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Cite Teaching Case
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MLA
To, Yenand Hansel Burley. "Principles for the Analysis of Large Complex Secondary Databases in Educational Settings." IGI Global, 2012. 132-145. Web. 21 May. 2024. doi:10.4018/978-1-60960-857-6.ch008
APA
To, Y. & Burley, H. (2012). Principles for the Analysis of Large Complex Secondary Databases in Educational Settings (pp. 132-145). doi:10.4018/978-1-60960-857-6.ch008
Chicago
To, Yenand Hansel Burley. "Principles for the Analysis of Large Complex Secondary Databases in Educational Settings," 132-145 (2012), accessed May 21, 2024. doi:10.4018/978-1-60960-857-6.ch008
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Principles for the Analysis of Large Complex Secondary Databases in Educational Settings
Yen To (The University of Southern Mississippi, USA) and
Hansel Burley
(Texas Tech University, USA)
Copyright:
© 2012
|
Pages:
145
EISBN13:
9781466609914
|
DOI:
10.4018/978-1-60960-857-6.ch008
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Abstract
A primary feature of institutional research work is prediction. When statistics are used as the primary analysis tool, much of this work depends upon ordinary least squares regression, which assumes that data have one level. However, much of the data in educational research, in general, and in higher education research, in particular, is multilevel or nested. This chapter explores multilevel data analysis, with a focus on exploring issues associated with sampling, weighting, design effects, and analysis of data. Additionally, it emphasizes the importance of considering contextual effects using as a reference large secondary datasets. The chapter will also explore opportunities and challenges presented by these types of data.
Full-text search over 191,700 research articles and chapters.
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