Factors influencing enrolment: A case study from Birth to Twenty, the 1990 birth cohort in Soweto–Johannesburg

Longitudinal studies offer significant advantages in rendering data commensurate with the complexity of human development. However, incomplete enrolment and attrition over time can introduce bias. Furthermore, there is a scarcity of evaluative information on cohorts in developing countries. This paper documents various strategies adopted to minimize loss to follow up and describes a retrospective analysis of a small group of families who were missed during initial enrolment and through several subsequent rounds of data collection of the Birth to Twenty (BT20) birth cohort in Soweto–Johannesburg, South Africa that began in 1990. A purposive case study approach was used, and 10 of the 119 families missed at enrolment were interviewed to investigate why these families were not enrolled into the study. The findings demonstrate that high mobility, both within urban areas and between urban and rural areas, are a major challenge for longitudinal studies in densely populated urban areas. In addition, enrolment was also affected by individuals changing their names, largely motivated to facilitate access to employment under Apartheid, as well as varying motivations for participating in research. Longitudinal studies in the developing country context must be mindful of the political, social and economic climate that influences enrolment and ongoing cohort maintenance.     

Climbing out of an economic crisis: A cycle of consumer sentiment and personal stress

This research examines bidirectional relationships between consumer sentiment and personal stress. It seeks to answer the question whether reduced consumer sentiment (consumer perceptions about the global, national and personal economic situation) raises personal stress levels, and whether increased personal stress levels depresses consumer sentiment. Finding such relationships would reveal a pathway from economic to personal well-being, as reflected in personal stress, and the other way around. A large longitudinal study in the Netherlands with a total of eight waves across three years (2012–2014), 4 waves during and 4 waves after the recent worldwide economic crisis, finds on average low personal stress levels among consumers, which is reassuring. Also and as expected, consumers are more positive about the state of the economy after than during the crisis. Importantly, more positive consumer sentiment indeed contributes to lower levels of personal stress. Moreover, the more personal stress consumers experience, the more pessimistic they are about the state of the economy after the crisis, whereas this connection is weaker during the crisis. This sheds new light on the pathways between consumer sentiment about the economy and their personal well-being.     

Data culture as a change agent for organizations serving families and children experiencing homelessness

Over the past decade there have been considerable developments in the use of data in the field of child and family homelessness. The development of high-quality data collection processes—including Housing Management Information Systems (HMIS), community point-in-time counts, and school district data and evaluation infrastructure—has given nonprofit and social sector leaders unprecedented access to client-level data. However, it remains a challenge for nonprofits and community-based organizations to engage in work with families experiencing homelessness and demonstrate meaningful impact across a variety of outcomes. In this policy brief, the authors discuss (1) challenges facing the field of child and family homelessness with respect to data use, (2) recent advancements in the use of data, and (3) strategies to create an organizational culture of data that makes use of recent advancements in data use and addresses current challenges facing the field. The brief makes the argument that fostering a data culture at the organizational level has the capacity to operate as an organizational change agent that improves programs.     

Change we can believe in: Comparing longitudinal network models on consistency,interpretability and predictive power

While several models for analysing longitudinal network data have been proposed, their main differences, especially regarding the treatment of time, have not been discussed extensively in the literature. However, differences in treatment of time strongly impact the conclusions that can be drawn from data. In this article we compare auto-regressive network models using the example of TERGMs – a temporal extensions of ERGMs – and process-based models using SAOMs as an example. We conclude that the TERGM has, in contrast to the ERGM, no consistent interpretation on tie-level probabilities, as well as no consistent interpretation on processes of network change. Further, parameters in the TERGM are strongly dependent on the interval length between two time-points. Neither limitation is true for process-based network models such as the SAOM. Finally, both compared models perform poorly in out-of-sample prediction compared to trivial predictive models.     

Measuring the Unmeasurable—A Study of Domestic Violence Risk Prediction and Management

ABSTRACT

The prevention of domestic violence (DV) have aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported DV cases that doubled over the past decade and the scarcity of social workers. However, most common collaborations for DV prevention, between academic researchers and advocacy groups or governments outsourcing, often fail to produce effective prevention strategies. Hence, Data for Social Good Initiative (D4SG) worked with Taipei City Government to improve the efficiency of DV prevention and risk management on two levels—project collaboration level and data level. On the project collaboration level, we adopted a platform strategy and utilize public-private partnership (PPP) to connect and empower change agents across data silos from pilot runs to actual project execution. On the data level, we helped social workers differentiate the risk level of new cases by building a repeat victimization risk prediction model using random forest method with the 2015 data from Taipei City government’s DV database. The accuracy and F1-measure of our model were 96.3% and 62.8%. This projects’ PPP approach and quantification method successfully improved DV prevention process. These methodologies have also been applied to other work fields including firework prediction, emergency healthcare management as a paradigm.     

Food for Thought: Analyzing Public Opinion on the Supplemental Nutrition Assistance Program

ABSTRACT

This project explores public opinion on the Supplemental Nutrition Assistance Program (SNAP) in news and social media outlets, and tracks elected representatives’ voting records on issues relating to SNAP and food insecurity. We used machine learning, sentiment analysis, and text mining to analyze national and state level coverage of SNAP in order to gauge perceptions of the program over time across these outlets. Results indicate that the majority of news coverage has negative sentiment, more partisan news outlets have more extreme sentiment, and that clustering of negative reporting on SNAP occurs in the Midwest. Our final results and tools will be displayed in an online application that the ACFB Advocacy team can use to inform their communication to relevant stakeholders.     

Measurement and decomposition of the Malmquist productivity index for parallel production systems

The conventional Malmquist productivity index (MPI), which ignores the internal structure of a production system when measuring changes in performance between two periods, may produce misleading results. This paper thus takes the operations of the component processes into account in investigating the MPI of parallel production systems. A relational data envelopment analysis (DEA) model is developed to measure the biennial MPIs of the system and internal processes at the same time, and it is shown that the former is a linear combination of the latter. This decomposition helps identify the processes that cause the decline in performance of the system. An example of 39 branches of a commercial bank, with deposits, sales, and services as the three major functions operating in parallel, is used to illustrate this approach.     

The determination of the least distance to the strongly efficient frontier in Data Envelopment Analysis oriented models: Modelling and computational aspects

Determining the least distance to the efficient frontier for estimating technical inefficiency, with the consequent determination of closest targets, has been one of the relevant issues in recent Data Envelopment Analysis literature. This new paradigm contrasts with traditional approaches, which yield furthest targets. In this respect, some techniques have been proposed in order to implement the new paradigm. A group of these techniques is based on identifying all the efficient faces of the polyhedral production possibility set and, therefore, is associated with the resolution of a NP-hard problem. In contrast, a second group proposes different models and particular algorithms to solve the problem avoiding the explicit identification of all these faces. These techniques have been applied more or less successfully. Nonetheless, the new paradigm is still unsatisfactory and incomplete to a certain extent. One of these challenges is that related to measuring technical inefficiency in the context of oriented models, i.e., models that aim at changing inputs or outputs but not both. In this paper, we show that existing specific techniques for determining the least distance without identifying explicitly the frontier structure for graph measures, which change inputs and outputs at the same time, do not work for oriented models. Consequently, a new methodology for satisfactorily implementing these situations is proposed. Finally, the new approach is empirically checked by using a recent PISA database consisting of 902 schools.