## Abstract

In this paper, we focus on PLS-SEM’s ability to handle models with observable binary outcomes. We examine the different ways in which a binary outcome may appear in a model and distinguish those situations in which a binary outcome is indeed problematic versus those in which one can easily incorporate it into a PLS-SEM analysis. Explicating such details enables IS researchers to distinguish different situations rather than avoid PLS-SEM altogether whenever a binary indicator presents itself. In certain situations, one can adapt PLS-SEM to analyze structural models with a binary observable variable as the endogenous construct. Specifically, one runs the PLS-SEM first stage as is. Subsequently, one uses the output for the binary variable and latent variable antecedents from this analysis in a separate logistic regression or discriminant analysis to estimate path coefficients for just that part of the structural model. We also describe a method—regularized generalized canonical correlation analysis (RGCCA)—from statistics, which is similar to PLS-SEM but unequivocally allows binary outcomes.

Original language | English |
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Article number | 23 |

Pages (from-to) | 400-419 |

Number of pages | 20 |

Journal | Communications of the Association for Information Systems |

Volume | 38 |

Issue number | 1 |

DOIs | |

State | Published - 2016 |

### Bibliographical note

Publisher Copyright:© 2016 by the Association for Information Systems.

## Keywords

- Binary endogenous variables
- PLS
- Partial least squares
- Structural equation modeling

## ASJC Scopus subject areas

- Information Systems