Activity Number:
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340
- SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #330414
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Title:
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Parameter Estimate Bias Resulting from Level 3 Sample Size Decisions
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Author(s):
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Tingqiao Chen* and Frank Lawrence and Wenjuan Ma
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Companies:
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and Michigan State University and Michigan State University
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Keywords:
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3-lvl HLM;
sample size;
simulation;
parameter bias;
absolute relative bias
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
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