective variable is therefore small if not zero, within a program. We believe it is therefore more appropriate to use data that was aggregated in a first step using the program as a break variable and to conduct the correlation analysis using the aggregated data afterwards. Also, the choice of the correlation method must be considered carefully since in many instances, the data will not fulfill the preconditions for a Pearson correlation.
Further, balanced repeated replication needs to be applied for variance estimation and the correct weights must be used in order to get unbiased estimates of the correlation coefficient and its sampling variance. The proper procedures to perform this kind of analysis will be introduced.


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