Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

A Comparison of Estimation Methods when an Interaction is Omitted from a Multilevel Model

Terhorst, Lauren (2008) A Comparison of Estimation Methods when an Interaction is Omitted from a Multilevel Model. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Primary Text

Download (2MB) | Preview

Abstract

One of the sources of inaccuracy in parameter estimates of multilevel models is omitted variable bias, caused by the omission of an important predictor. The purpose of this study was to examine the performance of six estimation procedures in estimating the fixed effects when a level-2 interaction term was omitted from a two-level hierarchical linear model. Four alternative estimators (FE, WLS1, WLS2, WLS3) based on the work of Frees (2001) and the Maximum Likelihood (FML, ReML) estimation methods were examined. Findings of the Monte Carlo study revealed that the FML and ReML methods were the least biased methods when a level-2 interaction was omitted from the multilevel model. FML and ReML produced the lowest RMSD values of all six estimation methods regardless of level-2 sample size, ICC, or effect sizes of the level-2 variables. The difference in the performance of the alternative and Maximum Likelihood (ML) procedures diminished as level-2 sample size and ICC increased. The bias in all six estimation methods did not differ much when the effect sizes of the level-2 predictors varied. When the methods were examined using the ECLS data, the results of the Monte Carlo study were confirmed. The ML methods were the least biased of all the methods when a level-2 interaction term was omitted from the model.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Terhorst, Laurenlat15@pitt.eduLAT15
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKim, Kevin Hkhkim@pitt.eduKHKIM
Committee MemberVotruba-al, Elizabethevotruba@pitt.eduEVOTRUBA
Committee MemberYe, Feifeifeifeiye@pitt.eduFEIFEIYE
Committee MemberBachman, Heather Jhbachman@pitt.eduHBACHMAN
Committee MemberLane, Suzannesl@pitt.eduSL
Date: 29 January 2008
Date Type: Completion
Defense Date: 12 November 2007
Approval Date: 29 January 2008
Submission Date: 27 November 2007
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Education > Psychology in Education
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: estimation methods; multilevel modeling; omitted variable bias
Other ID: http://etd.library.pitt.edu/ETD/available/etd-11272007-104235/, etd-11272007-104235
Date Deposited: 10 Nov 2011 20:06
Last Modified: 15 Nov 2016 13:52
URI: http://d-scholarship.pitt.edu/id/eprint/9811

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item