How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

Ongoing changes in social structures, orientation, and value systems confront us with the growing necessity to address and understand transforming patterns of tolerance as well as specific aspects, such as social tolerance. Based on hierarchical analyses of the latest World Values Survey (2005-08) and national statistics for 28 countries, we assess both individual and contextual aspects that influence an individual's perception of different social groupings. Using a social tolerance index that captures personal attitudes toward these groupings, we present an institutional theory of social tolerance. Our results show that specific institutional qualities, which reduce status anxiety, such as inclusiveness, universality, and fairness, prevail over traditional socio-economic, societal, cultural, and democratic explanations.

To avoid asking respondents questions that do not apply to them, surveys often use filter questions that determine routing into follow-up items. Filter questions can be asked in an interleafed format, in which follow-up questions are asked immediately after each relevant filter, or in a grouped format, in which follow-up questions are asked only after multiple filters have been administered. Most previous investigations of filter questions have found that the grouped format collects more affirmative answers than the interleafed format. This result has been digitalcommons.unl.edu E c k m a n E t a l . i n P u b l i c O P i n i O n Q u a r t e r l y ( 2 0 1 4 ) 2 taken to mean that respondents in the interleafed format learn to shorten the questionnaire by answering the filter questions negatively. However, this is only one mechanism that could produce the observed differences between the two formats. Acquiescence, the tendency to answer yes to yes/no questions, could also explain the results. We conducted a telephone survey that linked filter question responses to high-quality administrative data to test two hypotheses about the mechanism of the format effect. We find strong support for motivated underreporting and less support for the acquiescence hypothesis. This is the first clear evidence that the grouped format results in more accurate answers to filter questions. However, we also find that the underreporting phenomenon does not always occur. These findings are relevant to all surveys that use multiple filter questions.

This article contributes to an ongoing debate about how to measure sensitive topics in population surveys. We propose a novel technique that can be applied to the measurement of quantitative sensitive variables: the item sum technique (IST). This method is closely related to the item count technique (ICT), which was developed for the measurement of dichotomous sensitive items. First, we provide a description of our new technique and discuss how data collected by the IST can be analyzed. Second, we present the results of a CATI survey on undeclared work in Germany, in which the IST has been applied. Using an experimental design, we compare the IST to direct questioning. Our empirical results indicate that the IST is a promising data collection technique for sensitive questions. We conclude the article by discussing the limitations of the new technique and outlining possible improvements for future studies.