A methodological investigation of the Intermodal Preferential Looking paradigm: Methods of analyses,picture selection and data rejection criteria

The Intermodal Preferential Looking paradigm provides a sensitive measure of a child's online word comprehension. To complement existing recommendations (Fernald, Zangl, Portillo, & Marchman, 2008), the present study evaluates the impact of experimental noise generated by two aspects of the visual stimuli on the robustness of familiar word recognition with and without mispronunciations: the presence of a central fixation point and the level of visual noise in the pictures (as measured by luminance saliency). Twenty-month-old infants were presented with a classic word recognition IPL procedure in 3 conditions: without a fixation stimulus (No Fixation – noisiest condition), with a fixation stimulus before trial onset (Fixation, intermediate), and with a fixation stimulus, a neutral background and equally salient images (Fixation Plus – least noisy). Data were systematically analyzed considering a range of data selection criteria and dependent variables (proportion of looking time towards the target, longest look, and time-course analysis). Critically, the expected pronunciation and naming interaction was only found in the Fixation Plus condition. We discuss the impact of data selection criteria and the dependent variable choice on the modulation of these effects across the different conditions.     

Biased judgments of the effects of speed change on travel time,fuel consumption and braking: Individual differences in the use of simplifying rules producing the same biases

The main purpose of this study was to identify different cognitive rules that lead to a particular judgment bias. To fulfill this purpose, a new method Spectral analysis was introduced and applied. Participants judged time saved by driving faster, fuel saved by replacing a car and braking capacity at different speeds. These problems invite the time saving bias (e.g., time saved from speed increases at higher speeds overestimated), the miles per gallon, MPG illusion (misjudgment of fuel saved by replacing a car) and the braking capacity bias (overestimation of braking capacity after speed increase). The average results replicated the biases. Spectral analysis of individual participants and problems showed that a speed difference rule explained about half of the time saving judgments and about three fourth of the MPG judgments. A difference between speeds rule described about one third of the biased braking judgments and a ratio/proportion rule about one fifth of the time saving and MPG judgments. All rules give biased judgments in all three domains. The paper ends with a discussion of hierarchies of cognitive rules, applications of the results, and how to mitigate or avoid the biases and the risks associated with the biases.     

An overview of applied robust methods

Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no ‘best method’ in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.     

Decomposing the social gradient in children’s vocabulary skills at 3 years of age: A mediation analysis using data from a large representative cohort study

Disparities in children’s expressive language by socio-economic status are evident early in childhood and impact children’s development and educational attainment. This study investigated the processes by which maternal education, as a powerful indicator for socio-economic status, affects early expressive language. A nationally representative cohort study of 8,062 children resident in the Republic of Ireland were assessed on the British Ability Scales (BAS) Naming Vocabulary Test at 36 months. A significant difference of almost six points was found between the mean vocabulary test scores of children whose mothers had completed the minimum level of educational attainment compared with children whose mothers had a degree-level qualification. Mediation analysis revealed that 78% of the difference was explained by mediating variables, with differences in household income, parental practice, and material resources accounting for most of the variation. The findings support interventions which redress gaps in maternal education, income, and caregiving.     

Further extensions of precision medicine to behavior analysis: A demonstration using functional communication training

The potential applicability of concepts and methods of the paradigm of precision medicine to the field of applied behavior analysis is only beginning to be explored. Both precision medicine and applied behavior analysis seek to understand and classify clinical problems through identification of their causal pathways. Both aim to develop treatments directly targeting those causal pathways, which also requires an understanding of the mechanisms by which treatments produce change (treatment-action pathways). In the current study, we extend the data-analytic methods and concepts described by Hagopian et al. (2018) toward the identification of variables that predict response to functional communication training (FCT). We discuss emerging conceptual issues, including the importance of distinguishing predictive behavioral markers from predictor variables based on their purported involvement in the causal or treatment-action pathways. Making these discriminations is a complex undertaking that requires knowledge of these mechanisms and how they interact.     

Transformation to achieve a longitudinally stationary factor pattern matrix

Meredith's method of extracting a factorially invariant solution is adapted to longitudinal settings. An explorational estimation procedure is presented which attempts to identify the longitudinal factor components of an across occasion variance-covariance matrix. This is effected by transforming an initial factor pattern matrix to stationarity. The estimation is performed in two parts, the first employing a stepwise algorithm to ascertain the dimensionality and existence of the longitudinal components and the second being the direct estimation of the existing factor pattern.     

A note on structural models for the circumplex

Jöreskog (1974) developed a latent variable model for the covariance structure of the circumplex which, under certain conditions, includes a model for a patterned correlation matrix (Browne, 1977). This model is of limited usefulness, however, in that it employs a known matrix that is rank deficient for many problems. Furthermore, the model is inappropriate for the circumplex which contains negative covariances. This paper presents alternative models for the perfect circumplex and quasi-circumplex that avoids these difficulties, and that includes the important model for a patterned correlation circumplex matrix. Two numerical examples are provided.This research was supported in part by a grant from the Graduate School of the University of Minnesota. I wish to thank M. W. Browne for suggesting the final model presented in this paper. James Steiger and the Editor also made several valuable suggestions.