Longitudinal data over the past 20 years have seen a greater diffusion in the social sciences. Accompanying this growth was an interest in the methods for analyzing such data. Structural Equation Modeling (SEMs) and especially Partial Least Squares Path Modeling (PLS-PM) are a valuable way to analyze longitudinal data because it is both flexible and useful for answering common research questions. The aim of this paper is to demonstrate how PLS-PM can help us to analyze longitudinal data.
Longitudinal data analysis using PLS-PM approach
Corrado Crocetta;
2020-01-01
Abstract
Longitudinal data over the past 20 years have seen a greater diffusion in the social sciences. Accompanying this growth was an interest in the methods for analyzing such data. Structural Equation Modeling (SEMs) and especially Partial Least Squares Path Modeling (PLS-PM) are a valuable way to analyze longitudinal data because it is both flexible and useful for answering common research questions. The aim of this paper is to demonstrate how PLS-PM can help us to analyze longitudinal data.File in questo prodotto:
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