Measuring societal effects of transdisciplinary research projects: design and application of an evaluation method

Most Transdisciplinary Research (TdR) projects combine scientific research with the building of decision making capacity for the involved stakeholders. These projects usually deal with complex, societally relevant, real-world problems. This paper focuses on TdR projects, which integrate the knowledge of researchers and stakeholders in a collaborative transdisciplinary process through structured methods of mutual learning. Previous research on the evaluation of TdR has insufficiently explored the intended effects of transdisciplinary processes on the real world (societal effects). We developed an evaluation framework for assessing the societal effects of transdisciplinary processes. Outputs (measured as procedural and product-related involvement of the stakeholders), impacts (intermediate effects connecting outputs and outcomes) and outcomes (enhanced decision making capacity) are distinguished as three types of societal effects. Our model links outputs and outcomes of transdisciplinary processes via the impacts using a mediating variables approach. We applied this model in an ex post evaluation of a transdisciplinary process. 84 out of 188 agents participated in a survey. The results show significant mediation effects of the two impacts "network building" and "transformation knowledge". These results indicate an influence of a transdisciplinary process on the decision making capacity of stakeholders, especially through social network building and the generation of knowledge relevant for action.     

Human Capital,Social Support,and Economic Well-being among Rural,Low-income Mothers: A Latent Growth Curve Analysis

The purpose of this study was to better understand human capital and social support in the long-term economic well-being of rural, low-income mothers in the US. Three waves of data from a multi-state, longitudinal investigation tracking the well-being of rural families, known as “Rural Families Speak,” were used to test two latent growth curve models of economic well-being. Results indicated that human capital alone is not a good predictor of economic well-being over time for this sample. A model of economic well-being that includes both social support and human capital provides a better fit for these data. Findings suggest that social support is a key contributor to long-term economic success for this sample. Implications for public policy are presented.

When do couples sign the one-child certificate in urban China?

This paper analyzes a central part of China's one-child policy: when do eligible couples sign the one-child certificate and what are important socioeconomic determinants of this decision? We use proportional hazard models applied to micro-data from the 1985 Chinese Fertility Survey to study this question. Our results for urban residents in Hebei and Shaanxi indicate that a couple's socioeconomic characteristics significantly affect the timing of signing the certificate. In particular, education of the husband and wife, household wealth, and the age at marriage increase the probability of signing the certificate at an earlier time, while living space decreases the probability. Living in extended family structure or a male first child (to test for son preference) apparently have little effect.     

Assessing switchability for biosimilar products: modelling approaches applied to children's growth

The present paper describes two statistical modelling approaches that have been developed to demonstrate switchability from the original recombinant human growth hormone (rhGH) formulation (Genotropin^®) to a biosimilar product (Omnitrope^®) in children suffering from growth hormone deficiency. Demonstrating switchability between rhGH products is challenging because the process of growth varies with the age of the child and across children. The first modelling approach aims at predicting individual height measured at several time‐points after switching to the biosimilar. The second modelling approach provides an estimate of the deviation from the overall growth rate after switching to the biosimilar, which can be regarded as an estimate of switchability. The results after applying these approaches to data from a randomized clinical trial are presented. The accuracy and precision of the predictions made using the first approach and the small deviation from switchability estimated with the second approach provide sufficient evidence to conclude that switching from Genotropin^® to Omnitrope^® has a very small effect on growth, which is neither statistically significant nor clinically relevant. Copyright © 2015 John Wiley & Sons, Ltd.     

Speaking Acquaintances or Helpers in Need: Participation in Civic Associations and Individual Social Capital

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - It is widely believed that civic associations are capable to produce social capital, here understood as an individual...     

A copula-based multivariate hidden Markov model for modelling momentum in football

AStA Advances in Statistical Analysis - We investigate the potential occurrence of change points—commonly referred to as “momentum shifts”—in the dynamics of football...     

Contracting with Imperfect Commitment and the Revelation Principle: The Single Agent Case

This paper extends the revelation principle to environments in which the mechanism designer cannot fully commit to the outcome induced by the mechanism. We show that he may optimally use a direct mechanism under which truthful revelation is an optimal strategy for the agent. In contrast with the conventional revelation principle, however, the agent may not use this strategy with probability one. Our results apply to contracting problems between a principal and a single agent. By reducing such problems to well‐defined programming problems they provide a basic tool for studying imperfect commitment.