CLC Estimator App for Survey Research

CLC Estimator, Congeneric Latent Construct Estimator, is a free and open-source app to estimate latent unidimensional constructs via congeneric approaches in survey research. 

Presenting the CLC Estimator App

In Social Sciences, researchers often deal with latent variables to infer complex constructs like personality traits or organizational characteristics that cannot be measured directly. Traditionally, researchers have used simple average or sum scores of the items to estimate these variables. However, congeneric approaches may provide a more accurate and reliable method which assigns a different weight to each item based on its unique variance for the estimation of the latent variable (see Methodological Notes for more detailed information).

Although congeneric methods may yield more accurate outcomes than parallel-based scoring techniques (sum score or average), most statistical packages lack straightforward access to congeneric estimations methods. To tackle this problem, we developed the CLC Estimator, which enables social scientists to seamlessly employ congeneric approaches for estimating unidimensional latent constructs in survey research (see Usage Notes for more detailed information). 

The CLC Estimator app grounds on a peer-reviewed academic study published in Multivariate Behavioral Research entitled "CLC Estimator: A Tool for Latent Construct Estimation via Congeneric Approaches in Survey Research" and contributes to the ongoing debate on the topic (see, for example, Kuhfeld and Soland, 2022;  McNeish and Wolf, 2020; McNeish, 2022).

The source code (R and Shiny) for the CLC Estimator is available on GitHub at: https://github.com/LeoEgidi/clc