Are hospital ratings systems transparent? An examination of Consumer Reports and the Leapfrog Hospital Safety Grade.

The health care industry is complex, dynamic, and large. In such uncertain environments where a great deal of revenue is at stake, competition and comparative claims flourish. One such manifestation is hospital ratings systems. This research examines two influential hospital ratings to explore whether the hospital ratings of each system was straightforward and reproducible. Regressions and structural equations models were fit to examine the relationships among the hospital ratings constructs. Both hospital ratings systems were excellent in their transparency and reproducibility. The Consumer Reports and Leapfrog ratings systems can confidently tout that their hospital scores reflect what they claim to measure. The unique aspects of each system are also noted.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R2 will remain undisturbed (which is also good).

Mean centering helps alleviate "micro" but not "macro" multicollinearity.

There seems to be confusion among researchers regarding whether it is good practice to center variables at their means prior to calculating a product term to estimate an interaction in a multiple regression model. Many researchers use mean centered variables because they believe it's the thing to do or because reviewers ask them to, without quite understanding why. Adding to the confusion is the fact that there is also a perspective in the literature that mean centering does not reduce multicollinearity. In this article, we clarify the issues and reconcile the discrepancy. We distinguish between "micro" and "macro" definitions of multicollinearity and show how both sides of such a debate can be correct. To do so, we use proofs, an illustrative dataset, and a Monte Carlo simulation to show the precise effects of mean centering on both individual correlation coefficients as well as overall model indices. We hope to contribute to the literature by clarifying the issues, reconciling the two perspectives, and quelling the current confusion regarding whether and how mean centering can be a useful practice.