The Pew Research Center’s survey, Jewish Americans in 2020, was designed to provide estimates of the size of the US Jewish population, sociodemographic data on issues such as intermarriage, child-rearing, engagement in Jewish communal life, and a description of American Jewish attitudes. A sophisticated sample design was employed to ensure accurate and generalizable assessments of the population. Because Jews are a small sub-group and the US government does not collect census data on religious groups, creating estimates is a non-trivial task. The focus of this paper is on the validity of Pew’s estimate of 7.5 million US Jewish adults and children, 2.4% of the overall US population. The estimate is an important standalone indicator and is the basis for assessments of current Jewish attitudes and behavior. This paper considers the underlying construct of Jewish identity and its operationalization by Pew and evaluates the convergent validity of Pew’s findings. The efforts to define “who is a Jew” in sociodemographic surveys is described, and a set of methodological challenges to creating estimates are considered. The results of this review indicate that Pew’s criteria for inclusion in the population estimate comports with long-standing views of how to assess the Jewish population. Furthermore, Pew’s estimate of 7.5 million Jewish Americans is consistent with other recent demographic studies of the population. Their conclusions about a growing US Jewish population suggest a new narrative of American Jewish life that reflects the diversity of ways in which Jewish identity is expressed.
The present study tests the validity of a data synthesis approach to population estimates of religiously defined groups. This is particularly important in places like the United States, where there is no definitive source of official data on its population's religious composition, and researchers must rely on costly, large‐scale surveys, or congregational membership studies. Each approach has limitations, especially for estimation of small religious groups and for estimation within small geographic areas. Without official statistics, the degree of bias in estimates is unknown. Data synthesis, specifically Bayesian multilevel estimation with poststratification, offers a useful alternative that maximizes the utility of data across all sources to estimate multiple groups from the same sources of data. This method also facilitates comparison of groups. This study provides evidence of the validity of the approach by synthesizing data from Canada, a country that includes questions about religious identification in its national census.
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