Cameron N. McIntosh

Reflections on Partial Least Squares Path Modeling

The purpose of the present article is to take stock of a recent exchange in Organizational Research Methods between critics and proponents of partial least squares path modeling (PLS-PM). The two target articles were centered around six principal issues, namely whether PLS-PM: (a) can be truly characterized as a technique for structural equation modeling (SEM), (b) is able to correct for measurement error, (c) can be used to validate measurement models, (d) accommodates small sample sizes, (e) is able to provide null hypothesis tests for path coefficients, and (f) can be employed in an exploratory, model-building fashion. We summarize and elaborate further on the key arguments underlying the exchange, drawing from the broader methodological and statistical literature to offer additional thoughts concerning the utility of PLS-PM and ways in which the technique might be improved. We conclude with recommendations as to whether and how PLS-PM serves as a viable contender to SEM approaches for estimating and evaluating theoretical models.

Pitfalls in subgroup analysis based on growth mixture models: a commentary on Van Leeuwen et al. (2012).

ObjectivesThis article is a brief commentary in response to “van Leeuwen et al. (Qual Life Res 21:1499–1508, 2012)”Methods and resultsThe commentary argues that in the context of mixture modeling, assigning individuals to specific subgroups for conducting a secondary set of analyses ignores the original uncertainty in group membership, thereby biasing any subsequent results and inference.ConclusionsAlternative approaches to subgroup analysis that attempt to preserve uncertainty in group membership are discussed and illustrated.

Spatial mobility and organised crime

This special issue focuses on a phenomenon studied by only a handful of organised crime scholars to date – criminal group mobility. The contributions in this issue evolved out of discussion papers commissioned by the Department of Public Safety Canada in 2010 for the 12th National and 15th International Metropolis conferences, and cover a wide variety of economic, social, and law enforcement issues related to the mobility of criminal groups. In this introductory article, we provide the general background and context for the collection, as well as a brief overview of each of the four papers. It is our hope that this special issue will inspire further rigorous research on this important topic, as well as help contribute toward the development of effective strategies for preventing the spread of organised crime.

A cautionary note on the finite sample behavior of maximal reliability.

Several calls have been made for replacing coefficient &agr; with more contemporary model-based reliability coefficients in psychological research. Under the assumption of unidimensional measurement scales and independent measurement errors, two leading alternatives are composite reliability and maximal reliability. Of these two, the maximal reliability statistic, or equivalently Hancock’s H, has received a significant amount of attention in recent years. The difference between composite reliability and maximal reliability is that the former is a reliability index for a scale mean (or unweighted sum), whereas the latter estimates the reliability of a scale score where indicators are weighted differently based on their estimated reliabilities. The formula for the maximal reliability weights has been derived using population quantities; however, their finite-sample behavior has not been extensively examined. Particularly, there are two types of bias when the maximal reliability statistic is calculated from sample data: (a) the sample maximal reliability estimator is a positively biased estimator of population maximal reliability, and (b) the true reliability of composites formed with maximal reliability weights calculated from sample data is on average less than the population reliability. Both effects are more pronounced in small-sample scenarios (e.g., <100). We also demonstrate that the composite reliability estimator for equally weighted composite exhibits substantially less bias, which makes it a more appropriate choice for the small-sample case.