Power of Latent Growth Modeling for Detecting Linear Growth: Number of Measurements and Comparison With Other Analytic Approaches

The authors investigated 2 issues concerning the power of latent growth modeling (LGM) in detecting linear growth: the effect of the number of repeated measurements on LGM's power in detecting linear growth and the comparison between LGM and some other approaches in terms of power for detecting...

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Bibliographic Details
Published in:The Journal of experimental education 2005-01, Vol.73 (2), p.121-139
Main Authors: Xitao, Fan, Xiaotao, Fan
Format: Article
Language:English
Subjects:
Analytics
Behavioral Sciences
Comparative analysis
dependent t test
Educational research
Educational sciences
Educational statistics
Generalized linear models
Growth models
latent growth modeling
Mathematical growth
Measurement, Statistics, and Research Design
Modeling
Monte Carlo
Monte Carlo Methods
Monte Carlo simulation
Multilevel models
Parametric models
Proportions
Reading Tests
repeated measures ANOVA
Sample Size
Statistical Analysis
statistical power
Structural Equation Models
t test
T tests
Theories, epistemology
Trajectories
Variance analysis
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Summary:The authors investigated 2 issues concerning the power of latent growth modeling (LGM) in detecting linear growth: the effect of the number of repeated measurements on LGM's power in detecting linear growth and the comparison between LGM and some other approaches in terms of power for detecting linear growth. A Monte Carlo simulation design was used, with 3 crossed factors (growth magnitude, number of repeated measurements, and sample size) and 1,000 replications within each cell condition. The major findings were as follows: For 3 repeated measurements, a substantial proportion of samples failed to converge in structural equation modeling; the number of repeated measurements did not show any effect on the statistical power of LGM in detecting linear growth; and the LGM approach outperformed both the dependent t test and repeated-measures analysis of variance (ANOVA) in terms of statistical power for detecting growth under the conditions of small growth magnitude and small to moderate sample size conditions. The multivariate repeated-measures ANOVA approach consistently underperformed the other tests.
ISSN:0022-0973
1940-0683
DOI:10.3200/JEXE.73.2.121-139