Birth outcome, not pregnancy process: Reply to van der Veen

In a recent article (Frisbie. Forbes. and Pullum 1996) we documented racial/ethnic differences in birth outcomes according to a more fine-grained classification than has typically been employed in the demographic literature. In his commentary, van der Veen focuses on the measurement of one of the dimensions of that classification, maturity of the infant. as proxied by the fetal growth ratio. The crux of the critique is easily seen in van der Veen's statement that “all of my disagreements with Frisbie et al. 's method arise from their particular use of a postnatal standard for the assessment of intrauterine growth.” Our critic misunderstands our objective: He fails to realize our interest in birth outcome, not pregnancy process, and does not perceive that our intent was to extend the research extant in both the demographic and public-health literatures in which patently postnatal (i.e., ex utero) measures are taken as outcomes interesting in their own right and/or as risk factors for infant mortality and infant and childhood morbidity. Specifically, he does recognize that we purposefully expanded our focus to include moderately compromised births to determine if they were at higher risk than the normal births with whom they are conventionally categorized. Our discussion draws on research cited in the original article, on studies cited by our critic, and on a few more recent investigations. Although we have never argued that ours is the only, or even the best, approach in all cases, we try to clarify the rationale for, and adduce additional empirical evidence of, the utility of the method we used.     

An Investigation of Measures of Modern and Old-Fashioned Sexism

Working from the Principle of Beneficence we make the case that there is a need for adequate measures of sexism in men and women. Swim, Aikin, Hall and Hunter (1995) suggest that modern sexism represents a constellation of beliefs in which individuals: (a) disavow women's present-day discrimination; (b) reject women's demands for political and economic power; and (c) disapprove of policies designed to promote gender equality. To assess this construct, Swim et al. (1995) developed the Modern Sexism Scale (MSS), and to date, few studies have assessed its reliability and factor structure. Using data from 187 Canadian participants, confirmatory factor analysis was used to assess the unidimensionality of the MSS and Swim and associates' (1995) Old-Fashioned Sexism Scale (OFSS). Results do not support Swim et al.'s conceptualization of modern sexism as a unidimensional construct; however, the OFSS fit a unidimensional model. The implications of these findings are discussed.     

A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.     

APPLICATIONS OF SPECTRAL ANALYSIS

The basic concepts and application of spectral analysis are explained. Stationary time series and autocorrelation are first defined. Autocorrelation is related to the familiar concepts of variance and covariance. The use of autocorrelation analysis is explained in estimating the interdependent relationship of a time series over discrete time lags. In order to measure the behavior of the time series using autocorrelation, it would be necessary to examine a very large number of autocorrelation lags. Alternatively, the technique of Fourier analysis can be used to transform the autocorrelation function of the time series into a continuous function, termed a spectrum. The spectrum has a one to one correspondence to the autocorrelation for the time series and has the advantage of representing all possible autocorrelations over the discrete time lags. The spectrum can then be examined as a measure of the behavior of the time series. Spectral analysis indicates the reliability of the analysis of autocorrelated variables when familiar statistical techniques such as sample means and variances are used. The application of spectral analysis to management science problems in three general areas is illustrated: (1) inventory demand, (2) transportation simulation, and (3) stock market price behavior. Spectral analysis was used to detect cycles and trends in the data. Analyses were focused on the spectrum which provides a measure of the relative contribution of cycles in a band of frequencies to the total variance of the data.     

CONFLICT RESOLUTION IN INVESTMENT DECISIONS: IMPLEMENTATION OF GOAL PROGRAMMING METHODOLOGY FOR DUAL-PURPOSE FUNDS*

Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.     

RETURN ON INVESTMENT AS A CRITERION FOR INVENTORY MODELS

In the past most inventory formulations have utilized cost minimization or profit maximization as an optimizing criterion. When viewed from the standpoint of the owner or investor, maximizing the return on investment (ROI) is an appropriate criterion for many types of inventories. This paper proposes ROI as a criterion for inventory models and derives optimal reorder rules for some common assumptions. An economic order quantity that differs greatly from the traditional formulas is discussed. The paper also enumerates the conditions under which ROI is an appropriate criterion and contrasts it to the traditional cost minimization and profit maximization criteria.