DieTryin: An R package for data collection, automated data entry, and post-processing of network-structured economic games, social networks, and other roster-based dyadic data

Cody T Ross,Daniel Redhead

doi:10.3758/s13428-021-01606-5

Cody T Ross, Daniel Redhead

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https://doi.org/10.3758/s13428-021-01606-5

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Researchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.

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In the psychological and sociological sciences, there is a keen history of developing and applying social network methods (Borgatti et al 2009)—i.e., methods that quantify relationships between individuals
In order to make the use of powerful, roster-based research methodologies easier for field researchers working in large field-sites, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and processed as rapidly as name-generator-based data
There are some benefits: (i) there is no work on the back-end to link names to personal identifiers, as the roster can be constructed to differentiate between individuals with the same name a priori, and (ii) recall bias is minimized by presenting each focal respondent with a prime—be it a name or a photograph—of each alter in the community

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In the psychological and sociological sciences, there is a keen history of developing and applying social network methods (Borgatti et al 2009)—i.e., methods that quantify relationships (termed edges or ties) between individuals (termed nodes or actors). There are some benefits: (i) there is no work on the back-end to link names to personal identifiers, as the roster can be constructed to differentiate between individuals with the same name a priori, and (ii) recall bias is minimized by presenting each focal respondent with a prime—be it a name or a photograph—of each alter in the community While these trade-offs need to be considered on a case-by-case basis, DieTryin aims to increase the feasibility of rosterbased methods by automating the data collection and entry process so that the time burden scales linearly with sample size. Once all data are entered, they can be compiled into a single edge-list using the compile data function

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