TY - CHAP
T1 - Principles for the analysis of large complex secondary databases in educational settings
AU - To, Yen
AU - Burley, Hansel
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84898572149&partnerID=8YFLogxK
U2 - 10.4018/978-1-60960-857-6.ch008
DO - 10.4018/978-1-60960-857-6.ch008
M3 - Chapter
AN - SCOPUS:84898572149
SN - 9781609608576
SP - 132
EP - 145
BT - Cases on Institutional Research Systems
PB - IGI-Global
ER -