Applying Principal Component Regression to Factors Associated with College Students’ Frequency ofCooking Their Own Breakfast

Abstract

Cooking breakfast independently is associated with healthier eating habits, but its frequency among college. Several 

interrelated factors influence students. This study aimed to model the weekly frequency of cooking breakfast independently 

(Y) using principal component regression (PCR) with four predictors: confidence in cooking ability (X1), frequency of not 

cooking because food ingredients had run out (X2), frequency of cooking because food outside the home was more expensive 

(X3), and frequency of preparing breakfast to take as a packed meal (X4). The four predictors were selected from 15 

candidate questions based on conceptual relevance and examined using Spearman’s correlation across 12 numerical 

indicators; X1 and X3 were significantly correlated with Y, while X2 and X4 were retained despite not being significant. 

The data came from 150 college students who completed an online form, with no missing values. The analysis included 

descriptive statistics, Pearson’s correlation, standardisation, principal component analysis (PCA), and PCR, with the number 

of components selected through 5-fold cross-validation based on the minimum RMSE. Two principal components explained 

61.01% of the variance in the predictors and produced a cross-validation RMSE of 1.5882. The model was jointly significant 

(F = 18.65; p = 6.03×10−8), with an R² of 0.2024, an adjusted R² of 0.1916, and a cross-validated R² of 0.1556. On the 

original scale, X1, X3, and X4 were positively associated with Y, whereas X2 was negatively associated with Y. The model’s 

explanatory power was low to moderate; therefore, interpret the results as associations rather than causal relationships.