This article proposes the Shiny app ‘CLC Estimator’ –Congeneric Latent Construct Estimator– to address the problem of estimating latent unidimensional constructs via congeneric approaches. While congeneric approaches provide more rigorous results than suboptimal parallel-based scoring methods, most statistical packages do not provide easy access to congeneric approaches. To address this issue, the CLC Estimator allows social scientists to use congeneric approaches to estimate latent unidimensional constructs smoothly. The present app provides a novel solution to the challenge of limited access to congeneric estimation methods in survey research.

CLC Estimator: a tool for latent construct estimation via congeneric approaches in survey research

Marzi, Giacomo;
2023-01-01

Abstract

This article proposes the Shiny app ‘CLC Estimator’ –Congeneric Latent Construct Estimator– to address the problem of estimating latent unidimensional constructs via congeneric approaches. While congeneric approaches provide more rigorous results than suboptimal parallel-based scoring methods, most statistical packages do not provide easy access to congeneric approaches. To address this issue, the CLC Estimator allows social scientists to use congeneric approaches to estimate latent unidimensional constructs smoothly. The present app provides a novel solution to the challenge of limited access to congeneric estimation methods in survey research.
2023
Latent construct estimation, congeneric approaches, Shiny app, scale scores, social sciences
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/22998
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