Premium
Robust transformation with applications to structural equation modelling
Author(s) -
Yuan KeHai,
Chan Wai,
Bentler Peter M.
Publication year - 2000
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711000159169
Subject(s) - outlier , kurtosis , transformation (genetics) , skewness , multivariate statistics , normality , robust statistics , structural equation modeling , data transformation , multivariate normal distribution , computer science , mathematics , power transform , econometrics , statistics , data mining , artificial intelligence , biochemistry , chemistry , consistency (knowledge bases) , data warehouse , gene
Data sets in social and behavioural sciences are seldom normal. Influential cases or outliers can lead to inappropriate solutions and problematic conclusions in structural equation modelling. By giving a proper weight to each case, the influence of outliers on a robust procedure can be minimized. We propose using a robust procedure as a transformation technique, generating a new data matrix that can be analysed by a variety of multivariate methods. Mardia's multivariate skewness and kurtosis statistics are used to measure the effect of the transformation in achieving approximate normality. Since the transformation makes the data approximately normal, applying a classical normal theory based procedure to the transformed data gives more efficient parameter estimates. Three procedures for parameter evaluation and model testing are discussed. Six examples illustrate the various aspects with the robust transformation.