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Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
Simon Hoellerbauer. “Why Join? How Civil Society Organizations’ Attributes Signal Congruence and Impact Community Engagement.” Journal of Experimental Political Science, Online FirstView
Civil society organizations (CSOs) can facilitate collective action. This makes understanding what shapes whether people are likely to engage with CSOs critically important. This paper argues that whether an organization is perceived as congruent --- similar to an individual in values --- is a key determinant of whether individuals will engage with it. I use a conjoint survey experiment to test how organizational attributes signaling congruence influence respondents' willingness to attend a hypothetical organization's meetings. I find that individuals are more likely to choose organizations that are more likely to be congruent with them, except when it comes to funding. These findings imply that an individual's level of comfort with a CSO matters for engagement; thus, CSOs need to consider how they match to their publics when reaching out to potential joiners. Furthermore, donors seeking to support CSOs need to pay attention to their impact on perceptions of congruence.
Simon Hoellerbauer. “A Mixture Model Approach to Assessing Measurement Error in Surveys Using Reinterviews.”* Forthcoming at the Journal of Survey Statistics and Methodology.
Researchers are often unsure about the quality of the data collected by third-party actors, such as survey firms. They are reliant on survey firms to provide them with estimates of data quality and to identify observations that are problematic, potentially because they have been falsified or poorly collected. This may be because of the inability to measure data quality effectively at scale and the difficulty with communicating which observations may be the source of measurement error. To address these issues, I propose the QualMix model, a mixture modeling approach to deriving estimates of survey data quality in situations in which two sets of responses exist for all or certain subsets of respondents. I apply this model to the context of survey reinterviews, a common form of data quality assessment used to detect falsification and data collection problems during enumeration. Through simulation based on real-world data, I demonstrate that the model successfully identifies incorrect observations and recovers latent enumerator and survey data quality. I further demonstrate the model's utility by applying it to reinterview data from a large survey fielded in Malawi, using it to identify significant variation in data quality across observations generated by different enumerators.