Data harmonization is a topic of growing importance to demographers, who increasingly conduct domestic or international comparative research. Many self-reported survey items cannot be directly compared across demographic groups or countries because these groups differ in how they use subjective response categories. Anchoring vignettes, already appearing in numerous surveys worldwide, promise to overcome this problem. However, many anchoring vignettes have not been formally evaluated for adherence to the key measurement assumptions of vignette equivalence and response consistency. This article tests these assumptions in some of the most widely fielded anchoring vignettes in the world: the health vignettes in the World Health Organization (WHO) Study on Global AGEing and Adult Health (SAGE) and World Health Survey (WHS) (representing 10 countries; n = 52,388), as well as similar vignettes in the Health and Retirement Study (HRS) (n = 4,528). Findings are encouraging regarding adherence to response consistency, but reveal substantial violations of vignette equivalence both cross-nationally and across socioeconomic groups. That is, members of different sociocultural groups appear to interpret vignettes as depicting fundamentally different levels of health. The evaluated anchoring vignettes do not fulfill their promise of providing interpersonally comparable measures of health. Recommendations for improving future implementations of vignettes are discussed.

First, sufficient conditions are given for a triangular array of random vectors such that the sequence of related random step functions converges towards a (not necessarily time homogeneous) diffusion process. These conditions are weaker and easier to check than the existing ones in the literature, and they are derived from a very general semimartingale convergence theorem due to Jacod and Shiryaev, which is hard to use directly. Next, sufficient conditions are given for the convergence of stochastic integrals of random step functions, where the integrands are functionals of the integrators. This result covers situations which cannot be handled by existing ones.

This paper introduces a non-negative integer-valued autoregressive (INAR) process with seasonal structure of first order, which is an extension of the standard INAR(1) model proposed by Al-Osh and Alzaid [First-order integer-valued autoregressive (INAR(1)) process. J Time Ser Anal. 1987;8:261–275]. The main properties of the model are derived such as its stationarity and autocorrelation function (ACF), among others. The conditional least squares and conditional maximum likelihood estimators of the model parameters are studied and their asymptotic properties are established. Some detailed discussion is dedicated to the case where the marginal distribution of the process is Poisson. A Monte Carlo experiment is conducted to evaluate and compare the performances of these estimators for finite sample sizes. The standard Yule–Walker approach is also considered for comparison purposes. The empirical results indicate that, in general, the conditional maximum likelihood estimator presents much better performance in terms of bias and mean square error. The model is illustrated using a real data set.

Smart cities offer services to their inhabitants which make everyday life easier beyond providing a feedback channel to the city administration. For instance, a live timetable service for public transportation or real-time traffic jam notification can increase the efficiency of travel planning substantially. Traditionally, the implementation of these smart city services require the deployment of some costly sensing and tracking infrastructure. As an alternative, the crowd of inhabitants can be involved in data collection via their mobile devices. This emerging paradigm is called mobile crowd-sensing or participatory sensing. In this paper, we present our generic framework built upon XMPP (Extensible Messaging and Presence Protocol) for mobile participatory sensing based smart city applications. After giving a short description of this framework we show three use-case smart city application scenarios, namely a live transit feed service, a soccer intelligence agency service and a smart campus application, which are currently under development on top of our framework.