Using item-specific instructional information in achievement modeling

The problem of detecting instructional sensitivity (item basis) in test items is considered. An illustration is given which shows that for tests with many biased items, traditional item bias detection schemes give a very poor assessment of bias. A new method is proposed instead. This method extends item response theory (IRT) by including item-specific auxiliary measurement information related to opportunity-to-learn. Item-specific variation in measurement relations across students with varying opportunity-to-learn is allowed for.This paper was presented at the 1987 AERA meeting in Washington, DC. This research was supported by grant OERI-G-86-003 from the Office of Educational Research and Improvement, Department of Education. The author thanks Michael Hollis and Chih-fen Kao for valuable research assistance, and appreciates valuable comments made by an anonymous reviewer.     

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.     

What analytic metaphysics can do for scientific metaphysics

The apparent chasm between two camps in metaphysics, analytic metaphysics and scientific metaphysics, is well recognized. I argue that the relationship between them is not necessarily a rivalry; a division of labour that resembles the relationship between pure mathematics and science is possible. As a case study, I look into the metaphysical underdetermination argument for ontic structural realism, a well-known position in scientific metaphysics, together with an argument for the position in analytic metaphysics known as ontological nihilism. I argue that we can ascribe the same schema to both arguments, which indicates that analytic metaphysics can offer an abstract model that scientific metaphysics may find useful.     

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)

In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.     

Structural connectivity between the hippocampus and cortical/subcortical area relates to cognitive impairment in schizophrenia but not in mood disorders

Cognitive impairment in schizophrenia and other psychiatric disorders is a challenge to be overcome in order to maintain patients' quality of life and social function. The neurological pathogenesis of cognitive impairment requires further elucidation. In general, the hippocampus interacts between the cortical and subcortical areas for information processing and consolidation and has an important role in memory. We examined the relationship between structural connectivity of the hippocampus and cortical/subcortical areas and cognitive impairment in schizophrenia, major depressive disorder and bipolar disorder. Subjects comprised 21 healthy controls, 19 patients with schizophrenia, 20 patients with bipolar disorder and 18 patients with major depressive disorder. Diffusion-weighted tensor images data were processed using ProbtrackX2 to calculate the structural connectivity between the hippocampus and cortical/subcortical areas. Cognitive function was assessed using the Brief Assessment of Cognition in schizophrenia composite score. Hippocampal structural connectivity index was significantly correlated with composite score in the schizophrenia group but not in the healthy control, major depressive disorder or bipolar disorder groups. There were no statistically significant differences in hippocampal structural connectivity index between the four groups. Structural connectivity between the hippocampus and cortical/subcortical areas is suggested to be a pathophysiological mechanism of comprehensive cognitive impairment in schizophrenia.     

Some properties of estimated scale invariant covariance structures

Scale invariance is a property shared by many covariance structure models employed in practice. An example is provided by the well-known LISREL model subject only to classical normalizations and zero constraints on the parameters. It is shown that scale invariance implies that the estimated covariannce matrix must satisfy certain equations, and the nature of these equations depends on the fitting function used. In this context, the paper considers two classes of fitting functions: weighted least squares and the class of functions proposed by Swain.Constructive comments by the referees are greatly appreciated. The author gratefully acknowledges Michael Browne's interest in his work.     

Selective mutism in children: A structural family therapy approach to treatment

When children refuse to speak in some social settings, but can understand and speak in other settings, the diagnosis of selective mutism must be considered. A review of the literature describes the disorder, the various types of mutism, and related family dynamics. A family systems approach to treatment using structural family therapy is suggested. The case history presented illustrates one type of mutism as well as the family rules and hierarchy which appeared to be maintaining it. Structural and strategic family systems theory techniques that were used to move the family toward resolution of this problem are discussed.Diane Wolf Tatem, MA, MS, completed graduate work in marriage and family therapy at New Mexico State University, Las Cruces, NM. She is employed at Life Management Center of El Paso's Child Development Services and also has a private practice in marriage and family therapy in El Paso, Texas. Robert L. DelCampo, PhD is a professor of family science at New Mexico State University and maintains a private practice with Associates for Marriage and Family Therapy in Las Cruces, NM. Please address all correspondence to Dr. DelCampo at Box 3470, New Mexico State University, Las Cruces, NM, 88003-3470.Appreciation is expressed to Dr. Charles Huber, New Mexico State University in Las Cruces, for his insightful critique of the final draft of this paper. This paper was presented at the Texas Association for Marriage and Family Therapy Annual Conference, January 25–29, 1995 in Dallas, TX.     

Antecedents and Consequences of Victimization of Lesbian, Gay, and Bisexual Young People: A Structural Model Comparing Rural University and Urban Samples

Research indicates that antigay victimization is widespread and that lesbian, gay, and bisexual young people may be very vulnerable to such victimization. The current study builds upon previous work by Hershberger and D'Augelli (1995), who studied the consequences of sexual orientation-based victimization in 194 urban lesbian, gay, and bisexual youths. Using structural equation modeling, the present study models both antecedents and consequences (including psychological distress, self-esteem, and suicidality) of victimization via a secondary analysis of their data set. In addition, a second sample of 54 lesbian, gay, and bisexual youths from a rural university setting was examined to cross-validate and generalize the relationships found in urban settings. Results indicated that a revised model of victimization exhibited sufficient fit to the urban sample data and provided preliminary support for the generalizability of the model beyond the initial sample. Additional similarities were found between the urban and rural university community samples, including a high prevalence of reported suicide attempts: 42% of the urban sample and 32% of the rural university sample had attempted suicide at least once. Results indicated that victimization based on sexual orientation has similar correlates for young people in different community settings.