Sex selection with biased technologies and its effect on the population sex ratio

Current biomedical research on sex selection techniques may soon offer couples the opportunity to choose the sex of their children with greater certainty. A technique planned for marketing by mid-1978 can increase the probability of bearing a son to as much as 0.90. However, couples who wish to improve their chances of bearing a daughter have no such opportunity. The purpose of this paper is two-fold. First, a decision-making model is provided which describes how couples should choose among alternative sex-selection methods so as to maximize the probability of bearing their desired number of sons and daughters. Second, the effect of the widespread use of sex-selection techniques on the population sex ratio is explored. It is shown that even if populations have unbiased sex preferences, or sex preferences biased towards daughters, the use of biased sex-selection technologies may result in very high population sex ratios.     

Microcomputers as work stations for sociologists

Microcomputers are making their way into the departments, institutions and homes of most sociologists. After a brief overview of the directions of computer and microcomputer evolution and of the microcomputer's present uses, I offer specific advice about the use and purchase of microcomputers for sociologists engaged in research, teaching and administration.     

Evaluation of multilevel decision trees

The evaluation of decision trees under uncertainty is difficult because of the required nested operations of maximizing and averaging. Pure maximizing (for deterministic decision trees) or pure averaging (for probability trees) are both relatively simple because the maximum of a maximum is a maximum, and the average of an average is an average. But when the two operators are mixed, no simplification is possible, and one must evaluate the maximization and averaging operations in a nested fashion, following the structure of the tree. Nested evaluation requires large sample sizes (for data collection) or long computation times (for simulations).     

Inference about permutation parameter in large samples

The estimation problem of a permutation parameter on the basis of a random sample of increasing size is considered. A necessary and sufficient condition for the existence of an estimator, asymptotically fully efficient for two different distributions families, is derived. We also study the application of this result to cyclic groups of order two and three.