This study investigated the factor structure and factorial group and time invariance of the 17-item and 9-item versions of the Utrecht Work Engagement Scale (UWES; Schaufeli et al. (2002b) Journal of Happiness Studies 3:71-92). Furthermore, the study explored the rank-order stability of work engagement. The data were drawn from five different studies (N = 9,404), including a three-year longitudinal study (n = 2,555), utilizing five divergent occupational samples. Confirmatory factor analysis supported the hypothesized correlated three-factor structure-vigor, dedication, absorption-of both UWES scales. However, while the structure of the UWES-17 did not remain the same across the samples and time, the structure of the UWES-9 remained relatively unchanged. Thus, the UWES-9 has good construct validity and use of the 9-item version can be recommended in future research. Moreover, as hypothesized, Structural Equation Modeling showed high rank-order stabilities for the work engagement factors (between 0.82 and 0.86). Accordingly, work engagement seems to be a highly stable indicator of occupational well-being.

Background: Analyses from the Jyväskylä Longitudinal Study of Dyslexia project show that the key childhood predictors (phonological awareness, short-term memory, rapid naming, expressive vocabulary, pseudoword repetition, and letter naming) of dyslexia differentiate the group with reading disability (n ¼ 46) and the group without reading problems (n ¼ 152) at the end of the 2nd grade. These measures were employed at the ages of 3.5, 4.5 and 5.5 years and information regarding the familial risk of dyslexia was used to find the most sensitive indices of an individual child's risk for reading disability. Methods: Age-specific and across-age logistic regression models were constructed to produce the risk indices. The predictive ability of the risk indices was explored using the ROC (receiver operating curve) plot. Information from the logistic models was further utilised in illustrating the risk with probability curve presentations. Results: The logistic regression models with familial risk, letter knowledge, phonological awareness and RAN provided a prediction probability above .80 (area under ROC). Conclusions: The models including familial risk status and the three above-mentioned measures offer a rough screening procedure for estimating an individual child's risk for reading disability at the age of 3.5 years. Probability curves are presented as a method of illustrating the risk.

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The aim of the longitudinal study was to investigate whether a computer application designed for remedial reading training can enhance letter knowledge, reading accuracy, fluency, and spelling of at-risk children. The participants, 7-year-old Finnish school beginners (N=166), were assigned to 1 of 3 groups: (a) regular remedial reading intervention (n=25), (b) computer-assessed reading intervention (n=25), and (c) mainstream reading instruction (n=116). Based on the results, computer-assisted remedial reading intervention was highly beneficial, whereas regular type of intervention was less successful. The results indicated that at-risk children require computer-based letter-name and letter-sound training to acquire adequate decoding and spelling skills, and to reach the level of their non-at-risk peers.

Present findings are drawn from the Jyväskylä Longitudinal Study of Dyslexia (JLD) in which about 100 children with familial risk of dyslexia and 100 control children have been followed from birth. In this paper we report data on reading development of the JLD children and their classmates, a total of 1750 children from four measurement points during the first two school years. In the total sample we examined whether heterogeneous developmental paths can be identified based on profiles of 1st through 2nd grade word recognition and reading comprehension skills after controlling for the classroom membership effect. Secondly we studied what kind of early language and literacy skill profiles and reading experiences characterize the children in the follow-up with differing reading development. Our analyses included comparisons of the reading development of the JLD children with and without familial risk for dyslexia. The mixture modelling procedure resulted in five reading subtypes: (1) 'Poor readers' with poor skills in both word recognition and reading comprehension, (2) 'Slow readers' with somewhat below average word recognition combined with faster than average growth in reading comprehension, (3) 'Poor comprehenders' with average word recognition combined with slower than average reading comprehension development, (4) 'Average readers' with average skills in both word recognition and reading comprehension, and (5) 'Good readers' with high level of performance in both reading skills. The children with familial risk for dyslexia performed on average at a lower level in all reading tasks than both their classmates and controls and they were over-represented in the reading subtypes with deficient fluent word recognition. Differences were found in the early language and literacy skill development of the reading subtypes.Reading Development Subtypes