Appendix B:
Methodological Report
Contents
Introduction .............................................................................................................................................. 1
Model Specifications and Model Results ................................................................................................. 1
Model 1: Intercept-Only ...................................................................................................................... 1
Model 2: Population Model & Survey Weights ................................................................................... 4
Comparison of Survey-Specific and Cross-survey Estimates ............................................................ 11
Model 3: Inclusion of Year of Survey ............................................................................................... 15
Model 4: Based to Single year: 2004 ................................................................................................ 17
Model 5: Based to subset of years: 2004-2008 ................................................................................ 19
Population Counts and Proportions by State: 2010 ........................................................................... 21
Weighting: Survey of American Jews 2010 ........................................................................................... 23
Sample Composition .......................................................................................................................... 23
State-Metropolitan Clusters................................................................................................................ 23
Knowledge Network Sampling Weight ............................................................................................. 26
Adjustments for non-response ............................................................................................................ 28
Adjustments for Representativeness of the Sample ........................................................................... 30
Relation of Post-stratification Variables to Outcomes ....................................................................... 30
Post-Stratification 1: Raking to Demos & State-Metro Clusters....................................................... 36
Post-Stratification 2: Raking to Demos & Region x Metropolitan Status......................................... 39
Post-Stratification 3: Full Post-Stratification .................................................................................... 40
Post-Stratification 4: Next Steps Model-based Estimation ............................................................... 42
Additional Considerations .................................................................................................................. 43
Handling of JBO Sample ............................................................................................................... 44
Trimming ....................................................................................................................................... 45
Weight summary ................................................................................................................................ 45
Table of Tables
Table B1: Model Results, Jewish likelihood as a function of random intercepts for survey. .......................................3
Table B2: Results from Hierarchical Bayes. .................................................................................................................6
Table B3: results of regression Jewish likelihood on survey weights. ..........................................................................8
Table B4: Unweighted, Weighted and Cross-survey estimate for each sample survey, 2000-2008. ......................... 11
Table B5: Results from multilevel analysis with laplace approximation, Jewish likelihood on demographics and
year of survey. ............................................................................................................................................................. 15
Table B6: Jewish population estimates using single year (2004) model, post-stratified to 2004 CPS........................ 17
Table B7: Results from Hierarchical Bayes population model for 2004-2008 ........................................................... 19
Table B8: Total Jewish Population by State: 2010. .................................................................................................. 21
Table B9: SAJ 2010 sample size within state-metropolitan clusters. ......................................................................... 23
Table B10: Comparison of SSRI estimates with Knowledge Network’s Jewish Panel and the SAJ 2010 sample. ... 24
Table 11: Demographic characteristics of SAJ 2010 sample weighted by sample weight. ........................................ 27
Table B12: Demographic characteristics of SAJ 2010 sample weighted by nonresponse weight .............................. 29
Table B13: Bivariate relationships of having a bar/bat mitzvah, years of Jewish education and intermarriage with
age, education, race, metropolitan status and geographic region. ................................................................................ 32
Table B14: Bivariate relationships of attending Jewish life cycle events with age, education, race, metropolitan
status and geographic region. ...................................................................................................................................... 33
Table B15: Bivariate relationships of Reading Hebrew and synagogue membership with age, education, race,
metropolitan status and geographic region. ................................................................................................................. 34
Table B16: Bivariate relationships of synagogue honors with age, education, race, metropolitan status and
geographic region. ....................................................................................................................................................... 35
Table B17: Demographic characteristics of NJPS 2010 sample with demographic and state groups raking
adjustments. ................................................................................................................................................................. 36
Table B18: Comparison of the KN Sampling weight estimates and post-stratification weight estimates. ................. 41
Table B19: Logistic regression JBO vs. JBRI Secular on age, race, education, state and metropolitan area. ........... 44
Table B20: Comparison of estimates of survey outcomes for different weighting scenarios. .................................... 46
Table of Figures
Figure B1: Trace plots for the intercept and sigma. ......................................................................................................3
Figure B2: State level estimates of Jewish population based to 2004 single year model. ......................................... 18
Figure B3: Distribution of KN Panel and SAJ 2010 respondents by state groups...................................................... 25
Figure B4: Distribution of sample weights for SAJ 2010........................................................................................... 26
Figure B5: sample weights adjusted for non-response for SAJ 2010. ....................................................................... 28
Figure B6: Comparison of KN sampling weight to raked weight by age group. ........................................................ 38
Figure B7: Comparison of MSE and variance of unweighted estimates across distribution of basic demographics,
estimates with KN sampling weight and estimates after raking. ................................................................................. 39
Figure B8: Comparison of MSE and variance of unweighted estimates, KN sampling weight, 8 state cluster raking,
and 9 region raking. ..................................................................................................................................................... 40
Figure B9: Comparison of MSE and variance of unweighted estimates, KN sampling estimates, 8 state cluster
raking, 9 region raking, and full post-stratification estimates. ..................................................................................... 42
Methodological Report
Elizabeth Tighe
Raquel Magidin de Kramer
Begli Nursahedov
Janet Aronson
October 2011
Introduction
This report provides the methodological and analytic details associated with the crosssurvey analysis of the US Jewish population 2000-2008. This includes specification of
the statistical models and model results. Also included are details associated with the use
of cross-survey results in the analysis of the Survey of American Jews 2010 (Saxe, 2010).
Several alternatives for the use of Jewish population totals in post-stratification
adjustments are examined.
Model Specifications and Model Results
Model 1: Intercept-Only
Prior to fitting a post-stratification model, an intercept-only model was fit to examine the
degree of variance across surveys in estimates of the Jewish population before taking into
account sampling and other variables. Three models were fit. The first was run as a
classical multilevel using LMER (Bates, 2011). LMER applies a LaPlace approximation,
which performs well under some conditions and not so well under others -- such as ours
where there is near zero variance and large discrepancies in cluster sizes. The LMER
model with LaPlace approximation was compared to two Hierarchical Bayes models.1
One model used non-informative priors (explain). The other incorporated the LMER
estimates as informative priors.2 All analyses were run on Linux/Ubuntu calling JAGS
(Just Another Gibbs Sampler) from R.
1
Bayesian models were run using JAGS (Plummer, 2010) with rjags (Plummer, 2011) and
R2Jags (Su & Yajima, 2011) on Ubuntu Linux.
2
This was done given the estimate of the Jewish population was expected to be low – between
less than 1% to no higher than 6% based on analysis of the individual surveys (See Table B4) and
the variance across surveys was also estimated to be low – with estimates varying within this
restricted range. The Markov chain Monte Carlo algorithms sample from the entire solution
space (0% to 100%) and can be slow to converge if the proposed variance is either too large or
too small (see Roberts and Rosenthal, 2001). One approach to speed convergence is to use the
variance estimate from a maximum likelihood model, or in this case the LaPlace approximation
from the LMER model, scaled by a factor from 2 to 10 (Congdon, 2009).
1
The LMER model fit a basic multilevel model for the dichotomous outcome (Jewish/Not
Jewish) with random intercepts for survey:
mod1 <- lmer(curreljw ~ (1|survf), family=binomial(link="logit"))
The Bayesian model with uninformative priors was specified as:
model{
for (i in 1:N){
curreljw[i] ~ dbin(p.bound[i], 1)
p.bound[i] <- max(0, min(1,p[i]))
logit(p[i]) <- b.cons + b.surv[survs[i]]
}
b.cons ~ dnorm(0,.01)
for (j in 1:n.surv){
b.surv[j] ~ dnorm(0, tau.surv)
}
tau.surv <- pow(sigma.surv, -2)
sigma.surv ~ dunif(0,10)
}
With proposal distributions based to the LMER estimates, this model was modified as:
model{
for (i in 1:Ndm){
curreljw[i] ~ dbin(p.bound[i], 1)
p.bound[i] <- max(0, min(1,p[i]))
logit(p[i]) <- b.cons + b.surv[survs[i]]
}
b.cons ~ dnorm(-3.8,155)
for (j in 1:n.surv){
b.surv[j] ~ dnorm(0, tau.surv)
}
tau.surv <- pow(sigma.surv, -2)
sigma.surv ~ dunif(0,.7)
}
Both of these models yielded similar results to the LMER model (see Table B1). Trace
plots for the third model, displayed in Figure B1, indicate that the proposal variances
2
perform well. The figure displays plots for the intercept and sigma, which were the
slowest parameters to converge.
TABLE B1: MODEL RESULTS, JEWISH LIKELIHOOD AS A FUNCTION OF RANDOM INTERCEPTS FOR
SURVEY.
JAGS w/noninformative priors
LMER
Intercept
JAGS w/ LMER priors
Est.
SE
Est.
SE
Est.
SE
-3.84
0.026
-3.84
0.025
-3.84
0.025
Survey Variance
Tau
21.696
22.034
Variance
0.046
0.046
0.045
ICC
0.014
0.014
0.014
MOR
1.225
1.226
1.224
Deviance
68977
68867
68867
FIGURE B1: TRACE PLOTS FOR THE INTERCEPT AND SIGMA.
3
Model 2: Population Model & Survey Weights
The full post-stratification model specification submitted to JAGS – using results from an
initial fit using LMER to specify proposal distributions included random intercepts for
survey along with fixed effects for demographic variables and fixed effects for state and
metropolitan status3:
model{
for (i in 1:Ndm){
curreljw[i] ~ dbin(p.bound[i], 1)
p.bound[i] <- max(0, min(1,p[i]))
XBSt1[i] <- b.st1*st1[i] + b.st2*st2[i] + b.st3*st3[i] + b.st5*st5[i] +
b.st6*st6[i] + b.st7*st7[i] + b.st8*st8[i] + b.st9*st9[i] +
b.st10*st10[i] + b.st11*st11[i] + b.st12*st12[i] + b.st13*st13[i] +
b.st14*st14[i] + b.st15*st15[i] + b.st16*st16[i] + b.st17*st17[i] +
b.st18*st18[i] + b.st19*st19[i] + b.st20*st20[i] + b.st21*st21[i] +
b.st22*st22[i] + b.st23*st23[i] + b.st24*st24[i] + b.st25*st25[i]
XBSt2[i] <- b.st26*st26[i] + b.st27*st27[i] + b.st28*st28[i] + b.st29*st29[i] +
b.st30*st30[i] + b.st31*st31[i] + b.st32*st32[i] + b.st33*st33[i] +
b.st34*st34[i] + b.st35*st35[i] + b.st36*st36[i] + b.st37*st37[i] +
b.st38*st38[i] + b.st39*st39[i] + b.st40*st40[i] + b.st41*st41[i] +
b.st42*st42[i] + b.st43*st43[i] + b.st44*st44[i] + b.st45*st45[i] +
b.st46*st46[i] + b.st47*st47[i] + b.st48*st48[i] + b.st49*st49[i]
XBDem[i] <- b.cons + b.racb*racb[i] + b.rach*rach[i] + b.raco*raco[i] +
b.edu*eduncg[i] + b.age1*age1[i] + b.age2*age2[i] + b.age3*age3[i] +
b.age5*age5[i] + b.age6*age6[i] + b.met*metstat[i] +
b.exa1*eduncg[i]*age1[i] + b.exa2*eduncg[i]*age2[i] +
b.exa3*eduncg[i]*age3[i] +
b.exa5*eduncg[i]*age5[i] + b.exa6*eduncg[i]*age6[i]
logit(p[i]) <- XBDem[i] + XBSt1[i] + XBSt2[i] + b.surv[survs[i]] }
3
States were included as fixed rather than random effects to reduce the influence of outlier states,
particularly states such as Utah or the Dakotas where the estimated size of the population is so
small (recommendation from David Rindskopf).
4
b.cons ~ dnorm(-3.74,35)
b.racb ~ dnorm(-2.39,7)
b.rach ~ dnorm(-1.66,13)
b.raco ~ dnorm(-1.19,16)
b.age1 ~ dnorm(-0.12,11)
b.age2 ~ dnorm(-0.35,37)
b.age3 ~ dnorm(-0.27,47)
b.age5 ~ dnorm(0.11,50)
b.age6 ~ dnorm(0.22,50)
b.edu ~ dnorm(-1.49,25)
b.met ~ dnorm(1.24,32)
b.exa1 ~ dnorm(0.88,7)
b.exa2 ~ dnorm(0.24,9)
b.exa3 ~ dnorm(0.18,11)
b.exa5 ~ dnorm(0.18,13)
b.exa6 ~ dnorm(0.41,16)
b.st1 ~ dnorm(-1.88,3)
b.st2 ~ dnorm(-0.57,11)
b.st3 ~ dnorm(-1.91,2)
b.st5 ~ dnorm(-0.73,9)
b.st6 ~ dnorm(-0.07,11)
b.st7 ~ dnorm(-0.82,2)
b.st8 ~ dnorm(1.04,5)
b.st9 ~ dnorm(0.28,41)
b.st10 ~ dnorm(-0.79,11)
b.st11 ~ dnorm(-1.72,2)
b.st12 ~ dnorm(-0.37,21)
b.st13 ~ dnorm(-1.8,4)
b.st14 ~ dnorm(-1.95,2)
b.st15 ~ dnorm(-1.7,2)
b.st16 ~ dnorm(-1.87,2)
b.st17 ~ dnorm(-1.86,3)
b.st18 ~ dnorm(-1,3)
b.st19 ~ dnorm(0.41,21)
b.st20 ~ dnorm(0.13,24)
b.st21 ~ dnorm(-0.93,12)
b.st22 ~ dnorm(-1.39,6)
b.st23 ~ dnorm(-2.13,1)
b.st24 ~ dnorm(-1.13,7)
b.st25 ~ dnorm(-2.14,1)
b.st26 ~ dnorm(-1.97,1)
b.st27 ~ dnorm(-0.27,5)
b.st28 ~ dnorm(-0.86,3)
b.st29 ~ dnorm(0.53,32)
b.st30 ~ dnorm(-0.7,3)
b.st31 ~ dnorm(0.89,55)
b.st32 ~ dnorm(-1.2,8)
b.st33 ~ dnorm(-3.12,0.3)
b.st34 ~ dnorm(-0.91,16)
b.st35 ~ dnorm(-1.83,2)
b.st36 ~ dnorm(-1.12,5)
b.st37 ~ dnorm(-0.35,28)
b.st38 ~ dnorm(-0.62,3)
b.st39 ~ dnorm(-1.57,3)
b.st40 ~ dnorm(-3.28,0.25)
b.st41 ~ dnorm(-1.82,5)
b.st42 ~ dnorm(-1.32,15)
b.st43 ~ dnorm(-2.49,1)
b.st44 ~ dnorm(-0.15,3)
b.st45 ~ dnorm(-1.02,10)
b.st46 ~ dnorm(-1.12,9)
b.st47 ~ dnorm(-1.62,2)
b.st48 ~ dnorm(-1.65,5)
b.st49 ~ dnorm(-1.02,1)
for (j in 1:n.surv){ b.surv[j] ~
dnorm(0, tau.surv) }
tau.surv <- pow(sigma.surv, -2)
sigma.surv ~ dunif(0,.4)
5
Results from this model are displayed in Table B2 (column 1). Column 2 displays results
after including the original survey weights as a covariate in the population model.
Column 3 displays the same for the subset of surveys where the original survey weight
was significantly related to the likelihood of respondents identifying as Jewish. See
Table B3 for results of regressions of Jewish likelihood on survey weights.
TABLE B2: RESULTS FROM HIERARCHICAL BAYES.
Cross-Survey
Estimates
Est.
SD
Inclusion of
Survey Weight
Est.
SD
Subset Sig. Survey
Weight
Est.
SD
Random Intercept for Survey
Tau
Variance
ICC
MOR
Deviance
89.15
88.42
278.10
206.72
962.45
0.01
0.004
0.001
0.003
0.001
0.0003
1.11
1.06
1.03
58256
33042
19899
2560.39
Fixed Effects
Final Weight
--
--
<.001
<.001
<.001
<.001
-3.75
0.06
-3.83
0.09
-3.68
0.11
18-24 years
-0.12
0.09
-0.10
0.12
-0.15
0.17
25-34 years
-0.35
0.05
-0.31
0.07
-0.41
0.09
35-44 years
-0.27
0.04
-0.25
0.06
-0.34
0.07
55-64 years
0.11
0.04
0.13
0.05
0.11
0.07
65 years and older
0.22
0.04
0.20
0.05
0.20
0.07
-1.49
0.05
-1.41
0.07
-1.45
0.10
Black non-Hispanic
-2.40
0.11
-2.55
0.16
-2.55
0.22
Hispanic
-1.66
0.09
-1.67
0.11
-1.75
0.16
Other non-Hispanic
-1.19
0.08
-1.25
0.10
-1.21
0.13
18-24 years
0.88
0.11
0.88
0.15
1.02
0.20
25-34 years
0.23
0.10
0.23
0.13
0.15
0.18
35-44 years
0.18
0.08
0.16
0.11
0.16
0.15
55-64 years
0.17
0.08
0.10
0.10
0.13
0.13
65 years and older
0.41
0.07
0.35
0.09
0.29
0.12
1.24
0.05
1.29
0.08
1.23
0.10
Alabama
-1.88
0.19
-2.04
0.26
-2.10
0.37
Arizona
-0.79
0.09
-0.77
0.12
-0.98
0.17
Arkansas
-1.76
0.27
-1.66
0.34
-1.69
0.44
Colorado
-0.37
0.06
-0.32
0.08
-0.28
0.10
Connecticut
-1.82
0.16
-1.88
0.20
-1.79
0.25
Intercept
Age
Education
Less than College
Race/Ethnicity
Age x Education
Metropolitan Status
In Metro
State
6
Delaware
-1.97
0.23
-2.12
0.35
-2.10
0.45
District of Columbia
-1.72
0.21
-1.99
0.31
-2.07
0.42
Florida
-1.89
0.20
-1.96
0.29
-2.50
0.48
Georgia
-1.87
0.21
-1.65
0.26
-1.64
0.32
Idaho
-1.01
0.20
-0.98
0.25
-1.13
0.38
Illinois
0.41
0.06
0.41
0.08
0.45
0.11
Indiana
-0.58
0.09
-0.53
0.11
-0.42
0.15
0.13
0.06
0.10
0.07
0.05
0.11
Kansas
-0.93
0.08
-0.94
0.11
-0.93
0.15
Kentucky
-1.39
0.13
-1.28
0.17
-1.43
0.22
Louisiana
-2.17
0.36
-2.46
0.53
-2.53
0.68
Maine
-1.13
0.12
-1.12
0.16
-1.14
0.19
Maryland
-2.22
0.46
-2.07
0.60
-2.00
0.68
Massachusetts
-2.02
0.33
-2.25
0.49
-2.18
0.58
Michigan
-0.27
0.14
-0.15
0.17
-0.08
0.21
Minnesota
-0.88
0.20
-0.59
0.23
-0.60
0.31
Mississippi
0.53
0.05
0.53
0.07
0.56
0.09
Missouri
-1.93
0.28
-1.94
0.36
-2.11
0.49
Montana
-0.71
0.18
-0.67
0.23
-0.73
0.31
Nebraska
0.90
0.04
0.85
0.05
0.80
0.06
Nevada
-1.20
0.10
-1.41
0.15
-1.44
0.18
New Hampshire
-3.42
0.95
-2.94
0.99
-2.36
0.87
New Jersey
-0.90
0.07
-0.94
0.10
-0.90
0.12
New Mexico
-1.84
0.21
-1.70
0.27
-1.68
0.36
New York
-1.11
0.14
-1.25
0.18
-1.33
0.25
North Carolina
-0.35
0.06
-0.35
0.07
-0.37
0.09
North Dakota
-0.63
0.20
-0.83
0.27
-1.07
0.42
Ohio
-1.57
0.17
-1.30
0.20
-1.07
0.23
Oklahoma
-3.62
0.94
-3.07
1.01
-2.47
0.90
Oregon
-1.83
0.15
-2.16
0.23
-2.49
0.36
Pennsylvania
-1.32
0.08
-1.21
0.09
-1.15
0.12
Rhode Island
-2.53
0.34
-2.75
0.49
-2.62
0.56
South Carolina
-0.16
0.20
-0.39
0.29
-0.08
0.32
South Dakota
-1.02
-1.12
-1.67
-1.65
-1.07
-0.73
-0.07
-0.85
1.03
0.28
0.10
0.10
0.28
0.14
0.38
0.10
0.09
0.26
0.15
0.05
-0.97
-1.33
-2.03
-1.74
-1.02
-0.78
-0.07
-0.91
1.08
0.32
0.13
0.14
0.41
0.19
0.48
0.13
0.10
0.35
0.19
0.06
-0.98
-1.52
-2.12
-2.00
-0.49
-0.63
-0.12
-1.46
1.17
0.31
0.16
0.20
0.59
0.27
0.46
0.15
0.15
0.55
0.24
0.07
Iowa
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Note: The reference group are those age 45-54, white non-Hispanic, college graduates, residing in nonmetropolitan California.
7
TABLE B3: RESULTS OF REGRESSION JEWISH
LIKELIHOOD ON SURVEY WEIGHTS.
Survey ID
S113
S40
S103
S112
S116
S10
S106
S130
S124
S94
S58
S90
S12
S18
S134
S97
S6
S65
S107
S37
S41
S84
S129
S2
S11
S31
S16
S75
S81
S125
S117
S137
S4
S14
S82
S32
S9
S42
S96
S62
S83
S71
S63
S72
S132
S59
B
-1.328
-0.205
-1.717
-1.290
-1.653
-1.138
-2.370
-1.184
-1.112
-3.382
-4.311
-2.677
-0.751
-1.093
-2.261
-4.748
-1.666
-1.556
-1.773
-1.620
-0.462
-1.486
-1.184
-1.019
-.502
-.519
-.799
-2.598
-1.376
2.305
-.995
-1.369
.324
-.181
-1.317
-.739
.508
-.503
-1.378
-1.913
-1.557
-1.873
-1.018
-1.610
-.001
-2.283
8
S.E.
.212
.036
.358
.328
.443
.311
.721
.374
.366
1.146
1.502
.962
.279
.407
.843
1.772
.622
.596
.686
.631
.180
.586
.474
.415
.205
.214
.335
1.102
.587
.984
.430
.592
.141
.079
.583
.328
.228
.228
.629
.885
.730
.882
.486
.772
.001
1.171
Sig.
.000
.000
.000
.000
.000
.000
.001
.002
.002
.003
.004
.005
.007
.007
.007
.007
.007
.009
.010
.010
.010
.011
.012
.014
.014
.015
.017
.018
.019
.019
.021
.021
.022
.022
.024
.024
.026
.027
.028
.031
.033
.034
.036
.037
.047
.051
S33
S92
S85
S24
S80
S105
S93
S51
S43
S69
S39
S86
S88
S127
S34
S22
S60
S64
S115
S89
S118
S47
S76
S74
S66
S73
S15
S61
S29
S70
S126
S79
S101
S100
S36
S28
S52
S57
S111
S67
S30
S5
S109
S131
S19
S114
S13
S8
S44
S139
-.235
-1.292
-.762
-.443
-1.047
-1.277
-1.447
-.218
-.725
-1.181
-.428
-.896
-1.016
-1.159
-.163
.528
-1.229
-1.274
-.517
-.793
-.701
-.122
-1.652
-.975
-1.052
-1.256
-.004
-.966
-.187
-1.005
.256
-1.593
1.206
-.340
-.186
.175
-.356
-.475
.336
-.647
-.306
-.366
-.497
.000
-.218
-.268
.136
-.455
.156
-.266
9
.121
.673
.403
.236
.563
.692
.789
.119
.396
.647
.235
.494
.583
.667
.094
.309
.735
.772
.315
.489
.433
.075
1.030
.610
.658
.806
.002
.630
.122
.690
.181
1.127
.881
.255
.142
.137
.281
.377
.283
.561
.266
.319
.435
.000
.200
.246
.125
.424
.147
.263
.052
.055
.059
.060
.063
.065
.067
.067
.067
.068
.069
.070
.081
.082
.084
.087
.094
.099
.100
.105
.105
.107
.109
.110
.110
.119
.123
.125
.126
.145
.157
.158
.171
.183
.190
.201
.206
.208
.236
.248
.249
.251
.254
.262
.275
.277
.277
.283
.286
.311
S87
S20
S128
S98
S91
S53
S38
S7
S121
S17
S3
S77
S45
S49
S136
S138
S120
S55
S140
S35
S99
S95
S50
S78
S102
S104
S133
S25
S21
S1
S54
S119
S135
S27
-.372
.288
-.546
-.195
-.420
-.106
-.057
-.460
-.520
-.183
.166
-.369
-.079
-.076
-.366
-.102
-.503
-.036
-.180
.038
-.235
-.187
-.060
-.181
-.079
.063
-.072
-.023
.042
-.039
-.004
.005
.006
-.001
10
.376
.291
.578
.210
.460
.120
.072
.578
.675
.251
.229
.521
.118
.121
.590
.182
.912
.066
.349
.076
.488
.451
.148
.486
.259
.208
.261
.097
.261
.312
.091
.150
.185
.163
.322
.322
.345
.352
.361
.376
.424
.426
.442
.466
.469
.479
.502
.529
.535
.575
.581
.588
.606
.617
.630
.678
.687
.709
.760
.762
.782
.813
.873
.901
.969
.973
.974
.997
Comparison of Survey-Specific and Cross-survey Estimates
TABLE B4: UNWEIGHTED, WEIGHTED AND CROSS-SURVEY ESTIMATE FOR EACH SAMPLE SURVEY, 2000-2008.
Survey
ID
1
2
3
4
7
9
Survey Shop
Sample
Size
ISR/NORC
1006
ISR/NORC
801
ISR/NORC
1212
ISR/NORC
2320
PSRAI
PSRAI
10
PSRAI
11
PSRAI
12
14
PSRAI
SRBI
15
U-Indiana SurvRes
17
ISR/NORC
18
19
ISR/NORC
ISR/NORC
20
ISR/NORC
21
ISR/NORC
23
26
PSRAI
PSRAI
27
PSRAI
28
PSRAI
29
30
PSRAI
TNS
32
UConn SurvRes
33
UConn SurvRes
34
36
UConn SurvRes
NewEng SurvRes
42
SRBI
44
Gallup
45
46
Gallup
Gallup
47
PSRAI
48
OpResearch
49
50
ICR
PSRAI
51
CBS/NYTimes
52
TNS
Unweighted
Prop.
Survey Specific
Lower
Upper
Prop.
95% CI 95% CI
Cross-Survey Model-Base
Lower
Upper
Prop.
95% CI 95% CI
0.027
0.020
0.013
0.031
0.020
0.016
0.024
0.020
0.018
0.011
0.031
0.018
0.015
0.022
0.029
0.021
0.014
0.030
0.020
0.017
0.025
0.017
0.012
0.011
0.006
0.027
0.022
0.017
0.014
0.021
1000
0.012
0.013
0.017
0.013
0.020
3142
0.022
0.019
0.015
0.024
0.019
0.015
0.022
3002
0.020
0.018
0.014
0.023
0.018
0.015
0.021
3000
0.018
0.016
0.012
0.021
0.018
0.015
0.021
3204
0.021
0.022
0.017
0.029
0.018
0.015
0.021
2988
0.017
0.015
0.010
0.020
0.017
0.014
0.020
1018
0.029
0.031
0.022
0.044
0.019
0.016
0.023
0.022
0.022
0.014
0.036
0.020
0.017
0.024
0.018
0.015
0.007
0.030
0.018
0.015
0.021
0.020
0.021
0.012
0.035
0.019
0.016
0.022
0.017
0.019
0.011
0.032
0.019
0.016
0.022
0.017
0.022
0.010
0.016
0.029
0.030
0.019
0.015
0.022
1500
0.019
0.025
0.020
0.016
0.024
2041
0.013
0.012
0.008
0.018
0.017
0.013
0.020
2002
0.016
0.013
0.009
0.019
0.017
0.014
0.020
2002
0.020
0.019
0.013
0.026
0.018
0.015
0.021
3002
0.022
0.018
0.014
0.024
0.018
0.015
0.021
3003
0.013
0.014
0.010
0.019
0.017
0.014
0.020
1012
0.030
0.019
0.013
0.027
0.019
0.016
0.023
1000
0.024
0.016
0.010
0.026
0.019
0.015
0.023
1000
0.030
0.030
0.021
0.043
0.019
0.015
0.023
1003
0.056
0.025
0.020
0.033
2000
0.016
0.017
0.011
0.025
0.017
0.014
0.020
1642
0.024
0.020
0.014
0.029
0.019
0.015
0.022
882
0.032
0.025
0.016
0.037
0.019
0.016
0.024
876
0.041
0.031
0.022
0.046
0.020
0.017
0.025
1501
0.013
0.015
0.009
0.025
0.016
0.013
0.019
1029
0.030
0.020
0.013
0.030
0.020
0.015
0.025
1617
0.025
0.014
0.010
0.020
0.019
0.016
0.023
1503
0.021
0.018
0.012
0.026
0.018
0.015
0.022
1134
0.021
0.017
0.010
0.030
0.018
0.014
0.021
1204
0.028
0.023
0.016
0.034
0.020
0.016
0.024
2813
2747
2791
4475
2023
11
53
54
55
56
SRBI
Gallup
ICR
SRBI
57
Gallup
58
Gallup
59
60
PSRAI
Gallup
61
Gallup
62
CBS/NYTimes
63
64
Gallup
ICR
65
PSRAI
66
Gallup
67
68
TNS
PSRAI
69
Gallup
70
TNS
72
73
PSRAI
Gallup
74
Gallup
75
TNS
76
77
TNS
TNS
78
TNS
79
TNS
80
81
TNS
TNS
82
TNS
83
TNS
84
85
TNS
TNS
86
TNS
87
TNS
88
91
TNS
PSRAI
92
PSRAI
93
PSRAI
94
95
SRBI
CBS/NYTimes
96
CBS/NYTimes
97
CBS/NYTimes
98
CBS/NYTimes
1001
0.028
0.025
0.017
0.036
1003
0.026
0.016
0.011
0.024
1010
0.029
0.018
0.012
0.028
990
0.024
0.023
0.015
0.034
1002
0.015
0.014
0.008
0.025
1002
0.030
0.021
0.014
0.032
1405
0.018
0.016
0.011
0.025
1006
0.027
0.022
0.014
0.035
1003
0.023
0.016
0.010
0.026
1145
0.019
0.013
0.008
0.021
1040
0.029
0.018
0.013
0.027
1026
0.023
0.016
0.010
0.026
2558
0.011
0.011
0.006
0.019
1015
0.020
0.018
0.011
0.029
969
0.034
0.025
0.017
0.035
2009
0.023
0.019
0.014
0.026
1000
0.034
0.026
0.018
0.037
1005
0.018
0.014
0.009
0.023
1703
0.017
0.015
0.010
0.021
1005
0.021
0.016
0.010
0.027
1029
0.017
0.013
0.007
0.021
1007
0.018
0.014
0.009
0.024
1202
0.024
0.020
0.013
0.029
1201
0.018
0.014
0.009
0.022
1202
0.027
0.022
0.015
0.032
1004
0.016
0.013
0.008
0.022
1004
0.035
0.026
0.018
0.037
1007
0.020
0.016
0.010
0.026
1036
0.023
0.020
0.013
0.032
1229
0.029
1003
0.020
0.015
0.009
0.025
1002
0.021
0.018
0.011
0.028
1001
0.024
0.018
0.012
0.028
1001
0.024
0.017
0.011
0.026
1006
0.020
0.016
0.010
0.026
1502
0.022
0.018
0.012
0.026
1110
0.027
0.021
0.014
0.032
1130
0.019
0.021
0.012
0.036
1251
0.024
0.019
0.012
0.030
1104
0.020
0.013
0.008
0.022
1126
0.028
0.024
0.014
0.041
989
0.025
0.024
0.013
0.042
829
0.023
0.012
0.007
0.021
12
0.020
0.016
0.024
0.019
0.016
0.022
0.020
0.016
0.024
0.019
0.015
0.023
0.017
0.013
0.020
0.019
0.016
0.024
0.017
0.014
0.021
0.019
0.016
0.023
0.018
0.014
0.022
0.018
0.015
0.022
0.019
0.016
0.023
0.018
0.015
0.022
0.017
0.014
0.020
0.018
0.014
0.021
0.020
0.016
0.025
0.019
0.016
0.023
0.021
0.017
0.025
0.018
0.014
0.022
0.017
0.014
0.020
0.018
0.015
0.022
0.017
0.013
0.021
0.018
0.015
0.022
0.019
0.015
0.023
0.018
0.014
0.022
0.020
0.016
0.025
0.018
0.014
0.021
0.022
0.018
0.027
0.018
0.014
0.022
0.019
0.015
0.023
0.020
0.016
0.024
0.018
0.014
0.022
0.019
0.015
0.023
0.019
0.015
0.023
0.019
0.016
0.023
0.018
0.015
0.022
0.019
0.016
0.023
0.018
0.015
0.022
0.019
0.016
0.023
0.018
0.014
0.021
0.020
0.016
0.024
0.018
0.014
0.021
0.019
0.016
0.023
0.019
0.015
0.023
99
100
101
102
CBS/NYTimes
CBS/NYTimes
CBS/NYTimes
CBS/NYTimes
103
Gallup
104
Gallup
105
106
Gallup
Gallup
107
Gallup
108
Gallup
110
111
GreenbergRosner
PSRAI
112
TNS
114
TNS
115
116
TNS
TNS
117
TNS
118
SRBI
119
120
SRBI
TNS
121
PSRAI
122
PSRAI
123
124
SRBI
RTI
125
PSRAI
126
PSRAI
127
128
PSRAI
PSRAI
129
SRBI
130
SRBI
131
132
SRBI
SRBI
133
PSRAI
134
PSRAI
135
136
PSRAI
PSRAI
137
PSRAI
138
PSRAI
139
140
PSRAI
PSRAI
141
PSRAI
142
PSRAI
143
PSRAI
894
0.025
0.020
0.012
0.033
1125
0.015
0.014
0.008
0.025
1276
0.024
0.025
0.013
0.044
992
0.009
0.008
0.004
0.016
1007
0.027
0.018
0.012
0.029
1010
0.033
0.025
0.017
0.036
1014
0.024
0.017
0.011
0.029
1027
0.022
0.018
0.011
0.032
883
0.021
0.013
0.008
0.020
885
0.028
0.022
0.014
0.034
897
0.017
0.022
0.013
0.036
1512
0.020
0.016
0.011
0.023
1028
0.017
0.014
0.008
0.024
1208
0.018
0.013
0.009
0.020
1207
0.018
0.015
0.009
0.023
1008
0.027
0.026
0.017
0.038
1004
0.016
0.015
0.008
0.028
58373
0.021
81422
0.022
1004
0.021
0.018
0.011
0.028
2528
0.018
0.017
0.012
0.024
2007
0.020
0.017
0.012
0.024
35009
0.019
0.017
0.016
0.019
2610
0.013
0.015
0.006
0.035
2802
0.024
0.020
0.015
0.026
813
0.012
962
0.013
0.014
0.008
0.026
1038
0.016
0.017
0.010
0.028
960
0.017
0.022
0.012
0.038
1043
0.021
0.022
0.013
0.037
2254
0.016
0.016
0.011
0.023
651
0.021
3620
0.018
0.016
0.012
0.020
1733
0.024
0.020
0.014
0.028
1488
0.011
0.008
0.004
0.017
1516
0.020
0.023
0.013
0.040
1501
0.017
0.016
0.010
0.025
503
0.012
2551
0.031
850
0.020
1515
0.027
0.021
0.037
0.020
0.019
0.013
0.028
1508
0.017
0.017
0.011
0.026
1503
0.024
0.019
0.013
0.028
13
0.018
0.015
0.023
0.018
0.015
0.022
0.017
0.014
0.021
0.018
0.015
0.022
0.016
0.013
0.020
0.018
0.015
0.022
0.019
0.016
0.023
0.018
0.015
0.022
0.018
0.015
0.022
0.018
0.014
0.022
0.019
0.016
0.023
0.018
0.014
0.022
0.018
0.015
0.022
0.017
0.014
0.021
0.017
0.014
0.021
0.018
0.014
0.021
0.020
0.016
0.024
0.017
0.013
0.021
0.019
0.018
0.020
0.018
0.017
0.019
0.018
0.015
0.022
0.019
0.015
0.022
0.018
0.015
0.021
0.017
0.016
0.019
0.018
0.014
0.021
0.019
0.016
0.023
0.017
0.014
0.021
0.017
0.014
0.021
0.017
0.014
0.021
0.018
0.014
0.021
0.018
0.015
0.022
0.017
0.014
0.020
0.019
0.015
0.023
0.018
0.015
0.021
0.019
0.016
0.023
0.017
0.013
0.020
0.019
0.015
0.023
0.017
0.014
0.020
0.017
0.013
0.021
0.021
0.018
0.025
0.019
0.015
0.023
0.018
0.014
0.022
0.018
0.014
0.021
144
145
146
147
148
PSRAI
PSRAI
PSRAI
PSRAI
PSRAI
1502
0.029
0.025
0.017
0.035
1505
0.016
0.014
0.009
0.023
1089
0.020
0.020
0.012
0.033
2264
0.024
0.023
0.017
0.031
752
0.027
Samples not include in Cross-survey estimate due to Missing Post-stratification Variables
6
16
Harris
Mitofsky
25
SRBI
31
UConn SurvRes
35
UConn SurvRes
39
SRC UMD
40
Harris
41
MarketFacts
43
Gallup
90
ICR
89
KnowledgeNetworks
109
SRBI
71
LA Times
3048
0.016
0.015
0.011
0.021
2002
0.019
0.019
0.013
0.028
5603
0.019
0.016
0.013
0.019
1015
0.019
0.016
0.010
0.027
1002
0.021
0.018
0.010
0.031
1824
0.011
0.013
0.008
0.021
2846
0.024
0.014
0.011
0.019
10204
0.017
0.012
0.010
0.015
1721
0.028
0.025
0.017
0.035
1001
0.008
0.007
0.004
0.015
3596
0.020
0.021
0.010
0.044
2910
0.023
0.020
0.016
0.026
1603
0.032
0.014
0.009
0.021
14
0.018
0.015
0.022
0.020
0.016
0.024
0.017
0.014
0.020
0.018
0.015
0.022
0.019
0.016
0.022
Model 3: Inclusion of Year of Survey
LMER model:
mod <- lmer(curreljw ~ racecat + agecat + edu + eduncg:agecat +
metstat + state + year +
(1|survs), family=binomial(link="logit"))
All categorical variables (racecat, agecat, education, metropolitan status, state and year)
were entered as “factors” in the model with the largest group within each factor as the
reference group. For year, the reference group was the year 2004.
TABLE B5: RESULTS FROM MULTILEVEL ANALYSIS WITH LAPLACE
APPROXIMATION, JEWISH LIKELIHOOD ON DEMOGRAPHICS AND YEAR OF
SURVEY.
Cross-Survey Estimates
Est.
SD
z value
Pr(>|z|)
Tau
--
--
--
--
Variance
0
0
--
--
ICC
--
--
--
--
MOR
--
--
--
--
58241
--
--
--
-3.74
0.08
-49.53
<.001
2001
0.06
0.04
1.59
0.11
2002
0.05
0.10
0.50
0.62
2003
-0.08
0.06
-1.26
0.21
2005
0.03
0.05
0.49
0.62
2006
0.03
0.05
0.58
0.56
2007
0.01
0.05
0.19
0.85
2008
-0.08
0.04
-2.01
0.04
2009
-0.01
0.05
-0.18
0.86
18-24 years
-0.12
0.09
-1.27
0.21
25-34 years
-0.36
0.05
-6.88
0.00
35-44 years
-0.28
0.05
-6.01
0.00
55-64 years
0.11
0.04
2.49
0.01
65 years and older
0.22
0.04
4.91
0.00
-1.49
0.06
-23.63
<.001
Random Intercept for Survey
Deviance
Fixed Effects
Intercept
Year
Age
Education
Less than College
15
Race/Ethnicity
Black non-Hispanic
-2.39
0.12
-20.41
<.001
Hispanic
-1.66
0.09
-18.67
<.001
Other non-Hispanic
-1.19
0.08
-15.09
<.001
18-24 years
0.88
0.12
7.17
0.00
25-34 years
0.24
0.11
2.24
0.03
35-44 years
0.18
0.10
1.88
0.06
55-64 years
0.17
0.09
1.97
0.05
65 years and older
0.41
0.08
5.17
0.00
1.24
0.06
20.59
<.001
Alabama
-1.88
0.21
-8.85
<.001
Arizona
-0.57
0.10
-5.82
0.00
Arkansas
-1.91
0.29
-6.55
0.00
Colorado
-0.73
0.11
-6.84
0.00
Connecticut
-0.07
0.09
-0.74
0.46
Delaware
-0.82
0.27
-3.01
0.00
District of Columbia
1.03
0.15
6.75
0.00
Florida
0.28
0.05
5.64
0.00
Georgia
-0.79
0.10
-7.97
0.00
Idaho
-1.72
0.30
-5.64
0.00
Illinois
-0.37
0.07
-5.28
0.00
Indiana
-1.80
0.16
-11.13
<.001
Iowa
-1.95
0.25
-7.69
0.00
Kansas
-1.70
0.23
-7.48
0.00
Kentucky
-1.87
0.23
-8.23
<.001
Louisiana
-1.85
0.22
-8.37
<.001
Maine
Age x Education
Metropolitan Status
In Metro
State
-1.00
0.21
-4.68
0.00
Maryland
0.41
0.07
5.91
0.00
Massachusetts
0.13
0.06
2.05
0.04
Michigan
-0.93
0.09
-10.14
<.001
Minnesota
-1.39
0.14
-10.20
<.001
Mississippi
-2.13
0.38
-5.60
0.00
Missouri
-1.13
0.12
-9.17
<.001
Montana
-2.14
0.50
-4.26
0.00
Nebraska
-1.97
0.34
-5.87
0.00
Nevada
-0.27
0.15
-1.87
0.06
New Hampshire
-0.86
0.21
-4.01
0.00
New Jersey
0.53
0.06
9.52
<.001
New Mexico
-0.71
0.19
-3.63
0.00
0.89
0.04
21.04
<.001
New York
16
North Carolina
-1.20
0.11
-10.87
<.001
North Dakota
-3.11
1.00
-3.11
0.00
Ohio
-0.91
0.08
-11.30
<.001
Oklahoma
-1.83
0.23
-7.86
0.00
Oregon
-1.12
0.14
-7.97
0.00
Pennsylvania
-0.35
0.06
-5.81
0.00
Rhode Island
-0.61
0.21
-2.99
0.00
South Carolina
-1.58
0.18
-8.58
<.001
South Dakota
-3.28
1.00
-3.27
0.00
Tennessee
-1.83
0.17
-10.85
<.001
Texas
-1.32
0.08
-15.92
<.001
Utah
-2.49
0.36
-7.00
0.00
Vermont
-0.14
0.22
-0.65
0.51
Virginia
-1.02
0.10
-10.20
<.001
Washington
-1.12
0.11
-10.27
<.001
West Virginia
-1.62
0.29
-5.56
0.00
Wisconsin
-1.65
0.15
-10.84
<.001
Wyoming
-1.02
0.41
-2.46
0.01
Note: The reference group are those age 45-54, white non-Hispanic, college
graduates, residing in non-metropolitan California and survey year 2004.
Model 4: Based to Single year: 2004
Estimates were similar to those obtained across the set of nine years (compare Table B6
to Table 2 of main report). The primary difference is that the smaller cell sizes
associated with estimation within particular states lead to less reliable (wider confidence
intervals) associated with state-level estimates (see Figure B2).
TABLE B6: JEWISH POPULATION ESTIMATES USING SINGLE YEAR (2004) MODEL, POST-STRATIFIED TO 2004
CPS.
Total US Adultsa
Total Jewish Population
Pct of Total US
adults
Pop
Total All Groups
Age
18-24 years
25-34 years
35-44 years
45-54 years
55-64 years
65+ years
Pct
213,308,957
27,643,318
38,948,429
43,300,765
40,786,805
28,186,364
34,443,276
13.0
18.3
20.3
19.1
13.2
16.1
Total US Jewish Adults
Pct(CI)
Pop.
Lower 95% Upper 95%
CI
CI
1.9
(1.7,2.1)
4,023,000
3,715,873 4,393,809
1.9
1.3
1.4
2.0
2.3
2.7
(1.6,2.2)
(1.1,1.5)
(1.3,1.6)
(1.8,2.2)
(2.0,2.5)
(2.4,3.1)
520,898
504,386
617,964
796,997
638,603
943,842
431,193
618,416
439,740
579,687
544,098
703,396
716,353
888,963
571,055
717,319
833,900 1,056,509
17
Pct of Total US
Jewish adults
Pct. (CI)
12.9
12.5
15.4
19.8
15.9
23.5
(11.1,14.9)
(11.2,14.0)
(13.9,16.9)
(18.4,21.3)
(14.5,17.2)
(21.7,25.4)
Education
Non-College
College Grad Plus
159,546,537
53,762,420
74.8
25.2
1.1
4.3
(1.0,1.2)
(3.9,4.6)
1,733,954
2,288,736
1,556,352 1,935,457
2,114,055 2,493,855
43.1
56.9
(40.7,45.2)
(54.8,59.3)
Race
White, non-Hisp
Black, non-Hisp.
Hispanic
Other non-Hisp.
150,951,210
23,825,461
26,496,449
12,035,838
70.8
11.2
12.4
5.6
2.5
0.2
0.3
1.2
(2.3,2.7)
(0.1,0.3)
(0.2,0.5)
(1.0,1.6)
3,736,654 3,450,691 4,081,138
51,074
31,557
75,528
86,977
57,824
120,902
147,984
115,980
189,798
92.9
1.3
2.2
3.7
(91.6,94.0)
(0.8,1.9)
(1.5,2.9)
(2.9,4.6)
173,843,153
81.5
2.2
(2.1,2.5)
3,898,138
96.9
(96.3,97.4)
39,465,804
18.5
0.3
(0.3,0.4)
124,552
3.1
(2.6,3.7)
Metropolitan
Metro
Non-Metro
3,601,446 4,261,066
101,712
151,602
Notes: Source: 2004 Current Population Survey, March Supplement.
FIGURE B2: STATE LEVEL ESTIMATES OF JEWISH POPULATION BASED TO 2004 SINGLE YEAR MODEL.
18
Model 5: Based to subset of years: 2004-2008
Parameter estimates are displayed in Table B7and mirror those observed in the full set of
surveys from 2000 to 2008.
TABLE B7: RESULTS FROM HIERARCHICAL BAYES POPULATION MODEL FOR 2004-2008
Cross-Survey
Estimates
Est.
SD
R-hat
Random Intercepts for Survey
Tau
69
Variance
0.014
ICC
0.004
MOR
Deviance
1.07
1.12
41863.18
Fixed Effects
Intercept
-4.11
0.06
1.05
0.92
0.08
1.02
25-34 years
0.03
0.10
1.03
35-44 years
-0.02
0.09
1.02
55-64 years
0.39
0.07
1.03
65 years and older
0.71
0.06
1.03
1.56
0.06
1.04
Black non-Hispanic
-2.41
0.13
1.01
Hispanic
-1.73
0.11
1.01
Other non-Hispanic
-1.15
0.09
1.00
18-24 years
-0.99
0.14
1.01
25-34 years
-0.39
0.12
1.03
35-44 years
-0.26
0.10
1.01
55-64 years
-0.26
0.09
1.02
65 years and older
-0.47
0.08
1.02
-1.34
0.07
1.01
Alabama
-1.88
0.25
1.00
Arizona
-0.55
0.11
1.01
Arkansas
-2.00
0.34
1.00
Colorado
-0.61
0.12
1.00
0.02
0.10
1.00
-0.54
0.28
1.00
Age
18-24 years
Education
College Grad
Race/Ethnicity
Age x Education
Metropolitan Status
Non Metro
State
Connecticut
Delaware
19
District of Columbia
1.25
0.16
1.00
Florida
0.33
0.05
1.00
Georgia
-0.76
0.11
1.00
Idaho
-1.59
0.31
1.00
Illinois
-0.33
0.07
1.00
Indiana
-1.65
0.17
1.00
Iowa
-1.84
0.27
1.00
Kansas
-1.85
0.28
1.00
Kentucky
-2.20
0.30
1.00
Louisiana
-1.95
0.27
1.00
Maine
-0.91
0.23
1.00
0.43
0.08
1.00
Maryland
Massachusetts
0.20
0.07
1.00
Michigan
-0.81
0.10
1.00
Minnesota
-1.31
0.14
1.00
Mississippi
-2.35
0.51
1.01
Missouri
-1.13
0.14
1.00
Montana
-1.90
0.50
1.00
Nebraska
-1.71
0.33
1.00
Nevada
-0.18
0.16
1.00
New Hampshire
-0.66
0.23
1.00
New Jersey
0.54
0.06
1.00
New Mexico
-0.53
0.19
1.00
0.90
0.04
1.00
North Carolina
-1.10
0.11
1.00
North Dakota
-3.15
1.06
1.00
Ohio
-0.91
0.09
1.00
Oklahoma
-1.93
0.29
1.00
Oregon
-1.21
0.16
1.00
Pennsylvania
-0.29
0.06
1.00
Rhode Island
-0.71
0.25
1.00
South Carolina
-1.51
0.21
1.00
South Dakota
-3.22
1.06
1.00
Tennessee
-1.89
0.21
1.00
Texas
-1.25
0.09
1.00
Utah
-2.55
0.41
1.01
Vermont
0.01
0.24
1.00
Virginia
-0.99
0.12
1.00
Washington
-1.28
0.13
1.00
West Virginia
-1.49
0.32
1.00
Wisconsin
-1.79
0.19
1.00
New York
Wyoming
-0.95
0.43
1.00
Note: Reference group, age 45-54, white non-Hispanic, college graduates , residing in metropolitan California.
20
Population Counts and Proportions by State: 2010
TABLE B8: TOTAL JEWISH POPULATION BY STATE: 2010.
Total US
Adults
Prop. Jewish of All
Adults
Est.
Pct(CI)
Alabama
3,579,052
0.3
(0.2, 0.5)
Arizona
4,800,864
1.5
(1.2, 1.8)
Arkansas
2,144,490
0.3
(0.1, 0.5)
California
27,322,887
2.3
(2.2, 2.5)
Colorado
3,727,626
1.8
(1.4, 2.2)
Connecticut
2,667,563
3.4
(2.8, 4.0)
672,703
1.4
(0.8, 2.2)
Delaware
482,035
7.4
(5.6, 9.4)
Florida
14,368,065
3.5
(3.2, 3.8)
Georgia
7,078,286
1.1
(0.9, 1.3)
Idaho
1,104,481
0.5
(0.3, 0.8)
Washington, DC
Illinois
9,570,396
1.9
(1.6, 2.1)
Indiana
4,721,604
0.5
(0.3, 0.7)
Iowa
2,280,184
0.4
(0.2, 0.6)
Kansas
2,032,547
0.4
(0.2, 0.7)
Kentucky
3,265,707
0.2
(0.1, 0.4)
Louisiana
3,292,002
0.3
(0.2, 0.5)
Maine
1,027,470
1.0
(0.6, 1.5)
Maryland
4,318,837
3.9
(3.4, 4.5)
Massachusetts
5,175,248
4.2
(3.7, 4.7)
Michigan
7,454,419
1.2
(1.0, 1.4)
Minnesota
3,954,543
0.8
(0.6, 1.0)
Mississippi
2,064,766
0.2
(0.1, 0.4)
Missouri
4,535,180
0.8
(0.6, 1.1)
Montana
754,160
0.3
(0.1, 0.7)
Nebraska
1,321,501
0.5
(0.2, 0.8)
Nevada
1,964,403
1.9
(1.4, 2.6)
New Hampshire
1,029,031
1.5
(0.9, 2.1)
New Jersey
6,608,420
4.7
(4.2, 5.2)
New Mexico
1,460,277
1.2
(0.8, 1.7)
14,719,227
5.8
(5.4, 6.2)
New York
7,012,892
0.7
(0.6, 0.9)
488,495
0.2
(0.0, 0.6)
Ohio
8,746,658
1.0
(0.8, 1.2)
Oklahoma
2,708,166
0.4
(0.2, 0.6)
Oregon
2,970,906
0.8
(0.6, 1.1)
North Carolina
North Dakota
21
Total Jewish Adults
Population
12,200
95% lower
7,200
95%
Upper
18,400
72,000
59,100
88,200
6,300
3,000
10,900
629,000
590,400
672,900
67,000
52,800
81,300
90,200
73,800
107,400
9,500
5,500
15,100
35,600
26,800
45,500
503,100
460,200
547,800
75,200
60,700
92,100
5,700
2,900
9,400
180,900
156,400
205,300
22,900
16,200
31,400
8,400
4,700
12,900
8,500
4,600
13,600
7,800
4,000
12,800
11,200
6,100
17,900
10,400
6,300
15,200
170,000
148,600
193,800
216,900
190,300
244,200
90,500
74,300
107,800
30,800
22,600
39,600
3,400
1,100
7,500
37,900
27,600
48,600
2,400
800
5,300
6,300
3,000
10,800
38,000
27,500
50,200
15,100
9,500
22,000
309,100
278,200
343,900
17,500
11,700
24,300
856,500
799,300
912,200
51,700
40,900
63,800
800
< 50
2,700
86,500
72,200
101,900
9,600
5,000
15,600
24,400
17,700
31,900
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
9,591,280
2.0
(1.8, 2.3)
806,505
1.7
(1, 2.5)
3,394,958
0.5
(0.3, 0.7)
598,678
0.1
(0.0, 0.4)
4,761,287
0.4
(0.2, 0.5)
Texas
17,691,183
0.6
(0.5, 0.7)
Utah
1,922,109
0.2
(0.1, 0.5)
Vermont
494,178
2.0
(1.2, 3.0)
Virginia
5,859,015
1.1
(0.9, 1.3)
Washington
5,156,507
0.9
(0.7, 1.1)
West Virginia
1,412,776
0.5
(0.3, 0.9)
Wisconsin
4,266,034
0.5
(0.3, 0.7)
Wyoming
404,406
0.7
(0.3, 1.3)
22
194,100
172,900
217,700
13,400
7,900
20,400
16,800
10,900
24,400
800
< 50
2,700
18,000
11,600
25,600
103,500
86,400
120,400
4,600
1,800
8,700
9,800
6,000
15,000
62,600
50,500
77,100
45,800
35,300
57,800
7,500
3,700
12,600
20,000
13,800
28,000
2,800
1,000
5,400
Weighting: Survey of American Jews 2010
To identify factors necessary to include in weighting, the demographic composition of
the SAJ 2010 sample was compared to the US adult Jewish population. In addition, the
relationship of demographic variables to a representative sample of survey outcomes
were examined. Post-stratification is done for those demographic variables that are both
over- or under-represented relative to the total population and are related to survey
outcomes.
Sample Composition
Basic demographic information is collected as part of the KnowledgePanel. To compare
the KN sample to cross-survey estimates of the Jewish population, all KN demographic
variables were recoded into standard format. Age (in years) was recoded into 6
categories (18-24/25-34/35-44/45-54/55-64/ 65+). Race/Ethnicity, originally recorded as
5 categories (White non-Hispanic/Black non-Hispanic, Other non-Hispanic, Hispanic,
and More than Two Races non-Hispanic) was recoded into 4 categories of race, with the
2 or more category (n=22) included as Other non-Hispanic. Educational attainment was
recoded from 4 categories (Less than high school/High school/Some college/Bachelor’s
degree or higher) into two categories (Less than Bachelor’s/Bachelor’s degree or higher).
Also included was metropolitan status (metro/non-metro) and state of residence.4
State-Metropolitan Clusters
Given the sparseness of data at the state level, respondents were grouped into eight statemetropolitan clusters. These clusters were defined based on a combination of
distributions of the Jewish population as estimated in the most recent years of crosssurvey data and based on the number of cases in the SAJ 2010 sample, such that low
incidence regions would not be combined with higher incidence regions in a way that
could result in biasing estimates either upward or downward. Population models were
refit using these state-metro clusters rather than state and metropolitan areas to obtain
corresponding population estimates for analysis of the SAJ 2010 sample.
Table B9: SAJ 2010 sample size within state-metropolitan clusters.
State-metro Clusters
4
Cluster 1
New York state metro & DC
Cluster 2
Jewish
Population
Incidence
SAJ
2010
Sample
above 6%
150
New Jersey
just under 5%
60
Cluster 3
MD & MA metro
Just over 4%
72
Cluster 4
CT, VT, FL metro
3% - 4%
102
Cluster 5
PA & CA metro, NY non-metro
2% - 3%
194
All analyses limited to Continental U.S..
23
Cluster 6
AZ, CO, NH, RI, NV, IL metro
1.5% - 2%
97
Cluster 7
NC, MN, MO, WA, VA, WY, OR,
MI, DE, GA, OH, NM, ME metro &
CT, CA, VT, FL, MA, MD non-met
1% - 1.5%
144
Cluster 8
All other metro & non-metro areasa
Below 1%
75
Notes: a) Excludes Alaska & Hawaii.
Demographic composition of the sample is displayed in Table B10. Included are
distributions of each variable for the total US adult population and the US adult Jewish
population (based on cross-survey estimates) in comparison to the full KN Jewish panel
and the subset of panel members who responded to the SAJ 2010 survey. Since the crosssurvey analysis is based to those who identify as Jewish in response to questions about
religious affiliation, the sample is further separated into those who identified as Jewish by
religion and those who were added to the sample based on responses to the two follow-up
questions.
TABLE B10: COMPARISON OF SSRI ESTIMATES WITH KNOWLEDGE NETWORK’S JEWISH PANEL AND THE SAJ 2010 SAMPLE.
US Adults:
CPS 2010
Population
%
Jewish Population:
SSRI 2010
Est. & Lower
Upper
KN All
Jewish
Panel
Members
N
%
SAJ 2010
Respondents
SAJ 2010
Jewish by
Religion
SAJ 2010
Other Jewish
N
%
N
%
N
%
23
2.1
17
1.9
6
3.2
Age
Age 18-24 years
12.8
12.6
11.4
13.8
34
Age 25-34 years
17.9
13.3
12.3
14.3
108
8.2
73
6.8
53
5.9
20
10.7
Age 35-44 years
17.6
13.5
12.7
14.4
167
12.6
128
11.8
92
10.3
36
19.3
Age 45-54 years
19.4
18.2
17.3
19.1
254
19.2
209
19.3
176
19.7
33
17.6
Age 55-64 years
15.4
18.9
17.9
19.9
374
28.3
317
29.2
273
30.5
44
23.5
Age 65 years plus
16.8
23.5
22.3
24.6
385
29.1
331
30.7
283
31.7
48
25.7
27.3
72.7
59.7
40.3
58.2
38.8
61.2
41.8
960
362
72.6
27.4
801
280
74.1
25.9
658
236
73.6
26.4
143
44
76.5
23.5
68.3
11.8
13.9
5.9
91.3
1.5
3.4
3.9
90.3
1.1
2.7
3.3
92.3
1.9
4.1
4.5
1234
8
36
44
93.3
0.6
2.7
3.3
1015
3
26
37
93.9
0.3
2.4
3.4
843
2
21
28
94.3
0.2
2.3
3.1
172
1
5
9
92
0.5
2.7
4.8
6.1
2.9
4.0
7.3
15.8
8.4
20.2
36.3
20.9
7.4
9.2
13.9
19.6
8.8
12.7
7.7
19.8
6.7
8.4
13
18.5
8
11.8
7.1
22
8.1
9.9
14.8
20.6
9.5
13.5
8.4
217
81
101
151
296
134
225
115
16.4
6.1
7.7
11.4
22.4
10.2
17
8.7
171
69
83
118
250
108
189
91
15.8
6.4
7.7
10.9
23.2
10
17.5
8.4
150
60
71
102
194
97
144
74
16.8
6.7
8
11.4
21.7
10.9
16.1
8.3
21
9
12
16
56
11
45
17
11.2
4.8
6.4
8.6
29.9
5.9
24.1
9.1
Education
College
Non-College
Race/Ethnicity
White-NonHispanic
Black-NonHispanic
Hispanic
Other-NonHispanic
State Group
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
24
2.6
Even though RDD and other general population surveys tend to under-represent younger
age groups and over-represent older age groups (cf., Keeter, et al., 2000), the KN Jewish
panel, along with the subset who responded to the SAJ 2010 survey, does so to an even
greater degree. For typical RDD surveys, percentages aged 55 years and older tend to be
just a few percentages higher than the US as a whole, whereas the KN Jewish panel is
over 60% aged 55 years and older. The KN Jewish panel also over-represents college
graduates and under-represents -- to a lesser degree -- racial/ethnic minorities. These
differences are true of the sample overall as well as for the subgroups who identify by
religion and those who do not.
There are also disparities in the representation of the panel and sample by geographic
dispersion. Clusters 1 through 4 are under-represented (see Figure B3). These are the
geographic areas with the highest Jewish population incidence, New York metropolitan,
New Jersey, Maryland and Massachusetts metropolitan areas, and Connecticut, Vermont
and Florida metropolitan areas. State clusters 5, 6, and to the largest degree 7 are overrepresented. These include the metropolitan areas of Pennsylvania and California, the
non-metropolitan areas of New York, and metropolitan areas in states such as North
Carolina, Georgia, Ohio, Minnesota, Missouri, Wyoming, and Michigan.
FIGURE B3: DISTRIBUTION OF KN PANEL AND SAJ 2010 RESPONDENTS BY STATE GROUPS.
25
Knowledge Network Sampling Weight
KN provides a base sampling weight to account for the idiosyncrasies of the panel
design, and potential over- or under-representation of particular groups. These
adjustments include under-sampling of telephone numbers unmatched to valid mailing
addresses, RDD selection proportional to the number of telephone landlines in the
household, oversampling of Chicago and Los Angeles, oversampling of the four largest
states and central region states, under-sampling of households not covered by the MSN
TV network service, oversampling of African-American and Hispanic telephone
exchanges, address-based sample phone matching, and ABS oversample stratification
adjustment which included oversampling of high density minority communities
(Knowledge Networks, 2010).
The average weight was .89, corresponding to an effective sample size of 958.5 Weights
ranged from .22 to 4.74, a ratio of 21.5 of lowest to highest. Weights were below 1 for
75% of the sample (see Figure B4).
FIGURE B4: DISTRIBUTION OF SAMPLE WEIGHTS FOR SAJ 2010.
The distribution of sample demographics after weighting for these factors is displayed in
Table 11. The weighting adjustments have little effect on the differences between the
sample and the US Jewish population as a whole. Disproportionate representation of
older adults and college graduates remain. Over- and under-representation of
metropolitan areas within states also remain. For respondents who identified as Jewish
5
The KN sampling weight was not standardized to the observed sample size. Thus, sample
counts sum to 958 rather than the observed 1,079 cases.
26
but not by religion, the sampling weights alter the distributions by metropolitan status
substantially, particularly with far greater weight given to Jews in non-metropolitan areas
of Texas, Ohio and Washington State.
TABLE 11: DEMOGRAPHIC CHARACTERISTICS OF SAJ 2010
SAMPLE WEIGHTED BY SAMPLE WEIGHT.
SAJ 2010
Respondents
N
Total All Groups
%
958
SAJ 2010
Jewish by
Religion
N
%
SAJ 2010
Other Jewish
N
787
%
171
Age
Age 18-24 years
23
2.4
18
2.2
6
3.3
Age 25-34 years
75
7.9
55
7.0
20
12.0
Age 35-44 years
115
12.0
84
10.7
31
18.0
Age 45-54 years
191
19.9
155
19.7
36
21.1
Age 55-64 years
271
28.3
227
28.9
44
25.8
Age 65 years plus
283
29.5
249
31.6
34
19.9
College
697
72.8
572
72.6
125
73.3
Non-College
261
27.2
215
27.4
46
26.7
901
94.1
742
94.3
159
93.2
.
Education
Race/Ethnicity
White-NonHispanic
Black-NonHispanic
4
0.4
3
0.4
1
0.5
Hispanic
21
2.2
18
2.2
4
2.1
Other-NonHispanic
32
3.3
25
3.1
7
4.2
Cluster 1
150
15.6
133
16.9
17
9.8
Cluster 2
75
7.9
63
8.0
12
7.1
Cluster 3
86
8.9
72
9.1
14
8.2
Cluster 4
115
12.0
96
12.2
19
11.0
Cluster 5
205
21.4
161
20.4
44
25.8
Cluster 6
90
9.4
81
10.3
9
5.5
Cluster 7
163
17.0
124
15.8
39
22.7
Cluster 8
75
7.8
58
7.4
17
9.8
State Group
27
Adjustments for non-response
Knowledge Networks made available sociodemographic information on all Jewish panel
members. Analyses were conducted to examine differences between the 1,082 panel
members who responded to the SAJ 2010 survey and the 267 who did not (Phillips,
2010). Analyses consisted of logistic regressions (weighted by the KN sampling weight)
predicting the likelihood of response (yes/no) as a function of demographic variables. It
was determined that age, household size and geographic region were significantly related
to the likelihood of responding. The KN sampling weight was, therefore, adjusted to
reflect the distributions of these variables in the full KN Jewish panel. Adjustments were
done using QBAL (Werner, 2004) and included 6 categories of age, 4 categories of
household size (1/2/3/4+) and nine categories of geographic region (Massachusetts, Other
New England, New York, Mid-Atlantic, Midwest, South Atlantic, Florida, South,
Mountain & Pacific). The adjustments appeared to have very little effect on the overall
sampling weights, with a mean similarly of .89, range .19 to 5.14 and just under 75% of
the sample with weights below 1 (see Figure B5).6
FIGURE B5: SAMPLE WEIGHTS ADJUSTED FOR NON-RESPONSE FOR SAJ 2010.
6
Typically adjustments for non-response are based to a sample frame. The adjustments here treat
the knowledge network sample as if it were a frame and balances in terms of how the 200 or so
panel members who didn’t respond differ from the 1,000 that did. Such an adjustment assumes
that the KN Jewish panel is representative of all US Jewish adults, which based on comparison to
Jewish population estimates, it appears not to be. Perhaps if it were a larger representative
sample such adjustments might make sense, but in this context, it’s not clear adjustments of the
sample achieved to the panel is useful.
28
Adjustments for non-response reduce somewhat the disparities by age, but overall appear
to have little effect on sample demographics (see Table B12).
TABLE B12: DEMOGRAPHIC CHARACTERISTICS OF SAJ 2010
SAMPLE WEIGHTED BY NONRESPONSE WEIGHT
SAJ 2010
Respondents
N
%
SAJ 2010
Jewish by
Religion
N
%
SAJ 2010
Other Jewish
N
%
Age
Age 18-24 years
27
2.8
20
2.6
7
4.1
Age 25-34 years
86
8.9
63
8.0
23
13.3
Age 35-44 years
125
13.0
91
11.6
33
19.2
Age 45-54 years
187
19.5
153
19.5
34
19.7
Age 55-64 years
265
27.7
222
28.2
43
25.2
Age 65 years plus
269
28.1
237
30.2
32
18.5
College
699
72.9
573
72.9
126
73.3
Non-College
259
27.1
213
27.1
46
26.7
900
94.0
740
94.2
160
93.1
Education
Race/Ethnicity
White-NonHispanic
Black-NonHispanic
4
0.4
3
0.4
1
0.5
Hispanic
22
2.3
18
2.3
4
2.2
Other-NonHispanic
32
3.4
25
3.2
7
4.3
Cluster 1
157
16.4
139
17.7
18
10.3
Cluster 2
75
7.8
63
8.0
12
6.8
State Group
Cluster 3
81
8.4
68
8.6
13
7.5
Cluster 4
122
12.8
102
12.9
21
12.1
Cluster 5
197
20.6
155
19.8
42
24.2
Cluster 6
87
9.1
78
9.9
9
5.1
Cluster 7
164
17.1
123
15.6
41
23.6
Cluster 8
76
7.9
58
7.4
18
10.2
29
Adjustments for Representativeness of the Sample
To account for the disproportionate representation of demographic groups within the
Jewish population, an additional stage of weighting was done to balance the sample
demographics to match Jewish population estimates for age, race, education, the
interaction of age and education interaction, as well as geographic region. This was done
for the subset of 892 respondents who identified as Jewish in response to religious
identification, which is the group most similar to those on which the population estimates
are also based.
Relation of Post-stratification Variables to Outcomes
Prior to weighting, relationships between outcomes and potential post-stratification
variables was examined. These variables included:







Had a Bar or Bat Mitzvah (As a child/As an adult/No)
Jewish education
o Years of supplementary school (0 years/1-5 years/6-12 years)
o Years of day school (0 years/Any years)
o Years of camping (0 years/1-3 years/4 or more years)
Intermarriage (Yes/No)
Ability to read and understand Hebrew (Don’t know Hebrew alphabet at all/Can
read the letters but not understand the words/Some of what I read/Most of what I
read/Everything I read)
Synagogue membership (Yes/Other in household/No)
Attending any Jewish life cycle event (brit, baby naming ceremony, bar/bat
mitzvah, wedding, shivah call) in past year (Yes any event, No)
Any synagogue honor (aliyah, read Torah, chanted Haftarah/other)
Relationships with post-stratification variables were examined using logistic (or
ordinal/multinomial) regressions. All analyses were conducted using the SVY functions
in STATA to take into account variance estimation using the sampling and non-response
weights. Table B13 displays bivariate statistics for bar/bat mitzvah, Jewish education and
intermarriage. Only 28 of the 892 respondents indicated that they had a bar or bat
mitzvah as an adult. This variable was, therefore, dichotomized into a 0/1, yes/no. Age
was significantly related to the likelihood of having a bar or bat mitzvah. All age groups
under age 44 were significantly more likely to have had a bar/bat mitzvah then older
adults. Age was also significantly related to Jewish education and intermarriage.
Education and race were significantly related to the number of years of supplemental
Jewish education, but were unrelated to the other variables. Metropolitan status was
related to the likelihood of intermarriage, but otherwise unrelated to the other measures.
State cluster was related to all of these outcome variables in some way.
30
Table B13 through Table B16 display results of bivariate tests of association for
reading/understanding Hebrew, synagogue membership, attending Jewish life cycle event
in the past year and receiving any synagogue honors in the past year. Again, age is
related to all of these activities, with the exception of some of the synagogue honors.
Being called to Torah for Aliyah, reading Torah and chanting Haftarah are unrelated to
age. Education is related to the likelihood of having a bar/bat mitzvah and attending a
wedding, but unrelated to attending a brit or making a shiva call. Race is unrelated to
these activities, with the exception of a weak, marginally significant relationship with
making a shiva call. Metropolitan status, and state cluster are also unrelated to these
activities, with the exception of shiva calls, which are significantly more likely in
metropolitan than non-metropolitan areas and more likely in the highest density Jewish
population state clusters.
31
TABLE B13: BIVARIATE RELATIONSHIPS OF HAVING A BAR/BAT MITZVAH, YEARS OF JEWISH EDUCATION AND INTERMARRIAGE WITH AGE, EDUCATION, RACE,
METROPOLITAN STATUS AND GEOGRAPHIC REGION.
Had Bar/Bat Mitzvah (n=812)
Coef. Std. Err.
Jewish Ed: Supplemental (n=869)
t
P>t
Coef. Std. Err.
0.143 -1.500
0.133
-0.678
Jewish Ed: Day School (n=864)
t
P>t
Coef. Std. Err.
0.130
-5.230
0.000
3.837
Intermarriage (n=527 married)
t
P>t
Coef.
Std. Err.
t
P>t
0.420
9.130
0.000
1.152
0.211
5.460
0.000
Age
Constant
-0.216
Age 18-24
3.122
1.063
2.940
0.003
2.345
0.664
3.530
0.000
0.536
1.125
0.480
0.634
Age 25-34
1.341
0.393
3.420
0.001
0.490
0.297
1.650
0.099
2.205
0.595
3.710
0.000
-0.871
0.498
-1.750
0.081
Age 35-44
0.649
0.292
2.220
0.027
0.274
0.268
1.020
0.307
2.033
0.521
3.900
0.000
-0.816
0.368
-2.220
0.027
Age 45-54
0.771
0.235
3.280
0.001
0.763
0.200
3.810
0.000
1.447
0.515
2.810
0.005
-0.942
0.295
-3.190
0.001
Age 55-64
0.256
0.205
1.250
0.212
0.607
0.170
3.560
0.000
1.187
0.518
2.290
0.022
-0.793
0.272
-2.920
0.004
0.367
0.093
3.960
0.000
-1.201
0.098 -12.210
0.000
2.537
0.170
14.920
0.000
0.512
0.115
4.460
0.000
0.196 -3.220
0.001
-0.464
0.180
-2.580
0.010
-0.261
0.348
-0.750
0.454
0.154
0.243
0.640
0.526
0.083
0.011
-1.102
0.091 -12.150
0.000
2.657
0.157
16.930
0.000
0.574
0.105
5.480
0.000
-14.879
0.841 -17.690
0.000
-11.439
0.851 -13.440
0.000
Education
Constant
College grad
-0.630
Race
Constant
0.210
Black nH
--
Hispanic
0.079
Other nH
-0.107
0.605
2.540
0.130
0.896
-0.822
0.467
-1.760
0.079
1.175
0.692
1.700
0.090
-0.071
0.575
-0.120
0.901
0.511 -0.210
0.834
0.113
0.520
0.220
0.827
0.618
0.684
0.900
0.367
-0.694
0.555
-1.250
0.212
Metropolitan
Constant
0.195
0.082
2.370
0.018
-1.090
0.091 -12.000
0.000
2.586
0.150
17.250
0.000
0.602
0.104
5.770
0.000
Non-Metro
0.229
0.486
0.470
0.638
-0.479
0.322
-1.490
0.138
-0.531
1.032
-0.510
0.607
-1.403
0.512
-2.740
0.006
Constant
0.198
0.177
1.120
0.262
-0.976
0.154
-6.330
0.000
3.025
0.355
8.530
0.000
0.180
0.222
0.810
0.420
Cluster 1
-0.088
0.270 -0.330
0.744
-0.064
0.204
-0.310
0.755
1.016
0.447
2.270
0.023
1.223
0.388
3.150
0.002
Cluster 2
0.184
0.344
0.530
0.593
-0.119
0.261
-0.460
0.649
0.141
0.734
0.190
0.848
0.599
0.422
1.420
0.156
Cluster 3
0.301
0.331
0.910
0.364
0.728
0.264
2.760
0.006
0.662
0.645
1.030
0.305
1.071
0.457
2.350
0.019
Cluster 4
-0.090
0.297 -0.300
0.762
-0.007
0.289
-0.020
0.982
0.179
0.626
0.290
0.775
1.099
0.415
2.650
0.008
Cluster 5
-0.264
0.295 -0.890
0.371
-0.049
0.257
-0.190
0.848
0.207
0.563
0.370
0.714
0.193
0.343
0.560
0.573
Cluster 6
0.116
0.265
0.440
0.662
0.604
0.255
2.370
0.018
0.341
0.495
0.690
0.491
-0.250
0.324
-0.770
0.440
Cluster 7
-0.104
0.345 -0.300
0.763
-0.218
0.276
-0.790
0.430
0.439
0.698
0.630
0.530
-0.356
0.378
-0.940
0.347
State Cluster
32
TABLE B14: BIVARIATE RELATIONSHIPS OF ATTENDING JEWISH LIFE CYCLE EVENTS WITH AGE, EDUCATION, RACE, METROPOLITAN STATUS AND GEOGRAPHIC
REGION.
Brit/Baby Naming (n=887)
Coef.
Std. Err.
t
Bar/Bat Mitzvah (n=884)
P>t
Coef.
Std. Err.
t
Wedding (n=886)
P>t
Coef.
Std. Err.
t
Shiva Call (n=884)
P>t
Coef.
Std. Err.
t
P>t
Age
Constant
-1.308
0.173 -7.550
0.000
-0.259
0.139
-1.860
0.063
-1.308
0.157
-8.320
0.000
-0.029
0.140
-0.210
0.837
Age 18-24
-1.040
1.057 -0.980
0.326
-1.177
0.718
-1.640
0.102
-0.480
0.758
-0.630
0.527
-2.240
0.883
-2.540
0.011
Age 25-34
-0.145
0.405 -0.360
0.721
-1.149
0.377
-3.050
0.002
0.817
0.351
2.330
0.020
-1.156
0.372
-3.110
0.002
Age 35-44
-0.796
0.370 -2.150
0.032
-0.071
0.275
-0.260
0.795
-0.286
0.315
-0.910
0.364
-1.177
0.315
-3.730
0.000
Age 45-54
-0.518
0.306 -1.690
0.091
-0.147
0.222
-0.660
0.508
-0.192
0.271
-0.710
0.479
-0.367
0.224
-1.640
0.102
Age 55-64
-0.171
0.247 -0.690
0.488
-0.527
0.204
-2.580
0.010
0.321
0.219
1.470
0.142
-0.168
0.199
-0.840
0.399
Constant
-1.476
0.114 -12.930
0.000
-0.393
0.090
-4.380
0.000
-1.097
0.100 -10.960
0.000
-0.337
0.090
-3.770
0.000
College grad
-0.313
0.242 -1.290
0.197
-0.566
0.188
-3.010
0.003
-0.477
0.205
-2.330
0.020
-0.136
0.190
-0.720
0.473
-1.542
0.104 -14.830
0.000
-0.534
0.081
-6.560
0.000
-1.210
0.091 -13.360
0.000
-0.323
0.081
-3.960
0.000
0.595
0.270
0.789
-0.255
0.479
-0.530
0.594
-0.363
0.587
-0.620
0.537
-0.773
0.554
-1.390
0.163
0.455
0.590
0.557
-1.069
0.507
-2.110
0.035
Education
Race
Constant
Black nH
Hispanic
0.159
Other nH
-0.384
0.529 -0.730
0.468
0.299
0.423
0.710
0.479
0.267
Constant
-1.522
0.101 -15.000
0.000
-0.517
0.080
-6.460
0.000
-1.202
0.089 -13.500
0.000
-0.331
0.080
-4.130
0.000
Non-Metro
-1.695
1.029 -1.650
0.100
-0.628
0.476
-1.320
0.187
-0.392
0.538
-0.730
0.466
-1.761
0.635
-2.780
0.006
Constant
-1.622
0.232 -6.990
0.000
-0.630
0.170
-3.700
0.000
-1.367
0.208
-6.570
0.000
-0.968
0.181
-5.350
0.000
Cluster 1
0.122
0.329
0.370
0.711
0.249
0.259
0.960
0.336
0.116
0.293
0.400
0.692
1.333
0.265
5.020
0.000
Cluster 2
0.181
0.409
0.440
0.658
0.327
0.326
1.000
0.317
0.272
0.368
0.740
0.459
1.527
0.350
4.360
0.000
Cluster 3
0.322
0.395
0.820
0.415
0.509
0.320
1.590
0.112
0.264
0.384
0.690
0.493
0.791
0.329
2.410
0.016
Cluster 4
-0.069
0.433 -0.160
0.873
0.215
0.297
0.720
0.469
0.391
0.326
1.200
0.231
0.961
0.294
3.270
0.001
Cluster 5
0.133
0.369
0.360
0.719
0.046
0.281
0.160
0.870
0.294
0.316
0.930
0.351
0.254
0.288
0.880
0.378
Cluster 6
0.162
0.326
0.500
0.620
-0.112
0.258
-0.430
0.665
0.227
0.297
0.770
0.444
0.213
0.266
0.800
0.425
Cluster 7
-0.543
0.470 -1.160
0.248
-0.583
0.335
-1.740
0.082
-0.544
0.392
-1.390
0.165
-0.455
0.348
-1.310
0.191
Metropolitan
State Cluster
33
TABLE B15: BIVARIATE RELATIONSHIPS OF READING HEBREW AND SYNAGOGUE
MEMBERSHIP WITH AGE, EDUCATION, RACE, METROPOLITAN STATUS AND GEOGRAPHIC
REGION.
Read & Understand Hebrew (n=890)
Coef.
Std. Err.
T
Synagogue Membership (n=894)
P>t
Coef.
Std. Err.
0.136 -1.580
0.113
-0.208
0.141
t
P>t
Age
Constant
-0.216
-1.480
0.139
Age 18-24
0.554
0.304
1.820
0.069
0.794
0.565
1.400
0.161
Age 25-34
0.857
0.264
3.250
0.001
-0.297
0.337
-0.880
0.378
Age 35-44
0.065
0.243
0.270
0.788
-0.279
0.278
-1.000
0.316
Age 45-54
0.664
0.215
3.090
0.002
0.080
0.226
0.360
0.723
Age 55-64
0.282
0.194
1.450
0.147
-0.403
0.201
-2.010
0.045
Education
Constant
-0.668
0.090 -7.420
0.000
-0.320
0.089
-3.590
0.000
College grad
-0.609
0.187 -3.260
0.001
-0.064
0.187
-0.340
0.733
Constant
-0.502
0.082 -6.090
0.000
-0.350
0.082
-4.290
0.000
Black nH
-14.787
0.840 -17.600
0.000
1.833
1.417
1.290
0.196
Hispanic
-0.331
0.438 -0.750
0.451
0.264
0.474
0.560
0.578
Other nH
0.550
0.393
1.400
0.162
-0.012
0.427
-0.030
0.978
Race
Metropolitan
Constant
-0.524
0.082 -6.420
0.000
-0.332
0.080
-4.140
0.000
Non-Metro
-0.660
0.414 -1.590
0.111
-0.169
0.424
-0.400
0.691
Constant
-0.289
0.156 -1.860
0.064
-0.655
0.176
-3.730
0.000
Cluster 1
0.376
0.251
1.500
0.135
0.201
0.269
0.750
0.455
Cluster 2
0.094
0.311
0.300
0.763
0.587
0.331
1.770
0.077
Cluster 3
0.656
0.276
2.380
0.018
0.440
0.321
1.370
0.170
Cluster 4
0.075
0.266
0.280
0.778
0.650
0.291
2.240
0.026
Cluster 5
-0.166
0.255 -0.650
0.514
0.099
0.282
0.350
0.725
Cluster 6
0.482
0.227
2.120
0.034
0.627
0.261
2.410
0.016
Cluster 7
0.206
0.302
0.680
0.496
0.034
0.316
0.110
0.915
State Cluster
34
TABLE B16: BIVARIATE RELATIONSHIPS OF SYNAGOGUE HONORS WITH AGE, EDUCATION, RACE, METROPOLITAN STATUS AND GEOGRAPHIC REGION.
Called to Torah for Aliyah (n=889)
Coef.
Std. Err.
t
Read Torah (n=885)
P>t
Coef.
-3.366
Std. Err.
t
Chanted Haftarah (n=886)
P>t
Coef.
0.000
-3.056
Std. Err.
t
Age
Constant
-1.092
0.167 -6.540
0.000
Age 18-24
-0.910
0.794 -1.150
0.252
Age 25-34
-0.639
0.468 -1.360
0.173
1.020
0.636
1.600
0.109
0.702
0.622
1.130
Age 35-44
-0.609
0.392 -1.550
0.120
0.516
0.736
0.700
0.483
-1.439
1.052
-1.370
Age 45-54
-0.054
0.259 -0.210
0.834
0.297
0.595
0.500
0.618
0.021
0.507
0.040
Age 55-64
-0.550
0.256 -2.150
0.032
0.426
0.463
0.920
0.358
0.104
0.433
0.240
Education
constant
-1.218
0.108 -11.290
0.000
-2.833
0.203 -13.990
0.000
-2.924
College grad
-0.618
0.263 -2.350
0.019
-1.060
0.685
-1.550
0.122
-0.600
Race
constant
-1.336
0.101 -13.240
0.000
-3.122
0.204 -15.310
0.000
-3.112
4.605
1.430
3.220
0.001
0.835
0.696
1.200
0.231
Black nH
0.327 -10.310
0.301 -10.160
P>t
0.000
Other (n=855)
Coef.
Std. Err.
t
P>t
-1.622
0.211
-7.680
0.000
-1.623
1.070
-1.520
0.130
0.260
-3.192
1.035
-3.080
0.002
0.172
-1.149
0.484
-2.370
0.018
0.967
0.314
0.305
1.030
0.304
0.810
-0.499
0.318
-1.570
0.117
0.196 -14.920
0.000
-1.771
0.132 -13.420
0.000
0.469
-1.280
0.202
-0.423
0.329
-1.280
0.200
0.191 -16.330
0.000
-1.881
0.127 -14.800
0.000
1.076
0.679
1.580
0.113
0.085
0.670
0.130
0.899
0.514
0.770
0.670
0.505
0.282
0.540
0.520
0.601
Hispanic
-0.490
0.653 -0.750
0.453
Other nH
-0.571
0.554 -1.030
0.303
Metropolitan
constant
-1.355
0.100 -13.550
0.000
-3.080
0.183 -16.800
0.000
-1.846
0.123 -15.050
0.000
Non-Metro
-0.344
0.532 -0.650
0.518
0.507
0.763
0.660
0.506
-1.314
1.033
-1.270
0.204
State Cluster
constant
-1.348
0.202 -6.660
0.000
-3.021
0.401
-7.540
0.000
-2.639
0.345
-7.650
0.000
-2.004
0.263
-7.630
0.000
Cluster 1
-0.068
0.340 -0.200
0.841
-0.372
0.620
-0.600
0.549
-0.461
0.545
-0.850
0.398
0.120
0.435
0.280
0.782
Cluster 2
0.581
0.367
1.580
0.114
0.560
0.763
0.730
0.464
-0.549
0.809
-0.680
0.498
0.169
0.469
0.360
0.718
Cluster 3
0.114
0.402
0.280
0.777
-0.061
0.716
-0.080
0.932
-0.279
0.629
-0.440
0.658
0.698
0.447
1.560
0.119
Cluster 4
0.319
0.333
0.960
0.338
-0.189
0.725
-0.260
0.795
-0.333
0.629
-0.530
0.597
0.611
0.406
1.500
0.133
Cluster 5
-0.284
0.362 -0.780
0.433
-0.059
0.716
-0.080
0.934
-0.796
0.683
-1.170
0.244
-0.356
0.430
-0.830
0.409
Cluster 6
-0.212
0.306 -0.690
0.488
0.344
0.602
0.570
0.568
-1.259
0.683
-1.840
0.065
-0.262
0.395
-0.660
0.508
Cluster 7
-1.066
0.509 -2.090
0.037
-1.003
0.839
-1.200
0.232
-0.290
0.643
-0.450
0.652
-0.124
0.510
-0.240
0.809
35
Post-Stratification 1: Raking to Demos & State-Metro Clusters
Since all of the potential post-stratification variables were related to outcomes of interest
in some way, all were included in post-stratification adjustments. The six categories of
age, four categories of race, two categories of education across the metropolitan and nonmetropolitan areas of the 48 states (plus DC) used in the SSRI population models result
in possible population estimates for over 4,700 demographic groups. Clearly with a
sample size of 1,000, there are many groups (i.e., post-stratification cells) for which there
are no corresponding observations in the KN sample. In such situations where there are
too few observations within a survey for some of the sub-groups to estimate them reliably
(or when population estimates for all of the sub-groups are not available), weights are
typically “raked”, such that the marginal distributions of the weighted totals for each
separate demographic variable align with the marginal distributions of the target
population. Thus, the weighted sample estimates for the six categories of age are aligned
to the population estimates for the same six categories of age, ignoring differences within
specific age categories that there might be between states, education levels, etc.. Raking
uses iterative procedures such that after adjustments to the weights for each variable are
made, the process is repeated until the weights no longer change after having adjusted for
previous variables in the model.
With revised population estimates based to the eight state-metro clusters, raking
adjustments were done using the rake procedure in the R Survey package. Table B17
displays sample composition variables after raking. Included for comparison are the
SSRI 2010 population estimates from revised state-metro model, along with unweighted
and weighted estimates based to the KN sampling weight. Two raked estimates are
included in the table. The first represents weighting to the total Jewish population. The
second represents these weights normalized to the observed sample size.
TABLE B17: DEMOGRAPHIC CHARACTERISTICS OF NJPS 2010 SAMPLE WITH DEMOGRAPHIC AND STATE GROUPS RAKING
ADJUSTMENTS.
Age
ssri cross-survey
estimates
Est. Lower Upper
unweighted
N
Est.
weighted with sampwt
SE
N
Est.
raked to dems & stategroup
SE
N
Est.
SE
raked to dems &
stategroup (normalized)
N
Est.
SE
18-24
0.127 0.115 0.140
17 0.019 0.0046
18 0.022 0.006
531,019
0.127
0.033
113
0.127
0.033
25-34
0.132 0.122 0.143
53 0.059 0.0079
55 0.070 0.010
552,756
0.132
0.019
118
0.132
0.019
35-44
0.134 0.126 0.143
92 0.103 0.0102
84 0.107 0.012
564,386
0.135
0.015
120
0.135
0.015
45-54
0.181 0.172 0.190
175 0.196 0.0133
155 0.197 0.015
754,467
0.180
0.019
161
0.180
0.019
55-64
0.188 0.179 0.198
272 0.305 0.0154
227 0.289 0.017
789,276
0.188
0.016
168
0.188
0.016
65+
0.238 0.226 0.249
283 0.317 0.0156
249 0.316 0.018
997,439
0.238
0.019
212
0.238
0.019
College
0.596 0.582 0.611
657 0.737 0.0148
572 0.726 0.018
2,501,024
0.597
0.030
533
0.597
533
No College
0.404 0.389 0.418
235 0.263 0.0148
215 0.274 0.018
1,688,319
0.403
0.030
360
0.403
360
White NH
0.913 0.903 0.923
842 0.944 0.0077
742 0.943 0.009
3,826,950
0.913
0.019
815
0.913
0.019
Black NH
0.015 0.011 0.019
2 0.002 0.0016
3 0.004 0.003
62,321
0.015
0.012
13
0.015
0.012
Education
Race
36
Hispanic
0.033 0.027 0.040
21 0.024 0.0051
18 0.022 0.005
140,586
0.034
0.009
30
0.034
0.009
Other NH
0.038 0.032 0.045
27 0.030 0.0057
25 0.031 0.006
159,485
0.038
0.013
34
0.038
0.013
Cluster 1
0.209 0.198 0.220
150 0.168 0.0125
159 0.202 0.015
875,983
0.209
0.020
187
0.209
0.020
Cluster 2
0.074 0.067 0.081
60 0.067 0.0084
133 0.169 0.015
310,037
0.074
0.011
66
0.074
0.011
Cluster 3
0.092 0.084 0.099
71 0.080 0.0091
63 0.080 0.011
382,931
0.091
0.016
82
0.091
0.016
Cluster 4
0.139 0.130 0.148
102 0.114 0.0107
72 0.091 0.012
582,736
0.139
0.028
124
0.139
0.028
Cluster 5
0.196 0.185 0.206
194 0.217 0.0138
96 0.122 0.013
820,784
0.196
0.020
175
0.196
0.020
Cluster 6
0.088 0.080 0.095
97 0.109 0.0104
161 0.204 0.015
367,813
0.088
0.011
78
0.088
0.011
Cluster 7
0.127 0.118 0.135
144 0.161 0.0123
81 0.103 0.011
531,414
0.127
0.018
113
0.127
0.018
Cluster 8
0.077 0.071 0.084
74 0.083 0.0092
124 0.158 0.014
317,645
0.076
0.011
68
0.076
0.011
State Cluster
The goal in weighting is to select variables and methods that decrease bias – the extent to
which sample estimates deviate from the population -- without increasing the standard
error such that the benefits of decreased bias are negated by the increase in variance. As
can be seen in Table B17, raking decreased bias for all of the demographic variables. For
example, the unweighted sample estimate of the proportion of the Jewish population aged
18 to 24 years of age was less than 2%. This underestimates the representation of this
group by nearly 10% when compared to the 11.4% observed in the cross-survey
population models. Raking to population distributions of age yielded a sample estimate
of 12.7% for this group, much more closely aligned to the population estimate. The
variance of the estimate, however, increases from an SE of .019 for the unweighted
estimate to .033 after raking.
Further, the range of weights increases dramatically, from a ratio of 22 (0.22 to 4.74) in
the sampling weight to 230 (0.10 to 23) in the raked weight. Figure B6 displays
comparison of the sampling weight to the raked weight, with points identified by age
group to examine whether larger weights are identified with particular age groups. There
are just 16 cases with weights greater than 5. Most of these clustered around 5 and less
than 10 and are primarily respondents in the youngest age group, the group most underrepresented in the sample. Not visible due to the clustering on the x-axis, but 30 cases
have weights below .2, primarily those aged 55 to 64 who are over-represented in the
sample.
37
FIGURE B6: COMPARISON OF KN SAMPLING WEIGHT TO RAKED WEIGHT BY AGE GROUP.
The MSE provides a metric by which to gauge changes in bias relative to increases in
variance. The lower the MSE, the better the weighting adjustments. Figure B7 displays
the MSE of unweighted estimates across distributions of basic demographics involved in
weighting in comparison to the MSE of estimates weighted by the KN sampling weight
and after raking. Although there is a small increase in variance after raking, as would be
expected, the MSE is greatly reduced after raking.
38
0.07
0.06
0.05
0.04
Variance
0.03
MSE
0.02
0.01
0
Unweighted
Sampling Weight
Raked State-Met
Clusters
FIGURE B7: COMPARISON OF MSE AND VARIANCE OF UNWEIGHTED ESTIMATES ACROSS DISTRIBUTION
OF BASIC DEMOGRAPHICS, ESTIMATES WITH KN SAMPLING WEIGHT AND ESTIMATES AFTER RAKING.
Post-Stratification 2: Raking to Demos & Region x Metropolitan Status
Prior to the development of state-metro clusters, the KN survey was post-stratified to
demographic groups of age, race, education, metropolitan status and 9 geographic
regions. These regions were based on the typical conceptual geographic clustering of US
states within census divisions, which are defined independently of the distribution of the
Jewish population. The exception, however, was that the highest density state of New
York, along with the state of Florida were included as separate regions. The nine
geographic clusters were:









New England: CT, ME, MA, NH, RI & VT
Mid-Atlantic: NJ & PA
New York State
Midwest: IL, IN, IA, KS, MI, MN, MO, NE, ND, OH, SD, WI
South Atlantic: DE, DC, GA, MD, NC, SC, VA, WV
Florida
South: AL, AR, KY, LA, MS, OK, TN, TX
Mountain: AZ, CO, ID, NV, NM, UT
West: CA, OR, WA
Rather than refitting the population model using these regions, state-level population
counts from the cross-survey estimates for 2010 were summed to obtain estimated
population counts for each of these nine regions to be used in raking. The key issue is
whether the use of these nine regions with metropolitan status within each reduces bias
without increasing variance to a greater degree than the use of the eight state-metro
39
clusters. Plotting overall MSE by variance across the demographic variables included in
the raking , performs similarly as raking to the 8 state-metro clusters (see Figure B8).
0.07
0.06
0.05
0.04
Variance
0.03
MSE
0.02
0.01
0
Unweighted
Sampling Weight
Raked State-Met
Clusters
Raked 9 Regions
FIGURE B8: COMPARISON OF MSE AND VARIANCE OF UNWEIGHTED ESTIMATES, KN SAMPLING WEIGHT,
8 STATE CLUSTER RAKING, AND 9 REGION RAKING.
Post-Stratification 3: Full Post-Stratification
In many contexts reliable population estimates for all possible combinations of the
demographic variables involved in post-stratification simply are not available. This is
one major motive for raking approaches to post-stratification. Results from the crosssurvey population model, however, can be used as this external source of data. Use of
these data to do full post-stratification was compared to the raking approaches above.
Although the cross-survey model yields estimates of demographic groups at the statelevel, there are simply too few cases in the KN survey sample to be able to estimate
distributions within states. Thus, we compared fully post-stratified weighting estimates
to the raked estimates based on the cross-survey distributions of demographic groups by
the eight state-metropolitan clusters.
Table B18 displays a comparison of the distributions of the weights. The fully poststratified weight has a standard deviation of 1.08 compared to 1.30 and 1.25 for raked
40
weights. Weight ratios (largest relative to smallest weight) are over 100 for all of the
weights and are highest for full post-stratification. Although variance is reduced slightly
under full post-stratification (see Figure B9), the MSE increases compared to the raked
weights, indicating an increase in bias compared to variance. In this context, full poststratification does not appear to be as useful as raking.
TABLE B18: COMPARISON OF THE KN SAMPLING WEIGHT ESTIMATES AND POST-STRATIFICATION
WEIGHT ESTIMATES.
Mean
KN Sampling Weight
Std.
Dev.
Minimum
Maximum
Sum
Weight
Ratio
.88
.50
.22
4.74
788.62
22.02
w/ Post-stratification raking to demographics
and 8 state-metro clusters
1.00
1.30
.09
23.44
894.00
254.85
w/ Post-stratification raking to demographics
and 9 regions
1.00
1.25
.11
18.75
894.00
174.73
w/ Full-postratification to demographics and 8
state-metro clusters
1.00
1.08
.03
15.42
894.00
494.83
41
0.07
0.06
0.05
0.04
Variance
0.03
MSE
0.02
0.01
0
Unweighted
Sampling
Weight
Raked State- Raked 9 Regions Full Post-Strat 8
Met Clusters
Clusters
FIGURE B9: COMPARISON OF MSE AND VARIANCE OF UNWEIGHTED ESTIMATES, KN SAMPLING
ESTIMATES, 8 STATE CLUSTER RAKING, 9 REGION RAKING, AND FULL POST-STRATIFICATION ESTIMATES.
Post-Stratification 4: Next Steps Model-based Estimation
Another alternative to weighting is to employ a model-based approach (cites) which
would be better suited to estimation of smaller cells and would combine the strengths of
the cross-survey model-based analysis method with the development of population
weights within a single survey. There are several methods yet to be explored to
implement this approach. The first is to employ calibration approaches to weighting
rather than the straight raking or post-stratification methods. Calibration offers a modelassisted approach that allows one to find a balance between the constraints and
limitations of the marginal approach of traditional raking and full cross-classification in
post-stratification. Another approach is to employ a fully Bayesian model-based
approach to estimation, including sampling variables in a Bayesian model similar to the
cross-survey population model and post-stratifying to Jewish population distributions
derived from the cross-survey model. A third model-based approach is to incorporate the
KN sample directly into the larger cross-survey data frame. This would enable one to
generate survey-specific estimates of small cells by capitalizing on the borrowing of
strength across all of the surveys that provide data on similar cells as those in the KN
42
sample. In addition, for all of these alternatives whether or not to trim/restrict the
weights to be closer in magnitude to the original sampling weights needs to be explored.
Each of these alternatives will be explored and compared to traditional raking approaches
in future work.
Another issue that needs to be dealt with in post-stratification is the use of fewer race
categories. At the national level an estimated 1.5% of Jews identify as Black nonHispanic and 3% Hispanic and there are significant differences in distributions of the
population by the four categories of race. Within the KN sample, however, only 3 of the
respondents were Black non-Hispanic, far less than 1% of the sample. Pooling these few
cases with the “Other non-Hispanic” group or comparing simply White non-Hispanic to
all others may reduce some of the extreme values for weights as they are currently
defined.
In addition, what geographic identifiers might be best for Jewish population estimate
needs further investigation. Our initial cross-survey models were based to the state-level.
It might be useful, however, to define geographic regions differently. Two alternatives
were examined in post-stratification based on the limitations of the KN sample. Other
alternatives should also be explored. In particular, both the 1990 NJPS and the 2000
NJPS included sampling at the metropolitan level. NJPS 2000, in particular, included
stratification of metropolitan areas based on estimated Jewish population density within
40 major metropolitan areas. For the subset of surveys that provides detailed geographic
identifiers, such stratification at the county level is feasible and could be replicated to
obtain data-based rather than guesstimates of Jewish population density associated with
sampled geographic regions.
Further, knowing how representative the KN sample of Jews is to the larger US
population of Jews on social factors other than basic demographics would be useful.
Although the cross-survey data set provides little data with which to examine the
representativeness of the sample in terms of its Jewish identity (e.g., knowledge of
Hebrew, years of Jewish day school, etc.), there is a critical mass of surveys that would
enable one to examine at least a metric of religious involvement, the frequency of
attending services. Many surveys also assess political ideology and philosophy and it
would be useful to examine how the KN sample compares to the larger US Jewish
population on these measures as well.
Additional Considerations
For analysis of the SAJ 2010 survey two additional factors were taken into consideration.
The first was the handling of the JBO sample, Jews who indicated there religious
identification was none but who identified as Jewish in response to follow-up questions.
The second was the trimming of weights to reduce over influential cases of extreme low
and high weights.
43
Handling of JBO Sample
The JBO sample was most similar to the JBRI group who identified as
Secular/Cultural/Just Jewish/No Religion in response to current denomination. These
two groups were compared on variables included in the post-stratification model. This
was done by regressing the dichotomous indicator (0=JBO, 1=JBRI Sec/Cul/JJ/NR) on
age, race, education, and state-metropolitan cluster. Comparisons were made weighting
each group by the base sampling weight, prior to making post-stratification adjustments.
There were a few marginally significant differences for age, education and statemetropolitan clusters (.05 < p < .01). None of these differences would be significant if
one controlled for the number of tests done with a Bonferroni or other adjustment.
TABLE B19: LOGISTIC REGRESSION JBO VS. JBRI SECULAR ON
AGE, RACE, EDUCATION, STATE AND METROPOLITAN AREA.
Coef.
Std. Err.
t
P>t
1.675
0.458
3.66
0
18-24 years
-1.088
0.890
-1.22
0.222
25-34 years
-1.141
0.842
-1.36
0.175
35-44 years
-1.732
0.802
-2.16
0.031
45-54 years
-1.586
0.663
-2.39
0.017
55-64 years
-0.703
0.567
-1.24
0.215
Non-college
-0.887
0.475
-1.87
0.062
18-24 years
1.125
1.352
0.83
0.406
25-34 years
0.655
0.925
0.71
0.479
35-44 years
1.352
0.867
1.56
0.119
45-54 years
1.731
0.746
2.32
0.021
55-64 years
0.717
0.665
1.08
0.281
Cluster 1
0.707
0.330
2.14
0.033
Cluster 2
0.447
0.535
0.84
0.404
Cluster 3
0.211
0.450
0.47
0.639
Cluster 4
0.220
0.459
0.48
0.632
Cluster 6
0.864
0.413
2.09
0.037
Cluster 7
-0.137
0.295
-0.46
0.644
Cluster 8
0.138
0.371
0.37
0.709
Intercept
To reflect the greater proportion of the full Jewish population who identify as
Secular/Cultural/Just Jewish/No Denomination or Religion, this sample was treated as
other Sec/Cul/JJ/NR respondents and pooled with the full sample for analysis. Poststratification adjustments, raked to demographics and state group clusters as described
above, were done across the full sample of 1,079 to create a full sample post-stratification
weight. The weight was normalized to the sample size. Note that these analyses weights
are not useful for purposes of total population estimation. They can, however, be used to
44
describe relative differences within the sample between secular and non-secularly
identified Jews.
Trimming
To reduce the influence of extremely high or low weights, weights were trimmed such
that the final weight (wif) was no more than twice and no less than half the base weight
(wib), that is .5wib ≤ wif ≤ 2wib. Trimming was done using the TrimWeight function in
the R Survey package (Lumley, 2011). Trimmed weights ranged from .11 to 9.5.
Weight summary
A summary of estimates of outcomes for different weighting scenarios are given in Table
B20.
45
TABLE B20: COMPARISON OF ESTIMATES OF SURVEY OUTCOMES FOR DIFFERENT WEIGHTING SCENARIOS.
Unweighted
N
Prop.
w/ Post-stratification
untrimmed
Sampling Weight
SE
Prop
SE
Deff
Prop
SE
w/ Post-stratification
trimmed
Deff
Prop
SE
Deff
Current Denomination
Sec/Cul/JJ/None
653
0.605
0.015
0.618
0.017
1.32
0.635
0.023
2.43
0.630
0.021
2.14
Reform
218
0.202
0.012
0.190
0.013
1.22
0.172
0.016
2.02
0.175
0.016
1.92
Conservative
152
0.141
0.011
0.139
0.012
1.40
0.134
0.015
2.19
0.136
0.015
2.11
Orthodox
41
0.038
0.006
0.039
0.007
1.33
0.051
0.013
3.47
0.051
0.012
3.00
Reconstructionist
15
0.014
0.004
0.013
0.004
1.13
0.008
0.002
0.74
0.008
0.002
0.75
0 years
321
0.315
0.015
0.316
0.017
1.36
0.343
0.023
2.47
0.347
0.022
2.17
1-6 years
431
0.423
0.015
0.422
0.018
1.33
0.364
0.022
2.15
0.370
0.021
1.88
7 + years
268
0.263
0.014
0.261
0.016
1.38
0.293
0.027
3.60
0.283
0.022
2.52
962
0.935
0.008
0.937
0.008
1.22
0.925
0.011
1.92
0.924
0.011
1.89
67
0.065
0.008
0.063
0.008
1.22
0.075
0.011
1.92
0.076
0.011
1.89
0 years
595
0.656
0.016
0.670
0.018
1.36
0.634
0.029
3.28
0.646
0.024
2.31
1-3 years
199
0.219
0.014
0.209
0.016
1.34
0.246
0.030
4.26
0.232
0.022
2.55
4 + years
113
0.125
0.011
0.122
0.013
1.43
0.120
0.016
2.32
0.122
0.016
2.24
None
455
0.424
0.015
0.439
0.018
1.37
0.437
0.024
2.58
0.441
0.022
2.18
Read letters
344
0.321
0.014
0.319
0.016
1.31
0.333
0.025
3.07
0.325
0.021
2.22
Some of what read
224
0.209
0.012
0.201
0.014
1.37
0.182
0.017
2.19
0.186
0.017
2.08
50
0.047
0.006
0.041
0.006
1.04
0.048
0.008
1.70
0.048
0.008
1.68
Education
Supplementary School
Day School
0 years
1 + years
Camping
Hebrew Comprehension
Most or all of what read
Bar/Bat Mitvah
46
Yes
483
0.530
0.017
0.541
0.019
1.35
0.571
0.026
2.46
0.564
0.023
2.02
345
0.320
0.014
0.320
0.017
1.38
0.315
0.022
2.51
0.319
0.021
2.25
234
0.218
0.013
0.221
0.015
1.35
0.206
0.020
2.56
0.208
0.019
2.34
Brit
167
0.156
0.011
0.149
0.012
1.28
0.134
0.014
1.68
0.137
0.013
1.62
Bar Mitzvah
361
0.338
0.014
0.322
0.016
1.28
0.292
0.020
2.05
0.298
0.019
1.86
Wedding
245
0.229
0.013
0.208
0.014
1.20
0.196
0.016
1.83
0.200
0.016
1.72
Shiva call
385
0.361
0.015
0.353
0.017
1.35
0.324
0.021
2.20
0.330
0.020
1.97
360
0.572
0.02
0.560
0.023
1.31
0.575
0.025
1.65
0.575
0.025
1.62
Synagogue Membership
Yes
Any Synagogue Honors
Yes
Past Year Attend
In-Marriage
Jewish Spouse
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