Early formal schooling: Are we promoting achievement or anxiety?

The resurgence of interest in early childhood education has been accompanied by an active debate about the most developmentally beneficial approach to preschool instruction. Some experts and the major early childhood organizations in this country advocate a child-centered or developmentally appropriate approach, whereas other experts advocate a teacher-directed, didactic approach. Until recently, the empirical evidence available to inform this controversy has been woefully thin. This article reviews the social and theoretical parameters of this debate about preschool instruction, articulates the core dimensions that have been portrayed as distinguishing child- from teacher-directed curricula, and examines the available research that can inform the debate. Our conclusions support those who encourage the adoption of child-centered curricula. Didactic approaches appear to undermine young children's motivation in and enjoyment of school in the short term and to have either neutral or negative ramifications for long-term achievement, despite some evidence of short-term gains.     

High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature

Although the Bock–Aitkin likelihood-based estimation method for factor analysis of dichotomous item response data has important advantages over classical analysis of item tetrachoric correlations, a serious limitation of the method is its reliance on fixed-point Gauss-Hermite (G-H) quadrature in the solution of the likelihood equations and likelihood-ratio tests. When the number of latent dimensions is large, computational considerations require that the number of quadrature points per dimension be few. But with large numbers of items, the dispersion of the likelihood, given the response pattern, becomes so small that the likelihood cannot be accurately evaluated with the sparse fixed points in the latent space. In this paper, we demonstrate that substantial improvement in accuracy can be obtained by adapting the quadrature points to the location and dispersion of the likelihood surfaces corresponding to each distinct pattern in the data. In particular, we show that adaptive G-H quadrature, combined with mean and covariance adjustments at each iteration of an EM algorithm, produces an accurate fast-converging solution with as few as two points per dimension. Evaluations of this method with simulated data are shown to yield accurate recovery of the generating factor loadings for models of upto eight dimensions. Unlike an earlier application of adaptive Gibbs sampling to this problem by Meng and Schilling, the simulations also confirm the validity of the present method in calculating likelihood-ratio chi-square statistics for determining the number of factors required in the model. Finally, we apply the method to a sample of real data from a test of teacher qualifications.     

Simultaneous classification and multidimensional scaling with external information

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or multidimensional scaling and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.     

On local influence analysis of full information item factor models

The full information item factor (FIIF) model is very useful for analyzing relations of dichotomous variables. In this article, we present a feasible procedure to assess local influence of minor perturbations for identifying influence aspects of the FIIF model. The development is based on a Q-displacement function which is closely related with the Monte Carlo EM algorithm in the ML estimation. In the E-step of this algorithm, the conditional expectations are approximated by sample means of observations simulated by the Gibbs sampler from the appropriate conditional distributions. It turns out that these observations can be utilized for computing the building blocks of the proposed diagnostic measures. The diagnoses are based on the conformal normal curvature that can be computed easily. A number of interesting perturbation schemes are considered. The methodology is illustrated with two real examples.The research is fully supported by a grant (CUHK 4356/00H) from the Research Grant Council of the Hong Kong Special Administration Region. The authors are thankful to the Editor, Associate Editor, anonymous reviewers, and W.Y. Poon for valuable comments for improving the paper, and to ICPSR and the relevant founding agency for allowing us to use of their data. The assistance of Michael Leung and Esther Tam is gratefully acknowledged.     

Prevalence of Attention-Deficit/Hyperactivity Disorder Symptoms in 4- to 17-Year-Old Children in the General Population

The purposes of this study were (a) to estimate the prevalence of Attention-Deficit/Hyperactivity Disorder (AD/HD) symptoms in the general preschool and school population; and (b) to analyze the influence of gender, age, and socioeconomic status (SES) variables on AD/HD symptoms. Out of the 80,000 preschool and schoolchildren living in Manizales, Colombia, a random sample of 540 children was selected. Two gender, three age (4- to 5-year olds, 6- to 11 -year olds, and 12- to 17-year olds), and three SES (low, middle, and high) groups were used. The 18 DSM-IV symptoms corresponding to AD/HD Criterion A were assessed on a scale of 0 (never) to 3 (almost always). All three demographic variables established statistically significant differences: AD/HD symptoms were more frequent in 6-to 11-year-old, low-SES, male participants. DSM-IV Criterion A for AD/HD was fulfilled by 19.8% of the boys and 12.3% of the girls. However, this difference was marginally significant only in the AD/HD Subtype I: Combined. It was concluded that demographic variables are significant correlates of the AD/HD diagnosis. The prevalence found in this study was higher than usually reported, even though only the symptomatic DSM-IV AD/HD criterion was analyzed. We failed to confirm the assumed AD/HD gender ratio.     

Linguistic characteristics of neurotic, borderline and psychotic personality organization

Thirty patients were subjected to a Structural Interview by means of which 10 of them received the diagnosis NPO (Neurotic Personality Organization), 10 BPO (Borderline Personality Organization) and 10 PPO (Psychotic Personality Organization). About 2500 words and groups of words were extracted from the patients' utterances for analysis. The word-frequencies thus found were analyzed by a PLS discriminant analysis which yielded two significant principal components (main dimensions) explaining 57% of the variance. This analysis showed that the three groups of patients are well separated from one another and that there is a definite correlation between personality organization and linguistic variables. The main features of the BPO-patients in this study seem to be that they refer to positions outside themselves and their language is impersonal; we see this as an exemple of a vacillating identity. The NPO-group is characterized by an intense and rich language, signs of a more advanced symbolizing ability, deixis and high level defenses. The language of the PPO-patients is poor and its predominant feature is a lack of words; in our interpretation this indicates foreclosure and a lack of identity.     

The Relative Contribution of Diagnostic and Psychosocial Variables to Severity of Suicidal Ideation

The present study examined the relative contribution of diagnostic and psychosocial factors to the prediction of severity of suicidal ideation in a sample of 78 college students. Using hierarchical regression analyses to control for each set of factors, psychosocial variables were found to consistently predict level of suicidal ideation. In contrast, diagnostic variables including diagnosis of substance abuse and composite scores for depression and anxiety were unrelated to level of suicidality. An examination of individual diagnoses revealed no predictable relationships with severity of suicidality in this population. The results were discussed from several methodological viewpoints.     

Is the link between forebrain size and feeding innovations caused by confounding variables? A study of Australian and North American birds

The short notes of ornithology journals feature new and unusual feeding behaviours, which, when systematically collated, could provide a quantitative estimate of behavioural flexibility in different bird groups. Previous studies suggest that taxonomic variation in the frequency of new behaviours (innovations) is correlated with variation in relative forebrain size. Recent work on primates shows, however, that observer bias can affect innovation frequency. We assess this possibility in birds via three estimates in North America and Australia: the number of full-length papers in academic journals, the frequency of photographs in birding magazines and a questionnaire on reporting bias given to ornithologists at a meeting. We also look at sampling effects due to single journal sources by doing a split-half analysis of our North American database (The Wilson Bulletin vs. six other journals) and adding three new Australian journals to the one we had used previously. In multiple regressions that also included species number per taxon, none of the potential biases could account for the correlation between forebrain size and innovation frequency. Species number was the best predictor of full-length paper frequency, which was the best predictor of photograph numbers. Ornithologists are not preferentially interested in innovative, large-brained taxa, suggesting that the correlation between innovations and neural substrate size is not a spurious effect of the biases examined here. Accepted after revision: 27 July 2001 Electronic Publication     

A Polychoric Instrumental Variable (PIV) Estimator for Structural Equation Models with Categorical Variables

This paper presents a new polychoric instrumental variable (PIV) estimator to use in structural equation models (SEMs) with categorical observed variables. The PIV estimator is a generalization of Bollen’s (Psychometrika 61:109–121, 1996) 2SLS/IV estimator for continuous variables to categorical endogenous variables. We derive the PIV estimator and its asymptotic standard errors for the regression coefficients in the latent variable and measurement models. We also provide an estimator of the variance and covariance parameters of the model, asymptotic standard errors for these, and test statistics of overall model fit. We examine this estimator via an empirical study and also via a small simulation study. Our results illustrate the greater robustness of the PIV estimator to structural misspecifications than the system-wide estimators that are commonly applied in SEMs. Kenneth Bollen gratefully acknowledges support from NSF SES 0617276, NIDA 1-RO1-DA13148-01, and DA013148-05A2. Albert Maydeu-Olivares was supported by the Department of Universities, Research and Information Society (DURSI) of the Catalan Government, and by grant BSO2003-08507 from the Spanish Ministry of Science and Technology. We thank Sharon Christ, John Hipp, and Shawn Bauldry for research assistance. The comments of the members of the Carolina Structural Equation Modeling (CSEM) group are greatly appreciated. An earlier version of this paper under a different title was presented by K. Bollen at the Psychometric Society Meetings, June, 2002, Chapel Hill, North Carolina.