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The current study examined professional caregivers' perceived and physiological stress, and associations with the quality of care they provide. Participants were 55 female caregivers from childcare homes and 46 female caregivers from childcare centers in the Netherlands. In both types of settings, equivalent measures and procedures were used. On non-work days, caregivers' salivary cortisol levels decreased between 11 am and 3 pm, whereas on work days, caregivers' cortisol levels remained at the same level during this period. Caregivers' cortisol levels and perceived stress did not differ across the two types of settings. In home-based childcare, caregivers offered higher-quality caregiving, compared to caregivers in center-based childcare. In home-based childcare--but not in center care--caregivers' negative appraisal was associated with less positive caregiver behavior. These findings suggest that work at childcare influences cortisol secretion in professional caregivers, and that perceived stress but not cortisol is associated with quality of care. (Contains 3 tables and 2 figures.)

In this paper, the statistical significance of the contribution of variables to the principal components in principal components analysis (PCA) is assessed nonparametrically by the use of permutation tests. We compare a new strategy to a strategy used in previous research consisting of permuting the columns (variables) of a data matrix independently and concurrently, thus destroying the entire correlational structure of the data. This strategy is considered appropriate for assessing the significance of the PCA solution as a whole, but is not suitable for assessing the significance of the contribution of single variables. Alternatively, we propose a strategy involving permutation of one variable at a time, while keeping the other variables fixed. We compare the two approaches in a simulation study, considering proportions of Type I and Type II error. We use two corrections for multiple testing: the Bonferroni correction and controlling the False Discovery Rate (FDR). To assess the significance of the variance accounted for by the variables, permuting one variable at a time, combined with FDR correction, yields the most favorable results. This optimal strategy is applied to an empirical data set, and results are compared with bootstrap confidence intervals.

A cross-country comparison of children's cortisol levels at child care was performed in relation to their cortisol levels at home and the quality and quantity of child care they received. Participants were toddlers visiting child care centers in Spanish Basque Country (N = 60) and the Netherlands (N = 25) with substantial variation in structural child care quality (group sizes, child:caregiver ratios). Salivary cortisol was measured at mid-morning and mid-afternoon, both at child care and at home. Children's actual experiences in child care (global quality), as measured with the Early Childhood Environment Rating Scale-Revised edition (ECERS-R), did not differ significantly across the two countries. Overall, children did not produce more cortisol at child care than at home. Results suggest that factors other than structural quality and quantity of care are responsible for cortisol diurnal changes. Global quality matters in explaining cortisol diurnal change in children visiting child care centers. (Contains 1 figure and 6 tables.)

In an intriguing account, the hypothesis is proposed that elevating effects of quantity of childcare on aggression might not, as other research has suggested, be dissipating over time, but instead be diffusing across groups of children. Paradoxically, this diffusion may also affect children with little or no experience with non-maternal care. If supported, this hypothesis may cause great concern about the increasing childcare use in western countries. In this commentary, we take a closer look at this hypothesis, focusing on limitations in ethnic and cultural diversity of the available data, on the inconclusive role of temperament, on the causal nature of some conclusions, and on the role of the father as a non-maternal caregiver.

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover nonlinear relationships between variables. Also, nonlinear PCA can deal with variables at their appropriate measurement level; for example, it can treat Likert-type scales ordinally instead of numerically. Every observed value of a variable can be referred to as a category. While performing PCA, nonlinear PCA converts every category to a numeric value, in accordance with the variable's analysis level, using optimal quantification. The authors discuss how optimal quantification is carried out, what analysis levels are, which decisions have to be made when applying nonlinear PCA, and how the results can be interpreted. The strengths and limitations of the method are discussed. An example applying nonlinear PCA to empirical data using the program CATPCA (J. J. Meulman, W. J. Heiser, & SPSS, 2004) is provided.

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate normality. For nonlinear PCA, however, standard options for establishing stability are not provided. The authors use the nonparametric bootstrap procedure to assess the stability of nonlinear PCA results, applied to empirical data. They use confidence intervals for the variable transformations and confidence ellipses for the eigenvalues, the component loadings, and the person scores. They discuss the balanced version of the bootstrap, bias estimation, and Procrustes rotation. To provide a benchmark, the same bootstrap procedure is applied to linear PCA on the same data. On the basis of the results, the authors advise using at least 1,000 bootstrap samples, using Procrustes rotation on the bootstrap results, examining the bootstrap distributions along with the confidence regions, and merging categories with small marginal frequencies to reduce the variance of the bootstrap results.

Expanding our knowledge on parenting practices of immigrant families is crucial for designing culturally sensitive parenting intervention programs in countries with high immigration rates. We investigated differences in patterns of parenting between second-generation immigrant and native families with young children. Authoritarian and authoritative control and sensitivity of second-generation Turkish immigrant mothers of 2-year-old children (n = 70) and native Dutch mothers (n = 70) were observed in the home and in the laboratory. Controlling for maternal age and education, Turkish immigrant mothers were less supportive, gave less clear instructions to their children, were more intrusive and were less authoritative in their control strategies than native Dutch mothers. No differences were found in authoritarian control. In both ethnic groups supportive presence, clarity of instruction, authoritative control, and low intrusiveness loaded on one factor. No differences between ethnic groups were found in gender-differentiated parenting. Maternal emotional connectedness to the Turkish culture was associated with less authoritative control, whereas more use of the Turkish language was related to more sensitivity. Even though mean level differences in parenting behaviors still exist between second-generation Turkish immigrant and native Dutch mothers, the patterns of associations between parenting behaviors were comparable for both groups. This suggests that existing parenting interventions for native families may be applicable to second-generation Turkish immigrants as well. Keywords Immigrant families * Observed parenting * Sensitivity * Control * Toddlers DOI 10.1007/s10826-009-9346-y

Missing data is a problem that occurs frequently in many scientific areas. The most sophisticated method for dealing with this problem is multiple imputation. Contrary to other methods, like listwise deletion, this method does not throw away information, and partly repairs the problem of systematic dropout. Although from a theoretical point of view multiple imputation is considered to be the optimal method, many applied researchers are reluctant to use it because of persistent misconceptions about this method. Instead of providing an(other) overview of missing data methods, or extensively explaining how multiple imputation works, this article aims specifically at rebutting these misconceptions, and provides applied researchers with practical arguments supporting them in the use of multiple imputation. [ABSTRACT FROM AUTHOR]