Organizational variables needed for effective execution of population planning programmes in Pakistan

Although the organizational and administrative aspects of family planning and population programs are basic to their ultimate success, they have been little studied. Some of the organizational variables necessary to effective implementation of population planning programs in Pakistan are outlined. These variables are of 2 types: 1) intraorganizational; and 2) interorganizational. Field workers must have an adequate idea of the role they should play and an adequacy for such a role. Care should be taken in the selection of field workers. They should be assigned to areas where their clients will have socioeconomic backgrounds similar to their own. Family planning field workers should have regular opportunities to communicate with other types of field workers in other programs. Both vertical and horizontal communication among family planning workers is important. The success of family planning programs will ultimately depend on the effecting of social changes and larger changes in social values. In other words, family planning programs should occur in the larger context of development programs. Interdepartmental communication among various agencies involved with family planning and population is necessary.     

The analysis of multifactor experiments designed to contrast the within- and between-subjects manipulation of the independent variables

Experiments are frequently performed in which the major question of interest is whether the effect of the substantive independent variable depends on its manipulation within as opposed to between subjects. Erlebacher (1977) discussed the design of such experiments in which one independent variable is manipulated, and presented a method of analysis by an analysis of variance technique. This paper discusses the analysis of three different experimental designs in which the within-subjects/between-subjects design type is one factor and two substantive independent variables are manipulated. A rule is also presented that can be used to generate statistical tests in a generalized multifactor design.     

Symmetry-based methodology for decision-rule identification in same-different experiments

The standard practice of reducing every same-different data set to two numbers (hits and false alarms) is wasteful, because the response pattern to all four stimulus pairs carries information about the decision rule adopted by the observer. We describe eight rules organized in three families: differencing, covert classification, and likelihood ratio. We prove that each family produces a characteristic pattern of (in)equalities among the response probabilities. We propose two simple qualitative tests. Is the performance on stimulus pairs AA and BB statistically indistinguishable? If not, differencing and likelihood-ratio strategies can be rejected. Is the performance on pairs AB and BA indistinguishable? If not, covert classification can be rejected. We present algorithms for fitting two covert-classification models and illustrate the new methodology in a perceptual learning experiment on visual motion-direction discrimination. The standard assumption of symmetric decision criteria was violated.     

Factor analysis for non-normal variables

Factor analysis for nonnormally distributed variables is discussed in this paper. The main difference between our approach and more traditional approaches is that not only second order cross-products (like covariances) are utilized, but also higher order cross-products. It turns out that under some conditions the parameters (factor loadings) can be uniquely determined. Two estimation procedures will be discussed. One method gives Best Generalized Least Squares (BGLS) estimates, but is computationally very heavy, in particular for large data sets. The other method is a least squares method which is computationally less heavy. In one example the two methods will be compared by using the bootstrap method. In another example real life data are analyzed.This paper has partly been written while the author was a visiting scholar at the Department of Psychology, University of California, Los Angeles. He wants to thank Peter Bentler who made this stay at UCLA possible and for his valuable contributions to this paper. This research was supported by the Netherlands Organization for the Advancement of Pure Research (Z.W.O) under number R56-150 and by USPHS Grant DA01070.     

Retinal location and string position as important variables in visual information processing

Three experiments were conducted to isolate the effects of retinal locus and string position in tachistoscopic letter recognition. Retinal locus proved to be an important variable even when its range was restricted to less than a degree from the center of the fovea. Performance was maximal at the center of the fovea, dropping off rapidly to about 1.5 deg from the center. From that distance on, the decline in performance was quite gradual. String position was also an important factor. Retinal locus and string position interacted in such a way that the end positions were less affected by retinal locus than the middle positions. It was also found that processing order, as distinct from report order, was a significant component of the string position effect.     

Measures of invariance and comparability in factor analysis for fixed variables

New procedures are presented for measuring invariance and matching factors for fixed variables and for fixed or different subjects. Two of these, the coefficient of invariance for factor loadings and the coefficient of factor similarity, utilize factor scores computed from the different sets of factor loadings and one of the original standard score matrices. Another, the coefficient of subject invariance, is obtained by using one of the sets of factor loadings in conjunction with the different standard score matrices. These coefficients are correlations between factor scores of the appropriate matrices. When the best match of factors is desired, rather than degree of resemblance, the method of assignment is proposed.     

Regression estimation and post-stratification in factor analysis

Regression estimation and poststratification are methods used in survey sampling to estimate a population mean, when additional information is available for some auxiliary variables. The extension of these methods to factor analysis is considered. These methods may be used either to improve the efficiency of estimation or to adjust for the effects of nonrandom selection. The estimation procedure may be formulated in a LISREL framework.The author is grateful to the referees for their comments.     

Reinforcer identification and evaluation of choice within an educational setting

Current research on preference methodology and the effects of choice with children who are typically developing is limited. This study evaluated two preference methodologies for identifying reinforcers across two choice options within the classroom. These methods include a teacher-generated list of reinforcers, a student-generated list of reinforcers, student choice from both lists, and teacher choice from both lists. Four elementary aged-children with no diagnosed learning or developmental disabilities participated. Both preference methods were effective in identifying reinforcers for all participants. Moreover, both choice options resulted in increases in responding. Results are discussed with respect to reinforcement and the effects of “choice.”     

A criterion for selecting variables in a regression analysis

Methods are given for deciding whether to use some or no predictor variables in a regression analysis. Previously obtained results on the more general problem, whether to usek ork — r predictor variables are reviewed with emphasis on applications.     

Models for the analysis of alternative sources of growth in correlated stochastic variables

An approach to the analysis of multivariate time series is presented in which linear structural relationships among multiple stochastic variables are investigated. A number of alternative structural models are considered for the case of two stochastic variables. Each model represents a possible hypothesis concerning the relationship of growth in one variable to growth in the second. Both symmetric and asymmetric models are considered. Extensions of two of the models to three variables are illustrated by means of a numerical example. Implications of the models for the problem of detecting change in multivariate time series are discussed.This paper is based in part on a paper read at the Psychometric Society Meetings, Princeton, N. J., Mar. 30–31, 1972. The research was supported by the Committe on Basic Research in Education, National Academy of Sciences, Grant No. OEG-0-9-140396-4497(010), Project No. 9-0396.     

Top-down gain control in the auditory system: evidence from identification and discrimination experiments

The influence of intensity range in auditory identification and intensity discrimination experiments is well documented and is usually attributed to nonsensory factors. Recent studies, however, have suggested that the stimulus range effect might be sensory in origin. To test this notion, in one set of experiments, we had listeners identify the individual tones in a set. One baseline condition consisted of identifying four 1-kHz, low-intensity tones; the other consisted of identifying four 1-kHz, high-intensity tones. In the experimental conditions, these baseline tone sets were augmented by adding a fifth tone at either 1 or 5 kHz. Added 5-kHz tones had little effect on identification accuracy for the four baseline tones. When an added 1-kHz tone differed substantially in intensity from the four baseline tones, it adversely affected performance, with the addition of a high-intensity tone to a set of low-intensity tones having a more deleterious effect than the addition of a low-intensity tone to a set of high-intensity tones. These and further results, obtained in an exploration of this asymmetrical range effect in a third identification experiment and in two intensity-discrimination experiments, were consistent with the notion of a nonlinear amplifier under top-down control whose functions include protection against sensory overload from loud sounds. The identification data were well described by a signal-detection model using equal-variance Laplace distributions instead of the usual Gaussian distributions.     

Simple structure in component analysis techniques for mixtures of qualitative and quantitative variables

Several methods have been developed for the analysis of a mixture of qualitative and quantitative variables, and one, called PCAMIX, includes ordinary principal component analysis (PCA) and multiple correspondence analysis (MCA) as special cases. The present paper proposes several techniques for simple structure rotation of a PCAMIX solution based on the rotation of component scores and indicates how these can be viewed as generalizations of the simple structure methods for PCA. In addition, a recently developed technique for the analysis of mixtures of qualitative and quantitative variables, called INDOMIX, is shown to construct component scores (without rotational freedom) maximizing the quartimax criterion over all possible sets of component scores. A numerical example is used to illustrate the implication that when used for qualitative variables, INDOMIX provides axes that discriminate between the observation units better than do those generated from MCA.The Netherlands organization for scientific research (NWO) is gratefully acknowledged for funding this project. This research was conducted while the author was supported by a PSYCHON-grant (560-267-011) from this organization. The author is obliged to Jos ten Berge for his comments on an earlier version.