A monte carlo evaluation of three nonmetric multidimensional scaling algorithms

Psychometrika ◽  
1972 ◽  
Vol 37 (4) ◽  
pp. 461-486 ◽  
Author(s):  
Ian Spence
Keyword(s):  
Monte Carlo ◽  
Multidimensional Scaling ◽  
Nonmetric Multidimensional Scaling ◽  
Monte Carlo Evaluation ◽  
Scaling Algorithms

A monte carlo evaluation of interactive multidimensional scaling

Psychometrika ◽  
1976 ◽  
Vol 41 (1) ◽  
pp. 43-64 ◽  
Author(s):  
Roger A. Girard ◽  
Norman Cliff
Keyword(s):  
Monte Carlo ◽  
Multidimensional Scaling ◽  
Monte Carlo Evaluation

A Monte Carlo evaluation of growth mixture modeling

2021 ◽  
pp. 1-14
Author(s):  
Tiffany M. Shader ◽  
Theodore P. Beauchaine
Keyword(s):  
Monte Carlo ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Fit Indices ◽  
Linear Quadratic ◽  
Growth Mixture ◽  
True Number ◽  
Monte Carlo Evaluation ◽  
Wrong Number ◽  
State Of Affairs

Abstract Growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, are quite popular in developmental and clinical science. However, research addressing the validity of GMM-identified latent subgroupings is limited. This Monte Carlo simulation tests the efficiency of GMM in identifying known subgroups (k = 1–4) across various combinations of distributional characteristics, including skew, kurtosis, sample size, intercept effect size, patterns of growth (none, linear, quadratic, exponential), and proportions of observations within each group. In total, 1,955 combinations of distributional parameters were examined, each with 1,000 replications (1,955,000 simulations). Using standard fit indices, GMM often identified the wrong number of groups. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well only when one group had steep growth (whether linear, quadratic, or exponential). When three to four groups were simulated, GMM was effective primarily when intercept effect sizes and sample sizes were large, an uncommon state of affairs in real-world applications. When conditions were less ideal, GMM often underestimated the correct number of groups when the true number was between two and four. Results suggest caution in interpreting GMM results, which sometimes get reified in the literature.

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