Fading Frames: Data Presentation and Framing Effects*

This article describes three experiments that investigate the impact of data presentation on framing, which is the differential evaluation of objectively equivalent information given different frames of reference. The experiments were designed to vary the amount of information and the format of presentation (tables versus graphs) provided to the decision maker to gain an understanding of the mechanism underlying framing. The inferred information model proposed by Johnson and Levin [11] is utilized to explain the results of the three experiments in terms of the redistribution of weights attached by the decision makers to the attributes of the prospective outcomes. The results of the three experiments indicate that framing effects are observed only when the presentation of data facilitates the shifting of weights among the attributes of the available alternatives. The results further indicate that framing is eliminated as more information is displayed. However, presentation of additional information does not eliminate framing when a graphical format is utilized. Finally, no framing effects are observed when uneven amounts of information are used to describe two alternatives in the same decision task.     

Evaluating Effects of Training within a Company: Methods,Problems and One Application

ABSTRACT: In the Netherlands, interest in the measurement of the effects of training has increased. In most cases this measurement has been done by drawing conclusions on analyses with samples of firms. However, individual companies will be more interested in the effects of training within their own organisation. In this paper we discuss possible methods of measuring the effects of training within an organisation. A crucial element of such an analysis is the use of performance indicators of individual workers. A first application of such a method in the Civil Service shows positive effects of training. However, the size of the effects should not be overrated.     

The ‘Dark Side’ of Social Capital: A Cross-National Examination of the Relationship Between Social Capital and Violence in Africa

Social Indicators Research - Research and policy circles often emphasize the importance of social capital in achieving social transformation and economic development. There is also, however,...     

Juggling Between Parental and School Expectations: The Development of Domain‐Specific Acculturation Orientations in Early Adolescence

We examined how perceived acculturation expectations from parents and school, and ethnic discrimination predicted early adolescents’ heritage and mainstream acculturation orientations at home (private domain) and in school (public domain) one year later. We surveyed 263 early adolescents of immigrant background in Germany (M_age = 10.44 years, 60% female). Multigroup path analyses revealed that perceived acculturation expectations and ethnic discrimination were more strongly related to adolescents’ private than public acculturation orientations. Parental heritage expectations were the strongest predictor of adolescents’ acculturation orientations. Boys were more susceptible than girls to ethnic discrimination and acculturation expectations in school, which affected their private and public acculturation orientations. Results highlight the importance of integrating domain‐specific and gendered experiences when analyzing adolescents’ acculturative development.     

Accurate estimation for extra-Poisson variability assuming random effect models

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets.