Using confirmatory composite analysis to assess emergent variables in business research

Henseler, J., & Schuberth, F. (2020). Using confirmatory composite analysis to assess emergent variables in business research. Journal of Business Research, 120, 147-156. https://doi.org/10.1016/j.jbusres.2020.07.026Confirmatory composite analysis (CCA) was invented by Jörg Henseler and Theo K. Dijkstra in 2014 and elaborated by Schuberth et al. (2018b) as an innovative set of procedures for specifying and assessing composite models. Composite models consist of two or more interrelated constructs, all of which emerge as linear combinations of extant variables, hence the term ‘emergent variables’. In a recent JBR paper, Hair et al. (2020) mistook CCA for the measurement model evaluation step of partial least squares structural equation modeling. In order to clear up potential confusion among JBR readers, the paper at hand explains CCA as it was originally developed, including its key steps: model specification, identification, estimation, and assessment. Moreover, it illustrates the use of CCA by means of an empirical study on business value of information technology. A final discussion aims to help analysts in business research to decide which type of covariance structure analysis to use.publishersversionpublishe

Learning Topic Models and Latent Bayesian Networks Under Expansion Constraints

Unsupervised estimation of latent variable models is a fundamental problem central to numerous applications of machine learning and statistics. This work presents a principled approach for estimating broad classes of such models, including probabilistic topic models and latent linear Bayesian networks, using only second-order observed moments. The sufficient conditions for identifiability of these models are primarily based on weak expansion constraints on the topic-word matrix, for topic models, and on the directed acyclic graph, for Bayesian networks. Because no assumptions are made on the distribution among the latent variables, the approach can handle arbitrary correlations among the topics or latent factors. In addition, a tractable learning method via $\ell_1$ optimization is proposed and studied in numerical experiments.Comment: 38 pages, 6 figures, 2 tables, applications in topic models and Bayesian networks are studied. Simulation section is adde

An Introduction To Multi-Battery Factor Analysis: Overcoming Method Artefacts

Examination of participant\u27s responses to factor or scale scores provides useful insights, but analysis of such scores from multiple measures or batteries is sometimes confounded by methodological artefacts. This paper provides a short primer into the use of multi-trait, multi-method (MTMM) correlational analysis and multi-battery factor analysis (MBFA). The principles of both procedures are outlined and a case study is provided from the author\u27s research into 233 teacher\u27s responses to 22 scale scores drawn from five batteries. The batteries were independently developed measures of teacher\u27s thinking about the nature and purpose of assessment, teaching, learning, curriculum, and teacher efficacy. Detailed procedures for using Cudeck\u27s (1982) MBFACT software are provided. Both MTMM and MBFA analyses identified an appropriate common trait across the five batteries, whereas joint factor analysis of the 22 scale scores confounded the common trait with a battery or method artefact. When researchers make use of multiple measures, they ought to take into account the impact of method artefacts when analyzing scale scores from multiple batteries. The multi-battery factor analysis procedure and MBFACT software provide a robust procedure for exploring how scales inter-relate. Accessed 16,966 times on https://pareonline.net from May 29, 2007 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right