Higher-order models with reflective indicators: A rejoinder to a recent call for their abandonment

Abstract

Purpose

Higher-order factor models have recently been dismissed as a ‘misleading’, ‘meaningless’, and ‘needless’ approach for modeling multidimensional constructs (Lee and Cadogan, 2013; L & C, 2013 hereafter). The purpose of this paper is to show that – in contrast to L & C’s (2013) verdict – higher-order factor models are still a legitimate operationalization option for multidimensional constructs.

Design/methodology/approach

Basic conceptual and statistical premises of L & C’s (2013) arguments against higher-order factor models are scrutinized both conceptually and statistically as to their logic and validity.

Findings

A thorough analysis of L & C’s (2013) arguments shows that they are fundamentally flawed both conceptually and statistically, rendering their conclusions invalid.

Research limitations/implications

Researchers should not remove the well-established higher-order factor models from their methodological toolkit. Furthermore, empirical findings should not automatically be considered suspect simply because higher-factor models have been used to model multidimensional constructs.

Originality/value

So far, L & C’s (2013) arguments against higher-order factor models have gone unchallenged in the literature. This rejoinder is a first, much needed attempt to protect applied researchers from getting the false impression that by using higher-factor models, they rely on a “misleading” or “meaningless” modeling approach.

Citation

Temme, D. and Diamantopoulos, A. (2016), "Higher-order models with reflective indicators: A rejoinder to a recent call for their abandonment", Journal of Modelling in Management, Vol. 11 No. 1, pp. 180-188. https://doi.org/10.1108/JM2-05-2014-0037