No abstract

L. R. James, R. G. Demaree, and G. Wolf (1984) introduced rWG(J) to estimate interrater agreement for a group. This index is calculated by comparing an observed group variance with an expected random variance. As researchers have gained experience using this index, several questions have arisen. What are the consequences of replacing values beyond the unit interval by 0? What is the dependence of rWG(J) on the group size? The authors' simulations show that a positive bias is caused by the truncation, but for large population values of rWG(J) it is negligible. Also, in this case, the group size has no effect on the expected value of rWG(J). For inference on rWG(J), researchers can exploit the availability of computers to simulate data from the hypothesized distribution and then compare the simulation results for rWG(J) with the actual values. In addition, it is shown how the bootstrap method can be used for comparing the indices of 2 groups.

The authors investigated the moderating role of unit-level performance resources on the distress-mediated relationship between the intensity of involvement in workplace critical incidents and problematic drinking behavior (i.e., drinking to cope). Building on recent developments in hierarchical linear modeling, the authors tested a cross-level, moderated-mediation model using data from 1,481 firefighters in 144 companies. The findings indicate that (a) there is a significant, distress-mediated association between intensity of involvement in such incidents and drinking to cope, which varies by company (i.e., unit), and (b) the adequacy of unit-level performance resources explains much of this cross-unit variance and attenuates both individual-level mediation stages (i.e., intensity of involvement in critical incidents 3 distress, and distress 3 drinking to cope). Implications regarding the role of unit resources adequacy as a vulnerability factor in stressor-strain relations are discussed.

The most popular index of agreement has been r WG(J) ; more recently, the AD M(J) index also has been used. This study addresses two problems: first, how to test the statistical significance of r WG(J) and AD M(J) and, second, how to infer from the indices that were evaluated for each group about the agreement of the ensemble of groups. The authors extend the inference based on either r WG(J) or AD M(J) by focusing on multiple-item scales and on the whole ensemble of groups. Their method is based on simulations, as was done by Dunlap, Burke, and Smith-Crowe (2003) and by Cohen, Doveh, and Eick (2001). The tests are illustrated on the data of Bliese, Halverson, and Schriesheim (2002) pertaining to a sample of 2,042 U.S Army soldiers in 49 U.S. Army companies. Software for our procedures is available both as a SAS code and in the Multilevel Modeling in R package (Bliese, 2006).

In their seminal paper, Edwards and Parry (1993) presented the polynomial regression as a better alternative to applying difference score in the study of congruence. While this method is increasingly applied in congruence research, its complexity relative to other methods for assessing congruence (e.g., difference score methods) was one of the main claims against its use. The objective of this work is to gain additional insight into the use of polynomial regression in the area of social and behavioral sciences. First, we elaborate on the meaning and the inference based on the coefficients obtained by applying polynomial regression and explain the mathematical terms that are used to describe response surfaces. Then, we introduce additional inference methods and illustrate their application on a real life example from the area of supportive exchanges, using data collected by the Smithers Institute at Cornell University (supported by the National Institute on Alcohol Abuse and Alcoholism). Our work is aimed to provide a guide to researchers who apply polynomial regression in order to assess the effect of congruence between two constructs and enable better understanding and interpretation of the results obtained.