A Comparison of Estimation Procedures for Quantile Logistic Growth Curves

Identifying an individual’s standing on estimated quantile growth curves including lower 10 percentile and upper 10 percentile growth curve, for example, is one way to describe individual differences in developmental process of psychological characteristics. The present study introduced two different approaches for quantile logistic growth curve estimation, i.e., two-step nonlinear regression model and quantile regression model with transformation. An application of the two procedures was illustrated using a real data example of the word understanding test scores obtained from 563 infants. The quantile regression model performed better than the two-step nonlinear regression model in estimation of quantile growth curves. Quantile regression model did not show rank-reversed estimated quantile scores but the two-step nonlinear regression method did. In addition, the former also showed higher accuracy in classification proportion than the latter. It was discussed why we need to use more elaborated statistical models to estimate quantile growth curves.

keywords: 성장 곡선, 분위수 회귀모형, 비선형 회귀모형, 로지스틱 성장 함수, growth curve, quantile regression, nonlinear regression, logistic growth function

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Article Contents

Vol.33 No.4

A Comparison of Estimation Procedures for Quantile Logistic Growth Curves

Abstract

Reference

Korean Journal of Psychology: General