Effects of Time-Varying Parent Input on Children's Language Outcomes Differ for Vocabulary and Syntax

Early linguistic input is a powerful predictor of children's language outcomes. We investigated two novel questions about this relationship: Does the impact of language input vary over time, and does the impact of time-varying language input on child outcomes differ for vocabulary and for syntax? Using methods from epidemiology to account for baseline and time-varying confounding, we predicted 64 children's outcomes on standardized tests of vocabulary and syntax in kindergarten from their parents' vocabulary and syntax input when the children were 14 and 30 months old. For vocabulary, children whose parents provided diverse input earlier as well as later in development were predicted to have the highest outcomes. For syntax, children whose parents' input substantially increased in syntactic complexity over time were predicted to have the highest outcomes. The optimal sequence of parents' linguistic input for supporting children's language acquisition thus varies for vocabulary and for syntax

Educational psychology in latin america: with linear hierarchical models

Research in clinical psychology, since its inception, has been aimed at analyzing, predicting and explaining the effect of treatments, by studying the change of patients in the course of them. To study the effects of therapy, research based on quantitative analysis models has historically used classical methods of parametric statistics, such as Pearson correlations, least squares regressions Student’s T-Tests and Variance Analysis (ANOVA). Hierarchical linear models (HLMs) represent a fundamental statistical strategy for research in psychotherapy, as they allow to overcome dependence on the observations usually presented in your data. The objective of this work is to present a guide to understanding, applying and reporting HLMs to study the effects of psychotherapy

A comparison of walk-in counselling and the wait list model for delivering counselling services

Background: Walk-in counselling has been used to reduce wait times but there are few controlled studies to compare outcomes between walk-in and the traditional model of service delivery. Aims: To compare change in psychological distress by clients receiving services from two models of service delivery, a walk-in counselling model and a traditional counselling model involving a wait list Method: Mixed methods sequential explanatory design including quantitative comparison of groups with one pre-test and two follow ups, and qualitative analysis of interviews with a subsample. 524 participants 16 years and older were recruited from two Family Counselling Agencies; the General Health Questionnaire assessed change in psychological distress; prior use of other mental health and instrumental services was also reported. Results: Hierarchical linear modelling revealed clients of the walk-in model improved faster and were less distressed at the 4-week follow-up compared to the traditional service delivery model. At the 10-week follow-up, both groups had improved and were similar. Participants receiving instrumental services prior to baseline improved more slowly. Qualitative interviews confirmed participants valued the accessibility of the walk-in model. Conclusions: This study improves methodologically on previous studies of walk-in counselling, an approach to service delivery that is not conducive to randomized controlled trials

Equity in mathematics and science outcomes: characteristics associated with high and low achievement on PISA 2006 in Ireland

Equity in education is a key concern internationally; however, it is rare that this issue is examined separately for low- and high-achieving students and concurrently across different subject domains. This study examines student and school background characteristics associated with low and high achievement in mathematics and science on the Programme for International Student Assessment. Based on the results of a multilevel multinomial model of achievement for each domain, findings indicate that a greater number of the variables examined are associated with low rather than high achievement. At student level, home language, intention to leave school early, socioeconomic status, grade level, cultural capital, and books in the home are significantly associated with achievement in mathematics and science. At school level, only school average socioeconomic status is statistically significant in the models. Significant gender differences are found in the distribution of high and low achievers, which vary across the domains. In mathematics, females are more likely to be low achievers while males are more likely to be high achievers. In science, gender interacts with early school-leaving intent whereas males intending to leave school early are more likely to be in the low-achieving group than females intending to leave early. Conclusions emphasise the need for targeting resources aimed at promoting equity in outcomes at student level as well as at school level. Future work may extend the current analyses by incorporating domain-specific variables or examining cross-country differences

Safety climate and increased risk: The role of deadlines in design work

Although much research indicates positive safety climate is associated with reduced safety risk, we argue this association is not universal and may even be reversed in some contexts. Specifically, we argue that positive safety climate can be associated with increased safety risk when there is pressure to prioritize production over safety and where workers have some detachment from the consequences of their actions, such as found in engineering design work. We used two indicators of safety risk: use of heuristics at the individual level and design complexity at the design team level. Using experience sampling data (N = 165, 42 design teams, k = 5752 observations), we found design engineers’ perceptions of team positive safety climate were associated with less use of heuristics when engineers were not working to deadlines, but more use of heuristics when engineers were working to deadlines. Independent ratings were obtained of 31 teams’ designs of offshore oil and gas platforms (N = 121). For teams that worked infrequently to deadlines, positive team safety climate was associated with less design complexity. For teams that worked frequently to deadlines, positive team safety climate was associated with more design complexity

Fitting multilevel models in complex survey data with design weights: Recommendations

Abstract Background Multilevel models (MLM) offer complex survey data analysts a unique approach to understanding individual and contextual determinants of public health. However, little summarized guidance exists with regard to fitting MLM in complex survey data with design weights. Simulation work suggests that analysts should scale design weights using two methods and fit the MLM using unweighted and scaled-weighted data. This article examines the performance of scaled-weighted and unweighted analyses across a variety of MLM and software programs. Methods Using data from the 2005–2006 National Survey of Children with Special Health Care Needs (NS-CSHCN: n = 40,723) that collected data from children clustered within states, I examine the performance of scaling methods across outcome type (categorical vs. continuous), model type (level-1, level-2, or combined), and software (Mplus, MLwiN, and GLLAMM). Results Scaled weighted estimates and standard errors differed slightly from unweighted analyses, agreeing more with each other than with unweighted analyses. However, observed differences were minimal and did not lead to different inferential conclusions. Likewise, results demonstrated minimal differences across software programs, increasing confidence in results and inferential conclusions independent of software choice. Conclusion If including design weights in MLM, analysts should scale the weights and use software that properly includes the scaled weights in the estimation.</p

Optimising experimental design for high-throughput phenotyping in mice: a case study

To further the functional annotation of the mammalian genome, the Sanger Mouse Genetics Programme aims to generate and characterise knockout mice in a high-throughput manner. Annually, approximately 200 lines of knockout mice will be characterised using a standardised battery of phenotyping tests covering key disease indications ranging from obesity to sensory acuity. From these findings secondary centres will select putative mutants of interest for more in-depth, confirmatory experiments. Optimising experimental design and data analysis is essential to maximise output using the resources with greatest efficiency, thereby attaining our biological objective of understanding the role of genes in normal development and disease. This study uses the example of the noninvasive blood pressure test to demonstrate how statistical investigation is important for generating meaningful, reliable results and assessing the design for the defined research objectives. The analysis adjusts for the multiple-testing problem by applying the false discovery rate, which controls the number of false calls within those highlighted as significant. A variance analysis finds that the variation between mice dominates this assay. These variance measures were used to examine the interplay between days, readings, and number of mice on power, the ability to detect change. If an experiment is underpowered, we cannot conclude whether failure to detect a biological difference arises from low power or lack of a distinct phenotype, hence the mice are subjected to testing without gain. Consequently, in confirmatory studies, a power analysis along with the 3Rs can provide justification to increase the number of mice used