Supplementary Information - PNAS

Supplementary Information‘Biased hate crime perceptions may reveal supremacist sympathies’

ContentsSupplementary Information A: Annotated Analyses .................................................. 1Supplementary Information B: Full Survey of Study 1 ............................................. 145Supplementary Information C: Full Surveys of Studies 2a and 2b ............................ 201Supplementary Information D: Full Survey of Study 3 ............................................. 304Supplementary Information References ..................................................................... 361

www.pnas.org/cgi/doi/10.1073/pnas.1916883117

Supplementary Information AAnnotated Analyses of ‘Biased hate crime perceptions may reveal supremacist sympathies’

Jannis Kreienkamp, N. Pontus Leander, Maximilian Agostini

24-Feb-2020

ContentsData Import and Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Study 1: Pittsburgh - Thousand Oaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Study 2a: Christchurch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45Study 2b: Utrecht . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Meta Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Study 3: El Paso and Dayton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Software Info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

The data sets are available at: https://osf.io/p9yca/

Note. Box-plots display the interquartile range (IQR, center box), and the whiskers extend 1.5*IQR from the lower and upper hinge. The white point indicates the mean and the white center line indicates the median.

Data Import and CleaningRaw Data

In a first step we import the raw Qualtrics data, which was downloaded as an SPSS file.

Data format and number of participants in the raw data:

## List of 5## $ PittTsdOaks :Classes 'tbl_df', 'tbl' and 'data.frame': 770 obs. of 404 variables:## $ PittTsdOaksCleaned:Classes 'tbl_df', 'tbl' and 'data.frame': 380 obs. of 513 variables:## $ Christchurch :Classes 'tbl_df', 'tbl' and 'data.frame': 828 obs. of 449 variables:## $ Utrecht :Classes 'tbl_df', 'tbl' and 'data.frame': 544 obs. of 376 variables:## $ ElPasoDayton :Classes 'tbl_df', 'tbl' and 'data.frame': 910 obs. of 425 variables:

1

https://osf.io/p9yca/

Cleaned Data

The raw SPSS file has been pre-processed to include filter variables.All studies had pre-established exclusion criteria. For all studies, data quality was considered insufficientwhen the same IP address was used multiple times (all duplicate IP participants were removed), participantsstraight-lined on multiple scales with reversed items, or entered nonsensical text entries in open text fields(e.g., survey feedback field).We had the following filter criteria and drop-outs per shooting:

1. Pittsburgh and Thousand Oaks:• did not respond on either of the two waves: n = 337 (note: this was not originally set up to be a

multi-wave study; we only sought to recontact participants after the Thousand Oaks shooting aweek later).

• missing on key variables: n = 11• data quality: n = 2

2. A. Christchurch:• data quality: n = 37• ethnicity: n = 146 (note: Because we were interested in the extend to which people would endorse

white nationalistic beliefs, we excluded anyone who did not identify as “white”).• missing on key variables: n = 2

3. B. Utrecht:• data quality: n = 36• ethnicity: n = 24 (note: Because we were interested in the extend to which people would endorse


4. El Paso and Dayton:• data quality: n = 0• ethnicity: n = 63 (note: Because we were interested in the extend to which people would endorse


Data format and number of participants in the cleaned data:

## List of 5## $ PittTsdOaks :Classes 'tbl_df', 'tbl' and 'data.frame': 380 obs. of 513 variables:## $ Christchurch :Classes 'tbl_df', 'tbl' and 'data.frame': 637 obs. of 449 variables:## $ ChristchurchAllEthn:Classes 'tbl_df', 'tbl' and 'data.frame': 784 obs. of 449 variables:## $ Utrecht :Classes 'tbl_df', 'tbl' and 'data.frame': 478 obs. of 376 variables:## $ ElPasoDayton :Classes 'tbl_df', 'tbl' and 'data.frame': 837 obs. of 425 variables:

2

Study 1: Pittsburgh - Thousand OaksThe first major study is comprised of two data sets that were collected after the Pittsburgh synagogueshooting in 2018 (11 killed, 6 injured), wherein by the gunman expressed anti-Semitic prejudices online priorto the attack and a week later, following the Thousand Oaks country-western bar shooting (13 killed, 10-12injured), whereby the gunman had not declared any particular prejudices. Given the close temporal proximityof the two shootings, we used a repeated measures design in which we re-contacted the same participants toassess their perceptions of both shootings.

We had two main hypotheses:1. The first hypothesis was that anti-Semitic prejudice would predict hate crime perceptions only after thePittsburgh synagogue shooting (a likely hate crime against Jews) and not the Thousand Oaks bar shooting.2. The second hypothesis was that disempowerment would indirectly predict hate crime perceptions via itseffects on radical nationalism and Antisemitism.

The demographics of the final sample (i.e., valid data for both shootings) are:

Table 1: Gender (Pittsburgh)

Gender Frequency Percentagefemale 222 58.42male 158 41.58

Table 2: Religious Affiliation (Pittsburgh)

Religious Affiliation Frequency PercentageProtestant (Anglican, Orthodox, Baptist, Lutheran) 110 28.95Catholic (including roman catholic and orthodox) 69 18.16None 59 15.53Agnostic 50 13.16Atheist 33 8.68Other 33 8.68Jewish 8 2.11Muslim 7 1.84Buddhist 6 1.58Hindu 4 1.05Sikh 1 0.26

3

Table 3: Ethnicity (Pittsburgh)

Ethnicity Frequency PercentageWhite 257 67.63Black or African American 49 12.89Asian 26 6.84Hispanic / Latino / Latina 22 5.79White & Hispanic / Latino / Latina 11 2.89Other 7 1.84American Indian or Alaska Native 2 0.53White & Asian 2 0.53Native Hawaiian or Pacific Islander 1 0.26White & American Indian or Alaska Native 1 0.26White & Black or African American 1 0.26White & Black or African American & Other 1 0.26

Table 4: Age (Pittsburgh)

Age Range Frequency Percentage18-24 40 10.5325-34 130 34.2135-44 94 24.7445-54 68 17.8955-64 31 8.1665+ 17 4.47

Table 5: Education (Pittsburgh)

Education Frequency PercentageSome High School or Less 1 0.26High School Graduate / GED 36 9.47Some College 84 22.11College Graduate 196 51.58Graduate Degree 63 16.58

Table 6: Gun Ownership Proportions (Pittsburgh)

Gun Ownership Frequency Percentageno gun owner 278 73.16gun owner 102 26.84

4

Scale Construction

In order to test our two hypotheses we first assess all variables and create appropriate scale variables.

Hate Crime Attribution Pittsburgh

We first assess hate crime perceptions and other ascribed motives in Pittsburgh and check the relation (i.e.,correlation) of hate crime perceptions to other perceived motives.

Table 7: Descriptives of Motives (Pittsburgh)

n mean sd min max range sereligion 374 0.6604 1.943 -3 3 6 0.1005ideology 360 0.5889 1.981 -3 3 6 0.1044power 374 1.4733 1.548 -3 3 6 0.0800compensation 375 1.2400 1.644 -3 3 6 0.0849hate 380 2.3553 1.270 -3 3 6 0.0651mental 379 1.8153 1.445 -3 3 6 0.0742ease 378 1.2275 1.919 -3 3 6 0.0987culture 375 0.7253 1.931 -3 3 6 0.0997other 176 1.4148 1.543 -3 3 6 0.1163

hate

−3

2−

32

−3

2−

32

−3 0 3

−3 0 3

*r = 0.12[0.01, 0.21]

religion

*r = 0.12[0.02, 0.22]

***r = 0.40[0.31, 0.48]

ideology

−3 0 3

−3 0 3

***r = 0.37[0.28, 0.46]

*r = 0.12[0.02, 0.22]

***r = 0.24[0.14, 0.33]

power

***r = 0.36[0.27, 0.44]

r = 0.06[−0.05, 0.16]

***r = 0.20[0.1, 0.3]

***r = 0.54[0.47, 0.61]

compensation

−3 0 3

−3 0 3

***r = 0.38[0.29, 0.47]

*r = 0.11[0.01, 0.21]

*r = 0.13[0.02, 0.23]

***r = 0.28[0.19, 0.37]

***r = 0.30[0.2, 0.39]

mental

***r = 0.28[0.18, 0.37]

**r = 0.14[0.04, 0.24]

*r = 0.13[0.02, 0.23]

***r = 0.19[0.09, 0.29]

***r = 0.27[0.17, 0.36]

**r = 0.15[0.05, 0.25]

ease

−3 0 3

−3 0 3

***r = 0.21[0.11, 0.31]

***r = 0.17[0.07, 0.27]

***r = 0.19[0.09, 0.29]

***r = 0.18[0.08, 0.28]

*r = 0.13[0.03, 0.23]

.r = 0.10[0, 0.2]

***r = 0.34[0.24, 0.42]

culture

−3

2***r = 0.30[0.16, 0.43]

r = 0.02[−0.13, 0.17]

−3

2 r = 0.00[−0.16, 0.15]

*r = 0.18[0.03, 0.32]

−3

2 r = 0.01[−0.13, 0.16]

*r = 0.17[0.02, 0.31]−

32***r = 0.29[0.15, 0.42]

.r = 0.14[0, 0.29]

−3 0 3

−3

2other

5

0

100

200

−2 0 2Hate Crime Attribution Pittsburgh

coun

tDistribution of Hate Crime Attribution after the Pittsburgh Shooting

Table 8: Hate Crime Attribution: Item Descriptives (Pittsburgh)

vars n mean sd median trimmed mad min max range skew kurtosis sehate 380 2.355 1.27 3 2.674 0 -3 3 6 -2.436 6.054 0.0651

6

In a next step we compare whether hate crime perceptions were indeed higher in Pittsburgh than any of theother motives. This also functions as a check to ensure that the participants indeed identified the shooting asa hate crime.

ideology

religion

culture

ease

compensation

other

power

mental

hate

0.0 0.5 1.0 1.5 2.0 2.5Average Rating

Mot

ive

Motive Rating Means (with SE)

#### Simultaneous Tests for General Linear Hypotheses#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesPB),## random = ~1 | id, method = "ML")#### Linear Hypotheses:## Estimate Std. Error z value Pr(>|z|)## hate - religion == 0 1.696 0.112 15.19 <0.00001 ***## hate - ideology == 0 1.766 0.113 15.65 <0.00001 ***## hate - power == 0 0.880 0.112 7.88 <0.00001 ***## hate - compensation == 0 1.112 0.112 9.97 <0.00001 ***## hate - mental == 0 0.541 0.111 4.87 <0.00001 ***## hate - ease == 0 1.131 0.111 10.16 <0.00001 ***## hate - culture == 0 1.631 0.112 14.62 <0.00001 ***## hate - other == 0 0.924 0.142 6.52 <0.00001 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## (Adjusted p values reported -- single-step method)

##

7

## Simultaneous Confidence Intervals#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesPB),## random = ~1 | id, method = "ML")#### Quantile = 2.661## 95% family-wise confidence level###### Linear Hypotheses:## Estimate lwr upr## hate - religion == 0 1.696 1.399 1.993## hate - ideology == 0 1.766 1.466 2.066## hate - power == 0 0.880 0.583 1.178## hate - compensation == 0 1.112 0.815 1.409## hate - mental == 0 0.541 0.245 0.837## hate - ease == 0 1.131 0.835 1.427## hate - culture == 0 1.631 1.334 1.928## hate - other == 0 0.924 0.547 1.302

The results suggest that hate crime perceptions were indeed higher than any of the other motive perceptions(even, after controlling for the multiple tests).

8

Robust test

Given that the repeated measures ANOVA and the follow up contrasts are parametric tests we also checkedtheir assumptions and offer robust alternatives where necessary.

−6−4−2

024

−2 0 2Theoretical quantiles (predicted values)

Res

idua

ls

Dots should be plotted along the line

Non−normality of residuals and outliers

0.00.10.20.3

−4 −2 0 2 4Residuals

Den

sity

Distribution should look like normal curve

Non−normality of residuals

−4−2

024

−2 0 2Fitted values

Res

idua

ls

Amount and distance of points scattered above/below line is equal or randomly spread

Homoscedasticity (constant variance of residuals)

9

other

culture

ease

mental

hate

compensation

power

ideology

religion

−4 −2 0 2 4

Residuals

Gro

upFollow−Up Sphericity

Visual inspection of the qq-plot and the distribution of the residuals suggested only slight left tail (which didnot warrant a suspicion that the assumption of residual normality was violated) but a further inspection ofthe sphericity (and homoscedasticity) assumption indicated differences in the variances between the categories.This was also corroborated by the ration of largest group variance to smallest variance = 2.43, which is largerthan the recommended 1.5 rule of thumb.Even though with large sample sizes and largely balanced data (i.e., almost all participants responded on allmotives) the tests are quite robust to violations of homoscedasticity we still offer a robust heteroscedasticrepeated measurement ANOVA. We removed the ‘other’ from the analyses because some of the robustanalyses need fully balanced designs and 204 participants did not rate any additional motivations. For theoverall robust ANOVA we, however, offer a bootstrapped alternative that is able to deal with missing data.For the follow-up comparisons we present the relevant Hate crime subset of a full robust post-hoc test with aHochberg’s approach to control for the family-wise error (FWE).

## [1] "robust heteroscedastic repeated measurement ANOVA: F(5.8, 2040.53) = 56.04, p < 0.001"

## [1] "robust heteroscedastic bootstrapped repeated measurement ANOVA without the 'other'category removed: F = 23.5 with a critical value of 2.03 and given that F > F_critical,the bootstrapped test equally indicates an overall statistical significance."

Table 9: Follow-up Compare Hate Motive - robust (Pittsburgh)

Group1 Group2 psi hat ci.lower ci.upper p.value p.crit sigreligion hate -1.666 -2.031 -1.301 0 0.002 TRUEideology hate -1.745 -2.117 -1.374 0 0.002 TRUEpower hate -0.873 -1.139 -0.606 0 0.002 TRUEcompensation hate -1.099 -1.379 -0.819 0 0.002 TRUEhate mental 0.496 0.242 0.749 0 0.003 TRUEhate ease 1.125 0.795 1.454 0 0.002 TRUEhate culture 1.589 1.243 1.935 0 0.002 TRUE

10

As expected the robust follow-ups mirrored the parametric contrasts.

11

Hate Crime Attribution Thousand Oaks

We similarly assess the motive perceptions in Thousand Oaks but do not expect “hate” to be the mostimportant perceived motive.

Table 10: Descriptives of Motives (Thousand Oaks)

n mean sd median trimmed mad min max range skew kurtosis sereligion 360 -0.7778 1.942 -0.5 -0.9375 2.224 -3 3 6 0.3053 -1.0857 0.1024ideology 357 -0.6162 2.068 0.0 -0.7561 2.965 -3 3 6 0.2173 -1.2948 0.1094power 377 1.4111 1.622 2.0 1.6700 1.483 -3 3 6 -1.1147 0.7430 0.0835compensation 378 1.4815 1.583 2.0 1.7467 1.483 -3 3 6 -1.2934 1.3152 0.0814hate 380 1.4868 1.579 2.0 1.7368 1.483 -3 3 6 -1.0809 0.6470 0.0810mental 379 2.2454 1.200 3.0 2.5049 0.000 -3 3 6 -1.8715 3.5228 0.0616ease 380 1.3053 1.920 2.0 1.6020 1.483 -3 3 6 -0.9099 -0.3235 0.0985culture 379 0.7784 1.949 1.0 0.9672 1.483 -3 3 6 -0.6167 -0.6899 0.1001other 149 1.1275 1.362 1.0 1.1074 1.483 -3 3 6 0.1372 -1.0895 0.1116

hate

−3

2−

32

−3

2−

32

−3 0 3

−3 0 3

***r = 0.32[0.22, 0.41]

religion

***r = 0.37[0.28, 0.46]

***r = 0.72[0.66, 0.76]

ideology

−3 0 3

−3 0 3

***r = 0.35[0.26, 0.43]

***r = 0.18[0.08, 0.28]

***r = 0.22[0.12, 0.32]

power

***r = 0.39[0.3, 0.47]

.r = 0.09[−0.01, 0.2]

.r = 0.10[0, 0.2]

***r = 0.52[0.44, 0.59]

compensation

−3 0 3

−3 0 3

***r = 0.23[0.13, 0.32]

.r = −0.09[−0.19, 0.01]

r = −0.08[−0.18, 0.02]

***r = 0.19[0.1, 0.29]

**r = 0.15[0.05, 0.24]

mental

***r = 0.28[0.19, 0.37]

*r = 0.11[0, 0.21]

.r = 0.09[−0.01, 0.19]

**r = 0.15[0.05, 0.25]

*r = 0.12[0.01, 0.21]

***r = 0.22[0.12, 0.31]

ease

−3 0 3

−3 0 3

**r = 0.14[0.04, 0.24]

r = 0.03[−0.08, 0.13]

r = 0.08[−0.03, 0.18]

*r = 0.13[0.03, 0.23]

r = 0.07[−0.03, 0.17]

*r = 0.10[0, 0.2]

***r = 0.28[0.18, 0.37]

culture−

32

r = −0.01[−0.17, 0.15]

*r = −0.19[−0.35, −0.03]

−3

2 r = −0.09[−0.26, 0.08]

r = −0.04[−0.2, 0.12]

−3

2 r = 0.01[−0.15, 0.17]

***r = 0.29[0.13, 0.43]

−3

2 r = −0.04[−0.2, 0.12]

r = 0.09[−0.07, 0.25]

−3 0 3

−3

2other

12

0

30

60

90

120

−2 0 2Hate Crime Attribution Thousand Oaks

coun

tDistribution of Hate Crime Attribution after the Thousand Oaks Shooting

Table 11: Hate Crime Attribution: Item Descriptives (Thousand Oaks)

vars n mean sd median trimmed mad min max range skew kurtosis sehate 380 1.487 1.579 2 1.737 1.483 -3 3 6 -1.081 0.647 0.081

13

We again formally contrast hate crime perceptions from the other motives.

religion

ideology

culture

other

ease

power

compensation

hate

mental

−1 0 1 2Average Rating

Mot

ive


#### Simultaneous Tests for General Linear Hypotheses#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesTO),## random = ~1 | id, method = "ML")#### Linear Hypotheses:## Estimate Std. Error z value Pr(>|z|)## hate - religion == 0 2.27029 0.11568 19.63 <0.001 ***## hate - ideology == 0 2.09787 0.11595 18.09 <0.001 ***## hate - power == 0 0.07833 0.11422 0.69 0.99## hate - compensation == 0 0.00373 0.11413 0.03 1.00## hate - mental == 0 -0.75599 0.11405 -6.63 <0.001 ***## hate - ease == 0 0.18158 0.11397 1.59 0.49## hate - culture == 0 0.71154 0.11405 6.24 <0.001 ***## hate - other == 0 0.32676 0.15447 2.12 0.19## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## (Adjusted p values reported -- single-step method)

#### Simultaneous Confidence Intervals##

14

## Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesTO),## random = ~1 | id, method = "ML")#### Quantile = 2.663## 95% family-wise confidence level###### Linear Hypotheses:## Estimate lwr upr## hate - religion == 0 2.27029 1.96220 2.57837## hate - ideology == 0 2.09787 1.78907 2.40666## hate - power == 0 0.07833 -0.22586 0.38251## hate - compensation == 0 0.00373 -0.30022 0.30769## hate - mental == 0 -0.75599 -1.05972 -0.45226## hate - ease == 0 0.18158 -0.12194 0.48510## hate - culture == 0 0.71154 0.40781 1.01527## hate - other == 0 0.32676 -0.08464 0.73816

The data suggests that mental health issues were perceived as a stronger motive of the shooter than hate andprejudice were.

15

Robust test


−5.0−2.5

0.02.5


Res

idua

ls



0.00.10.20.3

−5.0 −2.5 0.0 2.5Residuals

Den

sity



−5.0−2.5

0.02.5


Res

idua

ls



16

other

culture

ease

mental

hate

compensation

power

ideology

religion

−5.0 −2.5 0.0 2.5

Residuals

Gro


Visual inspection of the qq-plot and the distribution of the residuals indicated no violation of the normality ofresiduals assumption but a further inspection of the sphericity (and homoscedasticity) assumption indicateddifferences in the variances between the categories. This was also corroborated by the ration of largest groupvariance to smallest variance = 2.97, which is larger than the recommended 1.5 rule of thumb.Even though with large sample sizes and largely balanced data (i.e., almost all participants responded on allmotives) the tests are quite robust to violations of homoscedasticity we still offer a robust heteroscedasticrepeated measurement ANOVA. We removed the ‘other’ from the analyses because some of the robustanalyses need fully balanced designs and 231 participants did not rate any additional motivations. For theoverall robust ANOVA we, however, offer a bootstrapped alternative that is able to deal with missing data.For the follow-up comparisons we present the relevant Hate crime subset of a full robust post hoc test with aHochberg’s approach to control for the family-wise error (FWE).

## [1] "robust heteroscedastic repeated measurement ANOVA: F(5, 1746.45) = 169.31, p < 0.001"

## [1] "robust heteroscedastic bootstrapped repeated measurement ANOVA without the 'other'category removed: F = 37.57 with a critical value of 2.03 and given that F > F_critical,the bootstrapped test equally indicates an overall statistical significance."

Table 12: Follow-up Compare Hate Motive - robust (Thousand Oaks)

Group1 Group2 psi hat ci.lower ci.upper p.value p.crit sigreligion hate -2.306 -2.653 -1.958 0.000 0.003 TRUEideology hate -2.106 -2.457 -1.755 0.000 0.002 TRUEpower hate -0.031 -0.323 0.261 0.735 0.025 FALSEcompensation hate 0.000 -0.283 0.283 1.000 0.050 FALSEhate mental -0.749 -1.037 -0.460 0.000 0.004 TRUEhate ease 0.226 -0.125 0.577 0.044 0.009 FALSEhate culture 0.729 0.335 1.122 0.000 0.005 TRUE

17


Change in Hate Crime Attributions:

In a final step, we then check for differences in “Motivated by Hate” between the two within subject samples.Here we would expect that hate crime perceptions were significantly higher for the Pittsburgh synagogueshooting than after the Thousand Oaks Bar shooting. This paired samples t-test also offers a sort of“manipulation check” to ensure that Pittsburgh was indeed, on average, more strongly perceived as a hatecrime than Thousand Oaks was (within participants).Given the differences in distribution between the re-contacts we also offer a rank based robust alternative.

#### Paired t-test#### data: hatePBTO$PB_Mot_PB_5 and hatePBTO$TO_Mot_PB_5## t = 9.1, df = 379, p-value <0.0000000000000002## alternative hypothesis: true difference in means is not equal to 0## 95 percent confidence interval:## 0.6812 1.0556## sample estimates:## mean of the differences## 0.8684

#### Wilcoxon signed rank test with continuity correction#### data: hatePBTO$PB_Mot_PB_5 and hatePBTO$TO_Mot_PB_5## V = 24988, p-value <0.0000000000000002## alternative hypothesis: true location shift is not equal to 0

The test results (both parametric and non-parametric) indicate that Pittsburgh was indeed, on average, morestrongly perceived as a hate crime than Thousand Oaks was (within participants).

Disempowerment Pittsburgh

For our antecedent Disempowerment measure after the Pittsburgh shooting we assess scale-ability andcombine the individual items into a single scale score.

Reliability Analysis of Disempowerment:

A spearman correlation matrix of 3 items was calculated and submitted to Reliability analysis.

The overall Cronbach’s Alpha was 0.79. Furthermore, deleting item(s) 3 may improve reliability. A gls factoranalysis was conducted. Items were regressed to a single factor. Their loadings are the following:

Table 13: Disempowerment: Item Total Correlations (Pittsburgh)Item Corr. to scale Factor Loading Mean SDPB_fail1 0.6922 0.8510 2.900 1.250PB_fail2 0.6777 0.8201 2.637 1.279PB_fail3 0.5217 0.5753 2.324 1.230

18

Table 14: Disempowerment: Item Descriptives (Pittsburgh)

vars n mean sd median trimmed mad min max range skew kurtosis sePB_fail1 380 2.900 1.250 3 2.875 1.483 1 5 4 -0.0134 -1.0163 0.0641PB_fail2 380 2.637 1.279 3 2.559 1.483 1 5 4 0.2611 -1.0460 0.0656PB_fail3 380 2.324 1.230 2 2.201 1.483 1 5 4 0.5313 -0.7193 0.0631

PB_fail1

12

34

5

1 2 3 4 5

1 2 3 4 5

***r = 0.70

[0.65, 0.75]

PB_fail2

12

34

5

***r = 0.49

[0.41, 0.57]

***r = 0.48

[0.4, 0.55]

1 2 3 4 5

12

34

5

PB_fail3

Table 15: Disempowerment: Scale Descriptives (Pittsburgh)

vars n mean sd min max range seDisempowerment 380 2.62 1.053 1 5 4 0.054

Disempowerment Thousand Oaks

To check that our antecedent Disempowerment measure did not change meaningfully after the Pittsburghshooting, we also assessed disempowerment after the Pittsburgh shooting.We first assess scale-ability and combine the individual items into a single scale score, in order to comparethem with the previous ratings after the Pittsburgh shooting.




19

Table 16: Disempowerment: Item Total Correlation (Thousand Oaks)Item Corr. to scale Factor Loading Mean SDTO_fail1 0.6329 0.7902 2.950 1.162TO_fail2 0.6317 0.7876 2.668 1.269TO_fail3 0.5075 0.5794 2.208 1.163

Table 17: Disempowerment: Item Descriptives (Thousand Oaks)

vars n mean sd median trimmed mad min max range skew kurtosis seTO_fail1 380 2.950 1.162 3 2.938 1.483 1 5 4 0.0467 -0.7929 0.0596TO_fail2 380 2.668 1.269 2 2.586 1.483 1 5 4 0.3360 -0.9750 0.0651TO_fail3 380 2.208 1.163 2 2.069 1.483 1 5 4 0.7963 -0.2382 0.0597

TO_fail1

12

34

5

1 2 3 4 5

1 2 3 4 5

***r = 0.63

[0.57, 0.69]

TO_fail2

12

34

5

***r = 0.48

[0.4, 0.55]

***r = 0.47

[0.39, 0.55]

1 2 3 4 5

12

34

5

TO_fail3

Table 18: Disempowerment: Scale Descriptives (Thousand Oaks)


Change in Disempowerment:

In a final step, we then check for differences in Disempowerment between the two within subject samples.Here we expect disempowerment to not change significantly over the period of approximately one week andto remain a stable between subjects difference.

We ran a paired samples t-test (see Null Hypothesis Test Result below) as well as an equivalence test (see

20

Equivalence Test Result below) to ensure that the difference was not larger than a minimal effect size ofinterest (in our case a Cohen’s D of 0.3; also see Lakens, Scheel, & Isager, 2018. Equivalence Testing forPsychological Research: A Tutorial. https://doi.org/10.1177/2515245918770963 ).

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

Mean Difference

Equivalence bounds −0.237 and 0.237Mean difference = 0.011

TOST: 90% CI [−0.055;0.078] significant NHST: 95% CI [−0.068;0.091] non−significant

## TOST results:## t-value lower bound: 6.13 p-value lower bound: 0.000000001## t-value upper bound: -5.57 p-value upper bound: 0.00000002## degrees of freedom : 379#### Equivalence bounds (Cohen's d):## low eqbound: -0.3## high eqbound: 0.3#### Equivalence bounds (raw scores):## low eqbound: -0.237## high eqbound: 0.237#### TOST confidence interval:## lower bound 90% CI: -0.055## upper bound 90% CI: 0.078#### NHST confidence interval:## lower bound 95% CI: -0.068## upper bound 95% CI: 0.091##

21

https://doi.org/10.1177/2515245918770963

## Equivalence Test Result:## The equivalence test was significant, t(379) = -5.567, p = 0.0000000246, given equivalence bounds## of -0.237 and 0.237 (on a raw scale) and an alpha of 0.05.## Null Hypothesis Test Result:## The null hypothesis test was non-significant, t(379) = 0.281, p = 0.779, given an alpha of 0.05.## Based on the equivalence test and the null-hypothesis test combined, we can conclude that## the observed effect is statistically not different from zero and statistically equivalent to zero.

Antisemitism Pittsburgh

For our antecedent Antisemitism measure we assess scale-ability and combine the individual items into asingle scale score.Because endorsement of a Antisemitic beliefs was assumed to be a stable belief this between subjects differencewas only measured once (after the initial Pittsburgh synagogue shooting).

Reliability Analysis of Antisemitism:



Table 19: Antisemitism: Item Total Correlation (Pittsburgh)Item Corr. to scale Factor Loading Mean SDPB_antisemitism_8 0.9065 0.9328 2.118 1.380PB_antisemitism_4 0.8948 0.9195 2.129 1.377PB_antisemitism_6 0.8832 0.9123 2.171 1.431PB_antisemitism_5 0.8664 0.8926 2.026 1.341PB_antisemitism_7 0.8619 0.8878 2.345 1.449PB_antisemitism_3 0.7526 0.7838 2.571 1.520PB_antisemitism_2 0.7249 0.7517 1.713 1.171PB_antisemitism_1 0.6625 0.6760 2.439 1.608

Table 20: Antisemitism: Item Descriptives (Pittsburgh)

vars n mean sd median trimmed mad min max range skew kurtosis sePB_antisemitism_1 380 2.439 1.608 2 2.220 1.483 1 6 5 0.7835 -0.6379 0.0825PB_antisemitism_2 380 1.713 1.171 1 1.447 0.000 1 6 5 1.6505 1.8803 0.0601PB_antisemitism_3 380 2.571 1.520 2 2.424 1.483 1 6 5 0.5064 -0.9937 0.0779PB_antisemitism_4 380 2.129 1.377 2 1.905 1.483 1 6 5 1.0563 0.0244 0.0706PB_antisemitism_5 380 2.026 1.341 1 1.793 0.000 1 6 5 1.2023 0.4153 0.0688PB_antisemitism_6 380 2.171 1.431 2 1.934 1.483 1 6 5 1.1005 0.2277 0.0734PB_antisemitism_7 380 2.345 1.449 2 2.145 1.483 1 6 5 0.7994 -0.4638 0.0743PB_antisemitism_8 380 2.118 1.380 2 1.891 1.483 1 6 5 1.0736 0.0937 0.0708

22

PB_antisemitism_11

41

41

4

1 3 5

14

1 3 5

***r = 0.50[0.43, 0.58]

PB_antisemitism_2

***r = 0.46[0.37, 0.53]

***r = 0.53[0.46, 0.6]

PB_antisemitism_3

1 3 5

1 3 5

***r = 0.57[0.49, 0.63]

***r = 0.70[0.65, 0.75]

***r = 0.69[0.63, 0.74]

PB_antisemitism_4

***r = 0.54[0.47, 0.61]

***r = 0.70[0.64, 0.74]

***r = 0.65[0.59, 0.7]

***r = 0.90[0.88, 0.92]

PB_antisemitism_5

1 3 5

1 3 5

***r = 0.57[0.49, 0.63]

***r = 0.66[0.6, 0.71]

***r = 0.71[0.66, 0.76]

***r = 0.80[0.77, 0.84]

***r = 0.77[0.72, 0.81]

PB_antisemitism_6

***r = 0.60[0.53, 0.66]

***r = 0.61[0.54, 0.67]

***r = 0.77[0.72, 0.81]

***r = 0.77[0.73, 0.81]

***r = 0.74[0.69, 0.78]

***r = 0.83[0.79, 0.85]

PB_antisemitism_7

1 3 5

1 3 5

14***r = 0.56[0.49, 0.63]

***r = 0.70[0.64, 0.74]

14***r = 0.71[0.66, 0.76]

***r = 0.88[0.86, 0.9]

14***r = 0.86[0.83, 0.88]

***r = 0.83[0.79, 0.86]

14***r = 0.80[0.76, 0.83]

PB_antisemitism_8

Table 21: Antisemitism: Scale Descriptives (Pittsburgh)

vars n mean sd min max range seantisemitism 380 2.189 1.203 1 6 5 0.0617

Christian Nationalism Pittsburgh

For our antecedent Christian nationalism measure after the Pittsburgh shooting we assess scale-ability andcombine the individual items into a single scale score.Because endorsement of a christian nationalist ideology was assumed to be a stable belief this betweensubjects difference was only measured once (after the initial Pittsburgh synagogue shooting).

Reliability Analysis of Christian Nationalism:



Table 22: Christian Nationalism: Item Total Correlations (Pittsburgh)Item Corr. to scale Factor Loading Mean SDPB_christ_2 0.8047 0.8740 2.537 1.424PB_christ_6 0.7798 0.8573 2.424 1.419PB_christ_1 0.7744 0.8504 2.137 1.358PB_christ_5 0.7302 0.7708 3.003 1.493PB_christ_4 0.6387 0.6771 3.139 1.365PB_christ_3 0.4887 0.5130 2.224 1.285

23

Table 23: Christian Nationalism: Item Descriptives (Pittsburgh)

vars n mean sd median trimmed mad min max range skew kurtosis sePB_christ_1 380 2.137 1.358 1 1.947 0.000 1 5 4 0.7862 -0.7512 0.0697PB_christ_2 380 2.537 1.424 2 2.421 1.483 1 5 4 0.3386 -1.2541 0.0730PB_christ_3 380 3.776 1.285 4 3.947 1.483 1 5 4 -0.7472 -0.5451 0.0659PB_christ_4 380 3.139 1.365 3 3.174 1.483 1 5 4 -0.2644 -1.1195 0.0700PB_christ_5 380 3.003 1.493 3 3.003 1.483 1 5 4 -0.1420 -1.4145 0.0766PB_christ_6 380 2.424 1.419 2 2.280 1.483 1 5 4 0.4669 -1.1272 0.0728

PB_christ_1

13

51

35

1 2 3 4 5

13

5

1 2 3 4 5

***r = 0.77[0.72, 0.8]

PB_christ_2

***r = 0.34[0.25, 0.43]

***r = 0.39[0.3, 0.47]

PB_christ_3

1 2 3 4 5

1 2 3 4 5

***r = 0.52[0.44, 0.59]

***r = 0.58[0.51, 0.64]

***r = 0.30[0.2, 0.38]

PB_christ_4

***r = 0.59[0.52, 0.65]

***r = 0.61[0.55, 0.67]

***r = 0.38[0.29, 0.46]

***r = 0.68[0.62, 0.73]

PB_christ_5

1 2 3 4 5

1 2 3 4 5

13

5

***r = 0.72[0.67, 0.77]

***r = 0.74[0.69, 0.78]

13

5

***r = 0.34[0.25, 0.43]

***r = 0.54[0.47, 0.61]

13

5

***r = 0.63[0.56, 0.68]

PB_christ_6

Table 24: Christian Nationalism: Scale Descriptives (Pittsburgh)

vars n mean sd min max range seDisempowerment 380 3.003 0.9234 1.167 5.167 4 0.0474

24

Mixed Models

Antisemitism was hypothesized to predict (lower) hate crime perceptions only after the Pittsburgh synagogueshooting and not the Thousand Oaks bar shooting. A mixed linear model was conducted, treating antisemitismas a continuous, between-subjects predictor (standardized), and hate crime attributions as a within-subjectsoutcome variable (Pittsburgh vs. Thousand Oaks).

Hatet = antisemitism ∗ shootingt + ε(1|participant)

Repeated Measures ANOVARMAnova = aov(hate ~ antisem.z*shooting + Error(id), dt$PittTsdOaks.long)

#summary(lme(hate.c ~ antisem.c*shooting, random=~1|mergeWorkerID, dt$PittTsdOaks.long)m <- lmerTest::lmer(hate ~ antisem.z*shooting+(1|id), dt$PittTsdOaks.long)class(m) <- "lmerMod"

summary(RMAnova)

#### Error: id## Df Sum Sq Mean Sq F value Pr(>F)## antisem.z 1 46 46.0 20.3 0.0000089 ***## Residuals 378 857 2.3## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#### Error: Within## Df Sum Sq Mean Sq F value Pr(>F)## shooting 1 143 143.3 84.16 <0.0000000000000002 ***## antisem.z:shooting 1 9 9.1 5.37 0.021 **## Residuals 378 644 1.7## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1eta_sq(RMAnova, partial = T)

term partial.etasq stratumantisem 0.067 idResiduals 0.571 idshooting 0.182 Withinantisem:shooting 0.014 Within

We indeed find a two-way within subjects interaction indicating that antisemitism was mainly a predictor of hate crime attributions after the Pittsburgh synagogue shooting but not the Thousand Oaks Bar shooting (also see line graphs below).Pseudo R2(from mixed models regression): ω0

2 = 33.69%

25

Simple Slopes Follow-up

Simple Slope Pittsburgh:

#### Call:## lm(formula = hate ~ antisem.z, data = dt$PittTsdOaks.long[dt$PittTsdOaks.long$shooting ==## "Pittsburgh", ])#### Residuals:## Min 1Q Median 3Q Max## -5.604 -0.575 0.396 0.864 2.735#### Coefficients:## Estimate Std. Error t value Pr(>|t|)## (Intercept) 2.0478 0.0801 25.58 < 0.0000000000000002 ***## antisem.z -0.5628 0.0802 -7.02 0.00000000001 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#### Residual standard error: 1.56 on 378 degrees of freedom## Multiple R-squared: 0.115, Adjusted R-squared: 0.113## F-statistic: 49.3 on 1 and 378 DF, p-value: 0.0000000000103

## 2.5 % 97.5 %## (Intercept) 1.8904 2.2052## antisem.z -0.7204 -0.4052

Simple Slope Thousand Oaks:

#### Call:## lm(formula = hate ~ antisem.z, data = dt$PittTsdOaks.long[dt$PittTsdOaks.long$shooting ==## "Thousand Oaks", ])#### Residuals:## Min 1Q Median 3Q Max## -3.949 -1.283 0.236 2.051 2.768#### Coefficients:## Estimate Std. Error t value Pr(>|t|)## (Intercept) 0.770 0.101 7.61 0.00000000000022 ***## antisem.z -0.182 0.101 -1.79 0.074 .## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#### Residual standard error: 1.97 on 378 degrees of freedom## Multiple R-squared: 0.00844, Adjusted R-squared: 0.00581## F-statistic: 3.22 on 1 and 378 DF, p-value: 0.0737

## 2.5 % 97.5 %## (Intercept) 0.5709 0.96859## antisem.z -0.3807 0.01752

26

Correlation Pittsburgh:

## Call:corr.test(x = dt$PittTsdOaks %>% select(PB_Mot_PB_5, antisem))## Correlation matrix## PB_Mot_PB_5 antisem## PB_Mot_PB_5 1.00 -0.28## antisem -0.28 1.00## Sample Size## [1] 380## Probability values (Entries above the diagonal are adjusted for multiple tests.)## PB_Mot_PB_5 antisem## PB_Mot_PB_5 0 0## antisem 0 0#### Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci## raw.lower raw.r raw.upper raw.p lower.adj upper.adj## PB_M_-antsm -0.37 -0.28 -0.19 0 -0.37 -0.19

Correlation Thousand Oaks:

## Call:corr.test(x = dt$PittTsdOaks %>% select(TO_Mot_PB_5, antisem))## Correlation matrix## TO_Mot_PB_5 antisem## TO_Mot_PB_5 1.00 -0.09## antisem -0.09 1.00## Sample Size## [1] 380## Probability values (Entries above the diagonal are adjusted for multiple tests.)## TO_Mot_PB_5 antisem## TO_Mot_PB_5 0.00 0.09## antisem 0.09 0.00#### Confidence intervals based upon normal theory. To get bootstrapped values, try cor.ci## raw.lower raw.r raw.upper raw.p lower.adj upper.adj## TO_M_-antsm -0.19 -0.09 0.01 0.09 -0.19 0.01

Robust Mixed Models Regression:

To guard against misspecifications due to violations of normality we also ran a robust mixed models regression.

## Robust linear mixed model fit by DAStau## Formula: hate ~ antisem.z * shooting + (1 | id)## Data: dt$PittTsdOaks.long#### Scaled residuals:## Min 1Q Median 3Q Max## -3.170 -0.740 0.148 0.556 1.506#### Random effects:## Groups Name Variance Std.Dev.## id (Intercept) 0.00 0.00## Residual 3.27 1.81## Number of obs: 760, groups: id, 380##

27

## Fixed effects:## Estimate Std. Error t value## (Intercept) 2.1930 0.0952 23.05## antisem.z -0.5457 0.0953 -5.73## shootingThousand Oaks -1.3007 0.1346 -9.67## antisem.z:shootingThousand Oaks 0.3377 0.1347 2.51#### Correlation of Fixed Effects:## (Intr) antsm. shtnTO## antisem.z 0.000## shtngThsndO -0.707 0.000## antsm.z:sTO 0.000 -0.707 0.000#### Robustness weights for the residuals:## 617 weights are ~= 1. The remaining 143 ones are summarized as## Min. 1st Qu. Median Mean 3rd Qu. Max.## 0.424 0.668 0.826 0.804 0.951 0.998#### Robustness weights for the random effects:## All 380 weights are ~= 1.#### Rho functions used for fitting:## Residuals:## eff: smoothed Huber (k = 1.345, s = 10)## sig: smoothed Huber, Proposal II (k = 1.345, s = 10)## Random Effects, variance component 1 (id):## eff: smoothed Huber (k = 1.345, s = 10)## vcp: smoothed Huber, Proposal II (k = 1.345, s = 10)

## 2.5 % 97.5 %## (Intercept) 2.00647 2.3795## antisem.z -0.73242 -0.3589## shootingThousand Oaks -1.56443 -1.0369## antisem.z:shootingThousand Oaks 0.07366 0.6018

tl;dr: All effects remain significant within the robust model.

28

Line Graph:

1

2

Pittsburgh Thousand OaksShooting

Hat

e C

rime

Attr

ibut

ion

Antisemitism −1sd mean +1sd

Antisemitism over Time (+/− 1SE)

0

1

2

3

−1sd mean +1sdAntisemitism

Hat

e C

rime

Attr

ibut

ion

Shooting Pittsburgh Thousand Oaks

Effect of Antisemitism by Shooting (95%CI)

29

Path Model

Correlation of key variables

fail.pb.c

−1

13

−1 0 1 2

−5

−3

−1

−1 0 1 2 3 4

***r = 0.31

[0.22, 0.4]

antisem.c

*r = 0.11

[0.01, 0.21]

***r = 0.48

[0.4, 0.56]

christ.c

−1 0 1 2

−5 −3 −1 0

−1

12

**r = −0.14

[−0.23, −0.04]

***r = −0.28

[−0.37, −0.19]

−1

12

**r = −0.16

[−0.25, −0.06]

hate.pb.c

Path Model Pittsburgh

Output from the the SPSS PROCESS Macro:Note, that given the univariate partially non-normal distributions we also offer bootstrapped results of theregression parameters.

Run MATRIX procedure:

*************** PROCESS Procedure for SPSS Version 3.3 ******************

Written by Andrew F. Hayes, Ph.D. www.afhayes.comDocumentation available in Hayes (2018). www.guilford.com/p/hayes3

**************************************************************************Model : 6

Y : ZhateX : Zfail_pb

M1 : ZchristM2 : Zantisem

SampleSize: 380

**************************************************************************OUTCOME VARIABLE:Zchrist

Model Summary

30

R R-sq MSE F df1 df2 p.0930 .0086 .9940 3.2968 1.0000 378.0000 .0702

Modelcoeff se t p LLCI ULCI

constant .0000 .0511 .0000 1.0000 -.1006 .1006Zfail_pb .0930 .0512 1.8157 .0702 -.0077 .1937

Standardized coefficientscoeff

Zfail_pb .0930

**************************************************************************OUTCOME VARIABLE:Zantisem

Model SummaryR R-sq MSE F df1 df2 p

.5525 .3052 .6984 82.8205 2.0000 377.0000 .0000


constant .0000 .0429 .0000 1.0000 -.0843 .0843Zfail_pb .2713 .0431 6.2917 .0000 .1865 .3560Zchrist .4568 .0431 10.5937 .0000 .3720 .5415


Zfail_pb .2713Zchrist .4568

**************************************************************************OUTCOME VARIABLE:Zhate


.2964 .0879 .9194 12.0729 3.0000 376.0000 .0000


constant .0000 .0492 .0000 1.0000 -.0967 .0967Zfail_pb -.0609 .0520 -1.1705 .2425 -.1631 .0414Zchrist -.0916 .0564 -1.6262 .1047 -.2024 .0192Zantisem -.2173 .0591 -3.6776 .0003 -.3335 -.1011


Zfail_pb -.0609Zchrist -.0916Zantisem -.2173

************************** TOTAL EFFECT MODEL ****************************OUTCOME VARIABLE:

31

Zhate


.1376 .0189 .9837 7.2915 1.0000 378.0000 .0072


constant .0000 .0509 .0000 1.0000 -.1000 .1000Zfail_pb -.1376 .0509 -2.7003 .0072 -.2377 -.0374


Zfail_pb -.1376

************** TOTAL, DIRECT, AND INDIRECT EFFECTS OF X ON Y **************

Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs-.1376 .0509 -2.7003 .0072 -.2377 -.0374 -.1376 -.1376

Direct effect of X on YEffect se t p LLCI ULCI c'_ps c'_cs-.0609 .0520 -1.1705 .2425 -.1631 .0414 -.0609 -.0609

Indirect effect(s) of X on Y:Effect BootSE BootLLCI BootULCI

TOTAL -.0767 .0230 -.1251 -.0341Ind1 -.0085 .0072 -.0254 .0022Ind2 -.0590 .0199 -.1017 -.0231Ind3 -.0092 .0063 -.0233 .0011

Partially standardized indirect effect(s) of X on Y:Effect BootSE BootLLCI BootULCI

TOTAL -.0767 .0240 -.1276 -.0331Ind1 -.0085 .0070 -.0248 .0023Ind2 -.0590 .0209 -.1053 -.0221Ind3 -.0092 .0064 -.0239 .0011

Completely standardized indirect effect(s) of X on Y:Effect BootSE BootLLCI BootULCI

TOTAL -.0767 .0238 -.1261 -.0334Ind1 -.0085 .0070 -.0244 .0023Ind2 -.0590 .0209 -.1050 -.0221Ind3 -.0092 .0064 -.0238 .0011

Indirect effect key:Ind1 Zfail_pb -> Zchrist -> ZhateInd2 Zfail_pb -> Zantisem -> ZhateInd3 Zfail_pb -> Zchrist -> Zantisem -> Zhate

*********** BOOTSTRAP RESULTS FOR REGRESSION MODEL PARAMETERS ************

OUTCOME VARIABLE:

32

Zchrist

Coeff BootMean BootSE BootLLCI BootULCIconstant .0000 -.0007 .0505 -.1019 .0983Zfail_pb .0930 .0942 .0546 -.0104 .2008

----------

OUTCOME VARIABLE:Zantisem

Coeff BootMean BootSE BootLLCI BootULCIconstant .0000 .0004 .0423 -.0803 .0843Zfail_pb .2713 .2715 .0471 .1805 .3653Zchrist .4568 .4562 .0435 .3726 .5435

----------

OUTCOME VARIABLE:Zhate

Coeff BootMean BootSE BootLLCI BootULCIconstant .0000 -.0012 .0500 -.1013 .0934Zfail_pb -.0609 -.0617 .0476 -.1604 .0256Zchrist -.0916 -.0916 .0504 -.1947 .0043Zantisem -.2173 -.2181 .0650 -.3467 -.0897

*********************** ANALYSIS NOTES AND ERRORS ************************

Level of confidence for all confidence intervals in output:95.0000

Number of bootstrap samples for percentile bootstrap confidence intervals:5000

------ END MATRIX -----

Note. Please note that we were mainly interested in the model after a hate crime and have not yet formallyhypothesized what the model would predict after Pittsburgh.

33

Gender Differences: Path Model Pittsburgh (as per request of reviewer)

Given that we did not have any explicit predictions about gender differences and added this exploratory stepas part of the reviewing process, we used a data driven approach to determine the specific moderating stagesof gender. To do so, we first split the data file by gender and reran the path analysis:

Path Analysis for Women:




**************************************************************************Model : 6


M1 : ZChristM2 : Zantisem

SampleSize: 222

**************************************************************************OUTCOME VARIABLE:ZChrist


.0659 .0043 1.0361 .9585 1.0000 220.0000 .3286


constant -.0581 .0684 -.8502 .3961 -.1929 .0766Zfail_pb -.0671 .0686 -.9791 .3286 -.2023 .0680


Zfail_pb -.0659



.4446 .1977 .4555 26.9808 2.0000 219.0000 .0000


constant -.2423 .0454 -5.3364 .0000 -.3318 -.1528Zfail_pb .1064 .0456 2.3352 .0204 .0166 .1962

34

ZChrist .3175 .0447 7.1035 .0000 .2294 .4056


Zfail_pb .1416ZChrist .4309



.2211 .0489 .8853 3.7342 3.0000 218.0000 .0120


constant .0152 .0673 .2265 .8211 -.1174 .1479Zfail_pb -.0220 .0643 -.3429 .7320 -.1488 .1047ZChrist -.0568 .0691 -.8210 .4125 -.1930 .0795Zantisem -.2372 .0942 -2.5184 .0125 -.4229 -.0516


Zfail_pb -.0230ZChrist -.0603Zantisem -.1857

************************** TOTAL EFFECT MODEL ****************************OUTCOME VARIABLE:Zhate


.0400 .0016 .9208 .3533 1.0000 220.0000 .5528


constant .0804 .0645 1.2475 .2136 -.0466 .2075Zfail_pb -.0384 .0646 -.5944 .5528 -.1658 .0890


Zfail_pb -.0400


Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs-.0384 .0646 -.5944 .5528 -.1658 .0890 -.0401 -.0400

Direct effect of X on YEffect se t p LLCI ULCI c'_ps c'_cs-.0220 .0643 -.3429 .7320 -.1488 .1047 -.0230 -.0230

35


TOTAL -.0164 .0200 -.0596 .0211Ind1 .0038 .0076 -.0074 .0237Ind2 -.0252 .0164 -.0638 -.0004Ind3 .0051 .0067 -.0063 .0207


TOTAL -.0171 .0214 -.0629 .0217Ind1 .0040 .0079 -.0077 .0252Ind2 -.0263 .0179 -.0686 -.0004Ind3 .0053 .0073 -.0066 .0233


TOTAL -.0171 .0211 -.0625 .0219Ind1 .0040 .0079 -.0077 .0253Ind2 -.0263 .0177 -.0676 -.0004Ind3 .0053 .0073 -.0066 .0232

Indirect effect key:Ind1 Zfail_pb -> ZChrist -> ZhateInd2 Zfail_pb -> Zantisem -> ZhateInd3 Zfail_pb -> ZChrist -> Zantisem -> Zhate


OUTCOME VARIABLE:ZChrist

Coeff BootMean BootSE BootLLCI BootULCIconstant -.0581 -.0583 .0676 -.1894 .0753Zfail_pb -.0671 -.0657 .0732 -.2086 .0775

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.2423 -.2425 .0462 -.3313 -.1487Zfail_pb .1064 .1051 .0499 .0097 .2057ZChrist .3175 .3181 .0429 .2337 .4037

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .0152 .0134 .0708 -.1344 .1497Zfail_pb -.0220 -.0224 .0614 -.1550 .0899

36

ZChrist -.0568 -.0569 .0630 -.1869 .0629Zantisem -.2372 -.2402 .1003 -.4451 -.0489




------ END MATRIX -----

Path Analysis for Men:




**************************************************************************Model : 6



SampleSize: 158



.3189 .1017 .8547 17.6558 1.0000 156.0000 .0000


constant .0590 .0737 .8011 .4243 -.0865 .2046Zfail_pb .3094 .0736 4.2019 .0000 .1640 .4549


Zfail_pb .3189


Model Summary

37

R R-sq MSE F df1 df2 p.6713 .4507 .7702 63.5906 2.0000 155.0000 .0000


constant .3026 .0701 4.3176 .0000 .1642 .4411Zfail_pb .4239 .0737 5.7477 .0000 .2782 .5696ZChrist .5596 .0760 7.3637 .0000 .4095 .7098


Zfail_pb .3610ZChrist .4625



.3519 .1238 .9814 7.2545 3.0000 154.0000 .0001


constant -.0394 .0837 -.4703 .6388 -.2048 .1260Zfail_pb -.1273 .0917 -1.3880 .1671 -.3084 .0539ZChrist -.1526 .0997 -1.5312 .1278 -.3495 .0443Zantisem -.1518 .0907 -1.6744 .0961 -.3309 .0273


Zfail_pb -.1217ZChrist -.1416Zantisem -.1704



.2535 .0642 1.0347 10.7095 1.0000 156.0000 .0013


constant -.0993 .0811 -1.2254 .2223 -.2595 .0608Zfail_pb -.2651 .0810 -3.2725 .0013 -.4252 -.1051


Zfail_pb -.2535


38




TOTAL -.1379 .0514 -.2365 -.0358Ind1 -.0472 .0290 -.1130 .0018Ind2 -.0644 .0437 -.1493 .0231Ind3 -.0263 .0198 -.0695 .0099


TOTAL -.1315 .0524 -.2357 -.0322Ind1 -.0451 .0269 -.1052 .0019Ind2 -.0614 .0440 -.1512 .0217Ind3 -.0251 .0199 -.0709 .0090


TOTAL -.1318 .0522 -.2372 -.0320Ind1 -.0451 .0267 -.1045 .0018Ind2 -.0615 .0439 -.1501 .0216Ind3 -.0251 .0197 -.0701 .0089




Coeff BootMean BootSE BootLLCI BootULCIconstant .0590 .0576 .0744 -.0872 .2045Zfail_pb .3094 .3112 .0763 .1599 .4565

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .3026 .3056 .0681 .1758 .4383Zfail_pb .4239 .4239 .0763 .2748 .5685ZChrist .5596 .5630 .0816 .4065 .7256

----------

39


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0394 -.0424 .0868 -.2218 .1165Zfail_pb -.1273 -.1314 .0819 -.3077 .0121ZChrist -.1526 -.1542 .0859 -.3320 .0059Zantisem -.1518 -.1493 .1012 -.3460 .0546




------ END MATRIX -----

Path Analysis with gender and gender X disempowerment:

To assess the differences between the groups, we inspected (1) the differences in betas (as an indicationof effect size; given the reduced sample sizes we chose a difference of at least .3 as practically relevant),(2) the overlap of the confidence intervals, as well as (3) a statistical comparison of betas as suggested byCohen, Cohen, West, and Aiken (2003, https://doi.org/10.4324/9780203774441 ) - with t = b1−b2√

se2b1+se2

b2and

df = n1 + n2 − 4.Summary:

Table 25: Gender Differences in Path Parameters (Pittsburgh)

Path βwomen[95%CI] pwomen βmen[95%CI] pmen δBetas t df pdiff

Disempowerment →Radical Nationalism -0.07 [-0.2, 0.07] 0.329 0.31 [0.16, 0.45] 0.000 0.38 3.74 376 0.000Disempowerment →Prejudice 0.11 [0.02, 0.2] 0.020 0.42 [0.28, 0.57] 0.000 0.32 3.66 376 0.000Radical Nationalism →Prejudice 0.32 [0.23, 0.41] 0.000 0.56 [0.41, 0.71] 0.000 0.24 2.75 376 0.006Disempowerment →Hate Crime Perceptions -0.02 [-0.15, 0.1] 0.732 -0.13 [-0.31, 0.05] 0.167 0.11 0.94 376 0.348Radical Nationalism →Hate Crime Perceptions -0.06 [-0.19, 0.08] 0.413 -0.15 [-0.35, 0.04] 0.128 0.10 0.79 376 0.430Prejudice →Hate Crime Perceptions -0.24 [-0.42, -0.05] 0.013 -0.15 [-0.33, 0.03] 0.096 0.09 0.65 376 0.514

Inspection of the two subgroup analyses indicated that the effects of disempowerment on christiannationalism and Antisemitism were the effects that notably differed between men and women (i.e.,non-overlapping CIs, statistically significant difference in betas, difference in standardized beta largerthan .3). We, therefore, reran the original path model with gender and gender X disempowermentas additional predictors of all down-stream outcomes (i.e., christian nationalism, Antisemitism, andhate crime attributions). It should be noted that the effect of Nationalism on Antisemitism was also

40

https://doi.org/10.4324/9780203774441

statistically different between the groups but the effect size difference did not reach the pre-set threshold of 0.3.




**************************************************************************Model : 6



Covariates:PB_gende failXgen

SampleSize: 380



.2162 .0467 .9609 6.1455 3.0000 376.0000 .0004


constant .0004 .0511 .0087 .9930 -.1000 .1009Zfail_pb .1211 .0511 2.3695 .0183 .0206 .2217PB_gende .0586 .0511 1.1467 .2522 -.0419 .1590failXgen .1883 .0511 3.6825 .0003 .0877 .2888


Zfail_pb .1211PB_gende .0578failXgen .1880



.6394 .4088 .5975 64.8282 4.0000 375.0000 .0000


41

constant .0372 .0403 .9243 .3559 -.0420 .1165Zfail_pb .2918 .0406 7.1836 .0000 .2119 .3716ZChrist .4069 .0407 10.0055 .0000 .3269 .4869PB_gende .2744 .0404 6.7992 .0000 .1950 .3537failXgen .1794 .0410 4.3712 .0000 .0987 .2601


Zfail_pb .2918ZChrist .4069PB_gende .2708failXgen .1792



.3003 .0902 .9220 7.4159 5.0000 374.0000 .0000


constant -.0021 .0501 -.0417 .9668 -.1006 .0964Zfail_pb -.0738 .0538 -1.3723 .1708 -.1797 .0320ZChrist -.0912 .0569 -1.6045 .1094 -.2030 .0206Zantisem -.1961 .0641 -3.0571 .0024 -.3222 -.0700PB_gende -.0261 .0531 -.4904 .6241 -.1305 .0784failXgen -.0460 .0523 -.8800 .3794 -.1487 .0568


Zfail_pb -.0738ZChrist -.0912Zantisem -.1961PB_gende -.0257failXgen -.0459



.1990 .0396 .9681 5.1656 3.0000 376.0000 .0016


constant -.0095 .0513 -.1846 .8536 -.1103 .0914Zfail_pb -.1518 .0513 -2.9577 .0033 -.2527 -.0509PB_gende -.0899 .0513 -1.7528 .0805 -.1907 .0109failXgen -.1134 .0513 -2.2090 .0278 -.2143 -.0125

Standardized coefficients

42

coeffZfail_pb -.1518PB_gende -.0887failXgen -.1132





TOTAL -.0779 .0241 -.1276 -.0319Ind1 -.0111 .0082 -.0304 .0013Ind2 -.0572 .0216 -.1023 -.0165Ind3 -.0097 .0057 -.0228 -.0007


TOTAL -.0779 .0253 -.1311 -.0317Ind1 -.0111 .0080 -.0296 .0013Ind2 -.0572 .0228 -.1055 -.0162Ind3 -.0097 .0058 -.0230 -.0008


TOTAL -.0779 .0251 -.1298 -.0317Ind1 -.0111 .0079 -.0295 .0013Ind2 -.0572 .0227 -.1052 -.0162Ind3 -.0097 .0058 -.0229 -.0008




Coeff BootMean BootSE BootLLCI BootULCIconstant .0004 -.0002 .0509 -.0986 .0994Zfail_pb .1211 .1212 .0529 .0182 .2249PB_gende .0586 .0601 .0502 -.0395 .1600failXgen .1883 .1881 .0529 .0824 .2916

----------

43


Coeff BootMean BootSE BootLLCI BootULCIconstant .0372 .0374 .0418 -.0432 .1210Zfail_pb .2918 .2920 .0435 .2058 .3779ZChrist .4069 .4066 .0426 .3244 .4902PB_gende .2744 .2739 .0417 .1911 .3556failXgen .1794 .1787 .0445 .0915 .2641

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0021 -.0053 .0506 -.1083 .0902Zfail_pb -.0738 -.0755 .0491 -.1758 .0172ZChrist -.0912 -.0918 .0520 -.1972 .0054Zantisem -.1961 -.1971 .0705 -.3330 -.0570PB_gende -.0261 -.0253 .0548 -.1359 .0773failXgen -.0460 -.0459 .0482 -.1430 .0499




NOTE: Variables names longer than eight characters can produce incorrect output.Shorter variable names are recommended.

------ END MATRIX -----

Controlling for gender and the disempowerment by gender interaction, the model interpretation stayedconsistent and non of the original key pathways became unstable. Interestingly, however, the weakest path(disempowerment –> nationalism) became slightly stronger, whereas the stronger effect of nationalism –>Antisemitism was slightly reduced by the inclusion of the interaction effect. The effect of Antisemitism onhate crime perceptions was also slightly reduced - this differences was, however, not statistically significant inthe comparison of the subgroup betas.

44

Study 2a: ChristchurchIn 2019 there was the Christchurch mosque shootings in New Zealand (51 killed, 49 injured), perpetrated byan avowed white nationalist.This data set seeks to replicate the path model and further test the in-group defense mechanism.

We first report tables for the reported the reported demographic information.

Table 26: Gender (Christchurch)

Gender Frequency PercentageMale 438 68.76Female 199 31.24

Table 27: Age (Christchurch)


Table 28: Education (Christchurch)

Education Frequency PercentageSome Secondary School or Less 43 6.75Secondary School (or equivalent) 146 22.92Some Tertiary education 188 29.51Bachelor’s degree (or equivalent) 153 24.02Postgraduate degree 107 16.80

Table 29: Gun Ownership Proportions (Christchurch)

Gun Ownership Frequency Percentagegun owner 321 50.39no gun owner 316 49.61

45

Scale Construction

Hate Crime Attributions

We first assess hate crime perceptions and other ascribed motives after Christchurch and check the relation(i.e., correlation) of hate crime perceptions to other perceived motives.

Table 30: Descriptives of Motives (Christchurch)


hate

−3

2−

32

−3

2−

32

−3 0 3

−3 0 3

*r = 0.09[0.01, 0.17]

religion

***r = 0.50[0.44, 0.56]

***r = 0.25[0.17, 0.32]

ideology

−3 0 3

−3 0 3

***r = 0.42[0.36, 0.48]

**r = 0.11[0.03, 0.19]

***r = 0.39[0.32, 0.45]

power

***r = 0.27[0.2, 0.34]

***r = 0.14[0.07, 0.22]

***r = 0.23[0.15, 0.3]

***r = 0.37[0.3, 0.44]

compensation

−3 0 3

−3 0 3

***r = 0.20[0.12, 0.27]

***r = 0.20[0.13, 0.28]

***r = 0.19[0.11, 0.26]

***r = 0.15[0.07, 0.23]

***r = 0.26[0.19, 0.33]

mental

***r = 0.25[0.17, 0.32]

r = −0.02[−0.1, 0.06]

***r = 0.17[0.1, 0.25]

***r = 0.19[0.11, 0.26]

***r = 0.18[0.1, 0.25]

***r = 0.17[0.1, 0.25]

ease

−3 0 3

−3 0 3

***r = 0.13[0.06, 0.21]

r = 0.06[−0.02, 0.14]

***r = 0.20[0.12, 0.27]

***r = 0.24[0.16, 0.31]

***r = 0.20[0.12, 0.28]

***r = 0.25[0.18, 0.33]

***r = 0.43[0.36, 0.49]

culture

−3

2 r = 0.10[−0.05, 0.24]

r = 0.12[−0.03, 0.26]

−3

2*r = 0.16[0.02, 0.3]

r = 0.07[−0.07, 0.21]

−3

2 r = 0.01[−0.13, 0.15]

r = −0.05[−0.2, 0.09]

−3

2.r = 0.12[−0.02, 0.26]

.r = 0.13[−0.01, 0.27]

−3 0 3

−3

2other

Because “Motivated by Hate” had such a high correlation with “Motivated by Ideology” and “Motivated byPower”, we ran an exploratory factor analysis to assess whether hatred, ideology, and power would load ontothe same latent factor.

46

2 4 6 8

0.0

0.5

1.0

1.5

2.0

2.5

Parallel Analysis Scree Plots for Shooter Motives

Factor Number

eige

n va

lues

of p

rinci

pal f

acto

rs FA Actual Data FA Simulated Data FA Resampled Data

## Parallel analysis suggests that the number of factors = 4

The scree plot suggests that at least two factors are necessary in order to explain a sufficient amount ofvariation. The Parallel analysis (the scree plot of a simulated data set from a random data matrix with thesame sample size), suggests that up to 5 factors are reasonable (i.e., the cross-over point).When conducting factor analyses with the suggested numbers of factors, only a 5 factor solution reached thecommon model fit rules of thumb (i.e., RMSR < 0.05, RMSEA < 0.05, and TLI > 0.9).

## Factor Analysis using method = minres## Call: fa(r = dt$Christchurch %>% select(starts_with("mot_cc"), -ends_with("TEXT")) %>%## dplyr::rename(religion = Mot_CC_01, ideology = Mot_CC_02,## power = Mot_CC_03, compensation = Mot_CC_04, hate = Mot_CC_05,## mental = Mot_CC_06, ease = Mot_CC_07, culture = Mot_CC_08,## other = Mot_CC_10) %>% mutate_all(funs(na_if(., ""))) %>%## mutate_all(funs(na_if(., -99))) %>% select(hate, everything()) %>%## as.data.frame(), nfactors = 5, rotate = "Varimax", fm = "minres")## Standardized loadings (pattern matrix) based upon correlation matrix## MR1 MR3 MR2 MR4 MR5 h2 u2 com## hate 0.97 0.21 0.06 0.03 0.04 1.00 0.0041 1.1## religion 0.06 0.08 -0.02 0.58 0.17 0.38 0.6240 1.2## ideology 0.44 0.28 0.14 0.34 0.02 0.41 0.5931 2.9## power 0.22 0.97 0.08 0.04 0.03 1.00 0.0045 1.1## compensation 0.18 0.32 0.16 0.11 0.27 0.25 0.7538 3.4## mental 0.14 0.08 0.22 0.17 0.57 0.43 0.5719 1.7## ease 0.19 0.10 0.60 -0.06 0.04 0.41 0.5911 1.3## culture 0.06 0.17 0.67 0.07 0.13 0.50 0.5019 1.2## other 0.09 0.03 0.21 0.28 -0.29 0.21 0.7892 3.1#### MR1 MR3 MR2 MR4 MR5## SS loadings 1.29 1.21 0.95 0.58 0.53

47

## Proportion Var 0.14 0.13 0.11 0.06 0.06## Cumulative Var 0.14 0.28 0.38 0.45 0.51## Proportion Explained 0.28 0.27 0.21 0.13 0.12## Cumulative Proportion 0.28 0.55 0.76 0.88 1.00#### Mean item complexity = 1.9## Test of the hypothesis that 5 factors are sufficient.#### The degrees of freedom for the null model are 36 and the objective function was## 1.36 with Chi Square of 860## The degrees of freedom for the model are 1 and the objective function was 0#### The root mean square of the residuals (RMSR) is 0## The df corrected root mean square of the residuals is 0.02#### The harmonic number of observations is 414 with the empirical chi square 0.49 with## prob < 0.49. The total number of observations was 637 with Likelihood## Chi Square = 0.71 with prob < 0.4#### Tucker Lewis Index of factoring reliability = 1.013## RMSEA index = 0 and the 90 % confidence intervals are 0 0.099## BIC = -5.74## Fit based upon off diagonal values = 1## Measures of factor score adequacy## MR1 MR3 MR2 MR4 MR5## Correlation of (regression) scores with factors 1.00 1.00 0.78 0.68 0.65## Multiple R square of scores with factors 0.99 0.99 0.60 0.46 0.43## Minimum correlation of possible factor scores 0.99 0.98 0.21 -0.09 -0.14

Table 31: Factor Loadings Shooter Motives (Christchurch)

MR1 MR3 MR2 MR4 MR5hate 0.972religion 0.581ideology 0.437 0.345power 0.969compensation 0.318mental 0.569ease 0.598culture 0.669other

48

Factor Analysis

hate

ideology

power

compensation

culture

ease

religion

mental

other

MR11

0.4

MR31

0.3

MR20.7

0.6

MR40.6

MR50.6

The factor analysis suggests that after the Christchurch shooting, “ideology” is a multidimensional motive inNew Zealand. Yet the “hate” motive remains the mayor contributor to its own factor and seems to be largelyindependent of the other motives.

We, therefore, continued using the hate crime attributions as our key dependent variable:

49

0

100

200

300

400

−2 0 2Hate Crime Attribution Thousand Oaks

coun

tDistribution of Hate Crime Attribution after the Thousand Oaks Shooting

Table 32: Hate Crime Attribution: Scale Descriptives (Christchurch)

vars n mean sd min max range seHate 637 2.38 1.099 -3 3 6 0.0435

50

As with the the previous data sets we checked whether hate crime perceptions were indeed higher thanany of the other motives after the Christchurch shooting. This also functions as a check to ensure that theparticipants indeed identified the shooting as a hate crime.

culture

religion

compensation

mental

ease

other

power

ideology

hate

0.0 0.5 1.0 1.5 2.0 2.5Average Rating

Mot

ive


#### Simultaneous Tests for General Linear Hypotheses#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesCC),## random = ~1 | id, method = "ML")#### Linear Hypotheses:## Estimate Std. Error z value Pr(>|z|)## hate - religion == 0 1.7757 0.0898 19.76 <0.001 ***## hate - ideology == 0 0.2252 0.0894 2.52 0.074 .## hate - power == 0 0.3538 0.0893 3.96 <0.001 ***## hate - compensation == 0 1.6372 0.0897 18.25 <0.001 ***## hate - mental == 0 1.4655 0.0895 16.37 <0.001 ***## hate - ease == 0 1.4666 0.0895 16.40 <0.001 ***## hate - culture == 0 1.7974 0.0899 20.00 <0.001 ***## hate - other == 0 1.3664 0.1355 10.08 <0.001 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## (Adjusted p values reported -- single-step method)

##

51

## Simultaneous Confidence Intervals#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesCC),## random = ~1 | id, method = "ML")#### Quantile = 2.667## 95% family-wise confidence level###### Linear Hypotheses:## Estimate lwr upr## hate - religion == 0 1.7757 1.5361 2.0153## hate - ideology == 0 0.2252 -0.0133 0.4636## hate - power == 0 0.3538 0.1156 0.5921## hate - compensation == 0 1.6372 1.3979 1.8765## hate - mental == 0 1.4655 1.2267 1.7043## hate - ease == 0 1.4666 1.2281 1.7052## hate - culture == 0 1.7974 1.5578 2.0371## hate - other == 0 1.3664 1.0050 1.7278

The results suggest that hate crime perceptions were indeed higher than any of the other motive perceptions(except for ideology, which became non-significant after multi-test alpha correction).

52

Robust test


−5.0−2.5

0.02.5


Res

idua

ls



0.00.10.20.3

−5.0 −2.5 0.0 2.5Residuals

Den

sity



−5.0−2.5

0.02.5


Res

idua

ls



53

other

culture

ease

mental

hate

compensation

power

ideology

religion

−5.0 −2.5 0.0 2.5

Residuals

Gro


Visual inspection of the qq-plot and the distribution of the residuals suggested a slight slight violation ofnormality and a further inspection of the sphericity (and homoscedasticity) assumption indicated differencesin the variances between the categories. This was also corroborated by the ration of largest group variance tosmallest variance = 4.1, which is larger than the recommended 1.5 rule of thumb.Even though with large sample sizes and largely balanced data (i.e., almost all participants responded on allmotives) the tests are quite robust to violations of homoscedasticity we still offer a robust heteroscedasticrepeated measurement ANOVA. Given the violations of normality we additionally offer a Friedman rank sumtest that does not assume normality. We removed the ‘other’ from the analyses because some of the robustanalyses need fully balanced designs and 451 participants did not rate any additional motivations. For theoverall robust ANOVA we, however, offer a bootstrapped alternative that is able to deal with missing data.For the follow-up comparisons we present the relevant Hate crime subset of a full robust post-hoc test with aHochberg’s approach to control for the family-wise error (FWE).


## [1] "robust heteroscedastic bootstrapped repeated measurement ANOVA without the 'other'## category removed: F = 33.1 with a critical value of 2.23 and given that F > F_critical,## the bootstrapped test equally indicates an overall statistical significance."

#### Friedman rank sum test#### data: value and variable and id## Friedman chi-squared = 912, df = 7, p-value <0.0000000000000002


54

Table 33: Follow-up Compare Hate Motive - robust (Christchurch)

Group1 Group2 psi hat ci.lower ci.upper p.value p.crit sigreligion hate -1.781 -2.088 -1.473 0 0.003 TRUEideology hate -0.241 -0.406 -0.077 0 0.004 TRUEpower hate -0.361 -0.533 -0.188 0 0.004 TRUEcompensation hate -1.648 -1.896 -1.400 0 0.002 TRUEhate mental 1.456 1.194 1.717 0 0.002 TRUEhate ease 1.483 1.225 1.741 0 0.002 TRUEhate culture 1.801 1.531 2.071 0 0.002 TRUE

Islamoprejudice

For our antecedent Islamoprejudice measure we assess scale-ability and combine the individual items into asingle scale score.

Reliability Analysis of Islamoprejudice:



Table 34: Islamoprejudice: Item Total Correlations (Christchurch)Item Corr. to scale Factor Loading Mean SDislamoPred_01 0.5854 0.7667 2.830 1.276islamoPred_03 0.5860 0.7663 2.879 1.270islamoPred_05 0.5832 0.6954 3.135 1.334islamoPred_04_R 0.5253 0.5306 2.527 1.318islamoPred_06_R 0.4456 0.4566 2.769 1.277islamoPred_02_R 0.3715 0.3880 1.915 1.108

Table 35: Islamoprejudice: Item Descriptives (Christchurch)

vars n mean sd min max range seislamoPred_01 637 2.830 1.276 1 5 4 0.0506islamoPred_03 637 2.879 1.270 1 5 4 0.0503islamoPred_05 637 3.135 1.334 1 5 4 0.0529islamoPred_02_R 637 1.915 1.108 1 5 4 0.0439islamoPred_04_R 637 2.527 1.318 1 5 4 0.0522islamoPred_06_R 637 2.769 1.277 1 5 4 0.0506

55

islamoPred_011

35

13

5

1 2 3 4 5

13

5

1 2 3 4 5

***r = 0.69[0.65, 0.73]

islamoPred_03

***r = 0.54[0.49, 0.6]

***r = 0.54[0.48, 0.59]

islamoPred_05

1 2 3 4 5

1 2 3 4 5

***r = 0.21[0.14, 0.29]

***r = 0.22[0.14, 0.29]

***r = 0.20[0.12, 0.27]

islamoPred_02_R

***r = 0.31[0.24, 0.38]

***r = 0.32[0.25, 0.39]

***r = 0.41[0.34, 0.47]

***r = 0.33[0.26, 0.4]

islamoPred_04_R

1 2 3 4 5

1 2 3 4 5

13

5

***r = 0.26[0.19, 0.33]

***r = 0.26[0.19, 0.33]

13

5

***r = 0.34[0.27, 0.4]

***r = 0.27[0.19, 0.34]

13

5

***r = 0.47[0.41, 0.53]

islamoPred_06_R

Table 36: Islamoprejudice: Scale Descriptives (Christchurch)

vars n mean sd min max range seIslamoprejudice 637 2.676 0.8674 1 5 4 0.0344

Disempowerment

For our antecedent Disempowerment measure after the Christchurch shooting we assess scale-ability andcombine the individual items into a single scale score.




Table 37: Disempowerment: Item Total Correlations (Christchurch)Item Corr. to scale Factor Loading Mean SDdisemp2 0.6993 0.8885 2.622 1.220disemp1 0.6465 0.7727 2.906 1.210disemp3 0.5058 0.5592 1.929 1.316

56

Table 38: Disempowerment: Item Descriptives (Christchurch)

vars n mean sd median trimmed mad min max range skew kurtosis sedisemp1 637 2.906 1.210 3 2.883 1.483 1 5 4 0.0793 -0.7843 0.0479disemp2 637 2.622 1.220 3 2.567 1.483 1 5 4 0.1688 -0.9537 0.0483disemp3 637 1.929 1.316 1 1.675 0.000 1 5 4 1.2370 0.2365 0.0522

disemp1

12

34

5

1 2 3 4 5

1 2 3 4 5

***r = 0.70

[0.65, 0.73]

disemp2

12

34

5

***r = 0.50

[0.44, 0.56]

***r = 0.54

[0.48, 0.59]

1 2 3 4 5

12

34

5

disemp3

Table 39: Disempowerment: Scale Descriptives (Christchurch)


White Nationalism

For our antecedent White nationalism measure after the Christchurch shooting we assess scale-ability andcombine the individual items into a single scale score.

Reliability Analysis of White Nationalism:


The overall Cronbach’s Alpha was 0.64. A gls factor analysis was conducted. Items were regressed to a singlefactor. Their loadings are the following:

57

Table 40: White Nationalism: Item Total Correlations (Christchurch)Item Corr. to scale Factor Loading Mean SDbs2 0.4937 0.7133 2.557 1.419bs3 0.4566 0.6195 2.292 1.145bs1 0.3933 0.5012 2.703 1.331

Table 41: White Nationalism: Item Descriptives (Christchurch)

vars n mean sd median trimmed mad min max range skew kurtosis sebs1 637 2.703 1.331 3 2.630 1.483 1 5 4 0.1775 -1.1222 0.0527bs2 637 2.557 1.419 2 2.448 1.483 1 5 4 0.4111 -1.1380 0.0562bs3 637 2.292 1.145 2 2.157 1.483 1 5 4 0.8262 -0.0158 0.0454

bs1

12

34

5

1 2 3 4 5

1 2 3 4 5

***r = 0.38

[0.31, 0.45]

bs2

12

34

5

***r = 0.36

[0.29, 0.43]

***r = 0.49

[0.42, 0.54]

1 2 3 4 5

12

34

5

bs3

Table 42: White Nationalism: Scale Descriptives (Christchurch)

vars n mean sd min max range seWhite Nationalism 637 2.518 1.012 1 5 4 0.0401

58

Path Model

Correlation Key Variables

fail.cc.c

−1

12

−1 0 1 2

−5

−3

−1

−1 0 1 2

***r = 0.47

[0.41, 0.53]

white.cc.c

***r = 0.27

[0.2, 0.34]

***r = 0.30

[0.22, 0.37]

islam.cc.c

−1 0 1 2

−5 −3 −1 0

−1

12

***r = −0.15

[−0.23, −0.08]

**r = −0.12

[−0.2, −0.04]

−1

12

***r = −0.14

[−0.22, −0.06]

hate.cc.c

Path Model Christchurch





**************************************************************************Model : 6

Y : ZhateX : Zfail

M1 : Zwhite.nM2 : Zislam

SampleSize: 637

**************************************************************************OUTCOME VARIABLE:Zwhite.n

59


.4715 .2223 .7805 181.5095 1.0000 635.0000 .0000


constant .0002 .0350 .0065 .9948 -.0685 .0690Zfail .4720 .0350 13.4725 .0000 .4032 .5408


Zfail .4715

**************************************************************************OUTCOME VARIABLE:Zislam


.3310 .1095 .8940 38.9954 2.0000 634.0000 .0000


constant .0004 .0375 .0107 .9915 -.0732 .0740Zfail .1684 .0425 3.9599 .0001 .0849 .2519Zwhite.n .2164 .0425 5.0942 .0000 .1330 .2998


Zfail .1683Zwhite.n .2165



.1877 .0352 .9693 7.7089 3.0000 633.0000 .0000


constant .0005 .0390 .0128 .9898 -.0761 .0771Zfail -.1062 .0448 -2.3685 .0182 -.1942 -.0181Zwhite.n -.0414 .0451 -.9183 .3588 -.1300 .0472Zislam -.0997 .0414 -2.4100 .0162 -.1809 -.0185


Zfail -.1061Zwhite.n -.0415Zislam -.0997

************************** TOTAL EFFECT MODEL ****************************

60



.1527 .0233 .9782 15.1500 1.0000 635.0000 .0001


constant .0004 .0392 .0114 .9909 -.0765 .0774Zfail -.1527 .0392 -3.8923 .0001 -.2297 -.0756


Zfail -.1527



Direct effect of X on YEffect se t p LLCI ULCI c'_ps c'_cs-.1062 .0448 -2.3685 .0182 -.1942 -.0181 -.1062 -.1061


TOTAL -.0465 .0275 -.1006 .0046Ind1 -.0196 .0244 -.0685 .0275Ind2 -.0168 .0097 -.0381 -.0004Ind3 -.0102 .0058 -.0230 -.0003


TOTAL -.0465 .0273 -.1004 .0046Ind1 -.0196 .0244 -.0686 .0271Ind2 -.0168 .0095 -.0375 -.0004Ind3 -.0102 .0057 -.0226 -.0003


TOTAL -.0465 .0274 -.1002 .0046Ind1 -.0196 .0245 -.0690 .0271Ind2 -.0168 .0095 -.0375 -.0004Ind3 -.0102 .0057 -.0225 -.0003

Indirect effect key:Ind1 Zfail -> Zwhite.n -> ZhateInd2 Zfail -> Zislam -> ZhateInd3 Zfail -> Zwhite.n -> Zislam -> Zhate


61

OUTCOME VARIABLE:Zwhite.n

Coeff BootMean BootSE BootLLCI BootULCIconstant .0002 .0005 .0347 -.0672 .0680Zfail .4720 .4714 .0416 .3873 .5532

----------

OUTCOME VARIABLE:Zislam

Coeff BootMean BootSE BootLLCI BootULCIconstant .0004 .0005 .0380 -.0735 .0741Zfail .1684 .1678 .0440 .0795 .2545Zwhite.n .2164 .2165 .0454 .1292 .3069

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .0005 .0000 .0390 -.0766 .0747Zfail -.1062 -.1067 .0431 -.1918 -.0233Zwhite.n -.0414 -.0413 .0517 -.1445 .0568Zislam -.0997 -.0995 .0498 -.1983 -.0032











62

**************************************************************************Model : 6

Y : ZhateX : Zfail


SampleSize: 199



.3441 .1184 .7743 26.4518 1.0000 197.0000 .0000


constant .0253 .0625 .4054 .6856 -.0979 .1485Zfail .3367 .0655 5.1431 .0000 .2076 .4658


Zfail .3441



.2987 .0892 .7958 9.5976 2.0000 196.0000 .0001


constant -.1551 .0634 -2.4477 .0153 -.2801 -.0301Zfail .1995 .0707 2.8215 .0053 .0600 .3389Zwhite.n .1572 .0722 2.1764 .0307 .0148 .2997





.2093 .0438 .8595 2.9778 3.0000 195.0000 .0327

63


constant .1230 .0669 1.8396 .0673 -.0089 .2549Zfail -.1384 .0749 -1.8471 .0662 -.2862 .0094Zwhite.n -.1233 .0760 -1.6224 .1063 -.2731 .0266Zislam .0277 .0742 .3732 .7094 -.1187 .1741


Zfail -.1405Zwhite.n -.1225Zislam .0274



.1756 .0308 .8624 6.2647 1.0000 197.0000 .0131


constant .1157 .0659 1.7544 .0809 -.0144 .2457Zfail -.1729 .0691 -2.5029 .0131 -.3092 -.0367


Zfail -.1756





TOTAL -.0345 .0367 -.1085 .0383Ind1 -.0415 .0255 -.0950 .0070Ind2 .0055 .0185 -.0315 .0437Ind3 .0015 .0054 -.0094 .0139


TOTAL -.0367 .0398 -.1178 .0397Ind1 -.0441 .0285 -.1045 .0071Ind2 .0059 .0193 -.0335 .0446Ind3 .0016 .0056 -.0099 .0140

64


TOTAL -.0350 .0382 -.1141 .0371Ind1 -.0421 .0276 -.1024 .0067Ind2 .0056 .0185 -.0319 .0435Ind3 .0015 .0053 -.0095 .0134





----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.1551 -.1569 .0631 -.2813 -.0343Zfail .1995 .1991 .0697 .0581 .3359Zwhite.n .1572 .1571 .0739 .0157 .3007

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .1230 .1226 .0675 -.0154 .2472Zfail -.1384 -.1395 .0671 -.2768 -.0142Zwhite.n -.1233 -.1237 .0707 -.2615 .0208Zislam .0277 .0269 .0879 -.1410 .2038





------ END MATRIX -----

65





**************************************************************************Model : 6

Y : ZhateX : Zfail


SampleSize: 438



.5213 .2718 .7754 162.7066 1.0000 436.0000 .0000


constant -.0162 .0421 -.3852 .7003 -.0989 .0665Zfail .5270 .0413 12.7556 .0000 .4458 .6082


Zfail .5213



.3455 .1194 .9256 29.4886 2.0000 435.0000 .0000


constant .0729 .0460 1.5838 .1140 -.0176 .1633Zfail .1409 .0529 2.6626 .0080 .0369 .2448Zwhite.n .2490 .0523 4.7591 .0000 .1462 .3519



66



.1952 .0381 1.0110 5.7325 3.0000 434.0000 .0007


constant -.0434 .0482 -.8998 .3687 -.1381 .0514Zfail -.0973 .0557 -1.7462 .0815 -.2069 .0122Zwhite.n -.0097 .0561 -.1732 .8626 -.1200 .1005Zislam -.1390 .0501 -2.7749 .0058 -.2375 -.0406


Zfail -.0971Zwhite.n -.0098Zislam -.1392



.1400 .0196 1.0258 8.7133 1.0000 436.0000 .0033


constant -.0528 .0484 -1.0904 .2761 -.1479 .0424Zfail -.1403 .0475 -2.9518 .0033 -.2337 -.0469


Zfail -.1400





TOTAL -.0430 .0388 -.1217 .0310Ind1 -.0051 .0368 -.0774 .0668Ind2 -.0196 .0112 -.0444 -.0016

67

Ind3 -.0182 .0093 -.0392 -.0028


TOTAL -.0420 .0374 -.1151 .0312Ind1 -.0050 .0359 -.0745 .0650Ind2 -.0192 .0107 -.0423 -.0016Ind3 -.0179 .0088 -.0372 -.0028


TOTAL -.0429 .0381 -.1182 .0318Ind1 -.0051 .0366 -.0764 .0667Ind2 -.0195 .0108 -.0432 -.0017Ind3 -.0182 .0089 -.0378 -.0029




Coeff BootMean BootSE BootLLCI BootULCIconstant -.0162 -.0172 .0413 -.0987 .0610Zfail .5270 .5257 .0459 .4355 .6137

----------



----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0434 -.0431 .0467 -.1379 .0437Zfail -.0973 -.0980 .0569 -.2074 .0176Zwhite.n -.0097 -.0089 .0695 -.1482 .1257Zislam -.1390 -.1387 .0585 -.2563 -.0249


Level of confidence for all confidence intervals in output:

68

95.0000



------ END MATRIX -----

Path Analysis controlling for gender:


se2b1+se2

b2and


Table 43: Gender Differences in Path Parameters (Christchurch)


Disempowerment –>Radical Nationalism 0.34 [0.21, 0.47] 0.000 0.53 [0.45, 0.61] 0.000 0.19 2.46 553 0.014Disempowerment –>Prejudice 0.2 [0.06, 0.34] 0.005 0.14 [0.04, 0.24] 0.008 0.06 0.66 553 0.507Radical Nationalism –>Prejudice 0.16 [0.01, 0.3] 0.031 0.25 [0.15, 0.35] 0.000 0.09 1.03 553 0.304Disempowerment –>Hate Crime Perceptions -0.14 [-0.29, 0.01] 0.066 -0.1 [-0.21, 0.01] 0.082 0.04 0.44 553 0.660Radical Nationalism –>Hate Crime Perceptions -0.12 [-0.27, 0.03] 0.106 -0.01 [-0.12, 0.1] 0.863 0.11 1.20 553 0.230Prejudice –>Hate Crime Perceptions 0.03 [-0.12, 0.17] 0.709 -0.14 [-0.24, -0.04] 0.006 0.17 1.86 553 0.063

Inspection of the two subgroup analyses indicated that non of the pathways reached our criterium for addinggender as an interaction term (i.e., non-overlapping CIs, statistically significant differnce in betas, differencein standardized beta larger than .3). It should be noted, however, that the effect of disempowerment onChristian Nationalism was statistically different between the two samples. However, the effect did not reachour cut-off for adjusted relevance (|β1 − β2| > .3). Below we show the full path analysis output when addinggender as a simple control variable.




**************************************************************************Model : 6

69

https://doi.org/10.4324/9780203774441

Y : ZhateX : Zfail


Covariates:gender.c

SampleSize: 637



.4720 .2228 .7812 90.8656 2.0000 634.0000 .0000


constant .0091 .0378 .2418 .8090 -.0651 .0833Zfail .4729 .0351 13.4804 .0000 .4040 .5418gender.c -.0475 .0756 -.6282 .5301 -.1960 .1010


Zfail .4724gender.c -.0220



.3477 .1209 .8840 29.0077 3.0000 633.0000 .0000


constant -.0427 .0402 -1.0621 .2886 -.1216 .0362Zfail .1627 .0423 3.8450 .0001 .0796 .2459Zwhite.n .2194 .0422 5.1925 .0000 .1364 .3023gender.c .2297 .0805 2.8552 .0044 .0717 .3878


Zfail .1627Zwhite.n .2195gender.c .1065


70


.2003 .0401 .9660 6.6040 4.0000 632.0000 .0000


constant .0289 .0421 .6880 .4917 -.0536 .1115Zfail -.1039 .0448 -2.3203 .0206 -.1918 -.0160Zwhite.n -.0452 .0451 -1.0032 .3161 -.1338 .0433Zislam -.0913 .0415 -2.1969 .0284 -.1729 -.0097gender.c -.1516 .0847 -1.7912 .0737 -.3179 .0146


Zfail -.1038Zwhite.n -.0453Zislam -.0913gender.c -.0703



.1717 .0295 .9736 9.6269 2.0000 634.0000 .0001


constant .0322 .0422 .7642 .4450 -.0506 .1151Zfail -.1496 .0392 -3.8194 .0001 -.2265 -.0727gender.c -.1695 .0844 -2.0079 .0451 -.3353 -.0037


Zfail -.1496gender.c -.0786



Direct effect of X on YEffect se t p LLCI ULCI c'_ps c'_cs-.1039 .0448 -2.3203 .0206 -.1918 -.0160 -.1039 -.1038


TOTAL -.0457 .0273 -.1005 .0077Ind1 -.0214 .0245 -.0700 .0269Ind2 -.0149 .0091 -.0350 .0004

71

Ind3 -.0095 .0058 -.0225 .0002


TOTAL -.0457 .0270 -.0987 .0078Ind1 -.0214 .0244 -.0692 .0266Ind2 -.0149 .0089 -.0348 .0005Ind3 -.0095 .0057 -.0221 .0003


TOTAL -.0457 .0271 -.0990 .0079Ind1 -.0214 .0244 -.0695 .0261Ind2 -.0149 .0089 -.0348 .0004Ind3 -.0095 .0057 -.0221 .0003




Coeff BootMean BootSE BootLLCI BootULCIconstant .0091 .0078 .0385 -.0686 .0836Zfail .4729 .4733 .0405 .3923 .5524gender.c -.0475 -.0462 .0758 -.1921 .1018

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0427 -.0428 .0392 -.1193 .0352Zfail .1627 .1634 .0438 .0771 .2507Zwhite.n .2194 .2189 .0459 .1272 .3094gender.c .2297 .2291 .0776 .0719 .3810

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .0289 .0291 .0405 -.0530 .1069Zfail -.1039 -.1033 .0438 -.1899 -.0189Zwhite.n -.0452 -.0453 .0517 -.1481 .0574Zislam -.0913 -.0911 .0491 -.1882 .0030gender.c -.1516 -.1524 .0807 -.3106 .0084

72





------ END MATRIX -----

73

Study 2b: UtrechtA week after the Christchurch Shooting in New Zealand, there was the shooting in the Dutch city of Utrecht(3 killed, 7 injured), perpetrated by a Turkish-born immigrant.If hate crime perceptions indeed reflect in-group defense, then disempowerment-fueled prejudice should predicthate crime recognition in opposing directions for Utrecht. Because in the Netherlands the perpetrator was aTurkish-born immigrant, the inter-group context is reversed as compared to the previous studies, providing acontext in which disempowered Whites may seek to “win” victim status for themselves (Shnabel & Nadler,2008), so in-group defense would be reflected by stronger claims that the gunman was motivated by hatredand prejudice. Therefore, in the Netherlands, Islamoprejudice would positively predict claims that the Utrechtshooting was a hate crime.We first report tables for the reported the reported demographic information.

Table 44: Gender (Utrecht)

Gender Frequency PercentageMan 248 51.88Vrouw 229 47.91Anders, namelijk 1 0.21

Note. Translations: ’Man’: man; ’Vrouw’: woman; ’Anders, namelijk’: other, namely

Table 45: Age (Utrecht)

Age Range Frequency Percentage18-24 22 4.6025-34 70 14.6435-44 63 13.1845-54 85 17.7855-64 107 22.3865+ 130 27.20Wil ik niet zeggen 1 0.21

Note. Translations: ’Wil ik niet zeggen’: I don’t want to say

Table 46: Education (Utrecht)

Education Frequency PercentageBasisschool 8 1.67LBO 37 7.74MAVO, MULO, VMBO 95 19.87HAVO 33 6.90VWO 16 3.35MBO 128 26.78HBO 93 19.46WO, universiteit 65 13.60onbekend 1 0.21Anders, namelijk 2 0.42

Note. For an explanation of the Dutch education system see: https://en.wikipedia.org/wiki/Education_in_the_Netherlands; Translation: ’unbekend’: unknown; ’Anders, namelijk’: other, namely

74

https://en.wikipedia.org/wiki/Education_in_the_Netherlands

https://en.wikipedia.org/wiki/Education_in_the_Netherlands

Table 47: Gun Ownership Proportions (Christchurch)

Gun Ownership Frequency PercentageNee 474 99.16Ja 4 0.84

Note. Translations: ’Nee’: no; ’Ja’: yes

Scale Construction

Hate Crime Attributions

We first assess hate crime perceptions and other ascribed motives after Utrecht and check the relation (i.e.,correlation) of hate crime perceptions to other perceived motives.

Table 48: Descriptives of Motives (Utrecht)


hate

−3

2−

32

−3

2−

32

−3 0 3

−3 0 3

***r = 0.40[0.32, 0.47]

religion

***r = 0.41[0.33, 0.48]

***r = 0.69[0.64, 0.73]

ideology

−3 0 3

−3 0 3

***r = 0.32[0.24, 0.4]

**r = 0.14[0.05, 0.22]

***r = 0.24[0.15, 0.32]

power

***r = 0.17[0.08, 0.25]

r = −0.03[−0.12, 0.06]

r = 0.06[−0.03, 0.15]

***r = 0.34[0.26, 0.42]

compensation

−3 0 3

−3 0 3

*r = 0.11[0.02, 0.2]

r = −0.06[−0.15, 0.03]

r = −0.04[−0.13, 0.05]

***r = 0.21[0.12, 0.29]

***r = 0.27[0.18, 0.35]

mental

***r = 0.29[0.21, 0.37]

***r = 0.19[0.1, 0.28]

***r = 0.17[0.08, 0.26]

*r = 0.11[0.02, 0.2]

r = 0.05[−0.04, 0.14]

r = 0.07[−0.02, 0.16]

ease

−3 0 3

−3 0 3

**r = 0.13[0.04, 0.22]

*r = 0.10[0.01, 0.19]

.r = 0.09[0, 0.18]

***r = 0.21[0.12, 0.29]

***r = 0.28[0.2, 0.37]

***r = 0.16[0.08, 0.25]

***r = 0.34[0.25, 0.41]

culture

−3

2 r = 0.12[−0.05, 0.29]

r = −0.07[−0.24, 0.11]

−3

2 r = −0.07[−0.24, 0.1]

*r = 0.19[0.02, 0.35]

−3

2 r = 0.00[−0.17, 0.17]

**r = 0.24[0.08, 0.4]

−3

2 r = −0.01[−0.18, 0.16]

*r = 0.19[0.02, 0.35]

−3 0 3

−3

2other

75

0

50

100

150

−2 0 2Hate Crime Attribution

coun

tDistribution of Hate Crime Attribution after the Utrecht Shooting

Table 49: Hate Crime Attribution: Scale Descriptives (Utrecht)

vars n mean sd min max range seHate 478 1.617 1.331 -3 3 6 0.0609

76

As with the the previous data sets we checked whether hate crime perceptions were indeed higher than anyof the other motives after the Utrecht shooting. This also functions as a check to ensure that the participantsindeed identified the shooting as a hate crime.

culture

compensation

other

religion

power

ideology

ease

hate

mental

0.0 0.5 1.0 1.5 2.0Average Rating

Mot

ive


#### Simultaneous Tests for General Linear Hypotheses#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesUtr),## random = ~1 | id, method = "ML")#### Linear Hypotheses:## Estimate Std. Error z value Pr(>|z|)## hate - religion == 0 0.6619 0.0949 6.98 <0.001 ***## hate - ideology == 0 0.4662 0.0947 4.92 <0.001 ***## hate - power == 0 0.5696 0.0948 6.01 <0.001 ***## hate - compensation == 0 1.1340 0.0948 11.96 <0.001 ***## hate - mental == 0 -0.4455 0.0945 -4.71 <0.001 ***## hate - ease == 0 0.2566 0.0945 2.71 0.044 *## hate - culture == 0 1.5800 0.0947 16.69 <0.001 ***## hate - other == 0 0.9470 0.1464 6.47 <0.001 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## (Adjusted p values reported -- single-step method)

##

77

## Simultaneous Confidence Intervals#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesUtr),## random = ~1 | id, method = "ML")#### Quantile = 2.664## 95% family-wise confidence level###### Linear Hypotheses:## Estimate lwr upr## hate - religion == 0 0.6619 0.4091 0.9147## hate - ideology == 0 0.4662 0.2138 0.7185## hate - power == 0 0.5696 0.3169 0.8222## hate - compensation == 0 1.1340 0.8814 1.3867## hate - mental == 0 -0.4455 -0.6974 -0.1936## hate - ease == 0 0.2566 0.0047 0.5085## hate - culture == 0 1.5800 1.3278 1.8322## hate - other == 0 0.9470 0.5568 1.3372

Mean scores on the dependent variable “. . . Hatred of others; prejudice” were lower than in the Orlando,Pittsburgh, or Christchurch studies. Scores on the item were still higher than most of the other possiblemotivations. Only, scores on “Mental illness” were higher, presumably due to the fact that the Dutch nationalmedia emphasized the gunman’s history of mental illness in its coverage of the shooting.

78

Robust test


−5.0−2.5

0.02.5


Res

idua

ls



0.00.10.20.3

−5.0 −2.5 0.0 2.5Residuals

Den

sity



−5.0−2.5

0.02.5


Res

idua

ls



79

other

culture

ease

mental

hate

compensation

power

ideology

religion

−5.0 −2.5 0.0 2.5

Residuals

Gro


Visual inspection of the qq-plot and the distribution of the residuals suggested no violation of normalitybut a further inspection of the sphericity (and homoscedasticity) assumption indicated differences in thevariances between the categories. This was also corroborated by the ration of largest group variance tosmallest variance = 1.94, which is larger than the recommended 1.5 rule of thumb.Even though with large sample sizes and largely balanced data (i.e., almost all participants responded on allmotives) the tests are quite robust to violations of homoscedasticity we still offer a robust heteroscedasticrepeated measurement ANOVA. We removed the ‘other’ from the analyses because some of the robustanalyses need fully balanced designs and 345 participants did not rate any additional motivations. For theoverall robust ANOVA we, however, offer a bootstrapped alternative that is able to deal with missing data.For the follow-up comparisons we present the relevant Hate crime subset of a full robust post-hoc test with aHochberg’s approach to control for the family-wise error (FWE).



Table 50: Follow-up Compare Hate Motive - robust (Utrecht)

Group1 Group2 psi hat ci.lower ci.upper p.value p.crit sigreligion hate -0.676 -0.920 -0.432 0.000 0.003 TRUEideology hate -0.487 -0.723 -0.251 0.000 0.004 TRUEpower hate -0.585 -0.839 -0.330 0.000 0.004 TRUEcompensation hate -1.170 -1.469 -0.870 0.000 0.002 TRUEhate mental -0.422 -0.681 -0.162 0.000 0.005 TRUEhate ease 0.285 0.028 0.542 0.001 0.009 TRUEhate culture 1.591 1.280 1.902 0.000 0.002 TRUE

80


Islamoprejudice

For our antecedent Islamoprejudice measure we assess scale-ability and combine the individual items into asingle scale score.

Reliability Analysis of Islamoprejudice:



Table 51: Islamoprejudice: Item Total Correlations (Utrecht)Item Corr. to scale Factor Loading Mean SDislamoPred_01 0.6933 0.7894 -0.4046 1.304islamoPred_03 0.6755 0.7749 0.2704 1.284islamoPred_05 0.6424 0.7197 0.4780 1.238islamoPred_04_R 0.6202 0.6704 -0.3187 1.236islamoPred_02_R 0.5099 0.5617 -1.0230 1.039islamoPred_06_R 0.4673 0.5190 0.2621 1.061

Table 52: Islamoprejudice: Item Descriptives (Utrecht)

vars n mean sd median trimmed mad min max range skew kurtosis seislamoPred_01 477 -0.4046 1.304 0 -0.4909 1.483 -2 2 4 0.2484 -1.0919 0.0597islamoPred_03 477 0.2704 1.284 0 0.3368 1.483 -2 2 4 -0.2862 -0.9847 0.0588islamoPred_05 477 0.4780 1.238 1 0.5770 1.483 -2 2 4 -0.4242 -0.7844 0.0567islamoPred_02_R 478 -1.0230 1.039 -1 -1.1693 1.483 -2 2 4 0.8968 0.1144 0.0475islamoPred_04_R 477 -0.3187 1.236 0 -0.3969 1.483 -2 2 4 0.3139 -0.8747 0.0566islamoPred_06_R 477 0.2621 1.061 0 0.2585 1.483 -2 2 4 0.0541 -0.5396 0.0486

81

islamoPred_01−

20

2−

20

2

−2 0 1 2

−2

02

−2 0 1 2

***r = 0.66[0.61, 0.71]

islamoPred_03

***r = 0.54[0.48, 0.6]

***r = 0.56[0.49, 0.62]

islamoPred_05

−2 0 1 2

−2 0 1 2

***r = 0.39[0.31, 0.46]

***r = 0.40[0.32, 0.47]

***r = 0.36[0.28, 0.44]

islamoPred_02_R

***r = 0.48[0.41, 0.55]

***r = 0.46[0.39, 0.53]

***r = 0.53[0.46, 0.59]

***r = 0.45[0.37, 0.52]

islamoPred_04_R

−2 0 1 2

−2 0 1 2

−2

02

***r = 0.44[0.36, 0.51]

***r = 0.36[0.28, 0.44]

−2

02

***r = 0.34[0.26, 0.42]

***r = 0.27[0.18, 0.35]

−2

02

***r = 0.39[0.31, 0.47]

islamoPred_06_R

Table 53: Islamoprejudice: Scale Descriptives (Utrecht)

vars n mean sd min max range seIslamoprejudice 478 -0.1224 0.8798 -2 2 4 0.0402

Disempowerment

For our antecedent Disempowerment measure after the Utrecht shooting we assess scale-ability and combinethe individual items into a single scale score.




Table 54: Disempowerment: Item Total Correlations (Utrecht)Item Corr. to scale Factor Loading Mean SDdisemp02 0.7313 0.9203 2.575 1.385disemp01 0.6809 0.8069 2.630 1.322disemp03 0.4992 0.5395 2.013 1.187

82

Table 55: Disempowerment: Item Descriptives (Utrecht)

vars n mean sd median trimmed mad min max range skew kurtosis sedisemp01 478 2.630 1.322 3 2.539 1.483 1 5 4 0.2547 -1.0945 0.0605disemp02 478 2.575 1.385 2 2.471 1.483 1 5 4 0.3806 -1.1296 0.0633disemp03 478 2.013 1.187 2 1.836 1.483 1 5 4 0.8918 -0.2297 0.0543

disemp01

12

34

5

1 2 3 4 5

1 2 3 4 5

***r = 0.74

[0.7, 0.78]

disemp02

12

34

5

***r = 0.44

[0.36, 0.51]

***r = 0.51

[0.44, 0.57]

1 2 3 4 5

12

34

5

disemp03

Table 56: Disempowerment: Scale Descriptives (Utrecht)


White Nationalism

For our antecedent White nationalism measure after the Utrecht shooting we assess scale-ability and combinethe individual items into a single scale score.

Reliability Analysis of White Nationalism:



83

Table 57: White Nationalism: Item Total Correlations (Utrecht)Item Corr. to scale Factor Loading Mean SDBS02 0.4531 0.6754 2.550 1.1115BS03 0.4194 0.5876 2.157 0.9758BS01 0.3692 0.4886 2.320 1.0680

Table 58: White Nationalism: Item Descriptives (Utrecht)

vars n mean sd median trimmed mad min max range skew kurtosis seBS01 478 2.320 1.0680 2 2.250 1.483 1 5 4 0.3691 -0.7188 0.0488BS02 475 2.550 1.1115 3 2.514 1.483 1 5 4 0.1792 -0.7931 0.0510BS03 477 2.157 0.9758 2 2.050 1.483 1 5 4 0.6981 0.0501 0.0447

BS01

12

34

5

1 2 3 4 5

1 2 3 4 5

***r = 0.34

[0.26, 0.42]

BS02

12

34

5

***r = 0.31

[0.22, 0.39]

***r = 0.42

[0.35, 0.49]

1 2 3 4 5

12

34

5

BS03

Table 59: White Nationalism: Scale Descriptives (Utrecht)

vars n mean sd min max range seWhite Nationalism 478 2.343 0.7939 1 5 4 0.0363

84

Path Model

Correlation Key Variables

fail.nl.c

−1

12

−1 0 1 2

−4

−2

0

−1 0 1 2

***r = 0.45

[0.37, 0.52]

white.nl.c

***r = 0.41

[0.34, 0.49]

***r = 0.50

[0.43, 0.57]

islam.nl.c

−2 −1 0 1 2

−4 −2 0 1

−1

12**

r = 0.13

[0.04, 0.22]

***r = 0.16

[0.08, 0.25]

−2

01

2

***r = 0.23

[0.15, 0.32]

hate.nl.c

Path Model Utrecht





**************************************************************************Model : 6

Y : ZhateX : Zfail


SampleSize: 478


Model Summary

85

R R-sq MSE F df1 df2 p.4486 .2012 .7979 119.9041 1.0000 476.0000 .0000


constant .0030 .0409 .0732 .9417 -.0773 .0833Zfail .4472 .0408 10.9501 .0000 .3670 .5275


Zfail .4486



.5477 .3000 .7066 101.7619 2.0000 475.0000 .0000


constant .0012 .0384 .0304 .9758 -.0744 .0767Zfail .2359 .0430 5.4857 .0000 .1514 .3204Zwhite.n .4015 .0431 9.3093 .0000 .3168 .4863





.2400 .0576 .9484 9.6590 3.0000 474.0000 .0000


constant -.0015 .0445 -.0348 .9723 -.0891 .0860Zfail .0231 .0514 .4490 .6536 -.0779 .1240Zwhite.n .0583 .0543 1.0723 .2841 -.0485 .1650Zislam .1929 .0532 3.6291 .0003 .0885 .2974


Zfail .0231Zwhite.n .0582Zislam .1934

************************** TOTAL EFFECT MODEL ****************************OUTCOME VARIABLE:

86

Zhate


.1295 .0168 .9853 8.1132 1.0000 476.0000 .0046


constant -.0009 .0454 -.0202 .9839 -.0901 .0883Zfail .1293 .0454 2.8484 .0046 .0401 .2185


Zfail .1295


Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs.1293 .0454 2.8484 .0046 .0401 .2185 .1293 .1295

Direct effect of X on YEffect se t p LLCI ULCI c'_ps c'_cs.0231 .0514 .4490 .6536 -.0779 .1240 .0231 .0231


TOTAL .1062 .0268 .0545 .1608Ind1 .0261 .0253 -.0239 .0762Ind2 .0455 .0161 .0173 .0808Ind3 .0346 .0117 .0134 .0598


TOTAL .1062 .0274 .0540 .1612Ind1 .0261 .0254 -.0238 .0776Ind2 .0455 .0161 .0173 .0812Ind3 .0346 .0118 .0135 .0597


TOTAL .1064 .0275 .0542 .1615Ind1 .0261 .0255 -.0237 .0777Ind2 .0456 .0161 .0174 .0805Ind3 .0347 .0117 .0134 .0594



OUTCOME VARIABLE:

87

Zwhite.n


----------



----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0015 -.0034 .0441 -.0913 .0811Zfail .0231 .0219 .0498 -.0795 .1143Zwhite.n .0583 .0573 .0559 -.0525 .1673Zislam .1929 .1934 .0562 .0801 .3028











88

**************************************************************************Model : 6

Y : ZhateX : Zfail


SampleSize: 229



.4400 .1936 .6819 54.4865 1.0000 227.0000 .0000


constant -.0773 .0560 -1.3817 .1684 -.1876 .0330Zfail .4275 .0579 7.3815 .0000 .3134 .5416


Zfail .4400



.5197 .2700 .6836 41.8025 2.0000 226.0000 .0000


constant -.1187 .0563 -2.1093 .0360 -.2296 -.0078Zfail .2396 .0646 3.7101 .0003 .1123 .3668Zwhite.n .3903 .0665 5.8724 .0000 .2593 .5212





.2405 .0578 .9861 4.6049 3.0000 225.0000 .0038

89


constant -.0103 .0682 -.1511 .8800 -.1448 .1242Zfail .0073 .0799 .0909 .9276 -.1502 .1647Zwhite.n .1250 .0857 1.4583 .1462 -.0439 .2938Zislam .1707 .0799 2.1368 .0337 .0133 .3281





.1208 .0146 1.0223 3.3640 1.0000 227.0000 .0679


constant -.0454 .0685 -.6624 .5084 -.1804 .0896Zfail .1301 .0709 1.8341 .0679 -.0097 .2698


Zfail .1208


Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs.1301 .0709 1.8341 .0679 -.0097 .2698 .1280 .1208



TOTAL .1228 .0447 .0343 .2115Ind1 .0534 .0378 -.0212 .1291Ind2 .0409 .0229 .0031 .0919Ind3 .0285 .0157 .0020 .0627


TOTAL .1208 .0450 .0330 .2099Ind1 .0526 .0379 -.0202 .1300Ind2 .0402 .0226 .0030 .0915Ind3 .0280 .0153 .0020 .0611

90


TOTAL .1141 .0427 .0310 .1990Ind1 .0496 .0359 -.0187 .1215Ind2 .0380 .0212 .0028 .0857Ind3 .0265 .0145 .0019 .0581




Coeff BootMean BootSE BootLLCI BootULCIconstant -.0773 -.0782 .0581 -.1882 .0364Zfail .4275 .4268 .0592 .3123 .5398

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.1187 -.1193 .0554 -.2292 -.0128Zfail .2396 .2394 .0690 .1037 .3755Zwhite.n .3903 .3892 .0730 .2421 .5308

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0103 -.0118 .0739 -.1617 .1281Zfail .0073 .0072 .0802 -.1610 .1576Zwhite.n .1250 .1240 .0863 -.0508 .2842Zislam .1707 .1723 .0810 .0138 .3270





------ END MATRIX -----

91





**************************************************************************Model : 6

Y : ZhateX : Zfail


SampleSize: 248



.4224 .1784 .9007 53.4192 1.0000 246.0000 .0000


constant .0788 .0615 1.2811 .2014 -.0424 .2000Zfail .4360 .0597 7.3088 .0000 .3185 .5535


Zfail .4224



.5291 .2800 .7106 47.6320 2.0000 245.0000 .0000


constant .1227 .0548 2.2372 .0262 .0147 .2307Zfail .1982 .0585 3.3909 .0008 .0831 .3134Zwhite.n .3887 .0566 6.8636 .0000 .2771 .5002



92



.2324 .0540 .9234 4.6451 3.0000 244.0000 .0035


constant .0072 .0631 .1138 .9095 -.1172 .1316Zfail .0256 .0682 .3752 .7078 -.1087 .1599Zwhite.n .0129 .0705 .1828 .8551 -.1260 .1517Zislam .2122 .0728 2.9129 .0039 .0687 .3556





.1126 .0127 .9560 3.1576 1.0000 246.0000 .0768


constant .0407 .0634 .6425 .5211 -.0841 .1656Zfail .1092 .0615 1.7770 .0768 -.0118 .2303


Zfail .1126


Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs.1092 .0615 1.7770 .0768 -.0118 .2303 .1112 .1126



TOTAL .0836 .0316 .0234 .1497Ind1 .0056 .0327 -.0600 .0717Ind2 .0421 .0211 .0072 .0891

93

Ind3 .0360 .0161 .0089 .0720


TOTAL .0852 .0330 .0233 .1538Ind1 .0057 .0334 -.0604 .0734Ind2 .0428 .0216 .0072 .0919Ind3 .0366 .0168 .0089 .0745


TOTAL .0862 .0334 .0234 .1551Ind1 .0058 .0337 -.0607 .0741Ind2 .0433 .0218 .0073 .0925Ind3 .0371 .0169 .0089 .0754





----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .1227 .1221 .0558 .0144 .2301Zfail .1982 .1990 .0622 .0762 .3197Zwhite.n .3887 .3897 .0577 .2758 .5044

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant .0072 .0069 .0630 -.1198 .1277Zfail .0256 .0250 .0638 -.1052 .1490Zwhite.n .0129 .0105 .0741 -.1348 .1582Zislam .2122 .2141 .0782 .0547 .3632


Level of confidence for all confidence intervals in output:

94

95.0000



------ END MATRIX -----

Path Analysis controlling for gender:


se2b1+se2

b2and


Table 60: Gender Differences in Path Parameters (Utrecht)


Disempowerment →Radical Nationalism 0.43 [0.31, 0.54] 0.000 0.44 [0.32, 0.55] 0.000 0.01 0.10 473 0.919Disempowerment →Prejudice 0.24 [0.11, 0.37] 0.000 0.2 [0.08, 0.31] 0.001 0.04 0.48 473 0.635Radical Nationalism →Prejudice 0.39 [0.26, 0.52] 0.000 0.39 [0.28, 0.5] 0.000 0.00 0.02 473 0.985Disempowerment →Hate Crime Perceptions 0.01 [-0.15, 0.16] 0.928 0.03 [-0.11, 0.16] 0.708 0.02 0.17 473 0.862Radical Nationalism –>Hate Crime Perceptions 0.13 [-0.04, 0.29] 0.146 0.01 [-0.13, 0.15] 0.855 0.11 1.01 473 0.313Prejudice →Hate Crime Perceptions 0.17 [0.01, 0.33] 0.034 0.21 [0.07, 0.36] 0.004 0.04 0.38 473 0.701

Inspection of the two subgroup analyses indicated that non of the pathways reached our criterion for addinggender as an interaction term (i.e., non-overlapping CIs, statistically significant difference in betas, differencein standardized beta larger than .3). Below we show the full path analysis output when adding gender as asimple control variable.




**************************************************************************Model : 6

Y : ZhateX : Zfail

95

https://doi.org/10.4324/9780203774441


Covariates:gender.c

SampleSize: 477



.4562 .2081 .7940 62.2789 2.0000 474.0000 .0000


constant .0017 .0408 .0405 .9677 -.0786 .0819Zfail .4322 .0417 10.3623 .0000 .3503 .5142gender.c .1559 .0835 1.8664 .0626 -.0082 .3201


Zfail .4333gender.c .0780



.5603 .3140 .6951 72.1601 3.0000 473.0000 .0000


constant -.0025 .0382 -.0658 .9476 -.0776 .0726Zfail .2167 .0432 5.0139 .0000 .1318 .3016Zwhite.n .3892 .0430 9.0570 .0000 .3048 .4737gender.c .2425 .0785 3.0910 .0021 .0883 .3967


Zfail .2163Zwhite.n .3876gender.c .1209


Model Summary

96

R R-sq MSE F df1 df2 p.2413 .0582 .9496 7.2970 4.0000 472.0000 .0000


constant -.0043 .0447 -.0957 .9238 -.0920 .0835Zfail .0184 .0518 .3549 .7228 -.0835 .1203Zwhite.n .0598 .0544 1.0988 .2724 -.0471 .1667Zislam .1929 .0537 3.5896 .0004 .0873 .2985gender.c .0189 .0926 .2036 .8388 -.1631 .2008


Zfail .0184Zwhite.n .0597Zislam .1936gender.c .0094



.1347 .0181 .9858 4.3791 2.0000 474.0000 .0130


constant -.0045 .0455 -.0997 .9206 -.0939 .0849Zfail .1185 .0465 2.5496 .0111 .0272 .2098gender.c .0867 .0931 .9310 .3523 -.0963 .2696


Zfail .1187gender.c .0433


Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs.1185 .0465 2.5496 .0111 .0272 .2098 .1185 .1187



TOTAL .1001 .0262 .0499 .1548Ind1 .0258 .0242 -.0222 .0736Ind2 .0418 .0153 .0156 .0759Ind3 .0325 .0110 .0129 .0562

97


TOTAL .1001 .0269 .0497 .1567Ind1 .0258 .0243 -.0219 .0746Ind2 .0418 .0154 .0157 .0765Ind3 .0325 .0111 .0128 .0569


TOTAL .1003 .0270 .0500 .1566Ind1 .0259 .0244 -.0218 .0749Ind2 .0419 .0154 .0156 .0766Ind3 .0325 .0112 .0130 .0572




Coeff BootMean BootSE BootLLCI BootULCIconstant .0017 .0020 .0405 -.0767 .0805Zfail .4322 .4326 .0448 .3430 .5221gender.c .1559 .1560 .0807 .0010 .3173

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0025 -.0019 .0385 -.0779 .0762Zfail .2167 .2163 .0460 .1250 .3069Zwhite.n .3892 .3896 .0448 .3043 .4776gender.c .2425 .2428 .0784 .0903 .3977

----------


Coeff BootMean BootSE BootLLCI BootULCIconstant -.0043 -.0043 .0446 -.0928 .0842Zfail .0184 .0178 .0518 -.0850 .1190Zwhite.n .0598 .0593 .0553 -.0501 .1687Zislam .1929 .1938 .0564 .0828 .3054gender.c .0189 .0172 .0943 -.1689 .2025


98




------ END MATRIX -----

99

Meta AnalysisIn a final step we aim to summarize the path model results of the three studies (after Pittsburgh, Christchurch,and Utrecht). With this analysis we further test the robustness of the model while taking into accountdifferences in magnitude across the studies. Below we report the full results of the parametric and bootstrappedmeta analyses for each of the (direct and indirect) path ways. The names of the paths are set up to reflect allinvolved variables separated in order by a period (i.e., predictor.outcome, e.g., Fail.RadNat indicates thepath from disempowerment to Radical Nationalism).We assess the overall effect sizes by estimating a random effects model using the t-statistic of the standardizedbetas, sample size N, the number of direct predictors of the outcome variables, as well as the R2 of theoutcome variable (this allows us to extract semi-partial correlations).

Meta Analysis as reportet in the main text

Table 61: Parametric Meta-Analysis (S1 controlled)

Path βP ittsburgh[95%CI] βChristchurch[95%CI] βUtrecht[95%CI] βMeta[95%CI] p Meta sigDisempowerment toRadical Nationalism 0.12 [0.02, 0.22] 0.47 [0.41, 0.53] 0.45 [0.38, 0.52] 0.35 [0.13, 0.57] 0.0019 **Disempowerment toPrejudice 0.29 [0.19, 0.38] 0.15 [0.09, 0.21] 0.21 [0.14, 0.28] 0.21 [0.14, 0.29] 0.0000 ***Radical Nationalism toPrejudice 0.4 [0.3, 0.49] 0.19 [0.13, 0.25] 0.36 [0.29, 0.43] 0.32 [0.19, 0.44] 0.0000 ***Disempowerment toHate Crime -0.07 [-0.17, 0.03] -0.09 [-0.15, -0.03] -0.02 [-0.09, 0.05] -0.06 [-0.11, -0.01] 0.0121 *Radical Nationalism toHate Crime -0.08 [-0.18, 0.02] -0.04 [-0.1, 0.02] -0.05 [-0.12, 0.02] -0.05 [-0.1, 0] 0.0416 *Prejudice toHate Crime -0.15 [-0.25, -0.05] -0.09 [-0.15, -0.03] -0.16 [-0.23, -0.09] -0.13 [-0.18, -0.08] 0.0000 ***

Total Effect -0.15 [-0.25, -0.05] -0.15 [-0.21, -0.09] -0.13 [-0.2, -0.06] -0.14 [-0.19, -0.09] 0.0000 ***

100

101

Table 62: Bootstrapped Meta-Analysis (S1 controlled)

Path βP ittsburgh[95%CI] βChristchurch[95%CI] βUtrecht[95%CI] βMeta[95%CI] p Meta sigDisempowerment toRadical Nationalism 0.12 [0.02, 0.22] 0.47 [0.39, 0.55] 0.45 [0.36, 0.53] 0.35 [0.13, 0.57] 0.0019 **Disempowerment toPrejudice 0.29 [0.19, 0.39] 0.17 [0.09, 0.25] 0.24 [0.15, 0.32] 0.23 [0.16, 0.3] 0.0000 ***Radical Nationalism toPrejudice 0.41 [0.30, 0.51] 0.22 [0.14, 0.30] 0.4 [0.32, 0.49] 0.34 [0.22, 0.46] 0.0000 ***Disempowerment toHate Crime -0.07 [-0.18, 0.03] -0.11 [-0.19, -0.03] -0.02 [-0.11, 0.06] -0.07 [-0.12, -0.02] 0.0095 **Radical Nationalism toHate Crime -0.09 [-0.19, 0.01] -0.04 [-0.12, 0.04] -0.06 [-0.14, 0.03] -0.06 [-0.12, 0.00] 0.0339 *Prejudice toHate Crime -0.2 [-0.30, -0.09] -0.1 [-0.18, -0.02] -0.19 [-0.28, -0.11] -0.15 [-0.22, -0.09] 0.0000 ***

Indirect 1 -0.01 [-0.11, 0.09] -0.02 [-0.1, 0.06] -0.03 [-0.11, 0.06] -0.01 [-0.03, 0.00] 0.0667

Indirect 2 -0.06 [-0.16, 0.05] -0.02 [-0.10, 0.06] -0.05 [-0.13, 0.04] -0.03 [-0.06, -0.01] 0.0067 **

SecondaryIndirect -0.01 [-0.11, 0.09] -0.01 [-0.09, 0.07] -0.03 [-0.12, 0.05] -0.01 [-0.03, 0.00] 0.0148 *Note.Indirect 1: Disempowerment to Radical Nationalism to Hate CrimeIndirect 2: Disempowerment to Prejudice to Hate CrimeSecondary Indirect: Disempowerment to Radical Nationalism to Prejudice to Hate Crime

102

103

Meta Analysis without any controlling for gender

Table 63: Parametric Meta-Analysis (without controlling)

Path βP ittsburgh[95%CI] βChristchurch[95%CI] βUtrecht[95%CI] βMeta[95%CI] p Meta sigDisempowerment toRadical Nationalism 0.09 [-0.01, 0.19] 0.47 [0.41, 0.53] 0.45 [0.38, 0.52] 0.34 [0.10, 0.58] 0.0050 **Disempowerment toPrejudice 0.27 [0.17, 0.37] 0.15 [0.09, 0.21] 0.21 [0.14, 0.28] 0.21 [0.14, 0.28] 0.0000 ***Radical Nationalism toPrejudice 0.45 [0.36, 0.55] 0.19 [0.13, 0.25] 0.36 [0.29, 0.43] 0.33 [0.18, 0.48] 0.0000 ***Disempowerment toHate Crime -0.06 [-0.16, 0.04] -0.09 [-0.15, -0.03] -0.02 [-0.09, 0.05] -0.06 [-0.11, -0.01] 0.0163 *Radical Nationalism toHate Crime -0.08 [-0.18, 0.02] -0.04 [-0.10, 0.02] -0.05 [-0.12, 0.02] -0.05 [-0.10, 0.00] 0.0407 *Prejudice toHate Crime -0.18 [-0.28, -0.08] -0.09 [-0.15, -0.03] -0.16 [-0.23, -0.09] -0.14 [-0.20, -0.09] 0.0000 ***

Total Effect -0.14 [-0.24, -0.04] -0.15 [-0.21, -0.09] -0.13 [-0.20, -0.06] -0.14 [-0.19, -0.09] 0.0000 ***

104

105

Table 64: Bootstrapped Meta-Analysis (without controlling)

Path βP ittsburgh[95%CI] βChristchurch[95%CI] βUtrecht[95%CI] βMeta[95%CI] p Meta sigDisempowerment toRadical Nationalism 0.09 [-0.01, 0.20] 0.47 [0.39, 0.55] 0.45 [0.36, 0.53] 0.34 [0.10, 0.58] 0.0051 **Disempowerment toPrejudice 0.27 [0.17, 0.38] 0.17 [0.09, 0.25] 0.24 [0.15, 0.32] 0.22 [0.16, 0.28] 0.0000 ***Radical Nationalism toPrejudice 0.46 [0.35, 0.56] 0.22 [0.14, 0.30] 0.4 [0.32, 0.49] 0.36 [0.22, 0.50] 0.0000 ***Disempowerment toHate Crime -0.06 [-0.17, 0.05] -0.11 [-0.19, -0.03] -0.02 [-0.11, 0.06] -0.07 [-0.12, -0.01] 0.0137 *Radical Nationalism toHate Crime -0.09 [-0.20, 0.01] -0.04 [-0.12, 0.04] -0.06 [-0.14, 0.03] -0.06 [-0.12, -0.01] 0.0305 *Prejudice toHate Crime -0.22 [-0.32, -0.11] -0.1 [-0.18, -0.02] -0.19 [-0.28, -0.11] -0.16 [-0.24, -0.09] 0.0000 ***

Indirect 1 -0.01 [-0.11, 0.10] -0.02 [-0.10, 0.06] -0.03 [-0.11, 0.06] -0.01 [-0.02, 0.00] 0.1063

Indirect 2 -0.06 [-0.17, 0.05] -0.02 [-0.10, 0.06] -0.05 [-0.13, 0.04] -0.04 [-0.06, -0.01] 0.0060 **

SecondaryIndirect -0.01 [-0.12, 0.10] -0.01 [-0.09, 0.07] -0.03 [-0.12, 0.05] -0.01 [-0.03, 0.00] 0.0185 *Note.Indirect 1: Disempowerment to Radical Nationalism to Hate CrimeIndirect 2: Disempowerment to Prejudice to Hate CrimeSecondary Indirect: Disempowerment to Radical Nationalism to Prejudice to Hate Crime

106

107

Meta Analysis all studies controlling for gender

Table 65: Parametric Meta-Analysis (all studies controlled)

Path βP ittsburgh[95%CI] βChristchurch[95%CI] βUtrecht[95%CI] βMeta[95%CI] p Meta sigDisempowerment toRadical Nationalism 0.12 [0.02, 0.22] 0.47 [0.41, 0.53] 0.42 [0.35, 0.50] 0.34 [0.13, 0.55] 0.0018 **Disempowerment toPrejudice 0.29 [0.19, 0.38] 0.14 [0.08, 0.20] 0.19 [0.12, 0.26] 0.21 [0.12, 0.29] 0.0000 ***Radical Nationalism toPrejudice 0.40 [0.30, 0.49] 0.19 [0.13, 0.25] 0.34 [0.27, 0.42] 0.31 [0.19, 0.43] 0.0000 ***Disempowerment toHate Crime -0.07 [-0.17, 0.03] -0.09 [-0.15, -0.03] -0.02 [-0.09, 0.06] -0.06 [-0.11, -0.01] 0.0152 *Radical Nationalism toHate Crime -0.08 [-0.18, 0.02] -0.04 [-0.10, 0.02] -0.05 [-0.12, 0.02] -0.05 [-0.1, 0.00] 0.0350 *Prejudice toHate Crime -0.15 [-0.25, -0.05] -0.09 [-0.15, -0.03] -0.16 [-0.23, -0.09] -0.13 [-0.18, -0.08] 0.0000 ***

Total Effect -0.15 [-0.25, -0.05] -0.15 [-0.21, -0.09] -0.12 [-0.19, -0.04] -0.14 [-0.19, -0.09] 0.0000 ***

108

109

Table 66: Bootstrapped Meta-Analysis (all studies controlled)

Path βP ittsburgh[95%CI] βChristchurch[95%CI] βUtrecht[95%CI] βMeta[95%CI] p Meta sigDisempowerment toRadical Nationalism 0.12 [0.02, 0.22] 0.47 [0.39, 0.55] 0.43 [0.34, 0.52] 0.34 [0.13, 0.56] 0.0018 **Disempowerment toPrejudice 0.29 [0.19, 0.39] 0.16 [0.08, 0.24] 0.22 [0.13, 0.30] 0.22 [0.15, 0.30] 0.0000 ***Radical Nationalism toPrejudice 0.41 [0.3, 0.51] 0.22 [0.14, 0.3] 0.39 [0.30, 0.48] 0.34 [0.22, 0.46] 0.0000 ***Disempowerment toHate Crime -0.07 [-0.18, 0.03] -0.1 [-0.18, -0.02] -0.02 [-0.11, 0.07] -0.07 [-0.12, -0.02] 0.0111 *Radical Nationalism toHate Crime -0.09 [-0.19, 0.01] -0.05 [-0.12, 0.03] -0.06 [-0.15, 0.03] -0.07 [-0.13, -0.01] 0.0293 *Prejudice toHate Crime -0.20 [-0.30, -0.09] -0.09 [-0.17, -0.01] -0.19 [-0.28, -0.11] -0.15 [-0.23, -0.08] 0.0001 ***

Indirect 1 -0.01 [-0.11, 0.09] -0.02 [-0.10, 0.06] -0.03 [-0.11, 0.06] -0.01 [-0.03, 0.00] 0.0625

Indirect 2 -0.06 [-0.16, 0.05] -0.01 [-0.09, 0.06] -0.04 [-0.13, 0.05] -0.03 [-0.06, -0.01] 0.0104 *

Secondary Indirect -0.01 [-0.11, 0.09] -0.01 [-0.09, 0.07] -0.03 [-0.12, 0.06] -0.01 [-0.02, 0.00] 0.0116 *Note.Indirect 1: Disempowerment to Radical Nationalism to Hate CrimeIndirect 2: Disempowerment to Prejudice to Hate CrimeSecondary Indirect: Disempowerment to Radical Nationalism to Prejudice to Hate Crime

110

111

Study 3: El Paso and DaytonIn summer 2019 Americans suffered two consecutive mass shootings in the span of 24 hours, the El PasoWalmart shooting (22 killed, 24 injured), wherein the gunman expressed White nationalist, anti-Immigrantprejudices prior to the attack, and the Dayton bar shooting (10 killed, 27 injured), where the gunman hadnot declared any such prejudice. We used a repeated measures design to test whether biased hate crimeperceptions after El Paso were linked to disempowered individuals perceiving Hispanic immigrants as arealistic threat or a symbolic threat to the United States.

We first report tables for the reported the reported demographic information.

Table 67: Gender (El Paso / Dayton)

Gender Frequency PercentageMale 427 51.02Female 410 48.98

Table 68: Age (El Paso / Dayton)


Table 69: Education (El Paso / Dayton)

Education Frequency PercentageSome High School or Less 9 1.08High School Graduate / GED 125 14.93Some College 240 28.67College Graduate 269 32.14Graduate Degree 194 23.18

Table 70: Gun Ownership Proportions (El Paso / Dayton)

Gun Ownership Frequency PercentageI own this 421 50.3I do not own this 416 49.7

112

Scale Construction

In order to test our two hypotheses we first assess all variables and create appropriate scale variables.

Hate Crime Attribution El Paso

We first assess hate crime perceptions and other ascribed motives in El Paso and check the relation (i.e.,correlation) of hate crime perceptions to other perceived motives.

Table 71: Descriptives of Motives (El Paso)

n mean sd min max range sereligion 804 -0.7438 1.883 -3 3 6 0.0664ideology 827 1.3386 1.639 -3 3 6 0.0570power 832 1.5661 1.486 -3 3 6 0.0515compensation 828 0.9179 1.713 -3 3 6 0.0595hate 837 2.2736 1.092 -3 3 6 0.0377mental 837 1.7945 1.406 -3 3 6 0.0486ease 827 0.7860 2.204 -3 3 6 0.0766culture 832 1.0817 1.768 -3 3 6 0.0613other 258 0.8915 1.529 -3 3 6 0.0952

religion

−3

2−

32

−3

2−

32

−3 0 3

−3 0 3

***r = 0.22[0.16, 0.29]

ideology

***r = 0.14[0.07, 0.21]

***r = 0.35[0.29, 0.41]

power

−3 0 3

−3 0 3

***r = 0.17[0.1, 0.23]

***r = 0.26[0.2, 0.33]

***r = 0.50[0.45, 0.55]

compensation

r = 0.05[−0.02, 0.12]

***r = 0.45[0.39, 0.5]

***r = 0.46[0.41, 0.51]

***r = 0.36[0.3, 0.42]

hate

−3 0 3

−3 0 3

r = 0.05[−0.02, 0.12]

**r = 0.10[0.04, 0.17]

***r = 0.20[0.13, 0.27]

***r = 0.20[0.13, 0.26]

***r = 0.26[0.19, 0.32]

mental

**r = 0.11[0.04, 0.18]

***r = 0.26[0.2, 0.32]

***r = 0.16[0.09, 0.22]

***r = 0.14[0.07, 0.2]

***r = 0.40[0.34, 0.45]

r = 0.01[−0.06, 0.08]

ease

−3 0 3

−3 0 3

**r = 0.09[0.02, 0.16]

***r = 0.17[0.1, 0.24]

***r = 0.27[0.21, 0.33]

***r = 0.19[0.12, 0.25]

***r = 0.26[0.2, 0.32]

***r = 0.29[0.23, 0.35]

***r = 0.15[0.08, 0.21]

culture

−3

2 r = −0.09[−0.21, 0.03]

***r = 0.29[0.18, 0.4]

−3

2***r = 0.35[0.24, 0.45]

**r = 0.17[0.05, 0.28]

−3

2***r = 0.38[0.28, 0.48]

r = 0.05[−0.07, 0.17]

−3

2***r = 0.33[0.22, 0.44]

**r = 0.18[0.06, 0.29]

−3 0 3

−3

2other

113

0

100

200

300

400

500

−3 −2 −1 0 1 2 3

Hate Crime Attribution El Paso

coun

tDistribution of Hate Crime Attribution after the El Paso Shooting

Table 72: Hate Crime Attribution: Item Descriptives (El Paso)

vars n mean sd median trimmed mad min max range skew kurtosis sehate 837 2.274 1.092 3 2.487 0 -3 3 6 -1.785 3.67 0.0377

114

In a next step we compare whether hate crime perceptions were indeed higher in El Paso than any of theother motives. This also functions as a check to ensure that the participants indeed identified the shooting asa hate crime.

religion

ease

other

compensation

culture

ideology

power

mental

hate

0 1 2Average Rating

Mot

ive


#### Simultaneous Tests for General Linear Hypotheses#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesEP),## random = ~1 | id, method = "ML")#### Linear Hypotheses:## Estimate Std. Error z value Pr(>|z|)## hate - religion == 0 3.0065 0.0738 40.73 <0.000000001 ***## hate - ideology == 0 0.9340 0.0732 12.75 <0.000000001 ***## hate - power == 0 0.7059 0.0731 9.65 <0.000000001 ***## hate - compensation == 0 1.3561 0.0732 18.52 <0.000000001 ***## hate - mental == 0 0.4791 0.0730 6.56 <0.000000001 ***## hate - ease == 0 1.4860 0.0732 20.29 <0.000000001 ***## hate - culture == 0 1.1912 0.0731 16.29 <0.000000001 ***## hate - other == 0 1.3382 0.1087 12.31 <0.000000001 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## (Adjusted p values reported -- single-step method)

##

115

## Simultaneous Confidence Intervals#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesEP),## random = ~1 | id, method = "ML")#### Quantile = 2.664## 95% family-wise confidence level###### Linear Hypotheses:## Estimate lwr upr## hate - religion == 0 3.006 2.810 3.203## hate - ideology == 0 0.934 0.739 1.129## hate - power == 0 0.706 0.511 0.901## hate - compensation == 0 1.356 1.161 1.551## hate - mental == 0 0.479 0.285 0.674## hate - ease == 0 1.486 1.291 1.681## hate - culture == 0 1.191 0.996 1.386## hate - other == 0 1.338 1.049 1.628

The results suggest that hate crime perceptions were indeed higher than any of the other motive perceptions(even, after controlling for the multiple tests).

116

Robust test


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117

other

culture

ease

mental

hate

compensation

power

ideology

religion

−5.0 −2.5 0.0 2.5

Residuals

Gro


Visual inspection of the qq-plot and the distribution of the residuals suggested only slight left tail (which didnot warrant a suspicion that the assumption of residual normality was violated) but a further inspection ofthe sphericity (and homoscedasticity) assumption indicated differences in the variances between the categories.This was also corroborated by the ration of largest group variance to smallest variance = 4.08, which is largerthan the recommended 1.5 rule of thumb.Even though with large sample sizes and largely balanced data (i.e., almost all participants responded on allmotives) the tests are quite robust to violations of homoscedasticity we still offer a robust heteroscedasticrepeated measurement ANOVA. We removed the ‘other’ from the analyses because some of the robustanalyses need fully balanced designs and 579 participants did not rate any additional motivations. For theoverall robust ANOVA we, however, offer a bootstrapped alternative that is able to deal with missing data.For the follow-up comparisons we present the relevant Hate crime subset of a full robust post-hoc test with aHochberg’s approach to control for the family-wise error (FWE).


## [1] "robust heteroscedastic bootstrapped repeated measurement ANOVA without the 'other'## category removed: F = 75.28 with a critical value of 2 and given that F > F_critical,## the bootstrapped test equally indicates an overall statistical significance."


Hate Crime Attribution Dayton, Ohio

We similarly assess the motive perceptions in Dayton but do not expect “hate” to be the most importantperceived motive.

118

Table 73: Follow-up Compare Hate Motive - robust (El Paso)

Group1 Group2 psi hat ci.lower ci.upper p.value p.crit sigreligion hate -2.888 -3.329 -2.446 0 0.002 TRUEideology hate -0.936 -1.243 -0.628 0 0.002 TRUEpower hate -0.635 -0.909 -0.360 0 0.002 TRUEcompensation hate -1.365 -1.699 -1.032 0 0.002 TRUEhate mental 0.518 0.203 0.833 0 0.003 TRUEhate ease 1.329 0.941 1.717 0 0.001 TRUEhate culture 1.032 0.687 1.377 0 0.001 TRUEhate other 1.281 0.966 1.596 0 0.001 TRUE

Table 74: Descriptives of Motives (Dayton)

n mean sd median trimmed mad min max range skew kurtosis sereligion 796 -0.6935 1.835 0 -0.8213 1.483 -3 3 6 0.1263 -0.9202 0.0650ideology 828 0.8297 1.776 1 1.0271 1.483 -3 3 6 -0.6397 -0.2346 0.0617power 833 1.5738 1.400 2 1.7421 1.483 -3 3 6 -1.0693 1.2233 0.0485compensation 833 1.0408 1.634 1 1.2354 1.483 -3 3 6 -0.7666 0.2387 0.0566hate 837 1.7802 1.358 2 1.9717 1.483 -3 3 6 -1.2704 1.7028 0.0469mental 834 1.9329 1.298 2 2.1347 1.483 -3 3 6 -1.3350 1.8284 0.0450ease 827 0.7570 2.159 1 0.9442 2.965 -3 3 6 -0.5471 -1.0386 0.0751culture 832 1.0144 1.751 1 1.2312 1.483 -3 3 6 -0.7178 -0.2022 0.0607other 258 0.7248 1.483 0 0.7067 0.000 -3 3 6 0.2413 -0.3016 0.0923

religion

−3

2−

32

−3

2−

32

−3 0 3

−3 0 3

***r = 0.41[0.35, 0.47]

ideology

**r = 0.10[0.03, 0.17]

***r = 0.27[0.21, 0.34]

power

−3 0 3

−3 0 3

***r = 0.13[0.06, 0.2]

***r = 0.23[0.16, 0.29]

***r = 0.56[0.51, 0.6]

compensation

***r = 0.14[0.07, 0.21]

***r = 0.42[0.36, 0.47]

***r = 0.42[0.36, 0.47]

***r = 0.34[0.27, 0.4]

hate

−3 0 3

−3 0 3

r = 0.03[−0.04, 0.1]

***r = 0.18[0.12, 0.25]

***r = 0.32[0.25, 0.38]

***r = 0.26[0.19, 0.32]

***r = 0.29[0.23, 0.35]

mental

***r = 0.13[0.07, 0.2]

***r = 0.14[0.07, 0.2]

***r = 0.17[0.1, 0.24]

***r = 0.19[0.13, 0.26]

***r = 0.33[0.27, 0.39]

.r = 0.07[0, 0.13]

ease

−3 0 3

−3 0 3

**r = 0.10[0.03, 0.17]

***r = 0.19[0.12, 0.25]

***r = 0.32[0.25, 0.38]

***r = 0.31[0.25, 0.37]

***r = 0.24[0.17, 0.3]

***r = 0.27[0.2, 0.33]

***r = 0.17[0.1, 0.24]

culture

−3

2*r = −0.13[−0.25, −0.01]

r = 0.06[−0.07, 0.18]

−3

2***r = 0.29[0.18, 0.4]

***r = 0.22[0.1, 0.33]−

32***r = 0.28[0.16, 0.39]

*r = 0.16[0.04, 0.27]

−3

2***r = 0.24[0.12, 0.35]

***r = 0.27[0.16, 0.38]

−3 0 3

−3

2other

119

0

100

200

300

−3 −2 −1 0 1 2 3

Hate Crime Attribution Dayton

coun

tDistribution of Hate Crime Attribution

after the Dayton Shooting

Table 75: Hate Crime Attribution: Item Descriptives (Dayton)

vars n mean sd median trimmed mad min max range skew kurtosis sehate 837 1.78 1.358 2 1.972 1.483 -3 3 6 -1.27 1.703 0.0469

120

We again formally contrast hate crime perceptions from the other motives.

religion

other

ease

ideology

culture

compensation

power

hate

mental

0 1 2Average Rating

Mot

ive


#### Simultaneous Tests for General Linear Hypotheses#### Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesDO),## random = ~1 | id, method = "ML")#### Linear Hypotheses:## Estimate Std. Error z value Pr(>|z|)## hate - religion == 0 2.4673 0.0729 33.82 <0.001 ***## hate - ideology == 0 0.9458 0.0722 13.11 <0.001 ***## hate - power == 0 0.2071 0.0720 2.88 0.027 *## hate - compensation == 0 0.7399 0.0720 10.27 <0.001 ***## hate - mental == 0 -0.1532 0.0720 -2.13 0.185## hate - ease == 0 1.0244 0.0722 14.19 <0.001 ***## hate - culture == 0 0.7663 0.0721 10.63 <0.001 ***## hate - other == 0 0.9663 0.1072 9.02 <0.001 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## (Adjusted p values reported -- single-step method)

#### Simultaneous Confidence Intervals##

121

## Multiple Comparisons of Means: User-defined Contrasts###### Fit: lme.formula(fixed = value ~ variable, data = na.omit(motivesDO),## random = ~1 | id, method = "ML")#### Quantile = 2.665## 95% family-wise confidence level###### Linear Hypotheses:## Estimate lwr upr## hate - religion == 0 2.4673 2.2729 2.6616## hate - ideology == 0 0.9458 0.7535 1.1381## hate - power == 0 0.2071 0.0152 0.3991## hate - compensation == 0 0.7399 0.5479 0.9319## hate - mental == 0 -0.1532 -0.3451 0.0387## hate - ease == 0 1.0244 0.8321 1.2167## hate - culture == 0 0.7663 0.5743 0.9583## hate - other == 0 0.9663 0.6807 1.2519

The data suggests that mental health issues were perceived as a stronger motive of the shooter than hate andprejudice were.

Robust test


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other

culture

ease

mental

hate

compensation

power

ideology

religion

−6 −4 −2 0 2 4

Residuals

Gro


Visual inspection of the qq-plot and the distribution of the residuals indicated no violation of the normality ofresiduals assumption but a further inspection of the sphericity (and homoscedasticity) assumption indicateddifferences in the variances between the categories. This was also corroborated by the ration of largest groupvariance to smallest variance = 2.77, which is larger than the recommended 1.5 rule of thumb.Even though with large sample sizes and largely balanced data (i.e., almost all participants responded on allmotives) the tests are quite robust to violations of homoscedasticity we still offer a robust heteroscedasticrepeated measurement ANOVA. We removed the ‘other’ from the analyses because some of the robustanalyses need fully balanced designs and 579 participants did not rate any additional motivations. For theoverall robust ANOVA we, however, offer a bootstrapped alternative that is able to deal with missing data.For the follow-up comparisons we present the relevant Hate crime subset of a full robust post-hoc test with aHochberg’s approach to control for the family-wise error (FWE).



Table 76: Follow-up Compare Hate Motive - robust (Thousand Oaks)

Group1 Group2 psi hat ci.lower ci.upper p.value p.crit sigreligion hate -2.216 -2.642 -1.791 0.000 0.002 TRUEideology hate -0.980 -1.324 -0.635 0.000 0.001 TRUEpower hate -0.192 -0.471 0.087 0.027 0.005 FALSEcompensation hate -0.731 -1.067 -0.394 0.000 0.002 TRUEhate mental -0.065 -0.371 0.241 0.491 0.013 FALSEhate ease 1.086 0.682 1.489 0.000 0.002 TRUEhate culture 0.739 0.385 1.093 0.000 0.003 TRUEhate other 0.967 0.621 1.314 0.000 0.001 TRUE

123


Change in Hate Crime Attributions:

In a final step, we then check for differences in “Motivated by Hate” between the two within subject samples.Here we would expect that hate crime perceptions were significantly higher for the El Paso Walmart shootingthan after the Dayton Bar shooting. This paired samples t-test also offers a sort of “manipulation check”to ensure that El Paso was indeed, on average, more strongly perceived as a hate crime than Dayton was(within participants).Given the differences in distribution between the re-contacts we also offer a rank based robust alternative.

−3

−2

−1

0

1

2

3

El Paso Dayton

Shooting

Hat

e C

rime

Attr

ibut

ions

Distributions Hate Crime Attributions by shooting

#### Paired t-test#### data: hateEPDO$Mot_EP_05 and hateEPDO$Mot_DO_05## t = 12, df = 836, p-value <0.0000000000000002## alternative hypothesis: true difference in means is not equal to 0## 95 percent confidence interval:## 0.4152 0.5716## sample estimates:## mean of the differences## 0.4934

The test results (both parametric and non-parametric) indicate that El Paso was indeed, on average, morestrongly perceived as a hate crime than Dayton was (within participants).

Disempowerment

For our antecedent Disempowerment measure we assess scale-ability and combine the individual items into asingle scale score.


124



Table 77: Disempowerment: Item Total Correlations (El Paso / Dayton)Item Corr. to scale Factor Loading Mean SDfail2 0.6250 0.8605 -0.6093 1.111fail1 0.5727 0.7236 -0.2784 1.137fail3 0.4075 0.4634 -1.3405 0.928

Table 78: Disempowerment: Item Descriptives (El Paso / Dayton)

vars n mean sd median trimmed mad min max range skew kurtosis sefail1 837 -0.2784 1.137 0 -0.3100 1.483 -2 2 4 0.1343 -0.6685 0.0393fail2 837 -0.6093 1.111 -1 -0.6811 1.483 -2 2 4 0.3073 -0.6976 0.0384fail3 837 -1.3405 0.928 -2 -1.4978 0.000 -2 2 4 1.3776 1.3881 0.0321

fail1

−2

−1

01

2

−2 −1 0 1 2

−2 −1 0 1 2

***r = 0.63

[0.59, 0.67]

fail2

−2

−1

01

2

***r = 0.37

[0.31, 0.43]

***r = 0.43

[0.37, 0.48]

−2 −1 0 1 2

−2

−1

01

2fail3

Table 79: Disempowerment: Scale Descriptives (El Paso / Dayton)


125

Realistic Threat Perception

For our antecedent Intergroup Threat measures we assess scale-ability and combine the individual items intoa single scale score.We measured both symbolic and realistic threat perceptions.

Table 80: Realistic Threat: Item Descriptives (El Paso)

vars n mean sd median trimmed mad min max range skew kurtosis sethreat_realistic_01 837 5.021 2.307 5 5.006 1.483 1 10 9 0.0369 -0.400 0.0797threat_realistic_02 837 5.303 1.986 5 5.317 1.483 1 10 9 -0.0065 0.335 0.0687

threat_realistic_01

2 4 6 8 10

24

68

10

2 4 6 8 10

24

68

10

***

r = 0.67

[0.63, 0.7]

threat_realistic_02

Table 81: Realistic Threat: Scale Descriptives (El Paso / Dayton)

vars n mean sd min max range serealistic threat 837 5.162 1.96 1 10 9 0.0677

126

Symbolic Threat Perception

Table 82: Symbolic Threat: Item Descriptives (El Paso)

vars n mean sd median trimmed mad min max range skew kurtosis sethreat_symbolic_01 837 4.172 2.328 4 4.013 2.965 1 10 9 0.4150 -0.4241 0.0805threat_symbolic_02 837 4.229 2.406 4 4.049 2.965 1 10 9 0.4442 -0.4731 0.0832

threat_symbolic_01

2 4 6 8 10

24

68

10

2 4 6 8 10

24

68

10

***

r = 0.86

[0.84, 0.88]

threat_symbolic_02

Table 83: Symbolic Threat: Scale Descriptives (El Paso / Dayton)

vars n mean sd min max range sesymbolic threat 837 4.201 2.284 1 10 9 0.079

Mixed Models

Symbolic Threat was hypothesized to predict (lower) hate crime perceptions only after the El Paso Walmartshooting and not the Dayton bar shooting. A mixed linear model was conducted, treating Symbolic Threat asa continuous, between-subjects predictor (standardized), and hate crime attributions as a within-subjectsoutcome variable (El Paso vs. Dayton).

Hatet = symbolic.threat ∗ shootingt + ε(1|participant)

127

Repeated Measures ANOVA (Raw Data)

We first looked at the conditional change of the raw hate crime perception values. However, histograms ofthe dependent variables already showed strong ceiling effects in earlier steps.

Table 84: Repeated Measures ANOVA (Raw Data)hate

Predictors Estimates CI p(Intercept) 1.65 *** 1.56 – 1.75 <0.001symbolic.threat -0.12 * -0.22 – -0.01 0.025shooting 1.18 *** 1.02 – 1.35 <0.001symbolic.threat * shooting -0.15 -0.32 – 0.02 0.086Nid 350Observations 700

? p<0.05; ** p<0.01; *** p<0.001

Table 85: Effect Size (Raw Data)term partial.etasq stratumsymbolic.threat.z 0.0205 idResiduals 0.5886 idshooting 0.3726 id:shootingsymbolic.threat.z:shooting 0.0084 id:shooting

128

−4−2

02

0 1 2Theoretical quantiles (predicted values)

Res

idua

lsDots should be plotted along the line


(Intercept)

−2 0 2−2

−1

0

1

Standard normal quantiles

Ran

dom

effe

ct q

uant

iles

0.00.20.40.6

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Den

sity



As expected, the model with the untransformed dependent variables did, show severe deviations from normality.We, therefore, checked the effects of shooting and symbolic threat on hate crime perceptions using (1) a novelrobust approach, (2) a transformed dependent variable, and (3) a standardized dependent variable model.

The simple transformation of the hate crime perceptions was done in the following manner:Hatetransformed = ( (Hate+3)2

6 )− 31. make all values positive (+3)2. square transform (ˆ2)3. return to original range (/6)4. return to original midpoint (-3)

## Robust linear mixed model fit by DAStau## Formula: hate ~ symbolic.threat.z * shooting.n + (1 | id)## Data: dt$ElPasoDayton.long#### Scaled residuals:## Min 1Q Median 3Q Max## -5.684 -0.518 -0.091 0.803 1.811#### Random effects:## Groups Name Variance Std.Dev.## id (Intercept) 0.000 0.000## Residual 0.991 0.995## Number of obs: 700, groups: id, 350#### Fixed effects:## Estimate Std. Error t value

129

## (Intercept) 1.7772 0.0386 46.0## symbolic.threat.z -0.1083 0.0404 -2.7## shooting.n 1.1555 0.0772 15.0## symbolic.threat.z:shooting.n -0.2142 0.0807 -2.7#### Correlation of Fixed Effects:## (Intr) symb.. shtng.## symblc.thr. -0.034## shooting.n 0.000 0.000## symblc.t.:. 0.000 0.000 -0.034#### Robustness weights for the residuals:## 598 weights are ~= 1. The remaining 102 ones are summarized as## Min. 1st Qu. Median Mean 3rd Qu. Max.## 0.237 0.419 0.691 0.638 0.743 0.988#### Robustness weights for the random effects:## All 350 weights are ~= 1.#### Rho functions used for fitting:## Residuals:## eff: smoothed Huber (k = 1.345, s = 10)## sig: smoothed Huber, Proposal II (k = 1.345, s = 10)## Random Effects, variance component 1 (id):## eff: smoothed Huber (k = 1.345, s = 10)## vcp: smoothed Huber, Proposal II (k = 1.345, s = 10)

## 2.5 % 97.5 %## (Intercept) 1.7016 1.85289## symbolic.threat.z -0.1874 -0.02920## shooting.n 1.0042 1.30685## symbolic.threat.z:shooting.n -0.3725 -0.05603

130

0.0

0.2

0.4

0.6

−6 −4 −2 0 2residual

dens

ity

Table 86: Repeated Measures ANOVA (Transformed)hate

Predictors Estimates CI p(Intercept) 0.91 *** 0.79 – 1.03 <0.001symbolic.threat -0.16 * -0.29 – -0.04 0.012shooting 1.73 *** 1.52 – 1.94 <0.001symbolic.threat.z * shooting -0.36 ** -0.58 – -0.14 0.001Nid 350Observations 700

? p<0.05; ** p<0.01; *** p<0.001

131

−4−2

02

0 1 2Theoretical quantiles (predicted values)

Res

idua



(Intercept)

−2 0 2

−1

0

1


Ran

dom

effe

ct q

uant

iles

0.00.10.20.30.4

−4 −2 0 2Residuals

Den

sity



Table 87: Repeated Measures ANOVA (Standardized)hate

Predictors Estimates CI p(Intercept) -0.28 *** -0.36 – -0.20 <0.001symbolic.threat -0.10 * -0.18 – -0.02 0.014shooting 0.50 *** 0.37 – 0.63 <0.001symbolic.threat.z * shooting -0.14 * -0.28 – -0.01 0.040Nid 350Observations 700

? p<0.05; ** p<0.01; *** p<0.001

132

−4−2

0

−1.5 −1.0 −0.5 0.0Theoretical quantiles (predicted values)

Res

idua



(Intercept)

−2 0 2

−1

0

1


Ran

dom

effe

ct q

uant

iles

0.000.250.500.75

−4 −2 0Residuals

Den

sity



With the transformed dependent variables (but not the robust analysis) we find a two-way within subjectsinteraction indicating that Symbolic Threat was mainly a predictor of hate crime attributions after the ElPaso Walmart shooting but not the Dayton Bar shooting (also see follow up below).Pseudo R2(from mixed models regression with simple transformation): ω2

0 = 44.18%

Simple Slopes Follow-up

Simple Slope El Paso:

#### Call:## lm(formula = hate.ep ~ symbolic.threat.z + hate.do.z, data = ed.scales)#### Residuals:## Min 1Q Median 3Q Max## -4.719 -0.349 0.093 0.464 3.263#### Coefficients:## Estimate Std. Error t value Pr(>|t|)## (Intercept) 2.2736 0.0304 74.9 < 0.0000000000000002 ***## symbolic.threat.z -0.1665 0.0308 -5.4 0.000000086 ***## hate.do.z 0.6006 0.0308 19.5 < 0.0000000000000002 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#### Residual standard error: 0.879 on 834 degrees of freedom## Multiple R-squared: 0.354, Adjusted R-squared: 0.352

133

## F-statistic: 228 on 2 and 834 DF, p-value: <0.0000000000000002

## 2.5 % 97.5 %## (Intercept) 2.2140 2.3332## symbolic.threat.z -0.2269 -0.1060## hate.do.z 0.5401 0.6611

Simple Slope Dayton:

#### Call:## lm(formula = hate.do ~ symbolic.threat.z + hate.ep.z, data = ed.scales)#### Residuals:## Min 1Q Median 3Q Max## -5.347 -0.347 0.418 0.686 2.875#### Coefficients:## Estimate Std. Error t value Pr(>|t|)## (Intercept) 1.7802 0.0384 46.35 <0.0000000000000002 ***## symbolic.threat.z -0.0377 0.0396 -0.95 0.34## hate.ep.z 0.7724 0.0396 19.49 <0.0000000000000002 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#### Residual standard error: 1.11 on 834 degrees of freedom## Multiple R-squared: 0.332, Adjusted R-squared: 0.33## F-statistic: 207 on 2 and 834 DF, p-value: <0.0000000000000002

## 2.5 % 97.5 %## (Intercept) 1.7048 1.8556## symbolic.threat.z -0.1155 0.0401## hate.ep.z 0.6946 0.8502

134

Line Graph:

0.0

0.5

1.0

1.5

2.0

El Paso DaytonShooting

Hat

e C

rime

Attr

ibut

ion

Symbolic Threat −1sd mean +1sd

Symbolic Threat over Time (+/− 1SE)

0.0

0.5

1.0

1.5

2.0

−1sd mean +1sdSymbolic Threat

Hat

e C

rime

Attr

ibut

ion

Shooting Dayton El Paso

Effect of Symbolic Threat by Shooting (95%CI)

135

Path Model

Correlation of key variables

fail.z

−1

12

−1 0 1 2 3

−5

−3

−1

−1 0 1 2

***r = 0.30

[0.24, 0.36]

symbolic.threat.z

***r = 0.27

[0.2, 0.33]

***r = 0.79

[0.77, 0.82]

realistic.threat.z

−2 −1 0 1 2

−5 −3 −1 0

−1

13

***r = −0.20

[−0.26, −0.13]

***r = −0.24

[−0.31, −0.18]

−2

02

***r = −0.19

[−0.26, −0.13]

hate.ep.z

Path Model Pittsburgh





**************************************************************************Model : 4

Y : Zhate.EPX : Zfail

M1 : ZsymboliM2 : Zrealist

SampleSize: 837

**************************************************************************OUTCOME VARIABLE:Zsymboli

Model Summary

136

R R-sq MSE F df1 df2 p,2992 ,0895 ,9116 82,0718 1,0000 835,0000 ,0000


constant ,0000 ,0330 ,0000 1,0000 -,0648 ,0648Zfail ,2992 ,0330 9,0593 ,0000 ,2343 ,3640


Zfail ,2992

**************************************************************************OUTCOME VARIABLE:Zrealist


,2650 ,0702 ,9309 63,0680 1,0000 835,0000 ,0000


constant ,0000 ,0333 ,0000 1,0000 -,0655 ,0655Zfail ,2650 ,0334 7,9415 ,0000 ,1995 ,3305


Zfail ,2650

**************************************************************************OUTCOME VARIABLE:Zhate.EP


,2766 ,0765 ,9268 23,0036 3,0000 833,0000 ,0000


constant ,0000 ,0333 ,0000 1,0000 -,0653 ,0653Zfail -,1363 ,0349 -3,9028 ,0001 -,2049 -,0678Zsymboli -,2163 ,0553 -3,9124 ,0001 -,3248 -,1078Zrealist ,0163 ,0547 ,2982 ,7656 -,0911 ,1237


Zfail -,1363Zsymboli -,2163Zrealist ,0163

************************** TOTAL EFFECT MODEL ****************************OUTCOME VARIABLE:Zhate.EP

137


,1967 ,0387 ,9625 33,6153 1,0000 835,0000 ,0000


constant ,0000 ,0339 ,0000 1,0000 -,0666 ,0666Zfail -,1967 ,0339 -5,7979 ,0000 -,2633 -,1301


Zfail -,1967


Total effect of X on YEffect se t p LLCI ULCI c_ps c_cs-,1967 ,0339 -5,7979 ,0000 -,2633 -,1301 -,1967 -,1967

Direct effect of X on YEffect se t p LLCI ULCI c'_ps c'_cs-,1363 ,0349 -3,9028 ,0001 -,2049 -,0678 -,1363 -,1363


TOTAL -,0604 ,0128 -,0880 -,0377Zsymboli -,0647 ,0191 -,1039 -,0287Zrealist ,0043 ,0155 -,0270 ,0345






OUTCOME VARIABLE:Zsymboli

Coeff BootMean BootSE BootLLCI BootULCIconstant ,0000 ,0004 ,0325 -,0637 ,0632Zfail ,2992 ,3002 ,0378 ,2261 ,3731

----------

OUTCOME VARIABLE:Zrealist

138

Coeff BootMean BootSE BootLLCI BootULCIconstant ,0000 ,0003 ,0328 -,0630 ,0653Zfail ,2650 ,2654 ,0380 ,1920 ,3381

----------

OUTCOME VARIABLE:Zhate.EP

Coeff BootMean BootSE BootLLCI BootULCIconstant ,0000 ,0000 ,0333 -,0646 ,0650Zfail -,1363 -,1367 ,0345 -,2069 -,0716Zsymboli -,2163 -,2160 ,0594 -,3312 -,0980Zrealist ,0163 ,0158 ,0582 -,1001 ,1315


Level of confidence for all confidence intervals in output:95,0000



139

Software InfoWe used RStudio Version 1.2.1335 to analyse all data (except for the path models for with we used thePROCESS macro (v.3.3) in SPSS (v.25) in order to stay consistent with current standard in the field).Below you can find the full R session info:

R version 3.6.2 (2019-12-12)

Platform: x86_64-apple-darwin15.6.0 (64-bit)

locale: en_US.UTF-8||en_US.UTF-8||en_US.UTF-8||C||en_US.UTF-8||en_US.UTF-8

attached base packages:

• grid• parallel• stats• graphics• grDevices• utils• datasets• methods• base

other attached packages:

• dplyr(v.0.8.4)• plyr(v.1.8.5)• multcomp(v.1.4-12)• TH.data(v.1.0-10)• MASS(v.7.3-51.4)• prettydoc(v.0.3.1)• gdata(v.2.18.0)• scales(v.1.1.0)• ggbeeswarm(v.0.6.0)• tinytex(v.0.19)• pander(v.0.6.3)• rlme(v.0.5)• WRS2(v.1.0-0)• MANOVA.RM(v.0.3.4)• fBasics(v.3042.89)• timeSeries(v.3062.100)• timeDate(v.3043.102)• car(v.3.0-6)• ggthemes(v.4.2.0)• gridExtra(v.2.3)• TOSTER(v.0.3.4)• GPArotation(v.2014.11-1)• kableExtra(v.1.1.0)• jtools(v.2.0.2)• metafor(v.2.1-0)• meta(v.4.10-0)• DiagrammeR(v.1.0.5.9000)• igraph(v.1.2.4.2)• lavaanPlot(v.0.5.1)• semPlot(v.1.1.2)• sjPlot(v.2.8.2)• sjstats(v.0.17.8)

140

• effects(v.4.1-4)• carData(v.3.0-3)• robustlmm(v.2.3)• nlme(v.3.1-142)• lme4(v.1.1-21)• Matrix(v.1.2-18)• lavaan(v.0.6-5)• devtools(v.2.2.1)• usethis(v.1.5.1)• knitr(v.1.28)• mada(v.0.5.9)• mvmeta(v.1.0.3)• ellipse(v.0.4.1)• mvtnorm(v.1.0-12)• Hmisc(v.4.3-0)• Formula(v.1.2-3)• survival(v.3.1-8)• lattice(v.0.20-38)• stargazer(v.5.2.2)• forcats(v.0.4.0)• stringr(v.1.4.0)• purrr(v.0.3.3)• readr(v.1.3.1)• tibble(v.2.1.3)• tidyverse(v.1.3.0)• tidyr(v.1.0.2)• data.table(v.1.12.8)• haven(v.2.2.0)• ggplot2(v.3.2.1)• psych(v.1.9.12.31)• pacman(v.0.5.1)

loaded via a namespace (and not attached):

• reports(v.0.1.4)• estimability(v.1.3)• SparseM(v.1.78)• qdapTools(v.1.3.3)• coda(v.0.19-3)• acepack(v.1.4.1)• rpart(v.4.1-15)• RCurl(v.1.98-1.1)• generics(v.0.0.2)• qdapRegex(v.0.7.2)• callr(v.3.4.1)• openNLP(v.0.2-7)• openNLPdata(v.1.5.3-4)• chron(v.2.3-54)• webshot(v.0.5.2)• xml2(v.1.2.2)• lubridate(v.1.7.4)• assertthat(v.0.2.1)• d3Network(v.0.5.2.1)• xfun(v.0.12)• rJava(v.0.9-11)

141

• hms(v.0.5.3)• evaluate(v.0.14)• DEoptimR(v.1.0-8)• fansi(v.0.4.1)• dbplyr(v.1.4.2)• readxl(v.1.3.1)• DBI(v.1.1.0)• Rsolnp(v.1.16)• htmlwidgets(v.1.5.1)• reshape(v.0.8.8)• stats4(v.3.6.2)• ellipsis(v.0.3.0)• backports(v.1.1.5)• pbivnorm(v.0.6.0)• insight(v.0.8.0)• survey(v.3.37)• vctrs(v.0.2.2)• quantreg(v.5.54)• remotes(v.2.1.0)• sjlabelled(v.1.1.3)• abind(v.1.4-5)• withr(v.2.1.2)• robustbase(v.0.93-5)• checkmate(v.1.9.4)• emmeans(v.1.4.4)• fdrtool(v.1.2.15)• prettyunits(v.1.1.1)• fastGHQuad(v.1.0)• mnormt(v.1.5-5)• cluster(v.2.1.0)• mi(v.1.0)• lazyeval(v.0.2.2)• crayon(v.1.3.4)• slam(v.0.1-47)• labeling(v.0.3)• pkgconfig(v.2.0.3)• wordcloud(v.2.6)• vipor(v.0.4.5)• pkgload(v.1.0.2)• ggm(v.2.3)• nnet(v.7.3-12)• rlang(v.0.4.4)• spatial(v.7.3-11)• lifecycle(v.0.1.0)• MatrixModels(v.0.4-1)• sandwich(v.2.5-1)• kutils(v.1.69)• modelr(v.0.1.5)• cellranger(v.1.1.0)• rprojroot(v.1.3-2)• regsem(v.1.3.9)• mc2d(v.0.1-18)• boot(v.1.3-23)• zoo(v.1.8-7)

142

• beeswarm(v.0.2.3)• reprex(v.0.3.0)• base64enc(v.0.1-3)• whisker(v.0.4)• processx(v.3.4.1)• png(v.0.1-7)• viridisLite(v.0.3.0)• rjson(v.0.2.20)• bitops(v.1.0-6)• parameters(v.0.4.1)• diagram(v.1.6.4)• visNetwork(v.2.0.9)• shape(v.1.4.4)• arm(v.1.10-1)• jpeg(v.0.1-8.1)• rockchalk(v.1.8.144)• venneuler(v.1.1-0)• ggeffects(v.0.14.1)• memoise(v.1.1.0)• magrittr(v.1.5)• compiler(v.3.6.2)• RColorBrewer(v.1.1-2)• plotrix(v.3.7-7)• cli(v.2.0.1)• lmerTest(v.3.1-1)• TMB(v.1.7.16)• pbapply(v.1.4-2)• ps(v.1.3.0)• magic(v.1.5-9)• htmlTable(v.1.13.3)• mgcv(v.1.8-31)• tidyselect(v.1.0.0)• stringi(v.1.4.5)• lisrelToR(v.0.1.4)• mixmeta(v.1.0.7)• sem(v.3.1-9)• mitools(v.2.4)• yaml(v.2.2.1)• OpenMx(v.2.15.5)• latticeExtra(v.0.6-29)• tools(v.3.6.2)• rio(v.0.5.16)• matrixcalc(v.1.0-3)• rstudioapi(v.0.11)• foreign(v.0.8-72)• farver(v.2.0.3)• BDgraph(v.2.62)• digest(v.0.6.23)• Rcpp(v.1.0.3)• broom(v.0.5.4)• xlsx(v.0.6.1)• gender(v.0.5.3)• performance(v.0.4.3)• httr(v.1.4.1)

143

• effectsize(v.0.1.1)• colorspace(v.1.4-1)• rvest(v.0.3.5)• XML(v.3.99-0.3)• fs(v.1.3.1)• truncnorm(v.1.0-8)• splines(v.3.6.2)• Scale(v.1.0.4)• xlsxjars(v.0.6.1)• sessioninfo(v.1.1.1)• xtable(v.1.8-4)• jsonlite(v.1.6.1)• nloptr(v.1.2.1)• corpcor(v.1.6.9)• NLP(v.0.2-0)• glasso(v.1.11)• testthat(v.2.3.1)• R6(v.2.4.1)• tm(v.0.7-7)• qdap(v.2.3.6)• pillar(v.1.4.3)• htmltools(v.0.4.0)• glue(v.1.3.1)• minqa(v.1.2.4)• codetools(v.0.2-16)• pkgbuild(v.1.0.6)• numDeriv(v.2016.8-1.1)• huge(v.1.3.4)• curl(v.4.3)• gtools(v.3.8.1)• qdapDictionaries(v.1.0.7)• zip(v.2.0.4)• openxlsx(v.4.1.4)• glmmTMB(v.0.2.3)• CompQuadForm(v.1.4.3)• rmarkdown(v.2.1)• qgraph(v.1.6.4)• desc(v.1.2.0)• munsell(v.0.5.0)• sjmisc(v.2.8.3)• reshape2(v.1.4.3)• gtable(v.0.3.0)• bayestestR(v.0.5.1)

144

Supplementary Information B

Full Survey of Study 1: Pittsburgh Synagogue Shooting / Thousand Oaks Bar Shooting

The following supplementary appendix includes the full survey print-outs for Study 1. This

study uses two repeated measures survey data sets from 2018. Participants were recruited

after the Pittsburgh synagogue shooting (see below ‘MTurk – Pittsburgh – Oct. 2018’) and

re-contacted after the Thousand Oaks bar shooting (see below ‘MTurk - Thousand Oaks –

Nov. 2018’).

MTurk - Pittsburgh - Oct. 2018

Start of Block: Informed Consent Timing First Click Last Click Page Submit Click Count Informed Consent Primary investigator: Dr. N. P. Leander, PhDThis university-based psychological study will ask about your beliefs, attitudes, and experiences regarding potential gun ownership and the use of firearms. Also included are some short questions about the recent Pittsburgh synagogue shooting. The study typically takes 10-15 minutes. Your privacy is important: Your participation is completely anonymous. No identifying information will be collected from you. Only members of the research team will have access to the survey data, but even they cannot link the data to any single person. Your rights: You can decide whether or not to participate in the study. You can leave the study at any time.

145

You may click "Next" to begin when ready. Browser Meta Info Browser Version Operating System Screen Resolution Flash Version Java Support User Agent

End of Block: Informed Consent Start of Block: Demographics

Gender

o Male

o Female

Age

o 18-24

o 25-34

o 35-44

o 45-54

o 55-64

o 65+

146

Racial/ethnic background

▢ White

▢ Black or African American

▢ Hispanic / Latino / Latina

▢ American Indian or Alaska Native

▢ Asian

▢ Native Hawaiian or Pacific Islander

▢ Other ________________________________________________ Timing First Click Last Click Page Submit Click Count Page Break

147

Education

o Some High School or Less

o High School Graduate / GED

o Some College

o College Graduate

o Graduate Degree

What is your annual income?

o Under $15,000

o $15,000 - $25,000

o $25,000 - $35,000

o $35,000 - $50,000

o $50,000 - $75,000

o $75,000 - $100,000

o $100,000 - $150,000

o $150,000 - $200,000

o $200,000 +

148

Timing First Click Last Click Page Submit Click Count Page Break

149

In what region of the USA do you live?

o West

o Midwest

o South

o Northeast Timing First Click Last Click Page Submit Click Count

End of Block: Demographics Start of Block: Knowledge questions

150

How knowledgeable are you about the recent mass shooting at the synagogue in Pittsburgh, Pennsylvania?

o Not at all knowledgeable

o Slightly knowledgeable

o Moderately knowledgeable

o Very knowledgeable

o Extremely knowledgeable Timing First Click Last Click Page Submit Click Count

End of Block: Knowledge questions Start of Block: Mass Shooting_Pittsburgh Pittsburgh synagogue shooting, Pennsylvania On Friday, October 27, 2018, a mass shooting occurred at the Tree of Life synagogue in Pittsburgh, Pennsylvania. 11 people were killed and 9 hospitalized, making it one of the deadliest attacks on Jews in American history. The gunman, Robert G. Bowers was arrested and charged with 29 federal crimes and 36 state crimes. Timing First Click Last Click Page Submit Click Count Page Break

151

On the next few screens are questions about your views on the mass shooting: (1) what might have prevented it; (2) what might have motivated the gunman. Timing First Click Last Click Page Submit Click Count Page Break

152

(1) The recent synagogue shooting in Pittsburgh, Pennsylvania might have been prevented if...

(Not applicable)

-3: Very doubtful -2 -1 0:

Neutral +1 +2 +3:

Very possible

...people at the

synagogue were

armed. o o o o o o o o

...stricter gun control laws were in place.

o o o o o o o o ...better mental

health care existed.

o o o o o o o o ...there was

more surveillance

of suspected radicals.

o o o o o o o o ...society was more cautious of Muslims.

o o o o o o o o Other o o o o o o o o


153

(2) What might have motivated the gunman to commit the mass shooting at the synagogue in Pittsburgh, Pennsylvania?

(Not applicable)


Neutral +1 +2 +3:

Very possible

Influence of religion o o o o o o o o

Influence of ideology / ISIS o o o o o o o o

Desire for power,

significance, or attention

o o o o o o o o Compensation

for inadequacy

(shame, insecurity,

self-hate, etc.)

o o o o o o o o Hatred of others,

prejudice o o o o o o o o Mental illness (psychopathy, abused, etc.) o o o o o o o o

Ease of access to firearms o o o o o o o o Cultural

exposure to violence (in

movies, news, entertainment,

etc.)

o o o o o o o o

Other o o o o o o o o

154


155

(3) Did the gunman have any of the following goals?

(Not applicable)


Neutral +1 +2 +3: Very possible

The gunman wanted respect

o o o o o o o o The

gunman wanted to be

important o o o o o o o o

The gunman wanted to be

famous o o o o o o o o

Timing First Click Last Click Page Submit Click Count

End of Block: Mass Shooting_Pittsburgh Start of Block: Owngun Next, we would like to ask you some gun-related questions. Page Break

156


Do you own a gun?

o Yes

o No

What type(s) of gun(s) do you personally own? (click all that apply)

▢ Handgun

▢ Precision rifle

▢ Modern sporting rifle (AR-15, AK-style)

▢ Shotgun

▢ Other (specify) ________________________________________________

▢ Not applicable / None of the above

157

Page Break

158

To what extent do you see your gun as a central part of who you are? Please indicate the picture which best describes how much your gun represents you as a person.

o Image:1.png

o Image:2.png

o Image:3.png

o Image:4.png

o Image:5.png

o Image:6.png

o Image:7.png

o Image:8.png Timing First Click Last Click Page Submit Click Count

End of Block: Owngun Start of Block: Wolfgang_GunControl_Laws Timing First Click Last Click Page Submit Click Count

159

In general, do you think the laws covering the sale of firearms should be made more strict, less strict, or kept as they are now?

o 1: Much less strict

o 2

o 3

o 4: Neutral

o 5

o 6

o 7: Much more strict

Do you support or oppose some kind of registry of all guns, at least at the state-government level?

o 1: Strongly oppose a gun registry

o 2

o 3

o 4: Neutral

o 5

o 6

o 7: Strongly support a gun registry

160

Do you support or oppose laws that create "gun free zones" at schools and other public places?

o 1: Strongly oppose "gun free zones"

o 2

o 3

o 4: Neutral

o 5

o 6

o 7: Strongly support "gun free zones"

End of Block: Wolfgang_GunControl_Laws Start of Block: gunbuy Timing First Click Last Click Page Submit Click Count

161

Do you intend to buy a gun in the next six months? If so, of what type(s)? (select all that apply)

▢ (No intention to buy a gun)

▢ Handgun

▢ Shotgun

▢ Precision rifle

▢ Modern Sporting Rifle

▢ Other ________________________________________________

Page Break

162


If you were to buy a gun relatively soon, what would be your main reasons?

(Not applicable)

1: Not a reason 2 3 4 5: Major

reason

Protection / Self-

defense o o o o o o Stocking up o o o o o o

2nd Amendment o o o o o o Collecting o o o o o o

Sport / Target

shooting o o o o o o Other

reason(s) o o o o o o

End of Block: gunbuy Start of Block: Symbolic Racism

163

It's really a matter of some people not trying hard enough; if blacks would only try harder they could be just as well off as whites.

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree

Irish, Italian, Jewish, and many other minorities overcame prejudice and worked their way up. Blacks should do the same.

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree

Some say that black leaders have been trying to push too fast. Others feel that they haven't pushed fast enough. What do you think?

o Going too slowly

o Moving at about the right speed

o Trying to push too fast

164

How much of the racial tension that exists in the United States today do you think blacks are responsible for creating?

o Not much at all

o Some

o Most

o All of it

How much discrimination against blacks do you feel there is in the United States today, limiting their chances to get ahead?

o None at all

o Just a little

o Some

o A lot Timing First Click Last Click Page Submit Click Count Page Break

165

Generations of slavery and discrimination have created conditions that make it difficult for blacks to work their way out of the lower class.

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree

Over the past few years, blacks have gotten less than they deserve.

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree

Over the past few years, blacks have gotten more economically than they deserve.

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree

166

Reports about racism in police forces are overblown. What do you think?

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree

Discrimination against blacks is no longer a problem.

o Strongly disagree

o Somewhat disagree

o Somewhat agree

o Strongly agree Timing First Click Last Click Page Submit Click Count

End of Block: Symbolic Racism Start of Block: QFS Next are some questions about your personal experiences and tendencies. Page Break

167

168

Think about your life right now and express your level of agreement with each of the following statements.

169

Strongly disagre

e

Disagree

Somewhat disagree

Neither agree nor

disagree

Somewhat agree

Agree

Strongly agree

I want to feel

significant. o o o o o o o I wish I

could be more

respected. o o o o o o o

I wish other people

accepted me more.

o o o o o o o I want to be

more valued by

people who are

important to me.

o o o o o o o

I wish I meant

more to other

people. o o o o o o o

I want to be more

valued by society.

o o o o o o o I wish other

people thought I

were significant.

o o o o o o o I wish I

were more appreciated by other


I want people to care more about me.

o o o o o o o

170

I wish other people to be more proud of

me. o o o o o o o

I want to be more

important. o o o o o o o Timing First Click Last Click Page Submit Click Count

End of Block: QFS Start of Block: Fail

Not a lot is done for people like me in America.

o Agree strongly

o Agree somewhat

o Neither agree nor disagree

o Disagree somewhat

o Disagree strongly

171

If I compare people like me against other Americans, my group is worse off.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

Recent events in society have increased my struggles in daily life.

o A great deal

o A lot

o A moderate amount

o A little

o Not at all Timing First Click Last Click Page Submit Click Count

End of Block: Fail Start of Block: demo_orient

172


In which state do you currently reside?

▼ Alabama ... I do not reside in the United States

What is your political orientation?

o 1: Extremely conservative

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o 9: Extremely liberal

173

Where do you stand with regards to the current U.S. President's policies?

o Oppose broadly

o More opposed than support

o Neutral / Mixed

o More support than oppose

o Support broadly

End of Block: demo_orient Start of Block: religaffil

What is your current religious affiliation, if any?

o Catholic (including roman catholic and orthodox)

o Protestant (Anglican, Orthodox, Baptist, Lutheran)

o Jewish

o Muslim

o Sikh

o Hindu

o Buddhist

o Atheist

o Agnostic

o None

o Other ________________________________________________ Page Break

174

175

About how often do you attend church (or other organized religious services)?

o Never

o Rarely / Almost never

o At least once a year

o At least once a month

o At least once a week

End of Block: religaffil Start of Block: Christianity

176

To what extent do you agree or disagree that ...

Strongly Disagree

Somewhat Disagree Undecided Somewhat

Agree Strongly Agree

... the federal government

should declare the

United States a Christian nation?

o o o o o

... the federal government

should advocate Christian values?

o o o o o ... the federal government

should enforce a

strict separation of church and

state?

o o o o o

... the federal government should allow the display of

religious symbols in

public spaces?

o o o o o

... the federal government should allow

prayer in public

schools?

o o o o o ... the

success of the United

States is part of God’s

plan.

o o o o o

177


End of Block: Christianity Start of Block: Jewish

Please select the picture that best describes your closeness to the Jewish people.

o Image:close1.png

o Image:close2.png

o Image:close3.png

o Image:close4.png

o Image:close5.png

o Image:close6.png

o Image:close7.png Page Break

178

To what extent do you agree or disagree with the following statements:

Strongly disagree Disagree Somewhat

disagree Somewhat

agree Agree Strongly Agree

Jews are responsible

for the death of Jesus Christ.

o o o o o o Jews use Christian blood for

ritual purposes.

o o o o o o Jews aim at influencing the world economy.

o o o o o o Jews act in

a secret way. o o o o o o

Jews often meet in hiding to

discuss their plans.

o o o o o o Jews would like to rule the world. o o o o o o

Jews would like to

control the international

financial institutions.

o o o o o o Jews

achieve their

collective goals by secret

agreements.

o o o o o o

End of Block: Jewish

179

Start of Block: mot2

Did either of the following motivate the gunman to commit the mass shooting at the synagogue in Pittsburgh, Pennsylvania?

(Not applicable)


Neutral +1 +2 +3:

Very possible

The current

president's speeches and public statements

o o o o o o o o Biased

reporting in the media

o o o o o o o o Timing First Click Last Click Page Submit Click Count

End of Block: mot2 Start of Block: Debrief What is your MTurk worker ID? (only to ensure that you receive credit)

________________________________________________________________ Page Break

180

Thank you very much for helping with this study. Your completion code is 9001. Please enter the code "9001" into the MTurk window. Debriefing: The goal of this university-based psychological study is to examine how the public thinks about mass shootings. We collect this data regularly to assess changes in public opinions over time, especially as it relates to gun ownership, personality, beliefs, and daily habits. The results will be used for scientific research purposes only. If you have any questions or concerns about the study or your participation, you are welcome to contact the lead investigator, Dr. N. P. Leander ([email protected]). You are also welcome to contact our university ethics board at [email protected]. Now that you know the purpose of this study, do you have any advice or suggestions to improve the survey experience? We appreciate any feedback you can offer.

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________ Timing First Click Last Click Page Submit Click Count

End of Block: Debrief

181

MTurk - Thousand Oaks – Nov. 2018

Start of Block: Informed Consent Timing First Click Last Click Page Submit Click Count Informed Consent You were invited to this study as a follow-up to a previous study following a mass shooting. Primary investigator: Dr. N. P. Leander, PhD This university-based psychological study will ask about your beliefs, attitudes, and experiences regarding potential gun ownership and the use of firearms. Also included are some short questions about the recent Thousand Oaks bar shooting. The study typically takes 5 minutes. Your privacy is important: Your participation is completely anonymous. No identifying information will be collected from you. Only members of the research team will have access to the survey data, but even they cannot link the data to any single person. Your rights: You can decide whether or not to participate in the study. You can leave the study at any time. You may click "Next" to begin when ready. Browser Meta Info Browser Version Operating System Screen Resolution Flash Version Java Support User Agent

End of Block: Informed Consent

182

Start of Block: Knowledge questions

How knowledgeable are you about the recent mass shooting at the bar in Thousand Oaks, California?






End of Block: Knowledge questions Start of Block: Mass Shooting_Thousand Oaks Thousand Oaks shooting, California On Wednesday, November 7, 2018, a mass shooting occurred at the Borderline Bar and Grill in Thousand Oaks, California. 12 people were killed and 24 hospitalized. The bar was hosting a College Country Night, which is attended by students from the area, including California Lutheran University, Pepperdine University, and California State University Channel Islands. The gunman, Ian David Long, died of a self-inflicted gunshot wound. Timing First Click Last Click Page Submit Click Count

183

Page Break

184


185

(1) The recent bar shooting in Thousand Oaks, California might have been prevented if...

(Not applicable)


Neutral +1 +2 +3:

Very possible

...people at the bar were






more surveillance



o o o o o o o o Other o o o o o o o o


186

(2) What might have motivated the gunman to commit the mass shooting at the bar in Thousand Oaks, California?

(Not applicable)


Neutral +1 +2 +3:

Very possible


Influence of ideology / ISIS o o o o o o o o

Desire for power,



for inadequacy

(shame, insecurity,

self-hate, etc.)


prejudice o o o o o o o o Mental illness (psychopathy, abused, etc.) o o o o o o o o




etc.)

o o o o o o o o


187


188

(3) Did the gunman have any of the following goals?

(Not applicable)


Neutral +1 +2 +3: Very possible

The gunman wanted respect

o o o o o o o o The

gunman wanted to be

important o o o o o o o o

The gunman wanted to be

famous o o o o o o o o


End of Block: Mass Shooting_Thousand Oaks Start of Block: Owngun Next, we would like to ask you some gun-related questions. Page Break

189


Do you own a gun?

o Yes

o No Page Break

190


o Image:1.png

o Image:2.png

o Image:3.png

o Image:4.png

o Image:5.png

o Image:6.png

o Image:7.png

o Image:8.png Timing First Click Last Click Page Submit Click Count

End of Block: Owngun Start of Block: gunbuy Timing First Click Last Click Page Submit Click Count

191



▢ Handgun

▢ Shotgun

▢ Precision rifle


▢ Other ________________________________________________

Page Break

192



(Not applicable)


reason

Protection / Self-

defense o o o o o o Stocking up o o o o o o

2nd Amendment o o o o o o Collecting o o o o o o

Sport / Target



End of Block: gunbuy Start of Block: Fail Next are some questions about your personal experiences and tendencies. Page Break

193


o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly


o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly


o A great deal

o A lot

o A moderate amount

o A little

o Not at all

194


End of Block: Fail Start of Block: Jewish

Please select the picture that best describes your closeness to the American people.

o Image:closeUS1.png






o Image:closeUS7.png Timing First Click Last Click Page Submit Click Count Page Break

195

Please select the picture that best describes your closeness to the Jewish people.

o Image:close1.png

o Image:close2.png

o Image:close3.png

o Image:close4.png

o Image:close5.png

o Image:close6.png

o Image:close7.png Timing First Click Last Click Page Submit Click Count

End of Block: Jewish Start of Block: Hero Pic Timing First Click Last Click Page Submit Click Count

196

First Responder confronts Thousand Oaks shooter On Wednesday, November 7, a gunman entered the Borderline Bar and Grill in Thousand Oaks, California, dressed entirely in black and armed with a .45 caliber Glock 21 semi-automatic pistol. After he shot the security guard at the entrance, he shot other guards and employees before moving to shoot patrons inside the club, killing 12 and wounding 24. Three minutes after receiving the first 911 calls, Ventura County Sheriff Sgt. Ron Helus and a California Highway Patrol officer arrived at the scene. Hearing gunshots coming from the building, Helus ran inside, where he was shot several times as he tried to stop the rampaging gunman. The Highway Patrol officer dragged Helus to safety outside, but he died from his injuries several hours later.

End of Block: Hero Pic Start of Block: Hero_mindset Timing First Click Last Click Page Submit Click Count

197

Sgt. Ron Helus, the first responder who intervened against the perpetrator of the Thousand Oaks bar shooting ...

Strongly disagree

Somewhat disagree

Neither disagree nor

agree

somewhat agree

strongly agree

... is a true hero. o o o o o ... is

someone I can relate to. o o o o o ...is someone

who acted out of civic

responsibility to protect

and defend.

o o o o o

End of Block: Hero_mindset Start of Block: trumpism

Did either of the following motivate the gunman to commit the mass shooting at the bar in Thousand Oaks, California?

(Not applicable)


Neutral +1 +2 +3:

Very possible

The current

president's speeches and public statements

o o o o o o o o Biased

reporting in the media

o o o o o o o o

198


End of Block: trumpism Start of Block: Debrief What is your MTurk worker ID? (only to ensure that you receive credit)

________________________________________________________________ Page Break

199

Thank you very much for helping with this study. Your completion code is 6552. Please enter the code "6552" into the MTurk window. Debriefing: The goal of this university-based psychological study is to examine how the public thinks about mass shootings. We collect this data regularly to assess changes in public opinions over time, especially as it relates to gun ownership, personality, beliefs, and daily habits. We are, therefore, especially thankful to you for taking the time to complete this study a second time. The results will be used for scientific research purposes only. If you have any questions or concerns about the study or your participation, you are welcome to contact the lead investigator, Dr. N. P. Leander ([email protected]). You are also welcome to contact our university ethics board at [email protected]. Now that you know the purpose of this study, do you have any advice or suggestions to improve the survey experience? We appreciate any feedback you can offer.

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________



200

Supplementary Information C

Full Surveys of Studies 2a and 2b: Christchurch Mosque Shootings and Utrecht Tram

Shooting

The following supplementary appendix includes the full survey print-outs for Study 3. This

study uses two independent survey datasets from 2019. Participants were recruited after the

Christchurch mosque shooting (see below ‘Qualtrics Panels – Christchurch (NZ) – Mar.

2019’) and after the Utrecht tram shooting (see below ‘Flycatcher – Utrecht (NL) – Mar.

2019’).

Qualtrics Panels – Christchurch (NZ) – Mar. 2019

Start of Block: Demographics

Gender

o Male

o Female

201

Age

o 18-24

o 25-34

o 35-44

o 45-54

o 55-64

o 65+


▢ White / Caucasian

▢ Māori

▢ Asian

▢ Pacific Islander


202

Education

o Some Secondary School or Less

o Secondary School (or equivalent)

o Some Tertiary education

o Bachelor's degree (or equivalent)

o Postgraduate degree


o Under $15,000

o $15,000 - $25,000

o $25,000 - $35,000

o $35,000 - $50,000

o $50,000 - $75,000

o $75,000 - $100,000

o $100,000 - $150,000

o $150,000 - $200,000

o $200,000 +

203

Do you own a gun?

o Yes

o No Timing First Click Last Click Page Submit Click Count Page Break

204

In what region of New Zealand do you live? (please choose the number corresponding to the region)

▼ 1 ... 16


End of Block: Demographics Start of Block: Informed Consent

205

Timing First Click Last Click Page Submit Click Count Informed Consent Primary investigator: Dr. N. P. Leander, PhDThis university-based psychological study will ask about your beliefs, attitudes, and experiences regarding guns and the use of firearms. Also included are some short questions about the recent mass shooting at the Christchurch mosques. The study typically takes 10-15 minutes. Your privacy is important: Your participation is completely anonymous. No identifying information will be collected from you. Only members of the research team will have access to the survey data, but even they cannot link the data to any single person. Your rights: You can decide whether or not to participate in the study. You can leave the study at any time. You may click "Next" to begin when ready. Browser Meta Info Browser Version Operating System Screen Resolution Flash Version Java Support User Agent

End of Block: Informed Consent Start of Block: BDW Next are a series of statements. Please indicate the extent to which you agree or disagree with each of them.

206

Although it may appear that things are constantly getting more dangerous and chaotic, it really isn’t so. Every era has its problems, and a person’s chances of living a safe, untroubled life are better today than ever before.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

Any day now, chaos and lawlessness could erupt around us. All the signs are pointing to it.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

207

There are many dangerous people in our society who will attack someone out of pure meanness, for no reason at all.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

Despite what one hears about “crime in the street,” there probably isn’t any more now than there ever has been.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

208

If a person takes a few sensible precautions, nothing bad is likely to happen to him or her; we do not live in a dangerous world.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly Timing First Click Last Click Page Submit Click Count Page Break

209

Every day, as society becomes more lawless and bestial, a person’s chances of being robbed, assaulted, and even murdered go up and up.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

My knowledge and experience tells me that the social world we live in is basically a safe, stable and secure place, in which most people are fundamentally good.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

210

It seems that every year there are fewer and fewer truly respectable people, and more and more persons with no morals at all, who threaten everyone else.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

The “end” is not near. People who think that earthquakes, wars, and famines mean God might be about to destroy the world are being foolish.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

211

My knowledge and experience tells me that the social world we live in is basically a dangerous and unpredictable place, in which good, decent and moral people’s values and way of life are threatened and disrupted by bad people.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly Timing First Click Last Click Page Submit Click Count

End of Block: BDW Start of Block: PLRA

212

What do you estimate is the likelihood the following will happen in your lifetime (in your future)?

1: Not at all 2 3 4 5: Extremely likely

1. Likelihood you will be mugged. o o o o o

2. Likelihood you will be violently attacked.

o o o o o 3. Likelihood your home

will be invaded by an armed burglar.

o o o o o 4. Likelihood you will be

present during a

mass shooting.

o o o o o Timing First Click Last Click Page Submit Click Count

End of Block: PLRA Start of Block: Owngun Timing First Click Last Click Page Submit Click Count

213


▢ Handgun

▢ Precision rifle


▢ Shotgun

▢ Other (specify) ________________________________________________


End of Block: Owngun Start of Block: gunbuy_AUS Timing First Click Last Click Page Submit Click Count

214



▢ Handgun

▢ Shotgun

▢ Precision rifle


▢ Other ________________________________________________

Page Break

215



(Not applicable)


reason

Protection / Self-

defence o o o o o o Stocking

up o o o o o o I have the right to do

so o o o o o o Collecting o o o o o o

Sport / Target



End of Block: gunbuy_AUS Start of Block: Mass Shooting_Christchurch Christchurch mosque shootings On Friday, March 15, 2019, a gunman attacked the Al Noor Mosque and the Linwood Islamic Centre in Christchurch. 50 people were killed and 50 hospitalized, making it the deadliest mass shooting in New Zealand's modern history. The accused gunman was arrested and charged.

216


217


218

(1) The mass shooting, at the mosques in Christchurch, might have been prevented if...

(Not applicable)


Neutral +1 +2 +3:

Very possible

...people at the

mosques were






more surveillance


o o o o o o o o ...society was more cautious of immigrants

from Islamic

countries.

o o o o o o o o

...society was more accepting

of immigrants

from Islamic

countries.

o o o o o o o o


219


220

(2) What might have motivated the gunman to commit the mass shooting at the mosques in Christchurch?

(Not applicable)


Neutral +1 +2 +3:

Very possible


Influence of ideology o o o o o o o o

Desire for power,



for inadequacy

(shame, insecurity,

self-hate, etc.)


prejudice o o o o o o o o Mental illness o o o o o o o o




etc.)

o o o o o o o o


221


End of Block: Mass Shooting_Christchurch Start of Block: Islamoprejudice

222

Agree or disagree:

Disagree strongly

Disagree somewhat

Neither agree nor disagree

Agree somewhat

Agree strongly

The Islamic world is

backward and unresponsive

to new realities.

o o o o o It is wrong to characterize the Islamic

world as one single uniform

formation.

o o o o o Islam is an

archaic, out-of-date religion, that is unable

to adjust to the present.

o o o o o Islam shares

the same universal ethical

principles as other major

world religions.

o o o o o

Islam has an aggressive side that

predisposes it toward

terrorism.

o o o o o It is good for

Islamic religious

education to be offered in

all communities that have a

large number of Muslim

schoolchildren.

o o o o o

223

Page Break

224

Please select the picture that best describes your closeness to the Muslim people.

o Image:Muslim01.png







End of Block: Islamoprejudice Start of Block: Behavioral towards Muslims

225

Agree or disagree:

Strongly disagree

Somewhat disagree


Somewhat agree

Strongly agree

I would be interested in

meeting immigrants from Islamic countries.

o o o o o I would like to spend

more time with

immigrants from Islamic countries.

o o o o o

I would protest

against the arrival of

immigrants from Islamic countries.

o o o o o

I would keep away from immigrants from Islamic countries.


End of Block: Behavioral towards Muslims Start of Block: New Zealand_Responses

226

In response to the Christchurch shootings, do you agree or disagree with the following:

Strongly disagree Disagree Somewhat

disagree Somewhat

agree Agree Strongly Agree

A national ban on

semiautomatic rifles.

o o o o o o New Zealand opening its

doors to more refugees.

o o o o o o The rugby

team, Canterbury Crusaders,

changing their name

because of the overtones

of religious intolerance.

o o o o o o


End of Block: New Zealand_Responses Start of Block: Fail Next are some questions about you in general.

227

Not a lot is done for people like me in New Zealand.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

If I compare people like me against other New Zealanders, my group is worse off.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly


o A great deal

o A lot

o A moderate amount

o A little

o Not at all

228


End of Block: Fail Start of Block: Ernestine_Discontent

I fear things will go wrong in society.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

I feel concerned when I think about the future of society.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

229

I am satisfied with society.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly Timing First Click Last Click Page Submit Click Count

End of Block: Ernestine_Discontent Start of Block: hero Timing First Click Last Click Page Submit Click Count

230

Might you ever consider drawing or discharging a firearm to...

Definitely not Probably not Might or

might not Probably

yes Definitely

yes

Save a vulnerable stranger in

distress o o o o o

Stop an active shooter

situation o o o o o Deter

intimidation by

troublemakers o o o o o

End of Block: hero Start of Block: Right to Kill_AUS

I have a right to kill another person...

Strongly disagree

Somewhat disagree


Somewhat agree

Strongly agree

...in self-defence. o o o o o

...to defend my family. o o o o o ...to defend my home. o o o o o


End of Block: Right to Kill_AUS Start of Block: Gun laws from las vegas

231



o 2

o 3

o 4: Neutral

o 5

o 6


Do you support or oppose some kind of government registry of all guns?


o 2

o 3

o 4: Neutral

o 5

o 6


232

"In general, if more people had guns, there would be less crime."

o Strongly disagree

o Somewhat disagree


o Somewhat agree


End of Block: Gun laws from las vegas Start of Block: HP_OP_Guns_short

233

With regards to owning guns...

1: Do not agree at all 2 3 4

5: Completely

agree

Gun ownership is in harmony with other things that are part of

me.

o o o o o

I have almost an obsessive

feeling for gun

ownership. o o o o o

Gun ownership

allows me to live a variety

of experiences.

o o o o o I have the impression

that gun ownership

controls me. o o o o o


me.

o o o o o

Gun ownership is so exciting

that I sometimes lose control

over it.

o o o o o

234


235


o Image:1.png

o Image:2.png

o Image:3.png

o Image:4.png

o Image:5.png

o Image:6.png

o Image:7.png

o Image:8.png Timing First Click Last Click Page Submit Click Count Page Break

236

Number of guns currently owned (Optional) (approximate if necessary)

▼ 1 ... More than 10


End of Block: HP_OP_Guns_short Start of Block: WAS_C

237

Agree or disagree:

Disagree strongly

Disagree somewhat


Agree somewhat

Agree strongly

People's misfortunes result from mistakes they have

made.

o o o o o Through our actions we can prevent bad things

from happening to

us.

o o o o o

If people took preventive

actions, most misfortunes

could be avoided.

o o o o o When bad

things happen, it is

typically because

people have not taken necessary actions to

protect themselves.

o o o o o

(Please select "Agree strongly" to proceed)


238

End of Block: WAS_C Start of Block: Knowledge questions

How knowledgeable are you about the recent mass shooting at the mosques in Christchurch?





o Extremely knowledgeable

How long has it been since you were last in Christchurch?

o 0 days

o one or two days

o several days

o weeks

o months

o years

o (I have never been to Christchurch)

239


End of Block: Knowledge questions Start of Block: Belief superiority

How important is your race/ethnic background to your identity?

o Not at all important

o Slightly important

o Moderately important

o Very important

o Extremely important Timing First Click Last Click Page Submit Click Count Page Break

240

How important is it that whites work together to change laws that are unfair to whites?

o Not at all important

o Slightly important

o Moderately important

o Very important

o Extremely important Timing First Click Last Click Page Submit Click Count Page Break

241

How much discrimination do whites face in New Zealand?

o None at all

o A little

o A moderate amount

o A lot

o A great deal Timing First Click Last Click Page Submit Click Count Page Break

242

How much more correct are your views on discrimination of whites than other beliefs about this issue?

o 1: No more correct than other viewpoints

o 2: Slightly more correct than other viewpoints

o 3: Somewhat more correct than other viewpoints

o 4: Much more correct than other viewpoints

o 5: Totally correct (mine is the only correct view) Timing First Click Last Click Page Submit Click Count

End of Block: Belief superiority Start of Block: Personal_Christchurch

Have you ever been a victim of a violent crime? (Optional)

o Yes

o No Timing First Click Last Click Page Submit Click Count

End of Block: Personal_Christchurch

243

Start of Block: relpol

What is your current religious affiliation, if any?

o Catholic (including roman catholic and orthodox)

o Protestant (Anglican, Orthodox, Baptist, Lutheran)

o Jewish

o Muslim

o Sikh

o Hindu

o Buddhist

o Atheist

o Agnostic

o None

o Other ________________________________________________

About how often do you attend church (or other organized religious services)?

o Never

o Rarely / Almost never

o At least once a year

o At least once a month

o At least once a week

244

How religious are you?

o 1: Not religious at all

o 2

o 3

o 4

o 5: Highly religious Timing First Click Last Click Page Submit Click Count Page Break

245


o 1: Left-wing

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o 9: Right wing

Do you support any of the current political parties? (Select all that apply)

▢ National Party

▢ Labour Party

▢ NZ First

▢ Green Party

▢ ACT

▢ Other / none of the above

246

In general, do you think the laws covering immigration should be made more strict, less strict, or kept as they are now?


o 2

o 3

o 4: Neutral

o 5

o 6

o 7: Much more strict Timing First Click Last Click Page Submit Click Count Page Break

247

Do you know anyone who is an immigrant from an Islamic country? (Select all that apply)

▢ Family member(s)

▢ Friend(s)

▢ Immigrants from Islamic countries live in my neighbourhood / community

▢ Co-worker(s) / teammate(s)

▢ Some other connection to immigrants from Islamic countries ________________________________________________

▢ No

▢ (skip question)

Compared to myself, Muslims as a group have...

o -2: Much less power and influence

o -1

o 0: About the same amount of power and influence

o 1

o 2: Much more power and influence

248

How, based on your expectations, do people of an Islamic background feel about people who are not of an Islamic background?

o -2: Very negative

o -1

o 0: Neutral

o +1

o +2: Very positive

End of Block: relpol Start of Block: Ed_alone

Do you have any children? If so, how many?

▼ No ... Other

Are you married and/or cohabiting with a romantic partner?

▢ Yes

▢ No

249

How many days has it been since you had a meaningful conversation with another person?

o 0 days

o one or two days

o several days

o weeks

o months

o years Timing First Click Last Click Page Submit Click Count

End of Block: Ed_alone Start of Block: WAS_BW_Bruggen2018

250

Agree or disagree:

Disagree strongly

Disagree somewhat


Agree somewhat

Agree strongly

The good things that happen in

this world far outnumber

the bad.

o o o o o There is

more good than evil in the world.

o o o o o If you look

closely enough, you will see that the world is

full of goodness

o o o o o

The world is a good place. o o o o o


End of Block: WAS_BW_Bruggen2018 Start of Block: Debrief Debriefing:The goal of this university-based psychological study is to examine how the public thinks about mass shootings. We collect this data regularly to assess changes in public opinions over time, especially as it relates to gun ownership, personality, beliefs, and preferences. Given that the Christchurch shootings targeted a minority group, we additionally assessed views on people from Islamic countries. The results will be used for scientific research purposes only. If you have any questions or concerns about the study or your participation, you are welcome to contact the lead investigator, Dr. N. P. Leander ([email protected]). You are also welcome to contact our

251

university ethics board at [email protected]. Now that you know the purpose of this study, do you have any advice or suggestions to improve the survey experience? We appreciate any feedback you can offer.

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________



252

Flycatcher – Utrecht (NL) – Mar. 2019

Start of Block: informedconsent_NL Doel van het onderzoek Dit onderzoek van de Rijksuniversiteit Groningen (Hoofdonderzoeker Dr. L.P. Leander) vraagt naar uw overtuigingen, meningen en ervaringen wat betreft het gebruik van wapens. Tevens stellen wij u vragen over het recente schietincident in Utrecht en aspecten die daar wellicht mee te maken hebben. Het duurt ongeveer 10 tot 15 minuten om de vragenlijst in te vullen. Instructie Lees telkens de instructies vooraf goed door en beantwoordt de vragen zo eerlijk mogelijk. Probeer niet te lang bij antwoorden na te denken. Vaak is uw eerste gedachte de beste. Er bestaan geen foute antwoorden en in dit onderzoek zal geen oordeel gegeven worden over uw mening. Met deelname aan dit onderzoek heeft u, de deelnemer, kennis genomen van het volgende: Mijn deelname is vrijwillig en ik mag mijn deelname aan deze studie op elk tijdstip stopzetten en de gegevens die verkregen zijn uit dit onderzoek terugkrijgen, laten verwijderen uit de database, of laten vernietigen. Ik mag weigeren te antwoorden op een vraag, of vragen overslaan die ik niet wil beantwoorden. Er kleven voor mij geen voordelen of nadelen aan deze keuze. Na deelname zal ik volledige informatie verkrijgen over mijn deelname en het doel van het onderzoek. Al mijn antwoorden zijn anoniem en geheel vertrouwelijk. Alle antwoorden zijn niet tot u herleidbaar en worden veilig opgeslagen. Uw antwoorden zijn enkel toegankelijk voor het onderzoeksteam en worden niet doorgegeven aan derden. Dit onderzoeksproject houdt zich aan de ethische standaarden voor onderzoek en beschermt te allen tijde de waardigheid, rechten, belangen, en veiligheid van deelnemers. Ik heb bovenstaande gelezen en stem in met deelname door op het "->" te klikken.

253

Browser Meta Info Browser Version Operating System Screen Resolution Flash Version Java Support User Agent

End of Block: informedconsent_NL Start of Block: Demographics_NL

Allereerst stellen wij u een aantal demografische vragen Wat is uw geslacht?

o Man

o Vrouw

o Anders, namelijk ________________________________________________

Wat is uw leeftijd?

o 18-24

o 25-34

o 35-44

o 45-54

o 55-64

o 65+

o Wil ik niet zeggen

254

Wat is uw etnische achtergrond (selecteer de categorie waar u zich het meest mee identificeert)?

▢ Europees

▢ Turks

▢ Marokkaans

▢ Indonesisch

▢ Surinaams

▢ Anders, namelijk ________________________________________________ Timing First Click Last Click Page Submit Click Count Page Break

255

Wat is uw huidige of hoogst behaalde opleiding?

o Basisschool

o LBO

o MAVO, MULO, VMBO

o HAVO

o VWO

o MBO

o HBO

o WO, universiteit

o onbekend

o Anders, namelijk ________________________________________________ Timing First Click Last Click Page Submit Click Count Page Break

256

In welke provincie woon je?

▼ Groningen ... Limburg


257

End of Block: Demographics_NL Start of Block: BDW_NL Hier volgen een aantal stellingen. Geef bij iedere stelling aan in hoeverre u het eens bent met de stelling.

Hoewel het kan lijken dat dingen steeds gevaarlijker en chaotischer worden, is het echt niet zo. Elk tijdperk heeft zijn problemen, en iemands kansen om een veilig, onbezorgd leven te leiden is beter dan ooit tevoren.

o Absoluut mee eens

o Beetje mee eens

o Niet mee eens of oneens

o Beetje mee oneens

o Absoluut mee oneens

Elke dag kan er chaos en wetteloosheid om ons heen losbarsten. Alle tekenen wijzen erop.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


258

Er zijn veel gevaarlijke mensen in onze samenleving die iemand zonder enige reden, maar uit pure slechtheid, kunnen aanvallen.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Ondanks wat men hoort over 'misdaad op straat', is er niet meer misdaad dan ooit.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Als een persoon een paar verstandige voorzorgsmaatregelen neemt, gebeurt er waarschijnlijk niets ergs met hem of haar; we leven niet in een gevaarlijke wereld.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


259


260

Aangezien de samenleving steeds wettelozer en beestachtiger wordt, gaan iemands kansen om bestolen, mishandeld en zelfs vermoord te worden omhoog.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Mijn kennis en ervaring vertelt me dat de sociale wereld waarin we leven in feite een veilige, stabiele en veilige plaats is, waarin de meeste mensen fundamenteel goed zijn.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


261

Het lijkt erop dat er steeds minder echt respectabele mensen zijn en meer personen zonder moraal die de rest bedreigen.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Het "einde" is niet nabij. Mensen die denken dat aardbevingen, oorlogen en hongersnood betekenen dat God op het punt staat de wereld te vernietigen zijn dwaas.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Mijn kennis en ervaring vertelt me dat de sociale wereld waarin we leven in feite een gevaarlijke en onvoorspelbare plaats is, waarin de goede, fatsoenlijke en morele waarden en levensstijl van mensen worden bedreigd en verstoord door slechte mensen.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


262


End of Block: BDW_NL Start of Block: PLRA_NL

Hoe waarschijnlijk acht u dat het volgende gebeurt in uw leven (in uw toekomst)?

Absoluut

niet 1

2 3 4 Absoluut wel 5

1. Waarschijnlijkheid

dat u wordt overvallen.

o o o o o 2.

Waarschijnlijkheid dat u met geweld

wordt aangevallen.

o o o o o 3.

Waarschijnlijkheid dat uw huis zal

worden binnengevallen

door een gewapende

inbreker.

o o o o o

4. Waarschijnlijkheid

dat u aanwezig zult zijn bij een grootschalige

schietpartij met veel doden en

gewonden.

o o o o o

263


End of Block: PLRA_NL Start of Block: MassShooting_UtrechtNL_Fixed Schietincident in Utrecht Op maandag 18 maart 2019 schoot een schutter op meerdere mensen in een tram bij het 24 Oktoberplein in Utrecht. Drie mensen werden gedood en een aantal mensen werden in het ziekenhuis opgenomen, waardoor dit het dodelijkste schietincident in Nederland is sinds 2011. De schutter, een 37-jarige inwoner van Utrecht, werd na een klopjacht op de dag van de aanval gearresteerd. Timing First Click Last Click Page Submit Click Count Page Break

264

De volgende vragen hebben betrekking op uw kijk op het schietincident: (1) wat had het schietincident kunnen voorkomen; (2) wat zou de schutter hebben gemotiveerd? Timing First Click Last Click Page Submit Click Count Page Break

265

(1) Hoe waarschijnlijk vindt u de onderstaande opties?

266

Het schietincident in Utrecht had voorkomen kunnen worden als ...

(Niet van

toepassing)

Erg onwaarschijnlij

k -3

-2

-1

Neutraal 0

+1

+2

Erg waarschijnlij

k +3

...als mensen in de buurt

gewapend zouden zijn geweest.

o o o o o o o o ...als er

strengere wetten in Nederland

zouden zijn voor het legaal bezit

van vuurwapens.

o o o o o o o o ..als de

geestelijke gezondsheidszorg in Nederland

beter was geweest.

o o o o o o o o ...als er meer toezicht op

vermoedelijke radicalen zou zijn geweest.

o o o o o o o o ...als de

maatschappij waakzamer zou zijn geweest wat

betreft immigranten uit

islamitische landen.

o o o o o o o o

...als de samenleving toleranter zou zijn tegenover

immigranten uit islamitische

landen

o o o o o o o o

Andere reden, namelijk o o o o o o o o

267


268

(2) Hoe waarschijnlijk vind u de onderstaande opties? Wat zou de schutter hebben gemotiveerd om de schietpartij in Utrecht te plegen?

Niet van

toepassing

Erg onwaarschijnlij

k -3

-2

-1

Neutraal 0

+1

+2

Erg waarschijnlij

k +3

Invloed van religie o o o o o o o o

Invloed van ideologie o o o o o o o o

Verlangen naar macht,

betekenis of aandacht

o o o o o o o o Compensatie

voor eigen ontoereikendhei

d (schaamte, onveiligheid, zelfhaat, etc.)

o o o o o o o o Haat tegen anderen,

vooroordelen o o o o o o o o Psychische problemen o o o o o o o o

Gemakkelijke toegang tot vuurwapens o o o o o o o o

Culturele blootstelling aan geweld (in films,

nieuws, entertainment,

etc.)

o o o o o o o o Andere

motivatie, namelijk o o o o o o o o

269


End of Block: MassShooting_UtrechtNL_Fixed Start of Block: Islamoprejudice_FixedNL

270

De volgende vragen hebben betrekking op de islam. Geef bij iedere stelling aan in hoeverre u het er mee eens of oneens bent.

Absoluut oneens

Enigszins oneens

Niet mee eens of oneens

Enigszins eens

Absoluut mee eens

De islamitische wereld is

achterlijk en reageert niet op

nieuwe realiteiten.

o o o o o Het is verkeerd

om de islamitische wereld te

karakteriseren als één uniforme

wereld.

o o o o o

De islam is een archaïsche, verouderde

religie die zich niet aan het heden kan aanpassen.

o o o o o

De islam deelt dezelfde

universele ethische

principes als andere grote

wereldreligies.

o o o o o

De islam heeft een agressieve kant waardoor terrorisme er mogelijk door

wordt.

o o o o o Het is goed dat een islamitische

religieuze opvoeding wordt aangeboden in

alle gemeenschappen

met een groot aantal

islamitische kinderen.

o o o o o

271


272

De afbeeldingen hieronder geven diverse mogelijkheden weer van de mate van overlap die u kunt ervaren tussen uzelf en immigranten met een islamitische achtergrond in Nederland. Selecteer de afbeelding die het beste deze relatie weergeeft.

o Image:Moslim07

o Image:Moslim06

o Image:Moslim05

o Image:Moslim04

o Image:Moslim03

o Image:Moslim02

o Image:Moslim01 Timing First Click Last Click Page Submit Click Count

End of Block: Islamoprejudice_FixedNL Start of Block: BehaviorMuslims_NL

Graag willen we weten hoe u uw contact ziet met immigranten uit islamitische landen in Nederland.

273

Geef bij iedere stelling aan in hoeverre u het ermee eens bent of niet.

Absoluut oneens

Enigszins oneens


Enigszins mee eens

Absoluut mee eens

Ik zou geïnteresseerd

zijn in het ontmoeten van immigranten

uit islamitische landen.

o o o o o

Ik zou graag meer tijd

doorbrengen met

immigranten uit islamitische

landen.

o o o o o

Ik zou protesteren

tegen de komst van

meer immigranten


o o o o o

Ik zou wegblijven van immigranten



End of Block: BehaviorMuslims_NL Start of Block: Disempowerment_NL Graag willen we meer weten over hoe u Nederland ziet en hoe u uw eigen positie in Nederland ervaart.

274

Geef bij iedere stelling aan in hoeverre u het ermee eens bent.

Er wordt niet veel gedaan voor mensen zoals ik in Nederland.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Als ik mensen zoals mijzelf vergelijk met andere mensen in Nederland, is mijn groep slechter af.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


275

Recente gebeurtenissen in de samenleving hebben mijn problemen in het dagelijks leven vergroot.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens

o Absoluut mee oneens Timing First Click Last Click Page Submit Click Count

End of Block: Disempowerment_NL Start of Block: Discontent_NL

Ik ben bang dat het fout zal gaan in de samenleving.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


276

Ik ben bezorgd wanneer ik denk aan de toekomst van de samenleving.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens


Ik ben tevreden over de samenleving.

o Absoluut mee eens

o Beetje mee eens


o Beetje mee oneens

o Absoluut mee oneens Timing First Click Last Click Page Submit Click Count

End of Block: Discontent_NL Start of Block: WAS_CNL

277

Geef bij onderstaande stellingen in hoeverre u het er mee eens bent.

Absoluut

mee oneens

Beetje mee

oneens


Beetje mee eens

Absoluut mee eens

De tegenslagen van mensen zijn het

gevolg van fouten die ze hebben gemaakt.

o o o o o Door ons gedrag

kunnen we voorkomen dat er

slechte dingen met ons gebeuren.

o o o o o Als mensen

voorzorgsmaatregelen nemen, kunnen de

meeste tegenslagen worden vermeden.

o o o o o Wanneer er slechte dingen gebeuren, komt dit meestal

doordat mensen niet de noodzakelijke

maatregelen hebben genomen om zichzelf

te beschermen.

o o o o o


End of Block: WAS_CNL Start of Block: RTK_NL

Graag willen wij nu weten hoe u denkt over het volgende onderwerp: geef bij iedere mogelijkheid aan in hoeverre u het er mee eens bent.

278

Ik heb het recht om iemand anders te vermoorden ...

Absoluut mee oneens

Beetje mee oneens


Beetje mee eens

Absoluut mee eens

...in geval van zelfverdediging o o o o o

...om mijn familie te

beschermen o o o o o ...om mijn huis te verdedigen o o o o o


End of Block: RTK_NL Start of Block: Knowledge

Hoe voelde u zich toen u hoorde over het schietincident in Utrecht?

Absoluut niet 1 2 3 4 Absoluut wel

5

Woedend o o o o o Verdrietig o o o o o Angstig o o o o o

Geschrokken o o o o o Verrast o o o o o

279

Wanneer was u voor het laatst in Utrecht?

o vandaag

o een paar dagen geleden

o een week geleden

o een aantal weken geleden

o een aantal maanden geleden

o jaren geleden

o Ik ben nooit in Utrecht geweest

Hoeveel weet u over het schietincident in Utrecht?

o Helemaal niets

o Een beetje

o Redelijk wat

o Best veel

o Heel erg veel Timing First Click Last Click Page Submit Click Count

End of Block: Knowledge Start of Block: Belief Superiority

280

Hoe belangrijk is uw ras of etnische achtergrond voor uw identiteit?

o Helemaal niet belangrijk

o Een beetje belangrijk

o Belangrijk

o Heel belangrijk

o Extreem belangrijk Timing First Click Last Click Page Submit Click Count Page Break

281

Hoe belangrijk is het volgens u dat blanke mensen samenwerken om maatregelen en initiatieven te veranderen die oneerlijk zijn tegenover blanken?

o Helemaal niet belangrijk

o Een beetje belangrijk

o Belangrijk

o Heel belangrijk

o Extreem belangrijk Timing First Click Last Click Page Submit Click Count Page Break

282

Hoeveel discriminatie ervaren blanke mensen in Nederland?

o Helemaal niet

o Een beetje

o Redelijk wat

o Veel

o Heel erg veel Timing First Click Last Click Page Submit Click Count Page Break

283

Zijn uw opvattingen wat betreft discriminatie van blanken beter dan opvattingen die andere mensen hebben over discriminatie van blanken?

o Niet beter dan andere opvattingen

o Een beetje beter dan andere opvattingen

o Beter dan andere opvattingen

o Veel beter dan andere opvattingen

o Alleen mijn opvattingen zijn juist Timing First Click Last Click Page Submit Click Count

End of Block: Belief Superiority Start of Block: Personal_NL

Bent u ooit het slachtoffer geweest van een gewelddadige misdaad?

o Ja

o Nee

Bent u in het bezit van een vuurwapen?

o Ja

o Nee

284


285

Welk (e) type (s) vuurwapen (s) bezit u persoonlijk? (klik op alles wat van toepassing is)

▢ pistool / revolver

▢ precisiegeweer

▢ modern sportgeweer (AR-15, AK-style)

▢ jachtgeweer

▢ anders (specificeer) ________________________________________________

▢ niet van toepassing / geen van bovenstaande


End of Block: Personal_NL Start of Block: Wantgun_NL

286


Wil je een vuurwapen bezitten? Zo ja, van welk type(s)? (selecteer alles wat van toepassing is)

▢ (ik wil geen vuurwapen bezitten)

▢ pistool/ revolver

▢ jachtgeweer

▢ precisiegeweer

▢ modern sportgeweer

▢ anders ________________________________________________


287

Page Break

288


Als u een vuurwapen zou bezitten, wat zouden dan belangrijke redenen hiervoor zijn?

(Niet van toepassing)

Absoluut geen reden

1

2 3 4 Absoluut

een reden 5

Bescherming / zelfverdediging o o o o o o Voorraad, om het in huis te

hebben o o o o o o Mijn recht o o o o o o

Verzamelen o o o o o o Sport of jacht o o o o o o

Anders, namelijk o o o o o o

End of Block: Wantgun_NL Start of Block: RelPol_NL

289

Wat is uw huidige religieuze overtuiging, indien van toepassing?

o Katholiek

o Protestant

o Joods

o Moslim

o Sikh

o Hindoe

o Boeddhist

o Atheist

o Agnost

o Niets

o Anders, namelijk ________________________________________________

Hoe vaak gaat u naar de kerk (of andere georganiseerde religieuze diensten)?

o Nooit

o Zelden

o Minstens een keer per jaar

o Minstens een keer per maand

o Minstens een keer per week

290

Hoe religieus bent u?

o Absoluut niet religieus1

o 2

o 3

o 4

o Heel erg religieus5 Timing First Click Last Click Page Submit Click Count Page Break

291

Wat is uw politieke voorkeur?

o Heel erg links1

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o Heel erg rechts9

292

Op welke partij heeft u bij de provinciale verkiezingen op 20 maart 2019 gestemd?

▢ VVD

▢ CDA

▢ D66

▢ Groenlinks

▢ PVDA

▢ PVV

▢ Forum voor Democratie

▢ Christenunie

▢ SGP

▢ Partij voor de Dieren

▢ 50Plus

▢ Denk

▢ SP

▢ Lokale/ provinciale partij

▢ Niet gestemd

▢ Wil niet zeggen

293

Op welke partij heeft u bij de tweede kamer verkiezingen in 2017 gestemd?

294

▢ VVD

▢ CDA

▢ D66

▢ Groenlinks

▢ PVDA

▢ PVV

▢ Forum voor Democratie

▢ Christenunie

▢ SGP

▢ Partij voor de Dieren

▢ 50Plus

▢ Denk

▢ SP

▢ Andere partij

▢ Niet gestemd

▢ Wil niet zeggen

295


296

Denkt u dat het immigratiebeleid veel strenger, veel minder streng of hetzelfde zou moeten zijn? zijn?

o Veel minder streng1

o 2

o 3

o Hetzelfde als nu4

o 5

o 6

o Veel strenger7

Hoeveel vertrouwen heeft u in de nationale regering?

o Geen vertrouwen1

o 2

o 3

o 4

o Veel vertrouwen5

297

De nationale regering behartigt de belangen van mij en anderen zoals ik.

o Helemaal mee eens

o Mee eens

o Noch mee oneens, noch mee eens

o Mee oneens

o Helemaal mee oneens

o (Weet ik niet) Timing First Click Last Click Page Submit Click Count Page Break

298

Kent u immigranten met een islamitische achtergrond? (Selecteer alles wat van toepassing is)

▢ ja, in mijn familie

▢ ja, in mijn vriendenkring

▢ Er wonen immigranten met een islamitische achtergrond bij mij in de buurt

▢ Ik werk samen en/of studeer met immigranten met een islamitische achtergrond

▢ een andere relatie met immigranten met een islamitische achtergrond, namelijk ________________________________________________

▢ Nee, ik ken geen immigranten met een islamitische achtergrond

▢ wil niet antwoorden

▢ Ik ben zelf een immigrant met een islamitische achtergrond

In vergelijking met mezelf hebben mensen met een islamitische achtergrond ...

o Veel minder macht en invloed-2

o -1

o Ongeveer evenveel macht en invloed0

o 1

o Veel meer macht en invloed2

299

Hoe denkt u dat mensen met een islamitische achtergrond zich voelen over mensen zonder islamitische achtergrond?

o Heel erg negatief-2

o -1

o Neutraal0

o 1

o Heel erg positief2 Timing First Click Last Click Page Submit Click Count

End of Block: RelPol_NL Start of Block: RelationsNL

Heeft u kinderen, en zo ja, hoeveeel?

o Nee

o Ja, 1 kind

o Ja, 2 kinderen

o Ja, 3 kinderen

o Ja, 4 kinderen

o Ja, 5 of meer kinderen

o Anders

o Anders, namelijk: ________________________________________________

300

Bent u getrouwd en / of woont u samen met een romantische partner?

o ja

o nee

Wanneer heeft u voor het laats een betekenisvol gesprek gehad met een ander persoon?

o Vandaag

o een of twee dagen geleden

o een aantal dagen geleden

o weken geleden

o maanden geleden

o jaren geleden Timing First Click Last Click Page Submit Click Count

End of Block: RelationsNL Start of Block: WAS_BIW_NL

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In welke mate bent u het eens met de volgende stellingen?

Absoluut mee oneens

Beetje mee oneens


Beetje mee eens

Absolut mee eens

De goede dingen die in deze wereld

gebeuren overtreffen de slechte dingen die gebeuren

o o o o o

Er is meer goed dan

kwaad in de wereld.

o o o o o Als je goed

genoeg kijkt, zul je zien dat er veel

goeds gebeurt in de

wereld.

o o o o o

De wereld is een goede

plek. o o o o o Timing First Click Last Click Page Submit Click Count

End of Block: WAS_BIW_NL Start of Block: debrief_NL Dit is het einde van de vragenlijst. Hartelijk dank voor uw deelname! Het doel van deze studie is om te onderzoeken hoe het publiek denkt over massale schietpartijen. We verzamelen deze gegevens regelmatig in diverse landen om veranderingen in publieke

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opinies in de loop van de tijd te beoordelen, vooral als het gaat om wapens, persoonlijkheid, overtuigingen en voorkeuren. Aangezien de schietpartij in Utrecht werd gepleegd door een migrant die geboren is in een land met een islamitische meerderheid, en dit feit in de media sterk naar voren kwam, hebben we bovendien overtuigingen over de islam gemeten. De resultaten zullen alleen worden gebruikt voor wetenschappelijke onderzoeksdoeleinden. Als u vragen of opmerkingen heeft over de studie of uw deelname, kunt u contact opnemen met de hoofdonderzoeker, Dr. N. P. Leander ([email protected]). Bij klachten over deze studie kunt u contact opnemen met onze ethische commssie ([email protected]). Nu u het doel van deze studie weet, heeft u advies of suggesties om de enquêtervaring te verbeteren? We stellen uw feedback op prijs. Klik op "-->" om de vragenlijst af te sluiten en terug te keren naar Flycatcher.

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________


End of Block: debrief_NL

303

Supplementary Information D

Full Survey of Study 3: El Paso Walmart Shooting / Dayton Bar Shooting

The following supplementary appendix includes the full survey print-out for Study 3. This

study uses a survey data set from 2019. Participants were recruited after the El Paso Walmart

shooting and the Dayton bar shooting had taken place (see below ‘Qualtrics – El Paso/Dayton

– Aug. 2019’).

Qualtrics – El Paso/Dayton – Aug. 2019

Start of Block: Demographics

Gender

o Male

o Female

Age

o 18-24

o 25-34

o 35-44

o 45-54

o 55-64

o 65+

304


▢ White

▢ Black or African American

▢ Hispanic / Latino / Latina

▢ American Indian or Alaska Native

▢ Asian

▢ Native Hawaiian or Pacific Islander


305

Education

o Some High School or Less

o High School Graduate / GED

o Some College

o College Graduate

o Graduate Degree


o Under $15,000

o $15,000 - $25,000

o $25,000 - $35,000

o $35,000 - $50,000

o $50,000 - $75,000

o $75,000 - $100,000

o $100,000 - $150,000

o $150,000 - $200,000

o $200,000 +

306

Below is a list of items, please indicate the objects that you currently own. (select all that apply)

I own this I do not own this

a sport utility vehicle (SUV) o o a boat o o

a playstation o o a typewriter o o

a firearm o o a musical instrument o o

an easel o o a fishing rod o o

a dehumidifier o o a swimming pool o o


307

In what region of the USA do you live?

o West

o Midwest

o South

o Northeast Timing First Click Last Click Page Submit Click Count

End of Block: Demographics Start of Block: Informed Consent Timing First Click Last Click Page Submit Click Count

308

Informed Consent Primary investigator: Dr. N. P. Leander, PhDThis university-based psychological study will ask about your beliefs, attitudes, and experiences regarding guns and the use of firearms, including questions about the recent mass shootings. The study typically takes 15 minutes. Your privacy is important: Your participation is completely anonymous. No identifying information will be collected from you. Only members of the research team will have access to the survey data, but even they cannot link the data to any single person. Your rights: You can decide whether or not to participate in the study. You can leave the study at any time. You may click "Next" to begin when ready. Browser Meta Info Browser Version Operating System Screen Resolution Flash Version Java Support User Agent

End of Block: Informed Consent Start of Block: Knowledge questions_El Paso

309

How knowledgeable are you about the recent mass shooting at the Walmart store in El Paso, Texas?






End of Block: Knowledge questions_El Paso Start of Block: Mass Shooting_El Paso El Paso Walmart shooting On August 3, 2019, a mass shooting occurred at a Walmart store in El Paso, Texas. Twenty-two people were killed and 24 were injured. Among the deceased were 13 Americans, 8 Mexican citizens, and 1 German citizen. The accused gunman, Patrick Crusius, was arrested and charged. Timing First Click Last Click Page Submit Click Count Page Break

310


311

(1) The mass shooting, at the Walmart store in El Paso, might have been prevented if...

(Not applicable)


Neutral +1 +2 +3:

Very possible

...people at the

Walmart were






more surveillance



o o o o o o o o ...society was more accepting

of Muslims. o o o o o o o o

Other o o o o o o o o Timing First Click Last Click Page Submit Click Count

312

Page Break

313

(2) What might have motivated the gunman to commit the mass shooting at the Walmart store in El Paso, Texas?

(Not applicable)


Neutral +1 +2 +3:

Very possible



Desire for power,



for inadequacy

(shame, insecurity,

self-hate, etc.)






etc.)

o o o o o o o o


314


315

With regards to the El Paso gunman:

Disagree strongly

Disagree somewhat


Agree somewhat

Agree strongly

It is hard to place all the fault on him

alone. o o o o o

What the gunman did is not entirely his

fault o o o o o

It is understandable

why he was driven to do

this. o o o o o


End of Block: Mass Shooting_El Paso Start of Block: Knowledge questions_Dayton

How knowledgeable are you about the recent mass shooting in Dayton, Ohio?





o Extremely knowledgeable

316


End of Block: Knowledge questions_Dayton Start of Block: Mass Shooting_Dayton Dayton bar shooting On August 4, 2019, at 1:05 a.m., a mass shooting occurred outside Ned Pepper's Bar in downtown Dayton, Ohio. Ten people were killed and at least 27 others were injured. The accused gunman, Connor Betts, was shot and killed by police within 30 seconds of the first shots being fired. Timing First Click Last Click Page Submit Click Count Page Break

317


318

(1) The mass shooting, at the bar in Dayton, might have been prevented if...

(Not applicable)


Neutral +1 +2 +3:

Very possible

...people at the bar were






more surveillance



o o o o o o o o ...society was more accepting

of Muslims. o o o o o o o o

Other o o o o o o o o Timing First Click Last Click Page Submit Click Count Page Break

319

320

(2) What might have motivated the gunman to commit the mass shooting at the bar in Dayton, Ohio?

(Not applicable)


Neutral +1 +2 +3:

Very possible



Desire for power,



for inadequacy

(shame, insecurity,

self-hate, etc.)






etc.)

o o o o o o o o


321


322

With regards to the Dayton gunman:

Disagree strongly

Disagree somewhat


Agree somewhat

Agree strongly

What the gunman did is not entirely his

fault o o o o o

External forces influenced him

to do this. o o o o o It is, in some

ways, understandable how a person

could be driven to do this.


End of Block: Mass Shooting_Dayton Start of Block: BDW Next are a series of statements. Please indicate the extent to which you agree or disagree with each of them.

323

Although it may appear that things are constantly getting more dangerous and chaotic, it really isn’t so. Every era has its problems, and a person’s chances of living a safe, untroubled life are better today than ever before.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

Any day now, chaos and lawlessness could erupt around us. All the signs are pointing to it.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

There are many dangerous people in our society who will attack someone out of pure meanness, for no reason at all.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

324

Despite what one hears about “crime in the street,” there probably isn’t any more now than there ever has been.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

If a person takes a few sensible precautions, nothing bad is likely to happen to him or her; we do not live in a dangerous world.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly Timing First Click Last Click Page Submit Click Count Page Break

325

Every day, as society becomes more lawless and bestial, a person’s chances of being robbed, assaulted, and even murdered go up and up.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

My knowledge and experience tells me that the social world we live in is basically a safe, stable and secure place, in which most people are fundamentally good.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

326

It seems that every year there are fewer and fewer truly respectable people, and more and more persons with no morals at all, who threaten everyone else.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

The “end” is not near. People who think that earthquakes, wars, and famines mean God might be about to destroy the world are being foolish.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

My knowledge and experience tells me that the social world we live in is basically a dangerous and unpredictable place, in which good, decent and moral people’s values and way of life are threatened and disrupted by bad people.

o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

327


End of Block: BDW Start of Block: Immigrant Prejudice

How often do you have any contact with Hispanic people when you are out and about? This could be on public transport, in the street, in shops or in the neighborhood.

o Never

o Less than once a month

o Once a month

o Several times a month

o Once a week

o Several times a week

o Every day

328

Thinking about contact with Hispanics, in general, is such contact hindering or facilitating the goals you have at that moment?

o 0 hindering my goals

o 1

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o 9

o 10 facilitating my goals Timing First Click Last Click Page Submit Click Count Page Break

329

Please list the most important needs or goals you have when in contact with Hispanics. (try to list at least two)

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________ Timing First Click Last Click Page Submit Click Count Page Break

330

Thinking about contact with Hispanics, in general, how bad or good is it?

o 0 Extremely bad

o 1

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o 9

o 10 Extremely good Timing First Click Last Click Page Submit Click Count Page Break

331

Hispanic people who come to live here, generally...

1 2 3 4 5 6 7 8 9 10

take jobs away o o o o o o o o o o create

new jobs

undermine the

cultural life

o o o o o o o o o o enrich

the cultural

life

make crime

problems worse

o o o o o o o o o o make crime

problems better

harm America's

culture o o o o o o o o o o benefit

America's culture


332

Below is a feeling thermometer. Ratings between 50 degrees and 100 degrees mean that you feel favorable and warm toward the group. Ratings between 0 degrees and 50 degrees mean that you don't feel favorable toward the group and that you don't care too much for that group. You would rate the group at the 50 degree mark if you don't feel particularly warm or cold toward the group.

How would you rate: Hispanics

o 100˚ Very warm or favorable feeling

o 85˚ Quite warm or favorable feeling

o 70˚ Fairly warm or favorable feeling

o 60˚ A bit more warm or favorable feeling than cold feeling

o 50˚ No feeling at all

o 40˚ A bit more cold or unfavorable feeling than warm feeling

o 30˚ Fairly cold or unfavorable feeling

o 15˚ Quite cold or unfavorable feeling

o 0˚ Very cold or unfavorable feeling Timing First Click Last Click Page Submit Click Count

End of Block: Immigrant Prejudice Start of Block: QFS Next are some questions about your personal experiences and tendencies.

333

Think about your life right now and express your level of agreement with each of the following statements.

Strongly disagre

e

Disagree

Somewhat disagree

Neither agree nor

disagree

Somewhat agree

Agree

Strongly agree

I wish I could be

more respected.

o o o o o o o I wish I meant

more to other


I wish other people

thought I were

significant. o o o o o o o

I wish I were more appreciated by other



o o o o o o o I want to be

more important. o o o o o o o


334

335

Think about your life and express your level of agreement with each of the following statements. I feel...

Strongly disagre

e

Disagree

Somewhat disagree

Neither agree nor

disagree

Somewhat agree

Agree

Strongly agree

I feel significant o o o o o o o

I wish I meant

more to other


I wish other people

thought I were

significant. o o o o o o o

I wish I were more appreciated by other



o o o o o o o I feel I have

the success I deserve

o o o o o o o Timing First Click Last Click Page Submit Click Count

336

End of Block: QFS Start of Block: Fail Next are some questions about you in general.


o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly


o Agree strongly

o Agree somewhat


o Disagree somewhat

o Disagree strongly

337


o A great deal

o A lot

o A moderate amount

o A little

o Not at all Timing First Click Last Click Page Submit Click Count

End of Block: Fail Start of Block: Owngun Timing First Click Last Click Page Submit Click Count

338


▢ Handgun

▢ Precision rifle


▢ Shotgun

▢ Other (specify) ________________________________________________


Page Break

339

How important are guns for you as a means of protection?

o Not at all

o A little

o A moderate amount

o A lot

o A great deal

How important are guns for you as a means of recreation?

o Not at all

o A little

o A moderate amount

o A lot

o A great deal Timing First Click Last Click Page Submit Click Count

End of Block: Owngun Start of Block: HP-OP Guns (short)

340

With regards to owning guns...

1: Do not agree at all 2 3 4

5: Completely

agree


me.

o o o o o

I have almost an obsessive

feeling for gun

ownership. o o o o o

Gun ownership

allows me to live a variety

of experiences.

o o o o o I have the impression

that gun ownership

controls me. o o o o o


me.

o o o o o

Gun ownership is so exciting

that I sometimes lose control

over it.

o o o o o

Gun ownership is important to

me. o o o o o

341


End of Block: HP-OP Guns (short) Start of Block: gunbuy Timing First Click Last Click Page Submit Click Count



▢ Handgun

▢ Shotgun

▢ Precision rifle


▢ Other ________________________________________________

342

End of Block: gunbuy Start of Block: hero Timing First Click Last Click Page Submit Click Count

Might you ever consider drawing or discharging a firearm to...

Definitely not Probably not Might or

might not Probably

yes Definitely

yes

Save a vulnerable stranger in

distress o o o o o

Stop an active shooter

situation o o o o o Deter

intimidation by

troublemakers o o o o o

End of Block: hero Start of Block: Gun laws from las vegas

343



o 2

o 3

o 4: Neutral

o 5

o 6


Do you support or oppose some kind of government registry of all guns?


o 2

o 3

o 4: Neutral

o 5

o 6


344

"In general, if more people had guns, there would be less crime."

o Strongly disagree

o Somewhat disagree


o Somewhat agree


End of Block: Gun laws from las vegas Start of Block: Gun Control vs Gun Rights

What do you think is more important – to control gun ownership or to protect the right of Americans to own guns?

o 1: Gun Control is much more important

o 2

o 3

o 4: Neutral

o 5

o 6

o 7: Gun Rights are much more important

345


End of Block: Gun Control vs Gun Rights Start of Block: Politics


o 1: Extremely conservative

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o 9: Extremely liberal

346

Where do you stand with regards to the current U.S. President's policies?

o Oppose broadly

o More opposed than support

o Neutral / Mixed

o More support than oppose

o Support broadly

In general, do you think the laws covering immigration should be made more strict, less strict, or kept as they are now?


o 2

o 3

o 4: Neutral

o 5

o 6


Agree or disagree:

347

Illegal immigrants should be detained by ICE until they can be deported?

o Strongly disagree

o Somewhat disagree


o Somewhat agree

o Strongly agree

"The El Paso gunman may have been encouraged by public figures who describe immigrants as a threat to the United States."

o Strongly disagree

o Somewhat disagree


o Somewhat agree


End of Block: Politics Start of Block: Extremism

348

Below, we will ask you about your thoughts, feelings, and behaviors related to goal pursuit. In each statement, please select an answer that describes you best.

349

Definitely disagree Disagree Slightly

disagree

Neither agree nor

disagree

Slightly agree Agree Definitely

agree

My life is usually

dominated by one main

pursuit/desire. o o o o o o o

I wish I meant more to other

people. o o o o o o o I wish other

people thought I

were significant.

o o o o o o o I wish I were

more appreciated

by other people.

o o o o o o o I want people to care more

about me. o o o o o o o I spend most of my time

thinking about the one goal that matters to me the

most.

o o o o o o o

When I decide on

something, I go for it like

my life depended on

it.

o o o o o o o

There is usually one

goal that looms large in

my mind. o o o o o o o

350

When I focus on my most important

goal, I easily forget other

things.

o o o o o o o Typically, my

happiness depends on the one thing that I value

most.

o o o o o o o There is only one thing that can make me happy in life.

o o o o o o o I react very

emotionally to anything that is related to

my most important

goal.

o o o o o o o


End of Block: Extremism Start of Block: Moral Foundations

351

When you decide whether something is right or wrong, to what extent are the following considerations relevant to your thinking? Please rate each statement using this scale:

352

not at all relevant

not very relevant

slightly relevant

somewhat relevant

very relevant

extremely relevant

Whether or not

someone suffered

emotionally o o o o o o

Whether or not

someone cared for someone weak or

vulnerable

o o o o o o

Whether or not some people were

treated differently

than others

o o o o o o

Whether or not

someone acted

unfairly o o o o o o

Whether or not

someone’s action

showed love for his

or her country

o o o o o o

Whether or not

someone did

something to betray his or her

group

o o o o o o

Whether or not

someone showed a

lack of respect for authority

o o o o o o

353

Whether or not

someone conformed

to the traditions of society

o o o o o o

Whether or not

someone violated

standards of purity

and decency

o o o o o o

Whether or not

someone did

something disgusting

o o o o o o Timing First Click Last Click Page Submit Click Count Page Break

354

355

Please read the following sentences and indicate your agreement or disagreement

356

strongly disagree

moderately disagree

slightly disagree

slightly agree

moderately agree

strongly agree

Compassion for those who are

suffering is the most crucial virtue.

o o o o o o

One of the worst things

a person could do is

hurt a defenseless

animal.

o o o o o o

When the government makes laws, the number

one principle

should be ensuring

that everyone is

treated fairly.

o o o o o o

Justice is the most important

requirement for a

society.

o o o o o o I am proud

of my country’s history.

o o o o o o People

should be loyal to their

family members, even when they have

done something

wrong.

o o o o o o

357

Respect for authority is something all children

need to learn.

o o o o o o Men and women

each have different

roles to play in society.

o o o o o o People

should not do things that are

disgusting, even if no

one is harmed.

o o o o o o

I would call some acts wrong on

the grounds that they are unnatural.

o o o o o o Timing First Click Last Click Page Submit Click Count

End of Block: Moral Foundations Start of Block: Debrief

In which state do you currently reside?

▼ Alabama ... I do not reside in the United States

358


359

Debriefing:The goal of this university-based psychological study is to examine how the public thinks about mass shootings. We collect this data regularly to assess changes in public opinions over time, especially as it relates to gun ownership, personality, beliefs, and preferences. Given that the El Paso shootings targeted a minority group, we additionally assessed views on people from Hispanic countries.

The results will be used for scientific research purposes only. If you have any questions or concerns about the study or your participation, you are welcome to contact the lead investigator, Dr. N. P. Leander ([email protected]). You are also welcome to contact our university ethics board at [email protected]. Now that you know the purpose of this study, do you have any advice or suggestions to improve the survey experience? We appreciate any feedback you can offer.

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________

________________________________________________________________



360

SI References

1. D. Lakens, A. M. Scheel, P. M. Isager, Equivalence Testing for PsychologicalResearch: A Tutorial. Adv. Methods Pract. Psychol. Sci. 1, 259–269 (2018).

2. N. Shnabel, A. Nadler, A needs-based model of reconciliation: satisfying the differentialemotional needs of victim and perpetrator as a key to promoting reconciliation. J. Pers.Soc. Psychol. 94, 116–132 (2008).

3. J. Cohen, P. Cohen, S. G. West, L. S. Aiken, Applied Multiple Regression/CorrelationAnalysis for the Behavioral Sciences (Routledge, 2003)https:/doi.org/10.4324/9780203774441.

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Documents

Supplementary Information - PNAS