Assessing anti-fat bias in preschoolers: a comparison of a computer generated line-drawn figure array and photographic figure array

This study examined differences in preschoolers' ratings of anti-fat bias and identification of current body size depending on the realism of the figure array used: computer generated line-drawn or photographic. Children reported strong anti-fat bias with both arrays. However, less extreme bias was elicited with the photographic figure array. In addition, children were inaccurate reporters of their current body size when both figure arrays were used. However, children were consistent in their selection of figures that were thinner than their actual body size. Children's consistent selection of thinner figures as representative of their current body size may be an additional indicator of anti-fat bias. Overall, these results suggest that some of the anti-fat bias observed in preschoolers is attributable to less realistic figure arrays. Therefore, photographic figure arrays are proposed as a better measurement tool in the assessment of anti-fat bias in preschoolers.     

Co-Sleeping among School-Aged Anxious and Non-Anxious Children: Associations with Sleep Variability and Timing

Little is known about the co-sleeping behaviors of school-aged children, particularly among anxious youth who commonly present for the treatment of sleep problems. The current study examined the occurrence of co-sleeping in both healthy and clinically anxious children and its associated sleep patterns. A total of 113 children (ages 6–12), 75 with primary generalized anxiety disorder and 38 healthy controls, participated along with their primary caregiver. Families completed structured diagnostic assessments, and parents reported on their child’s co-sleeping behaviors and anxiety severity. Children provided reports of anxiety severity and completed one week of wrist-based actigraphy to assess objective sleep patterns. A significantly greater proportion of anxious youth compared to healthy children co-slept, and greater anxiety severity was related to more frequent co-sleeping. Co-sleeping in anxious youth was associated with a delay in sleep timing and with greater sleep variability (i.e., more variable nightly sleep duration). All analyses controlled for child age, race/ethnicity, family income, and parental marital status. Co-sleeping is highly common in anxious school-aged children, with more than 1 in 3 found to co-sleep at least sometimes (2–4 times a week). Co-sleeping was even more common for youth with greater anxiety severity. Increased dependence on others to initiate and maintain sleep may contribute to poorer sleep in this population via shifted schedules and more variable sleep patterns.     

CHull: A generic convex-hull-based model selection method

When analyzing data, researchers are often confronted with a model selection problem (e.g., determining the number of components/factors in principal components analysis [PCA]/factor analysis or identifying the most important predictors in a regression analysis). To tackle such a problem, researchers may apply some objective procedure, like parallel analysis in PCA/factor analysis or stepwise selection methods in regression analysis. A drawback of these procedures is that they can only be applied to the model selection problem at hand. An interesting alternative is the CHull model selection procedure, which was originally developed for multiway analysis (e.g., multimode partitioning). However, the key idea behind the CHull procedure—identifying a model that optimally balances model goodness of fit/misfit and model complexity—is quite generic. Therefore, the procedure may also be used when applying many other analysis techniques. The aim of this article is twofold. First, we demonstrate the wide applicability of the CHull method by showing how it can be used to solve various model selection problems in the context of PCA, reduced K-means, best-subset regression, and partial least squares regression. Moreover, a comparison of CHull with standard model selection methods for these problems is performed. Second, we present the CHULL software, which may be downloaded from http://ppw.kuleuven.be/okp/software/CHULL/, to assist the user in applying the CHull procedure.     

A qualitative analysis of the vaccine intention–behaviour relationship: parents’ descriptions of their intentions,decision-making behaviour and planning processes towards HPV vaccination

Objective: The objective of this study is to identify factors influencing the vaccine intention–behaviour relationship. Design: A total of 445 parents who received a brief intervention to promote HPV vaccination were categorized based on their intentions post-intervention (yes/unsure/eventually/never) and subsequent adolescents’ vaccine status (yes/no). Fifty-one of these parents participated in qualitative interviews. Main Outcome Measures: Parents described their intentions, decision-making and planning processes towards vaccination. Framework analysis was used to analyse the data. Results: Parents in the ‘Yes/Yes’ category were knowledgeable about HPV/vaccine, described strong, stable intentions, considered themselves the primary decision-makers about vaccination and said they vaccinated immediately. ‘Yes/No’ parents described strong intentions and thought their adolescent was vaccinated OR described hesitant intentions, seeking advice/agreement from others and noting barriers to vaccination without solutions. ‘Unsure/Yes’ parents described their intentions as strengthening with information from credible sources and identified strategies for overcoming barriers. ‘Unsure/No’ and ‘Eventually/No’ parents had misinformation/negative beliefs regarding vaccination, described being ambivalent or non-supportive of vaccination and cited barriers to vaccination. ‘Never/No’ parents held negative beliefs about vaccination, described strong, stable intentions to NOT vaccinate, deferring the decision to others, and reported no planning towards vaccination. Conclusions: Intention characteristics and planning processes could moderate the vaccine intention–behaviour relationship, potentially serving as targets for future vaccine strategies.     

Intensity profiles of emotional experience over time

A full understanding of emotions and emotion characteristics can only be reached when their dynamic nature is taken into account. As such, a primary objective of the present study is to describe and account for variability in temporal profiles of experienced emotional intensity. Participants were asked to make detailed drawings of intensity profiles of recently experienced episodes of anger, sadness, joy and affection. Functional data analysis revealed three features that together accounted for 84% of the total variability: (i) steepness at onset; (ii) skewness; and (iii) the number of peaks. Emotions differed with regard to the first two features, with the rise at onset being steeper for sadness and joy and affection being the most negatively skewed emotion under study. Individual differences regarding each of the three features were found, however, they did not strongly generalise across emotions.     

The LMPCA program: A graphical user interface for fitting the linked-mode PARAFAC-PCA model to coupled real-valued data

In behavioral research, PARAFAC analysis, a three-mode generalization of standard principal component analysis (PCA), is often used to disclose the structure of three-way three-mode data. To get insight into the underlying mechanisms, one often wants to relate the component matrices resulting from such a PARAFAC analysis to external (two-way two-mode) information, regarding one of the modes of the three-way data. To this end, linked-mode PARAFAC-PCA analysis can be used, in which the three-way and the two-way data set, which have one mode in common, are simultaneously analyzed. More specifically, a PARAFAC and a PCA model are fitted to the three-way and the two-way data, respectively, restricting the component matrix for the common mode to be equal in both models. Until now, however, no software program has been publicly available to perform such an analysis. Therefore, in this article, the LMPCA program, a free and easy-to-use MATLAB graphical user interface, is presented to perform a linked-mode PARAFAC-PCA analysis. The LMPCA software can be obtained from the authors at http://ppw.kuleuven.be/okp/software/LMPCA. For users who do not have access to MATLAB, a stand-alone version is provided.