The Independent Variable Problem

This open access publication deals with the operationalisation of the welfare state as an independent variable. To study how welfare states affect social inequality, individual behaviour, attitudes and more in different countries, an empirical operationalisation of the welfare state or specific elements of social policy is required. However, this operationalisation is fraught with some important problems. These problems essentially relate to one point: while there are a large number of contributions dealing with the measurement of differences between welfare states per se and as a dependent variable, there is a lack of feasible recommendations for a standardised operationalisation of welfare stateness as an independent variable. So far, there has been no systematic investigation of how such different approaches may affect the results and their comparability. Also missing is an in-depth conceptual discussion of which features of the welfare state are particularly relevant for explaining certain effects. This book fills both gaps. First, it exposes the pitfalls of existing approaches and shows how much empirical results can vary depending on the operationalisation chosen. Second, it proposes a framework for a standardised conceptualisation and operationalisation of social policies as independent variables that constrains operational decisions in a theoretically meaningful way

The Independent Variable Problem

This open access publication deals with the operationalisation of the welfare state as an independent variable. To study how welfare states affect social inequality, individual behaviour, attitudes and more in different countries, an empirical operationalisation of the welfare state or specific elements of social policy is required. However, this operationalisation is fraught with some important problems. These problems essentially relate to one point: while there are a large number of contributions dealing with the measurement of differences between welfare states per se and as a dependent variable, there is a lack of feasible recommendations for a standardised operationalisation of welfare stateness as an independent variable. So far, there has been no systematic investigation of how such different approaches may affect the results and their comparability. Also missing is an in-depth conceptual discussion of which features of the welfare state are particularly relevant for explaining certain effects. This book fills both gaps. First, it exposes the pitfalls of existing approaches and shows how much empirical results can vary depending on the operationalisation chosen. Second, it proposes a framework for a standardised conceptualisation and operationalisation of social policies as independent variables that constrains operational decisions in a theoretically meaningful way

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty

Breznau N, Rinke EM, Wuttke A, et al. Observing Many Researchers Using the Same Data and Hypothesis Reveals a Hidden Universe of Uncertainty. 2021.This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to include conscious and unconscious decisions that researchers make during data analysis and that may lead to diverging results. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of research based on secondary data, we find that research teams reported widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predicted the wide variation in research outcomes. More than 90% of the total variance in numerical results remained unexplained even after accounting for research decisions identified via qualitative coding of each team’s workflow. This reveals a universe of uncertainty that is hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a new explanation for why many scientific hypotheses remain contested. It calls for greater humility and clarity in reporting scientific findings