The average rate of change for continuous time models

The average rate of change (ARC) is a concept that has been misunderstood in the applied longitudinal data analysis literature, where the slope from the straight-line change model is often thought of as though it were the ARC. The present article clarifies the concept of ARC and shows unequivocally the mathematical definition and meaning of ARC when measurement is continuous across time. It is shown that the slope from the straight-line change model generally is not equal to the ARC. General equations are presented for two measures of discrepancy when the slope from the straight-line change model is used to estimate the ARC in the case of continuous time for any model linear in its parameters, and for three useful models nonlinear in their parameters.     

Parental interrater reliability as a function of situational specificity and familiarity of target child

Child behavior ratings were made by 17 mothers and fathers of the same child. Agreement was computed for each parent pair under the following experimental conditions: (1) Ratings of their own child's behavior in general (CBC); (2) ratings of own child based on observations of him/her in a videotaped sample; (3) ratings of an unknown child based on observations of him/her in a videotaped sample; and (4) ratings of the videotaped sample of own child and unknown child using a one minute time-sampling procedure. Parents achieved higher rates of agreement (X=.81) than have previously been reported. Increasing the specificity of the behavior being rated did not significantly affect agreement. Those parent pairs who agreed the most did not necessarily spend a large amount of time in the same kind of situations with their child. Agreement was significantly greater when parents rated their own children's videotaped behavior sample as opposed to that of an unknown child.     

Accuracy in parameter estimation for targeted effects in structural equation modeling: sample size planning for narrow confidence intervals

In addition to evaluating a structural equation model (SEM) as a whole, often the model parameters are of interest and confidence intervals for those parameters are formed. Given a model with a good overall fit, it is entirely possible for the targeted effects of interest to have very wide confidence intervals, thus giving little information about the magnitude of the population targeted effects. With the goal of obtaining sufficiently narrow confidence intervals for the model parameters of interest, sample size planning methods for SEM are developed from the accuracy in parameter estimation approach. One method plans for the sample size so that the expected confidence interval width is sufficiently narrow. An extended procedure ensures that the obtained confidence interval will be no wider than desired, with some specified degree of assurance. A Monte Carlo simulation study was conducted that verified the effectiveness of the procedures in realistic situations. The methods developed have been implemented in the MBESS package in R so that they can be easily applied by researchers.     

Quality of Life in Heart Transplant Candidates

Research on quality of life with heart transplant patients is complicated by the physical and psychological variables associated with heart failure. This study examined quality of life with several instruments in order to sample more general and more idiosyncratic aspects of quality of life in this particular patient population. We also examined the relationship between various aspects of quality of life and several psychosocial variables, including sensitivity to social desirability effects. The quality of life measures used in this study were found to be significantly correlated with each other and with measures of psychological distress. Implications of these findings are discussed.     

A Methodological Review of Faith-Based Health Promotion Literature: Advancing the Science to Expand Delivery of Diabetes Education to Black Americans

Non-traditional avenues, such as faith-based organizations (FBOs), must be explored to expand delivery of diabetes self-management education (DSME) to benefit Black Americans with type 2 diabetes (T2D). The purpose of this study was to methodologically review the faith-based health promotion literature relevant to Blacks with T2D. A total of 14 intervention studies were identified for inclusion in the review. These studies detailed features of methods employed to affect health outcomes that DSME similarly targets. Analysis of the faith-based studies?? methodological features indicated most studies used (1) collaborative research approaches, (2) pre-experimental designs, (3) similar recruitment and retention strategies, and (4) culturally sensitive, behaviorally oriented interventions with incorporation of social support to achieve positive health outcomes in Black Americans. Findings indicate FBOs may be a promising avenue for delivering DSME to Black Americans. Informed by the findings, a focused discussion on advancing the science of faith-based interventions to expand delivery of DSME to Black Americans with diabetes is provided.     

On effect size

The call for researchers to report and interpret effect sizes and their corresponding confidence intervals has never been stronger. However, there is confusion in the literature on the definition of effect size, and consequently the term is used inconsistently. We propose a definition for effect size, discuss 3 facets of effect size (dimension, measure/index, and value), outline 10 corollaries that follow from our definition, and review ideal qualities of effect sizes. Our definition of effect size is general and subsumes many existing definitions of effect size. We define effect size as a quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest. Our definition of effect size is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest. Additionally, we review some important developments in the effect size literature and discuss the importance of accompanying an effect size with an interval estimate that acknowledges the uncertainty with which the population value of the effect size has been estimated. We hope that this article will facilitate discussion and improve the practice of reporting and interpreting effect sizes.