The ‘right time’ – negotiating the timing of interviews with vulnerable young people

Participant retention is a key factor in determining the success of longitudinal research. Challenges in re-locating and retaining participants over the long term are major issues for researchers working with young people who face adversity and experience frequent changes in circumstances. This article reports on a study of vulnerable young people and their transition into adulthood. Rather than the more conventional schedule-based approach to locating and re-interviewing young people, a relational process, the ‘right time’ framework, was used to facilitate young people's involvement in the study. Embedded in the ‘right time’ framework is recognition of the diverse and fluctuating circumstances that shape young people's availability for interviews. Several case examples are considered which amplify the way that the ‘right time’ framework allowed the research to navigate around these circumstances. The case examples highlight the value young people attached to being involved in the research, the influence on the ‘right time’ of wider relational tensions for young people and the need to negotiate researcher status as a different sort of adult. The ‘right time’ framework contributed to a high retention rate in the study generating a more representative sample and enhancing the subsequent data analysis by providing valuable insights into the lives of these vulnerable young people.     

Backfitting Random Varying-Coefficient Models with Time-Dependent Smoothing Covariates

Abstract.  In this paper, we propose a random varying-coefficient model for longitudinal data. This model is different from the standard varying-coefficient model in the sense that the time-varying coefficients are assumed to be subject-specific, and can be considered as realizations of stochastic processes. This modelling strategy allows us to employ powerful mixed-effects modelling techniques to efficiently incorporate the within-subject and between-subject variations in the estimators of time-varying coefficients. Thus, the subject-specific feature of longitudinal data is effectively considered in the proposed model. A backfitting algorithm is proposed to estimate the coefficient functions. Simulation studies show that the proposed estimation methods are more efficient in finite-sample performance compared with the standard local least squares method. An application to an AIDS clinical study is presented to illustrate the proposed methodologies.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

Fuzzy K-means clustering models for triangular fuzzy time trajectories

We focus our attention on the classification of fuzzy time trajectories with triangular membership function, described by a given set of individuals. To this purpose, we adopt a fullyinformational approach, explicitly recognizing the informational nature shared by the ingredients of the classification procedure: the observed data (Empirical Information) and the classification model (Theoretical Information). In particular, by supposing that the informational paradigm has a fuzzy nature, we suggest three fuzzy clustering models allowing the classification of the triangular fuzzy time trajectories, based on the analysis of the cross sectional and/or longitudinal characteristics of their components (centers and spreads). Two applicative examples are illustrated.     

Race and Population Change: A Longitudinal Look at Cleveland Neighborhoods

This paper investigates the relationship of racial composition to neighborhood population change from 1910 to 1990 in the Cleveland metropolitan area. To better understand the long-term dynamics of urban neighborhood change, we focus our analysis upon the longitudinal relationship of race, socioeconomic status, and life cycle stage to changes in neighborhood population densities. First, we find that the more established neighborhoods of the African-American community have experienced dramatic declines in population since 1950, a pattern that represents a clear change from the earlier part of the twentieth century. Second, population loss is experienced through a variety of mechanisms, including the demolition of dwellings, the increase in housing vacancy, and the decline of household size. Third, much of this population loss should be interpreted within the context of high economic distress, occurring most frequently in older African-American communities. Over time, economic distress appears to be more important than race in and of itself in leading to the loss of neighborhood populations.     

EFFECTS OF INTERNET USE AND SOCIAL RESOURCES ON CHANGES IN DEPRESSION

We examine how people's different uses of the Internet predict their later scores on a standard measure of depression, and how their existing social resources moderate these effects. In a longitudinal US survey conducted in 2001 and 2002, almost all respondents reported using the Internet for information, and entertainment and escape; these uses of the Internet had no impact on changes in respondents' level of depression. Almost all respondents also used the Internet for communicating with friends and family, and they showed lower depression scores six months later. Only about 20 percent of this sample reported using the Internet to meet new people and talk in online groups. Doing so changed their depression scores depending on their initial levels of social support. Those having high or medium levels of social support showed higher depression scores; those with low levels of social support did not experience these increases in depression. Our results suggest that individual differences in social resources and people's choices of how they use the Internet may account for the different outcomes reported in the literature.     

THE SOCIAL IMPACT OF BROADBAND HOUSEHOLD INTERNET ACCESS

Broadband changes everything. Or so we are told. But does it? There is only one way to find out - follow people who move from narrowband to broadband internet access and see what changes. This paper reports exactly this kind of analysis using data from a two wave European panel study (e-Living) and the lagged endogenous regression approach to see if switching to broadband increases the time spent online, the use of online communication services, the breadth of internet activities and the amount of online spend, and whether it decreases the time spent watching TV and the level of social leisure activities. The results suggest, in the main, that switching to broadband made little difference for this group of early broadband adopters who were already heavy internet users. There was no evidence of an online spend or social leisure substitution effect although there was evidence of a reduction in time spent watching television, and an increase in email in use, time spent online and breadth of internet use. In all cases however it was the previous levels of behaviour that were the most significant and switching to broadband was, in general, one of the least strong effects.     

农村幼儿园运用家庭和社区资源的纵向调查——以我国西部S省为例

家庭和社区参与的高质量学前教育是乡村振兴计划的基础。通过对我国西部S省农村地区的两次实地调查发现,受该地区园所特点、教育政策和传统观念等的影响,农村地区幼儿园运用家庭和社区资源总体较低但也有所改善,对家庭资源的运用高于社区资源,对社区历史文化资源的运用较为薄弱。基于此结果提出了运用家庭和社区资源是农村幼儿园发展的必然选择;利用政策杠杆发掘农村地区家庭和社区资源;职后培训提高农村幼儿园教师运用家庭和社区资源的实效性等建议。     

扁平超宽连续箱梁桥空间受力性能分析与研究

扁平超宽箱梁桥由于具有较大的宽跨比，其受力性能呈现明显的空间效应，通常的单梁模型计算已不能满足设计的精度要求，主要体现在宽箱梁各腹板纵向受力的差异性以及明显的剪力滞效应，同时由于大宽跨比使得横向预应力的作用不仅增加了顶板的压应力，也使得整体箱梁截面下缘出现拉应力，造成边腹板底部纵向裂缝，这些都给宽箱梁的设计带来了不利。因此为研究宽箱梁的空间效应，本文运用ANSYS有限元软件建立了全桥实体模型，计算了各腹板纵向受力差异及剪力滞效应，以及横向预应力对箱梁横向受力的影响，得到了一些有益的结论，为设计人员提供一定的参考。