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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Impact of an equality constraint on the class-specific residual variances in regression mixtures
T2 - a Monte Carlo simulation study
AU - Kim, Minjung
AU - Lamont, Andrea E.
AU - Jaki, Thomas
AU - Feaster, Daniel
AU - Howe, George
AU - Van Horn, M. Lee
N1 - The final publication is available at Springer via http://dx.doi.org/10.3758/s13428-015-0618-8
PY - 2016/6
Y1 - 2016/6
N2 - Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.
AB - Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.
KW - Regression mixture
KW - Differential effects
KW - Effect heterogeneity
KW - Residual variances
U2 - 10.3758/s13428-015-0618-8
DO - 10.3758/s13428-015-0618-8
M3 - Journal article
VL - 48
SP - 813
EP - 826
JO - Behavior Research Methods
JF - Behavior Research Methods
SN - 1554-351X
IS - 2
ER -