Factor Analysis & Linear Regression on Perfectionism Data
Applied exploratory factor analysis to a psychology dataset on perfectionism dimensions — reducing a complex web of survey responses into meaningful latent factors. Then used linear regression to model how those hidden factors predict real outcomes. The kind of statistical detective work that turns messy behavioral data into clear, actionable patterns.
Factor Analysis
Linear Regression
Statistical Modeling
Jamovi
Behavioral Data
This project explores data analysis techniques, specifically focusing on factor analysis and linear regression, to uncover underlying patterns and relationships within the dataset. By employing factor analysis, we reduce the dimensionality of the data, identifying key variables that significantly impact the outcome. Following this, linear regression is utilized to model and predict relationships between the dependent and independent variables, providing insights into the data’s structure and enhancing predictive capabilities.