scholarly journals Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation

2015 ◽  
Vol 50 (5) ◽  
pp. 504-519 ◽  
Author(s):  
Gina L. Mazza ◽  
Craig K. Enders ◽  
Linda S. Ruehlman
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
Item Level

scholarly journals Full-Information Maximum Likelihood Estimation of Brand Positioning Maps Using Supermarket Scanning Data

1991 ◽  
Vol 28 (4) ◽  
pp. 483-490 ◽  
Author(s):  
Eric Waarts ◽  
Martin Carree ◽  
Berend Wierenga
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Estimation Procedure ◽  
Full Information ◽  
Brand Positioning ◽  
Additional Advantage ◽  
Full Information Maximum Likelihood ◽  
Methodological Problems ◽  
Product Maps

The authors build on the idea put forward by Shugan to infer product maps from scanning data. They demonstrate that the actual estimation procedure used by Shugan has several methodological problems and may yield unstable estimates. They propose an alternative estimation procedure, full-information maximum likelihood (FIML), which addresses the problems and yields significantly improved results. An important additional advantage of the procedure is that the parameters of the preference distribution can be estimated simultaneously with the brand coordinates. Hence, it is not necessary to assume a fixed (uniform) distribution of preferences. An empirical application is presented in which the outcomes obtained from Shugan's procedure are compared with those from the proposed procedure.

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