The context of infants' giving gestures in mother-infant dyads: Typical giving gestures and those contingent on exploration and play

This study aimed to focus on a niche that has not yet been investigated in infants' gesture studies that is the effect of the prior context of one specific gestural behavior (gives) on maternal behavior. For this purpose, we recruited 23 infants at 11 and 13 months of age yielded 246 giving gesture bouts that were performed in three contexts: typical when the object was offered immediately, contingent on exploration, and contingent on play. The analysis revealed that maternal responses to infants' giving gestures varied and were affected by their age and gesture context. Hence, mothers amended their responses according to the background that generated each gesture. The number of verbal responses to infants' giving gestures decreased as the infants aged, whereas the number of pretense responses increased. For infants aged 11 months, mothers generally provided motor responses to typical gestures. However, for infants aged 13 months, this trend declined and was replaced by a strong positive correlation between giving gestures contingent on play and verbal responses. We concluded that the type of activity with objects prior to employing giving gestures could enhance infants' symbolic skills because caregivers monitor the contingent act that yields the gesture that shapes their response.     

The making of a ghetto: Spatially concentrated poverty in New York City in the 1980s

In recent years a debate has arisen over which of two mechanisms — class selective household mobility or spatially focused increases in poverty — has been the driving force in concentrating poverty in certain inner-city neighborhoods. This paper utilizes a multivariate analysis to identify the processes underlying areal income-class transition in New York City during 1978–1987, and the areal characteristics that predict a consistent path of change. By anchoring the analysis at the level of the individual housing unit, this study disentangles the competing mechanisms of poverty concentration and demonstrates that both selective mobility and shifts in income class contribute to areal income-class transition, but that the latter mechanism accounts for a greater amount of change. Further, after controlling for the proportion of minority residents and public housing units in the area, the results show that location in poor areas is associated with poor in-movement, nonpoor out-movement, and downward shifts in the income class among long-term residents. These mutually reinforcing processes lead to continued decline in extreme- and high-poverty areas, while processes in the opposite direction sustain the economic vitality of low-poverty areas.An earlier version of this paper was presented at the 1992 meetings of the Population Association of America in Denver, Colorado.     

A SEMIPARAMETRIC DISCRETE CHOICE MODEL: AN APPLICATION TO HOSPITAL MERGERS

We propose a computationally simple semiparametric discrete choice estimator to model rich consumer heterogeneity. We assume groups of observably similar consumers have similar preferences, but allow preferences to vary freely across these groups. Model flexibility is easily adjusted by setting a single tuning parameter; we suggest a cross‐validation method to do so. We analyze the model's properties in the context of hospital mergers, both analytically and via a Monte Carlo analysis. The model performs well for policy relevant substitution and welfare measures, even if misspecified, when the tuning parameter is set within the neighborhood of the value chosen by cross validation. (JEL C14, D12, I11, L41)     

Inclusion of binary proxy variables in logistic regression improves treatment effect estimation in observational studies in the presence of binary unmeasured confounding variables

We present a simulation study and application that shows inclusion of binary proxy variables related to binary unmeasured confounders improves the estimate of a related treatment effect in binary logistic regression. The simulation study included 60,000 randomly generated parameter scenarios of sample size 10,000 across six different simulation structures. We assessed bias by comparing the probability of finding the expected treatment effect relative to the modeled treatment effect with and without the proxy variable. Inclusion of a proxy variable in the logistic regression model significantly reduced the bias of the treatment or exposure effect when compared to logistic regression without the proxy variable. Including proxy variables in the logistic regression model improves the estimation of the treatment effect at weak, moderate, and strong association with unmeasured confounders and the outcome, treatment, or proxy variables. Comparative advantages held for weakly and strongly collapsible situations, as the number of unmeasured confounders increased, and as the number of proxy variables adjusted for increased.     

Race and ethnicity in housing: Turnover in New York City, 1978–1987

This study examines the patterns and predictors of housing turnovers among non-Hispanic whites, non-Hispanic blacks, Puerto Ricans, and other Hispanics in New York City during 1978-1987 to assess whether access to housing is distributed differentially by race and ethnicity. The data are taken from the triennial New York City Housing and Vacancy Survey. After controlling for household preferences, purchasing power, and quality characteristics of the housing unit, multinomial logistic regression results show the most consistent and significant predictors of turnover to be geographic and market-sector attributes. The findings suggest the presence of structural constraints in the housing market which effectively channel racial/ethnic groups to separate neighborhoods. The overall results are reminiscent of early studies of neighborhood transition by Duncan and Duncan (1957) and Taeuber and Taeuber (1965), and show that little progress has been made toward achieving equality in housing or informal social contact between racial/ethnic groups.     

ERROR-FREE MILESTONES IN ERROR PRONE MEASUREMENTS

A predictor variable or dose that is measured with substantial error may possess an error-free milestone, such that it is known with negligible error whether the value of the variable is to the left or right of the milestone. Such a milestone provides a basis for estimating a linear relationship between the true but unknown value of the error-free predictor and an outcome, because the milestone creates a strong and valid instrumental variable. The inferences are nonparametric and robust, and in the simplest cases, they are exact and distribution free. We also consider multiple milestones for a single predictor and milestones for several predictors whose partial slopes are estimated simultaneously. Examples are drawn from the Wisconsin Longitudinal Study, in which a BA degree acts as a milestone for sixteen years of education, and the binary indicator of military service acts as a milestone for years of service.     

Publication Decisions Revisited: The Effect of the Outcome of Statistical Tests on the Decision to Publish and Vice Versa

This article presents evidence that published results of scientific investigations are not a representative sample of results of all scientific studies. Research studies from 11 major journals demonstrate the existence of biases that favor studies that observe effects that, on statistical evaluation, have a low probability of erroneously rejecting the so-called null hypothesis (H ₀). This practice makes the probability of erroneously rejecting H ₀ different for the reader than for the investigator. It introduces two biases in the interpretation of the scientific literature: one due to multiple repetition of studies with false hypothesis, and one due to failure to publish smaller and less significant outcomes of tests of a true hypotheses. These practices distort the results of literature surveys and of meta-analyses. These results also indicate that practice leading to publication bias have not changed over a period of 30 years.     

An exact distribution-free test comparing two multivariate distributions based on adjacency

Summary.  A new test is proposed comparing two multivariate distributions by using distances between observations. Unlike earlier tests using interpoint distances, the new test statistic has a known exact distribution and is exactly distribution free. The interpoint distances are used to construct an optimal non-bipartite matching, i.e. a matching of the observations into disjoint pairs to minimize the total distance within pairs. The cross-match statistic is the number of pairs containing one observation from the first distribution and one from the second. Distributions that are very different will exhibit few cross-matches. When comparing two discrete distributions with finite support, the test is consistent against all alternatives. The test is applied to a study of brain activation measured by functional magnetic resonance imaging during two linguistic tasks, comparing brains that are impaired by arteriovenous abnormalities with normal controls. A second exact distribution-free test is also discussed: it ranks the pairs and sums the ranks of the cross-matched pairs.