Prevalence of responsible research practices among academics in The Netherlands

Background: Traditionally, research integrity studies have focused on research misbehaviors and their explanations. Over time, attention has shifted towards preventing questionable research practices and promoting responsible ones. However, data on the prevalence of responsible research practices, especially open methods, open codes and open data and their underlying associative factors, remains scarce. Methods: We conducted a web-based anonymized questionnaire, targeting all academic researchers working at or affiliated to a university or university medical center in The Netherlands, to investigate the prevalence and potential explanatory factors of 11 responsible research practices. Results: A total of 6,813 academics completed the survey, the results of which show that prevalence of responsible practices differs substantially across disciplines and ranks, with 99 percent avoiding plagiarism in their work but less than 50 percent pre-registering a research protocol. Arts and humanities scholars as well as PhD candidates and junior researchers engaged less often in responsible research practices. Publication pressure negatively affected responsible practices, while mentoring, scientific norms subscription and funding pressure stimulated them. Conclusions: Understanding the prevalence of responsible research practices across disciplines and ranks, as well as their associated explanatory factors, can help to systematically address disciplinary- and academic rank-specific obstacles, and thereby facilitate responsible conduct of research

The effect of incorrect prior information on trust behavior in adolescents

During adolescence, social cognition and the brain undergo major developments. Social interactions become more important, and adolescents must learn that not everyone can be trusted equally. Prior knowledge about the trustworthiness of an interaction partner may affect adolescents' expectations about the partner. However, the expectations based on prior knowledge can turn out to be incorrect, causing the need to respond adaptively during the interaction. In the current fMRI study, we investigated the effect of incorrect prior knowledge on adolescent trust behavior and on the neural processes of trust. Thirty-three adolescents (Mage = 17.2 years, SDage = 0.5 years) played two trust games with partners whose behavior was preprogrammed using an algorithm that modeled trustworthy behavior. Prior to the start of both games, participants received information suggesting that the partner in one game was untrustworthy (raising incorrect expectations) and the partner in the other game trustworthy (raising correct expectations). Results indicated that participants adapted their trust behavior following incorrect prior expectations. No evidence for a change in trust behavior was shown when prior expectations were correct. fMRI analyses revealed that when receiving the partner's response, activity in the dorsolateral prefrontal cortex and in the superior parietal gyrus were increased when participants had incorrect expectations about the partner compared to when participants had correct expectations. When making trust decisions, no significant differences in neural activity were found when comparing the two games. This study provides insight into how adolescent trust behavior and neural mechanisms are affected by expectations and provides an increased understanding of the factors that influence adolescent social interaction

HEXACO Personality Dimensions Do Not Predict Individual Differences in Adolescent Trust Behavior

Adolescence is an important developmental period for both trust behavior and personality maturation, and individual differences in trust decisions may be related to different personality traits. In the current study, a group of adolescents (n = 483, Mage = 13.5, SDage = 0.4) played two counterbalanced conditions of a multi-round trust game. In one condition, the partner displayed trustworthy behavior (the trustworthy condition), while the partner in the other condition played untrustworthy behavior (the untrustworthy condition). Three types of trust behavior were examined: initial trust behavior, the adaptation of trust behavior (trustworthy condition), and the adaptation of trust behavior (untrustworthy condition). Personality was measured using the Brief HEXACO Inventory. We expected the HEXACO personality dimensions of honesty–humility and agreeableness to be positively associated with initial trust behavior, but conscientiousness to be negatively related to initial trust behavior. The examination of the relationship between these dimensions and the adaptation of trust behavior were conducted on an exploratory basis. The investigation of the relationship between the remaining dimensions (emotionality, extraversion, and openness to experience) and the three types of trust behavior were also carried out on an exploratory basis. For each type of trust behavior, a hierarchical multiple regression analysis was undertaken to examine whether the HEXACO personality dimensions were related to trust behavior. Using frequentist analyses, no evidence was found that supported the HEXACO dimensions as significant predictors of the three types of trust behavior. Moreover, additional Bayesian analyses showed evidence that the hypothesized HEXACO dimensions (honesty–humility, agreeableness, and conscientiousness) did not outperform the non-hypothesized HEXACO dimensions (emotionality, extraversion, and openness to experience). The association between personality traits and trust might be less pronounced during adolescence as personality maturates across an individual’s lifespan. Additionally, due to a heightened sensitivity to the environment, contextual cues may affect adolescent decision-making processes, leaving less room for personality-driven behaviors

Systematic review indicates postnatal growth in term infants born small-for-gestational-age being associated with later neurocognitive and metabolic outcomes

We systematically reviewed papers published in English between 1994 and October 2015 on how postnatal weight gain and growth affect neurodevelopment and metabolic outcomes in term-born small-for-gestational-age (SGA) infants. Two randomised trials reported that enriched infant formulas that promoted early growth also increased fat mass, lean mass and blood pressure (BP), but had no effect on early neurocognitive outcomes. Meanwhile, 31 observational studies reported consistent positive associations between postnatal weight gain and growth with neurocognitive outcomes, adiposity, insulin resistance and BP. $\textbf{Conclusion}$: Few intervention studies exist, despite consistent positive associations between early growth and neurocognition in term-born SGA infants.The expert group received funding from the ILSI Europe Early Nutrition and Long-Term Health (formerly Metabolic Imprinting) Task Force

Postnatal growth in preterm infants and later health outcomes: a systematic review.

In preterm infants, poor postnatal growth is associated with adverse neurocognitive outcomes; conversely, rapid postnatal growth is supposedly harmful for future development of metabolic diseases. CONCLUSION: In this systematic review, observational studies reported consistent positive associations between postnatal weight or head growth and neurocognitive outcomes; however, there was limited evidence from the few intervention studies. Evidence linking postnatal weight gain to later adiposity and other cardiovascular disease risk factors in preterm infants was also limited.The expert group received funding from the ILSI Europe Metabolic Imprinting Task Force (please see acknowledgements for further information). Industry members of this task force are listed on the ILSI Europe website at www.ilsi.eu. KMG is supported by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and by the European Union’s Seventh Framework Programme (FP7/2007-2013), project EarlyNutrition under grant agreement no 289346.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1111/apa.1312

The creation of the Global Scales for Early Development (GSED) for children aged 0-3 years: combining subject matter expert judgements with big data.

INTRODUCTION: With the ratification of the Sustainable Development Goals, there is an increased emphasis on early childhood development (ECD) and well-being. The WHO led Global Scales for Early Development (GSED) project aims to provide population and programmatic level measures of ECD for 0-3 years that are valid, reliable and have psychometrically stable performance across geographical, cultural and language contexts. This paper reports on the creation of two measures: (1) the GSED Short Form (GSED-SF)-a caregiver reported measure for population-evaluation-self-administered with no training required and (2) the GSED Long Form (GSED-LF)-a directly administered/observed measure for programmatic evaluation-administered by a trained professional. METHODS: We selected 807 psychometrically best-performing items using a Rasch measurement model from an ECD measurement databank which comprised 66 075 children assessed on 2211 items from 18 ECD measures in 32 countries. From 766 of these items, in-depth subject matter expert judgements were gathered to inform final item selection. Specifically collected were data on (1) conceptual matches between pairs of items originating from different measures, (2) developmental domain(s) measured by each item and (3) perceptions of feasibility of administration of each item in diverse contexts. Prototypes were finalised through a combination of psychometric performance evaluation and expert consensus to optimally identify items. RESULTS: We created the GSED-SF (139 items) and GSED-LF (157 items) for tablet-based and paper-based assessments, with an optimal set of items that fit the Rasch model, met subject matter expert criteria, avoided conceptual overlap, covered multiple domains of child development and were feasible to implement across diverse settings. CONCLUSIONS: State-of-the-art quantitative and qualitative procedures were used to select of theoretically relevant and globally feasible items representing child development for children aged 0-3 years. GSED-SF and GSED-LF will be piloted and validated in children across diverse cultural, demographic, social and language contexts for global use

Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania

BackgroundGestational weight gain differs according to pre-pregnancy body mass index and is related to the risks of adverse maternal and child health outcomes. Gestational weight gain charts for women in different pre-pregnancy body mass index groups enable identification of women and offspring at risk for adverse health outcomes. We aimed to construct gestational weight gain reference charts for underweight, normal weight, overweight, and grades 1, 2 and 3 obese women and to compare these charts with those obtained in women with uncomplicated term pregnancies.MethodsWe used individual participant data from 218,216 pregnant women participating in 33 cohorts from Europe, North America, and Oceania. Of these women, 9065 (4.2%), 148,697 (68.1%), 42,678 (19.6%), 13,084 (6.0%), 3597 (1.6%), and 1095 (0.5%) were underweight, normal weight, overweight, and grades 1, 2, and 3 obese women, respectively. A total of 138, 517 women from 26 cohorts had pregnancies with no hypertensive or diabetic disorders and with term deliveries of appropriate for gestational age at birth infants. Gestational weight gain charts for underweight, normal weight, overweight, and grade 1, 2, and 3 obese women were derived by the Box-Cox t method using the generalized additive model for location, scale, and shape.ResultsWe observed that gestational weight gain strongly differed per maternal pre-pregnancy body mass index group. The median (interquartile range) gestational weight gain at 40weeks was 14.2kg (11.4-17.4) for underweight women, 14.5kg (11.5-17.7) for normal weight women, 13.9kg (10.1-17.9) for overweight women, and 11.2kg (7.0-15.7), 8.7kg (4.3-13.4) and 6.3kg (1.9-11.1) for grades 1, 2, and 3 obese women, respectively. The rate of weight gain was lower in the first half than in the second half of pregnancy. No differences in the patterns of weight gain were observed between cohorts or countries. Similar weight gain patterns were observed in mothers without pregnancy complications.ConclusionsGestational weight gain patterns are strongly related to pre-pregnancy body mass index. The derived charts can be used to assess gestational weight gain in etiological research and as a monitoring tool for weight gain during pregnancy in clinical practice.Peer reviewe

Child development with the D-score: turning milestones into measurement [version 1; peer review: 1 approved with reservations]

The chapter equips the reader with a basic understanding of robust psychometric methods that are needed to turn developmental milestones into measurements, introducing the fundamental issues in defining a unit for child development and demonstrates the relevant quantitative methodology. It reviews quantitative approaches to measuring child development; introduces the Rasch model in a non-technical way; shows how to estimate model parameters from real data; puts forth a set of principles for model evaluation and assessment of scale quality; analyses the relation between early D-scores and later intelligence; and compares the D-scores from three studies that all use the same instrument

mice: Multivariate Imputation by Chained Equations: 3.11.0

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations

The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation

Background: Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was to assess the performance of multiple imputation when estimating a logistic regression model when the prevalence of missing data for predictor variables is very high. Methods: Monte Carlo simulations were used to examine the performance of multiple imputation when estimating a multivariable logistic regression model. We varied the size of the analysis samples (N = 500, 1,000, 5,000, 10,000, and 25,000) and the prevalence of missing data (5–95% in increments of 5%). Results: In general, multiple imputation performed well across the range of scenarios. The exceptions were in scenarios when the sample size was 500 or 1,000 and the prevalence of missing data was at least 90%. In these scenarios, the estimated standard errors of the log-odds ratios were very large and did not accurately estimate the standard deviation of the sampling distribution of the log-odds ratio. Furthermore, in these settings, estimated confidence intervals tended to be conservative. In all other settings (i.e., sample sizes > 1,000 or when the prevalence of missing data was less than 90%), then multiple imputation allowed for accurate estimation of a logistic regression model. Conclusions: Multiple imputation can be used in many scenarios with a very high prevalence of missing data