The Drinkwater-Ross anthropometric fractionation of body mass: comparison with measured body mass and densitometrically estimated fat and fat-free masses

The Drinkwater-Ross anthropometric fractionation of body mass (mass = sigma skeletal, residual, fat and muscle masses), lean body mass (LBM = sigma skeletal, residual and muscle masses) and fat mass (FM) were compared with the measured body mass, together with the densitometrically estimated fat-free mass (FFM) and fat mass (FM), of 205 male (mean +/- S.D.: 74.66 +/- 10.55 kg; 10.1 +/- 3.7% BF by densitometry) and 177 female (mean +/- S.D.: 59.14 +/- 8.85 kg; 18.5 +/- 5.1% BF by densitometry) South Australian State representatives in a variety of sports. Most absolute differences (d) between the measured body masses and those resultant from the sum of the four fractionated masses (male: d = 2.15 kg or 2.9%; female: d = 1.27 kg or 2.2%) were within what one would expect from random day-to-day variation. However, this was not so for the comparisons between the fractionated LBM (male: d = 2.54 kg or 3.8%; female: d = 2.45 kg or 5.2%) and FM scores (male: d = 1.67 kg or 30.0%; female: d = 2.40 kg or 20.0%) and their densitometric counterparts. These differences are probably related to a combination of the densitometric and fractionation assumptions.     

A health/patient education database for family practice

Using pilot project funding from the W. K. Kellogg Foundation, the American Academy of Family Physicians Foundation (AAFP/F) developed a program by which health/patient education print materials were reviewed. Favorably reviewed materials were entered into a database accessible through the AAFP/F's Huffington Library. The review service and resulting database were designed to help the busy clinician identify scientifically accurate, reliable materials for use in patient education. The review process developed for the project is described, as is the database and its use by family physicians. Research findings from the pilot project are discussed, some of which assisted in planning the self-supporting second phase of the program.     

The Impact of Misspecifying Class-Specific Residual Variances in Growth Mixture Models

The purpose of this study was to examine the impact of misspecifying a growth mixture model (GMM) by assuming that Level-1 residual variances are constant across classes, when they do, in fact, vary in each subpopulation. Misspecification produced bias in the within-class growth trajectories and variance components, and estimates were substantially less precise than those obtained from a correctly specified GMM. Bias and precision became worse as the ratio of the largest to smallest Level-1 residual variances increased, class proportions became more disparate, and the number of class-specific residual variances in the population increased. Although the Level-1 residuals are typically of little substantive interest, these results suggest that researchers should carefully estimate and report these parameters in published GMM applications.     

Multiple Imputation Strategies for Multiple Group Structural Equation Models

Although structural equation modeling software packages use maximum likelihood estimation by default, there are situations where one might prefer to use multiple imputation to handle missing data rather than maximum likelihood estimation (e.g., when incorporating auxiliary variables). The selection of variables is one of the nuances associated with implementing multiple imputation, because the imputer must take special care to preserve any associations or special features of the data that will be modeled in the subsequent analysis. For example, this article deals with multiple group models that are commonly used to examine moderation effects in psychology and the behavioral sciences. Special care must be exercised when using multiple imputation with multiple group models, as failing to preserve the interactive effects during the imputation phase can produce biased parameter estimates in the subsequent analysis phase, even when the data are missing completely at random or missing at random. This study investigates two imputation strategies that have been proposed in the literature, product term imputation and separate group imputation. A series of simulation studies shows that separate group imputation adequately preserves the multiple group data structure and produces accurate parameter estimates.     

The incorporation of health and welfare risks into technological forecasting

The application of the outputs of scientific and technological development to industry snd society brings with it a shift in the pattern and distribution of health and safety risks to worker, user and third party. A quatitative method is suggested of measuring and controlling this reduction in an economic and socially acceptable way os that both growth and quality of life may be preserved.