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 / Cataldo, Rosanna; Crocetta, Corrado; Grassia, MARIA GABRIELLA; Marino, Marina. - (2020), pp. 1363-1368. (Intervento presentato al convegno SIS 2020 tenutosi a Pisa nel 2021).
Longitudinal data analysis using PLS-PM approach
Rosanna Cataldo;Maria Gabriella Grassia;Marina Marino
2020
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 | Dimensione | Formato | |
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