Treatment of Missing Data at The Second Level of Hierarchical Linear Models  

Oleh:

Gibson, Nicole Morgan ; Olejnik, Stephen

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 63 no. 2 (Apr. 2003), page 204-238.
Topik: LINEAR MODELS; missing data; hierarchical linear models; missing data treatments
Fulltext: 204.pdf (170.77KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: EE30.9 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The problem of missing data at the second level of a two - level hierarchical data structure was investigated. Using data generated to stimulate the 1982 high school and beyond data set, five missing data treatments - listwise deletion, overall mean substitution, group mean substitution, the expectation maximization (EM) algorithm, and multiple imputation - were examined under four manipulated conditions : number of level 2 variables, level 2 sample size, level 1 intercept - slope correlation and percentage of missing data. Listwise deletion, group mean substitution and the EM algorithm performed data, listwise deletion and the EM algorithm performed satisfactorily. Only listwise deletion performed well in estimating random effects except when the level 2 sample size was 30 and 40% of the data were missing. The practical implications of the findings are discussed.

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