The effect of multiple indicators on the power to detect inter-individual differences in change

被引:54
|
作者
von Oertzen, Timo [1 ]
Hertzog, Christopher [2 ]
Lindenberger, Ulman [1 ]
Ghisletta, Paolo [3 ,4 ]
机构
[1] Max Planck Inst Human Dev, D-14195 Berlin, Germany
[2] Georgia Inst Technol, Sch Psychol, Atlanta, GA 30332 USA
[3] Univ Geneva, Fac Psychol & Educ Sci, CH-1211 Geneva 4, Switzerland
[4] Distance Learning Univ, Geneva, Switzerland
来源
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY | 2010年 / 63卷 / 03期
关键词
GROWTH CURVE MODELS; INDIVIDUAL-DIFFERENCES; SAMPLE-SIZE; STATISTICAL POWER; COEFFICIENTS; RELIABILITY; MEMORY;
D O I
10.1348/000711010X486633
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Hertzog et al. evaluated the statistical power of linear latent growth curve models (LGCMs) to detect individual differences in change, i.e., variances of latent slopes, as a function of sample size, number of longitudinal measurement occasions, and growth curve reliability. We extend this work by investigating the effect of the number of indicators per measurement occasion on power. We analytically demonstrate that the positive effect of multiple indicators on statistical power is inversely related to the relative magnitude of occasion-specific latent residual variance and is independent of the specific model that constitutes the observed variables, in particular of other parameters in the LGCM. When designing a study, researchers have to consider trade-offs of costs and benefits of different design features. We demonstrate how knowledge about power equivalent transformations between indicator measurement designs allows researchers to identify the most cost-efficient research design for detecting parameters of interest. Finally, we integrate different formal results to exhibit the trade-off between the number of measurement occasions and number of indicators per occasion for constant power in LGCMs.
引用
收藏
页码:627 / 646
页数:20
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