Robert M. Pruzek

Immediate visual recall in poor and normal readers as a function of orthographic-linguistic familiarity.

Summary The current investigation directly assessed the hypothesis that the visualperceptual skills of poor readers are comparable to those of normal readers. Previous research suggested that spatial and other perceptual difficulties, as observed in poor readers, occur as a function of more basic problems in acquiring visual-verbal equivalents. The prediction was made that both poor and normal readers would encounter similar difficulties if these groups were presented with an unfamiliar orthography, as compared with normal readers who were becoming acquainted with the graphic and linguistic components of that orthography. Employing a visual recall task, the performance of non-Hebrew poor and normal readers were compared with one another as well as with those of normal readers learning Hebrew. Response variables consisted of total number correct, as well as omission, orientation, sequencing and substitution errors for different length Hebrew words. It was expected that the performance of the non-Hebrew groups would be comparable on all measures but that both groups would be inferior to the Hebrew group. The prediction was supported for total number correct, as well as for omission and orientation errors, but there were no reliable differences among the groups on the sequencing and substitution measures. Of additional interest was the finding that the non-Hebrew groups had particular difficulty in correctly orienting Hebrew letters that could be construed as disoriented Roman letters as well as the observation that the directional scanning tendencies of the non-Hebrew groups were identical. The results provide additional evidence for the suggestion that visual-spatial deficit is an unlikely cause of reading disability and indirectly support the notion that the disorder is attributable to dysfunction in visual-verbal integration.

Microcomputer-Based Instruction in Special Education.

This article reports findings of an evaluation study examining the instructional effectiveness of varying software environments for 120 preschool special needs children. Cognitive distancing principles were applied to classify children according to symbolic competence prior to instruction and to classify child-software interactions during instruction. An aptitude-by-treatment interaction design contrasted childrens learning in either Logo or instructional software contexts with a control condition. Dependent measures included indicators of preschool problem solving, skill acquisition, language development, cognitive development and affective development. Results indicated that a Logo-based environment enhanced childrens problem-solving skills and their acquisition of linguistic pragmatics as compared to counterparts in a control condition. In contrast, an instructional software condition promoted childrens acquisition of specific skills. Neither software environment enhanced childrens global levels of cognitive or of affective development. We conclude with a caution that the medium is not the message.

Influences of Social and Educational Environments on Creativity During Adolescence: Does SES Matter?

It is well established that there is an academic achievement gap between students from high and low socioeconomic family backgrounds. However, how being brought up and living in different socioeconomic backgrounds impacts adolescent development, particularly their creative capabilities and creativity-related personality traits, is not well understood. This study compared creative capabilities and traits of 8th grade students of 2 school districts: a suburban, upper-middle class community and an urban community with a large proportion of families under poverty, located in a northeastern state. The results provide compelling evidence for a creativity gap. The study also found a possible mediating role of academic achievement and intrinsic cognitive motivation, suggesting that the psychosocial processes and mechanisms leading to the creativity gap are tractable. Implications of the findings for optimal adolescent development and social interventions are discussed.

Weighted structural regression: a broad class of adaptive methods for improving linear prediction

Given a criterion variable and two or more predictors, applied linear prediction usually entails some form of OLS regression. But when there are several predictors, and especially when these are subject to non-ignorable errors of measurement, applications of OLS methods are often fraught with problems. Weighted structural regression (WSR) methods can mitigate many difficulties through the incorporation of prior structural models into analyses. WSR methods are sufficiently general to include OLS, ridge, reduced rank regression, as well as most covariance structural regression models, as special cases; many other regression methods, heretofore not available, are also included. In this article adaptive forms of WSR are developed and discussed. According to our bootstrapping studies the new methods have potential to recover known population regression weights and predict criterion score values routinely better than OLS with which they are compared. These new methods are scale free as well as simple to compute; they seem well suited to many prediction applications in behavioral research.

Rotation to simple processes: the effect of alternative rotation rules on observed patterns in time-ordered measurements

Two types of rotation are well known in psychometrics. The first of these is rotation to simple structure, widely known particularly in factor analysis and multidimensional scaling. Rotation to simple structure applies to the case where only a single scaling solution is available, and one wishes to array the configuration in a particular way relative to its axes; usually in such a way that each point projects as much of its variance as possible on a single axis and as little as possible on each of the other axes.The second type of rotation applies when two scaling solutions are available and one wishes to compare them to each other. In this case, one rotates one or both of the solutions until they fit as well as possible to each other by some criterion. Sometimes rescaling, stretching and shrinking may be involved. This type of rotation is usually referred to as “Procrustes” rotation.Neither of these rotation schemes is appropriate, howerver, when time series measurements are available. In the time-series situation, one is not particularly interested in the structure of the solution at any time, nor even in the comparison of any two structures adjacent in time. Rather one is interested in the underlying processes which may be found in the time series.The kinds of processes which emerge from a time series of multidimensional measurements, however, are heavily dependent on the rotation rules by which one relates each of the structures in the time series of structures to each of the others.In the present article, we discuss a weighted least squares rule, and illustrate the situations under which it is appropriate for discovering processes underlying time series measurements for Galileo-type scaling data. An example dealing with social perceptions of time is presented.

Developmental trends in the salience of meaning versus structural attributes of written words

SummaryThis study was designed to evaluate Gibsons contention that maximum sensitivity to the meanings of printed words does not occur until the individual has become a fluent reader. Also of interest was the degree to which reading ability would significantly influence the developmental course. These questions were evaluated by means of a sorting task which allowed subjects to group printed words on the basis of either semantic-syntactic (meaning) or graphic-phonologic (structural) attributes, and differential grouping tendencies along the age/grade continuum were the dependent measures specifically evaluated. Subjects consisted of normal and poor readers from grades 1–6, as well as ninth graders and college sophomores. The study was quasi-longitudinal in that available subjects from grades 2–5 were retested a year later to evaluate the reliability of developmental trends.Results were consistent with Gibsons theory. Sensitivity to word meanings was much more stable in fluent readers than it was in less fluent readers, although there was considerable individual variability noted at all levels. Poor readers were inclined to place words they could identify more often in meaning than in structural categories. However, structural categories that were utilized, were often idiosyncratic, suggesting that poor readers are less atuned to orthographic regularities than are normal readers.

A Class of Simple Methods for Exploratory Structural Analysis

Simple modifications of principal component methods are described that have distinct advantages for structural analysis of relations among educational and psychological variables. Advantages include the provision for the incorporation of prior beliefs about errors in the variables, computational efficiency, tractability for large battery analysis, and the availability of hypothesis testing procedures. The methods are contrasted theoretically and empirically with conventional principal component methods and with maximum likelihood factor analysis.

Introduction to the Special Issue on Propensity Score Methods in Behavioral Research

This issue includes six articles that present logic, methods, and models for causal analyses of observational data, in particular those based on propensity score (PS) methods. The articles include a general introduction to propensity score analysis (PSA), uses of PSA in mediation studies, issues involved in choosing covariates, challenges that often arise in PSA applications, hierarchical data issues and models, and an application in an educational testing context. In this editorial I briefly summarize each article and make a few recommendations that relate to future applications in this field: the first pertains to how propensity score (PS) work could profit by connecting it with stronger forms of randomized experiments, not just simple randomization; the second to how and why graphical methods could be used to greater advantage in PSA studies; then why it might be helpful to reconsider the meaning of the term “treatments” in observational studies and why conventional usage might be modified; and finally, to the distinction between retrospective and prospective approaches to observational study design, noting the advantages, when feasible, of the latter approach.

Factor Analysis: Exploratory

Exploratory factor analysis (EFA) provides a way to account for intercorrelations or covariances among all pairs of variables in a battery using a relatively small number of latent variables called common factors. The method can be especially helpful for analyzing structural relationships in situations where observed variables are approximately linearly related to one another, and when individual-level observations are fallible, that is, subject to errors of measurement. Exploratory factor analysis has gained prominence in many fields over many years, and it has been the subject of much praise as well as criticism. Psychometric and statistical practice has not fully caught up with contemporary appreciations of factor analytic and related methods, but past and recent developments suggest notable promise for continued applications in applied sciences. Keywords: common factor analysis; exploratory factor analysis; principal component analysis; multivariate methods

Some New Regression Methods for Predictive and Construct Validation

Summary Results for MTMM Data--Selected Criterion Variable _________________________________________________________________________ MSEs n=35 n=70 n=140 Method ---------------------- --------------------- ---------------------- OLS .421 .387 .426 .125 .142 .164 .064 .075 .079 RIDGE .309 .309 .310 .184 .200 .170 .085 .104 .101 m=1 m=2 m=3 m=1 m=2 m=3 m=1 m=2 m=3 MR .148 .163 .174 .075 .094 .086 .052 .056 .054 MR2 . 125 .134 .141 . 066 .086 .071 .047 .049 .046 GFI .140 .131 .134 .115 .100 .071 .105 .091 .056 RR .621 .244 .149 .590 .217 .095 .590 .203 .077 PMSEs OLS .089 .091 .082 .034 .034 .036 .016 .017 .016 RIDGE .072 .077 .069 .033 .035 .034 .016 .017 .016