Nonconvergence,improper solutions,and starting values in lisrel maximum likelihood estimation

In the framework of a robustness study on maximum likelihood estimation with LISREL three types of problems are dealt with: nonconvergence, improper solutions, and choice of starting values. The purpose of the paper is to illustrate why and to what extent these problems are of importance for users of LISREL. The ways in which these issues may affect the design and conclusions of robustness research is also discussed.     

Statistical consistency and hypothesis testing for nonmetric multidimensional scaling

The properties of nonmetric multidimensional scaling (NMDS) are explored by specifying statistical models, proving statistical consistency, and developing hypothesis testing procedures. Statistical models with errors in the dependent and independent variables are described for quantitative and qualitative data. For these models, statistical consistency often depends crucially upon how error enters the model and how data are collected and summarized (e.g., by means, medians, or rank statistics). A maximum likelihood estimator for NMDS is developed, and its relationship to the standard Shepard-Kruskal estimation method is described. This maximum likelihood framework is used to develop a method for testing the overall fit of the model.     

How serious a fallacy is inconsistency?

Consistency is often pictured as an indispensable requisite for rationality. The paper argues that this is overly rigoristic. Inconsistency can be treated as a matter of isolable singularities rather than an all-destructive disaster. The paper, supports and illustrates a perspective on which consistency can be seen as a desideratum rather than a totaly non-negotiable demand. The argumentation of the paper casts consistency in the role of a cognitive ideal rather than a sine qua non condition of rational process.     

On structural equation modeling with data that are not missing completely at random

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.The research of the first author was supported by grant No. SES-8312583 from the National Science Foundation and by a Spencer Foundation grant. We wish to thank Chuen-Rong Chan for drawing the path diagram.     

A note on the computation of the second-order derivatives of the elementary symmetric functions in the rasch model

Jansen (1984) gave explicit formulas for the computation of the second-order derivatives of the elementary symmetric functions. But they are only applicable to those pairs of items which have unequal parameters. It is shown here that for equal parameters similar explicit formulas do exist, too, facilitating the application of the Newton-Raphson procedure to estimate the parameters in the Rasch model and related models according to the conditional maximum likelihood principle.The author wishes to thank the reviewers for their helpful comments on an earlier draft of this paper.     

Estimation for structural equation models with missing data

A direct method in handling incomplete data in general covariance structural models is investigated. Asymptotic statistical properties of the generalized least squares method are developed. It is shown that this approach has very close relationships with the maximum likelihood approach. Iterative procedures for obtaining the generalized least squares estimates, the maximum likelihood estimates, as well as their standard error estimates are derived. Computer programs for the confirmatory factor analysis model are implemented. A longitudinal type data set is used as an example to illustrate the results.This research was supported in part by Research Grant DAD1070 from the U.S. Public Health Service. The author is indebted to anonymous reviewers for some very valuable suggestions. Computer funding is provided by the Computer Services Centre, The Chinese University of Hong Kong.     

Assertiveness and cognitive processing in interpersonal situations

This study examined social skills components that precede the delivery of a skilled overt interpersonal response. Using a cognitive-behavioral systems approach to assertiveness, a task analysis of how women receive and process information in interpersonal situations requiring an assertive response to men was performed. Forty women were assigned to high- or low-assertive groups based on their Rathus Assertiveness Schedule scores. In small group sessions, each woman viewed four videotaped problem situations requiring an assertive response to both pleasant and angry males. After viewing each scene, each woman completed three questionnaires: (a) receiving information, (b) processing-generation of alternatives and decision making, and (c) processing-generation of consequences. High- and low-assertive participants were found to differ in their evaluation of consequences, for response options, particularly how a male would likely to behave to them. High-assertive participants were judged to evaluate more correctly than low-assertive participants the likely behavior of males if response options were implemented. All participants generated more complex alternatives and more assertive responses to situations involving an angry male as compared to a pleasant male. No differences were found between groups in their ability to receive information accurately. Correlational results were supportive of a cognitive-behavioral systems approach of assertion, that is, the emission of a skilled response depends on a chain of preceding responses.Thanks are due to Vida Dyson and Lenard Jason for their comments on an early draft of this paper.     

Expertise reversal effect in a pen-tablet-based learning environment: The role of learningcentered emotions in the interplay between learner expertise and task complexity

The study investigated interactions between learner expertise and task complexity evaluated from both cognitive and affective perspectives. One hundred and seventy-three students, both novices and advanced learners, were asked to learn Japanese writing in a pen-tablet-based digital learning environment with varying task complexity levels. Cognitive load and learning-centred emotions were measured at intervals during learning, while writing performance was monitored by runtime tracking. Results indicated that while advanced learners performed better than novices across the range of task complexity, the moderate task complexity was shown to be superior in enhancing performance for both levels of expertise. Results for learning-centred emotions showed that advanced learners reported lower enjoyment and higher frustration when completing the low complexity task, whereas the moderately complex task was reported to be the most enjoyable and less frustrating for these learners. No significant difference in emotions was found across levels of task complexity for novices. Finally, a constructed composite indicator of cognitive-affective efficiency of instructional conditions showed a significant interaction between levels of learner expertise and task complexity primarily caused by affective factors.     

The Most Important Election of Our Lifetime: Focalism and Political Participation

This article argues that a psychological bias called “focalism” contributes to an overestimation of the differences between political candidates, which in turn increases participation and polarization. Focalism causes people to confuse the allocation of attention to things with the importance of those things. Because attention to politics typically centers on conflict, the result is an exaggeration of differences across the partisan divide. I test this intuition using an experimental design that provides all respondents with all the information they need to estimate how much Joe Biden and Donald Trump objectively disagreed on policy positions just before the 2020 election. I find that shifting attention—toward either those positions the candidates agreed or disagreed with each other on—influences beliefs about the differences between candidates. The effect exceeds that of identifying as a Democrat or as a Republican. Beyond those perceptions, focalism increases turnout intentions, perceptions of election importance, negative feelings towards the out-candidate, and affective polarization.     

SPIRITUAL INTELLIGENCE: PROCESSING DIFFERENT INFORMATION OR PROCESSING INFORMATION DIFFERENTLY?

This article introduces the concept of spiritual intelligence in terms of a natural human ability to take a different perspective on reality rather than an extraordinary ability to engage with a different/supernatural reality. From a cognitive perspective, spiritual intelligence entails a re-balancing of the two main modes of human cognition, with a prioritization of the holistic-intuitive mind over the conceptual one. From the psychological and phenomenological perspectives, it involves a different kind of engagement with information: slower, more participatory, less objectifying, and not focused entirely on problem solving. The article ends with a reflection on the theological implications of the proposed model and how such an account of spiritual intelligence as knowing differently might relate to theological anthropology and the theology of the spirit and the spiritual.