Finding Jews: Current Prospects and Future Opportunities
Benjamin Phillips, Ph.D. *
Paper for the Steinhardt Social Research Institute Conference on the
Socio-Demography of American Jews
Brandeis University, Waltham, MA
October 23, 2011
Understanding the socio-demographics of American Jews requires data. As we look to
the future, however, it is unclear where these data will come from. It is well known that
the then-United Jewish Communities announced in 2007 that it would not support a 2010
National Jewish Population Survey. 1 This paper will largely set aside the question of
funding and focus on how valid and reliable data about American Jews can be gathered
over the next 5 to 10 years.
Funding
Before turning to the technical issues, however, I will make a few brief observations
regarding communal funding for socio-demographic research. First, if sociodemographics are useful to the Jewish community, then up-to-date data will be required.
Second, most efforts at collecting representative data on American Jews at the national
level have been huge, expensive, infrequent, and consequently high risk affairs. At the
local level, surveys have similarly been infrequent and relatively large and expensive
endeavors. Jewish organizations would do better to think about smaller and thus less
expensive studies on a more frequent basis, lowering the risks involved considerably.
Third, money exists in the communal system for these tasks, although this must be
balanced against other funding priorities.
Challenges to Collecting Data
Cost
Before describing methods for collecting data on American Jews, I will first examine the
challenges facing attempts to collect high quality data on American Jews. The first is one
of cost. Jews are a rare population. While the number of American Jews is subject to
some dispute, it is likely that American Jews constitute two percent or less of the U.S.
population. If, for the sake of argument, two percent of U.S. households contained a Jew,
only one in fifty household interviews will be with a “Jewish household.” This means
that surveys spend much more money on interviewing non-Jews in order to find Jews
than they do on interviewing Jews about questions of interest. However, in many areas
within the U.S., Jewish incidence is higher and costs for local surveys will be
correspondingly lower.
*
Benjamin Phillips is a senior analyst/project director at Abt SRBI. Please address all
correspondence to: phillipsb@srbi.com; 55 Wheeler Street, Cambridge, MA 02138.
1
The financial challenge of screening for Jewish households was a true 10 or 20 years ago
as it is today. However, the costs of surveys have increased over time. Response rates to
surveys have declined, and more attempts to reach a household are required. 2 More
expensive techniques are used in order to maximize response rates. Changes in telephone
use mean surveys need to make calls to cell phones (see below), which also increase
costs. These challenges are daunting for any survey, but particularly so for American
Jews because of the multiplier effect of the costs of screening for Jewish households.
Bias
The second challenge facing surveys of American Jews is one of bias. Since the early
1980s, the mainstay of Jewish population studies has been making calls to landline
telephone households. That made since through the later 1990s, as landline telephone
access was likely near universal among Jews. With the rise of cell phones and increasing
numbers of people getting rid of landline phones, calling only landline numbers will
result in inaccurate estimates. The most recent authoritative figures available—spanning
July to December 2010—put the proportion of households with only cell phones at 30
percent. 3 And a further 16 percent of households received all or almost all their calls on
cell phones. 4 These trends have been ongoing since data collection began in 2003 and
have continued linearly and unabated since. Naturally, young adults are much more likely
to be only contactable by cell phones, with more than half of those aged 25-29 having
only cell phones. 5 While the situation is not likely to be as bad among Jews—whites in
general and people with higher socioeconomic status are less likely to be cell phone
only 6 —any random digit dialing survey that does not include a cell phone frame is likely
to suffer severe bias in estimates of Jewish young adults at the very least, and these
effects will only get worse as more people drop their landlines.
Sampling Error
The third major challenge—and unfortunately one that is ignored in Jewish research—is
sampling error. Because survey samples draw groups of people at random from the
broader population, different groups of people will give somewhat different answers. In
general, for a sufficiently large sample size, these errors are small and in any case do not
give rise to systematic bias. However, many of the steps taken to reduce the costs of
Jewish surveys tend to increase the amount of sampling error and, if left unchecked, this
can severely harm the accuracy of survey estimates.
Obtaining Accurate Estimates
Having discussed the challenges, I will turn to how we can get accurate estimates. In
order to obtain representative data, all Jews must have some probability of inclusion in
the sample. There are two realistic contenders for a sampling frame that includes all Jews.
The first is a combination of landline and cell phones. The second is address-based
sampling. I discount area-probability samples with in-person interviews as far too
expensive to be even considered.
2
Dual-Frame Landline and Cell Phone Random Digit Dialing
Combined landline and cell phone random digit dialing (RDD) designs as, for example,
Ukeles & Associates have used in recent studies, 7 cover (at least in theory) everyone with
a cell phone or a landline phone, which amounts to virtually all American Jews. Cell
phone frames are, for a variety of reasons, much more expensive than pure landline
frames. 8 As was noted earlier, though, just dialing landlines will result in unacceptable
bias, so there have no choice but to include cell phones if one wishes to obtain a
representative sample. Dual-frame RDD surveys are, however, most attractive at the
national level rather than for surveys in particular locales. Cell phone frames are far more
problematic for small areas because cell phone numbers are not strongly tied to
geography. Numbers are assigned in loose cellular rate centers, but it is easy to obtain
phones with numbers from nearby cellular rate centers. The connection between cell
phone numbers and geography is also weakened because many people will retain their
old cell phone number when they moved. This means that cell phone dialing is especially
inefficient in studies of small areas because many of the people reached will live outside
the area. More problematically, the frame will be biased against people who live on the
outskirts of the community (and are more likely to have out of sample cell phone
numbers) and recent movers, who are likely to be disproportionately young.
Using dual-frame landline and cell phone RDD designs solves the problem of bias, but
the challenges of cost and sampling error still remain. These need to be attacked
simultaneously in order to find the split between cell phone and landline sample dialed
that will yield the lowest sampling error under fixed costs: dial too few cell phones, and
sampling error will increase because the people reached will have low probabilities of
selection and large weights; dial too many cell phones, and costs will get out of hand, and
sampling error will be high because overall sample size is too low. Optimization will
require estimates of the incidence of Jews on the cell phone frame and the landline frame,
which are unknown. We can, however, reasonably proxy for Jewish incidence by
estimating coverage rates based on a college educated white population. More accurate
estimates could be derived from modeling landline and cell phone access on the National
Health Interview Survey for white college-educated people and adjusting for the
demographic composition of the Jewish population using Brandeis meta-analysis
estimates. 9
Address-Based Sampling
The alternative frame with full coverage is address-based sampling (ABS). 10 In this case,
a sample of addresses is drawn from the USPS Delivery Sequence File. The beauty of
this is that almost every American Jew lives at a given address and thus is included in the
frame. Addresses can be reached by mail, in which case one would need to send
households a short and simple paper survey, possibly with options for people to respond
via the web or dial in to be called. This would be very expensive because of the need to
screen for Jewish households and difficult if the Jewish purposes of the survey were
made explicit because non-Jewish households would be likely to return the survey at
lower rates than would Jewish households. Another option is to send only the questions
needed to screen for Jewish adults and either solicit a phone number or send back a more
3
detailed questionnaire by mail. This faces similar problems to the first option. Finally,
one can match up to 60 percent of the addresses to phone numbers and call this frame,
while relying on the type of mail techniques previously described to reach the remainder.
Because ABS surveys rely on mailing at least some respondents, they take longer than
phone surveys. Mail contact attempts need to be carefully structured to maximize
response rates. 11 While dialing address-matched households will increase response rates
and lower costs due to greater efficiency, it will increase sampling error because of the
lower weights for the number matched as opposed to the unmatched households. ABS
makes the most sense for studies of specific Jewish communities due to the shortcomings
of the cell phone frame at local levels described previously. To the best of my
knowledge, no-one has tried ABS in a Jewish community study and it would be very
helpful for the future of the field to do a systematic experiment in comparing ABS and
dual landline/cell phone RDD costs.
Reducing Survey Costs
ABS or dual-frame landline and cell phone RDD both have near complete coverage of
the Jewish population. Costs are very high, however, because of low Jewish incidence,
particularly at the national level. There are two main ways one can reduce costs without
decreasing sample size while maintaining representative designs. The first is
oversampling higher incidence areas and the second is including higher incidence frames
with less than full coverage. Where recent survey data that includes questions on religion
(and, ideally, secular Jewish identity) is available, this presents a third option for reducing
costs.
Oversampling
To oversample, one draws a disproportionate amount of sample from strata (typically
areas) with higher Jewish population incidence. Like balancing between landline and cell
phone frames, this must be done with care. If one takes too much sample from high
incidence areas, sampling error will be higher to due to the highly disproportionate
probabilities of selection, negative the increase in overall sample size that is possible
because of lower costs per case. Go too far in the other direction, however, and overall
sample size will be low because of higher cost per interview. Once again cost variance
optimization is in order to maximize the precision of the sample. In general, it is best to
oversample too little than too much, because the penalties in terms of sampling error are
much higher for disproportionate sampling than for lower overall sample size. At times,
one may need to drop very low incidence Jewish areas from RDD or ABS frames. This
introduces error in the form of bias but reduces sampling error due to larger sample sizes
as a result of decreased cost per case. This, too, can be handled with cost-variance
optimization by using mean squared error as the objective function to be minimized,
allowing trade-offs between variance and bias in pursuit of the lowest overall error.
High Incidence Frames
Another way of reducing costs is to combine a frame with high Jewish incidence but less
than full coverage of the Jewish population with a frame that has full coverage but lower
4
incidence. These frames include lists from Jewish organizations, particularly federations,
and a list of “Distinctive Jewish Frames” (DJNs), the origins of which go back to the
1940s. 12 Use of Jewish organizational lists and/or distinctive Jewish names has been the
province of local community studies, rather than national studies, which stand to gain the
most from including higher incidence sampling frames. 13 As long as a higher incidence
frame has been incorporated properly—and historically this has not been the case with
DJN frames—this process does not introduce bias. 14 Use of high incidence nonuniversal
frames does increase sampling error due to disproportionate probabilities of selection, but
this is balanced by lower per case costs, allowing a larger overall sample size. Costvariance optimization is once again called for in determining the proper mix between the
universal frame and the nonuniversal high incidence frame.
Rather than using DJN or list frames, however, I would suggest an ethnic names frame.
These are based on complex expert rules that look at both first and last names. 15 Thus, a
Ze’ev McMahon would most likely be classified as a Jew, as would a Kathy Silverstein,
but not a Kathy McMahon. This covered 27 percent of the Jewish population, more than
the CJP or DJN lists. 16 This recommendation is based on results from the 2005 Boston
Jewish Community Study. Had the study relied on the CJP list only, the high incidence
frame would have covered 20 percent of the Jewish population, and about 87 percent of
all people on the list were Jewish. 17 However, in an effort to increase the coverage of the
high incidence frame, the Boston study collected 85 lists together. This increased
coverage to 32 percent, with 71 percent of people on the combined list being Jewish. 18
The DJN list, had it been, would have covered only 12 percent of the Jewish population,
but Jews would have consisted of 92 percent of entries. 19 In contrast, the ethnic names
frame had lower Jewish incidence, at 70 percent, but this was still very efficient. 20 These
methods have been, for example, used in Pew surveys of the U.S. Muslim population. 21
Previously Identified Households
Another strategy used by Pew is to recontact households identified in their other surveys
who were, for example, Muslim. 22 This can be combined with other frames to reduce
costs further, although weighting can become very complicated.
Weighting Strategies
Whatever the method, a persistent problem facing surveys of Jews and other religious
groups in the U.S. is that these is not a good source of data about the Jewish population
that can be use to weight surveys to. In contrast, other survey ethnic or racial groups like
Asians, Hispanics, or African-Americans, have an abundance of data from the Census
Bureau. These data enable researchers to identify whether any segments of the population
are under- or over-represented and statistically adjust the sample so that it resembles the
population more closely. A solution—at least a partial one—is at hand for national
surveys of Jews based on the Steinhardt Institute’s meta-analysis project, which combines
surveys that asked religion (and the few that measured Jewish ethnicity) into a very large
dataset that enables estimates of the size and demographic characteristics of the adult
Jewish by religion population. 23 The existence of these data means that we can identify
5
weaknesses in national samples of Jews more effectively than ever before and design
weighting schemes to adjust for biases.
Reducing Risk
As was previously mentioned, surveys of American Jews have historically been large,
expensive, and infrequent affairs. Correspondingly, such surveys are high risk affairs
because any errors will taint data obtained at great cost, with the next survey likely to be
sponsored a decade hence. Jewish organizations would do well to adopt smaller, less
expensive, more frequent, and lower risk surveys. Instead of surveying 4,000 Jews once a
decade, why not survey 400 Jews a year for 10 years? If a given survey goes badly, the
amount of money invested is much smaller and lessons learned can be rapidly applied.
While a sample of 400 is not sufficient for making reasonable estimates of the Jewish
population as a whole, 2 years worth of data will net 800 cases, which are sufficient for
broad brush national estimates, and precision increases over time by bringing together
sample from multiple years. Another potential benefit of repeated samples is that one can
recontact some proportion of respondents from earlier surveys. In addition to potentially
reducing costs, this step has the advantage of allowing longitudinal analyses of Jewish
attitudes and behavior. The existence of such data over an extended period of time would,
for instance, resolve arguments about whether the apparent paradox of stable overall
levels of connection to Israel among American Jews with persistent higher levels of
connection to Israel among older American Jews is the product of lifecycle effects or
cohort or period effects. 24
Alternatives
The designs proposed above are evolutionary, not (with the exception of ABS),
revolutionary, and ignore a recent trend of surveys of Jews based on volunteer panels of
internet users for whom information on religion was available. 25 Why not consider these?
The internet, after all, is close to an ideal way to conduct surveys (one does not have to
pay for mail or survey interviewers). There are no problems when you email addresses
are available for all members of the population of interest. Unfortunately, for
representative surveys, there is no way to sample all internet users. Consequently, the
samples relied upon by the cited references were based on panels of volunteers recruited
in a nonprobabilistic fashion (e.g., open-enrollment on the panel company’s website, web
advertisements, and targeted email). These samples are not representative and do not
support the use of inferential statistics, not to mention that they exclude individuals
without internet access (leading to coverage error for poor and elderly populations). They
also exclude any secular Jews who answer “no religion” to the screening question on
religion. Accordingly, the American Association for Public Opinion Research concluded
in its report on online panels that “Researchers should avoid nonprobability online panels
when one of the research objectives is to accurately estimate population values.” 26 This is
particularly problematic with respect to surveys by the American Jewish Committee, 27 J
Street surveys, 28 and S3K, 29 which focus on top-line estimates of the attitudes and
behaviors of American Jews. 30
6
The one vendor of representative online panels is Knowledge Networks (KN). KN
recruits panel members using ABS (formerly, via RDD) and provides panel members
without internet access a laptop, avoiding noncoverage of noninternet households. KN
identifies the religion of panel members of a matter of course and has added a follow-up
to people of no religion asking whether they are Jewish. The KN panel has been used in
several Cohen Center projects. 31 While promising, it has relatively few Jews (achieved
sample sizes were around 1,000), very low response rates once recruitment into the panel
is factored in, 32 and has few Jewish young adults and Orthodox Jews. 33
Supplements
Although ABS and dual-frame landline and cell RDD surveys are the only feasible
frames for representative samples of American Jews, there are alternative approaches that
provide data that, although not representative, provide information that large-scale
representative surveys cannot. These are discussed below.
Social Media Tracking
Recent technological developments have presented an opportunity for gathering a new
kind of data. This is the analysis of social media. Social media tracking combines
elements of web search and content analysis, monitoring live streams of content like
Twitter “tweets” and Facebook updates to generate an ever-evolving picture of what
internet users are talking about. Because of its immediacy, social media can be used to
track constructs like “Israel” and “Jewish” by following trends of phrases associated with
these terms on a much smaller time scale than is possible with surveys. It is this
immediacy that no survey can hope to match. 34 Beyond simple lists of trending topics,
however, analyses can provide context and valence through sophisticated text mining
algorithms. While interesting in isolation, these techniques are most telling when
extended forward and backwards in time to track trends. These tools have been adopted
most quickly and thoroughly by businesses, as an extension of market research and
strategic communications functions. Introductions of new products, advertising
campaigns, and corporate crises can be monitored and, if necessary, responded to.
Academia has been slower to adopt social media techniques, likely due to the lack of
established standards and procedures and concerns over the representativeness of the
material. Research organizations like the Pew Foundation Project for Excellence in
Journalism have adopted these methods, likely presaging their broader use by academics
as standards for appropriate usage are developed. 35
When working with these data, it is, however, important to bear in mind their limitations.
Recent Pew estimates put American internet users at 79 percent of the population, with
59 percent of internet users (47 percent of adults) reporting using social networking
sites. 36 The proportion who regularly use these sites is smaller again, with Pew
estimating 52 percent of Facebook users and 33 percent of Twitter users using these
services daily or more. The user base is, as one would expect, disproportionately young
and those who make the most noise are picked up most. Thus, these data do not constitute
a representative sample and common sense suggests that the weight given to these results
should be in rough relationship to which the population of largely young, tech savvy, and
7
opinionated social media users has to the population of interest. While opinionated users
of social media may not be wholly representative, they are influential, and may be of
interest—with due caution—as a leading indicator of trends.
Internet Panels
Although volunteer internet panels are not a substitute for representative sampling
frames, their low cost and the rapidity with which it is possible to get data place them in a
similar category to social media tracking: low cost of entry enables researchers to field
frequent surveys. Although not representative of American Jews, the rapidity and
frequency with which it is possible to field surveys using volunteer internet panels allows
one to track trends. Reporting of such research should be careful to note the
nonrepresentative nature of the data and focus on trends rather than absolute levels of
attitudes and behaviors.
Conclusion
Understanding American Jewish demography requires up-to-date data. At present,
however, little is available. There will be no National Jewish Population Survey and no
Jewish organizations have taken up the organizational and financial yoke that the thenUnited Jewish Communities set aside. 37 National polls of American Jews produced by
advocacy organizations stand on extremely shaky ground. The situation is somewhat
better, but far short of ideal, at the sub-national level. Among the largest centers of
Jewish population, most have conducted surveys within the past decade, although these
are of varying quality. 38 Unless this situation changes, a repetition of this meeting in a
decade’s time will be an exercise in imagination rather than fact-based social science.
The dearth of funding for studies is understandable. Although all Jewish population
studies appear to generate some controversy, NJPS 2000-01 eclipsed them all, with more
ink likely generated by fights over the study’s quality than its findings. These arguments,
combined with the study’s great cost and the increasing difficulty of conducting high
quality surveys have apparently been read by potential funders as an end to the era of
representative national surveys of American Jews. Such pessimism is likely misplaced.
Now well-proven techniques—in the form of cell phone frames and ABS—have been
developed over the past decade to address biases associated with landline RDD frames
that surveys of American Jews have relied on. Matters are helped by the Steinhardt
Institute’s meta-analysis efforts, which have the potential to serve as the basis for
corrective weighting in the case of flawed samples. While surveying Jews remains an
expensive endeavor, costs for national studies can be lowered somewhat by adopting
techniques used on the local level, such as ethnic names lists, as well as careful attention
to sample allocation. Perhaps most importantly, risk can be minimized and costs spread
out by focusing on smaller, more frequent studies than once a decade behemoths that are
all or nothing affairs. New technologies, such as social media tracking, can collect data
that was heretofore inaccessible. The challenge facing Jewish social research is no longer
methodological, but is now practical. The tools needed to conduct high quality research
are available, what is needed now is the wherewithal to use them.
8
Notes
1
See, e.g., Saxe, Kadushin, and Phillips (2007).
2
See, e.g.. Curtin, Presser, and Singer (2005).
3
Blumberg and Luke (2011).
4
Blumberg and Luke (2011).
5
Blumberg and Luke (2011).
6
Blumberg and Luke (2011).
7
See, e.g., Jewish Community Federation of Baltimore (2010), Social Science Research
Solutions (2010).
8
Cell phone sample must be dialed by hand rather than using the more efficient
predictive dialer due to Federal Communications Commission regulations. Good practice
is to provide an incentive to cell phone callers for the costs they incurred or minutes they
lost as a result of the incoming (Lavrakas et al. 2010). Users of cell phones are often
engaged in activities that are too dangerous to conduct an interview (e.g., driving) or are
in places where responding to a survey would be difficult (e.g., while shopping),
requiring more calls. Techniques used to increase the efficiency of RDD calls, such as
list-assisted RDD, are impossible because cell phones are not listed in telephone
directories. For surveys of small areas—such as a typical Jewish community study—cell
phones frames are inefficient and incomplete because of the large numbers of people who
use cell phones from different cellular rate centers; thus many people reached with cell
phone numbers from selected rate centers will be out of sample while many cell phone
only users within the population of interest will have cell phones with numbers from
excluded cellular rate centers.
9
See Saxe et al. (2007).
10
This section draws on an internal presentation given at Abt Associates by Battaglia and
Frankel (2011).
11
See, e.g., Dillman, Smyth, and Christian (2008).
12
See Phillips (2007) and Ritterband, Kosmin, and Scheckner (1988) for additional
information on DJN frames.
13
NJPS 1970-71 was the exception, using a dual-frame list and area probability sample
technique, where areas were stratified by Jewish population incidence using DJNs (c.f.,
Phillips 2007).
9
14
See Phillips (2007:118-24).
15
See http://ethnictechnologies.com.
16
Phillips (2007:273).
17
Phillips (2007:273).
18
Phillips (2007:273).
19
Phillips (2007:273).
20
Phillips (2007:273).
21
Kohut (2007); Kohut and Lugo (2011).
22
Kohut (2007); Kohut and Lugo (2011).
23
Saxe et al. (2007);
24
See the Contemporary Jewry (2010, vol. 30(2-3)) special issue on the distancing
hypothesis, particularly Sasson, Kadushin, and Saxe (2010a and b) and Cohen and
Kelman (2010a and b).
25
American Jewish Committee (2005, 2006, 2007, 2008, 2009, 2010); Cohen and
Hoffman (2009); Cohen and Kelman (2007, 2008); Gerstein (2008, 2009, 2010).
26
Baker et al. (2010:4).
27
American Jewish Committee (2005, 2006, 2007, 2008, 2009, 2010).
28
Gerstein (2008, 2009, 2010).
29
Cohen and Hoffman (2009).
30
Cohen and Kelman (2007, 2008), by contrast, focus on comparative analysis of
behavior among different segments of the Jewish population. While still subject to bias
due to the poor coverage properties of the list frame, these results are at least a
comparison between groups that share the same selection biases.
31
Chertok et al. (2011); Sasson et al. (2010a).
32
Callegaro and DiSogra (2008).
33
Sasson et al. (2010b).
10
34
Nielsen has experimented with providing respondents with smart phones in return for
completing short surveys several times a day (Bailey et al. 2011; Bensky et al. 2011).
This is likely to be financially infeasible for surveys of American Jews.
35
A recent example tracked opinions regarding President Obama’s recent speech on the
Arab-Israeli conflict (http://pewresearch.org/pubs/2005/social-media-israel-palestinianconflict-obama-speech).
36
Hampton et al. (2011).
37
Barry Kosmin, Ariela Keysar, and colleagues have conducted surveys of religious
identification, using an approach that is similar to NJPS 1990 (Mayer, Kosmin, and
Keysar 2002). The most recent update (Kosmin and Keysar 2009) did not ask a detailed
battery of Jewish-specific items of Jewish respondents.
38
The glaring exception is Los Angeles, which accounts for a little under 10 percent of
American Jews and conducted its last survey in 1997.
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