Kolner Zeitschrift Fur Soziologie Und Sozialpsychologie最新文献

This article provides researchers with knowledge of how to design a high quality mixed methods research study. To design a mixed study, researchers must understand and carefully consider each of the dimensions of mixed methods design, and always keep an eye on the issue of validity. We explain the seven major design dimensions: purpose, theoretical drive, timing (simultaneity and dependency), point of integration, typological versus interactive design approaches, planned versus emergent design, and design complexity. There also are multiple secondary dimensions that need to be considered during the design process. We explain ten secondary dimensions of design to be considered for each research study. We also provide two case studies showing how the mixed designs were constructed.

While regional mortality inequalities in Germany tend to be relatively stable in the short run, over the course of the past century marked changes have occurred in the country's regional mortality patterns. These changes include not only the re-emergence of stark differences between eastern and western Germany after 1970, which have almost disappeared again in the decades after the reunification of Germany in 1990; but also substantial changes in the disparities between northern and southern Germany. At the beginning of the twentieth century, the northern regions in Germany had the highest life expectancy levels, while the southern regions had the lowest. Today, this mortality pattern is reversed. In this paper, we study these long-term trends in spatial mortality disparities in Germany since 1910, and link them with theoretical considerations and existing research on the possible determinants of these patterns. Our findings support the view that the factors which contributed to shape spatial mortality variation have changed substantially over time, and suggest that the link between regional socioeconomic conditions and recorded mortality levels strengthened over the last 100 years.

To investigate how economic conditions and crises affect mortality and its predictability in industrialized countries, we review the related literature, and we forecast mortality developments in Spain, Hungary, and Russia-three countries which have recently undergone major transformation processes following the introduction of radical economic and political reforms. The results of our retrospective mortality forecasts from 1991 to 2009 suggest that our model can capture major changes in long-term mortality trends, and that the forecast errors it generates are usually smaller than those of other well-accepted models, like the Lee-Carter model and its coherent variant. This is because our approach is capable of modeling (1) dynamic shifts in survival improvements from younger to older ages over time, as well as (2) substantial changes in long-term trends by optionally complementing the extrapolated mortality trends in a country of interest with those of selected reference countries. However, the forecasting performance of our model is limited (like that of every model): e.g., if mortality becomes extremely volatile-as was the case in Russia after the dissolution of the Soviet Union-generating a precise forecast will depend more on luck than on methodology and expert judgment. In general, we conclude that, on their own, recent economic changes appear to have minor effects on life expectancy in industrialized countries, but that the effects of these changes are greater if they occur in conjunction with other major social and political changes.

The social sciences have been reticent to integrate a biodemographic approach to the study of fertility choice and behaviour, resulting in theories and findings that are largely socially-deterministic. The aim of this paper is to first reflect on reasons for this lack of integration, provide a review of previous examinations, take stock of what we have learned until now and propose future research frontiers. We review the early foundations of proximate determinants followed by behavioural genetic (family and twin) studies that isolated the extent of genetic influence on fertility traits. We then discuss research that considers gene and environment interaction and the importance of cohort and country-specific estimates, followed by multivariate models that explore motivational precursors to fertility and education. The next section on molecular genetics reviews fertility-related candidate gene studies and their shortcomings and on-going work on genome wide association studies. Work in evolutionary anthropology and biology is then briefly examined, focusing on evidence for natural selection. Biological and genetic factors are relevant in explaining and predicting fertility traits, with socio-environmental factors and their interaction still key in understanding outcomes. Studying the interplay between genes and the environment, new data sources and integration of new methods will be central to understanding and predicting future fertility trends.