Data Preprocessing Methods for Second Language Acquisition in Mixed Effects Models

The investigation into lexical and conceptual representation in second language (L2) acquisition has garnered significant attention in linguistics and cognitive psychology as it sheds light on the intricate mechanisms underlying the development of language proficiency in non-native speakers. The issue of handling outlier data in response time (RT) measurements, a cornerstone in many psycholinguistic experiments, is meticulously examined. The paper argues that variance normalization as a preprocessing step offers a robust method for mitigating the influence of extreme values that can potentially distort the overall interpretation of the data. Variance normalization involves transforming the RT data such that each observation is expressed in terms of its deviation from the mean scaled by the standard deviation of the dataset, thereby ensuring that outliers do not disproportionately affect the statistical analyses.