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General growth mixture modeling (GGMM) was briefly reviewed and its merits for the researchers who aim to conduct a longitudinal study discussed. The GGMM refers to modeling with categorical latent variables that represent subpopulations where population membership is not known but is inferred from the data (Muthén & Muthén, 2004). Latent class growth analysis (LCGA) and growth mixture modeling (G
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counter-stereotypic, interventions that highlight this association can produce a lowering of the default stereotype of female l weak. The possibility of such strategies for inducing a shift in automatic stereotypes and the potential to track stereotypes through both behavioral and brain activation measures has the potential, in the future, to inform about stereotype representation, process, conten
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