Improved confidence intervals for differences between standardized effect sizes.

An evaluation of a difference between effect sizes from two dependent variables in a single study is likely to be based on differences between standard scores if raw scores on those variables are not scaled in comparable units of measurement. The standardization used for this purpose is usually sample-based rather than population-based, but the consequences of this distinction for the construction of confidence intervals on differential effects have not been systematically examined. In this article I show that differential effect confidence intervals (CIs) constructed from differences between the standard scores produced by sample-based standardization can be too narrow when those effects are large and dependent variables are highly correlated, particularly in within-subjects designs. I propose a new approach to the construction of differential effect CIs based on differences between adjusted sample-based standard scores that allow conventional CI procedures to produce Bonett-type CIs (Bonett, 2008) on individual effects. Computer simulations show that differential effect CIs constructed from adjusted standard scores can provide much better coverage probabilities than CIs constructed from unadjusted standard scores. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Studentized maximum root procedures for coherent analyses of two-factor fixed-effects designs.

The authors provide generalizations of R. J. Boik's (1993) studentized maximum root (SMR) procedure that allow for simultaneous inference on families of product contrasts including simple effect contrasts and differences among simple effect contrasts in coherent analyses of data from 2-factor fixed-effects designs. Unlike the F-based simultaneous test procedures (STPs) proposed by M. A. Betz and K. R. Gabriel (1978) for coherent analyses allowing for inferences on all factorial contrasts, SMR STPs are designed for analyses where each contrast of interest is a product contrast, which is usually the case in analyses of data from factorial experiments. When both factors have more than 2 levels, SMR STPs always provide more power and precision than F STPs for inferences on product contrasts.

Controlling the maximum familywise Type I error rate in analyses of multivariate experiments.

Multivariate experiments are often analyzed by multistage multiple-comparison procedures (MCPs) that prohibit univariate testing on individual dependent variables if an overall multivariate analysis of variance (MANOVA) test fails to reject the relevant overall null hypothesis. Although the sole function of the MANOVA test in such analyses is to control the overall Type I error rate, it is known that the most popular MANOVA-protected MCPs do not control the maximum familywise error rate (MFWER). In this article, we show that the MFWER associated with standard MANOVA-protected MCPs can be so large that the protection provided by the initial MANOVA test is illusory. We show that the MFWER can be controlled nonconservatively with modified protected MCPs and with single-stage MCPs that allow for the construction of simultaneous confidence intervals on effect sizes. We argue that, given the ease with which these MCPs can be implemented, there is no justification for continued use of the standard procedures.

Deduced inference in the analysis of experimental data.

Any set of confidence interval inferences on J - 1 linearly independent contrasts on J means, such as the two comparisons µ1 - µ2 and µ2 - µ3 on 3 means, provides a basis for the deduction of interval inferences on all other contrasts, such as the redundant comparison µ1 - µ3. Deduced inference does not inflate the experimentwise error rate beyond the level associated with the set of direct statistical inferences from which it is deduced. This article shows that although deduced inference is not as precise as direct inference, it can be a useful alternative to the kind of post hoc analysis that often follows analyses of sets of linearly independent contrasts. The article also illustrates applications of deduced inference following analyses of factorial experiments, where the problems often associated with popular analysis strategies can be avoided if inferences on simple effect contrasts are deduced from confidence intervals on main and interaction effect contrasts.

Psychological factors, immune function and recovery from major surgery.

OBJECTIVE: This study used a prospective design and the technique of structural modelling to examine the complex interrelations between psychological factors, immune status and complications after major surgery. METHODS: Twenty-nine women scheduled for elective cholecystectomy were studied prospectively. Information regarding medical history, health practices, life stressors, and coping strategies was obtained two weeks prior to admission. At this initial meeting, as well as three days after surgery, and at one month follow-up immunological tests were performed and the level of psychological distress was assessed. The study additionally included measures of post-operative complications, and infections and negative effect during follow-up. RESULTS: Pre-operative immune status emerged as a key variable exerting strong effects on subsequent immune function and, thereby producing significant, indirect effects on every recovery variable. Pre-operative distress was directly linked to increased mood disturbance at follow-up. Moreover, distress significantly influenced immune function both before and after surgery, which mediated a significant impact on most recovery variables. Active coping behaviour directly increased the risk of a complicated recovery. CONCLUSIONS: The study demonstrated that distress-induced changes in immune functioning have clinical relevance. Overall, the present findings suggest that recovery from surgery is facilitated in patients with a well-functioning immune system, a low-level of pre-operative distress and a passive coping disposition.