25 years of higher‐order confirmatory factor analysis in the organizational sciences: A critical review and development of reporting recommendations

We discuss how confirmatory factor analysis results should be used to examine potential higher‐order constructs and advocate that researchers present five types of evidence, which are as follows: (1) the ability of the higher‐order model to reproduce the observed covariation among manifest variables; (2) the ability of the higher‐order model to reproduce the observed covariation among manifest variables better than more parsimonious alternative models—and no less well than less parsimonious alternative models; (3) the ability of the higher‐order model to reproduce the observed covariation among lower‐order factors; (4) the ability of the higher‐order factor to explain variation in lower‐order factors; and (5) the ability of the higher‐order factor to explain variation in manifest variables. We illustrate how this type of evidence could be presented with a worked example and contrast our recommendations with the manner in which higher‐order confirmatory factor analysis has been used in the organizational sciences over the past 25 years to support claims regarding higher‐order constructs such as core self‐evaluations and transformational leadership. Our review shows that a substantial proportion of the 44 examined articles failed to present enough evidence to allow readers to understand the size and importance of higher‐order factors. Copyright © 2015 John Wiley & Sons, Ltd.     

On-Scene Injury Severity Prediction (OSISP) Algorithm for Truck Occupants

Objective: The aim of this study is to develop an on-scene injury severity prediction (OSISP) algorithm for truck occupants using only accident characteristics that are feasible to assess at the scene of the accident. The purpose of developing this algorithm is to use it as a basis for a field triage tool used in traffic accidents involving trucks. In addition, the model can be valuable for recognizing important factors for improving triage protocols used in Sweden and possibly in other countries with similar traffic environments and prehospital procedures.

Methods: The scope is adult truck occupants involved in traffic accidents on Swedish public roads registered in the Swedish Traffic Accident Data Acquisition (STRADA) database for calendar years 2003 to 2013. STRADA contains information reported by the police and medical data on injured road users treated at emergency hospitals. Using data from STRADA, 2 OSISP multivariate logistic regression models for deriving the probability of severe injury (defined here as having an Injury Severity Score [ISS] > 15) were implemented for light and heavy trucks; that is, trucks with weight up to 3,500 kg and ??16,500 kg, respectively. A 10-fold cross-validation procedure was used to estimate the performance of the OSISP algorithm in terms of the area under the receiver operating characteristic curve (AUC).

Results: The rate of belt use was low, especially for heavy truck occupants. The OSISP models developed for light and heavy trucks achieved cross-validation AUC of 0.81 and 0.74, respectively. The AUC values obtained when the models were evaluated on all data without cross-validation were 0.87 for both light and heavy trucks. The difference in the AUC values with and without use of cross-validation indicates overfitting of the model, which may be a consequence of relatively small data sets. Belt use stands out as the most valuable predictor in both types of trucks; accident type and age are important predictors for light trucks.

Conclusions: The OSISP models achieve good discriminating capability for light truck occupants and a reasonable performance for heavy truck occupants. The prediction accuracy may be increased by acquiring more data. Belt use was the strongest predictor of severe injury for both light and heavy truck occupants. There is a need for behavior-based safety programs and/or other means to encourage truck occupants to always wear a seat belt.