Please use this identifier to cite or link to this item:
Title: Multi-Level Analysis as Applied in Educational Research
Authors: Suacamram, Mayuree 
Lila, Somsak 
Issue Date: 2013
Publisher: Chulalongkorn University Printing House
University of the Thai Chamber of Commerce
Source: Mayuree Suacamram, Somsak Lila (2013) Multi-Level Analysis as Applied in Educational Research. University of the Thai Chamber of Commerce Journal Vol.33 No.1.
Journal: University of the Thai Chamber of Commerce Journal 
Abstract: Multi-level Analysis is used for analyzing nested data. It is for studying the effect of multi-level predicted variables that influence dependent variables. It can check the cross-level effect by taking the effect size in the lower level to be the dependent variable for the next level. There are four sub-models of multi-level analysis: 1) one way random effect ANOVA model or null model, 2) means as outcome regression model, 3) random-coefficient regression model and 4) intercepts and slopes as outcome model. It is more valid than the regression analysis because the maximum likelihood is used for estimating the coefficient, which is different from using the least square method in regression analysis. In addition, the result shows that multi-level analysis can explain variance of the dependent variable more extensively.
ISSN: 0125-2437
Rights: This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Appears in Collections:JEO: Journal Articles

Files in This Item:
File Description SizeFormat 
1191fulltext.pdf1.96 MBAdobe PDFThumbnail
Show full item record Recommend this item

Page view(s) 5

checked on Jul 11, 2019

Download(s) 5

checked on Jul 11, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.