Factor invariance of the Millon Clinical Multiaxial Inventory

Several factor analyses of the Millon Clinical Multiaxial Inventory (MCMI) have resulted in very similar solutions. Interpretation of this consistency is hampered by the fact that the 20 scales of the inventory share items. Overlapping items cause the scales to be linearly dependent and may create structure in the interscale correlation matrix which is separate from the subject response patterns. A factor analysis was performed on the matrix of item-overlap coefficients which describes the underlying artifactual structure of the instrument. Data from two new subject samples were factor analyzed and compared to previously published studies. Similarity coefficients among factors across studies were calculated.     

USING STIMULUS EQUIVALENCE PROCEDURES TO TEACH NAME-FACE MATCHING TO ADULTS WITH BRAIN INJURIES

On pretests, 3 men with brain injuries matched dictated names of three therapists to written names, but did not match dictated or written names to photos, produce correct names in response to photos, locate offices given written names, or name therapists on sight. Match-to-sample training established conditional relations between dictated names and photos. Posttests showed the emergence of untrained conditional relations involving photos and written names, indicating development of three classes of equivalent stimuli (each containing a dictated name, photo, and written name). For 1 participant, conditional relations involving photos of office nameplates were also examined, but did not emerge pre- or posttraining. Two participants produced names orally when given photos and sorted written names and faces together after training; the 3rd participant was unavailable for these posttests. After training, 1 participant located and named all three therapists in their offices.     

Beyond principal component analysis: A trilinear decomposition model and least squares estimation

The paper derives sufficient conditions for the consistency and asymptotic normality of the least squares estimator of a trilinear decomposition model for multiway data analysis.     

Influential observations in principal factor analysis

We propose a method for detecting influential observations in iterative principal factor analysis. For this purpose we derive the influence functionsI(x; LL ^T ) andI(x; ) for the common variance matrixT =LL ^T and the unique variance matrix , respectively, in the common factor decomposition =LL ^T + . A numerical example is given for illustration.The authors are grateful to Tomoyuki Tarumi and Atsuhiro Hayashi for their kind permission to use their software Seto/B for drawing Figures 1 and 2 and to anonymous reviewers for comments on the paper.     

The social phobia and anxiety inventory: Construct validity

The Social Phobia and Anxiety Inventory (SPAI) is a new instrument composed of social phobia and agoraphobia subscales. The latter scale is used to detect social anxiety that may result from agoraphobia. The SPAI's construct validity was assessed through several procedures. First, confirmatory factor analyses were conducted to validate the existence of the two subscales. Second, exploratory factor analyses examined the underlying structure of the social phobia subscale. Third, a Q factor procedure determined if different anxiety diagnostic groups could be differentiated by their SPAI response pattern. The results confirmed the utility of the two SPAI subscales and identified a number of dimensions contained within the social phobia subscale which differed depending upon the specific subject sample. In addition, the complaints of social phobies appeared more homogeneous than those of an agoraphobic comparison group. The results are discussed in terms of construct validity and the sensitivity of the SPAI to various dimensions of social phobia fears.This study was supported in part by NIMH Grants 41852, 30915, 18269, and 16884.     

Iterative least squares estimates of communality: Initial estimate need not affect stabilized value

A common criticism of iterative least squares estimates of communality is that method of initial estimation may influence stabilized values. As little systematic research on this topic has been performed, the criticism appears to be based on cumulated experience with empirical data sets. In the present paper, two studies are reported in which four types of initial estimate (unities, squared multiple correlations, highestr, and zeroes) and four levels of convergence criterion were employed using four widely available computer packages (BMDP, SAS, SPSS, and SOUPAC). The results suggest that initial estimates have no effect on stabilized communality estimates when a stringent criterion for convergence is used, whereas initial estimates appear to affect stabilized values employing rather gross convergence criteria. There were no differences among the four computer packages for matrices without Heywood cases.     

Multifactor measurement of bodily feelings: Conceptual development,scale construction,and empirical validation

A series of studies is described demonstrating a coherent exploratory and confirmatory factor-analytic approach to the scaling of self-reported bodily feelings. This approach attempts to optimize the orthogonality, parsimony, and meaningfulness of a multifactor inventory. Psychometric properties of the resulting 35-item Bodily Feeling Scale (BFS) are presented and its comparability with other inventories is discussed. A validity study using item-content-based scales of the Minnesota Multiphasic Personality Inventory showed both concurrent and discriminative validity of the BFS. Implications for clinical and research utility are discussed.Portions of this research were conducted while the first author was supported by a University Graduate Fellowship awarded by the Graduate School of Arts and Sciences at Vanderbilt University.Professor Nunnally passed away in August 1982. His major contributions to the several stages of this research program are recognized through coauthorship.     

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.     

Brain bases of English morphological processing: A comparison between Chinese-English,Spanish-English bilingual,and English monolingual children

How do early bilingual experiences influence children's neural architecture for word processing? Dual language acquisition can yield common influences that may be shared across different bilingual groups, as well as language-specific influences stemming from a given language pairing. To investigate these effects, we examined bilingual English speakers of Chinese or Spanish, and English monolinguals, all raised in the US (N = 152, ages 5–10). Children completed an English morphological word processing task during fNIRS neuroimaging. The findings revealed both language-specific and shared bilingual effects. The language-specific effects were that Chinese and Spanish bilinguals showed principled differences in their neural organization for English lexical morphology. The common bilingual effects shared by the two groups were that in both bilingual groups, increased home language proficiency was associated with stronger left superior temporal gyrus (STG) activation when processing the English word structures that are most dissimilar from the home language. The findings inform theories of language and brain development during the key periods of neural reorganization for learning to read by illuminating experience-based plasticity in linguistically diverse learners.     

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.