The Relationship Between Adolescents’ Attitudes Toward Their Fathers and Loneliness: A Cross-Cultural Study

Loneliness can be regarded as an epidemic of modern society that is becoming increasingly problematic for millions of people. Adolescents are particularly susceptible to the development of this kind of emotional distress. Although many causes of loneliness can be identified, some researchers are of the opinion that it can also be attributed to unpleasant childhood experiences that result in unfulfilled needs relating to intimacy. In this research study, the relationship between loneliness and adolescents’ attitudes towards their fathers was investigated. In order to achieve this goal, information was gathered from 1068 South African respondents. Biographical questionnaires were administered and a Loneliness Scale was applied. In addition, adolescents’ attitudes towards their fathers were measured by means of a Fathers Questionnaire. Steps were taken to determine which of the independent variables, namely gender, age, race, language and socio-economic status, contributed significantly to the prediction of loneliness. In order to process the acquired information, a stepwise multiple regression analysis was conducted. The most important finding of this study is that adolescents’ attitudes towards their fathers comprise the most prominent predictor of loneliness during adolescence. The results also indicated that all of the independent variables had a statistically significant influence on loneliness.     

Net Amount of Food Affects Autoshaped Response Rate, Response Latency, and Gape Amplitude in Pigeons

The contribution of net amount of food to conditioned response strength and topography was assessed in four pigeons under autoshaping contingencies. In each session, under one baseline phase and three replication phases, three trial types were presented sequentially: One conditioned stimulus (keylight) signaled one small pellet, another signaled one large pellet, and a third signaled seven small pellets which were weight-matched to one large pellet. Five dependent variables were response rates and latencies based on the occurrence of both keyswitch closures and gapes (beak openings) and gape amplitudes. In result, net amount of food, not pellet diameter or number, affected all dependent variables. Notably, gape amplitudes elicited by the seven-small pellet keylights were larger than the gape amplutides elicited by the one-small pellet keylights even though the gape amplitudes elicited by both unconditioned stimuli (one or seven pellets) were equally small. This mismatch between conditioned and unconditioned responses is incompatible with stimulus substitution accounts but is compatible with an associative strength account. Furthermore, the changes in the dependent variables were most likely determined by Pavlovian and not by inadvertent operant contingencies. The findings demonstrate that an analysis of classical conditioning benefits from the inclusion of topographical measures.     

Further extensions of precision medicine to behavior analysis: A demonstration using functional communication training

The potential applicability of concepts and methods of the paradigm of precision medicine to the field of applied behavior analysis is only beginning to be explored. Both precision medicine and applied behavior analysis seek to understand and classify clinical problems through identification of their causal pathways. Both aim to develop treatments directly targeting those causal pathways, which also requires an understanding of the mechanisms by which treatments produce change (treatment-action pathways). In the current study, we extend the data-analytic methods and concepts described by Hagopian et al. (2018) toward the identification of variables that predict response to functional communication training (FCT). We discuss emerging conceptual issues, including the importance of distinguishing predictive behavioral markers from predictor variables based on their purported involvement in the causal or treatment-action pathways. Making these discriminations is a complex undertaking that requires knowledge of these mechanisms and how they interact.     

Comparing variances of correlated variables

A test is proposed for the equality of the variances ofk 2 correlated variables. Pitman's test fork = 2 reduces the null hypothesis to zero correlation between their sum and their difference. Its extension, eliminating nuisance parameters by a bootstrap procedure, is valid for any correlation structure between thek normally distributed variables. A Monte Carlo study for several combinations of sample sizes and number of variables is presented, comparing the level and power of the new method with previously published tests. Some nonnormal data are included, for which the empirical level tends to be slightly higher than the nominal one. The results show that our method is close in power to the asymptotic tests which are extremely sensitive to nonnormality, yet it is robust and much more powerful than other robust tests.This research was supported by the fund for the promotion of research at the Technion.     

An alternative two stage least squares (2SLS) estimator for latent variable equations

The Maximum-likelihood estimator dominates the estimation of general structural equation models. Noniterative, equation-by-equation estimators for factor analysis have received some attention, but little has been done on such estimators for latent variable equations. I propose an alternative 2SLS estimator of the parameters in LISREL type models and contrast it with the existing ones. The new 2SLS estimator allows observed and latent variables to originate from nonnormal distributions, is consistent, has a known asymptotic covariance matrix, and is estimable with standard statistical software. Diagnostics for evaluating instrumental variables are described. An empirical example illustrates the estimator. I gratefully acknowledge support for this research from the Sociology Program of the National Science Foundation (SES-9121564) and the Center for Advanced Study in the Behavioral Sciences, Stanford, California. This paper was presented at the Interdisciplinary Consortium for Statistical Applications at Indiana University at Bloomington (March 2, 1994) and at the RMD Conference on Causal Modeling at Purdue University, West Lafayette, Indiana (March 3-5, 1994).     

A note on distributional properties of the Jöreskog-Sörbom fit indices

In introducing the LISREL model for systems of linear structural equations, Jöreskog and Sörbom proposed two goodness-of-fit indices, GFI and AGFI. Their asymptotic distributions and some statistical properties are discussed.The authors would like to acknowledge the helpful comments and suggestions from the Associate Editor and two anonymous reviewers.     

Some contributions to efficient statistics in structural models: Specification and estimation of moment structures

Current practice in structural modeling of observed continuous random variables is limited to representation systems for first and second moments (e.g., means and covariances), and to distribution theory based on multivariate normality. In psychometrics the multinormality assumption is often incorrect, so that statistical tests on parameters, or model goodness of fit, will frequently be incorrect as well. It is shown that higher order product moments yield important structural information when the distribution of variables is arbitrary. Structural representations are developed for generalizations of the Bentler-Weeks, Jöreskog-Keesling-Wiley, and factor analytic models. Some asymptotically distribution-free efficient estimators for such arbitrary structural models are developed. Limited information estimators are obtained as well. The special case of elliptical distributions that allow nonzero but equal kurtoses for variables is discussed in some detail. The argument is made that multivariate normal theory for covariance structure models should be abandoned in favor of elliptical theory, which is only slightly more difficult to apply in practice but specializes to the traditional case when normality holds. Many open research areas are described.     

The rank of reduced dispersion matrices

Psychometricians working in factor analysis and econometricians working in regression with measurement error in all variables are both interested in the rank of dispersion matrices under variation of the diagonal elements. Psychometricians concentrate on cases in which low rank can be attained, preferably rank one, the Spearman case. Econometricians cocentrate on cases in which the rank cannot be reduced below the number of variables minus one, the Frisch case. In this paper we give an extensive historial discussion of both fields, we prove the two key results in a more satisfactory and uniform way, we point out various small errors and misunderstandings, and we present a methodological comparison of factor analysis and regression on the basis of our results.Financial support by the Netherlands Organization for the Advancement of Pure Research (ZWO) is gratefully acknowledged.     

Application of model-selection criteria to some problems in multivariate analysis

A review of model-selection criteria is presented, with a view toward showing their similarities. It is suggested that some problems treated by sequences of hypothesis tests may be more expeditiously treated by the application of model-selection criteria. Consideration is given to application of model-selection criteria to some problems of multivariate analysis, especially the clustering of variables, factor analysis and, more generally, describing a complex of variables.     

Retrospective questions and correlations

In survey research it is not uncommon to ask questions of the following type: “How many times did you undertake actiona in reference periodT of length τ?.” The relationship is established between τ and the correlation of the number of reported actions with some background variable. To this end it is assumed that the process of actions satisfies a renewal model with individual heterogeneity. Also a model has to be formulated for possible recall effects. Applications are given in the field of medical consumption.