East Bay Jewish Community Study - Jewish Federation of the ...

Weighting and Imputation
The data were weighted in two steps. First, the total data (including both Jewish and non-
Jewish households) were weighted by gender, age, and region to reflect expected values
derived from Census data. 23 The low weight coefficient was .776 and the high weight coefficient
was 378. The coefficient of variation of weight was .967, which means the design effect from
the weighting process was 1.97. 24 By weighting the total data set that reflects the general
population to known values, we were able to get a more accurate estimate of the age
distribution of Jewish individuals. In other words, we used the weighted total data set to help us
set weighting parameters for the Jewish households in the data.
Region
It was necessary to weight region because we slightly oversampled regions with small Jewish
populations. Region weights were derived using the dialing information from the RDD portion of
the survey, which allowed us to calculate the incidence of Jewish households by region. These
incidence calculations were combined with Census data to obtain the expected proportion of the
Jewish population residing in each region. The data were then weighted to the correct regional
proportions.
Age
We determined the need to weight the age distribution after an extensive review of the survey
data, data from other Jewish surveys including the National Jewish Population Survey, 25 and the
2003 San Francisco Community survey, 26 and available Census data. Moreover, after
weighting the total data set (including Jewish and non-Jewish households) to known Census
targets, it was apparent that the unweighted age distribution among Jewish individuals skewed
too old.
This skew likely resulted from several factors that are inherent to all telephone survey research
and some factors that are specific to research among Jewish populations. A few of these
factors are listed below:
Older women are among the most likely to pick up the telephone. We try to mitigate this
effect by initially asking for the youngest male over the age of 18 in the household at the
time.
Cell phone penetration rates are considerably higher for younger people, and fewer and
fewer young people use a landline telephone. We added a listed cell phone sample to
try to address this concern. However, as mentioned previously, listed sample tends to
under-represent younger, more mobile, and lower income populations. In this case, the
listed cell sample did little to correct for the age imbalance. Employing a cell phone
sample that uses an RDD process to derive cell phone numbers would have been the
best way to reach more young people but proved to be prohibitively expensive, costing
nearly $300 per interview for this population.
23 2010 US Census Data. ―2010 Census Breakdowns for Napa County and Alameda County‖ Accessed June 1, 2011.
http://factfinder2.census.gov/main.html.
24 When we weight data, while we can reduce the biases due to sampling design and non-responses, the margin of
errors of the estimates will be larger. The MOE we are reporting factors in this increase in sampling error by larger
design effects.
25 ―Jewish Population in the United States‖ 2010. The estimate comes from a 2006 Informant or Internet update of a
1986 scientific study.
26 ―Jewish Community Study of San Francisco‖ 2003. Jewish Community Federation of San Francisco, the Peninsula,
Marin and Sonoma Counties
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