On structural models for analyzing change

The recommendation to base the analysis of multi-wave data upon explicit models for change is advocated. Several univariate and multivariate models are described, which emerge from an interaction between the classical test theory and the structural equation modeling approach. The resulting structural models for analyzing change reflect in some of their parameters substantively interesting aspects of intra- and interindividual change in follow-up studies. The models are viewed as an alternative to an ANOVA-based analysis of longitudinal data, and are illustrated on data from a cognitive intervention study of old adults (Bakes et al , 1986). The approach presents a useful means of analyzing change over time, and is applicable for purposes of (latent) growth curve analysis when analysis of variance assumptions are violated (e.g., Schaie & Hertzog, 1982; Morrison, 1976).     

Defining web ethics

The design of Web browsers has resulted in a transfer of power to Web users and developers who often lack an ethical framework in which to act. For example, the technology makes it simple to copy and use other people’s Web page formatting without their permission. The author argues that we need to educate more people about ethical Web practices, and the author asks for “rules of the road” which amateurs and professionals can understand and follow. This article discusses four areas of concern about Web development: the browser wars, information storage and retrieval, access for the handicapped, and cookies. For teachers, there are suggestions on how to use browsers to help students learn about Web ethics. “We are all idiots and we are going to make mistakes.” Scott Adams An earlier version of this paper was presented by the author at a mini-conference, Practicing and Teaching Ethics in Engineering and Computing, held during the Sixth Annual Meeting of the Association for Practical and Professional Ethics, Washington, D.C., March 8–9, 1997. This paper is one of a series edited by Michael C. Loui. See Volume 3, No. 4, 1997 for other papers in this series.     

Fitting multivariage normal finite mixtures subject to structural equation modeling

This paper is about fitting multivariate normal mixture distributions subject to structural equation modeling. The general model comprises common factor and structural regression models. The introduction of covariance and mean structure models reduces the number of parameters to be estimated in fitting the mixture and enables one to investigate a variety of substantive hypotheses concerning the differences between the components in the mixture. Within the general model, individual parameters can be subjected to equality, nonlinear and simple bounds constraints. Confidence intervals are based on the inverse of the Hessian and on the likelihood profile. Several illustrations are given and results of a simulation study concerning the confidence intervals are reported.     

Why Combine Logics?

Combining logics has become a rapidly expanding entreprise that is inspired mainly by concerns about modularity and the wish to join together tailor made logical tools into more powerful but still manageable ones. A natural question is whether it offers anything new over and above existing standard languages.By analysing a number of applications where combined logics arise, we argue that combined logics are a potentially valuable tool in applied logic, and that endorsements of standard languages often miss the point. Using the history of quantified modal logic as our main example, we also show that the use of combined structures and logics is a recurring theme in the analysis of existing logical systems.     

Multilevel covariance structure analysis of sibling substance use and intrafamily conflict

Conventional covariance structure analysis, such as factor analysis, is often applied to data that are obtained in a hierarchical fashion, such as siblings observed within families. A more appropriate specification is demonstrated which explicitly models the within-level and between-level covariance matrices of sibling substance use and intrafamily conflict. Participants were 267 target adolescents (mean age=13.11 years) and 318 siblings (mean age=15.03 years). The level of homogeneity within sibling clusters, and heterogeneity among families, was sufficient to conduct a multilevel covariance structure analysis (MCA). Parental and family-level variables consisting of marital status, socioeconomic status, marital discord, parent use, and modeling of substances were used to explain heterogeneity among families. Marital discord predicted intrafamily conflict, and parent marital status and modeling of substances predicted sibling substance use. Advantages and uses of hierarchical designs and conventional covariance structure software for multilevel data are discussed.     

On simulating non-normal distributions

Six different algorithms to generate widely different non-normal distributions are reviewed. These algorithms are compared in terms of speed, simplicity and generality of the technique. The advantages and disadvantages of using these algorithms are briefly discussed.The author would like to thank an anonymous referee for his comments and suggestions which lead to the inclusion of the section A Comparative Study.     

NORMAL PEER MODELS AND AUTISTIC CHILDREN'S LEARNING

Present research and legislation regarding mainstreaming autistic children into normal classrooms have raised the importance of studying whether autistic children can benefit from observing normal peer models. The present investigation systematically assessed whether autistic children's learning of discrimination tasks could be improved if they observed normal children perform the tasks correctly. In the context of a multiple baseline design, four autistic children worked on five discrimination tasks that their teachers reported were posing difficulty. Throughout the baseline condition the children evidenced very low levels of correct responding on all five tasks. In the subsequent treatment condition, when normal peers modeled correct responses, the autistic children's correct responding increased dramatically. In each case, the peer modeling procedure produced rapid achievement of the acquisition criterion which was maintained after the peer models were removed. These results are discussed in relation to issues concerning observational learning and in relation to the implications for mainstreaming autistic children into normal classrooms.     

A theory of consistency of ordering generalizable to tailored testing

Measures of consistency and completeness of order relations derived from test-type data are proposed. The measures are generalized to apply to incomplete data such as from tailored testing. The measures are based on consideration of the items-plus-persons by items-plus-persons matrix as an adjacency matrix in which a 1 means that the row element, whether item or person, dominated the column element. From this the number of item-item and person-person dominance relations can be derived. Indices of consistency are developed which compare the number of dominance relations in a matrix to the number that would occur if it were perfectly consistent and to the number in a random matrix; relations to other indices are noted.The research reported here was supported by the Office of Naval Research, Contract N00014-75-C-0684, NR150-373. The author wishes to thank Dr. Thomas J. Reynolds for a number of helpful discussions of this material.     

Conditional discrimination and equivalence relations: Control by negative stimuli

Three adult subjects were taught the following two-sample, two-comparison conditional discriminations (each sample is shown with its positive and negative comparison, in that order): A1-B1B2, A2-B2B1; B1-C1C2, B2-C2C1; and C1-D1D2, C2-D2D1. A teaching procedure was designed to encourage control by negative comparisons. Subjects were then tested for emergent performances that would indicate whether the baseline conditional discriminations were reflexive, symmetric, and transitive. The tests documented the emergence of two classes of equivalent stimuli: A1, B2, C1, D2 and A2, B1, C2, D1. These were the classes to be expected if the negative comparisons were the controlling comparisons in the baseline conditional discriminations. The negative comparisons, however, were not the comparisons that subjects were recorded as having chosen in the baseline conditional discriminations. Differential test results confirmed predictions arising from a stimulus-control analysis: In reflexivity tests (AA, BB, CC, DD), subjects chose comparisons that differed from the sample; one-node transitivity (AC, BD) and "equivalence" (CA, DB) tests also yielded results that were the opposite of those to be expected from control by positive comparisons; symmetry tests (BA, CB, DC), two-node transitivity (AD) tests, and two-node "equivalence" (DA) tests yielded results that were to be expected from control by either positive or negative comparisons.     

Classical test theory based and factor analytic structural models for analyzing change: A note on differences and similarities

This note is concerned with differences and similarities between structural models for analyzing change, which are conceptualized within two different modelling traditions: the one based on the classical test theory, and that within the factor-analytic approach. It is shown that these two possibilities lead to models for studying change, which are indistinguishable when using for data analytic purposes structural modeling programs, such as LISREL, EQS, COSAN, LISCOMP, RAMONA, EzPATH, SAS PROC CALIS. The reason for this data-analytic equivalence of the two conceptually different types of models is the confounding of their differences in the corresponding implied covariance matrix structures.