|
Activity Number:
|
433
- SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, July 31, 2018 : 2:00 PM to 2:45 PM
|
|
Sponsor:
|
Survey Research Methods Section
|
|
Abstract #332924
|
|
|
Title:
|
Parameter Estimate Bias Resulting from Level 3 Sample Size Decisions
|
|
Author(s):
|
Tingqiao Chen* and Frank Lawrence and Wenjuan Ma
|
|
Companies:
|
and Michigan State University and Michigan State University
|
|
Keywords:
|
3-lvl HLM;
sample size;
simulation;
parameter bias;
absolute relative bias
|
|
Abstract:
|
Acquiring level 3 units is more expensive than either level 1 or level 2 units (cf. Kalaian & Raudenbush, 1996). The effect of level 3 sample size decisions on level 1 parameter estimate bias is important but not known (McNeish & Stapleton, 2016). To address this shortcoming we study the impact of level-three sample size on parameter estimates when the parameters of interest were at level one. The model was a three-level linear model; the target parameter was a level-one predictor. Level-one and level-two sample sizes were fixed at 15 and 30 respectively (e.g., Kreft & de Leeuw, 1998) with variances fixed at 10 and 1. Level-three sample size varied from 5 to 30 by 5. Level-three variance ranged from 0.6 to 2.0 with ICC varying from 0.05 to 0.15 by 0.05 (e.g., Maas & Hox, 2005). Target parameter estimates were evaluated using absolute relative bias (ARB) and the parameter estimate standard error (SE). As level 3 sample size increased, both ARB and SE decreased in a non-linear fashion. The value of ARB and SE started to stabilize once the level 3 sample size reached 20.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2018 program
|
