Estimating Sample Size and Fitting Multilevel Model in Agricultural Experiments
Estimating Sample Size and Fitting Multilevel Model in Agricultural Experiments
Iftikhar Ud Din* and Qamruz Zaman
ABSTRACT
The study highlighted the utility of Multilevel Modeling (MLM) in the field of agriculture. Different areas of agriculture are pinpointed where hierarchical data structure call for the use of multilevel model. The study reveals that for such data structure highly significant improvement is achieved when we shift modeling approach from classical linear regression (CLRM) to multilevel modeling. The study also discussed an important problem in Multilevel Modeling (MLM) i.e. to find sufficient sample size for accurate estimation purposes. In MLM, apart from the general factors of sample size estimation, the test size, the effect size, SE (standard error) of the effect size and power of the test, additional factors like, magnitude of the ICC (Intra Class Correlation), total number of clusters, the number of parameters to be estimated, and the information whether the design is balance or unbalance play a significant role and discussed in the present study.
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