Title: Deviations from normality: Effects on growth curve models
Authors: Catarina Marques - Instituto Universitario de Lisboa (ISCTE-IUL) and Business Research Unit (BRU-IUL) (Portugal) [presenting]
Maria de Fatima Salgueiro - Instituto Universitario de Lisboa (ISCTE-IUL) and Business Research Unit (BRU-IUL) (Portugal)
Paula Vicente - Universidade Lusofona - Lisboa (Portugal)
Abstract: Latent growth curve models (LGCM) became recently a very popular technique for longitudinal data analysis: they allow individuals to have distinct growth trajectories over time. These patterns of change are summarized in relatively few parameters: the means and variances of the random effects (random intercept and slope), as well as the covariance between intercept and slope. Although the specified model structure imposes normality assumptions, the data analyst often faces data deviations from normality, implying mild, moderate or even severe values for skewness and/or kurtosis. The aim is to investigate the effect of data deviations from normality on the goodness of fit measures in LGCM. Using the VITA method to obtain data generating non-normal distributions, a Monte Carlo simulation study was conducted in order to assess the effects on the values of goodness of fit indices. LGCM with unconditional linear growth are considered. Three time points, and sample sizes ranging from 50 to 1000 observations are used. The impacts of such deviations on fit measures are discussed.