Survey of American Catholic Priests, 2020 (NGO Subsample)
CitationCranney, S., & Regnerus, M. (2022, September 29). Survey of American Catholic Priests, 2020 (NGO Subsample).
SummaryThe 2020 Survey of American Catholic Priests was funded and collected by the Austin Institute during the end of 2020 and the beginning of 2021. Questions were largely derived from the similar 2002 LA Times clergy survey. Relevant questions were updated (e.g., asking about attitudes toward Pope Francis as opposed to Pope John Paul II). New questions about various aspects of priestly life and well-being were included that were not asked in the 2002 survey. Samples were collected from two different sources (see Collection Procedures below). The respondents in this file were drawn from the email list of a Catholic NGO (not the Austin Institute).
The ARDA has added two additional variables to the original data set to enhance the users' experience on our site.
Data FileCases: 571
Weight Variable: WEIGHT_ANEKSRAKE_DISTINCT, WEIGHT_ANEKSRAKE_COMBINED
Initial analyses found strong predictive power for ordination year and type of priest (whether religious or diocesan), and these basic demographic variables were used for the weights (age is not reported in the OCD). In order to derive targets for these weights, software was written that converted the 2021 online OCD into a format that was query-able, and averages for percent of priests who are religious/diocesan was calculated, as well as percent of priests who were ordained pre-1981, 1981-1990, 1991-2000, 2001-2010, and post-2010.
With these targets weights were calculated using a raking algorithm. Specifically, R's aneksrake package was employed. Because the dataset is an amalgam of two different lists that, while similar in that they attempted to gather a large list of clergy emails, may have their own distinct subtle biases, two types of weights were calculated: one treating each survey's dataset as an independent, distinctive dataset, and one to be used in case the two datasets were combined into one. The former weight variable is labelled as weight_aneksrake_distinct and the latter weight_aneksrake_combined. Despite coming from two different sources, the two datasets show remarkable similarity across key measures after weights are independently applied.