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Previous research suggests that primary–secondary school transitions can be problematic, with some children experiencing a negative impact on academic outcomes and a decline in wellbeing. The negative impact of primary–secondary transition experiences can be long term and lead to young people not completing high school with implications for their subsequent education and employment. However, there are several gaps in existing studies as a result of weak research designs, small samples and the timing of data collection. To better understand children's primary–secondary school transition experiences and the factors which may impact on them, we undertook secondary analysis of data from the Growing Up in Scotland (GUS) study. The analysis focused on data collected when children were in the penultimate year of primary school (Primary 6/P6) and then when they were in the first year of secondary school (Secondary 1/S1). This led to a final sample size of 2559 children and their parents. Results contradict many previous studies which tend to find primary–secondary transitions as overwhelmingly negative. In contrast, we found that 36% of children experienced a positive transition and 42% a moderately positive transition, with only 22% children experiencing a negative transition. This is the first large‐scale, longitudinal and contemporaneous study able to provide a clear proportion of children with positive, moderate and negative transition experiences. Several factors seemed to play a part in children's transition experiences, namely gender, religion, household composition, socioeconomic status, child expectations, schoolwork, and relationships. Implications and recommendations for research, policy and practice have been outlined. Context and implicationsRationale for this study:Primary–secondary school transitions are considered to have a detrimental impact on educational and wellbeing outcomes. However, previous studies have several limitations and a more robust analysis of a longitudinal dataset with a large sample was required.Why the new findings matter:This study has highlighted that the majority of children had a positive or moderately positive transition experience. It clarified which factors might influence whether children will have positive or negative transitions.Implications for practitioners, policy makers and researchers:Findings suggest the importance of practitioners and policy makers acknowledging the myriad factors occurring alongside and potentially influencing a child's transition experiences. Similarly, it is important to be mindful of strategies to promote children's positive relationships in both primary and secondary school, especially with their teachers. Further, children's participation in sports, clubs and youth groups should be encouraged. Children's anxieties and misconceptions about transitions should be addressed in a timely manner, including enhanced transition for those with higher levels of concern or anxiety. Fostering and maintaining good school–parent relationships, through timely and relevant communication can facilitate positive transitions.The findings suggest the importance of undertaking research to understand how children from less advantaged socioeconomic backgrounds can be better supported. Similarly, research should be undertaken to understand why some children have negative expectations and are less positive about secondary school. Further, future research should focus on analysing GUS data, including that from S3 and S6, and linking it with administrative data related to attainment and wellbeing. [ABSTRACT FROM AUTHOR]

Perinatal mental health disorders affect a significant number of women with debilitating and potentially life-threatening consequences. Researchers in Nordic countries have access to high quality, population-based data sources and the possibility to link data, and are thus uniquely positioned to fill current evidence gaps. We aimed to review how Nordic studies have contributed to existing evidence on perinatal mental health. We summarized examples of published evidence on perinatal mental health derived from large population-based longitudinal and register-based data from Denmark, Finland, Iceland, Norway and Sweden. Nordic datasets, such as the Danish National Birth Cohort, the FinnBrain Birth Cohort Study, the Icelandic SAGA cohort, the Norwegian MoBa and ABC studies, as well as the Swedish BASIC and Mom2B studies facilitate the study of prevalence of perinatal mental disorders, and further provide opportunity to prospectively test etiological hypotheses, yielding comprehensive suggestions about the underlying causal mechanisms. The large sample size, extensive follow-up, multiple measurement points, large geographic coverage, biological sampling and the possibility to link data to national registries renders them unique. The use of novel approaches, such as the digital phenotyping data in the novel application-based Mom2B cohort recording even voice qualities and digital phenotyping, or the Danish study design paralleling a natural experiment are considered strengths of such research. Nordic data sources have contributed substantially to the existing evidence, and can guide future work focused on the study of background, genetic and environmental factors to ultimately define vulnerable groups at risk for psychiatric disorders following childbirth. [ABSTRACT FROM AUTHOR]

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users’ private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework. [ABSTRACT FROM AUTHOR]

Understanding the possible pitfalls of survey data is critical for empirical research. Among other things, poor data quality can lead to biased regression estimates, potentially resulting in incorrect interpretations that mislead researchers and policymakers alike. Common data problems include difficulties in tracking respondents and high survey attrition, enumerator error and bias, and respondent reporting error. This paper describes and analyzes these issues in Round 1 of the Kenyan Life Panel Survey (KLPS-1), collected in 2003-2005. The KLPS-1 is an innovative longitudinal dataset documenting a wide range of outcomes for Kenyan youths who had originally attended schools participating in a deworming treatment program starting in 1998. The careful design of this survey allows for examination of an array of data quality issues. First, we explore the existence and implications of sample attrition bias. Basic residential, educational, and mortality information was obtained for 88% of target respondents, and personal contact was made with 84%, an exceptionally high follow-up rate for a young adult population in a less developed country. Moreover, rates of sample attrition are nearly identical for respondents who were randomly assigned deworming treatment and for those who were not, a key factor in the validity of subsequent statistical analysis. One vital component of this success is the tracking of respondents both nationally and across international borders (in our case, into Uganda), thus we discuss in detail the costs and benefits of tracking movers. Finally, we study KLPS-1 data quality more broadly by examining enumerator error and bias, as well as survey response consistency. We conclude that the extent of enumerator error is low, with an average of less than one recording error per survey. Errors decrease over time as enumerator experience with the survey instrument increases, but increase over the course of multiple interviews within a single day, presumably due to fatigue. We do find some evidence that the enumerator-respondent match in terms of gender, ethnicity, and religion correlates with responses regarding trust of others and religious activities, suggesting some field officer bias on sensitive questions. Reporting reliability is analyzed using respondent re-surveys. These checks show high levels of consistency across survey/re-survey rounds for the respondent’s own characteristics and personal history,with lower reliability rates on questions asked about others’ characteristics. The steps taken in the design of KLPS-1 to avoid common errors in survey data collection greatly improved the quality of this panel dataset, and provide some valuable lessons for future field data collection projects.

Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome “community state-types” at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm’s flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.

Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome "community statetypes'' at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm's flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations. [ABSTRACT FROM AUTHOR]

Abstract Background Hypertension is a strong factor for stroke and coronary disease, and it has been found that 1 in 4 young adults are experiencing pre-hypertension in the United States This study was designed to examine the role of parent-adolescent relationships in the risk of developing (pre)hypertension in young adulthood, and to explore potential mediator(s). Methods Our analysis was based on the data from the National Longitudinal Study of Adolescent to Adult Health, Wave 1 (aged 13–18) and Wave 4 (aged 25–32) (N = 3,350). Three parent-adolescent relationships were extracted from a factor analysis, and four different specifications of (pre)hypertension were generated and tested individually. We applied generalised structural equation modelling to perform path analysis estimations. Results We found that mother-reported relationship quality had both a direct and an indirect effect via alcohol consumption, on the likelihood of (pre)hypertension in young adulthood. The path from relationship quality to (pre)hypertension via alcohol consumption was consistent with three different specifications of hypertension (pre-hypertension, clinical/experienced hypertension, and experienced hypertension), suggesting the path relation was evident. Our study also showed that both relationship quality and adolescent-reported maternal warmth/responsiveness were associated with the risk of clinical/experienced hypertension via mental health problems. Parental control was found to have a direct and protective effect on clinical/experienced hypertension. Conclusions Early family relationships in adolescence predict (pre)hypertension in young adulthood. Initiatives related to parent-adolescent relationships, and the associated effects on later alcohol consumption and mental health problems, may have a long implication on the risk of (pre)hypertension in adulthood. [ABSTRACT FROM AUTHOR]

The game of chess has often been used for psychological investigations, particularly in cognitive science. The clear-cut rules and well-defined environment of chess provide a model for investigations of basic cognitive processes, such as perception, memory, and problem solving, while the precise rating system for the measurement of skill has enabled investigations of individual differences and expertise-related effects. In the present study, we focus on another appealing feature of chess-namely, the large archive databases associated with the game. The German national chess database presented in this study represents a fruitful ground for the investigation of multiple longitudinal research questions, since it collects the data of over 130,000 players and spans over 25 years. The German chess database collects the data of all players, including hobby players, and all tournaments played. This results in a rich and complete collection of the skill, age, and activity of the whole population of chess players in Germany. The database therefore complements the commonly used expertise approach in cognitive science by opening up new possibilities for the investigation of multiple factors that underlie expertise and skill acquisition. Since large datasets are not common in psychology, their introduction also raises the question of optimal and efficient statistical analysis. We offer the database for download and illustrate how it can be used by providing concrete examples and a step-by-step tutorial using different statistical analyses on a range of topics, including skill development over the lifetime, birth cohort effects, effects of activity and inactivity on skill, and gender differences. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate how science, technology, engineering, and mathematics (STEM) project based learning (PBL) affected high-need students in the United States (U.S.) in terms of their academic achievement. Among high-need students in the U.S., we focused on two student groups: Hispanic and at-risk students. The participants included 528 students in the three STEM PBL high schools and 2688 students in non-STEM PBL schools in the same region. Approximately 71% of the participants were Hispanic, at-risk, or Hispanic and at-risk. Latent growth modeling with repeated measures was used to analyze the data. The data were mathematics tests from 2008 to 2010 of the Texas Assessment of Knowledge and Skills. STEM PBL instruction positively influenced Hispanic students' achievement in mathematics, but not at-risk students. The findings of this study imply that curriculum integrating STEM PBL may help Hispanic students having language and cultural differences improve communication and collaboration skills in classrooms. [ABSTRACT FROM AUTHOR]

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