Determining Predictor Importance In Multiple Regression Under Varied Correlational And Distributional Conditions

This study examines the performance of eight methods of predictor importance under varied correlational and distributional conditions. The proportion of times a method correctly identified the dominant predictor was recorded. Results indicated that the new methods of importance proposed by Budescu (1993) and Johnson (2000) outperformed commonly used importance methods

Preoperative coping strategies and distress predict postoperative pain and morphine consumption in women undergoing abdominal gynecologic surgery

Objectives The aim of the present study was to predict postoperative pain and morphine consumption based on preoperative psychosocial factors. Methods One hundred and twenty-two women completed measures of distress and coping 1 week before major abdominal gynecological surgery by laparotomy. Forty-eight hours after surgery, measures of pain and negative affect (NA) were completed, and morphine consumption was recorded from a patient-controlled analgesia pump. Four weeks after surgery, measures of pain and NA were completed. Results Multivariate analyses revealed that preoperative self-distraction coping (P=.039) positively predicted postoperative pain levels in the hospital, after accounting for the effects of age, concurrent NA, and morphine consumption. Emotional support (P=.031) and religious-based coping (P=.036) positively predicted morphine consumption in the hospital, after accounting for the effects of age, concurrent NA, and pain levels. Preoperative distress (P<.04 to .008) and behavioral disengagement (P=.034), emotional support (P=.049), and religious-based coping (P=.001) positively predicted pain levels 4 weeks after surgery, after accounting for the effects of age and concurrent NA. Conclusion The results suggest that preoperative psychosocial factors are associated with postoperative pain and morphine consumption

Select Conference Presentations

Temporary repository for select conference presentations and related material

Correcting bias in extreme groups design using a missing data approach

Extreme groups design (EGD) refers to the use of a screening variable to inform further data collection, such that only participants with the lowest and highest scores are recruited in subsequent stages of the study. It is an effective way to improve the power of a study under a limited budget, but produces biased standardized estimates. We demonstrate that the bias in EGD results from its inherent missing at random mechanism, which can be corrected using modern missing data techniques such as full information maximum likelihood. Further, we provide a tutorial on computing correlations in EGD data with FIML using R

We need to change how we compute RMSEA for nested model comparisons in structural equation modeling

Comparison of nested models is common in applications of structural equation modeling (SEM). When two models are nested, model comparison can be done via a chi-square difference test or by comparing indices of approximate fit. The advantage of fit indices is that they permit some amount of misspecification in the additional constraints imposed on the model, which is a more realistic scenario. The most popular index of approximate fit is the root mean square error of approximation (RMSEA). In this article, we argue that the dominant way of comparing RMSEA values for two nested models, which is simply taking their difference, is problematic and will often mask misfit. We instead advocate computing the RMSEA associated with the chi-square difference test. We are not the first to propose this idea, and we review numerous methodological articles that have suggested it. Nonetheless, these articles appear to have had little impact on actual practice. The modification of current practice that we call for may be particularly needed in the context of measurement invariance assessment. We illustrate the difference between the current approach and our advocated approach on three examples, where two involve multiple-group and longitudinal measurement invariance assessment and the third involves comparisons of models with different numbers of factors. We conclude with a discussion of limitations and future research directions

Paper-and-pencil or online? Evaluating mode effects on measures of emotional functioning and attachment

The viability of using the World Wide Web to collect data from three widely used instruments by clinicians and researchers was investigated. The instruments were the Inventory of Parental and Peer Attachment, the Negative Mood Regulation Scale, and the Trait Meta-Mood Scale. Data were collected from two comparable groups of college students, and differences in response patterns on paper-and-pencil and World Wide Web versions of the measures, at both the item level and scale score level, were documented. Although mode of administration effects were statistically significant, the magnitude of the effects was in general very small. The basic similarity of the properties of the measures using paper-and-pencil and online Internet modes of administration suggests the viability of the Internet for assessing these and other psychological phenomena