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.     

The representation of nonuniform motion: induced movement

Induced motion occurs when there is a misallocation of nonuniform motion. Theories of induced motion are reviewed with respect to the model for uniform motion recently proposed by Swanston, Wade, and Day. Theories based on single processes operating at one of the retinocentric, orbitocentric, egocentric, or geocentric levels are not able to account for all aspects of the phenomenon. It is therefore suggested that induced motion is a consequence of combining two different types of motion signals: one provides information by registering the motion with respect to the retina, orbit, and egocentre; the other provides information only on the relational motions between the pattern elements. Simple rules are given for defining a frame of reference for the relational motion process, which can result in a reallocation of the motion signals. It is proposed that the two signals in combination are weighted differentially, with the greater influence coming from the relational signals. Procedures for determining the weighting factors are described, and predictions from the model are examined.     

The perception of continuous curves in dot stimuli

Two categorisation experiments are reported in which the perceptual phenomenon that some simple arrays of discrete dots appear as a continuous curve whereas others are perceived as an angular contour or as consisting of separate groups of dots was investigated. Triplets of dots were presented in the first experiment, and complete or incomplete regular dot polygons (ie dots positioned on the vertices of imaginary regular polygons) in the second. In both experiments the perception of a curve versus an angle was determined mainly by the relative orientations of the dots, ie by the angles between successive virtual lines, whereas the lengths of the virtual lines had relatively little influence. In experiment 2 the number of displayed dots was shown to be a second independent factor for perceiving continuity. These results are in agreement with results from experiments on dipole textures discrimination, and suggest the psychological existence and importance of virtual lines in the visual processing of dot stimuli.     

Remembering left-right orientation of pictures

In a study of recognition memory for pictures, we observed an asymmetry in classifying test items as "same" versus "different" in left-right orientation: Identical copies of previously viewed items were classified more accurately than left-right reversals of those items. Response bias could not explain this asymmetry, and, moreover, correct "same" and "different" classifications were independently manipulable: Whereas repetition of input pictures (one vs. two presentations) affected primarily correct "same" classifications, retention interval (3 hr vs. 1 week) affected primarily correct "different" classifications. In addition, repetition but not retention interval affected judgments that previously seen pictures (both identical and reversed) were "old". These and additional findings supported a dual-process hypothesis that links "same" classifications to high familiarity, and "different" classifications to conscious sampling of images of previously viewed pictures.     

Multiple regression analysis of twin and sibling data

DeFries and Fulker (1985) employed a multiple regression analysis of twin data in which a cotwin's score is predicted from the proband's score, the coefficient of relationship (R = 1.0 for monozygotic twins and 0.5 for dizygotic twins), and their interaction to yield direct estimates of heritability (h²) and the proportion of variance due to common or shared environmental influences (c²). The purpose of the report is to extend this model to allow for the analysis of data from more than two familial relationships simultaneously. Data from identical twins, fraternal twins, and siblings pairs, in which one member of each pair was selected based on low reading performance, were analyzed. To analyze the data simultaneously, additional coefficients were added to the regression equation in order to differentiate shared environmental influences of twins and siblings. When the model was applied to the combined cognitive data, significant estimates for heritability were obtained in three of the six tests analyzed. The results also indicated a lack of justification for the separate twin and sibling environmental parameters, and a discussion of more parsimonious models is included.