Brandeis University
Maurice and Marilyn Cohen Center for Modern Jewish Studies
Generation Birthright Israel:
The Impact of an Israel Experience on
Jewish Identity and Choices
Technical Appendices
Leonard Saxe
Benjamin Phillips
Theodore Sasson
Shahar Hecht
Michelle Shain
Graham Wright
Charles Kadushin
October 2009
Table of Contents Appendix 1: Characteristics of Taglit‐Birthright Israel Applicant Population ........................................... 1 Appendix 2: Comparison of Earlier and Later Taglit Cohorts .................................................................... 9 Appendix 3: Methodology ......................................................................................................................... 10 Appendix 4: Tables ..................................................................................................................................... 28 Appendix 5: Survey Instruments.............................................................................................................. 111 Appendix 6: Contact Protocols ................................................................................................................ 125 References ....................................................................................................................................... 147 Appendix 1: Characteristics of Taglit‐Birthright Israel Applicant Population Tables shown in this appendix are based on the achieved sample weighted for nonresponse. Table 1: Sex Number of strata
=
7
Number of PSUs
=
1248
Number of obs
=
Population size
= 21584.067
Design df
=
------------------------------|
birthright
|
participant
|
(corrected)
Female |
no
yes
Total
----------+-------------------0 | 0.483
0.521
0.507
1 | 0.517
0.479
0.493
1.000
1.000
|
Total | 1.000
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(1)
=
1.6578
Design-based
F(1, 1241)
=
0.8501
1 P = 0.3567 1248
1241
Table 2. Age at Time of Survey Number of strata
=
7
Number of PSUs
=
1248
Number of obs
=
Population size
= 21584.067
Design df
=
------------------------------|
birthright
Responden |
t age in
|
2009
|
participant
(corrected)
no
yes
Total
----------+-------------------24 | 0.033
0.089
0.068
25 | 0.043
0.138
0.103
26 | 0.136
0.124
0.128
27 | 0.164
0.137
0.147
28 | 0.125
0.136
0.132
29 | 0.133
0.098
0.111
30 | 0.081
0.083
0.083
31 | 0.095
0.056
0.070
32 | 0.079
0.063
0.069
33 | 0.067
0.044
0.052
34 | 0.045
0.031
0.036
1.000
1.000
|
Total | 1.000
Mean
28.803 27.939
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(10)
=
55.6542
Design-based
F(7.61, 9444.45)=
3.3093
2 P = 0.0011
1248
1241
Table 3. Age at Time of Trip
Number of strata
=
7
Number of PSUs
=
1248
Number of obs
=
Population size
= 21584.067
Design df
=
------------------------------|
birthright
|
participant
RECODE of |
(corrected)
ageattrip |
no
yes
Total
----------+-------------------19 | 0.092
0.170
0.142
20 | 0.149
0.219
0.193
21 | 0.162
0.172
0.168
22 | 0.156
0.151
0.153
23 | 0.083
0.066
0.073
24 | 0.107
0.058
0.076
25 | 0.071
0.049
0.057
26 | 0.081
0.051
0.062
27 | 0.085
0.058
0.068
28 | 0.015
0.005
0.009
Total | 1.000
1.000
1.000
Mean | 22.613 21.735
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(9)
=
43.3217
Design-based
F(7.05, 8744.92)=
3.0695
3 P = 0.0031
1248
1241
Table 4. Ritual Practice Number of strata
=
7
Number of PSUs
=
1206
Number of obs
=
Population size
= 20825.948
Design df
=
------------------------------|
birthright
|
participant
|
(corrected)
hsritual2 |
no
yes
Total
----------+-------------------0 | 0.065
0.060
0.062
1 | 0.041
0.052
0.048
2 | 0.366
0.349
0.355
3 | 0.182
0.166
0.172
4 | 0.346
0.374
0.364
1.000
1.000
|
Total | 1.000
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(4)
=
1.9970
Design-based
F(3.97, 4759.25)=
0.2595
4 P = 0.9028
1206
1199
Table 5. Years of Supplementary Jewish School Number of strata
=
7
Number of PSUs
=
1205
Number of obs
=
Population size
= 20659.276
Design df
=
------------------------------years
|
attended
|
Jewish
|
birthright
supplemen |
participant
tary
|
(corrected)
school
|
no
yes
Total
----------+-------------------0 | 0.401
0.373
0.383
1 | 0.037
0.032
0.034
2 | 0.030
0.028
0.028
3 | 0.030
0.040
0.037
4 | 0.057
0.061
0.060
5 | 0.059
0.062
0.061
6 | 0.062
0.050
0.054
7 | 0.075
0.060
0.065
8 | 0.060
0.084
0.076
9 | 0.018
0.034
0.028
10 | 0.062
0.070
0.067
11 | 0.008
0.025
0.019
12 | 0.100
0.081
0.088
1.000
1.000
|
Total | 1.000
Mean
4.169
4.408
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(12)
Design-based
F(11.26, 13486.26)=
=
13.4234
0.6166
5 P = 0.8201
1205
1198
Table 6. Years of Jewish Day School Number of strata
=
7
Number of PSUs
=
1217
Number of obs
=
1217
Population size
=
21001.33
Design df
=
1210
------------------------------years
|
attended
|
birthright
Jewish
|
participant
day
|
(corrected)
school
|
no
yes
Total
----------+-------------------0 | 0.584
0.648
0.625
1 | 0.027
0.014
0.019
2 | 0.018
0.017
0.018
3 | 0.028
0.019
0.022
4 | 0.027
0.025
0.026
5 | 0.019
0.021
0.020
6 | 0.012
0.017
0.015
7 | 0.017
0.019
0.018
8 | 0.047
0.030
0.036
9 | 0.004
0.018
0.013
10 | 0.002
0.015
0.010
11 | 0.001
0.002
0.002
12 | 0.212
0.156
0.177
1.000
1.000
|
Mean
3.538
2.990
Total | 1.000
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(12)
=
Design-based
F(10.23, 12380.92)=
23.0836
1.2065
6 P = 0.2798
Table 7. Parental Intermarriage Number of strata
=
7
Number of PSUs
=
1222
Number of obs
=
Population size
= 21078.253
Design df
=
------------------------------birthright
|
participant
Parents
|
intermarr |
ied
(corrected)
|
no
yes
Total
----------+-------------------0 | 0.821
0.853
0.841
1 | 0.179
0.147
0.159
1.000
1.000
|
Total | 1.000
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(1)
=
2.1833
Design-based
F(1, 1215)
=
1.0737
7 P = 0.3003
1222
1215
Table 8. Denomination Raised Number of strata
=
7
Number of PSUs
=
1229
Number of obs
=
Population size
= 21223.253
Subpop. no. of obs =
= 21223.253
Design df
=
RECODE of |
relraised |
|
(denomina |
tion
|
birthright
raised
|
participant
(incl.
|
(corrected)
parent))
|
no
yes
Total
----------+-------------------Orthodox | 0.202
0.160
0.175
Conserva | 0.231
0.278
0.261
Reform/R | 0.244
0.231
0.236
Just Jew | 0.247
0.273
0.263
Other | 0.076
0.058
0.065
1.000
1.000
|
Total | 1.000
------------------------------Key:
column proportions
Pearson:
Uncorrected
chi2(4)
=
7.7313
Design-based
F(3.95, 4830.50)=
0.9458
8 1229
Subpop. size
-------------------------------
1
1229
P = 0.4355
1222
Appendix 2: Comparison of Earlier and Later Taglit Cohorts Table 1: Denomination at Time of Application ‐ Participants Winter 20022004
Winter 2007
Winter 2008
Orthodox
23%
5%
3%
Conservative
26%
28%
25%
Reform
21%
41%
43%
Just Jewish
27%
21%
23%
4%
5%
5%
100%
100%
100%
Other
Total
Note: Registration data. Table 2: Ritual Practice During High School Years ‐ Participants Winter 20022004
Winter 2007
Winter 2008
Score=4
33%
18%
12%
Score=3
21%
22%
21%
Score=2
34%
46%
53%
Score=1
6%
6%
7%
Score=0
6%
8%
7%
100%
100%
100%
Total
Note: Survey data. Score is sum of family held or attended a Seder, celebrated Hanukkah, regularly lit Shabbat candles, and kept kosher. “During your high school years, did your family [hold or attend a Seder/celebrate Hanukkah/keep kosher at home?” “During your high school years, did someone in your home regularly light Shabbat candles?” Table 3: Most Intense Form of Jewish Education Received ‐ Participants Winter 20022004
Winter 2007
Winter 2008
Day School
Supplementary
School
35%
22%
16%
48%
61%
62%
None
17%
17%
22%
Total
100%
100%
100%
Note: Survey data. 9 Appendix 3: Methodology Sampling Frame This study focuses on the applicants and participants in the first four years of Taglit’s operation. Earlier rounds were chosen as the focus of this study to make sure that sufficient time would have elapsed since the putative trip experience for the long‐term impact of Taglit to be observable and for individuals to have settled into adult communal roles. The eligible population for this study comprises eligible applicants (both participants and nonparticipants) to Taglit in the winter rounds of 2001, 2002, 2003 and 2004. In the 2001 round, data for nonparticipants was not available so only participants were selected. Applicants to winter rounds in 2002, 2003, and 2004 who did not participate in those rounds were only eligible for the study if that was the last year they applied to Taglit and were eligible for the program. This necessarily excluded those who were nonparticipants in the rounds in question but who later went on Taglit trips after 2004. Because these individuals went on the trip, but did so outside the timeframe of the study, they could neither be considered part of the “control group” or the sample of participants. The study attempts to measure the effect of Taglit 5‐8 years after returning from the trip, so individuals who went on the trip since 2004 are not within the scope of the analysis. The initial round (winter 1999‐2000) was omitted because of the poor quality of registration data. Winter rounds were chosen because of the availability of previous Cohen Center evaluation data (Saxe, et al., 2004; Saxe, et al., 2009; Saxe, et al., 2006b; Saxe, et al., 2007). Table 1. Applicant Pool Winter
Trip Year
2001
2002
2003
2004
Total
Total
Total Eligible Pct. of Total
Applicants Applicants Population
6,235
3,097
14%
14,119
6,618
31%
11,539
3,595
17%
18,060
8,339
39%
49,953
21,649
100%
Note: Winter 2001 includes only information on participants (nonparticipant information is not available). Winter 2001 applicants with missing information on age and sex were excluded from the sampling frame and treated as ineligible. The target population for this study was drawn from the Taglit registration database. While the registration form changed slightly from year to year, applicants were invariably asked to provide some amount of contact information, and in most cases, basic demographic information such as gender, date of birth, and Jewish affiliation. The database only contained data on participants for the first four rounds (winter 2000 to summer 2001) and data on both participants and nonparticipants for subsequent rounds. Because individuals could apply for multiple rounds, and provide different information each time, some individuals had multiple records in the database. In some cases these duplicate records were linked by a common ID number and were easily identified, but in other cases they were not and had to be flagged by 10 manual searching on names and/or other demographic information, such as date of birth. In order to correctly define the frame of this study, it was necessary to select a single record for each applicant from among all duplicate listings in the registration database. A new dataset was created that selected one record from among an individual’s multiple records using the following criteria: 1) If an applicant was a participant in one of the four rounds selected (winter 2001, 2002, 2003, or 2004), the application data from the round they went on was used. 2) If an applicant never went on a Taglit trip, but was an eligible applicant for one of the four rounds selected then the data for the latest of these four rounds that they applied for was used. 3) If an applicant was not an eligible applicant to one of the four rounds selected, their data was not included in the frame. 4) If an applicant went on a round other than one of the four selected, their data was not included in the frame. In creating this dataset, efforts were made to identify duplicate records in the registration database to ensure that the population of nonparticipant applicants to the rounds selected did not later participate in Taglit so as to ensure an untreated control group. However, not all such duplicate cases were identified before the study was begun, and during the course of data collection some individuals were found to be participants in later rounds, or otherwise ineligible for the frame. The section below on ineligible cases explicates this further. Finding respondents in the most mobile years of their lives with contact information ranging from five to eight years old was extremely challenging. To increase the odds of making contact with a respondent, primary and secondary postal addresses were updated using the U.S. Postal Service extended National Change of Address database, which includes all changes of address over a 48‐month period. After this, telephone verification and appends were performed. (Both were carried out by an external data services company, Tower Data.) Sample The target sample size for this survey was set at n=1,200. To assess the adequacy of the sample, a power analysis was conducted for a difference of 10 percent (55 percent vs. 45 percent) on a binary variable between participants and nonparticipants. This corresponds to an effect size (Hedge’s g) of .2, heuristically classified as “small” (Rosenthal, 1994). Rather than a classical power analysis, a Monte Carlo procedure was used to account for survey design effects (the sampling scheme is discussed subsequently). Over 10,000 replications, the sample design was sufficient to detect this difference 82.6 percent of the time at a significance level of .05, thus exceeding the usual 80 percent heuristic for statistical power. The sample that was drawn was designed to represent nonparticipants and participants during the first four years of Taglit’s operation, stratified to overrepresent older participants (who are more likely to be married/have their own families). Cases were eligible for inclusion in the 11 sample if they had a primary or secondary residence in the United States at the time of most‐
recent application to the program, had information on date of birth and sex, and had some contact information. The decision to further restrict the sampling frame to cases resident in the United States at the time of registration was taken in order to exclude an additional extraneous source of variation that may have interfered with the study’s ability to generate valid estimates of program impact. Cases lacking sex and/or date of birth were excluded because these variables were necessary for purposes of stratification, discussed next. Stratification, as used here, ensures that an adequate number of cases will be drawn from key analytic categories. The sampling scheme divided the sampling frame into exhaustive and mutually exclusive strata based on round, participant status, age (less than 30 or 30 and above), and sex. As only information on participants was available for the winter 2001 round, there are only 28 strata rather than the expected 32. The survey was designed to achieve the target sample size of n=1,200. The target values for the stratifying variables were decided upon as follows. For round, n=200 cases (all participants) from winter 2001 and an average of n=333.3 cases from winter 2002, 2003, and 2004 (operationalized as samples of size n=333, n=333, and n=334 respectively). For participant status, the targets were n=800 Taglit participants and n=400 nonparticipants. For age, the target values were evenly divided between cases under the age of 30 in 2009 (n=600) and of age 30 and older in 2009 (n=600). Last, the target sample sizes by sex were also equal (n=600). Because various combinations of allocations of sample to the various strata could achieve the target values of the stratifying variables, it was possible to optimize the sample allocation. The optimization criterion chosen was to minimize the ratio of the highest design weight to the lowest design weight. (Design weight is defined as the ratio of the population size of a given stratum to its sample size.) This method was chosen to minimize the design effect of statistics in the study (defined as the complex sample design variance divided by the variance had the study been a simple random sample). Optimization was carried out using MINLP, a mixed‐
integer nonlinear programming solver (Fletcher & Leyffer, n.d) in an AMPL environment (Fourer, Gay, & Kernighan, 2009): min
max
min
(1)
Subject to: 56
1,200 2
200 333 333 12 334 800 400 600 600 600 600 Where: ,
,…,
is the number of sampled units from the hth stratum 1,2, … ,
1,2 … ,
is the number of units in the hth stratum of the sampling frame r is the round of the survey (2001,2002,2003,2004) p is an indicator of participation in Taglit‐Birthright Israel (0,1) f is an indicator of being female (0,1) a is an indicator of being of age 30 or above (0,1) is the set of positive integers. 13 The resulting optimal sample allocation is shown in Table 2, together with the frame population at the time the sample was drawn (ineligible cases were discovered subsequently) and sample sizes under an even allocation scheme. Table 2. Characteristics of Sampling Strata Round
Status
Nonparticipant
Age
Under
30
30+
2001
Participant
Under
30
30+
Nonparticipant
Under
30
30+
2002
Participant
Under
30
30+
Nonparticipant
Under
30
30+
2003
Participant
Under
30
30+
Nonparticipant
Under
30
30+
2004
Participant
Under
30
30+
Total
Sex
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Frame
pop.
0
0
0
0
786
864
806
643
1,209
1,599
1,034
869
648
570
475
288
477
583
339
269
715
722
331
180
955
892
343
287
2,383
2,607
517
378
21,769
Even allocation
n
Weight
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
50.0
15.7
50.0
17.3
50.0
16.1
50.0
12.9
33.3
36.3
33.3
48.0
33.3
31.0
33.3
26.1
50.0
13.0
50.0
11.4
50.0
9.5
50.0
5.8
33.3
14.3
33.3
17.5
33.3
10.2
33.3
8.1
50.0
14.3
50.0
14.4
50.0
6.6
50.0
3.6
33.3
28.7
33.3
26.8
33.3
10.3
33.3
8.6
50.0
47.7
50.0
52.1
50.0
10.3
50.0
7.6
1,200
18.1
Optimal alloc.
n
Weight
0
0.0
0
0.0
0
0.0
0
0.0
22
35.7
50
17.3
45
17.9
83
7.7
28
43.2
37
43.2
40
25.9
56
15.5
43
15.1
35
16.3
59
8.1
35
8.2
18
26.5
62
9.4
43
7.9
12
22.4
92
7.8
43
16.8
40
8.3
23
7.8
21
45.5
25
35.7
36
9.5
22
13.0
55
43.3
69
37.8
58
8.9
48
7.9
1,200
18.1
Because the survey was carried out in multiple replicates and because response rates varied across strata, it was necessary to recalculate the proportion of the sample available in replicates after the first to attempt to meet the target stratum n’s. This was calculated as follows. For the initial replicate 300, where is the number of cases released in the tth 1,2, … ,5 replicate, cases were assigned to strata in the ratio ( ⁄ ) where the values for 14 were arrived at as described in Eq. 1, where n = 300. For the subsequent four replicates, the number of cases assigned to the hth stratum was calculated as: ,
,
1
1
(2)
Where: was the number of cases fielded in the tth replicate was the desired sample size (completed surveys) for the hth stratum was the number of cases remaining before reaching at replicate t‐1 ,
∑
,
in the hth stratum . The number of cases released in the four later replicates were 600, 600, 600, and 300. However, due to cases that were subsequently determined to be ineligible (see below), the number of eligible cases was 2,266 rather than the expected 2,400 (Table 3). The use of t as a signifier for replicate for may be misleading, because the t‐1th replicate remained in the field after the point at which was calculated, usually a day or two before the release of the tth replicate. This procedure assigned cases to each stratum in inverse proportion to the propensity of response for a given stratum at that point in time. Thus, a stratum that had many cases remaining in order to reach would receive proportionally more cases than a stratum approaching . Table 3. Replicates Replicate Size 1 2 3 4 5 Total 285 566 572 557 286 2266 Launch Reminder Completed date date Surveys 2/11/2009 2/12/2009
162
2/27/2009 3/4/2009
333
3/16/2009 3/18/2009
317
3/30/2009 4/1/2009
294
5/1/2009 5/5/2009
143
1249
Ineligible Cases Some individuals were selected for a replicate, but after the replicate was released were discovered to be ineligible to participate in the study (in some cases, ineligibles were discovered after an interview had been completed). Two types of ineligible cases were identified: 15 Ineligible to participate in the program The study was designed to survey eligible nonparticipant applicants to Taglit in the winter rounds of 2001‐04 and participants on those trips. Sample selection relied on the registration database correctly identifying eligible cases. However, some individuals were initially thought to have been eligible applicants but were later determined not eligible for the program, either because they were too old or young at the time of application, they were not Jewish, or they had some other experience that should have disqualified them from the program that was not correctly identified (or was mislabeled) in the registration database (such as studying at a Jewish seminary in Israel or going on a teen peer trip to Israel). Not included in study time period There were also cases where an individual was thought to be an eligible nonparticipant but was later found to have been a participant on a later round that was not in the scope of the study. Most often this was the result of duplicate entries in the registration database that were not merged together. Although the database was cleaned in an effort to eliminate duplicate entries, some duplicates remain, specifically where duplicates could not be eliminated by a computerized algorithm. For example, a respondent could have applied to the winter 2001 round as “Deborah Cohen” and not gone. Later she applied for the summer 2007 round as “Devorah Cohen” and gone on the trip, but was treated as a different person by the registration database, even though birth date and other demographic information make it clear that both records refer to the same individual. These out of frame cases were first discovered after the survey had been in the field for several weeks and discrepancies were found between survey response regarding participation in the program and information obtained from the registration database. In order to identify all out of frame cases included in the sample, the registration database was manually searched for potential duplicate records. In 58 cases, individuals were found to be participants on rounds that made them ineligible for the study. In nine cases, individuals who were thought to be nonparticipants were found to be participants on one of the rounds included in the study (winter 2001‐2004) or else on one of the summer rounds in the year in question (summer 2001‐2004). Since in these cases the respondent still went on a Taglit trip in the time frame selected, they were considered eligible for the study. In all, 46 individuals who had completed the survey were considered ineligible and removed from the sample (with their responses being discarded) because they were out of frame. An additional 67 nonrespondents were also removed from the sample for the same reason. In addition, six cases were determined to be deceased and were also removed from the sample (Table 4). Response rate and weighting calculations take these reductions in sample size into account. 16 Table 4. Cases Removed from Sample Due to Ineligibility Not included in study time period
Ineligible to participate in the program
Deceased
Total
Respondent
Yes
No
27
31
19
36
0
6
46
74
Total
58
54
6
119
Incentives Respondents were informed that completion of the full survey (either online or over the phone) would earn them a $20 Amazon.com gift code. At the end of the instrument respondents were asked to provide an email address to which the gift code could be sent. Respondents were given the opportunity to opt‐out of receiving a gift card; 29 out of the 1,249 total respondents (2 percent) did so. Pilot Study To test out the survey, instrument, and procedures a small pilot study was conducted before the main study, beginning on January 29, 2009 and ending on February 13, 2009. In all, 210 individuals from the sample frame were randomly selected and contacted by phone via the CATI1 system by Cohen Center staff (no email invitations were sent, all pilot study interviews were conducted over the phone). No additional searching or refusal conversion was attempted, although informants who knew the selected individual were asked to provide contact information, and close relatives were asked to complete the parent survey. After the pilot survey, minor changes were made to the main survey instrument, but not to the parent survey. Of the 34 respondents to the pilot study, 17 also selected for the replicates drawn for the main study. Whenever this was the case, the respondent was not contacted again, and their answers to the pilot study were imported into the data table for the main survey. Field Operations The survey was designed as a dual‐mode telephone and Web survey (a very small number of mail surveys were sent out in exceptional cases). Telephone interviewing was the predominant mode of data collection due to the poor quality of email contact information and the need to leverage any contact with the respondent into an interview. Interviewers were instructed to attempt to complete an interview whenever they made contact with a respondent. If necessary, callbacks were scheduled. While interviewers could send an email containing the unique URL of the survey for a given respondent, they were discouraged from doing so, except where the respondent would not or could not be interviewed over the telephone, to minimize the inevitable drop‐offs that accompany switching modes. Data collection began on February 11, 2009. Interviewing ended on July 31, 2009 and the survey was closed on August 4, 2009. 17 The Web survey was administered using an online instrument, created in LimeSurvey (Schmitz, 2009). 2 All potential respondents were first invited via email to take the survey online, but the majority of respondents were interviewed over the phone. Interviewing was carried out at the Cohen Center by a group of interviewers predominantly comprised of Brandeis University undergraduate and graduate students. All callers attended a training session led by a Cohen Center staff member. One or more full‐time Cohen Center staff supervised calling. Several of the most skilled callers were tasked with attempting refusal conversions. In general, the standard of interviewing was very high. The similar ages of callers and respondents assisted in developing rapport. In view of the characteristics of the target population, it seemed unlikely that professional interviewers would have achieved better results. Several poorly performing interviewers were let go. In cases where a Russian‐speaking parent was reached, interviewers transferred the case to one of several native speakers of Russian on the interviewing staff. In addition, respondents with Russian names were also allocated, where possible, to native Russian‐speaking interviewers. Cohen Center staff created a sophisticated Web‐accessible interface to manage phone interviewing and updating of contact information. Interviewers used the Web survey to read questions and input items. The Web survey was designed with multimode use in mind, with the aim of minimizing mode effects (Dillman, 2007). A short three or four‐question survey (depending on marital status) was used in situations where interviewers reached a parent or other close relative. Even after administering the parent survey, effort was still made to contact the actual respondent to administer the full survey; consequently, some respondents have data for both the parent and full survey. Contact protocol Within each replicate, extensive and diverse efforts were made to attempt to contact potential respondents. Email invitation: An email was sent to the primary (and secondary, if extant) email addresses that the individual provided to Taglit‐Birthright Israel as part of their registration, with a link to the survey. The respondent’s name was piped into the email, and different emails were sent to participants and nonparticipants (see Appendix 4). Bounce calling: Within each replicate, roughly 50 percent of email invitations bounced. Shortly after the invitation was sent, callers attempted to contact those whose email address had bounced. Initial contact attempts were made using phone numbers on file in the Birthright registration database. (See below for in‐depth discussion of calling protocol.) Callers asked any person with whom they spoke to provide contact information for the respondent, and close relatives were also asked to complete the parent survey. Email reminder: Approximately three days after the invitation was sent, an email reminder with a link to the survey was sent to all those who had not responded to the survey and whose email 18 addresses had not bounced. As with the initial invitations, different reminder emails were sent to Taglit participants and nonparticipants. Main calling: Once initial calling of bounced cases concluded, callers moved on to call all remaining nonrespondents using on‐file registration data. Calling protocol: Callers made every effort to locate potential respondents, or any individual who could provide up‐to‐date contact information using the contact data obtained through the registration system and through the data enhancement services. If a potential respondent was busy but wished to complete the survey at a different time, or was temporarily unavailable, a callback was scheduled. If a caller received additional contact information from a parent, roommate, friend, or other acquaintance of the respondent, then that information was entered into the CATI and an attempt to contact the respondent directly was made. If a new email address was obtained, the caller sent a customized email with a link to the survey to that address via the CATI interface. If an answering machine was reached, a voicemail was left. Phone numbers were re‐called multiple times over a period of up to six months and multiple voice mails were left if the number could potentially be linked to the respondent. Most calling was done between the hours of 6 pm and 8 pm (Eastern Time), but callers also tried to reach respondents during the day or late at night, to reach west coast or overseas respondents at convenient times. If a respondent was reached but was unable to complete the survey at that time, a call‐back was scheduled at the time of their choosing. Respondents were also given the phone number of a Cohen Center researcher who they could call at their convenience to complete the survey. A series of scripts and scenarios were provided to callers to cover most eventualities (see Appendix 4). Callers were, however, advised to use their judgment when they reached a person and make appropriate adjustments to the script. Lack of contact information was a more common issue for nonparticipants than for participants, because the data retained in the Taglit registration for nonparticipants in early rounds was significantly inferior to that retained for participants, especially in regards to names or contact information of close relatives, which was often the key to obtaining current contact information for respondents. Searching and additional efforts: If registration data failed to lead to a contact with the respondent, a team of searchers checked online sources for additional contact information for either the respondent or a close relative. This included the subscription‐based online data service sites www.intelius.com, www.emailfinder.com and www.publicrecordspro.com, as well as public white‐pages directories such as www.anywho.com. Searchers also used their own accounts on the social network site www.linkedin.com to locate contact information. Online alumni directories were also used in cases where a member of the research team had access to the directory of a school where a potential respondent was known to have attended. If these sites failed to provide valid contact information, searchers used search engines such as Google to attempt to find any information they could about potential respondent. Sometimes a personal website, wedding announcement, home‐business website or page for an event such as a political campaign was located, and any email addresses or phone numbers provided were used to try to contact the respondent or any other individual who might know how to reach them. 19 Although this was a time‐consuming process, all non‐contact cases that were believed to have bad contact information were searched in this way. If new contact information was found, then additional attempts were made to contact the respondent or an individual who might have additional contact information. In some cases the respondent’s current or former place of work was discovered and contacted in an effort to make contact with the respondent. Some respon‐
dents were located on the social networking site www.facebook.com, using a Facebook account created specifically for the purposes of the study, and were sent an online message via the Facebook interface. Researchers experimented with different message texts until one was found that had a significantly higher rate of response. In a handful of cases, a paper letter was mailed to respondents with a link to the online survey if a family member requested that a paper letter be sent. Refusal conversion: If contact was made with a respondent, but they refused to complete the survey, an attempt at “refusal conversion” was made approximately a month later by specially selected callers. If the respondent could not be persuaded to take the whole survey, they were asked to complete the “parent survey” and their answers to those questions folded into the data for the main instrument. Conversion attempts were only made to “soft” refusals, where it was felt that there was a reasonable chance of convincing the respondent to complete the survey. “Hard” refusals were not called back. 20 Final Dispositions and Outcome Rates Table 5. Final Dispositions and Outcome Rates for Respondent Interviews Participants
Outcome
n
907
59.4%
316
36.7%
1,223
51.2%
Partial Interview
15
1.0%
11
1.3%
26
1.1%
Refusal
69
4.5%
72
8.4%
141
5.9%
397
26.0%
263
30.5%
660
27.6%
9
0.6%
5
0.6%
14
0.6%
Unknown
96
6.3%
107
12.4%
203
8.5%
Deceased
4
0.3%
2
0.2%
6
0.3%
Ineligible
29
1.9%
85
9.9%
114
4.8%
Total Sample
1,526
100.0%
861
100.0%
2,387
100.0%
Cleaned Sample
1,497
776
2,273
Living Sample Est.
1,493
774
2,266
Total Population
14,550
8,840
23,390
Cleaned Population Est.
14,273
7,967
22,241
Living Population Est.
14,233
7,944
22,177
Noncontact
Other
Pct.
Overall
Pct.
Interview
n
Nonparticipants
n
Pct.
AAPOR Outcome Rate
Response Rate 3
60.8%
40.8%
54.0%
Response Rate 4
61.8%
42.3%
55.1%
Cooperation Rate 2
93.6%
80.4%
88.8%
Refusal Rate 2
4.6%
9.3%
6.2%
Contact Rate 2
67.0%
52.2%
61.9%
21 Table 6. Final Dispositions and Outcome Rates for Parent and Respondent Interviews
Participants
Outcome
n
Nonparticipants
Pct.
n
1,080
70.8%
432
50.2%
1,512
63.3%
40
2.6%
58
6.7%
98
4.1%
272
17.8%
178
20.7%
450
18.9%
5
0.3%
3
0.3%
8
0.3%
Unknown
96
6.3%
103
12.0%
199
8.3%
Deceased
4
0.3%
2
0.2%
6
0.3%
Ineligible
29
1.9%
85
9.9%
114
4.8%
Total Sample
1,526
100.0%
861
100.0%
2,387
100.0%
Cleaned Sample
1,497
776
2,273
Living Sample Est.
1,493
774
2,266
Total Population
14,550
8,840
23,390
Cleaned Population Est.
14,273
7,967
22,241
Living Population Est.
14,233
7,944
22,177
Interview
Refusal
Noncontact
Other
Pct.
Overall
n
Pct.
AAPOR Outcome Rate
Response Rate 4
72.4%
55.8%
66.7%
Cooperation Rate 2
96.0%
87.6%
93.4%
Refusal Rate 2
2.7%
7.5%
4.3%
Contact Rate 2
75.4%
63.7%
71.4%
Weighting Design Weights The design weights for a stratified survey are simply the inverse of the probability of selection: (3)
Thus each case is assigned a weight equal to the number of elements in the population of the frame it “represents.” However, the large number of strata (L = 28) proved problematic, as strata were sometimes represented by a single sampling unit in analyses, preventing the calculation of the standard error. Accordingly, strata were collapsed where there was only one difference in response propensity ( ,
.1). 3 This was calculated first by collapsing strata by sex and then by age. 22 This resulted in seven exhaustive and mutually exclusive weighting strata (
1,2, … , ) based on round and participant status. Thus design weights ( ) were calculated as: (4)
Poststratification Weights Having defined the design weights, poststratification weights were then calculated in order to adjust for any differences between the distribution of known characteristics of the sample and known characteristics of the frame (which were derived from the Taglit registration database). In addition to the characteristics used in initial stratification (participant status, age, and sex), information on denomination at time of application to the trip was available for the 2003 and 2004 rounds and part of the 2002 round. (Age was dichotomized into under 30 and 30 and older.) Due to the varying nature of the information available by round, poststratification weights ( ) were created by raking within weighting stratum, where the sum of the weights was set to remain constant. 4 (See p. 24 for a description of raking.) The subscript j (where 1,2, … , ) is used to distinguish poststratification weights, which could vary across cases within weighting stratum, compared to the design weights, , which remained constant within weighting stratum. Within weighting stratum 1 (2001 participants), raking took place on age and sex. Within weighting strata 2 and 3 (2002 participants and nonparticipants), marginal frequencies were raked to population frequencies for age, sex, and denomination at time of trip, where cases with unknown denomination were treated as a separate category. Weighting strata 4 to 7 (2003‐04 participants and nonparticipants) were raked on age, sex, and denomination. These raked weights, using a dichotomized age variable, were the final weights used in all analyses. However, a number of alternative weighting schemes were also considered and rejected, before settling on the final scheme. The alternative schemes were: 1) Unweighted (all cases received a weight of 1). 2) Design weights only. 3) Poststratification weights raked in a similar fashion to the description above, where age was categorized by year rather than being dichotomized. (Alternative raked weights) 4) Poststratification weights calculated within weighting stratum using cell weights on variables for which a significant difference in χ2 in survey response (p ≤ .1) was observed across variable categories. For example, in weighting stratum 3 (2002 participants), cell weights were calculated for the interaction of Orthodox denomination and dichotomized age, both of which were significantly related to survey response. (Cell weights) 23 Raking Raking, also known as sample balancing and iterative proportional fitting (Deming, 1943), is a procedure that adjusts the marginal frequencies of a survey to the known marginal frequencies of a population. For example, one might have a population divided on sex and handedness (left and right) as follows: Handed
R
L
Total
Population
Sex
M
F
.45
.45
.05
.05
.50
.50
Survey
Total
.90
.10
1
Handed
R
L
Total
M
.25
.05
.30
Sex
F
.60
.10
.70
Total
.85
.15
1
Compared to the population, right‐handers are somewhat underrepresented in the survey while left‐handers are somewhat overrepresented. Initially, all right‐handers would receive weights of .90/.85 (c. 1.06), while left‐handers would receive weights of (c. .67). The resulting adjusted table would then be: Handed
R
L
Total
M
.265
.033
.298
Sex
F
.635
.067
.702
Total
.900
.100
Subsequently, sex would be adjusted to match the desired marginal totals, with males receiving an additional weight of .50/.298 (c. 1.678) and females receiving a weight of .5/.702 (c. .712). After this transformation, the weighted frequencies would be: Sex
Handed
M
F Total
R
.444 .453 .897
L
.056 .047 .103
Total
.500 .500
Further raking would yield additional weights of c. 1.003 for men and .971 for women and a marginal frequency of .4998 for men and .5002 for women. Additional iterations could take place until a desired level of precision was reached. (Precision is defined in raking in terms of the sum of the weighted squares of the residuals, the difference between the expected and observed frequency in a cell; Battaglia, Izrael, Hoaglin, & Frankel, 2004; Deming & Stephan, 1940.) The final weights for each cell are approximately 1.783 for male right‐handers, 1.082 for male left‐handers, .757 for female right‐handers, and .459 for female left‐handers.
24 These analyses were conducted before the end of data collection, so the sample size differs from the final report. As the sensitivity analyses included 93.6 percent of the final number of achieved surveys, it was deemed unnecessary to rerun the analyses on the final dataset. Table 7. Characteristics of Weights Weight
Unweighted
Design weights
Alternate raked weights
Cell weights
Final raked weights
n
Mean
1,145
1,145
1,145
1,145
1,145
1.00
18.92
18.92
18.92
18.92
Std.
dev.
.00
9.96
17.43
10.37
16.22
Min
Max
1.00
9.18
2.73
7.48
5.02
1.00
40.78
123.90
41.79
91.99
Max:Min
ratio
1.00
4.44
45.38
5.59
18.32
In calculating the cell weights the differences between respondents and nonrespondents on the known demographic variables from the Taglit registration system were examined. The only statistically significant differences between respondents and nonrespondents were: •
•
For nonparticipants in 2003, nonrespondents were more likely to identify as Orthodox, and less likely to identify as “Just Jewish” than respondents For nonparticipants in 2004, nonrespondents were more likely to be female than respondents Although the cell weights had the most desirable statistical properties among the possible poststratified weighting schemes, with the lowest standard deviation and ratio of maximum to minimum weights, it was decided that statistical significance was an inappropriate criterion due to inadequate statistical power, as substantive differences between respondents and nonrespondents were not always statistically significant. Between the two sets of raked weights, it was decided that the often small number of cases with a particular age in a weighting stratum would lead to exaggerated standard errors and overly influential observations (Battaglia, et al., 2004; Brick, Montaquila, & Roth, 2003). For variables estimated on the entire sample, differences between the poststratified weighting schemes were small. 5 Consequently, the use of cell weights was rejected, and the final raked weights were used. The final weights were also compared to an optimally trimmed version of the final weighting scheme, in addition to the null weights and the design weights. 6 On a sensitivity analysis of 17 variables, the estimates were mostly within 1 to 2 percentage points of one another. The largest variation was observed for the predicted inmarriage rate of nonparticipants, which was estimated at 62 percent using the untrimmed weights and 69 percent using the trimmed weights. The standard errors of the trimmed weights were generally little smaller than those of the untrimmed weights. It was decided that for this application the bias introduced by the trimming was of greater concern than the decline in standard error and the untrimmed weights were accordingly used in all analyses. 25 Calculation of Confidence Intervals Confidence intervals in tables and figures in this report were calculated using Stata’s (2009) survey commands set up for a stratified survey (where the strata are defined as the weighting strata) with simple random sampling within strata with a finite population correction. Any reestimation of these survey data that does not take account of the complex survey design will result in incorrect calculation of confidence intervals (in general, the degree of error will be underestimated). Unweighted reanalyses will result in incorrect point estimates and confidence intervals. Survey Instrument The survey instrument was developed by the Cohen Center and included items used in previous evaluations of Taglit as well as new items intended to measure family status, background, and Jewish engagement. The number of questions was kept to a minimum due to the need to minimize refusals and partial interviews. Full telephone interviews averaged 10 minutes in length, with the shortest full interviews taking 7 minutes and the longest in excess of half an hour; long interviews were a product of talkative respondents, not the length of the instrument. The instrument underwent cognitive testing with a group of nine Jewish young adults resident in the greater Boston area. In January 2009, some items were revised to reflect feedback from the pilot study. As described above, a short three or four‐question survey (hereafter referred to as the “parent survey”) was also created in Lime Survey, and was administered over the phone in cases where contact was made with a close relative or friend of a respondent (Appendix 5). 26 Appendix 3: Notes 1
Computer assisted telephone interviewing 2
Cohen Center staff made some modifications to the source code of Lime Survey before using it for this study (LimeSurvey is open‐source software released under the terms of the GNU General Public License v. 2). These modifications were mainly to allow greater compatibility between Lime Survey and the in‐house CATI and bulk‐email sending systems. 3
This was between younger and older nonparticipants in round 5 (sex having been collapsed prior to this analysis). It was judged that the cell size for maintaining separate strata for older and younger nonparticipants would be insufficient. 4
Raking was carried out using QBAL (Werner, 2003). 5
Differences with respect to choice of spouse were larger. Estimates of the inmarriage rate of nonparticipants were 62.6 percent for the final weights, 64.2 percent for the alternate poststratified weights, and 68.0 percent for the cell weights, with the confidence intervals substantially overlapping. The estimated inmarriage rates for participants were 83.8 percent for the final weights, 84.3 percent for the alternate poststratified weights, and 80.7 percent for the cell weights. 6
The optimal trimming followed the procedures of Phillips (2009). Weights were trimmed at the levels associated with the lowest summed mean square error (MSE) between the weighted and unweighted by compressing weights with a value of greater than the optimum cut‐point of 5 percent the size of the maximum weight by an optimum 85 percent of their value above 5 percent of the maximum weight. This produced a lower MSE than shrinking (all weights were adjusted to the optimum power of .35), shrinking above a margin (weights above the optimum cut‐point of 5 percent of the maximum had their value above 5 percent of the maximum adjusted to the optimum power of .7), optimal trimming (all weights above the optimum cut‐
point of 5 percent of the maximum weight were trimmed to 5 percent of the maximum weight), or various heuristics (trimmed at 4 or 5 times the mean weight, trimmed at 5 or 6 times the interquartile range, trimmed at the 95th percentile, trimmed at the 99th percentile, trimmed at the 5th and 95th percentile, and trimmed at the 1st and 99th percentile). 27 Appendix 4: Tables Table 1: Trip was a “Disappointment” (Estimated Proportions) Survey: Proportion estimation
Number of strata =
Number of PSUs
=
7
902
Number of obs
Population size
Design df
=
902
= 13419.9
=
895
_prop_1: bridppt = not at all
_prop_2: bridppt = a little
_prop_4: bridppt = very much
-------------------------------------------------------------|
Linearized
| Proportion
Std. Err.
[95% Conf. Interval]
-------------+-----------------------------------------------_prop_1 |
.8675276
.0147601
.8385591
.8964961
_prop_2 |
.0701774
.0107575
.0490645
.0912902
somewhat |
.0426801
.009521
.0239941
.0613661
_prop_4 |
.019615
.005957
.0079236
.0313063
--------------------------------------------------------------
Table 2: Trip was “A Life‐Changing Experience” (Estimated Proportions) Survey: Proportion estimation
Number of strata =
Number of PSUs
=
7
904
Number of obs
Population size
Design df
=
904
= 13441.2
=
897
_prop_1: brilife = not at all
_prop_2: brilife = a little
_prop_4: brilife = very much
-------------------------------------------------------------|
Linearized
| Proportion
Std. Err.
[95% Conf. Interval]
-------------+-----------------------------------------------_prop_1 |
.1116681
.0130157
.0861232
.1372129
_prop_2 |
.161381
.0154012
.1311545
.1916075
somewhat |
.2774216
.0182812
.2415426
.3133006
_prop_4 |
.4495294
.0207083
.4088869
.4901718
--------------------------------------------------------------
28
Table 3: Perceived Impact of Trip on Connection to Israel (Estimated Proportion) Survey: Proportion estimation
Number of strata =
Number of PSUs
=
7
906
Number of obs
Population size
Design df
=
906
= 13398.6
=
899
_prop_1: briisrl = not at all
_prop_2: briisrl = a little
_prop_4: briisrl = very much
-------------------------------------------------------------|
Linearized
| Proportion
Std. Err.
[95% Conf. Interval]
-------------+-----------------------------------------------briisrl
|
_prop_1 |
.0296906
.0066282
.016682
.0426992
_prop_2 |
.0713771
.0104631
.0508421
.0919121
somewhat |
.1817351
.0158679
.1505927
.2128775
_prop_4 |
.7171972
.0184302
.681026
.7533684
--------------------------------------------------------------
Table 4: Connection to Israel by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1218
Number of obs
Population size
Design df
------------------------------------------------------A
|
connectio |
n to
|
Israel?
|
To what
|
extent do |
you
|
birthright participant (corrected)
feel...
|
no
yes
Total
----------+-------------------------------------------not at a |
0.074
0.015
0.037
| [0.044,0.122] [0.008,0.027] [0.024,0.055]
|
a little |
0.171
0.099
0.126
| [0.125,0.229] [0.077,0.126] [0.103,0.152]
|
somewhat |
0.253
0.315
0.292
| [0.196,0.321] [0.278,0.353] [0.260,0.326]
|
very muc |
0.502
0.572
0.546
| [0.431,0.573] [0.531,0.611] [0.509,0.582]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.94, 3556.59)=
45.0128
8.4961
29
P = 0.0000
=
1218
= 21020.361
=
1211
Table 5: Minimal Ordinal Logistic Regression Model of Sense of Connection to Israel Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1196
Number of obs
Population size
Design df
F(
3,
1187)
Prob > F
=
1196
= 20635.423
=
1189
=
31.63
=
0.0000
-----------------------------------------------------------------------------|
Linearized
conisr | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
3.747548
1.523637
3.25
0.001
1.687806
8.320932
hsritual2 |
2.142655
.2655062
6.15
0.000
1.680229
2.732348
hsritualpart |
.7261582
.1002781
-2.32
0.021
.5538161
.9521315
-------------+---------------------------------------------------------------/cut1 | -1.165281
.4306308
-2.71
0.007
-2.010162
-.3204001
/cut2 |
.5579482
.3774834
1.48
0.140
-.1826597
1.298556
/cut3 |
2.19167
.3713991
5.90
0.000
1.463
2.920341
------------------------------------------------------------------------------
Table 6: Sense of Connection to Israel by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0 hsritual2=`h' hsritualpart=0)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
0
0.0376
0.1418
0.3489
0.4717
hsritual2
2.7275419
95% Conf. Interval
[ 0.0189,
0.0562]
[ 0.1004,
0.1832]
[ 0.3039,
0.3939]
[ 0.3960,
0.5475]
hsritualpart
0
. prvalue, x(participant=1 hsritual2=`h' hsritualpart=`h')
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
1
0.0243
0.0982
0.2945
0.5830
hsritual2
2.7275419
95% Conf. Interval
[ 0.0144,
0.0342]
[ 0.0766,
0.1198]
[ 0.2588,
0.3301]
[ 0.5429,
0.6231]
hsritualpart
2.7275419
30
Table 7: Sense of Connection to Israel by Taglit Participation and High School Ritual Practice (Predicted Probabilities) ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
0
0.2377
0.3983
0.2635
0.1005
hsritual2
0
95% Conf. Interval
[ 0.0848,
0.3906]
[ 0.3193,
0.4772]
[ 0.1541,
0.3729]
[ 0.0347,
0.1663]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=1 hsritualpart=0)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
0
0.1270
0.3221
0.3577
0.1932
hsritual2
1
95% Conf. Interval
[ 0.0529,
0.2012]
[ 0.2375,
0.4067]
[ 0.2921,
0.4233]
[ 0.1125,
0.2738]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=2 hsritualpart=0)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
0
0.0636
0.2120
0.3853
0.3390
hsritual2
2
95% Conf. Interval
[ 0.0310,
0.0962]
[ 0.1532,
0.2709]
[ 0.3433,
0.4274]
[ 0.2604,
0.4177]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=3 hsritualpart=0)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
0
0.0307
0.1201
0.3256
0.5236
hsritual2
3
95% Conf. Interval
[ 0.0153,
0.0462]
[ 0.0829,
0.1573]
[ 0.2780,
0.3732]
[ 0.4460,
0.6012]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=4 hsritualpart=0)
31
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
0
0.0146
0.0620
0.2215
0.7019
hsritual2
4
95% Conf. Interval
[ 0.0060,
0.0232]
[ 0.0346,
0.0893]
[ 0.1591,
0.2839]
[ 0.6111,
0.7928]
hsritualpart
0
. prvalue, x(participant=1 hsritual2=0 hsritualpart=0)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
1
0.0768
0.2411
0.3869
0.2951
hsritual2
0
95% Conf. Interval
[ 0.0413,
0.1123]
[ 0.1814,
0.3009]
[ 0.3438,
0.4300]
[ 0.2243,
0.3660]
hsritualpart
0
. prvalue, x(participant=1 hsritual2=1 hsritualpart=1)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
1
0.0508
0.1798
0.3750
0.3945
hsritual2
1
95% Conf. Interval
[ 0.0293,
0.0722]
[ 0.1391,
0.2205]
[ 0.3322,
0.4178]
[ 0.3365,
0.4525]
hsritualpart
1
. prvalue, x(participant=1 hsritual2=2 hsritualpart=2)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
1
0.0332
0.1282
0.3351
0.5034
hsritual2
2
95% Conf. Interval
[ 0.0198,
0.0466]
[ 0.1012,
0.1553]
[ 0.2961,
0.3742]
[ 0.4599,
0.5469]
hsritualpart
2
. prvalue, x(participant=1 hsritual2=3 hsritualpart=3)
ologit: Predictions for conisr
Confidence intervals by delta method
32
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
1
0.0216
0.0885
0.2779
0.6120
hsritual2
3
95% Conf. Interval
[ 0.0127,
0.0306]
[ 0.0682,
0.1088]
[ 0.2428,
0.3130]
[ 0.5708,
0.6531]
hsritualpart
3
. prvalue, x(participant=1 hsritual2=4 hsritualpart=4)
ologit: Predictions for conisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
participant
1
0.0140
0.0597
0.2158
0.7105
hsritual2
4
95% Conf. Interval
[ 0.0077,
0.0203]
[ 0.0423,
0.0770]
[ 0.1782,
0.2535]
[ 0.6608,
0.7601]
hsritualpart
4
Table 8: Standardized Ordinal Logistic Regression Model of Sense of Connection to Israel Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1167
Number of obs
Population size
Design df
F(
8,
1153)
Prob > F
=
1167
= 19965.619
=
1160
=
12.35
=
0.0000
-----------------------------------------------------------------------------|
Linearized
conisr | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.718551
.3135338
2.97
0.003
1.201452
2.458208
female |
.75746
.1201263
-1.75
0.080
.5549142
1.033936
age |
.9445166
.0635685
-0.85
0.397
.8276784
1.077848
tripage |
1.05502
.077843
0.73
0.468
.9128317
1.219357
hsritual2 |
1.366134
.0995079
4.28
0.000
1.184207
1.576009
supschyrs1 |
1.003839
.0208545
0.18
0.854
.9637446
1.045601
dayschyrs1 |
1.102413
.02545
4.22
0.000
1.053594
1.153494
parintmar |
.5498957
.1205743
-2.73
0.006
.3576387
.845505
-------------+---------------------------------------------------------------/cut1 | -2.789903
.8770023
-3.18
0.002
-4.510592
-1.069215
/cut2 | -1.040962
.8595716
-1.21
0.226
-2.727451
.645527
/cut3 |
.6579665
.8529969
0.77
0.441
-1.015623
2.331556
------------------------------------------------------------------------------
33
Table 9: Confidence in Ability to Describe Situation in Israel by Participant (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1231
Number of obs
Population size
Design df
------------------------------------------------------If
|
someone
|
asked you |
about the |
current
|
situation |
in
|
Israel,
|
how
|
confident |
birthright participant (corrected)
you fe
|
no
yes
Total
----------+-------------------------------------------not at a |
0.196
0.101
0.136
| [0.145,0.260] [0.081,0.126] [0.113,0.164]
|
a little |
0.293
0.246
0.263
| [0.232,0.362] [0.214,0.281] [0.232,0.297]
|
somewhat |
0.294
0.430
0.381
| [0.234,0.362] [0.391,0.471] [0.346,0.416]
|
very con |
0.217
0.222
0.220
| [0.165,0.279] [0.190,0.258] [0.192,0.251]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.99, 3661.71)=
35.2618
6.2328
34
P = 0.0003
=
1231
= 21368.462
=
1224
Table 10: Minimal Ordinal Logistic Regression Model of Confidence in Ability to Describe the Situation in Israel Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1200
Number of obs
Population size
Design df
F(
3,
1191)
Prob > F
=
1200
= 20769.048
=
1193
=
13.17
=
0.0000
-----------------------------------------------------------------------------|
Linearized
cnfdntisr | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
3.930356
1.401647
3.84
0.000
1.952394
7.912184
hsritual2 |
1.630038
.1743858
4.57
0.000
1.321422
2.010732
hsritualpart |
.7268122
.0878649
-2.64
0.008
.573344
.9213595
-------------+---------------------------------------------------------------/cut1 | -.2866385
.3210877
-0.89
0.372
-.916598
.343321
/cut2 |
1.243213
.3170461
3.92
0.000
.6211833
1.865243
/cut3 |
2.984872
.3338695
8.94
0.000
2.329836
3.639909
------------------------------------------------------------------------------
Table 11: Confidence in Ability to Describe the Situation in Israel (Predicted Probabilities) . prvalue, x(participant=0 hsritual2=`h' hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
0
0.1653
0.3123
0.3615
0.1608
hsritual2
2.7275419
95% Conf. Interval
[ 0.1214,
0.2092]
[ 0.2677,
0.3570]
[ 0.3189,
0.4042]
[ 0.1195,
0.2022]
hsritualpart
0
. prvalue, x(participant=1 hsritual2=`h' hsritualpart=`h')
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
1
0.1074
0.2497
0.4031
0.2398
hsritual2
2.7275419
95% Conf. Interval
[ 0.0856,
0.1292]
[ 0.2179,
0.2815]
[ 0.3661,
0.4401]
[ 0.2074,
0.2722]
hsritualpart
2.7275419
35
Table 12: Confidence in Ability to Describe the Situation in Israel by Taglit Participation and High School Ritual Practice (Predicted Probabilities) . prvalue, x(participant=0 hsritual2=0 hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
0
0.4288
0.3473
0.1758
0.0481
hsritual2
0
95% Conf. Interval
[ 0.2747,
0.5830]
[ 0.2847,
0.4099]
[ 0.0962,
0.2554]
[ 0.0181,
0.0781]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=1 hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
0
0.3153
0.3648
0.2437
0.0761
hsritual2
1
95% Conf. Interval
[ 0.2159,
0.4148]
[ 0.3205,
0.4092]
[ 0.1773,
0.3101]
[ 0.0424,
0.1098]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=2 hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
0
0.2203
0.3458
0.3155
0.1184
hsritual2
2
95% Conf. Interval
[ 0.1618,
0.2788]
[ 0.3009,
0.3907]
[ 0.2668,
0.3641]
[ 0.0824,
0.1544]
hsritualpart
0
. prvalue, x(participant=0 hsritual2=3 hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
0
0.1477
0.2968
0.3758
0.1796
hsritual2
3
95% Conf. Interval
[ 0.1064,
0.1891]
[ 0.2513,
0.3423]
[ 0.3338,
0.4178]
[ 0.1339,
0.2253]
hsritualpart
0
36
. prvalue, x(participant=0 hsritual2=4 hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
0
0.0961
0.2332
0.4077
0.2630
hsritual2
4
95% Conf. Interval
[ 0.0585,
0.1338]
[ 0.1767,
0.2898]
[ 0.3691,
0.4462]
[ 0.1851,
0.3409]
hsritualpart
0
. prvalue, x(participant=1 hsritual2=0 hsritualpart=0)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
1
0.1604
0.3083
0.3656
0.1657
hsritual2
0
95% Conf. Interval
[ 0.1117,
0.2091]
[ 0.2589,
0.3576]
[ 0.3182,
0.4130]
[ 0.1179,
0.2136]
hsritualpart
0
. prvalue, x(participant=1 hsritual2=1 hsritualpart=1)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
1
0.1388
0.2879
0.3827
0.1905
hsritual2
1
95% Conf. Interval
[ 0.1050,
0.1727]
[ 0.2466,
0.3293]
[ 0.3435,
0.4219]
[ 0.1510,
0.2301]
hsritualpart
1
. prvalue, x(participant=1 hsritual2=2 hsritualpart=2)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
1
0.1198
0.2661
0.3961
0.2180
hsritual2
2
95% Conf. Interval
[ 0.0952,
0.1444]
[ 0.2319,
0.3003]
[ 0.3592,
0.4329]
[ 0.1853,
0.2508]
hsritualpart
2
. prvalue, x(participant=1 hsritual2=3 hsritualpart=3)
ologit: Predictions for cnfdntisr
37
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
1
0.1030
0.2436
0.4051
0.2483
hsritual2
3
95% Conf. Interval
[ 0.0815,
0.1246]
[ 0.2117,
0.2754]
[ 0.3679,
0.4423]
[ 0.2143,
0.2823]
hsritualpart
3
. prvalue, x(participant=1 hsritual2=4 hsritualpart=4)
ologit: Predictions for cnfdntisr
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_con|x):
x=
participant
1
0.0884
0.2209
0.4095
0.2813
hsritual2
4
95% Conf. Interval
[ 0.0657,
0.1110]
[ 0.1848,
0.2569]
[ 0.3718,
0.4471]
[ 0.2340,
0.3285]
hsritualpart
4
Table 13: Standardized Ordinal Logistic Regression Model of Confidence in Ability to Describe the Situation in Israel Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1171
Number of obs
Population size
Design df
F(
8,
1157)
Prob > F
=
1171
= 20099.244
=
1164
=
16.27
=
0.0000
-----------------------------------------------------------------------------|
Linearized
cnfdntisr | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.704219
.28475
3.19
0.001
1.227875
2.365357
female |
.2901681
.0414498
-8.66
0.000
.2192456
.3840327
age |
.8393819
.0509025
-2.89
0.004
.7452236
.9454371
tripage |
1.22146
.0789537
3.09
0.002
1.075972
1.386619
hsritual2 |
1.312349
.0973906
3.66
0.000
1.134528
1.518041
supschyrs1 |
.9660163
.017624
-1.90
0.058
.9320496
1.001221
dayschyrs1 |
1.005415
.0186159
0.29
0.771
.9695456
1.042611
parintmar |
.7886681
.1631809
-1.15
0.251
.5255226
1.183579
-------------+---------------------------------------------------------------/cut1 | -2.342125
.8362316
-2.80
0.005
-3.982815
-.701435
/cut2 | -.7225808
.8326879
-0.87
0.386
-2.356318
.9111562
/cut3 |
1.193039
.8397793
1.42
0.156
-.4546118
2.840689
------------------------------------------------------------------------------
38
Table 14: Use of Israeli News Sources by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1241
Number of obs
Population size
Design df
=
1241
= 21483.964
=
1234
------------------------------------------------------Israeli
|
news
|
sources. |
Have you |
used any |
of the
|
following |
to keep
|
track of |
birthright participant (corrected)
events
|
no
yes
Total
----------+-------------------------------------------not sele |
0.693
0.581
0.622
| [0.625,0.753] [0.541,0.620] [0.587,0.656]
|
yes |
0.307
0.419
0.378
| [0.247,0.375] [0.380,0.459] [0.344,0.413]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1234)
=
=
15.1486
7.6751
P = 0.0057
Table 15: Minimal Ordinal Logistic Regression Model of Use of Israeli News Sources Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
1241
Number of obs
Population size
Design df
F(
1,
1234)
Prob > F
=
1241
= 21483.964
=
1234
=
7.62
=
0.0059
-----------------------------------------------------------------------------|
Linearized
newsrcisra~i | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.622122
.2842554
2.76
0.006
1.150205
2.287661
------------------------------------------------------------------------------
39
Table 16: Use of Israeli News Sources by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0)
logit: Predictions for newsrcisraeli
Confidence intervals by delta method
Pr(y=yes|x):
Pr(y=not_sele|x):
x=
0.3074
0.6926
95% Conf. Interval
[ 0.2430,
0.3718]
[ 0.6282,
0.7570]
participant
0
. prvalue, x(participant=1)
logit: Predictions for newsrcisraeli
Confidence intervals by delta method
Pr(y=yes|x):
Pr(y=not_sele|x):
x=
0.4186
0.5814
95% Conf. Interval
[ 0.3790,
0.4582]
[ 0.5418,
0.6210]
participant
1
Table 17: Standardized Ordinal Logistic Regression Model of Use of Israeli News Sources Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
1177
Number of obs
Population size
Design df
F(
8,
1163)
Prob > F
=
1177
= 20156.144
=
1170
=
6.66
=
0.0000
-----------------------------------------------------------------------------|
Linearized
newsrcisra~i | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.954558
.3880578
3.38
0.001
1.323964
2.8855
female |
.4671763
.0784852
-4.53
0.000
.3359941
.649576
age |
.8476626
.0625077
-2.24
0.025
.7334818
.9796179
tripage |
1.173903
.0939622
2.00
0.045
1.003297
1.373521
hsritual2 |
1.314603
.1131133
3.18
0.002
1.110397
1.556363
supschyrs1 |
.9598374
.0215474
-1.83
0.068
.918479
1.003058
dayschyn |
1.548943
.3626937
1.87
0.062
.9783974
2.452197
parintmar |
.8897676
.2110577
-0.49
0.623
.5586733
1.417083
------------------------------------------------------------------------------
40
Table 18: Live in Israel by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1241
Number of obs
Population size
Design df
------------------------------------------------------RECODE of |
country
|
(What
|
country
|
do you
|
currently |
birthright participant (corrected)
live in?) |
no
yes
Total
----------+-------------------------------------------0 |
0.964
0.975
0.971
| [0.914,0.985] [0.962,0.984] [0.954,0.982]
|
1 |
0.036
0.025
0.029
| [0.015,0.086] [0.016,0.038] [0.018,0.046]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1234)
=
=
1.3277
0.5756
41
P = 0.4482
=
1241
= 21483.964
=
1234
Table 19: Traveled to Israel since Taglit by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1224
Number of obs
Population size
Design df
=
=
=
1224
21156.25
1217
------------------------------------------------------been to
|
Israel
|
since
|
applying |
to
|
Birthrigh |
birthright participant (corrected)
t
|
no
yes
Total
----------+-------------------------------------------0 |
0.651
0.599
0.618
| [0.579,0.716] [0.559,0.638] [0.582,0.653]
|
1 |
0.349
0.401
0.382
| [0.284,0.421] [0.362,0.441] [0.347,0.418]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1217)
=
=
3.2111
1.5744
P = 0.2098
Table 20: Perceived Impact of trip on their Connection to Jewish Heritage (Estimated Proportions) Survey: Proportion estimation
Number of strata =
Number of PSUs
=
7
906
Number of obs
Population size
Design df
=
906
= 13455.9
=
899
_prop_1: brijew = not at all
_prop_2: brijew = a little
_prop_4: brijew = very much
-------------------------------------------------------------|
Linearized
| Proportion
Std. Err.
[95% Conf. Interval]
-------------+-----------------------------------------------brijew
|
_prop_1 |
.0624522
.0095399
.0437293
.0811752
_prop_2 |
.0939226
.0120072
.0703572
.1174881
somewhat |
.2383245
.0171482
.2046693
.2719797
_prop_4 |
.6053007
.0199798
.5660881
.6445133
--------------------------------------------------------------
42
Table 21: Sense of Connection to a Worldwide Jewish Community by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1213
Number of obs
Population size
Design df
------------------------------------------------------A
|
connectio |
n to a
|
worldwide |
Jewish
|
community |
? To what |
extent do |
you
|
birthright participant (corrected)
feel...
|
no
yes
Total
----------+-------------------------------------------not at a |
0.050
0.022
0.032
| [0.026,0.093] [0.014,0.037] [0.021,0.049]
|
a little |
0.156
0.122
0.135
| [0.113,0.211] [0.098,0.151] [0.112,0.161]
|
somewhat |
0.342
0.355
0.350
| [0.277,0.413] [0.317,0.394] [0.316,0.385]
|
very muc |
0.453
0.501
0.483
| [0.383,0.524] [0.460,0.541] [0.446,0.519]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.99, 3600.65)=
10.2913
1.8583
43
P = 0.1347
=
=
=
1213
20930.85
1206
Table 22: Minimal Ordinal Logistic Regression Model of Sense of Connection to a Worldwide Jewish Community Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1213
Number of obs
Population size
Design df
F(
1,
1206)
Prob > F
=
=
=
=
=
1213
20930.85
1206
2.75
0.0976
-----------------------------------------------------------------------------|
Linearized
conwrldjcomm | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.30411
.2088608
1.66
0.098
.9524727
1.785567
-------------+---------------------------------------------------------------/cut1 | -3.232944
.2606312
-12.40
0.000
-3.744285
-2.721603
/cut2 | -1.440236
.1559108
-9.24
0.000
-1.746123
-1.13435
/cut3 |
.2385205
.1435796
1.66
0.097
-.0431731
.5202142
------------------------------------------------------------------------------
Table 23: Sense of Connection to a Worldwide Jewish Community by Taglit Participation (Predicted Probabilities) ologit: Predictions for conwrldjcomm
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0379
0.1536
0.3678
0.4407
95% Conf. Interval
[ 0.0193,
0.0566]
[ 0.1154,
0.1917]
[ 0.3254,
0.4103]
[ 0.3713,
0.5100]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for conwrldjcomm
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0294
0.1244
0.3395
0.5067
95% Conf. Interval
[ 0.0171,
0.0416]
[ 0.1000,
0.1487]
[ 0.3045,
0.3746]
[ 0.4675,
0.5460]
participant
1
44
Table 24: Standardized Ordinal Logistic Regression of Sense of Connection to a Worldwide Jewish Community Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1162
Number of obs
Population size
Design df
F(
8,
1148)
Prob > F
=
1162
= 19876.108
=
1155
=
8.42
=
0.0000
-----------------------------------------------------------------------------|
Linearized
conwrldjcomm | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.362714
.2444627
1.73
0.085
.9583942
1.937605
female |
.9776329
.1529762
-0.14
0.885
.7191895
1.328949
age |
.9125275
.0620297
-1.35
0.178
.7985907
1.04272
tripage |
1.058226
.0768338
0.78
0.436
.9177221
1.220242
hsritual2 |
1.150507
.0888757
1.81
0.070
.988703
1.338792
supschyrs1 |
1.027741
.0213594
1.32
0.188
.9866765
1.070515
dayschyn |
3.340447
.8738882
4.61
0.000
1.999354
5.581096
parintmar |
.6772634
.1458119
-1.81
0.071
.4439206
1.033261
-------------+---------------------------------------------------------------/cut1 | -4.037228
.9545137
-4.23
0.000
-5.910003
-2.164453
/cut2 | -2.207416
.911377
-2.42
0.016
-3.995556
-.4192764
/cut3 | -.3711335
.906411
-0.41
0.682
-2.14953
1.407263
------------------------------------------------------------------------------
45
Table 25: Sense of Connection to Jewish Customs and Traditions by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1215
Number of obs
Population size
Design df
------------------------------------------------------A
|
connectio |
n to
|
Jewish
|
customs
|
and
|
tradition |
s? To
|
what
|
extent do |
you
|
birthright participant (corrected)
feel...
|
no
yes
Total
----------+-------------------------------------------not at a |
0.042
0.036
0.038
| [0.019,0.090] [0.023,0.057] [0.025,0.058]
|
a little |
0.100
0.121
0.113
| [0.067,0.147] [0.098,0.148] [0.093,0.136]
|
somewhat |
0.329
0.295
0.307
| [0.266,0.398] [0.261,0.331] [0.275,0.341]
|
very muc |
0.530
0.548
0.541
| [0.458,0.600] [0.508,0.587] [0.505,0.577]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.93, 3533.89)=
2.5453
0.4165
46
P = 0.7362
=
1215
= 20957.627
=
1208
Table 26: Minimal Ordinal Logistic Regression Model of Sense of Connection to Jewish Customs and Traditions Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1215
Number of obs
Population size
Design df
F(
1,
1208)
Prob > F
=
1215
= 20957.627
=
1208
=
0.05
=
0.8171
-----------------------------------------------------------------------------|
Linearized
conjtrad | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.037568
.1654596
0.23
0.817
.758823
1.418707
-------------+---------------------------------------------------------------/cut1 | -3.199231
.256427
-12.48
0.000
-3.702323
-2.696139
/cut2 | -1.700694
.152885
-11.12
0.000
-2.000643
-1.400744
/cut3 | -.1422629
.1394507
-1.02
0.308
-.4158554
.1313295
------------------------------------------------------------------------------
Table 27: Sense of Connection to Jewish Customs and Traditions by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for conjtrad
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0392
0.1152
0.3101
0.5355
95% Conf. Interval
[ 0.0203,
0.0581]
[ 0.0860,
0.1443]
[ 0.2666,
0.3537]
[ 0.4675,
0.6035]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for conjtrad
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0378
0.1118
0.3057
0.5447
95% Conf. Interval
[ 0.0222,
0.0535]
[ 0.0887,
0.1349]
[ 0.2723,
0.3392]
[ 0.5047,
0.5847]
participant
1
47
Table 28: Standardized Ordinal Logistic Regression Model of Sense of Connection to Jewish Customs and Traditions Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1165
Number of obs
Population size
Design df
F(
8,
1151)
Prob > F
=
1165
= 19916.853
=
1158
=
19.43
=
0.0000
-----------------------------------------------------------------------------|
Linearized
conjtrad | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.091073
.1820572
0.52
0.602
.7864539
1.513682
female |
2.23151
.3505052
5.11
0.000
1.639683
3.03695
age |
.9714242
.0713563
-0.39
0.693
.841043
1.122017
tripage |
1.037948
.0758087
0.51
0.610
.8993764
1.197871
hsritual2 |
1.494784
.1190346
5.05
0.000
1.278567
1.747566
supschyrs1 |
1.060831
.0214812
2.92
0.004
1.019511
1.103826
dayschyrs1 |
1.159366
.0301563
5.69
0.000
1.101683
1.220069
parintmar |
.724374
.1667248
-1.40
0.161
.4611496
1.137847
-------------+---------------------------------------------------------------/cut1 | -1.478359
.9811932
-1.51
0.132
-3.403475
.4467561
/cut2 |
.0869848
.9622895
0.09
0.928
-1.801041
1.975011
/cut3 |
1.973675
.9642331
2.05
0.041
.0818352
3.865514
------------------------------------------------------------------------------
48
Table 29: Sense of Connection to the Local Jewish Community by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1214
Number of obs
Population size
Design df
------------------------------------------------------A
|
connectio |
n to the |
Jewish
|
community |
where you |
live? To |
what
|
extent do |
birthright participant (corrected)
you...
|
no
yes
Total
----------+-------------------------------------------not at a |
0.157
0.139
0.146
| [0.113,0.216] [0.114,0.170] [0.122,0.174]
|
a little |
0.143
0.179
0.166
| [0.100,0.200] [0.151,0.211] [0.141,0.194]
|
somewhat |
0.309
0.311
0.310
| [0.247,0.379] [0.275,0.349] [0.277,0.345]
|
very muc |
0.391
0.371
0.378
| [0.325,0.462] [0.332,0.411] [0.344,0.414]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(3.00, 3618.98)=
3.1436
0.5361
49
P = 0.6575
=
1214
= 20969.505
=
1207
Table 30: Standardized Ordinal Logistic Regression Model of Clear Sense of Jewish Background Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1214
Number of obs
Population size
Design df
F(
1,
1207)
Prob > F
=
1214
= 20969.505
=
1207
=
0.13
=
0.7194
-----------------------------------------------------------------------------|
Linearized
conlocaljc~m | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
.9461907
.1456511
-0.36
0.719
.699549
1.279792
-------------+---------------------------------------------------------------/cut1 | -1.801708
.1603289
-11.24
0.000
-2.116263
-1.487154
/cut2 | -.8280305
.1418285
-5.84
0.000
-1.106288
-.5497728
/cut3 |
.4614745
.137605
3.35
0.001
.1915028
.7314461
------------------------------------------------------------------------------
Table 31: Sense of Connection to the Local Jewish Community by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for conlocaljcomm
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.1416
0.1624
0.3093
0.3866
95% Conf. Interval
[ 0.1034,
0.1798]
[ 0.1298,
0.1950]
[ 0.2753,
0.3433]
[ 0.3227,
0.4506]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for conlocaljcomm
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.1485
0.1674
0.3105
0.3736
95% Conf. Interval
[ 0.1218,
0.1752]
[ 0.1401,
0.1947]
[ 0.2766,
0.3444]
[ 0.3350,
0.4122]
participant
1
50
Table 32: Standardized Ordinal Logistic Regression Model of Sense of Connection to the Local Jewish Community Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1164
Number of obs
Population size
Design df
F(
8,
1150)
Prob > F
=
1164
= 19928.731
=
1157
=
7.30
=
0.0000
-----------------------------------------------------------------------------|
Linearized
conlocaljc~m | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.017275
.1612158
0.11
0.914
.7454185
1.388278
female |
1.017116
.1473867
0.12
0.907
.765414
1.351588
age |
1.003941
.0621733
0.06
0.949
.8890761
1.133647
tripage |
.9979189
.0684393
-0.03
0.976
.8722822
1.141651
hsritual2 |
1.218225
.0862472
2.79
0.005
1.060233
1.39976
supschyrs1 |
1.034587
.0202413
1.74
0.082
.9956257
1.075073
dayschyrs1 |
1.108173
.0239312
4.76
0.000
1.0622
1.156135
parintmar |
.9188461
.1873495
-0.42
0.678
.6158913
1.370823
-------------+---------------------------------------------------------------/cut1 | -.8400347
.7717531
-1.09
0.277
-2.354227
.6741576
/cut2 |
.2047962
.7706648
0.27
0.790
-1.307261
1.716853
/cut3 |
1.601692
.77596
2.06
0.039
.0792456
3.124138
------------------------------------------------------------------------------
51
Table 33: Feel Good about Jewish Heritage by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1204
Number of obs
Population size
Design df
=
1204
= 20771.728
=
1197
------------------------------------------------------AGREE or |
DISAGREE. |
I feel
|
good
|
about my |
Jewish
|
birthright participant (corrected)
heritage. |
no
yes
Total
----------+-------------------------------------------strongly |
0.006
0.007
0.006
| [0.002,0.019] [0.003,0.013] [0.004,0.011]
|
somewhat |
0.012
0.014
0.014
| [0.004,0.035] [0.007,0.028] [0.008,0.024]
|
somewhat |
0.192
0.202
0.198
| [0.142,0.254] [0.172,0.237] [0.171,0.229]
|
strongly |
0.790
0.777
0.782
| [0.727,0.842] [0.741,0.809] [0.750,0.810]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.70, 3228.19)=
0.3069
0.0726
P = 0.9658
Table 34: Minimal Ordinal Logistic Regression Model of Feel Good about Jewish Heritage Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1204
Number of obs
Population size
Design df
F(
1,
1197)
Prob > F
=
1204
= 20771.728
=
1197
=
0.15
=
0.6955
-----------------------------------------------------------------------------|
Linearized
meimfeelgood | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
.9246341
.1850529
-0.39
0.695
.6243687
1.3693
-------------+---------------------------------------------------------------/cut1 | -5.101185
.3311667
-15.40
0.000
-5.750917
-4.451453
/cut2 | -3.945819
.2609412
-15.12
0.000
-4.457772
-3.433866
/cut3 | -1.325129
.1751934
-7.56
0.000
-1.66885
-.9814089
------------------------------------------------------------------------------
52
Table 35: Feel Good about Jewish Heritage by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for meimfeelgood
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0061
0.0129
0.1910
0.7900
95% Conf. Interval
[ 0.0021,
0.0100]
[ 0.0049,
0.0209]
[ 0.1387,
0.2433]
[ 0.7331,
0.8470]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for meimfeelgood
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0065
0.0139
0.2028
0.7767
95% Conf. Interval
[ 0.0026,
0.0104]
[ 0.0059,
0.0220]
[ 0.1710,
0.2345]
[ 0.7432,
0.8102]
participant
1
Table 36: Standardized Ordinal Logistic Regression Model of Feel Good about Jewish Heritage Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1156
Number of obs
Population size
Design df
F(
8,
1142)
Prob > F
=
1156
= 19769.564
=
1149
=
7.13
=
0.0000
-----------------------------------------------------------------------------|
Linearized
meimfeelgood | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
.8904348
.1929385
-0.54
0.592
.5820632
1.362179
female |
1.457098
.2710894
2.02
0.043
1.011482
2.099035
age |
.9871021
.0809128
-0.16
0.874
.840457
1.159334
tripage |
.979009
.0838703
-0.25
0.804
.8275391
1.158203
hsritual2 |
1.363177
.1318179
3.20
0.001
1.1276
1.64797
supschyrs1 |
1.007925
.0238458
0.33
0.739
.9622077
1.055813
dayschyrs1 |
1.085784
.0352364
2.54
0.011
1.018804
1.157167
parintmar |
.8455966
.2073237
-0.68
0.494
.5226913
1.367985
-------------+---------------------------------------------------------------/cut1 | -4.958421
1.029613
-4.82
0.000
-6.978554
-2.938289
/cut2 | -3.836177
1.041044
-3.68
0.000
-5.878737
-1.793617
/cut3 | -1.036512
1.057166
-0.98
0.327
-3.110705
1.037681
------------------------------------------------------------------------------
53
Table 37: Clear Sense of Jewish Background by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1217
Number of obs
Population size
Design df
=
1217
= 21005.064
=
1210
------------------------------------------------------AGREE or |
DISAGREE. |
I have a |
clear
|
sense of |
my Jewish |
backgroun |
d and
|
what it
|
birthright participant (corrected)
mean
|
no
yes
Total
----------+-------------------------------------------strongly |
0.030
0.016
0.021
| [0.013,0.070] [0.008,0.030] [0.012,0.036]
|
somewhat |
0.040
0.071
0.060
| [0.026,0.062] [0.054,0.094] [0.047,0.076]
|
somewhat |
0.276
0.315
0.300
| [0.216,0.344] [0.279,0.354] [0.268,0.335]
|
strongly |
0.653
0.598
0.618
| [0.584,0.717] [0.558,0.637] [0.583,0.653]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.78, 3366.84)=
10.3523
2.0824
P = 0.1053
Table 38: Minimal Ordinal Logistic Regression Model of Clear Sense of Jewish Background Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1217
Number of obs
Population size
Design df
F(
1,
1210)
Prob > F
=
1217
= 21005.064
=
1210
=
1.87
=
0.1718
-----------------------------------------------------------------------------|
Linearized
meimclbkgrnd | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
.7950618
.1333699
-1.37
0.172
.5720984
1.104921
-------------+---------------------------------------------------------------/cut1 | -3.988519
.3283505
-12.15
0.000
-4.632719
-3.34432
/cut2 | -2.579454
.173623
-14.86
0.000
-2.920089
-2.238818
/cut3 | -.6298387
.1481861
-4.25
0.000
-.9205689
-.3391086
------------------------------------------------------------------------------
54
Table 39: Clear Sense of Jewish Background by Participation (Predicted Probabilities) ologit: Predictions for meimclbkgrnd
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0182
0.0523
0.2771
0.6525
95% Conf. Interval
[ 0.0067,
0.0297]
[ 0.0366,
0.0680]
[ 0.2253,
0.3288]
[ 0.5866,
0.7183]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for meimclbkgrnd
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0228
0.0643
0.3141
0.5988
95% Conf. Interval
[ 0.0108,
0.0348]
[ 0.0474,
0.0811]
[ 0.2788,
0.3495]
[ 0.5596,
0.6381]
participant
1
Table 40: Standardized Ordinal Logistic Regression Model of Clear Sense of Jewish Background Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1170
Number of obs
Population size
Design df
F(
8,
1156)
Prob > F
=
=
=
=
=
1170
20011.73
1163
8.49
0.0000
-----------------------------------------------------------------------------|
Linearized
meimclbkgrnd | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
.75305
.1368086
-1.56
0.119
.5272574
1.075536
female |
1.092649
.1741211
0.56
0.578
.7992721
1.493712
age |
1.000154
.0726717
0.00
0.998
.8672685
1.1534
tripage |
.9665699
.0751882
-0.44
0.662
.829756
1.125942
hsritual2 |
1.373294
.1007466
4.32
0.000
1.189196
1.585893
supschyrs1 |
1.049014
.0207951
2.41
0.016
1.008997
1.090618
dayschyrs1 |
1.10747
.0294624
3.84
0.000
1.051148
1.166811
parintmar |
1.088217
.2663464
0.35
0.730
.6732293
1.75901
-------------+---------------------------------------------------------------/cut1 | -3.521939
.9228128
-3.82
0.000
-5.332504
-1.711375
/cut2 | -2.123159
.8615162
-2.46
0.014
-3.813459
-.432859
/cut3 | -.0383595
.8681558
-0.04
0.965
-1.741686
1.664967
------------------------------------------------------------------------------
55
Table 41: Sense of Belonging to the Jewish People by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1214
Number of obs
Population size
Design df
=
1214
= 20941.477
=
1207
------------------------------------------------------AGREE or |
DISAGREE. |
I have a |
strong
|
sense of |
belonging |
to the
|
Jewish
|
birthright participant (corrected)
people.
|
no
yes
Total
----------+-------------------------------------------strongly |
0.039
0.019
0.026
| [0.021,0.074] [0.012,0.030] [0.018,0.040]
|
somewhat |
0.113
0.059
0.079
| [0.076,0.164] [0.044,0.080] [0.062,0.101]
|
somewhat |
0.333
0.298
0.311
| [0.268,0.406] [0.262,0.336] [0.277,0.347]
|
strongly |
0.514
0.624
0.583
| [0.443,0.585] [0.584,0.662] [0.547,0.619]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.91, 3509.07)=
21.7875
4.2993
P = 0.0054
Table 42: Minimal Ordinal Logistic Regression Model of Sense of Belonging to the Jewish People Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1214
Number of obs
Population size
Design df
F(
1,
1207)
Prob > F
=
1214
= 20941.477
=
1207
=
9.65
=
0.0019
-----------------------------------------------------------------------------|
Linearized
meimstbelong | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.651379
.26665
3.11
0.002
1.202997
2.266884
-------------+---------------------------------------------------------------/cut1 | -3.317671
.2422758
-13.69
0.000
-3.792999
-2.842342
/cut2 | -1.839389
.1660806
-11.08
0.000
-2.165228
-1.51355
/cut3 | -.0212689
.1428291
-0.15
0.882
-.3014898
.258952
------------------------------------------------------------------------------
56
Table 43: Sense of Belonging to the Jewish People by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for meimstbelong
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0350
0.1022
0.3576
0.5053
95% Conf. Interval
[ 0.0189,
0.0510]
[ 0.0707,
0.1336]
[ 0.3081,
0.4070]
[ 0.4353,
0.5753]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for meimstbelong
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0215
0.0663
0.2844
0.6278
95% Conf. Interval
[ 0.0125,
0.0304]
[ 0.0493,
0.0833]
[ 0.2499,
0.3189]
[ 0.5899,
0.6658]
participant
1
57
Table 44: Standardized Ordinal Logistic Regression Model of Sense of Belonging to the Jewish People Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1166
Number of obs
Population size
Design df
F(
8,
1152)
Prob > F
=
1166
= 19939.313
=
1159
=
12.62
=
0.0000
-----------------------------------------------------------------------------|
Linearized
meimstbelong | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.819212
.3144772
3.46
0.001
1.295947
2.553758
female |
1.11194
.1762056
0.67
0.503
.8148037
1.517433
age |
.9255086
.0639947
-1.12
0.263
.8080946
1.059983
tripage |
1.078469
.0807938
1.01
0.313
.9310499
1.24923
hsritual2 |
1.333175
.1056439
3.63
0.000
1.14121
1.557431
supschyrs1 |
1.062475
.0231571
2.78
0.006
1.017998
1.108895
dayschyrs1 |
1.146875
.0321215
4.89
0.000
1.085553
1.211662
parintmar |
.5774639
.1362717
-2.33
0.020
.3634508
.9174956
-------------+---------------------------------------------------------------/cut1 | -2.753244
.8809472
-3.13
0.002
-4.481673
-1.024814
/cut2 | -1.246877
.8284373
-1.51
0.133
-2.872281
.3785281
/cut3 |
.8587187
.8396445
1.02
0.307
-.7886746
2.506112
------------------------------------------------------------------------------
58
Table 45: Spent Time Learning about Judaism by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1216
Number of obs
Population size
Design df
------------------------------------------------------AGREE or |
DISAGREE. |
I have
|
spent
|
time
|
trying to |
learn
|
more
|
about
|
Judaism, |
birthright participant (corrected)
such as
|
no
yes
Total
----------+-------------------------------------------strongly |
0.087
0.050
0.064
| [0.053,0.140] [0.034,0.072] [0.047,0.086]
|
somewhat |
0.109
0.105
0.107
| [0.072,0.161] [0.083,0.133] [0.086,0.131]
|
somewhat |
0.363
0.397
0.384
| [0.298,0.433] [0.359,0.436] [0.350,0.420]
|
strongly |
0.441
0.448
0.445
| [0.372,0.512] [0.408,0.489] [0.409,0.482]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(3.00, 3623.29)=
7.1512
1.2119
59
P = 0.3037
=
1216
= 21012.139
=
1209
Table 46: Standardized Ordinal Logistic Regression Model of Spent Time Learning about Judaism
Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1216
Number of obs
Population size
Design df
F(
1,
1209)
Prob > F
=
1216
= 21012.139
=
1209
=
0.50
=
0.4802
-----------------------------------------------------------------------------|
Linearized
meimlrn | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.12187
.182677
0.71
0.480
.8150803
1.544133
-------------+---------------------------------------------------------------/cut1 | -2.613966
.2145109
-12.19
0.000
-3.034821
-2.193111
/cut2 | -1.509878
.1621576
-9.31
0.000
-1.828019
-1.191736
/cut3 |
.2935495
.1448462
2.03
0.043
.0093716
.5777274
------------------------------------------------------------------------------
Table 47: Spent Time Learning about Judaism (Predicted Probabilities) ologit: Predictions for meimlrn
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0682
0.1127
0.3919
0.4271
95% Conf. Interval
[ 0.0415,
0.0950]
[ 0.0820,
0.1434]
[ 0.3501,
0.4337]
[ 0.3577,
0.4966]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for meimlrn
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.0613
0.1032
0.3800
0.4555
95% Conf. Interval
[ 0.0429,
0.0797]
[ 0.0807,
0.1258]
[ 0.3442,
0.4157]
[ 0.4161,
0.4949]
participant
1
60
Table 48: Standardized Ordinal Logistic Regression Model of Spent Time Learning about Judaism Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1169
Number of obs
Population size
Design df
F(
8,
1155)
Prob > F
=
1169
= 20018.805
=
1162
=
7.98
=
0.0000
-----------------------------------------------------------------------------|
Linearized
meimlrn | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.202455
.2184526
1.01
0.310
.8419141
1.717393
female |
.7569338
.114217
-1.85
0.065
.5629664
1.017732
age |
.9303045
.0598282
-1.12
0.262
.8200247
1.055415
tripage |
1.076764
.0766355
1.04
0.299
.9364303
1.238128
hsritual2 |
1.227384
.1075164
2.34
0.020
1.033569
1.457545
supschyrs1 |
1.011432
.022404
0.51
0.608
.9684168
1.056358
dayschyrs1 |
1.115006
.0260743
4.66
0.000
1.065004
1.167355
parintmar |
1.716389
.3614678
2.57
0.010
1.135449
2.594561
-------------+---------------------------------------------------------------/cut1 |
-2.25757
.9202906
-2.45
0.014
-4.063187
-.451953
/cut2 | -1.092112
.9030333
-1.21
0.227
-2.86387
.6796466
/cut3 |
.8408961
.9041759
0.93
0.353
-.9331039
2.614896
------------------------------------------------------------------------------
61
Table 49: Think about How Life Affected by Being Jewish (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1207
Number of obs
Population size
Design df
------------------------------------------------------AGREE or |
DISAGREE. |
I think a |
lot about |
how my
|
life will |
be
|
affected |
by my
|
birthright participant (corrected)
being
|
no
yes
Total
----------+-------------------------------------------strongly |
0.144
0.090
0.110
| [0.100,0.203] [0.070,0.115] [0.088,0.135]
|
somewhat |
0.155
0.169
0.164
| [0.113,0.210] [0.141,0.201] [0.139,0.191]
|
somewhat |
0.331
0.361
0.350
| [0.267,0.403] [0.323,0.401] [0.316,0.386]
|
strongly |
0.370
0.381
0.377
| [0.305,0.440] [0.342,0.421] [0.342,0.412]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.99, 3588.13)=
8.5808
1.5147
62
P = 0.2087
=
1207
= 20730.891
=
1200
Table 50: Minimal Ordinal Logistic Regression Model of Think about How Life Affected by Being Jewish Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1207
Number of obs
Population size
Design df
F(
1,
1200)
Prob > F
=
1207
= 20730.891
=
1200
=
0.89
=
0.3467
-----------------------------------------------------------------------------|
Linearized
meimaffect | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.158203
.1806842
0.94
0.347
.8528249
1.572931
-------------+---------------------------------------------------------------/cut1 |
-2.00237
.1779623
-11.25
0.000
-2.351522
-1.653218
/cut2 | -.8843903
.1462457
-6.05
0.000
-1.171316
-.5974645
/cut3 |
.5989274
.1423276
4.21
0.000
.3196888
.878166
------------------------------------------------------------------------------
Table 51: Think about How Life Affected by Being Jewish by Taglit Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for meimaffect
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.1190
0.1733
0.3531
0.3546
95% Conf. Interval
[ 0.0824,
0.1555]
[ 0.1382,
0.2084]
[ 0.3171,
0.3891]
[ 0.2907,
0.4184]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for meimaffect
Confidence intervals by delta method
Pr(y=strongly|x):
Pr(y=somewhat|x):
Pr(y=somewhat|x):
Pr(y=strongly|x):
x=
0.1044
0.1584
0.3483
0.3889
95% Conf. Interval
[ 0.0825,
0.1263]
[ 0.1316,
0.1852]
[ 0.3128,
0.3838]
[ 0.3510,
0.4267]
participant
1
63
Table 52: Standardized Ordinal Logistic Regression Model of Think about How Life Affected by Being Jewish Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1169
Number of obs
Population size
Design df
F(
8,
1155)
Prob > F
=
1169
= 20018.805
=
1162
=
7.98
=
0.0000
-----------------------------------------------------------------------------|
Linearized
meimlrn | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.202455
.2184526
1.01
0.310
.8419141
1.717393
female |
.7569338
.114217
-1.85
0.065
.5629664
1.017732
age |
.9303045
.0598282
-1.12
0.262
.8200247
1.055415
tripage |
1.076764
.0766355
1.04
0.299
.9364303
1.238128
hsritual2 |
1.227384
.1075164
2.34
0.020
1.033569
1.457545
supschyrs1 |
1.011432
.022404
0.51
0.608
.9684168
1.056358
dayschyrs1 |
1.115006
.0260743
4.66
0.000
1.065004
1.167355
parintmar |
1.716389
.3614678
2.57
0.010
1.135449
2.594561
-------------+---------------------------------------------------------------/cut1 |
-2.25757
.9202906
-2.45
0.014
-4.063187
-.451953
/cut2 | -1.092112
.9030333
-1.21
0.227
-2.86387
.6796466
/cut3 |
.8408961
.9041759
0.93
0.353
-.9331039
2.614896
------------------------------------------------------------------------------
64
Table 53: Belong to Jewish Congregation by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1213
Number of obs
Population size
Design df
------------------------------------------------------A
|
synagogue |
, temple, |
minyan,
|
havurah
|
or other |
Jewish
|
congregat |
ion. Do
|
you
|
birthright participant (corrected)
belong
|
no
yes
Total
----------+-------------------------------------------no |
0.574
0.523
0.542
| [0.502,0.643] [0.483,0.563] [0.505,0.578]
|
yes |
0.426
0.477
0.458
| [0.357,0.498] [0.437,0.517] [0.422,0.495]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1206)
=
=
2.8960
1.4520
65
P = 0.2285
=
1213
= 20973.288
=
1206
Table 53a. Belong to Jewish Congregation (Excluding Raised Orthodox, Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1214
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------A
|
synagogue |
, temple, |
minyan,
|
havurah
|
or other |
Jewish
|
congregat |
ion. Do
|
you
|
birthright participant (corrected)
belong
|
no
yes
Total
----------+-------------------------------------------no |
0.683
0.579
0.616
| [0.604,0.753] [0.535,0.623] [0.576,0.654]
|
yes |
0.317
0.421
0.384
| [0.247,0.396] [0.377,0.465] [0.346,0.424]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1207)
=
=
12.5332
5.0601
66
P = 0.0247
=
1214
= 21011.175
=
1015
= 17286.644
=
1207
Table 53b. Belong to Jewish Congregation (Raised Orthodox Only) Number of strata
Number of PSUs
=
=
7
1247
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
=
=
=
=
=
1247
21546.18
198
3686.644
1240
------------------------------------------------------A
|
synagogue |
, temple, |
minyan,
|
havurah
|
or other |
Jewish
|
congregat |
ion. Do
|
you
|
birthright participant (corrected)
belong
|
no
yes
Total
----------+-------------------------------------------no |
0.150
0.226
0.193
| [0.065,0.308] [0.150,0.325] [0.131,0.275]
|
yes |
0.850
0.774
0.807
| [0.692,0.935] [0.675,0.850] [0.725,0.869]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1240)
=
=
11.3199
0.8890
P = 0.3459
Table 54: Minimal Ordinal Logistic Regression Model of Belong to Jewish Congregation Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
1149
Number of obs
Population size
Design df
F(
6,
1137)
Prob > F
=
1149
= 19851.714
=
1142
=
15.55
=
0.0000
-----------------------------------------------------------------------------|
Linearized
syn | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.434153
.2841746
1.82
0.069
.9721928
2.115623
age |
.7534886
.0581805
-3.67
0.000
.6475623
.876742
tripage |
1.186023
.0959966
2.11
0.035
1.011868
1.390152
nevermarried |
.3490913
.0742988
-4.94
0.000
.2299226
.5300252
intermarried |
.2040842
.0613336
-5.29
0.000
.1131685
.3680385
kids |
3.119046
.8026968
4.42
0.000
1.882477
5.167895
------------------------------------------------------------------------------
67
Table 54a. Minimal Logistic Regression Model of Belong to Jewish Congregation (Excluding Orthodox) Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
1155
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
6,
1143)
Prob > F
=
1155
= 20051.04
=
956
= 16326.509
=
1148
=
8.18
=
0.0000
-----------------------------------------------------------------------------|
Linearized
syn | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.75935
.3846207
2.58
0.010
1.145698
2.701682
age |
.7751838
.062392
-3.16
0.002
.661945
.9077943
tripage |
1.166986
.0959941
1.88
0.061
.9930555
1.37138
nevermarried |
.4131005
.0951068
-3.84
0.000
.2629538
.6489811
intermarried |
.3177906
.0976628
-3.73
0.000
.1738903
.5807734
kids |
2.498175
.7059739
3.24
0.001
1.434905
4.349334
------------------------------------------------------------------------------
Table 55: Belong to Jewish Congregation by Participation (Predicted Probabilities) . prvalue, x(participant=0 age=`sa' tripage=`ta' nevermarried=`nm' ///
> intermarried=`im' kids=`k')
logit: Predictions for syn
Confidence intervals by delta method
Pr(y=yes|x):
Pr(y=no|x):
x=
participant
0
0.4081
0.5919
age
28.255623
95% Conf. Interval
[ 0.3272,
0.4890]
[ 0.5110,
0.6728]
tripage
22.056998
nevermarried
.53685603
intermarried
.13035474
kids
.19787483
. prvalue, x(participant=1 age=`sa' tripage=`ta' nevermarried=`nm' ///
> intermarried=`im' kids=`k')
logit: Predictions for syn
Confidence intervals by delta method
Pr(y=yes|x):
Pr(y=no|x):
x=
participant
1
0.4972
0.5028
age
28.255623
95% Conf. Interval
[ 0.4520,
0.5424]
[ 0.4576,
0.5480]
tripage
22.056998
68
nevermarried
.53685603
intermarried
.13035474
kids
.19787483
Table 55a. Belong to Jewish Congregation (Excluding Raised Orthodox) by Participation (Predicted Probabilities) . prvalue, x(participant=0 age=`sa' tripage=`ta' nevermarried=`nm' ///
> intermarried=`im' kids=`k')
logit: Predictions for syn
Confidence intervals by delta method
Pr(y=yes|x):
Pr(y=no|x):
x=
0.2988
0.7012
participant
0
age
28.443547
95% Conf. Interval
[ 0.2196,
0.3780]
[ 0.6220,
0.7804]
tripage
22.209354
nevermarried
.56433008
intermarried
.15768278
kids
.15910339
. prvalue, x(participant=1 age=`sa' tripage=`ta' nevermarried=`nm' ///
> intermarried=`im' kids=`k')
logit: Predictions for syn
Confidence intervals by delta method
Pr(y=yes|x):
Pr(y=no|x):
x=
0.4285
0.5715
participant
1
age
28.443547
95% Conf. Interval
[ 0.3812,
0.4757]
[ 0.5243,
0.6188]
tripage
22.209354
nevermarried
.56433008
intermarried
.15768278
kids
.15910339
Table 56: Standardized Ordinal Logistic Regression Model of Belong to Jewish Congregation Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
1104
Number of obs
Population size
Design df
F( 11,
1087)
Prob > F
=
1104
= 18950.113
=
1097
=
13.20
=
0.0000
-----------------------------------------------------------------------------|
Linearized
syn | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.555212
.3230139
2.13
0.034
1.034669
2.337639
female |
1.092978
.1999288
0.49
0.627
.7633731
1.564899
age |
.8011318
.0659652
-2.69
0.007
.6816141
.9416063
tripage |
1.190988
.1022599
2.04
0.042
1.006332
1.409527
hsritual2 |
1.445363
.1447535
3.68
0.000
1.187503
1.759216
supschyrs1 |
1.072975
.0289765
2.61
0.009
1.0176
1.131364
dayschyrs1 |
1.131402
.0312349
4.47
0.000
1.071746
1.19438
parintmar |
1.968259
.5553547
2.40
0.017
1.131477
3.42388
nevermarried |
.3540757
.0800927
-4.59
0.000
.2271642
.5518898
intermarried |
.2495358
.0818235
-4.23
0.000
.1311334
.4748459
kids |
2.733645
.7399295
3.72
0.000
1.607269
4.649387
------------------------------------------------------------------------------
69
Table 57: Volunteer for Jewish Causes by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1208
Number of obs
Population size
Design df
=
1208
= 20773.756
=
1201
------------------------------------------------------Jewish
|
causes.
|
In the
|
past
|
year,
|
have you |
volunteer |
ed
|
birthright participant (corrected)
for...?
|
no
yes
Total
----------+-------------------------------------------no |
0.707
0.659
0.676
| [0.639,0.767] [0.618,0.697] [0.641,0.710]
|
yes |
0.293
0.341
0.324
| [0.233,0.361] [0.303,0.382] [0.290,0.359]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1201)
=
=
2.9855
1.5132
P = 0.2189
Table 58: Minimal Ordinal Logistic Regression Model of Volunteer for Jewish Causes Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
1208
Number of obs
Population size
Design df
F(
1,
1201)
Prob > F
=
1208
= 20773.756
=
1201
=
1.51
=
0.2193
-----------------------------------------------------------------------------|
Linearized
voljew | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.249446
.2263888
1.23
0.219
.8756523
1.782803
------------------------------------------------------------------------------
70
Table 59: Standardized Logistic Regression Model of Volunteer for Jewish Causes Survey: Logistic regression
Number of strata
=
7
Number of PSUs
=
1160
Number of obs
=
1160
Population size
= 19784.884
Design df
=
1153
F(
8,
1146)
=
7.51
Prob > F
=
0.0000
-----------------------------------------------------------------------------|
Linearized
voljew | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.295892
.2490105
1.35
0.178
.8888642
1.889306
female |
1.139657
.1956973
0.76
0.447
.813684
1.59622
age |
.9121806
.0708393
-1.18
0.237
.783263
1.062317
tripage |
1.114329
.0941845
1.28
0.201
.9440457
1.315326
hsritual2 |
1.328432
.1141076
3.31
0.001
1.122399
1.572285
supschyrs1 |
1.025468
.0233653
1.10
0.270
.980634
1.072351
dayschyrs1 |
1.080326
.0252489
3.31
0.001
1.031906
1.131019
parintmar |
.6350548
.1782091
-1.62
0.106
.3661812
1.101353
------------------------------------------------------------------------------
Table 60: Jewish Religious Service Attendance by Participation (Estimated Proportions) Number of strata
Number of PSUs
Number of obs
Population size
Design df
------------------------------------------------------Frequency |
of
|
attendanc |
e at
|
religious |
birthright participant (corrected)
services |
no
yes
Total
----------+-------------------------------------------0 |
0.152
0.100
0.120
| [0.109,0.209] [0.080,0.126] [0.098,0.145]
|
1 |
0.378
0.374
0.375
| [0.312,0.448] [0.336,0.413] [0.341,0.411]
|
2 |
0.128
0.164
0.151
| [0.087,0.185] [0.136,0.197] [0.126,0.179]
|
3 |
0.075
0.095
0.087
| [0.048,0.115] [0.074,0.121] [0.070,0.108]
|
4 |
0.096
0.089
0.091
| [0.061,0.149] [0.068,0.114] [0.072,0.115]
|
5 |
0.171
0.178
0.175
| [0.123,0.233] [0.148,0.212] [0.149,0.206]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
=
=
7
1221
chi2(5)
=
F(4.95, 6004.23)=
10.4640
1.0884
71
P = 0.3643
=
1221
= 20952.674
=
1214
Table 61: Minimal Ordinal Logistic Regression Model of Jewish Religious Service Attendance Survey: Ordered logistic regression
Number of strata
=
7
Number of PSUs
=
1123
Number of obs
=
1123
Population size
= 19156.818
Design df
=
1116
F(
8,
1109)
=
25.94
Prob > F
=
0.0000
-----------------------------------------------------------------------------|
Linearized
relservrec~e | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.220921
.6765182
2.62
0.009
1.221703
4.037387
dayschyn |
9.577814
3.510601
6.16
0.000
4.665871
19.66075
dayschpart |
.4523605
.1870389
-1.92
0.055
.2009804
1.018159
supschyrs1 |
1.120327
.0419628
3.03
0.002
1.040944
1.205762
supschpart |
.9318707
.0395631
-1.66
0.097
.8573896
1.012822
intermarried |
.2002128
.0486774
-6.62
0.000
.1242556
.3226026
nevermarried |
.474961
.0905963
-3.90
0.000
.3266777
.690552
kids |
1.811837
.3586471
3.00
0.003
1.228694
2.671742
-------------+---------------------------------------------------------------/cut1 | -1.663453
.3309362
-5.03
0.000
-2.312781
-1.014126
/cut2 |
.6036253
.3173388
1.90
0.057
-.0190226
1.226273
/cut3 |
1.395861
.3236763
4.31
0.000
.7607782
2.030943
/cut4 |
1.953753
.329879
5.92
0.000
1.3065
2.601006
/cut5 |
2.613928
.3310371
7.90
0.000
1.964403
3.263454
------------------------------------------------------------------------------
Table 62: Jewish Religious Service Attendance by Taglit Participation (Predicted Probabilities) ologit: Predictions for relservrecode
Confidence intervals by delta method
95% Conf. Interval
Pr(y=0|x):
0.0997
[ 0.0692,
0.1303]
Pr(y=1|x):
0.4170
[ 0.3606,
0.4734]
Pr(y=2|x):
0.1858
[ 0.1518,
0.2197]
Pr(y=3|x):
0.1024
[ 0.0774,
0.1274]
Pr(y=4|x):
0.0838
[ 0.0577,
0.1099]
Pr(y=5|x):
0.1113
[ 0.0775,
0.1451]
participant
dayschyn
nevermarried
kids
x=
0
.23786122
.53685603
.19787483
dayschpart
supschyrs1
supschpart
intermarried
0
4.321089
0
.13035474
. prvalue, x(participant=1 dayschyn=`ds' dayschpart=`ds' supschyrs1=`ss' ///
> supschpart=`ss' intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0755
0.3654
0.1943
0.1174
0.1022
0.1452
participant
dayschyn
nevermarried
kids
x=
1
.23786122
.53685603
.19787483
95% Conf. Interval
[ 0.0574,
0.0937]
[ 0.3252,
0.4056]
[ 0.1601,
0.2285]
[ 0.0912,
0.1435]
[ 0.0780,
0.1263]
[ 0.1157,
0.1747]
dayschpart
supschyrs1
supschpart
intermarried
.23786122
4.321089
4.321089
.13035474
72
Table 63: Jewish Religious Service Attendance by Taglit Participation and Years of Supplementary School (Predicted Probabilities) . prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=0 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.2365
0.5128
0.1191
0.0517
0.0369
0.0429
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1398,
0.3333]
[ 0.4660,
0.5597]
[ 0.0774,
0.1608]
[ 0.0281,
0.0754]
[ 0.0173,
0.0565]
[ 0.0199,
0.0658]
dayschpart
supschyrs1
supschpart
intermarried
0
0
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=1 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.2166
0.5108
0.1275
0.0565
0.0407
0.0478
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1340,
0.2993]
[ 0.4632,
0.5584]
[ 0.0882,
0.1668]
[ 0.0334,
0.0797]
[ 0.0210,
0.0604]
[ 0.0246,
0.0710]
dayschpart
supschyrs1
supschpart
intermarried
0
1
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=2 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1980
0.5064
0.1359
0.0616
0.0449
0.0532
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1273,
0.2687]
[ 0.4573,
0.5555]
[ 0.0987,
0.1732]
[ 0.0388,
0.0844]
[ 0.0250,
0.0648]
[ 0.0298,
0.0767]
dayschpart
supschyrs1
supschpart
intermarried
0
2
0
.13035474
73
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=3 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1806
0.4996
0.1443
0.0669
0.0494
0.0593
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1196,
0.2416]
[ 0.4490,
0.5502]
[ 0.1086,
0.1800]
[ 0.0444,
0.0895]
[ 0.0291,
0.0697]
[ 0.0353,
0.0832]
dayschpart
supschyrs1
supschpart
intermarried
0
3
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=4 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1644
0.4906
0.1524
0.0725
0.0542
0.0659
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1107,
0.2180]
[ 0.4386,
0.5426]
[ 0.1176,
0.1873]
[ 0.0498,
0.0951]
[ 0.0332,
0.0752]
[ 0.0410,
0.0908]
dayschpart
supschyrs1
supschpart
intermarried
0
4
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=5 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1493
0.4795
0.1603
0.0782
0.0594
0.0733
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1008,
0.1979]
[ 0.4258,
0.5333]
[ 0.1256,
0.1949]
[ 0.0550,
0.1014]
[ 0.0373,
0.0816]
[ 0.0467,
0.0998]
dayschpart
supschyrs1
supschpart
intermarried
0
5
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=6 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
74
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1355
0.4665
0.1676
0.0841
0.0650
0.0814
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.0901,
0.1808]
[ 0.4099,
0.5231]
[ 0.1325,
0.2027]
[ 0.0598,
0.1085]
[ 0.0412,
0.0887]
[ 0.0521,
0.1106]
dayschpart
supschyrs1
supschpart
intermarried
0
6
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=7 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1227
0.4517
0.1744
0.0901
0.0708
0.0903
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.0790,
0.1664]
[ 0.3907,
0.5128]
[ 0.1385,
0.2102]
[ 0.0642,
0.1160]
[ 0.0448,
0.0969]
[ 0.0570,
0.1236]
dayschpart
supschyrs1
supschpart
intermarried
0
7
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=8 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1110
0.4355
0.1804
0.0961
0.0770
0.1000
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.0679,
0.1541]
[ 0.3678,
0.5031]
[ 0.1439,
0.2168]
[ 0.0682,
0.1240]
[ 0.0481,
0.1059]
[ 0.0612,
0.1389]
dayschpart
supschyrs1
supschpart
intermarried
0
8
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=9 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
75
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1003
0.4179
0.1855
0.1021
0.0835
0.1108
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.0573,
0.1433]
[ 0.3418,
0.4941]
[ 0.1487,
0.2223]
[ 0.0720,
0.1322]
[ 0.0511,
0.1158]
[ 0.0647,
0.1568]
dayschpart
supschyrs1
supschpart
intermarried
0
9
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=10 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0905
0.3993
0.1897
0.1079
0.0902
0.1224
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.0474,
0.1336]
[ 0.3130,
0.4856]
[ 0.1531,
0.2263]
[ 0.0757,
0.1401]
[ 0.0540,
0.1263]
[ 0.0674,
0.1775]
dayschpart
supschyrs1
supschpart
intermarried
0
10
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=11 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0815
0.3799
0.1928
0.1135
0.0971
0.1352
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.0384,
0.1247]
[ 0.2825,
0.4773]
[ 0.1568,
0.2287]
[ 0.0794,
0.1476]
[ 0.0568,
0.1373]
[ 0.0694,
0.2009]
dayschpart
supschyrs1
supschpart
intermarried
0
11
0
.13035474
. prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=12 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
0.0734
95% Conf. Interval
[ 0.0303,
0.1165]
76
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.3600
0.1947
0.1188
0.1041
0.1490
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
[
[
[
[
[
0.2509,
0.1596,
0.0832,
0.0597,
0.0707,
0.4690]
0.2298]
0.1543]
0.1485]
0.2273]
dayschpart
supschyrs1
supschpart
intermarried
0
12
0
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=0 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1224
0.4514
0.1745
0.0902
0.0710
0.0905
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0844,
0.1605]
[ 0.3958,
0.5070]
[ 0.1407,
0.2083]
[ 0.0652,
0.1153]
[ 0.0494,
0.0925]
[ 0.0591,
0.1218]
dayschpart
supschyrs1
supschpart
intermarried
0
0
0
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=1 ///
> supschpart=1 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1179
0.4454
0.1769
0.0925
0.0733
0.0941
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0837,
0.1520]
[ 0.3925,
0.4982]
[ 0.1437,
0.2100]
[ 0.0682,
0.1169]
[ 0.0523,
0.0942]
[ 0.0641,
0.1241]
dayschpart
supschyrs1
supschpart
intermarried
0
1
1
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=2 ///
> supschpart=2 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
0.1135
0.4392
0.1791
0.0948
95% Conf. Interval
[ 0.0827,
0.1442]
[ 0.3891,
0.4893]
[ 0.1464,
0.2119]
[ 0.0710,
0.1186]
77
Pr(y=4|x):
Pr(y=5|x):
0.0756
0.0978
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
[ 0.0550,
[ 0.0691,
0.0962]
0.1265]
dayschpart
supschyrs1
supschpart
intermarried
0
2
2
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=3 ///
> supschpart=3 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1092
0.4328
0.1813
0.0971
0.0780
0.1017
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0813,
0.1372]
[ 0.3852,
0.4803]
[ 0.1487,
0.2139]
[ 0.0736,
0.1206]
[ 0.0576,
0.0984]
[ 0.0742,
0.1292]
dayschpart
supschyrs1
supschpart
intermarried
0
3
3
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=4 ///
> supschpart=4 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1051
0.4262
0.1833
0.0993
0.0804
0.1057
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0794,
0.1308]
[ 0.3808,
0.4715]
[ 0.1506,
0.2159]
[ 0.0760,
0.1227]
[ 0.0601,
0.1008]
[ 0.0791,
0.1323]
dayschpart
supschyrs1
supschpart
intermarried
0
4
4
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=5 ///
> supschpart=5 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1011
0.4194
0.1851
0.1016
0.0829
0.1098
95% Conf. Interval
[ 0.0770,
0.1252]
[ 0.3757,
0.4632]
[ 0.1523,
0.2179]
[ 0.0782,
0.1250]
[ 0.0623,
0.1036]
[ 0.0838,
0.1358]
78
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
dayschpart
supschyrs1
supschpart
intermarried
0
5
5
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=6 ///
> supschpart=6 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0973
0.4125
0.1869
0.1038
0.0854
0.1141
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0742,
0.1203]
[ 0.3696,
0.4555]
[ 0.1538,
0.2199]
[ 0.0801,
0.1275]
[ 0.0642,
0.1066]
[ 0.0882,
0.1400]
dayschpart
supschyrs1
supschpart
intermarried
0
6
6
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=7 ///
> supschpart=7 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0936
0.4055
0.1885
0.1060
0.0880
0.1185
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0710,
0.1161]
[ 0.3624,
0.4486]
[ 0.1551,
0.2218]
[ 0.0819,
0.1302]
[ 0.0659,
0.1100]
[ 0.0921,
0.1449]
dayschpart
supschyrs1
supschpart
intermarried
0
7
7
.13035474
supschpart
intermarried
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=8 ///
> supschpart=8 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
participant
nevermarried
0.0900
0.3983
0.1899
0.1082
0.0905
0.1231
dayschyn
kids
95% Conf. Interval
[ 0.0674,
0.1126]
[ 0.3540,
0.4426]
[ 0.1562,
0.2236]
[ 0.0835,
0.1330]
[ 0.0674,
0.1137]
[ 0.0955,
0.1507]
dayschpart
79
supschyrs1
x=
.53685603
1
.19787483
0
0
8
8
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=9 ///
> supschpart=9 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0865
0.3910
0.1912
0.1104
0.0931
0.1278
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0635,
0.1095]
[ 0.3444,
0.4376]
[ 0.1572,
0.2251]
[ 0.0849,
0.1359]
[ 0.0687,
0.1176]
[ 0.0983,
0.1573]
dayschpart
supschyrs1
supschpart
intermarried
0
9
9
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=10 ///
> supschpart=10 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0832
0.3836
0.1923
0.1125
0.0958
0.1327
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0595,
0.1069]
[ 0.3338,
0.4334]
[ 0.1581,
0.2265]
[ 0.0862,
0.1388]
[ 0.0697,
0.1218]
[ 0.1006,
0.1649]
dayschpart
supschyrs1
supschpart
intermarried
0
10
10
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=11 ///
> supschpart=11 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0799
0.3761
0.1933
0.1145
0.0984
0.1377
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0553,
0.1045]
[ 0.3222,
0.4300]
[ 0.1588,
0.2277]
[ 0.0873,
0.1417]
[ 0.0706,
0.1261]
[ 0.1023,
0.1732]
dayschpart
supschyrs1
supschpart
intermarried
0
11
11
.13035474
80
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=12 ///
> supschpart=12 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0768
0.3686
0.1940
0.1165
0.1011
0.1429
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0512,
0.1024]
[ 0.3099,
0.4273]
[ 0.1595,
0.2286]
[ 0.0884,
0.1447]
[ 0.0714,
0.1307]
[ 0.1035,
0.1824]
dayschpart
supschyrs1
supschpart
intermarried
0
12
12
.13035474
Table 64: Jewish Religious Service Attendance by Taglit Participation and Day School Education (Predicted Probabilities) . prvalue, x(participant=0 dayschyn=0 dayschpart=0 supschyrs1=0 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.2365
0.5128
0.1191
0.0517
0.0369
0.0429
participant
dayschyn
nevermarried
kids
x=
0
0
.53685603
.19787483
95% Conf. Interval
[ 0.1398,
0.3333]
[ 0.4660,
0.5597]
[ 0.0774,
0.1608]
[ 0.0281,
0.0754]
[ 0.0173,
0.0565]
[ 0.0199,
0.0658]
dayschpart
supschyrs1
supschpart
intermarried
0
0
0
.13035474
supschpart
intermarried
. prvalue, x(participant=0 dayschyn=1 dayschpart=0 supschyrs1=0 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
participant
nevermarried
0.0313
0.2066
0.1702
0.1383
0.1534
0.3002
dayschyn
kids
95% Conf. Interval
[ 0.0147,
0.0480]
[ 0.1276,
0.2855]
[ 0.1275,
0.2128]
[ 0.1075,
0.1691]
[ 0.1145,
0.1923]
[ 0.1867,
0.4138]
dayschpart
81
supschyrs1
x=
.53685603
0
.19787483
1
0
0
0
.13035474
. prvalue, x(participant=1 dayschyn=0 dayschpart=0 supschyrs1=0 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.1224
0.4514
0.1745
0.0902
0.0710
0.0905
participant
dayschyn
nevermarried
kids
x=
1
0
.53685603
.19787483
95% Conf. Interval
[ 0.0844,
0.1605]
[ 0.3958,
0.5070]
[ 0.1407,
0.2083]
[ 0.0652,
0.1153]
[ 0.0494,
0.0925]
[ 0.0591,
0.1218]
dayschpart
supschyrs1
supschpart
intermarried
0
0
0
.13035474
. prvalue, x(participant=1 dayschyn=1 dayschpart=1 supschyrs1=0 ///
> supschpart=0 intermarried=`im' nevermarried=`nm' kids=`k')
ologit: Predictions for relservrecode
Confidence intervals by delta method
Pr(y=0|x):
Pr(y=1|x):
Pr(y=2|x):
Pr(y=3|x):
Pr(y=4|x):
Pr(y=5|x):
0.0312
0.2059
0.1699
0.1383
0.1536
0.3012
participant
dayschyn
nevermarried
kids
x=
1
1
.53685603
.19787483
95% Conf. Interval
[ 0.0182,
0.0442]
[ 0.1484,
0.2633]
[ 0.1331,
0.2067]
[ 0.1079,
0.1686]
[ 0.1167,
0.1905]
[ 0.2209,
0.3815]
dayschpart
supschyrs1
supschpart
intermarried
1
0
0
.13035474
82
Table 65: Standardized Ordinal Logistic Regression Model of Jewish Religious Service Attendance Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1106
Number of obs
Population size
Design df
F( 11,
1089)
Prob > F
=
=
=
=
=
1106
18903.19
1099
21.28
0.0000
-----------------------------------------------------------------------------|
Linearized
relservrec~e | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.272675
.2290408
1.34
0.181
.8940457
1.811655
female |
.8412257
.1245889
-1.17
0.243
.6290819
1.12491
age |
.9238484
.061279
-1.19
0.233
.8111069
1.052261
tripage |
1.036344
.0748919
0.49
0.621
.8993399
1.19422
hsritual2 |
1.47941
.133965
4.33
0.000
1.238582
1.767065
supschyrs1 |
1.056718
.0218327
2.67
0.008
1.014736
1.100437
dayschyrs1 |
1.117732
.0237476
5.24
0.000
1.072094
1.165312
parintmar |
1.22888
.3067288
0.83
0.409
.7530345
2.005413
nevermarried |
.4356225
.0874802
-4.14
0.000
.2937558
.6460024
intermarried |
.1879341
.0480476
-6.54
0.000
.1138009
.3103598
kids |
2.00688
.4054072
3.45
0.001
1.350149
2.983053
-------------+---------------------------------------------------------------/cut1 | -2.680133
.9299205
-2.88
0.004
-4.504753
-.8555127
/cut2 | -.3236459
.9206545
-0.35
0.725
-2.130085
1.482793
/cut3 |
.5035606
.922304
0.55
0.585
-1.306115
2.313236
/cut4 |
1.090442
.9265045
1.18
0.239
-.727476
2.908359
/cut5 |
1.783035
.934928
1.91
0.057
-.0514108
3.61748
------------------------------------------------------------------------------
83
Table 66: Attend Shabbat Meal by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1216
Number of obs
Population size
Design df
------------------------------------------------------In the
|
past
|
year,
|
have you |
had or
|
attended |
a special |
meal on
|
Shabbat.. |
birthright participant (corrected)
.?
|
no
yes
Total
----------+-------------------------------------------never |
0.291
0.246
0.262
| [0.232,0.358] [0.214,0.281] [0.232,0.295]
|
sometime |
0.358
0.402
0.386
| [0.292,0.430] [0.363,0.442] [0.351,0.422]
|
usually |
0.081
0.131
0.113
| [0.049,0.130] [0.104,0.164] [0.091,0.139]
|
always |
0.271
0.221
0.239
| [0.212,0.339] [0.189,0.256] [0.209,0.272]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.98, 3608.64)=
12.8836
2.1499
84
P = 0.0922
=
1216
= 20949.236
=
1209
Table 67: Minimal Ordinal Logistic Regression Model of Attend Shabbat Meal Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1140
Number of obs
Population size
Design df
F(
7,
1127)
Prob > F
=
1140
= 19690.869
=
1133
=
36.12
=
0.0000
-----------------------------------------------------------------------------|
Linearized
shabmeal | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.338575
.2574694
1.52
0.130
.9177887
1.952284
age |
.9074695
.0252274
-3.49
0.000
.8592975
.958342
nevermarrie |
.3021285
.0584482
-6.19
0.000
.2067029
.4416079
intermarrie |
.1893761
.049018
-6.43
0.000
.1139633
.3146916
kids |
1.559133
.3738449
1.85
0.064
.974016
2.495746
dayschyn |
15.22642
6.441121
6.44
0.000
6.639498
34.91889
dayschpart |
.3077248
.1371452
-2.64
0.008
.1283505
.7377808
-------------+---------------------------------------------------------------/cut1 | -4.294641
.8942488
-4.80
0.000
-6.049211
-2.540071
/cut2 | -2.196174
.8837646
-2.49
0.013
-3.930173
-.4621745
/cut3 | -1.381366
.8746005
-1.58
0.115
-3.097385
.3346524
------------------------------------------------------------------------------
Table 68: Standardized Ordinal Logistic Regression Model of Attend Shabbat Meal Survey: Ordered logistic regression Number of strata
Number of PSUs
=
=
7
1105
Number of obs
Population size
Design df
F( 11,
1088)
Prob > F
=
1105
= 18991.862
=
1098
=
28.44
=
0.0000
-----------------------------------------------------------------------------|
Linearized
shabmeal | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.06147
.1871315
0.34
0.735
.7510688
1.500153
female |
1.019863
.1582835
0.13
0.899
.7521232
1.382913
age |
.8452781
.0594462
-2.39
0.017
.7363271
.9703502
tripage |
1.100082
.0817906
1.28
0.200
.9507552
1.272862
hsritual2 |
1.86468
.1843356
6.30
0.000
1.535906
2.26383
supschyrs1 |
.9940477
.0209916
-0.28
0.777
.9537011
1.036101
dayschyrs1 |
1.116446
.0251991
4.88
0.000
1.068081
1.167001
parintmar |
1.003839
.2300053
0.02
0.987
.640349
1.573662
nevermarried |
.2928401
.0612512
-5.87
0.000
.194265
.4414346
intermarried |
.1764806
.0511217
-5.99
0.000
.0999665
.3115584
kids |
1.755333
.4320027
2.29
0.022
1.08303
2.844978
-------------+---------------------------------------------------------------/cut1 | -2.901173
1.005697
-2.88
0.004
-4.874478
-.9278684
/cut2 | -.5966233
.9978287
-0.60
0.550
-2.55449
1.361243
/cut3 |
.2596726
.9928639
0.26
0.794
-1.688452
2.207798
------------------------------------------------------------------------------
85
Table 69: Relationship Status by Participation (below age 30, Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1234
Number of obs
=
1234
Population size
= 21233.629
Subpop. no. of obs =
598
Subpop. size
= 14532.359
Design df
=
1227
------------------------------------------------------Relations |
hip
|
birthright participant (corrected)
status
|
no
yes
Total
----------+-------------------------------------------Married |
0.466
0.249
0.322
| [0.365,0.569] [0.208,0.295] [0.277,0.371]
|
Civil un |
0.013
0.000
0.004
| [0.002,0.084]
[0.001,0.030]
|
Engaged |
0.031
0.058
0.049
| [0.009,0.102] [0.038,0.088] [0.032,0.074]
|
Life par |
0.009
0.027
0.021
| [0.001,0.062] [0.014,0.051] [0.011,0.039]
|
Signific |
0.230
0.269
0.256
| [0.153,0.330] [0.224,0.319] [0.214,0.302]
|
Dating |
0.234
0.353
0.313
| [0.162,0.327] [0.304,0.406] [0.270,0.360]
|
Not dati |
0.017
0.044
0.035
| [0.003,0.079] [0.027,0.070] [0.022,0.055]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(6)
=
F(5.88, 7218.31)=
78.9055
3.1455
86
P = 0.0047
Table 70: Relationship Status by Participation (age 30 and above, Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1236
Number of obs
=
1236
Population size
= 21437.844
Subpop. no. of obs =
624
Subpop. size
= 6555.047
Design df
=
1229
------------------------------------------------------Relations |
hip
|
birthright participant (corrected)
status
|
no
yes
Total
----------+-------------------------------------------Married |
0.507
0.452
0.476
| [0.434,0.580] [0.405,0.500] [0.434,0.517]
|
Civil un |
0.004
0.004
0.004
| [0.001,0.026] [0.001,0.014] [0.001,0.012]
|
Engaged |
0.050
0.063
0.058
| [0.026,0.094] [0.043,0.093] [0.041,0.081]
|
Life par |
0.028
0.029
0.028
| [0.011,0.068] [0.017,0.049] [0.017,0.046]
|
Signific |
0.153
0.162
0.158
| [0.107,0.214] [0.129,0.201] [0.130,0.191]
|
Dating |
0.242
0.249
0.246
| [0.185,0.311] [0.209,0.294] [0.212,0.284]
|
Not dati |
0.017
0.040
0.030
| [0.005,0.052] [0.026,0.063] [0.020,0.047]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(6)
=
F(5.87, 7215.80)=
8.7586
0.6061
87
P = 0.7221 Table 71: Inmarriage (Including Orthodox) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
497
Number of obs
Population size
Design df
------------------------------------------------------Married
|
to Jewish |
spouse/ci |
vil
|
union/for |
mer
|
birthright participant (corrected)
spouse
|
no
yes
Total
----------+-------------------------------------------Intermar |
0.369
0.222
0.292
| [0.274,0.475] [0.175,0.278] [0.239,0.352]
|
Inmarrie |
0.631
0.778
0.708
| [0.525,0.726] [0.722,0.825] [0.648,0.761]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 490)
=
=
12.8885
7.1869
88
P = 0.0076
=
=
=
497
7860.355
490
Table 72: Inmarriage (Excluding Orthodox) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
587
Number of obs
=
587
Population size
= 9619.512
Subpop. no. of obs =
388
Subpop. size
= 5894.981
Design df
=
580
------------------------------------------------------Married
|
to Jewish |
spouse/ci |
vil
|
union/for |
mer
|
birthright participant (corrected)
spouse
|
no
yes
Total
----------+-------------------------------------------Intermar |
0.500
0.290
0.388
| [0.381,0.618] [0.231,0.358] [0.322,0.459]
|
Inmarrie |
0.500
0.710
0.612
| [0.382,0.619] [0.642,0.769] [0.541,0.678]
|
Total |
1.000
1.000
1.000
|
---------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 580)
=
=
27.0295
9.5572
P = 0.0021
Table 73: Minimal Ordinal Logistic Regression Model of Inmarriage (Excluding Orthodox) Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
583
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
3,
574)
Prob > F
=
=
=
=
=
=
=
583
9530.886
384
5806.355
576
9.35
0.0000
-----------------------------------------------------------------------------|
Linearized
inmar1 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.283157
.7700944
2.45
0.015
1.177135
4.428383
parintmar |
.1115136
.0728002
-3.36
0.001
.0309358
.4019703
parintmarp~t |
3.672458
2.755293
1.73
0.083
.8413775
16.02961
------------------------------------------------------------------------------
89
Table 74: Inmarriage (Excluding Orthodox) by Participation (Predicted Probabilities) logit: Predictions for inmar1
Confidence intervals by delta method
Pr(y=Inmarrie|x):
Pr(y=Intermar|x):
x=
participant
0
0.4647
0.5353
95% Conf. Interval
[ 0.3419,
0.5874]
[ 0.4126,
0.6581]
parintmar parintmarp~t
.20544791
0
. prvalue, x(participant=1 parintmar=`p' parintmarpart=`p')
logit: Predictions for inmar1
Confidence intervals by delta method
Pr(y=Inmarrie|x):
Pr(y=Intermar|x):
x=
participant
1
0.7214
0.2786
95% Conf. Interval
[ 0.6567,
0.7860]
[ 0.2140,
0.3433]
parintmar parintmarp~t
.20544791
.20544791
Table 75: Inmarriage (Excluding Orthodox) by Participation and Parental Intermarriage (Predicted Probabilities) . prvalue, x(participant=0 parintmar=1 parintmarpart=0)
logit: Predictions for inmar1
Confidence intervals by delta method
Pr(y=Inmarrie|x):
Pr(y=Intermar|x):
x=
participant
0
0.1319
0.8681
95% Conf. Interval
[-0.0003,
0.2640]
[ 0.7360,
1.0003]
parintmar parintmarp~t
1
0
. prvalue, x(participant=1 parintmar=1 parintmarpart=1)
logit: Predictions for inmar1
Confidence intervals by delta method
Pr(y=Inmarrie|x):
Pr(y=Intermar|x):
x=
participant
1
0.5602
0.4398
95% Conf. Interval
[ 0.4070,
0.7134]
[ 0.2866,
0.5930]
parintmar parintmarp~t
1
1
. prvalue, x(participant=0 parintmar=0 parintmarpart=0)
logit: Predictions for inmar1
Confidence intervals by delta method
95% Conf. Interval
90
Pr(y=Inmarrie|x):
Pr(y=Intermar|x):
x=
participant
0
0.5767
0.4233
[ 0.4433,
[ 0.2900,
0.7100]
0.5567]
parintmar parintmarp~t
0
0
. prvalue, x(participant=1 parintmar=0 parintmarpart=0)
logit: Predictions for inmar1
Confidence intervals by delta method
Pr(y=Inmarrie|x):
Pr(y=Intermar|x):
x=
participant
1
0.7567
0.2433
95% Conf. Interval
[ 0.6883,
0.8251]
[ 0.1749,
0.3117]
parintmar parintmarp~t
0
0
Table 76: Standardized Ordinal Logistic Regression of Inmarriage (Excluding Orthodox) Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
576
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
8,
562)
Prob > F
=
=
=
=
=
=
=
576
9443.985
377
5719.454
569
5.90
0.0000
-----------------------------------------------------------------------------|
Linearized
inmar1 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
3.036065
1.005073
3.35
0.001
1.584619
5.816976
female |
.3763632
.1190453
-3.09
0.002
.2022062
.7005188
age |
1.223458
.1830846
1.35
0.178
.9118837
1.641493
tripage |
.8122273
.1243912
-1.36
0.175
.6012287
1.097275
hsritual2 |
.9872191
.1773052
-0.07
0.943
.6937632
1.404804
supschyrs1 |
.9612583
.0402552
-0.94
0.346
.8853557
1.043668
dayschyrs1 |
1.10469
.0636007
1.73
0.084
.9865736
1.236948
parintmar |
.2459224
.0924069
-3.73
0.000
.1175633
.5144278
------------------------------------------------------------------------------
91
Table 77: Minimal Ordinal Logistic Regression Model of Inmarriage (Excluding Orthodox, Including Parent Survey Data) Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
706
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
1,
699)
Prob > F
=
=
=
=
=
=
=
706
9462.564
446
5690.792
699
5.10
0.0243
-----------------------------------------------------------------------------|
Linearized
inmar1a | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.808885
.4748358
2.26
0.024
1.080392
3.028591
------------------------------------------------------------------------------
Table 78: Minimal Ordinal Logistic Regression Model of Inmarriage (Excluding Orthodox, Respondent Data Only) Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
587
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
1,
580)
Prob > F
=
=
=
=
=
=
=
587
9619.512
388
5894.981
580
9.38
0.0023
-----------------------------------------------------------------------------|
Linearized
inmar1 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.443919
.713155
3.06
0.002
1.37778
4.335045
------------------------------------------------------------------------------
92
Table 79: Inmarriage (Excluding Orthodox, Including Engaged to Be Married) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
640
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
=
640
= 10453.788
=
441
= 6729.257
=
633
------------------------------------------------------Married/E |
ngaged to |
Jewish
|
spouse/ci |
vil
|
union/fia |
ncee/form |
birthright participant (corrected)
er spouse |
no
yes
Total
----------+-------------------------------------------Intermar |
0.508
0.300
0.392
| [0.394,0.621] [0.242,0.365] [0.330,0.457]
|
Inmarrie |
0.492
0.700
0.608
| [0.379,0.606] [0.635,0.758] [0.543,0.670]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 633)
=
=
28.6868
10.1576
P = 0.0015
Table 80: Minimal Ordinal Logistic Regression Model of Inmarriage (Excluding Orthodox, Including Engaged to Be Married) Survey: Logistic regression
Number of strata
Number of PSUs
=
=
7
557
Number of obs
Population size
Design df
F(
1,
550)
Prob > F
=
=
=
=
=
557
8821.599
550
7.58
0.0061
-----------------------------------------------------------------------------|
Linearized
inmar2 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.037397
.5265736
2.75
0.006
1.226292
3.38499
------------------------------------------------------------------------------
93
Table 81: Standardized Ordinal Logistic Regression Model of Inmarriage (Excluding Orthodox, Including Engaged to Be Married) Survey: Logistic regression
Number of strata
=
7
Number of PSUs
=
627
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
8,
613)
Prob > F
=
627
= 10238.667
=
428
= 6514.136
=
620
=
6.49
=
0.0000
-----------------------------------------------------------------------------|
Linearized
inmar2 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
3.121832
.9852525
3.61
0.000
1.679749
5.801961
female |
.4082322
.1207372
-3.03
0.003
.2283846
.7297059
age |
1.172146
.1645164
1.13
0.258
.8897696
1.544136
tripage |
.8574036
.1231338
-1.07
0.284
.6467003
1.136757
hsritual2 |
.915195
.155855
-0.52
0.603
.6550479
1.278658
supschyrs1 |
.9787921
.0394006
-0.53
0.595
.9043965
1.059307
dayschyrs1 |
1.131914
.0636207
2.20
0.028
1.013625
1.264009
parintmar |
.2379008
.0852439
-4.01
0.000
.1177062
.4808311
------------------------------------------------------------------------------
Table 82: Jewish Status of Spouses Raised as non‐Jews (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
358
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------Current
|
Jewish
|
status of |
spouses
|
not born |
birthright participant (corrected)
Jewish
|
no
yes
Total
----------+-------------------------------------------not Jewish|
0.954
0.790
0.881
| [0.886,0.983] [0.675,0.871] [0.817,0.925]
|
Jewish|
0.046
0.210
0.119
| [0.017,0.114] [0.129,0.325] [0.075,0.183]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 351)
=
=
22.9194
10.2459
94
P = 0.0015
=
=
=
=
=
358
6321.701
159
2597.17
351
Table 83: Importance of Marrying a Jew (Excluding Orthodox, Unmarried Respondents Only) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1235
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------Thinking |
about the |
future,
|
how
|
important |
is it to |
you to
|
marry
|
someone
|
birthright participant (corrected)
Jewish?
|
no
yes
Total
----------+-------------------------------------------not at a |
.2851
.1406
.1829
| [.1889,.4057] [.1037,.1878] [.1423,.2319]
|
a little |
.1366
.1512
.1469
| [.0783,.2277] [.1138,.1982] [.1139,.1875]
|
somewhat |
.2235
.2058
.211
| [.1448,.3284] [.1645,.2544] [.1723,.2557]
|
very muc |
.3548
.5024
.4592
| [.255,.4691]
[.4428,.562]
[.406,.5133]
|
Total |
1
1
1
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.98, 3663.00)=
42.0789
3.3288
95
P = 0.0190
=
1235
= 21364.239
=
524
= 9697.154
=
1228
Table 84: Minimal Ordinal Logistic Regression Model of Importance of Marrying a Jew (Excluding Orthodox, Unmarried Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1235
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
1,
1228)
Prob > F
=
1235
= 21364.239
=
524
= 9697.154
=
1228
=
6.72
=
0.0096
-----------------------------------------------------------------------------|
Linearized
futmarry | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.932652
.4911275
2.59
0.010
1.173903
3.181818
-------------+---------------------------------------------------------------/cut1 | -1.052401
.2540229
-4.14
0.000
-1.550768
-.5540338
/cut2 | -.2499311
.232529
-1.07
0.283
-.7061292
.2062669
/cut3 |
.6377751
.2307914
2.76
0.006
.1849861
1.090564
------------------------------------------------------------------------------
Table 85: Importance of Marrying a Jew (Excluding Orthodox, Unmarried Respondents Only) by Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for futmarry
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.2588
0.1791
0.2164
0.3457
95% Conf. Interval
[ 0.1633,
0.3543]
[ 0.1317,
0.2265]
[ 0.1725,
0.2603]
[ 0.2434,
0.4481]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for futmarry
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.1530
0.1342
0.2075
0.5053
95% Conf. Interval
[ 0.1142,
0.1917]
[ 0.0986,
0.1699]
[ 0.1661,
0.2489]
[ 0.4463,
0.5642]
participant
1
96
Table 86: Standardized Ordinal Logistic Regression Model of Importance of Marrying a Jew (Excluding Orthodox, Unmarried Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1218
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
8,
1204)
Prob > F
=
=
=
=
=
=
=
1218
21054.84
507
9387.755
1211
11.02
0.0000
-----------------------------------------------------------------------------|
Linearized
futmarry | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.494618
.4077814
1.47
0.141
.8751086
2.552693
female |
.905887
.1954618
-0.46
0.647
.5932364
1.383312
age |
.9584706
.0903007
-0.45
0.653
.7967169
1.153064
tripage |
1.040817
.10912
0.38
0.703
.8473139
1.27851
hsritual2 |
1.652775
.1821249
4.56
0.000
1.331444
2.051655
supschyrs1 |
1.038997
.0296028
1.34
0.180
.9825118
1.098729
dayschyrs1 |
1.028177
.0456521
0.63
0.532
.9424016
1.12176
parintmar |
.2608534
.0797061
-4.40
0.000
.1432332
.4750607
-------------+---------------------------------------------------------------/cut1 | -.5586666
1.251287
-0.45
0.655
-3.013597
1.896264
/cut2 |
.3430773
1.266794
0.27
0.787
-2.142276
2.828431
/cut3 |
1.431473
1.260985
1.14
0.257
-1.042484
3.90543
------------------------------------------------------------------------------
97
Table 87: Dating (Excluding Orthodox, Unmarried Respondents Only) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
981
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------How many |
Jewish
|
dates in |
past
|
year,
|
excluding |
birthright participant (corrected)
no dates |
no
yes
Total
----------+-------------------------------------------None |
0.398
0.267
0.304
| [0.260,0.554] [0.202,0.345] [0.240,0.377]
|
A few |
0.120
0.203
0.179
| [0.063,0.215] [0.145,0.275] [0.133,0.237]
|
about ha |
0.119
0.060
0.076
| [0.053,0.244] [0.029,0.120] [0.045,0.127]
|
Most |
0.084
0.118
0.108
| [0.039,0.169] [0.073,0.185] [0.072,0.160]
|
All |
0.280
0.352
0.332
| [0.161,0.439] [0.274,0.439] [0.264,0.407]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(4)
=
F(3.77, 3669.34)=
33.2048
1.6023
98
P = 0.1745
=
981
= 16653.727
=
270
= 4986.642
=
974
Table 88: Minimal Ordinal Logistic Regression Model of Dating (Excluding Orthodox, Unmarried Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
321
Number of obs
Population size
Design df
F(
2,
313)
Prob > F
=
=
=
=
=
321
5863.919
314
8.74
0.0002
-----------------------------------------------------------------------------|
Linearized
datejew1 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.14018
.386412
0.39
0.699
.5853039
2.221087
parintmar |
.291514
.0865938
-4.15
0.000
.1624923
.5229812
-------------+---------------------------------------------------------------/cut1 | -1.027568
.3360044
-3.06
0.002
-1.688673
-.366463
/cut2 | -.2864184
.3155721
-0.91
0.365
-.9073217
.3344848
/cut3 | -.0042633
.3149812
-0.01
0.989
-.6240039
.6154773
/cut4 |
.4551931
.320679
1.42
0.157
-.1757582
1.086144
------------------------------------------------------------------------------
99
Table 89: Standardized Ordinal Logistic Regression Model of Dating (Excluding Orthodox, Unmarried Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
972
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
8,
958)
Prob > F
=
=
=
=
=
=
=
972
16498.77
261
4831.685
965
3.12
0.0017
-----------------------------------------------------------------------------|
Linearized
datejew1 | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
1.436483
.5275463
0.99
0.324
.6987232
2.953218
female |
.8630933
.2623857
-0.48
0.628
.4752938
1.567304
age |
.9822612
.1214585
-0.14
0.885
.7706233
1.252022
tripage |
1.094683
.1405156
0.70
0.481
.8509214
1.408275
hsritual2 |
1.425101
.1967682
2.57
0.010
1.086851
1.868622
supschyrs1 |
.9624348
.0371471
-0.99
0.321
.8922288
1.038165
dayschyrs1 |
1.043697
.0608885
0.73
0.464
.9307938
1.170294
parintmar |
.4675781
.1579823
-2.25
0.025
.2409319
.9074317
-------------+---------------------------------------------------------------/cut1 |
1.459903
1.669079
0.87
0.382
-1.81554
4.735346
/cut2 |
2.309551
1.66879
1.38
0.167
-.9653244
5.584425
/cut3 |
2.636717
1.664499
1.58
0.114
-.6297394
5.903173
/cut4 |
3.162792
1.665407
1.90
0.058
-.105445
6.431029
------------------------------------------------------------------------------
100
Table 90: Wedding Officiation by Marriage Type (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
502
Number of obs
Population size
Design df
=
=
=
502
7940.294
495
------------------------------------------------------Status of |
officiant |
(s) at
|
weddings |
and civil | spouse/former spouse/deceased spouse Jewish
unions
|
0
1
Total
----------+-------------------------------------------Neither |
0.492
0.039
0.170
| [0.371,0.613] [0.022,0.068] [0.129,0.220]
|
Non-Jewi |
0.073
0.000
0.021
| [0.039,0.132]
[0.011,0.039]
|
Rabbi an |
0.107
0.000
0.031
| [0.047,0.229]
[0.013,0.071]
|
Rabbi on |
0.328
0.961
0.778
| [0.221,0.456] [0.932,0.978] [0.724,0.824]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.63, 1301.11)=
241.6491
43.1728
P = 0.0000
Table 91: Wedding Officiation (Intermarried Respondents with a Religious Officiant Only, Estimated Proportions) Survey: Proportion estimation
Number of strata =
Number of PSUs
=
7
1248
Number of obs
Population size
Subpop. no. obs
Subpop. size
Design df
=
1248
= 21584.1
=
67
= 1167.51
=
1241
_prop_2: wedcuoff1 = Non-Jewish clergy only
_prop_3: wedcuoff1 = Rabbi and non-Jewish clergy
_prop_4: wedcuoff1 = Rabbi only
-------------------------------------------------------------|
Linearized
| Proportion
Std. Err.
[95% Conf. Interval]
-------------+-----------------------------------------------wedcuoff1
|
_prop_2 |
.1431698
.0450596
.0547684
.2315712
_prop_3 |
.2113029
.0790029
.0563089
.3662969
_prop_4 |
.6455274
.0837489
.4812223
.8098324
--------------------------------------------------------------
101
Table 92: Wedding Officiation (Excluding Orthodox, Intermarriages Only) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1248
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------Status of |
officiant |
(s) at
|
weddings |
and civil |
birthright participant (corrected)
unions
|
no
yes
Total
----------+-------------------------------------------Neither |
0.484
0.507
0.493
| [0.310,0.662] [0.384,0.629] [0.374,0.613]
|
Non-Jewi |
0.039
0.124
0.073
| [0.014,0.109] [0.060,0.239] [0.039,0.132]
|
Rabbi an |
0.136
0.057
0.105
| [0.047,0.335] [0.028,0.115] [0.045,0.225]
|
Rabbi on |
0.341
0.311
0.329
| [0.189,0.534] [0.207,0.440] [0.224,0.455]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.81, 3487.49)=
48.0761
1.3760
102
P = 0.2495
=
1248
= 21584.067
=
134
= 2289.378
=
1241
Table 93: Ritual at Intermarriages by Officiation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1248
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
=
1248
= 21584.067
=
135
= 2296.157
=
1241
------------------------------------------------------sumketchu |
wedcuoffrabbionly
p
|
no
yes
Total
----------+-------------------------------------------0 |
0.608
0.035
0.424
| [0.464,0.735] [0.007,0.148] [0.311,0.545]
|
1 |
0.197
0.039
0.146
| [0.114,0.319] [0.014,0.104] [0.087,0.235]
|
2 |
0.195
0.927
0.430
| [0.101,0.344] [0.823,0.972] [0.314,0.555]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(2)
=
F(1.84, 2286.45)=
596.6469
37.3616
P = 0.0000
Table 94: Ritual at Inmarriages by Officiation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1245
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------sumketchu |
wedcuoffrabbionly
p
|
no
yes
Total
----------+-------------------------------------------0 |
0.379
0.002
0.029
| [0.190,0.614] [0.000,0.011] [0.015,0.058]
|
1 |
0.057
0.005
0.009
| [0.018,0.165] [0.001,0.022] [0.004,0.023]
|
2 |
0.563
0.993
0.961
| [0.336,0.767] [0.977,0.998] [0.932,0.978]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(2)
=
F(1.96, 2431.50)=
453.7663
98.6693
103
P = 0.0000
=
1245
= 21551.303
=
367
= 5644.137
=
1238
Table 95: Presence of Children by Marital Status (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1201
Number of obs
Population size
Design df
=
1201
= 20818.339
=
1194
------------------------------------------------------|
Married, in civil union, or
|
divorced/separated
Has child |
Not marr
Married
Total
----------+-------------------------------------------0 |
0.987
0.512
0.802
| [0.962,0.996] [0.454,0.570] [0.772,0.828]
|
1 |
0.013
0.488
0.198
| [0.004,0.038] [0.430,0.546] [0.172,0.228]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1194)
=
=
405.7194
144.6207
P = 0.0000
Table 96: Presence of Children (Married Respondents Only) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1240
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------|
birthright participant (corrected)
Has child |
no
yes
Total
----------+-------------------------------------------0 |
0.473
0.548
0.512
| [0.374,0.574] [0.485,0.609] [0.454,0.570]
|
1 |
0.527
0.452
0.488
| [0.426,0.626] [0.391,0.515] [0.430,0.546]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(1)
F(1, 1233)
=
=
6.9961
1.5246
104
P = 0.2172
=
1240
= 21485.873
=
508
= 8120.596
=
1233
Table 97: Importance of Raising Jewish Children (Childless Respondents Only) by Participation (Estimated Proportions) . svy: tab futchild participant, col ci format(%10.3fc)
(running tabulate on estimation sample)
Number of strata
Number of PSUs
=
=
7
916
Number of obs
Population size
Design df
------------------------------------------------------Thinking |
about the |
future,
|
how
|
important |
is it to |
you to
|
raise
|
your
|
children |
birthright participant (corrected)
Jew
|
no
yes
Total
----------+-------------------------------------------not at a |
0.076
0.055
0.062
| [0.041,0.136] [0.037,0.080] [0.044,0.086]
|
a little |
0.127
0.046
0.073
| [0.078,0.200] [0.032,0.067] [0.053,0.100]
|
somewhat |
0.235
0.156
0.182
| [0.168,0.317] [0.126,0.192] [0.151,0.218]
|
very muc |
0.562
0.742
0.682
| [0.474,0.646] [0.701,0.780] [0.641,0.721]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.99, 2718.09)=
36.1393
6.3505
105
P = 0.0003
=
916
= 16454.046
=
909
Table 98: Minimal Ordinal Logistic Regression Model of Importance of Raising Jewish Children (Childless Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
916
Number of obs
Population size
Design df
F(
1,
909)
Prob > F
=
916
= 16454.046
=
909
=
15.61
=
0.0001
-----------------------------------------------------------------------------|
Linearized
futchild | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.207665
.442443
3.95
0.000
1.489754
3.271536
-------------+---------------------------------------------------------------/cut1 | -2.259588
.2128225
-10.62
0.000
-2.677269
-1.841908
/cut2 | -1.385255
.1878459
-7.37
0.000
-1.753917
-1.016593
/cut3 | -.2619255
.17319
-1.51
0.131
-.6018242
.0779732
------------------------------------------------------------------------------
Table 99: Importance of Raising Jewish Children (Childless Respondents Only, Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for futchild
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0945
0.1056
0.2347
0.5651
95% Conf. Interval
[ 0.0588,
0.1302]
[ 0.0654,
0.1459]
[ 0.1855,
0.2839]
[ 0.4817,
0.6485]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for futchild
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0452
0.0567
0.1567
0.7415
95% Conf. Interval
[ 0.0278,
0.0625]
[ 0.0386,
0.0748]
[ 0.1262,
0.1871]
[ 0.7020,
0.7810]
participant
1
106
Table 100: Standardized Ordinal Logistic Regression Model of Importance of Raising Jewish Children (Childless Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
881
Number of obs
Population size
Design df
F(
8,
867)
Prob > F
=
881
= 15743.704
=
874
=
14.90
=
0.0000
-----------------------------------------------------------------------------|
Linearized
futchild | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.253363
.4876533
3.75
0.000
1.473554
3.44585
female |
1.596483
.3252138
2.30
0.022
1.070357
2.381222
age |
.9772733
.090465
-0.25
0.804
.8149146
1.171979
tripage |
1.016885
.0978226
0.17
0.862
.8419265
1.228201
hsritual2 |
1.649723
.1676271
4.93
0.000
1.351453
2.013821
supschyrs1 |
1.075308
.0282354
2.77
0.006
1.021295
1.132178
dayschyrs1 |
1.083485
.0402398
2.16
0.031
1.007317
1.165412
parintmar |
.5321424
.1336296
-2.51
0.012
.325073
.8711135
-------------+---------------------------------------------------------------/cut1 | -1.075062
1.163111
-0.92
0.356
-3.357879
1.207755
/cut2 | -.0092381
1.176311
-0.01
0.994
-2.317962
2.299486
/cut3 |
1.35093
1.187771
1.14
0.256
-.9802862
3.682146
------------------------------------------------------------------------------
107
Table 101: Importance of Raising Jewish Children (Intermarried and Childless Respondents Only) by Participation (Estimated Proportions) Number of strata
Number of PSUs
=
=
7
1192
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
------------------------------------------------------Thinking |
about the |
future,
|
how
|
important |
is it to |
you to
|
raise
|
your
|
children |
birthright participant (corrected)
Jew
|
no
yes
Total
----------+-------------------------------------------not at a |
0.033
0.116
0.065
| [0.008,0.130] [0.039,0.297] [0.026,0.152]
|
a little |
0.209
0.101
0.167
| [0.070,0.478] [0.048,0.202] [0.072,0.340]
|
somewhat |
0.543
0.200
0.409
| [0.313,0.755] [0.100,0.359] [0.260,0.578]
|
very muc |
0.216
0.583
0.358
| [0.089,0.437] [0.421,0.730] [0.234,0.506]
|
Total |
1.000
1.000
1.000
|
------------------------------------------------------Key: column proportions
[95% confidence intervals for column proportions]
Pearson:
Uncorrected
Design-based
chi2(3)
=
F(2.91, 3450.54)=
237.1065
4.4368
108
P = 0.0045
=
1192
= 20865.981
=
79
= 1578.071
=
1185
Table 102: Minimal Ordinal Logistic Regression Model of Importance of Raising Jewish Children (Intermarried and Childless Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1192
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
1,
1185)
Prob > F
=
1192
= 20865.981
=
79
= 1578.071
=
1185
=
3.38
=
0.0663
-----------------------------------------------------------------------------|
Linearized
futchild | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
2.895718
1.675033
1.84
0.066
.9308378
9.008212
-------------+---------------------------------------------------------------/cut1 | -2.385748
.4576653
-5.21
0.000
-3.283673
-1.487824
/cut2 | -.9224066
.4198801
-2.20
0.028
-1.746198
-.0986154
/cut3 |
.9733361
.4031668
2.41
0.016
.1823358
1.764336
------------------------------------------------------------------------------
Table 103: Importance of Raising Jewish Children (Intermarried and Childless Respondents Only) by Participation (Predicted Probabilities) . prvalue, x(participant=0)
ologit: Predictions for futchild
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0843
0.2002
0.4413
0.2742
95% Conf. Interval
[ 0.0150,
0.1535]
[ 0.0364,
0.3640]
[ 0.2597,
0.6229]
[ 0.1170,
0.4315]
participant
0
. prvalue, x(participant=1)
ologit: Predictions for futchild
Confidence intervals by delta method
Pr(y=not_at_a|x):
Pr(y=a_little|x):
Pr(y=somewhat|x):
Pr(y=very_muc|x):
x=
0.0308
0.0899
0.3568
0.5225
95% Conf. Interval
[-0.0186,
0.0802]
[-0.0041,
0.1840]
[ 0.2052,
0.5085]
[ 0.3211,
0.7238]
participant
1
109
Table 104: Standardized Ordinal Logistic Regression Model of Importance of Raising Jewish children (Intermarried and Childless Respondents Only) Survey: Ordered logistic regression
Number of strata
Number of PSUs
=
=
7
1190
Number of obs
Population size
Subpop. no. of obs
Subpop. size
Design df
F(
8,
1176)
Prob > F
=
1190
= 20826.181
=
76
= 1531.492
=
1183
=
2.77
=
0.0049
-----------------------------------------------------------------------------|
Linearized
futchild | Odds Ratio
Std. Err.
t
P>|t|
[95% Conf. Interval]
-------------+---------------------------------------------------------------participant |
10.36331
7.345996
3.30
0.001
2.579345
41.63779
female |
.7825186
.5088551
-0.38
0.706
.218479
2.80272
age |
.8276688
.1863058
-0.84
0.401
.5321786
1.287229
tripage |
1.118632
.2882294
0.44
0.664
.6747458
1.854533
hsritual2 |
1.149504
.2924906
0.55
0.584
.6977516
1.893738
supschyrs1 |
1.317049
.1060204
3.42
0.001
1.124634
1.542384
dayschyrs1 |
1.14785
.0828567
1.91
0.056
.9962742
1.322487
parintmar |
1.382155
.8481856
0.53
0.598
.4146349
4.607312
-------------+---------------------------------------------------------------/cut1 | -4.124882
3.697927
-1.12
0.265
-11.38011
3.130344
/cut2 | -1.987222
3.953491
-0.50
0.615
-9.743857
5.769414
/cut3 |
.421641
4.057969
0.10
0.917
-7.539977
8.383259
------------------------------------------------------------------------------
110
Appendix 5: Survey Instruments Part 1: Main Survey Instrument Taglit‐Birthright Long Term Evaluation 1. These are tumultuous times. In the past month, how often have you actively sought news about... Never Once Once a Every Once a Several Times week few days Day a Day The economy? o o o o o o Health care? o o o o o o The wars in Iraq and Afganistan? o o o o o o Israel? o o o o o o 2. During the recent conflict in Gaza and southern Israel have you used any of the following, to keep track of events? □ Israeli news sources (Ha'aretz, Jerusalem Post, etc.) □ Jewish news sources (Forward, Jewlicious, Jewschool, etc.) □ Other news sources (CNN, NPR, New York Times, etc.) □ YouTube, Facebook or other social networking sites □ Word of mouth, personal email □ Other: ________ 3. If someone asked you about the current situation in Israel, how confident do you feel in your ability to give a good explanation? • Not at all confident • A little confident • Somewhat confident • Very confident 111 4. What country do you currently live in? •
•
•
•
United States (continue) Canada (go to 6) Israel (go to 6) Other: ________ (go to 6) 5. What zip code do you live in? ____________________ 6. Since turning 18 how many times, if any, have you been to Israel? • Never (go to question 11) • Once (continue) • Twice (continue) • 3 times (continue) • 4 times (continue) • 5 or more times (continue) (If you have been to Israel since turning 18 answer the following question) 7. How long was your longest visit to Israel since you turned 18? • Two weeks or less • More than two weeks but less than one month • One to two months • Three to nine months • More than nine months (If you have been to Israel since turning 18 answer the following question) 8. What was the last year you were in Israel? o 1992 o 1993 o 1994 o 1995 o 1996 o 1997 o 1998 o 1999 o 2000 o 2001 o 2002 o 2003 o 2004 o 2005 o 2006 o 2007 o 2008 o 2009 (If you have been to Israel since turning 18 answer the following question) 9. Did you ever go on a Birthright Israel trip? • Yes (continue) • No (go to question 11) 112 10. Thinking back on your Birthright Israel trip, would you say that the trip...... Not at All A Little Somewhat Made you feel closer to Israel? o o o Very Much o Made you feel closer to your Jewish heritage? o o o o Was a disappointment o o o o Was a life‐changing experience o o o o 113 A Little about You 11. To what extent do you feel... Not at All A Little Somewhat A connection to Israel? o o o Very Much o A connection to a worldwide Jewish community? o o o o A connection to the Jewish community where you live? A connection to Jewish customs and traditions? o o o o o o o o 12. Are you currently a student? • Yes (continue) • No (go to 15) (If you are not currently a student) 13. What is the last grade or level of schooling you have completed? • High school or less • Associates degree (AA, AN, etc.) • Bachelors degree (BA, BS, etc) • Masters degree (MA, MBA, MSW, etc.) • Professional degree (JD,MD, etc.) • Doctoral degree (PhD, etc.) • Other: ________ 14. What was your major or field of study? ____________________(go to 17) (If you are currently not a student) 15. What level of schooling are you currently enrolled in? • High school or less • Associates degree (AA, AN, etc.) • Bachelors degree (BA, BS, etc) • Masters degree (MA, MBA, MSW, etc.) • Professional degree (JD,MD, etc) • Doctoral degree (PhD, etc.) • Other: ________ 16. What is your major or field of study? ____________________ 114 What is your marital status? • Never married (go to 33) • Engaged to be married (continue) • Living with a life partner (go to 27) • Married (go to 21) • In a civil union (go to 21) • Separated / divorced (go to 23) • Widowed (go to 25) 17. Is your fiancé/fiancée... • Jewish • Protestant • Catholic • No Religion • Other: ________ 18. Was your fiancé/fiancée raised... • Jewish • Protestant • Catholic • No Religion • Other: ________ 19. Which of the following will officiate at your wedding ceremony? □ A rabbi or cantor □ A non‐Jewish clergy member □ A judge or justice of the peace □ Other: ________ Go to question 38 (If you are currently married or in a civil union) 20. Is your spouse... • Jewish • Protestant • Catholic • No Religion • Other: ________ (If you are currently married or in a civil union) 21. Was your spouse raised... • Jewish • Protestant • Catholic • No Religion • Other: ________ If married, go to question 29, if in a civil union go to question 31 115 (If you are currently divorced or separated) 22. Is your former spouse... • Jewish • Protestant • Catholic • No Religion • Other: ________ 23. Was your former spouse raised... • Jewish • Protestant • Catholic • No Religion • Other: ________ Go to question 29 (If you are currently widowed) 24. Was your spouse... • Jewish • Protestant • Catholic • No Religion • Other: ________ 25. Was your spouse raised... • Jewish • Protestant • Catholic • No Religion • Other: ________ Go to question 29 (If you are living with a life partner) 26. Is your life partner... • Jewish • Protestant • Catholic • No Religion • Other: ________ 27. Was your life partner raised... • Jewish • Protestant • Catholic • No Religion • Other: ________ 116 Go to question 38 (If you have ever been married) 28. Who officiated at your wedding ceremony? □ A rabbi or cantor □ A non‐Jewish clergy member □ A judge or justice of the peace □ Other: ________ 29. Did your wedding ceremony include........ □ Breaking a glass? □ Signing a Ketubah (Jewish marriage contract)? □ A chuppah (Jewish wedding canopy)? Go to question 38 (If you are currently in a civil union) 30. Who officiated at your civil union ceremony? □ A rabbi or cantor □ A non‐Jewish clergy member □ A judge or justice of the peace □ Other: ________ 31. Did your civil union ceremony include... □ Breaking a glass? □ Signing a Ketubah (Jewish marriage contract)? □ A Chuppah (Jewish wedding canopy)? Go to question 38 (If not currently married ‐ includes divorced/widowed/separated) 32. Do you have significant other (e.g. boyfriend or girlfriend)? • Yes (continue) • No (go to 36) 33. Is your significant other... • Jewish • Protestant • Catholic • No Religion • Other: ________ 34. Was your significant other raised... • Jewish • Protestant • Catholic • No Religion • Other: ________ 117 35. In the past year, how many of the people you dated were Jewish? • Did not date • None • A few • About half • Most • All 36. Thinking about the future, how important is it to you to marry someone Jewish? • Not Important • A little Important • Somewhat Important • Very Important 37. How many children do you have, if any? • 0 (go to 41) • 1 (continue) • 2 (continue) • 3 (continue) • 4 or more (continue) 118 (If you have one child or more) 38. Did you have a Jewish circumcision or naming ceremony for your oldest child? • Yes • No (If you have one child or more) 39. Is your oldest child... • Female • Male (If you have one child or more) 40. Are you raising your oldest child... • Jewish • Protestant • Catholic • No religion • Jewish and another religion • Have not decided yet • Other: ________ (If you currently have no children) 41. How important is it to you to raise your children Jewish? • Not Important • A little Important • Somewhat Important • Very Important 42. How many of your close friends are Jewish? • None • A few • Half • Most • All 43. Do you belong to ... A synagogue, temple, minyan, havurah or other Jewish congregation? A JCC or YMHA/YWHA? Any other Jewish organization besides a JCC or Jewish congregation? 119 Yes No O O O O O O 44. In the past year how often, if at all, have you attended some type of organized Jewish religious service? • Never • Once • Two or three times • Every few months • About once a month • Two or three times a month • Once a week or more 45. In the past year, have you had or attended a special meal on Shabbat... • Never • Sometimes • Usually • Always 46. Do you consider yourself to be...? • Secular/Culturally Jewish • Just Jewish • Reform • Conservative • Reconstructionist • Orthodox • No religion • Other: ________ 47. In the past year, have you volunteered for... Yes No Jewish causes? O O Non‐Jewish causes? O O 48. How much do you AGREE or DISAGREE with the following statements? Strongly Somewhat Somewhat Disagree Disagree Agree I have spent time trying to learn more about Judaism, such as its history, o o o traditions, and customs Strongly Agree o I think a lot about how my life will be affected by my being Jewish o o o o I have a clear sense of my Jewish background and what it means for me o o o o I have a strong sense of belonging to the Jewish people I feel good about my Jewish heritage o o o o o o o o 120 Your Upbringing 49. Were you raised… • Secular/Culturally Jewish • Just Jewish • Reform • Conservative • Reconstructionist • Orthodox • No religion • Other: ________ 50. Were you raised by... • Two Jews • A Jew and a non‐Jew • Two non‐Jews • A Jew • A non‐Jew 51. During your high school years, did someone in your home regularly light Shabbat candles? • Yes • No 52. During your high school years, did your family hold or attend a Seder? • Yes • No 53. During your high school years, did your family celebrate Hanukkah? • Yes • No 54. During your high school years, did your family keep kosher at home? • Yes • No 55. Did you attend an overnight camp that had Shabbat services or a Jewish educational program while growing up? • Yes (continue) • No (go to 58) 121 56. For how many years did you attend such a camp? •
•
•
•
•
•
•
•
1 or less 2 3 4 5 6 7 8 or more 57. During grades 1‐12, did you ever attend a supplementary Jewish school, like Hebrew or Sunday school? •
•
Yes (continue) No (go to 60) 58. For how many years did you attend such a school? •
•
•
•
•
•
•
•
•
•
•
•
•
Did not Attend 1 or less 2 3 4 5 6 7 8 9 10 11 12 or more 59. During grades 1‐12, did you ever attend a full time Jewish day school? •
•
Yes (continue) No (final thoughts) 60. For how many years did you attend such a school? • Did not Attend • 1 or less • 2 • 3 • 4 • 5 • 6 • 7 • 8 • 9 • 10 • 11 • 12 or more 122 Final thoughts Ok, we're almost done. Before we conclude the survey, I'd like to ask you one open‐ended question about your life since going on Birthright Israel. (For participants) 61. During the years since your trip, can you think of any decisions that you made that were influenced by your experience on Birthright Israel ( for example decisions about jobs, relationships, religious observance, how you spend your free time, etc.)? ______________________________________________________________________________
______________________________________________________________________________
________________________ (For nonparticipants) 62. Ok, we're almost done. Is there anything else you would like to add about your Jewish identity? ______________________________________________________________________________
______________________________________________________________________________
________________________ Thank you for completing this survey. Please provide your email address so that we can send you your gift card. ___________________ □ I would like to be notified when the results of this study are published. 123 Part 2: Parent Survey Instrument What is your relationship to {FIRSTNAME}? • Mother • Father • Sibling • Grandparent • Spouse • Other: ________ Was {FIRSTNAME} raised... • Secular/Culturally Jewish • Just Jewish • Reform • Conservative • Reconstructionist • Orthodox • No religion • Other: ________ What is {FIRSTNAME}'s marital status • Never married • Engaged to be married • Living with a life partner • Married • In a civil union • Seperated / divorced • Widowed Is {FIRSTNAME}'s spouse Jewish? • Yes • No Is the {FIRSTNAME}s fiancé/fiancée Jewish? • Yes • No 124 Appendix 6: Contact Protocols Part 1: Calling Protocol The Jewish Young Adult Study 2009 General Purpose of this study Learn about the current lives of young Jewish adults, in particular applicants to and participants in the early Birthright Israel trip cohorts (2001, 2002, 2003, 2004, 2005). Study Mode This is a dual mode study. Respondents are first contacted by email, if contact fails they are then contacted by phone. The research team The study is conducted by the Cohen Center for Modern Jewish Studies at Brandeis University. Co‐principal investigators: Prof. Len Saxe and Prof. Ted Sasson Project managers: Shahar Hecht and Ben Phillips Calling supervisor: Monica Pevzner Calling schedule In general, calls will be made Monday‐Thurs from 5‐8pm. Calls will be conducted on occasional Sunday afternoons between 3‐5 depending on the project’s needs. Call backs can be scheduled for any workday 9‐8pm. We DO NOT make calls on Friday nights and on Saturday. How to access the calling system Go to cc.cmjs.org. Enter your email and password Click on “Surveys” (on the top), then on “CATI MAIN” (on the left bar). Launch CATI. How to place a call? Dial 8+1+NUMBER. Before you place a call – make sure you know what time it is at the destination you are calling. How do you know who to call? You will receive a paper list/electronic file of people you will be calling that night. When in doubt of whether a person should be called consult with a supervisor. Goal of phone calls Complete a respondent questionnaire. 125
How to achieve this goal 1. Read the call history and check the status of survey completion (respondent and parent) before you pick up the phone. Do not call people on a Tuesday if the note says “please call back on a Wednesday or Thursday, not a Monday or Tuesday.” Do not call a parent number again if the parent already refused or already completed the parent survey. 2. Call the first phone number listed for the respondent If Do Respondent picks 1. Record status of phone # up 2. Complete respondent survey Parents pick up 1.
Record status and type of phone # 2.
Obtain new contact info for respondent: PHONE #: Ask what time zone respondent is in EMAIL: Get email also (especially if reluctant to provide phone number) If person is out of the country – we want their phone number/email. If respondent is in Israel check “In Israel” box. 3.
Complete parent survey 4.
Update phone number status to parents 5.
Call respondent using new contact info. In comments, indicate which number is the number to call to reach respondent (i.e., “call 555‐555‐5555”). If respondent lives with parents make sure to indicate this in the comment section. 6.
If only got email – select “Emailed survey” disposition and send the invitation email. Bad number 1.
A bad number is a disconnected line, a fax, a person indicating that you’ve reached the wrong number. VM with different names are not necessarily a bad number. 2.
Record status of phone # 3.
Call next number on the list 4.
Choose disposition of “no good phone numbers” if you exhausted the list of phone numbers for a particular respondent. Voice mail 1.Record status of phone # 2. Leave message on every working number even if you are not sure if the number is correct. Mark phone status as “Active”. If not sure about getting to the right place mark phone type as “unknown”. Use comments to explain the situation. There can be only one phone number of each type. When you select a type you will be overwriting the existing information. However, the history of changes can be viewed by clicking on the “view history” button. 3. Only leave one voice message per phone. After a VM has been left, keep calling until someone picks up. 4. Call next number for respondent if exists. 126
Refusals Break‐off 1. A hard refusal is a situation in which it is clear that we should NOT call again. 2. A soft refusal is a situation in which there might be a chance that a parent may change his/her mind about giving us contact info OR a respondent will change their mind about completing the survey. If respondent drops off mid‐survey you will need to save responses. Enter username: CMJS password: CMJS. Select disposition “call back later” and enter details in comments. Dispositions In order to update a record YOU MUST select a disposition and enter a comment. After you update the record, the CATI will take you back to the last case you were working on. Respondent survey completed VM‐Message left – If you left a message on a VM of respondents/parents/unknown number. Indicate in the comment box whether the message inbox belongs to the individual or is automated. VM‐no message left – If a VM regarding the study has already been left for this number. Left message with individual – If you spoke to a parent/other who would not give out respondents contact info but was willing to take a message. Email survey – Only use this disposition if the only contact information provided by parents is an email address. When this disposition is selected a new window opens. You will be asked whether you want to send various types of emails. ALWAYS check that the email address displayed matches the new email you got. Select REGULAR EMAIL. You will be able to view the text of the message to verify that this is the text you want to send. Call back later – If a respondent/parent requested to be called at a different time. Make sure to give specific details in the comments. Language barrier – If you are unable to communicate effectively with the person you are talking to on the phone. Use this disposition for cases where a Russian speaker (or other) can convey information more effectively. Make sure to leave details in the comments. Soft Refusal – If there is a chance to get a different outcome if we call again. Hard refusal – If it is clear we should NOT call again. 127
No good phone numbers – If ALL phone numbers for a person are bad. No Answer – if phone rings but doesn’t pick up and no VM Got new contact info – If parent survey is completed and ALSO got new contact info select this disposition to save the new info entered. Proceed to call respondent using new number. Refusal conversion – These cases were marked originally as either Soft Refusal or Left Message with Individual. Call these cases back to try to convince them to change their minds and do the survey. Daytime phone call – As a result of searching, you may speak with the individual that would like to be called during the daytime. Late call – If an individual would like to be called after typical calling hours (8 pm). Other – Only use this disposition if there is no other disposition that describes the situation. 128
Voice Mail Script – Evening Calls For Home/Cell Numbers: Voice mail Participants: “Hi, I’m calling from Brandeis University for [NAME]. We’re surveying young adults who applied to Birthright Israel in the past 8 years. If [NAME] completes the survey he/she will receive a $20 Amazon.com gift card. Please call Monica at 781‐736‐3821 or email us at study@brandeis.edu to arrange a time to complete the survey. If unsure whether VM belongs to person: “Hi, I’m calling from Brandeis University. I’m trying to get in touch with [FULL NAME]. We’re surveying young adults who applied to Birthright Israel in the past 8 years. If [NAME] completes the survey he/she will receive a $20 Amazon.com gift card. Please call Monica at 781‐736‐3821 or email us at study@brandeis.edu to arrange a time to complete the survey. Voice mail Non‐Participants: “Hi, I’m calling from Brandeis University for [NAME]. We’re surveying Jewish young adults. If [NAME] completes the survey he/she will receive a $20 Amazon.com gift card. Please call Monica at 781‐736‐3821 or email us at study@brandeis.edu to arrange a time to complete the survey. If unsure whether VM belongs to person: “Hi, I’m calling from Brandeis University. I’m trying to get in touch with [FULL NAME]. We’re surveying Jewish young adults. If [NAME] completes the survey he/she will receive a $20 Amazon.com gift card. Please call Monica at 781‐736‐3821 or email us at study@brandeis.edu to arrange a time to complete the survey. For Work number – Daytime Calls When calling a work number of the respondent or their relative, please leave a generic voicemail for them. Two examples are written below. Of Respondent: Hi, I’m calling from Brandeis University for [NAME]. We’re surveying young adults, when [NAME] completes the survey he/she will receive a $20 Amazon.com gift card. Please call Monica at 781‐736‐3821 or email us at study@brandeis.edu to arrange a time to complete the survey. Of Relative: Hi, I’m calling from Brandeis University for [RELATIVES FULL NAME]. I’m trying to get in touch with [RESPONDENTS FULL NAME]. We’re surveying young adults, when [NAME] completes the survey he/she will receive a $20 Amazon.com gift card. Please call Monica at 781‐736‐3821 or email us at study@brandeis.edu. 129
Calling protocol The script below is intended to be a starting point rather than something you must read word‐for‐word on every occasion. You should adjust what you say depending on your sense of the person you are speaking to. Above all, sound like a person, not a recorded message! Hello, can I please speak to [RESPONDENT NAME]? If seems not known at this phone number [Probe for information:] Do you know where I can reach [RS]? If known: Go to “If known but doesn’t live there” script If not known: Thank you very much. If known but doesn’t live there: For Participants: I’m calling from Brandeis University. My name is [YOUR FIRST NAME]. We are conducting a study of young adults who went a Birthright Israel trip in the past 8 years. We would like to contact [RS FIRST NAME] to participate in the study. Would you be able to give me her/his contact information? [If reluctant]: Many organizations here in North America look to our research to better understand Jewish young adults and the kinds of Jewish experiences they are looking for in their lives right now. We will send [RS FIRST NAME] a $20 Amazon gift certificate if s/he completes the short survey. For Non‐participants: I’m calling from Brandeis University. My name is [YOUR FIRST NAME]. We are conducting a study of Jewish young adults. We would like to contact [RS FIRST NAME] to participate in the study. Would you be able to give me her/his contact information? [If reluctant]: Many organizations here in North America look to our research to better understand Jewish young adults and the kinds of Jewish experiences they are looking for in their lives right now. We will send [RS FIRST NAME] a $20 Amazon gift card if s/he completes the short survey. [For privacy reasons, some parents will be willing to pass on our contact info, but will not give out their child’s contact info. Give them Monica’s contact info.] [Whether or not contact info is given:] What is your relationship to [RS FIRST NAME]? [If a relative:] I have three short questions about [RS FIRST NAME] so that we have some information about her/him in case we can’t get in touch with her/him. [GO TO PARENT SURVEY] 130
Use your judgment as to whether informant can answer the questions on parent questionnaire. As a general guideline don’t open parent survey for employers, children under age 18, former roommate, etc. If you are unsure whether the informant can give you the information you need fill out a PAPER PARENT SURVEY and indicate the name of the respondent. Inform your supervisor. If calling a work number of the individual’s parent For Participants: I’m calling from Brandeis University. My name is [YOUR NAME]. We are looking for [RS NAME] regarding a study we are conducting of young adults who applied to a Birthright Israel trip in the past 8 years. The survey will take about 15 minutes and when they complete the survey they will receive a $20 Amazon.com gift card to thank them for their time. [RS FIRST NAME]’s responses will help us understand how Birthright Israel impacts Jewish young adults. For Non‐Participants: I’m calling from Brandeis University. My name is [YOUR FIRST NAME]. We are looking for [RS NAME] regarding a study we are conducting of Jewish young adults. The survey will take about 15 minutes and when you complete the survey you will receive a $20 Amazon.com gift card to thank you for your time. If lives there but unavailable [work, etc]: For Participants: I’m calling from Brandeis University. My name is [YOUR NAME]. We are conducting a study of young adults who applied to a Birthright Israel trip in the past 8 years. What would the best way for me to get in touch with her/him? [Get call back time and/or other phone number]. What is your relationship to [RS NAME]? [If a relative:] I have three short questions about [RS FIRST NAME] so that we have some information about her/him in case we can’t get in touch with her/him. [GO TO PARENT SURVEY] For Non‐participants: I’m calling from Brandeis University. My name is [YOUR FIRST NAME]. We are conducting a study of Jewish young adults. What would the best way for me to get in touch with her/him? [Get call back time and/or other phone number]. What is your relationship to [RS NAME]? [If a relative:] I have three short questions about [RS FIRST NAME] so that we have some information about her/him in case we can’t get in touch with her/him. [GO TO PARENT SURVEY] If parents/person who picks up the phone speak Russian If parents/relatives who pick up the phone are Russian speakers and there is a language barrier – If there is a caller who is a Russian speaker close by call them over and hand over the call. If not – “I will ask a colleague who speaks Russian to call you later”. Select a disposition of “Language barrier” and make sure to notify supervisor that this case needs to be handed over to a Russian speaker. 131
If respondent For Participants: I’m calling from Brandeis University. My name is [YOUR NAME]. We are conducting a study of young adults who applied to a Birthright Israel trip in the past 8 years. The survey will take about 15 minutes and when you complete the survey you will receive a $20 Amazon.com gift card to thank you for your time. Your responses will help us understand how Birthright Israel impacts Jewish young adults. For Non‐Participants: I’m calling from Brandeis University. My name is [YOUR FIRST NAME]. We are conducting a study of Jewish young adults. The survey will take about 15 minutes and when you complete the survey you will receive a $20 Amazon.com gift card to thank you for your time. May I start with the first question? If unwilling/unable to take survey at this point: Is there a better time that we could call you? [Be mindful of our calling hour limits. Try to press for a specific time].
132
FAQs I’m not Jewish This study uses a very broad definition of what being Jewish is. We are interested in people who consider themselves to be Jewish or who have Jewish backgrounds. I’m not engaged in the Jewish community/I am not a practicing Jew We’re still interested in hearing from you. We want to learn about what young adult Jews care about regardless of their level of involvement. Let’s start with the first question… I didn’t go on the trip We’re collecting data from all those who were interested in the program. Since most Birthright applicants end up going on the trip, it is even more important that we hear from as many non‐participants as possible, so we have enough data to work with. Let’s start with the first question… I hated the trip This is exactly the place to voice your opinion. We need to hear from all those who were interested in the program. We need to report a diversity of voices. How did you get my phone number? For Participants: We found your phone number via the contact information you provided to Birthright Israel when you applied for the trip. For Non‐Participants: You were on a list of people who applied to go on a Birthright Israel trip in the past 8 years. [If necessary]: Birthright Israel has let us use their contact list for the sole purpose of this study. [If insist that they did not apply for the trip]: Your name must have gotten on our list by mistake. I apologize for the inconvenience. Thank you for your time. What are you going to use my contact information for? We will only use your contact information for the purposes of this study. We will not put you on any mailing or calling lists or share your information with any other organization. How long will this take? The survey is short and will take less than 15 minutes to complete. What’s in this for me? When you complete the survey you will receive a $20 Amazon.com gift card. You will receive your gift card within 48 hours of completing the survey. Also, many organizations here in North America look to our research to better understand Jewish young adults and the kinds of Jewish experiences they are looking for in their lives right now. 133
Why is the survey important? For participants: The information we provide is meant to help Birthright Israel understand its impact on Jewish young adults. Many organizations here in North America look to our research to better understand Jewish young adults and the kinds of Jewish experiences they are looking for in their lives right now. For non‐participants: Many organizations here in North America look to our research to better understand Jewish young adults and the kinds of Jewish experiences they are looking for in their lives right now. What is the purpose of this study? For participants: The information collected in this study is meant to help Birthright Israel understand the long‐term impact of the trip on Jewish young adults. For non‐participants: The information collected in this study is meant to assess the attitudes of Jewish young adults toward Israel and the Jewish community Who is funding this study? The Jewish Young Adult Study is funded by Taglit‐Birthright Israel, The Jim Joseph Foundation and the Charles and Lynn Schusterman Family Foundation. How will the information be used? The data we collect will help Jewish organizations understand Jewish young adults and the kinds of Jewish experiences they are looking for in their lives right now. Who will see the data? Your responses are kept strictly confidential. All survey data is used for research purposes only. Our analyses and reports present data from all respondents in groups. We don't report the responses of any particular individual and no individual will be identified. I feel uncomfortable discussing certain issues If you are still uncomfortable, feel free to skip that particular question. I don’t have time The survey is short and will take about 15 minutes to complete. We can start with a few questions now and finish at a later date. [If insists that can’t do it now:] Is there a better time I can call? This survey is very important and you will receive a $20 Amazon.com gift card for your time. [Let the supervisor know immediately so they can reschedule the call.] [If that doesn’t work:] 134
This survey can also be completed on the Web. Is there an email address I can send the survey link to you? [If that doesn’t work AND you DO NOT have a completed parent survey:] Could you please answer 2 quick questions so we know something about the people who don’t want to participate in this study? [Complete parent survey and mark relationship as “other” and in the textbox indicate “self”. How do I know this is legitimate? I can give you the name and contact information of my supervisor. Also, you can check our website on the internet. Would you like me to give you the URL for our site? (www.brandeis.edu/cmjs). Shahar’s Contact info: Shahar Hecht Research Associate shecht@brandeis.edu 781‐736‐3948 The study’s email account: study@brandeis.edu Monica’s phone number: 781‐736‐3821 135
When things go wrong People may be upset by Birthright or Israel or something else. Be sympathetic. If they had a bad experience with Taglit, let them know that this survey is the perfect place for them to make their feelings known. If the person is upset with Israel, we want to hear from everybody, regardless of what they think of Israel. If the respondent won’t do the survey, thank them very much for their time and hang up. If someone asks to speak with the supervisor, that’s OK. Find the supervisor. People may be abusive. If this happens, simply say “Thank you very much for your time” and hang up. You don’t need to wait for a pause in the tirade. Make a comment in the CATI system.
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Instructions for reading out questions In general An ideal interview should sound something like a conversation. Rather than forcing respondents to listen to every choice every time, we’re going to use conversational tricks like pauses to attempt to elicit answers. If the pause doesn’t get a person to answer, we gently prompt them by reading response categories. If a respondent stops you while you are reading the response categories, don’t make them listen to them all. However, don’t assume you know what a respondent would answer. If a respondent gives an answer that doesn’t fit into a category, gently try to prompt them into one by first rereading the response categories (“Would that be ‘somewhat agree’ or ‘strongly agree’?”) and, if that fails, say that you understand that the categories do not quite fit, but which one would be the most appropriate. If that doesn’t work, move onto the next question. SKIP THE INTRO PAGE OF THE WEB SURVEY Table questions Read the beginning of the question (above the table) the first time you read it. For the next response, you should supply the stem, pause, and if the respondent doesn’t answer, then the response categories. For example, the first questions should be read as follows: These are tumultuous times. In the past month, how often have you actively sought news about the economy? Never, once, once a week, every few days, once a day, several times a day? How often have you actively sought news about health care? [pause for a moment] Never, once, once a week, every few days, once a day, several times a day? You don’t need to read all the categories for the second and later items in the table. Just record the first answer the respondent chooses. Check box items Read out question then item, ending with a question mark, then pause for response. Then read out next item and pause. For example, the second question should be read as follows: During the recent conflict in Gaza and southern Israel, have you used any of the following to keep track of events? Israeli news sources? Pause. [If necessary: Yes or no] Jewish news sources? Pause. [If necessary: Yes or no] 137
IF RESP SEEMS CONFUSED REPEAT STEM: Have you used Jewish news sources to keep track of events? How about other news sources to keep track of events? For this question no need to read out parenthetical unless necessary for prompting. Other text boxes Choose “other” when respondent’s answer doesn’t fit any of the categories. If answer is ambiguous, probe for specifics. For example: Was your spouse raised? And the response is “Mixed”. Probe: What religions would those be? Specific questions Did your wedding/civil union ceremony include any of the following Jewish elements? READ PARANTHETICAL * Breaking a glass? * Signing a Ketubah, a Jewish marriage contract? * A Chuppah, a Jewish wedding canopy? Questions about children If respondent has only one child, omit the word “oldest” from all subsequent questions. If a respondent has multiple children and asks you why we chose the oldest, the reason is that we don’t want them to have to go through every child, so we just picked the oldest. Open‐ended question at the end of the survey If the respondent says something quotable, please record it verbatim and place it in quotes, to differentiate from your paraphrasing. Here’s an example. RS found Israel fascinating. Loved BRI: “Birthright is the greatest thing since sliced bread!” Don’t be afraid to ask the person to pause while you catch up. “Can you just give me a moment to catch up? Your comments are really great and I want to make sure I get them down.” Remember to ask for specifics—for example, if a person says BRI made them more involved you should ask in what way? What to do if someone cuts you off in the middle of the survey? We only have a few more questions left. You will be eligible for your $20 gift card when we’re done. Ending the survey Great! We’re done! I just need to get your email address so we can email you your Amazon gift card. SPELL OUT EMAIL BACK TO RESPONDENT. [If they want to know what 138
their email will be used for – “We will use your email only for the purposes of this study. We won’t add you to any mailing list or give out your information to any other organization”. Would you like to be notified when the results of this study are published? Thank you! Entering an email address at the end of the survey is mandatory. If respondent declines the gift card check the “no gift, thanks!” box. If respondents refuse to give their email address, they will not receive the gift card. If they are fine with this, enter the study account in the address box: study@brandeis.edu and check the “no gift” box. 139
Part 2: Internet Searching Protocol Jewish Young Adult Study 2009 Standard Search Protocol General Purpose of this study Learn about the current lives of young Jewish adults, in particular applicants to and participants in the early Birthright Israel trip cohorts (2001, 2002, 2003, 2004). Study Mode This is a dual mode study. Respondents are first contacted by email, if contact fails they are then contacted by phone. The research team The study is conducted by the Cohen Center for Modern Jewish Studies at Brandeis University. Co‐principal investigators: Prof. Len Saxe and Prof. Ted Sasson Project managers: Shahar Hecht and Ben Phillips Calling supervisor: Monica Pevzner Calling schedule In general, calls will be made Monday‐Thurs from 5‐8pm. We might make calls on Sundays depending on the project’s needs. Call backs can be scheduled for any workday 9‐8pm. We DO NOT make calls on Friday nights and on Saturday. How to access the calling system Go to cc.cmjs.org. Enter your email and password Click on “Surveys” (on the top), then on “CATI MAIN” (on the left bar). Launch CATI. How to place a call? Dial 8+1+NUMBER. Before you place a call – make sure you know what time it is at the destination you are calling. Goal of phone calls Complete a respondent questionnaire. Which cases are researched? Cases are designated as “search cases” in the following situations: - All phone numbers listed for a case are no good. - A parent/relative was reached, but refused to give direct contact information about respondent. - No answer on listed numbers on multiple tries (most likely wrong number). 140
Goal of searching Finding new contact information (email, phone number, street address) for survey participants. How do I know which cases are to be researched? You will receive a list of names and the replicate they are in. Lists will usually be published on Google.docs. Your name will be assigned to specific cases. How to achieve getting new info goal 1. “Know your case” ‐ Check previous history/dispositions The first thing you should do when reviewing any case is familiarize yourself with the information available about the case: - Call history: Calling dispositions are displayed at the top of the page. - Status of contact info: Status of phone numbers and emails is listed in the “Contact information” section. - Previous search results: To view a full history (including dispositions, changes to contact info, emails sent and search results) click on the + sign for “Hide/Show communication history”. If the case has already been searched to some extent, there should be a history log of all data found and the previous searcher’s advice on how to proceed next. - BRI data: Click on the “View BRI data to show more information about the case. This information may include additional phone numbers, names of relatives, addresses and information about education. 2. Identify your search goal – what information are you looking for? In some cases the goal will already be defined – Shahar will enter a “Need more research” disposition with specific instructions about what you should be looking for (e.g. “Look for Monica, not parents”). When no instructions exist, ask yourself: what do I already know about this case? What do I need to know? Who are the people I am searching for? Our goal is to get in touch with the respondent. If we can’t find direct contact for them we want to talk to their parents to gather information about the respondent and perhaps get direct contact info. If we can’t find any contact information we want to record any residual information that will help us learn about the people we can’t find. The priorities for searching are: ‐ Primary contact information for respondent (phone number (home, cell, work), email, address) ‐ Indirect contact info: phone numbers for parents, siblings, spouse ‐ Indirect information about respondent: Information about marriage, Jewish background, kids. 141
3. Triangulate and verify information When searching for a person use multiple data sources (see below) to make sure that the information you are finding actually refers to the person in question. There are many people who might share the same name. We need to try and avoid mistaken identity cases. The information entered into the system and handed to callers should be as accurate as possible. Each case is only researched ONCE. Exhaust all avenues in this search. Enter all GOOD data into the CATI for callers to act on. Check all questionable sources of data and rule them out if necessary. If necessary, pick us the phone and dial numbers you are not sure about. You might need to make some phone calls to verify that numbers are disconnected or that a person actually works at the company you found him at. 4. Record your actions and findings Describe your search process and your findings in the “Add to history” box in the “communication history” section. Use a coherent narrative explaining your logic and the avenues you researched. All of your findings should be recorded in the history log IN ADDITION to being entered into the CATI. Use a word/text document if you feel more comfortable writing and editing your findings before entering them into the log. This is going to be the only record of your search efforts and will be the bases for deciding how to proceed with a case. It needs to be coherent to people other than yourself. 5. Reach a conclusion At the end of the search process you should reach a conclusion about what should happen with the case. If you found possible numbers for a respondent and then possible numbers for parents and then also a Facebook account, describe the steps that need to be followed: e.g. “Call Anna at 4455 number, then try 3344. If nothing try her mother Nichole at 5566. If fails no more information.” 142
Research tools 1. Intelius –People Search by Name Login to www.Intelius.com with the following information: Username: xxxx@brandeis.edu Password: xxxxx Run a people search for the person by name. Sometimes the name is an unusual one and there is only one hit, but other times there can be hundreds of results. To narrow down your results, or to verify the correctness of any single result, always take a look at the age, location, and relatives provided by Intelius. We have a birthday and a location for everybody in our database, and in many cases we have multiple addresses and parents’ names or other guardians as well. If you think you’ve found a probable match, select “view details” and consume a credit. This will display the details of all the hits on the page, so don’t worry if there is more than one hit you wanted to investigate, you will see them all. Note any numbers found in the history. Cross check these numbers with anywho and record the results (see below for details on anywho). Note: If you include a middle initial in your search, you could get an entirely new set of results from running the same search without it. Note also: Don’t be afraid to consume a credit if you’re not certain. If you don’t think there’s much of a chance or if there’s no phone number listed then there’s no need to try. 2. Public Records Pro – Public Records Login to www.PublicRecordsPro.com with the following information: Username: xxxxx@brandeis.edu Password: xxxxx Click on “people search” to search by name. Similarly to Intelius, you may get many results or very few. If there are too many results to sort through manually, you can refine your search by including a middle initial, birth year or location. If there are not too many hits though it is better not to do this, as PRP does not have a birth year on record for every hit. In addition, the location data we have may be outdated, so you may be preventing yourself from seeing the up to date location on record. If you think you’ve found a probable match, record the number in the history. PRP keeps track of the times during which the number was registered, which can be used in judging the potential usefulness of any hit, so include that data in the log as well. Cross check these numbers with anywho and record the results (see below for details on anywho). 143
3. Emailfinder Login to www.Emailfinder.com with the following information: Username: xxxxx@brandeis.edu Password: xxxxx Email Search: Conduct a “Search by Email Address” for the email address in the BRI Data. Emailfinder works by scanning a long list of other websites to see if this email address is on file for them, and then it pulls all that info out. Sometimes this results in concrete information being reported such as location or phone number, but this information is generally pulled from a source that may not be up to date and which should always be double checked if possible. Emailfinder lists the sites it finds as it finds them. It is also worth running a “Find an Email Address” search for the person’s name, though it can often be difficult to sort through the results. If a strong looking match is found, run it through the “Search by Email” again and see if you can verify it. People Search: A people search for a person will provide a list of “free results” listing addresses and phone numbers and in some cases relatives. At the bottom of this list there is another list of cases for which a “background report” is available. This list provides invaluable information about the person you are looking for. It will list different spellings for the person’s name and other names they may be known as. This is extremely useful in finding women who have changed their name after getting married. Other information provided is a list of all locations known for the person and a list of people known to be related to the person. Note that it is not clear how relatives are matched to person and it may be difficult to determine the relationship to the respondent. Record all the information you find on Emailfinder and use it in combination with other sources to find contact information. 4. LinkedIn You do not need an account to use LinkedIn. Simply do a keyword search for the person’s name and note the results. If there are too many results, LinkedIn allows you to sort by school, so enter any school information known (from BRI Data or previous searching) to attempt to narrow down the list. If you have already located the page via Emailfinder, this is obviously unnecessary. If current job information seems current, look up the company the person works at and establish contact either through email or phone. If there is no publically available company directory call the main number and ask to be transferred or inquire about further info. Follow the phone script as necessary. 144
5. Facebook Login to www.Facebook.com with the following information: Username: xxxxx@brandeis.edu Password: xxxxx Search for the person by name. If there are too many results, Facebook allows you to sort by any network, so you can try entering any schools or locations where the person was known to be. Note any probable hits found along with their network info in the history. 6. Google Google the person’s name and take a quick look around. Do they have a personal website? Are they well published on the internet? See if you can get any new contact information. If you already have some decent information from the above searches, your Googling can stop here. If the above searches have yielded no data, and especially if they have produced peripheral evidence without actually providing contact information, an extensive Google search is warranted. Take a look past the first few pages, as sometimes they have posted in lesser known forums or sent publicly recorded emails that will feature contact information but won’t be popular enough to be in the top 10 or 20. Try searching for their name followed by a school they attended, or a job they worked, or their current location, to see if anything uniquely identifiable comes up. Be creative and persistent, you will be surprised at what you can find publicly. Always note wedding and birth announcements. Include the link but also describe the info as some of these webpages are not permanent and the info might not be available later on. It is not uncommon to discover a wedding announcement on a google search or note a newly hyphenated last name on facebook. If no information can be found for the person themselves, a search for their spouse will often be successful in reaching our person. Be sure to note the indirect source in the history and in any dispositions. Note: In cases where a marriage and corresponding new last name is discovered, a repetition of the above steps with the new name should take place even before searching for a spouse. Note: You may often encounter the site www.whitepages.com on your google search. This is a good site but it is very similar to other sources we use, so record any information found there but it is not necessarily something you should seek out every time. 145
7. Anywho Although it is listed last, anywho is best used in tandem with many of the above searches. www.anywho.com is a white pages service similar to many that we use, but what makes it unique is that it offers a reverse phone number lookup feature for free. This is almost exclusively what we will use it for. The reverse phone number lookup feature can be found here (http://www.anywho.com/rl.html), or you can navigate easily to it from the main page. It is extremely easy to use too – simply enter the number and press find. Anywho does not have a perfect track record and is not meant to be used as an ultimate verification check, merely as a tool to help us increase the accuracy of our searches. Some numbers will turn up as belonging to the person or their relatives but will be outdated. Anywho does not have a record of mobile numbers, and if you enter a mobile number into the field it will return a “not available” message similar to a number that is simply out of service or unlisted. However, it is generally fairly accurate and hits confirmed by it should always be considered more reliable than hits disconfirmed by it. 146
References Battaglia, M. P., Izrael, D., Hoaglin, D. C., & Frankel, M. R. (2004). Practical Considerations in Raking Survey Data. Paper presented at the American Association for Public Opinion Research. Brick, J. M., Montaquila, J. M., & Roth, S. (2003). Identifying Problems With Raking Estimators. Paper presented at the annual meeting of the American Statistical Association, San Francisco, CA. Dillman, D. A. (2007). Mail and Internet Surveys: The Tailored Design Method (2nd ed.). Hoboken, NJ: John Wiley & Sons. Fletcher, R., & Leyffer, S. (n.d). MINLPBB [computer software]. Dundee, United Kingdom: University of Dundee. Fourer, R., Gay, D. M., & Kernighan, B. W. (2009). AMPL (Version 1.6) [computer program]. Albuquerque, NM: AMPL Optimization. Phillips, B. (2009). A New Approach to Optimal Weight Trimming and Compression. Paper presented at the American Association for Public Opinion Research. Rosenthal, R. (1994). Parametric Measures of Effect Size. In H. Cooper & L. Hedges (Eds.), The Handbook of Research Synthesis (pp. 231‐244). New York: Russell Sage Foundation. Werner, J. (2003). QBAL (Version 1.52M) [computer program]. Pittsfield, MA: Jan Werner Data Processing. 147 The Maurice and Marilyn Cohen Center for Modern Jewish Studies at Brandeis University is a
multi-disciplinary research institute dedicated to the study of American Jewry and the development
of religious and cultural identity.
Brandeis University