Analysis of longitudinal data with destructive sampling using linear mixed models

TL;DR

This paper introduces a mixed linear model approach for analyzing longitudinal data with destructive sampling, demonstrating its advantages over traditional models through simulations and real-world student test data.

Contribution

It proposes a novel mixed linear model methodology specifically designed for destructive sampling in longitudinal studies, outperforming existing regression and multivariate models.

Findings

The proposed model reduces mean square error compared to traditional methods.

Application to Colombian student test data shows practical effectiveness.

Demonstrates advantages of the methodology in real-life scenarios.

Abstract

This paper proposes an analysis methodology for the case where there is longitudinal data with destructive sampling of observational units, which come from experimental units that are measured at all times of the analysis. A mixed linear model is proposed and compared with regression models with fixed and mixed effects, among which is a similar that is used for data called pseudo-panel, and one of multivariate analysis of variance, which are common in statistics. To compare the models, the mean square error was used, demonstrating the advantage of the proposed methodology. In addition, an application was made to real-life data that refers to the scores in the Saber 11 tests applied to students in Colombia to see the advantage of using this methodology in practical scenarios.