Data Cleaning

merged_data <- merged_data %>% 
  mutate(
    binary_var = ifelse(
      is.na(control_instruction_),  # Check if experiment_instruct__1 is NA
      1,                             # If TRUE, set binary_var to 1
      ifelse(
        control_instruction_ == 1,    # Check if control_instruction is 1
        0,                           # If TRUE, set binary_var to 0
        1                            # Otherwise, set binary_var to 1
      )
    )
  )

# descriptives 
merged_data <- merged_data %>% rename(Age = Age.x)
merged_data$Age <- as.numeric(merged_data$Age)

merged_data <- remove_labels(merged_data)

# Calculate z-scores
merged_data$ladder_latino_zscore <-  scale(merged_data$ladder_latino_)
merged_data$ladder_white_zscore <-  scale(merged_data$ladder_white_)

Guide to Variables

ladder_latino = Where would you place the average Latino person on the ladder in relation to other people in the United States? Please tick the box that corresponds to the selected rung. 1-10

ladder_white = Where would you place the average White person on the ladder in relation to other people in the United States? Please tick the box that corresponds to the selected rung. 1-10

undoc_imm__2 = In the United States, what percentage of immigrants are undocumented, meaning that they have either entered the country illegally or stayed longer than allowed? 0 to 100

preference_latino_ = Do you support the preferential hiring of Latino people? Strongly oppose, oppose, neither support nor oppose, support, strongly support

preference_white = Do you support the preferential hiring of poor White people? Strongly oppose, oppose, neither support nor oppose, support, strongly support

job_threat_ = To what degree are you worried about losing your job or not finding a job? Not at all, a bit, a lot, extremely

crime_threat_ = Have you felt unsafe from crime in your home? Not at all, a bit, a lot, extremely

household_finance = Thinking about your household’s finances today, do you feel your household is: Financially secure, not financially secure

household_financial_ = How would you rate your household’s financial situation today? poor, only fair, good, excellent

economic_conditions_ = How would you rate economic conditions in the country today? poor, only fair, good, excellent

imm_amvalue_ = Immigrants bring their foreign language and culture. Do you think we should shut the door to immigration? 1 = strongly disagree 5 = strongly agree

imm_urban_ = Immigrants have been found to revitalize urban areas. Do you think we should promote immigration? 1 = strongly disagree 5 = strongly agree

imm_develop_ = Immigrants have been found to create new businesses. Do you think we need to increase the number of immigrants? 1 = strongly disagree 5 = strongly agree

imm_burden_ = immigrants are a burden on our country becuase they take our jobs, housing, and health care. 1 = strongly disagree 5 = strongly agree

imm_strength_ = immigrants today strengthen our country because of their hard work and talents. 1 = strongly disagree 5 = strongly agree

ImmigBilingual = Replace multi-year bilingual instruction in schools (designed to accommodate children born in other countries) with instruction only in English after one year. Strongly oppose, oppose, neither support nor oppose, support, strongly support

DACA = Charge undocumented immigrants attending college a higher tuition rate at state colleges and universities even if they grew up and graduated high school in the United States. Strongly oppose, oppose, neither support nor oppose, support, strongly support

undoc_policy = What is your preferred policy on undocumented or illegal immigration?

anti_imm_att is an aggregate measure of”imm_strength_r”, “imm_burden_”, “imm_develop_r”, “imm_urban_r”, “imm_amvalue_” this variable is coded with 5 being more anti and 1 being more pro.

all_anti_policies is an aggregate measure of “zDACA”, “zanti_policy”, “zImmigIDr”, “zImmigBilingual” with 5 being anti and 1 being pro.

Baseline Models - testing if our experimental condiiton is assocaited with all relevant outcomes

Regression Models Summary - Model 1
	ladder_latino_zscore
Predictors	Estimates	CI	p
(Intercept)	-0.01	-0.08 – 0.06	0.716
binary var	0.03	-0.07 – 0.12	0.615
Observations	1599
R² / R² adjusted	0.000 / -0.000

Regression Models Summary - Model 2
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	27.80	26.23 – 29.36	<0.001
binary var	1.79	-0.37 – 3.96	0.104
Observations	1599
R² / R² adjusted	0.002 / 0.001

Regression Models Summary - Model 3
	preference_latino_
Predictors	Estimates	CI	p
(Intercept)	2.49	2.41 – 2.57	<0.001
binary var	0.03	-0.08 – 0.13	0.628
Observations	1599
R² / R² adjusted	0.000 / -0.000

Regression Models Summary - Model 4
	preference_white
Predictors	Estimates	CI	p
(Intercept)	2.43	2.35 – 2.51	<0.001
binary var	0.09	-0.02 – 0.19	0.101
Observations	1599
R² / R² adjusted	0.002 / 0.001

Regression Models Summary - Model 6
	job_threat_
Predictors	Estimates	CI	p
(Intercept)	1.82	1.75 – 1.89	<0.001
binary var	0.07	-0.03 – 0.16	0.151
Observations	1599
R² / R² adjusted	0.001 / 0.001

Regression Models Summary - Model 7
	crime_threat_
Predictors	Estimates	CI	p
(Intercept)	1.39	1.34 – 1.44	<0.001
binary var	0.09	0.03 – 0.16	0.005
Observations	1599
R² / R² adjusted	0.005 / 0.004

Regression Models Summary - Model 8
	household_finance
Predictors	Estimates	CI	p
(Intercept)	1.50	1.46 – 1.53	<0.001
binary var	0.02	-0.03 – 0.07	0.422
Observations	1599
R² / R² adjusted	0.000 / -0.000

Regression Models Summary - Model 9
	household_financial_
Predictors	Estimates	CI	p
(Intercept)	2.26	2.20 – 2.32	<0.001
binary var	-0.05	-0.14 – 0.03	0.233
Observations	1599
R² / R² adjusted	0.001 / 0.000

Regression Models Summary - Model 10
	economic_conditions_
Predictors	Estimates	CI	p
(Intercept)	1.86	1.80 – 1.91	<0.001
binary var	-0.01	-0.09 – 0.07	0.812
Observations	1599
R² / R² adjusted	0.000 / -0.001

Regression Models Summary - Model 11
	imm_amvalue_
Predictors	Estimates	CI	p
(Intercept)	2.33	2.23 – 2.43	<0.001
binary var	0.15	0.02 – 0.29	0.027
Observations	1599
R² / R² adjusted	0.003 / 0.002

Regression Models Summary - Model 12
	imm_urban_
Predictors	Estimates	CI	p
(Intercept)	2.91	2.82 – 3.00	<0.001
binary var	-0.04	-0.16 – 0.09	0.559
Observations	1599
R² / R² adjusted	0.000 / -0.000

Regression Models Summary - Model 13
	imm_develop_
Predictors	Estimates	CI	p
(Intercept)	2.78	2.70 – 2.87	<0.001
binary var	-0.08	-0.20 – 0.03	0.160
Observations	1599
R² / R² adjusted	0.001 / 0.001

Regression Models Summary - Model 14
	imm_burden_
Predictors	Estimates	CI	p
(Intercept)	2.46	2.36 – 2.56	<0.001
binary var	0.13	-0.01 – 0.26	0.063
Observations	1599
R² / R² adjusted	0.002 / 0.002

Regression Models Summary - Model 15
	imm_strength_
Predictors	Estimates	CI	p
(Intercept)	3.63	3.54 – 3.71	<0.001
binary var	-0.14	-0.26 – -0.02	0.027
Observations	1599
R² / R² adjusted	0.003 / 0.002

Regression Models Summary - Model 16
	ImmigBilingual
Predictors	Estimates	CI	p
(Intercept)	2.71	2.62 – 2.81	<0.001
binary var	0.03	-0.10 – 0.15	0.700
Observations	1599
R² / R² adjusted	0.000 / -0.001

Regression Models Summary - Model 17
	DACA
Predictors	Estimates	CI	p
(Intercept)	2.25	2.15 – 2.34	<0.001
binary var	0.14	0.01 – 0.27	0.035
Observations	1598
R² / R² adjusted	0.003 / 0.002

Regression Models Summary - Model 18
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	27.80	26.23 – 29.36	<0.001
binary var	1.79	-0.37 – 3.96	0.104
Observations	1599
R² / R² adjusted	0.002 / 0.001

Summary: Main effect of experiemntal condition on crime threat. Crime threat increases with new census latino race question. Significance for Imm_amvalue - with new question people think we should shut the door on immigration more. Significance with imm_strength_ - people think that less that immigration strengthens our country. Significance for DACA - people who see race question think we should charge DACA students higher tuition.

Examining interaction of experimental condition and where people think Latinos are on the social ladder

Regression Models Summary - Model 19
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	20.56	-9.89 – 51.01	0.186
Age	0.18	0.11 – 0.26	<0.001
Sex [Female]	-0.18	-30.54 – 30.18	0.991
Sex [Male]	-1.72	-32.07 – 28.64	0.912
ladder latino zscore	-1.42	-2.97 – 0.12	0.070
binary var	1.74	-0.41 – 3.88	0.112
ladder latino zscore × binary var	3.70	1.56 – 5.84	0.001
Observations	1599
R² / R² adjusted	0.026 / 0.022

Regression Models Summary - Model 20
	preference_latino_
Predictors	Estimates	CI	p
(Intercept)	2.11	0.58 – 3.63	0.007
Age	-0.00	-0.01 – 0.00	0.074
Sex [Female]	0.60	-0.92 – 2.12	0.439
Sex [Male]	0.47	-1.05 – 1.99	0.542
ladder latino zscore	0.05	-0.03 – 0.13	0.213
binary var	0.03	-0.08 – 0.14	0.612
ladder latino zscore × binary var	-0.08	-0.19 – 0.03	0.138
Observations	1599
R² / R² adjusted	0.007 / 0.003

Regression Models Summary - Model 21
	preference_white
Predictors	Estimates	CI	p
(Intercept)	3.45	1.97 – 4.94	<0.001
Age	-0.00	-0.00 – 0.00	0.577
Sex [Female]	-0.98	-2.46 – 0.50	0.193
Sex [Male]	-0.98	-2.45 – 0.50	0.193
ladder latino zscore	0.01	-0.07 – 0.08	0.816
binary var	0.08	-0.02 – 0.19	0.110
ladder latino zscore × binary var	-0.02	-0.12 – 0.09	0.739
Observations	1599
R² / R² adjusted	0.003 / -0.001

Regression Models Summary - Model 22
	job_threat_
Predictors	Estimates	CI	p
(Intercept)	2.34	1.03 – 3.64	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	0.14	-1.16 – 1.45	0.827
Sex [Male]	-0.06	-1.36 – 1.24	0.929
ladder latino zscore	-0.02	-0.09 – 0.04	0.493
binary var	0.07	-0.02 – 0.16	0.129
ladder latino zscore × binary var	0.01	-0.09 – 0.10	0.895
Observations	1599
R² / R² adjusted	0.046 / 0.042

Regression Models Summary - Model 23
	crime_threat_
Predictors	Estimates	CI	p
(Intercept)	1.83	0.89 – 2.77	<0.001
Age	0.00	-0.00 – 0.00	0.313
Sex [Female]	-0.44	-1.38 – 0.49	0.353
Sex [Male]	-0.54	-1.48 – 0.39	0.255
ladder latino zscore	-0.00	-0.05 – 0.05	0.924
binary var	0.09	0.03 – 0.16	0.007
ladder latino zscore × binary var	0.05	-0.02 – 0.11	0.167
Observations	1599
R² / R² adjusted	0.014 / 0.010

Regression Models Summary - Model 24
	household_finance
Predictors	Estimates	CI	p
(Intercept)	1.47	0.78 – 2.16	<0.001
Age	0.00	-0.00 – 0.00	0.279
Sex [Female]	0.04	-0.65 – 0.73	0.903
Sex [Male]	-0.08	-0.77 – 0.61	0.815
ladder latino zscore	-0.05	-0.08 – -0.01	0.008
binary var	0.02	-0.03 – 0.07	0.415
ladder latino zscore × binary var	0.02	-0.03 – 0.07	0.408
Observations	1599
R² / R² adjusted	0.022 / 0.019

Regression Models Summary - Model 25
	household_financial_
Predictors	Estimates	CI	p
(Intercept)	1.52	0.33 – 2.71	0.013
Age	0.00	-0.00 – 0.00	0.865
Sex [Female]	0.62	-0.56 – 1.81	0.304
Sex [Male]	0.85	-0.34 – 2.03	0.162
ladder latino zscore	0.11	0.05 – 0.17	0.001
binary var	-0.05	-0.13 – 0.03	0.237
ladder latino zscore × binary var	-0.08	-0.16 – 0.01	0.076
Observations	1599
R² / R² adjusted	0.026 / 0.022

Regression Models Summary - Model 26
	economic_conditions_
Predictors	Estimates	CI	p
(Intercept)	1.23	0.16 – 2.30	0.025
Age	0.01	0.01 – 0.01	<0.001
Sex [Female]	0.13	-0.94 – 1.20	0.815
Sex [Male]	0.38	-0.69 – 1.45	0.488
ladder latino zscore	0.09	0.04 – 0.15	0.001
binary var	-0.01	-0.09 – 0.06	0.775
ladder latino zscore × binary var	-0.07	-0.15 – 0.00	0.067
Observations	1599
R² / R² adjusted	0.052 / 0.048

Regression Models Summary - Model 27
	imm_amvalue_
Predictors	Estimates	CI	p
(Intercept)	2.42	0.53 – 4.31	0.012
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	-0.63	-2.51 – 1.26	0.515
Sex [Male]	-0.55	-2.43 – 1.34	0.569
ladder latino zscore	-0.10	-0.19 – -0.00	0.047
binary var	0.15	0.01 – 0.28	0.030
ladder latino zscore × binary var	0.20	0.06 – 0.33	0.004
Observations	1599
R² / R² adjusted	0.023 / 0.019

Regression Models Summary - Model 28
	imm_urban_
Predictors	Estimates	CI	p
(Intercept)	5.53	3.81 – 7.26	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	-2.00	-3.72 – -0.28	0.022
Sex [Male]	-1.93	-3.65 – -0.21	0.027
ladder latino zscore	0.12	0.03 – 0.21	0.007
binary var	-0.04	-0.16 – 0.08	0.527
ladder latino zscore × binary var	-0.15	-0.28 – -0.03	0.013
Observations	1599
R² / R² adjusted	0.040 / 0.036

Regression Models Summary - Model 29
	imm_develop_
Predictors	Estimates	CI	p
(Intercept)	4.57	2.93 – 6.21	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	-1.23	-2.86 – 0.40	0.139
Sex [Male]	-1.15	-2.78 – 0.48	0.166
ladder latino zscore	0.09	0.01 – 0.18	0.028
binary var	-0.08	-0.20 – 0.03	0.154
ladder latino zscore × binary var	-0.17	-0.29 – -0.06	0.003
Observations	1599
R² / R² adjusted	0.036 / 0.033

Regression Models Summary - Model 30
	imm_burden_
Predictors	Estimates	CI	p
(Intercept)	0.36	-1.54 – 2.26	0.709
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.46	-0.43 – 3.35	0.131
Sex [Male]	1.52	-0.37 – 3.41	0.115
ladder latino zscore	-0.11	-0.21 – -0.02	0.020
binary var	0.13	-0.00 – 0.26	0.057
ladder latino zscore × binary var	0.21	0.08 – 0.34	0.002
Observations	1599
R² / R² adjusted	0.030 / 0.027

Regression Models Summary - Model 31
	imm_strength_
Predictors	Estimates	CI	p
(Intercept)	5.47	3.77 – 7.17	<0.001
Age	-0.01	-0.01 – -0.01	<0.001
Sex [Female]	-1.43	-3.12 – 0.26	0.097
Sex [Male]	-1.44	-3.13 – 0.25	0.095
ladder latino zscore	0.10	0.01 – 0.19	0.024
binary var	-0.14	-0.26 – -0.02	0.023
ladder latino zscore × binary var	-0.15	-0.27 – -0.03	0.013
Observations	1599
R² / R² adjusted	0.021 / 0.017

Regression Models Summary - Model 32
	ImmigBilingual
Predictors	Estimates	CI	p
(Intercept)	2.24	0.50 – 3.99	0.012
Age	0.02	0.02 – 0.03	<0.001
Sex [Female]	-0.62	-2.36 – 1.13	0.487
Sex [Male]	-0.41	-2.15 – 1.33	0.645
ladder latino zscore	-0.06	-0.14 – 0.03	0.216
binary var	0.02	-0.10 – 0.14	0.750
ladder latino zscore × binary var	0.11	-0.02 – 0.23	0.094
Observations	1599
R² / R² adjusted	0.066 / 0.063

Regression Models Summary - Model 33
	DACA
Predictors	Estimates	CI	p
(Intercept)	1.29	-0.54 – 3.12	0.166
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.22	-1.61 – 2.04	0.817
Sex [Male]	0.45	-1.37 – 2.28	0.626
ladder latino zscore	-0.01	-0.10 – 0.09	0.913
binary var	0.14	0.01 – 0.27	0.035
ladder latino zscore × binary var	0.16	0.03 – 0.29	0.015
Observations	1598
R² / R² adjusted	0.039 / 0.035

Summary:

-Significance for undoc_imm_2 - Meaning that people who view latinos as higher on the social ladder and see the new latino race question there is a 3.7% increase in the percent of Latinx people they think are undocumented.

-Significace for imm_amvalue - meaning that people who see latinos as higher on the social ladder are more likely to want to close the doors on immigration when shown the new latino race question.

-Significance for imm_urban - For people who see latinos as high on the social ladder, there is a decrease of wanting to promote immigration in urban areas for people who see the Latino race question.

Significance for imm_develop - For those who see latinos as higher on the social ladder there is a lower likelihood in thinking we should increase the number of immigrants for bussiness development for people who see the latino race question.

Significance for imm_burden - people who see the latino race question and think that they are higher on the ladder think immigrants are more of a burden.

Significance for imm_strength - people who see the latino race question and think that they are higher on the ladder think less than immigrants strengthen our country.

Significance for DACA - people who see the latino race question and think that they are higher on the ladder think we should charge DACA students more for college.

Examining interaction of experimental condition and where people think Whites are on the social ladder

Regression Models Summary - Model 35
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	23.04	-7.40 – 53.47	0.138
Age	0.18	0.10 – 0.25	<0.001
Sex [Female]	-2.31	-32.65 – 28.03	0.881
Sex [Male]	-3.99	-34.32 – 26.35	0.797
ladder white zscore	-2.80	-4.36 – -1.23	<0.001
binary var	1.77	-0.37 – 3.91	0.105
ladder white zscore × binary var	2.23	0.08 – 4.38	0.042
Observations	1599
R² / R² adjusted	0.026 / 0.022

Regression Models Summary - Model 36
	preference_white
Predictors	Estimates	CI	p
(Intercept)	3.39	1.91 – 4.86	<0.001
Age	-0.00	-0.00 – 0.00	0.697
Sex [Female]	-0.93	-2.40 – 0.54	0.215
Sex [Male]	-0.92	-2.39 – 0.55	0.219
ladder white zscore	0.13	0.05 – 0.21	0.001
binary var	0.08	-0.02 – 0.19	0.116
ladder white zscore × binary var	-0.05	-0.15 – 0.05	0.336
Observations	1599
R² / R² adjusted	0.013 / 0.009

Regression Models Summary - Model 37
	preference_latino_
Predictors	Estimates	CI	p
(Intercept)	1.98	0.48 – 3.49	0.010
Age	-0.00	-0.01 – 0.00	0.127
Sex [Female]	0.70	-0.80 – 2.19	0.363
Sex [Male]	0.58	-0.92 – 2.08	0.446
ladder white zscore	0.23	0.15 – 0.31	<0.001
binary var	0.03	-0.08 – 0.13	0.643
ladder white zscore × binary var	-0.11	-0.21 – 0.00	0.052
Observations	1599
R² / R² adjusted	0.034 / 0.030

Regression Models Summary - Model 38
	job_threat_
Predictors	Estimates	CI	p
(Intercept)	2.28	0.98 – 3.59	0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	0.20	-1.11 – 1.50	0.768
Sex [Male]	-0.00	-1.30 – 1.30	0.996
ladder white zscore	0.01	-0.06 – 0.07	0.874
binary var	0.07	-0.02 – 0.16	0.134
ladder white zscore × binary var	0.05	-0.04 – 0.15	0.251
Observations	1599
R² / R² adjusted	0.047 / 0.044

Regression Models Summary - Model 39
	crime_threat_
Predictors	Estimates	CI	p
(Intercept)	1.86	0.92 – 2.80	<0.001
Age	0.00	-0.00 – 0.00	0.366
Sex [Female]	-0.47	-1.40 – 0.47	0.329
Sex [Male]	-0.57	-1.50 – 0.37	0.236
ladder white zscore	-0.03	-0.08 – 0.01	0.174
binary var	0.09	0.03 – 0.16	0.006
ladder white zscore × binary var	0.05	-0.01 – 0.12	0.116
Observations	1599
R² / R² adjusted	0.014 / 0.010

Regression Models Summary - Model 40
	household_finance
Predictors	Estimates	CI	p
(Intercept)	1.46	0.77 – 2.15	<0.001
Age	0.00	-0.00 – 0.00	0.266
Sex [Female]	0.05	-0.64 – 0.74	0.878
Sex [Male]	-0.07	-0.76 – 0.62	0.843
ladder white zscore	-0.01	-0.05 – 0.02	0.468
binary var	0.02	-0.03 – 0.07	0.432
ladder white zscore × binary var	-0.00	-0.05 – 0.05	0.910
Observations	1599
R² / R² adjusted	0.018 / 0.014

Regression Models Summary - Model 41
	household_financial_
Predictors	Estimates	CI	p
(Intercept)	1.54	0.35 – 2.74	0.011
Age	0.00	-0.00 – 0.00	0.842
Sex [Female]	0.59	-0.60 – 1.78	0.328
Sex [Male]	0.81	-0.38 – 2.00	0.180
ladder white zscore	0.05	-0.01 – 0.11	0.092
binary var	-0.05	-0.13 – 0.03	0.250
ladder white zscore × binary var	-0.06	-0.14 – 0.03	0.171
Observations	1599
R² / R² adjusted	0.020 / 0.016

Regression Models Summary - Model 42
	economic_conditions_
Predictors	Estimates	CI	p
(Intercept)	1.20	0.13 – 2.26	0.028
Age	0.01	0.01 – 0.01	<0.001
Sex [Female]	0.15	-0.92 – 1.21	0.785
Sex [Male]	0.40	-0.66 – 1.47	0.458
ladder white zscore	0.12	0.06 – 0.17	<0.001
binary var	-0.01	-0.09 – 0.06	0.772
ladder white zscore × binary var	-0.05	-0.13 – 0.03	0.194
Observations	1599
R² / R² adjusted	0.059 / 0.055

Regression Models Summary - Model 43
	imm_amvalue_
Predictors	Estimates	CI	p
(Intercept)	2.57	0.69 – 4.46	0.008
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	-0.76	-2.64 – 1.12	0.428
Sex [Male]	-0.69	-2.57 – 1.19	0.470
ladder white zscore	-0.19	-0.28 – -0.09	<0.001
binary var	0.15	0.02 – 0.28	0.027
ladder white zscore × binary var	0.09	-0.04 – 0.23	0.168
Observations	1599
R² / R² adjusted	0.029 / 0.025

Regression Models Summary - Model 44
	imm_urban_
Predictors	Estimates	CI	p
(Intercept)	5.40	3.70 – 7.10	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	-1.89	-3.59 – -0.20	0.029
Sex [Male]	-1.81	-3.51 – -0.11	0.037
ladder white zscore	0.25	0.17 – 0.34	<0.001
binary var	-0.04	-0.16 – 0.08	0.501
ladder white zscore × binary var	-0.11	-0.23 – 0.01	0.064
Observations	1599
R² / R² adjusted	0.060 / 0.057

Regression Models Summary - Model 45
	imm_develop_
Predictors	Estimates	CI	p
(Intercept)	4.42	2.80 – 6.04	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	-1.11	-2.72 – 0.51	0.181
Sex [Male]	-1.01	-2.63 – 0.60	0.219
ladder white zscore	0.22	0.14 – 0.30	<0.001
binary var	-0.09	-0.20 – 0.03	0.140
ladder white zscore × binary var	-0.11	-0.23 – 0.00	0.053
Observations	1599
R² / R² adjusted	0.051 / 0.048

Regression Models Summary - Model 46
	imm_burden_
Predictors	Estimates	CI	p
(Intercept)	0.51	-1.37 – 2.40	0.593
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.34	-0.54 – 3.22	0.162
Sex [Male]	1.39	-0.49 – 3.27	0.147
ladder white zscore	-0.27	-0.36 – -0.17	<0.001
binary var	0.13	-0.00 – 0.26	0.051
ladder white zscore × binary var	0.19	0.05 – 0.32	0.006
Observations	1599
R² / R² adjusted	0.044 / 0.040

Regression Models Summary - Model 47
	imm_strength_
Predictors	Estimates	CI	p
(Intercept)	5.34	3.66 – 7.03	<0.001
Age	-0.01	-0.01 – -0.00	<0.001
Sex [Female]	-1.33	-3.01 – 0.35	0.122
Sex [Male]	-1.32	-3.00 – 0.36	0.123
ladder white zscore	0.20	0.11 – 0.28	<0.001
binary var	-0.14	-0.26 – -0.02	0.021
ladder white zscore × binary var	-0.09	-0.21 – 0.03	0.157
Observations	1599
R² / R² adjusted	0.033 / 0.030

Regression Models Summary - Model 48
	ImmigBilingual
Predictors	Estimates	CI	p
(Intercept)	2.35	0.61 – 4.08	0.008
Age	0.02	0.02 – 0.03	<0.001
Sex [Female]	-0.69	-2.42 – 1.04	0.432
Sex [Male]	-0.50	-2.23 – 1.23	0.573
ladder white zscore	-0.23	-0.32 – -0.14	<0.001
binary var	0.02	-0.10 – 0.14	0.719
ladder white zscore × binary var	0.15	0.03 – 0.28	0.014
Observations	1599
R² / R² adjusted	0.081 / 0.077

Regression Models Summary - Model 49
	DACA
Predictors	Estimates	CI	p
(Intercept)	1.47	-0.36 – 3.30	0.114
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.06	-1.76 – 1.88	0.946
Sex [Male]	0.29	-1.53 – 2.11	0.757
ladder white zscore	-0.18	-0.28 – -0.09	<0.001
binary var	0.14	0.01 – 0.27	0.031
ladder white zscore × binary var	0.13	0.00 – 0.26	0.049
Observations	1598
R² / R² adjusted	0.041 / 0.038

Summary: significance for undoc_imm_2, imm_burden, immig_bilinguil, and DACA.

Testing two interactions

Regression Models Summary - Model 51
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	21.28	-9.07 – 51.62	0.169
Age	0.18	0.10 – 0.25	<0.001
Sex [Female]	-0.58	-30.84 – 29.68	0.970
Sex [Male]	-2.26	-32.51 – 28.00	0.884
ladder white zscore	-2.60	-4.22 – -0.97	0.002
binary var	1.76	-0.38 – 3.89	0.107
ladder latino zscore	-0.68	-2.29 – 0.92	0.405
ladder white zscore × binary var	1.16	-1.09 – 3.41	0.312
binary var × ladder latino zscore	3.42	1.18 – 5.66	0.003
Observations	1599
R² / R² adjusted	0.034 / 0.029

Regression Models Summary - Model 52
	preference_white
Predictors	Estimates	CI	p
(Intercept)	3.41	1.94 – 4.89	<0.001
Age	-0.00	-0.00 – 0.00	0.689
Sex [Female]	-0.95	-2.42 – 0.52	0.204
Sex [Male]	-0.94	-2.41 – 0.52	0.208
ladder white zscore	0.14	0.06 – 0.22	0.001
binary var	0.08	-0.02 – 0.19	0.113
ladder latino zscore	-0.03	-0.11 – 0.05	0.437
ladder white zscore × binary var	-0.05	-0.16 – 0.06	0.389
binary var × ladder latino zscore	-0.01	-0.12 – 0.10	0.895
Observations	1599
R² / R² adjusted	0.014 / 0.009

Regression Models Summary - Model 53
	preference_latino_
Predictors	Estimates	CI	p
(Intercept)	2.03	0.53 – 3.54	0.008
Age	-0.00	-0.01 – 0.00	0.123
Sex [Female]	0.64	-0.85 – 2.14	0.399
Sex [Male]	0.53	-0.97 – 2.03	0.487
ladder white zscore	0.24	0.16 – 0.32	<0.001
binary var	0.03	-0.08 – 0.13	0.631
ladder latino zscore	-0.02	-0.10 – 0.06	0.643
ladder white zscore × binary var	-0.09	-0.20 – 0.03	0.135
binary var × ladder latino zscore	-0.06	-0.17 – 0.05	0.270
Observations	1599
R² / R² adjusted	0.036 / 0.032

Regression Models Summary - Model 54
	job_threat_
Predictors	Estimates	CI	p
(Intercept)	2.31	1.00 – 3.61	0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	0.17	-1.13 – 1.47	0.797
Sex [Male]	-0.03	-1.33 – 1.27	0.965
ladder white zscore	0.01	-0.06 – 0.08	0.709
binary var	0.07	-0.02 – 0.16	0.130
ladder latino zscore	-0.03	-0.10 – 0.04	0.445
ladder white zscore × binary var	0.06	-0.04 – 0.16	0.233
binary var × ladder latino zscore	-0.01	-0.11 – 0.08	0.785
Observations	1599
R² / R² adjusted	0.048 / 0.043

Regression Models Summary - Model 55
	crime_threat_
Predictors	Estimates	CI	p
(Intercept)	1.83	0.90 – 2.77	<0.001
Age	0.00	-0.00 – 0.00	0.359
Sex [Female]	-0.44	-1.38 – 0.50	0.358
Sex [Male]	-0.54	-1.48 – 0.40	0.259
ladder white zscore	-0.04	-0.09 – 0.01	0.164
binary var	0.09	0.03 – 0.16	0.007
ladder latino zscore	0.01	-0.04 – 0.06	0.754
ladder white zscore × binary var	0.04	-0.03 – 0.11	0.236
binary var × ladder latino zscore	0.03	-0.04 – 0.10	0.333
Observations	1599
R² / R² adjusted	0.015 / 0.010

Regression Models Summary - Model 56
	household_finance
Predictors	Estimates	CI	p
(Intercept)	1.48	0.78 – 2.17	<0.001
Age	0.00	-0.00 – 0.00	0.275
Sex [Female]	0.04	-0.65 – 0.73	0.910
Sex [Male]	-0.09	-0.78 – 0.60	0.808
ladder white zscore	0.00	-0.04 – 0.04	0.967
binary var	0.02	-0.03 – 0.07	0.415
ladder latino zscore	-0.05	-0.08 – -0.01	0.011
ladder white zscore × binary var	-0.01	-0.06 – 0.04	0.725
binary var × ladder latino zscore	0.02	-0.03 – 0.07	0.368
Observations	1599
R² / R² adjusted	0.023 / 0.018

Regression Models Summary - Model 57
	household_financial_
Predictors	Estimates	CI	p
(Intercept)	1.52	0.33 – 2.71	0.012
Age	0.00	-0.00 – 0.00	0.822
Sex [Female]	0.61	-0.57 – 1.80	0.310
Sex [Male]	0.84	-0.35 – 2.03	0.166
ladder white zscore	0.02	-0.04 – 0.09	0.477
binary var	-0.05	-0.13 – 0.03	0.236
ladder latino zscore	0.10	0.04 – 0.16	0.002
ladder white zscore × binary var	-0.04	-0.13 – 0.05	0.363
binary var × ladder latino zscore	-0.06	-0.15 – 0.02	0.158
Observations	1599
R² / R² adjusted	0.026 / 0.022

Regression Models Summary - Model 58
	economic_conditions_
Predictors	Estimates	CI	p
(Intercept)	1.20	0.13 – 2.26	0.028
Age	0.01	0.01 – 0.01	<0.001
Sex [Female]	0.15	-0.92 – 1.21	0.787
Sex [Male]	0.40	-0.66 – 1.47	0.458
ladder white zscore	0.10	0.04 – 0.15	0.001
binary var	-0.01	-0.09 – 0.06	0.759
ladder latino zscore	0.06	0.01 – 0.12	0.026
ladder white zscore × binary var	-0.03	-0.11 – 0.05	0.443
binary var × ladder latino zscore	-0.06	-0.14 – 0.01	0.111
Observations	1599
R² / R² adjusted	0.062 / 0.057

Regression Models Summary - Model 59
	imm_amvalue_
Predictors	Estimates	CI	p
(Intercept)	2.48	0.60 – 4.36	0.010
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	-0.67	-2.55 – 1.21	0.484
Sex [Male]	-0.60	-2.48 – 1.27	0.530
ladder white zscore	-0.17	-0.27 – -0.07	0.001
binary var	0.15	0.02 – 0.28	0.027
ladder latino zscore	-0.05	-0.15 – 0.05	0.348
ladder white zscore × binary var	0.03	-0.11 – 0.17	0.635
binary var × ladder latino zscore	0.19	0.05 – 0.33	0.007
Observations	1599
R² / R² adjusted	0.035 / 0.030

Regression Models Summary - Model 60
	imm_urban_
Predictors	Estimates	CI	p
(Intercept)	5.46	3.76 – 7.16	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	-1.95	-3.65 – -0.25	0.024
Sex [Male]	-1.86	-3.56 – -0.17	0.031
ladder white zscore	0.24	0.15 – 0.33	<0.001
binary var	-0.04	-0.16 – 0.08	0.502
ladder latino zscore	0.05	-0.04 – 0.14	0.255
ladder white zscore × binary var	-0.07	-0.20 – 0.06	0.275
binary var × ladder latino zscore	-0.14	-0.27 – -0.01	0.029
Observations	1599
R² / R² adjusted	0.063 / 0.059

Regression Models Summary - Model 61
	imm_develop_
Predictors	Estimates	CI	p
(Intercept)	4.50	2.88 – 6.12	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	-1.19	-2.80 – 0.43	0.150
Sex [Male]	-1.10	-2.71 – 0.52	0.184
ladder white zscore	0.21	0.12 – 0.30	<0.001
binary var	-0.09	-0.20 – 0.03	0.142
ladder latino zscore	0.03	-0.05 – 0.12	0.453
ladder white zscore × binary var	-0.06	-0.18 – 0.06	0.306
binary var × ladder latino zscore	-0.16	-0.28 – -0.04	0.008
Observations	1599
R² / R² adjusted	0.057 / 0.052

Regression Models Summary - Model 62
	imm_burden_
Predictors	Estimates	CI	p
(Intercept)	0.43	-1.46 – 2.31	0.658
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.43	-0.45 – 3.30	0.136
Sex [Male]	1.48	-0.40 – 3.35	0.123
ladder white zscore	-0.25	-0.36 – -0.15	<0.001
binary var	0.13	-0.00 – 0.26	0.051
ladder latino zscore	-0.04	-0.14 – 0.06	0.413
ladder white zscore × binary var	0.13	-0.01 – 0.27	0.067
binary var × ladder latino zscore	0.18	0.04 – 0.32	0.012
Observations	1599
R² / R² adjusted	0.049 / 0.044

Regression Models Summary - Model 63
	imm_strength_
Predictors	Estimates	CI	p
(Intercept)	5.41	3.72 – 7.09	<0.001
Age	-0.01	-0.01 – -0.00	<0.001
Sex [Female]	-1.39	-3.07 – 0.29	0.105
Sex [Male]	-1.38	-3.06 – 0.30	0.106
ladder white zscore	0.18	0.09 – 0.27	<0.001
binary var	-0.14	-0.26 – -0.02	0.021
ladder latino zscore	0.05	-0.04 – 0.14	0.304
ladder white zscore × binary var	-0.04	-0.17 – 0.08	0.521
binary var × ladder latino zscore	-0.15	-0.27 – -0.02	0.022
Observations	1599
R² / R² adjusted	0.037 / 0.032

Regression Models Summary - Model 64
	ImmigBilingual
Predictors	Estimates	CI	p
(Intercept)	2.30	0.56 – 4.03	0.009
Age	0.02	0.02 – 0.03	<0.001
Sex [Female]	-0.64	-2.37 – 1.09	0.466
Sex [Male]	-0.45	-2.18 – 1.29	0.614
ladder white zscore	-0.23	-0.32 – -0.14	<0.001
binary var	0.02	-0.10 – 0.14	0.728
ladder latino zscore	0.01	-0.08 – 0.10	0.836
ladder white zscore × binary var	0.13	0.00 – 0.26	0.046
binary var × ladder latino zscore	0.07	-0.06 – 0.20	0.275
Observations	1599
R² / R² adjusted	0.083 / 0.078

Regression Models Summary - Model 65
	DACA
Predictors	Estimates	CI	p
(Intercept)	1.35	-0.47 – 3.17	0.146
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.18	-1.63 – 2.00	0.844
Sex [Male]	0.41	-1.40 – 2.22	0.657
ladder white zscore	-0.20	-0.30 – -0.10	<0.001
binary var	0.14	0.01 – 0.27	0.033
ladder latino zscore	0.05	-0.04 – 0.15	0.293
ladder white zscore × binary var	0.08	-0.05 – 0.22	0.225
binary var × ladder latino zscore	0.14	0.01 – 0.28	0.040
Observations	1598
R² / R² adjusted	0.052 / 0.047

Summary:

significance for undoc_imm_2, imm_amvalue, imm_urban, imm_develop, imm_burden_, imm_strength, DACA all with Latino Ladder x binary_var interaction

Creating Aggregate Policy Measures

# Combo should appear in my code but check it. Would be an average of immigrants do this, i want immigrants, reverse scoring in necessary. Same for policies but before averaging policies need to be z scored because they have diff scales
# Assuming you have a data frame `merged_data` containing the variables mentioned in the SPSS code.

# Initializing anti_policy variable
merged_data$anti_policy <- 1

# Updating anti_policy based on conditions
merged_data$anti_policy[merged_data$undoc_policy == 6] <- 4
merged_data$anti_policy[merged_data$undoc_policy == 4] <- NA  # 99 is recoded as missing value (NA)
merged_data$anti_policy[merged_data$undoc_policy == 5] <- 3
merged_data$anti_policy[merged_data$undoc_policy == 3] <- 2

# Recoding ImmigID - Allow immigrants to use an ID issues by a foreign country 
merged_data$ImmigIDr <- merged_data$ImmigID
merged_data$ImmigIDr[merged_data$ImmigID == 1] <- 5 # oppose 
merged_data$ImmigIDr[merged_data$ImmigID == 2] <- 4
merged_data$ImmigIDr[merged_data$ImmigID == 3] <- 3
merged_data$ImmigIDr[merged_data$ImmigID == 4] <- 2
merged_data$ImmigIDr[merged_data$ImmigID == 5] <- 1 # support

# Recoding multiple variables - immigrants today strengthen our country because of their hard work 
merged_data$imm_strength_r <- merged_data$imm_strength_
merged_data$imm_strength_r[merged_data$imm_strength_ == 1] <- 5  # disagree
merged_data$imm_strength_r[merged_data$imm_strength_ == 2] <- 4
merged_data$imm_strength_r[merged_data$imm_strength_ == 3] <- 3
merged_data$imm_strength_r[merged_data$imm_strength_ == 4] <- 2
merged_data$imm_strength_r[merged_data$imm_strength_ == 5] <- 1 # agree

merged_data$imm_develop_r <- merged_data$imm_develop_
merged_data$imm_develop_r[merged_data$imm_develop_ == 1] <- 5 #disagree
merged_data$imm_develop_r[merged_data$imm_develop_ == 2] <- 4
merged_data$imm_develop_r[merged_data$imm_develop_ == 3] <- 3
merged_data$imm_develop_r[merged_data$imm_develop_ == 4] <- 2
merged_data$imm_develop_r[merged_data$imm_develop_ == 5] <- 1 # agree 

merged_data$imm_urban_r <- merged_data$imm_urban_
merged_data$imm_urban_r[merged_data$imm_urban_ == 1] <- 5 #Disagree
merged_data$imm_urban_r[merged_data$imm_urban_ == 2] <- 4
merged_data$imm_urban_r[merged_data$imm_urban_ == 3] <- 3
merged_data$imm_urban_r[merged_data$imm_urban_ == 4] <- 2
merged_data$imm_urban_r[merged_data$imm_urban_ == 5] <- 1 # agree

# Computing anti_imm_att
merged_data$anti_imm_att <- rowMeans(merged_data[, c("imm_strength_r", "imm_burden_", "imm_develop_r", "imm_urban_r", "imm_amvalue_")], na.rm = TRUE)

# Using scale to calculate z-scores
merged_data$zDACA <- scale(merged_data$DACA, center = TRUE, scale = TRUE)
merged_data$zanti_policy <- scale(merged_data$anti_policy, center = TRUE, scale = TRUE)
merged_data$zImmigIDr <- scale(merged_data$ImmigIDr, center = TRUE, scale = TRUE)
merged_data$zImmigBilingual <- scale(merged_data$ImmigBilingual, center = TRUE, scale = TRUE)

# Computing all_anti_policies
merged_data$all_anti_policies <- rowMeans(merged_data[, c("zDACA", "zanti_policy", "zImmigIDr", "zImmigBilingual")], na.rm = TRUE)

anti_imm_att is an aggregate measure of”imm_strength_r”, “imm_burden_”, “imm_develop_r”, “imm_urban_r”, “imm_amvalue_” this variable is coded with 5 being anti immigrant and 1 being pro immigrant

all_anti_policies is an aggregate measure of “zDACA”, “zanti_policy”, “zImmigIDr”, “zImmigBilingual” this variable is coded with 5 being anti immigrant and 1 being pro immigrant

Interaction with anti immigrant attitudes and experimental group

Regression Models Summary - Model 66
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	15.79	-11.19 – 42.78	0.251
Age	0.07	0.00 – 0.13	0.046
Sex [Female]	-12.70	-39.41 – 14.01	0.351
Sex [Male]	-14.23	-40.94 – 12.47	0.296
anti imm att	8.34	7.11 – 9.57	<0.001
binary var	-4.06	-8.98 – 0.86	0.106
anti imm att × binary var	1.75	0.09 – 3.41	0.038
Observations	1599
R² / R² adjusted	0.246 / 0.243

Regression Models Summary - Model 67
	preference_white
Predictors	Estimates	CI	p
(Intercept)	3.66	2.19 – 5.13	<0.001
Age	0.00	-0.00 – 0.00	0.627
Sex [Female]	-0.82	-2.28 – 0.63	0.268
Sex [Male]	-0.82	-2.28 – 0.63	0.268
anti imm att	-0.17	-0.23 – -0.10	<0.001
binary var	0.04	-0.23 – 0.31	0.761
anti imm att × binary var	0.02	-0.07 – 0.11	0.635
Observations	1599
R² / R² adjusted	0.030 / 0.027

Regression Models Summary - Model 68
	preference_latino_
Predictors	Estimates	CI	p
(Intercept)	2.60	1.30 – 3.90	<0.001
Age	0.00	-0.00 – 0.01	0.060
Sex [Female]	1.18	-0.11 – 2.46	0.073
Sex [Male]	1.05	-0.24 – 2.33	0.109
anti imm att	-0.51	-0.56 – -0.45	<0.001
binary var	0.15	-0.09 – 0.38	0.225
anti imm att × binary var	-0.02	-0.10 – 0.06	0.572
Observations	1599
R² / R² adjusted	0.291 / 0.288

Regression Models Summary - Model 69
	job_threat_
Predictors	Estimates	CI	p
(Intercept)	2.35	1.03 – 3.67	<0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	0.16	-1.15 – 1.46	0.814
Sex [Male]	-0.05	-1.35 – 1.26	0.945
anti imm att	-0.01	-0.07 – 0.05	0.699
binary var	0.04	-0.20 – 0.28	0.768
anti imm att × binary var	0.01	-0.07 – 0.09	0.756
Observations	1599
R² / R² adjusted	0.045 / 0.042

Regression Models Summary - Model 70
	crime_threat_
Predictors	Estimates	CI	p
(Intercept)	1.77	0.83 – 2.71	<0.001
Age	0.00	-0.00 – 0.00	0.765
Sex [Female]	-0.54	-1.47 – 0.39	0.254
Sex [Male]	-0.64	-1.57 – 0.29	0.176
anti imm att	0.07	0.03 – 0.12	0.001
binary var	0.12	-0.05 – 0.29	0.164
anti imm att × binary var	-0.01	-0.07 – 0.04	0.647
Observations	1599
R² / R² adjusted	0.025 / 0.021

Regression Models Summary - Model 71
	household_finance
Predictors	Estimates	CI	p
(Intercept)	1.45	0.75 – 2.15	<0.001
Age	0.00	-0.00 – 0.00	0.464
Sex [Female]	0.03	-0.66 – 0.72	0.937
Sex [Male]	-0.09	-0.78 – 0.60	0.788
anti imm att	0.02	-0.01 – 0.05	0.230
binary var	-0.02	-0.15 – 0.11	0.736
anti imm att × binary var	0.01	-0.03 – 0.06	0.522
Observations	1599
R² / R² adjusted	0.021 / 0.017

Regression Models Summary - Model 72
	household_financial_
Predictors	Estimates	CI	p
(Intercept)	1.62	0.42 – 2.82	0.008
Age	0.00	-0.00 – 0.00	0.473
Sex [Female]	0.68	-0.51 – 1.87	0.261
Sex [Male]	0.90	-0.29 – 2.09	0.137
anti imm att	-0.07	-0.13 – -0.02	0.008
binary var	-0.03	-0.25 – 0.18	0.757
anti imm att × binary var	-0.00	-0.08 – 0.07	0.952
Observations	1599
R² / R² adjusted	0.028 / 0.024

Regression Models Summary - Model 73
	economic_conditions_
Predictors	Estimates	CI	p
(Intercept)	1.55	0.55 – 2.55	0.002
Age	0.01	0.01 – 0.01	<0.001
Sex [Female]	0.39	-0.60 – 1.37	0.439
Sex [Male]	0.64	-0.35 – 1.62	0.206
anti imm att	-0.27	-0.32 – -0.23	<0.001
binary var	0.01	-0.17 – 0.19	0.924
anti imm att × binary var	0.00	-0.06 – 0.06	0.903
Observations	1599
R² / R² adjusted	0.193 / 0.190

Regression Models Summary - Model 74
	imm_amvalue_
Predictors	Estimates	CI	p
(Intercept)	1.36	0.34 – 2.38	0.009
Age	-0.00	-0.00 – 0.00	0.246
Sex [Female]	-1.76	-2.77 – -0.75	0.001
Sex [Male]	-1.68	-2.69 – -0.67	0.001
anti imm att	1.02	0.98 – 1.07	<0.001
binary var	0.07	-0.12 – 0.25	0.467
anti imm att × binary var	-0.01	-0.07 – 0.05	0.729
Observations	1599
R² / R² adjusted	0.720 / 0.719

Regression Models Summary - Model 75
	imm_urban_
Predictors	Estimates	CI	p
(Intercept)	6.57	5.82 – 7.32	<0.001
Age	-0.00	-0.00 – -0.00	0.006
Sex [Female]	-0.94	-1.68 – -0.20	0.013
Sex [Male]	-0.87	-1.62 – -0.13	0.021
anti imm att	-0.98	-1.02 – -0.95	<0.001
binary var	0.09	-0.04 – 0.23	0.175
anti imm att × binary var	-0.01	-0.06 – 0.04	0.677
Observations	1599
R² / R² adjusted	0.820 / 0.819

Regression Models Summary - Model 76
	imm_develop_
Predictors	Estimates	CI	p
(Intercept)	5.54	4.76 – 6.32	<0.001
Age	-0.00	-0.00 – -0.00	0.046
Sex [Female]	-0.22	-0.99 – 0.55	0.582
Sex [Male]	-0.14	-0.91 – 0.63	0.727
anti imm att	-0.92	-0.96 – -0.89	<0.001
binary var	-0.02	-0.16 – 0.13	0.827
anti imm att × binary var	0.01	-0.04 – 0.06	0.645
Observations	1599
R² / R² adjusted	0.785 / 0.784

Regression Models Summary - Model 77
	imm_burden_
Predictors	Estimates	CI	p
(Intercept)	-0.80	-1.57 – -0.02	0.045
Age	-0.00	-0.00 – 0.00	0.911
Sex [Female]	0.24	-0.53 – 1.01	0.542
Sex [Male]	0.30	-0.47 – 1.07	0.441
anti imm att	1.11	1.07 – 1.14	<0.001
binary var	0.04	-0.10 – 0.19	0.541
anti imm att × binary var	-0.01	-0.06 – 0.04	0.619
Observations	1599
R² / R² adjusted	0.840 / 0.839

Regression Models Summary - Model 78
	imm_strength_
Predictors	Estimates	CI	p
(Intercept)	6.45	5.72 – 7.18	<0.001
Age	0.00	0.00 – 0.00	0.001
Sex [Female]	-0.36	-1.08 – 0.36	0.326
Sex [Male]	-0.37	-1.09 – 0.35	0.315
anti imm att	-0.96	-1.00 – -0.93	<0.001
binary var	0.03	-0.10 – 0.17	0.611
anti imm att × binary var	-0.02	-0.07 – 0.02	0.281
Observations	1599
R² / R² adjusted	0.823 / 0.822

Regression Models Summary - Model 79
	ImmigBilingual
Predictors	Estimates	CI	p
(Intercept)	1.39	-0.04 – 2.82	0.057
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	-1.28	-2.70 – 0.14	0.077
Sex [Male]	-1.07	-2.49 – 0.34	0.137
anti imm att	0.70	0.63 – 0.76	<0.001
binary var	0.20	-0.06 – 0.46	0.133
anti imm att × binary var	-0.09	-0.18 – -0.00	0.043
Observations	1599
R² / R² adjusted	0.384 / 0.382

Regression Models Summary - Model 80
	DACA
Predictors	Estimates	CI	p
(Intercept)	0.54	-0.87 – 1.95	0.451
Age	0.00	0.00 – 0.01	0.012
Sex [Female]	-0.68	-2.08 – 0.71	0.337
Sex [Male]	-0.45	-1.84 – 0.94	0.526
anti imm att	0.77	0.71 – 0.83	<0.001
binary var	0.13	-0.13 – 0.39	0.327
anti imm att × binary var	-0.03	-0.11 – 0.06	0.561
Observations	1598
R² / R² adjusted	0.439 / 0.437

Summary:

Signficance for undoc_imm_2, immigbilingual

Testing interaction with all_anti_policies and binary_var

Regression Models Summary - Model 81
	undoc_imm__2
Predictors	Estimates	CI	p
(Intercept)	38.96	11.52 – 66.40	0.005
Age	0.02	-0.05 – 0.09	0.578
Sex [Female]	-10.30	-37.62 – 17.03	0.460
Sex [Male]	-13.39	-40.72 – 13.93	0.337
all anti policies	11.68	9.84 – 13.52	<0.001
binary var	1.35	-0.58 – 3.28	0.171
all anti policies × binary var	1.97	-0.50 – 4.44	0.119
Observations	1599
R² / R² adjusted	0.211 / 0.208

Regression Models Summary - Model 82
	preference_white
Predictors	Estimates	CI	p
(Intercept)	3.15	1.70 – 4.61	<0.001
Age	0.00	-0.00 – 0.01	0.226
Sex [Female]	-0.85	-2.30 – 0.61	0.253
Sex [Male]	-0.81	-2.26 – 0.64	0.273
all anti policies	-0.29	-0.38 – -0.19	<0.001
binary var	0.09	-0.01 – 0.20	0.076
all anti policies × binary var	0.06	-0.07 – 0.19	0.392
Observations	1599
R² / R² adjusted	0.037 / 0.033

Regression Models Summary - Model 83
	preference_latino_
Predictors	Estimates	CI	p
(Intercept)	1.10	-0.18 – 2.39	0.092
Age	0.01	0.00 – 0.01	<0.001
Sex [Female]	1.09	-0.19 – 2.37	0.094
Sex [Male]	1.06	-0.22 – 2.34	0.104
all anti policies	-0.77	-0.86 – -0.69	<0.001
binary var	0.05	-0.04 – 0.14	0.257
all anti policies × binary var	-0.02	-0.13 – 0.10	0.743
Observations	1599
R² / R² adjusted	0.299 / 0.296

Regression Models Summary - Model 84
	job_threat_
Predictors	Estimates	CI	p
(Intercept)	2.29	0.99 – 3.60	0.001
Age	-0.01	-0.02 – -0.01	<0.001
Sex [Female]	0.17	-1.14 – 1.47	0.801
Sex [Male]	-0.03	-1.33 – 1.27	0.962
all anti policies	-0.03	-0.12 – 0.06	0.495
binary var	0.07	-0.02 – 0.16	0.128
all anti policies × binary var	0.01	-0.11 – 0.13	0.844
Observations	1599
R² / R² adjusted	0.045 / 0.042

Regression Models Summary - Model 85
	crime_threat_
Predictors	Estimates	CI	p
(Intercept)	1.98	1.04 – 2.91	<0.001
Age	-0.00	-0.00 – 0.00	0.964
Sex [Female]	-0.52	-1.46 – 0.41	0.269
Sex [Male]	-0.64	-1.57 – 0.29	0.179
all anti policies	0.11	0.05 – 0.17	0.001
binary var	0.09	0.02 – 0.15	0.008
all anti policies × binary var	-0.03	-0.11 – 0.06	0.507
Observations	1599
R² / R² adjusted	0.024 / 0.020

Regression Models Summary - Model 86
	household_finance
Predictors	Estimates	CI	p
(Intercept)	1.47	0.77 – 2.16	<0.001
Age	0.00	-0.00 – 0.00	0.361
Sex [Female]	0.05	-0.64 – 0.75	0.879
Sex [Male]	-0.07	-0.76 – 0.62	0.841
all anti policies	0.01	-0.04 – 0.06	0.659
binary var	0.02	-0.03 – 0.07	0.447
all anti policies × binary var	0.01	-0.06 – 0.07	0.866
Observations	1599
R² / R² adjusted	0.017 / 0.013

Regression Models Summary - Model 87
	household_financial_
Predictors	Estimates	CI	p
(Intercept)	1.47	0.28 – 2.67	0.016
Age	0.00	-0.00 – 0.00	0.547
Sex [Female]	0.63	-0.56 – 1.82	0.298
Sex [Male]	0.86	-0.33 – 2.05	0.157
all anti policies	-0.07	-0.15 – 0.01	0.098
binary var	-0.05	-0.13 – 0.04	0.274
all anti policies × binary var	0.01	-0.10 – 0.12	0.831
Observations	1599
R² / R² adjusted	0.021 / 0.017

Regression Models Summary - Model 88
	economic_conditions_
Predictors	Estimates	CI	p
(Intercept)	0.83	-0.18 – 1.84	0.108
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.30	-0.70 – 1.31	0.556
Sex [Male]	0.59	-0.41 – 1.60	0.248
all anti policies	-0.37	-0.43 – -0.30	<0.001
binary var	0.00	-0.07 – 0.07	0.974
all anti policies × binary var	0.03	-0.06 – 0.12	0.474
Observations	1599
R² / R² adjusted	0.158 / 0.155

Regression Models Summary - Model 89
	imm_amvalue_
Predictors	Estimates	CI	p
(Intercept)	3.91	2.45 – 5.37	<0.001
Age	-0.00	-0.01 – 0.00	0.059
Sex [Female]	-1.37	-2.83 – 0.08	0.064
Sex [Male]	-1.44	-2.89 – 0.02	0.053
all anti policies	1.15	1.05 – 1.24	<0.001
binary var	0.11	0.01 – 0.22	0.032
all anti policies × binary var	-0.01	-0.14 – 0.12	0.909
Observations	1599
R² / R² adjusted	0.417 / 0.415

Regression Models Summary - Model 90
	imm_urban_
Predictors	Estimates	CI	p
(Intercept)	4.05	2.84 – 5.26	<0.001
Age	0.00	-0.00 – 0.00	0.962
Sex [Female]	-1.27	-2.48 – -0.07	0.038
Sex [Male]	-1.06	-2.27 – 0.14	0.084
all anti policies	-1.13	-1.22 – -1.05	<0.001
binary var	-0.00	-0.09 – 0.08	0.963
all anti policies × binary var	-0.05	-0.16 – 0.06	0.383
Observations	1599
R² / R² adjusted	0.526 / 0.524

Regression Models Summary - Model 91
	imm_develop_
Predictors	Estimates	CI	p
(Intercept)	3.14	1.97 – 4.30	<0.001
Age	0.00	-0.00 – 0.00	0.631
Sex [Female]	-0.51	-1.67 – 0.65	0.388
Sex [Male]	-0.30	-1.46 – 0.86	0.615
all anti policies	-1.07	-1.15 – -1.00	<0.001
binary var	-0.05	-0.13 – 0.03	0.234
all anti policies × binary var	-0.04	-0.14 – 0.07	0.504
Observations	1599
R² / R² adjusted	0.513 / 0.511

Regression Models Summary - Model 92
	imm_burden_
Predictors	Estimates	CI	p
(Intercept)	2.11	0.85 – 3.36	0.001
Age	-0.00	-0.01 – -0.00	0.017
Sex [Female]	0.60	-0.65 – 1.85	0.348
Sex [Male]	0.49	-0.75 – 1.74	0.439
all anti policies	1.37	1.28 – 1.45	<0.001
binary var	0.09	-0.00 – 0.18	0.052
all anti policies × binary var	-0.03	-0.14 – 0.08	0.620
Observations	1599
R² / R² adjusted	0.578 / 0.577

Regression Models Summary - Model 93
	imm_strength_
Predictors	Estimates	CI	p
(Intercept)	3.97	2.80 – 5.15	<0.001
Age	0.01	0.00 – 0.01	<0.001
Sex [Female]	-0.69	-1.86 – 0.48	0.248
Sex [Male]	-0.56	-1.73 – 0.62	0.352
all anti policies	-1.13	-1.21 – -1.05	<0.001
binary var	-0.10	-0.18 – -0.02	0.016
all anti policies × binary var	-0.05	-0.15 – 0.06	0.368
Observations	1599
R² / R² adjusted	0.531 / 0.529

Regression Models Summary - Model 94
	ImmigBilingual
Predictors	Estimates	CI	p
(Intercept)	3.82	2.69 – 4.94	<0.001
Age	0.01	0.00 – 0.01	<0.001
Sex [Female]	-1.36	-2.48 – -0.24	0.018
Sex [Male]	-1.31	-2.43 – -0.19	0.022
all anti policies	1.32	1.24 – 1.39	<0.001
binary var	-0.02	-0.10 – 0.06	0.633
all anti policies × binary var	-0.10	-0.20 – 0.00	0.054
Observations	1599
R² / R² adjusted	0.614 / 0.613

Regression Models Summary - Model 95
	DACA
Predictors	Estimates	CI	p
(Intercept)	3.13	1.99 – 4.27	<0.001
Age	-0.00	-0.01 – -0.00	0.010
Sex [Female]	-0.73	-1.86 – 0.41	0.209
Sex [Male]	-0.66	-1.79 – 0.47	0.253
all anti policies	1.33	1.26 – 1.41	<0.001
binary var	0.10	0.02 – 0.18	0.017
all anti policies × binary var	0.05	-0.05 – 0.15	0.340
Observations	1598
R² / R² adjusted	0.629 / 0.628

Summary:

No interaction significance

Models testing association between aggreggate measures and experimental condition

Regression Models Summary - Model 104
	all_anti_policies
Predictors	Estimates	CI	p
(Intercept)	-1.24	-2.30 – -0.18	0.022
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.58	-0.47 – 1.64	0.279
Sex [Male]	0.71	-0.35 – 1.76	0.188
binary var	0.03	-0.04 – 0.11	0.417
Observations	1599
R² / R² adjusted	0.063 / 0.060

Regression Models Summary - Model 105
	anti_imm_att
Predictors	Estimates	CI	p
(Intercept)	1.10	-0.48 – 2.67	0.172
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.04	-0.53 – 2.61	0.196
Sex [Male]	1.04	-0.53 – 2.61	0.195
binary var	0.11	-0.00 – 0.22	0.059
Observations	1599
R² / R² adjusted	0.029 / 0.026

Summary:

No significance

Models testing interaction of White/Latino ladder and experimental binary variable

Regression Models Summary - Model 96
	all_anti_policies
Predictors	Estimates	CI	p
(Intercept)	-1.93	-2.67 – -1.19	<0.001
Age	0.01	0.01 – 0.01	<0.001
Sex [Female]	0.46	-0.28 – 1.20	0.220
Sex [Male]	0.57	-0.17 – 1.31	0.133
politics	0.36	0.34 – 0.38	<0.001
ladder latino zscore	-0.07	-0.11 – -0.03	<0.001
binary var	-0.02	-0.07 – 0.04	0.565
ladder latino zscore × binary var	0.06	0.01 – 0.12	0.018
Observations	1599
R² / R² adjusted	0.540 / 0.538

Regression Models Summary - Model 97
	anti_imm_att
Predictors	Estimates	CI	p
(Intercept)	1.04	-0.53 – 2.61	0.195
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.10	-0.47 – 2.67	0.169
Sex [Male]	1.10	-0.47 – 2.67	0.169
ladder latino zscore	-0.10	-0.18 – -0.03	0.010
binary var	0.11	-0.00 – 0.22	0.056
ladder latino zscore × binary var	0.18	0.07 – 0.29	0.002
Observations	1599
R² / R² adjusted	0.035 / 0.031

Regression Models Summary - Model 98
	all_anti_policies
Predictors	Estimates	CI	p
(Intercept)	-1.18	-2.23 – -0.14	0.026
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.55	-0.49 – 1.59	0.300
Sex [Male]	0.67	-0.38 – 1.71	0.210
ladder white zscore	-0.17	-0.22 – -0.12	<0.001
binary var	0.03	-0.04 – 0.11	0.380
ladder white zscore × binary var	0.10	0.03 – 0.18	0.006
Observations	1599
R² / R² adjusted	0.089 / 0.086

Regression Models Summary - Model 99
	anti_imm_att
Predictors	Estimates	CI	p
(Intercept)	1.18	-0.37 – 2.74	0.136
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.98	-0.57 – 2.53	0.216
Sex [Male]	0.97	-0.58 – 2.52	0.221
ladder white zscore	-0.22	-0.30 – -0.14	<0.001
binary var	0.11	0.00 – 0.22	0.050
ladder white zscore × binary var	0.12	0.01 – 0.23	0.035
Observations	1599
R² / R² adjusted	0.051 / 0.048

Summary:

-Significance with all_anti_policies - when people think Latinos or whites are high on the ladder, they are more anti-immigrant when seeing the new latino race question

-Significance with anti_imm_att - when people think Latinos or whites are high on the ladder, they are more anti-immigrant when seeing the new latino race question

Testing both interactions

Regression Models Summary - Model 100
	all_anti_policies
Predictors	Estimates	CI	p
(Intercept)	-1.25	-2.29 – -0.21	0.019
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.61	-0.42 – 1.65	0.246
Sex [Male]	0.73	-0.31 – 1.77	0.167
ladder latino zscore	0.01	-0.05 – 0.06	0.851
binary var	0.03	-0.04 – 0.11	0.391
ladder white zscore	-0.17	-0.23 – -0.12	<0.001
ladder latino zscore × binary var	0.10	0.02 – 0.17	0.013
binary var × ladder white zscore	0.07	-0.00 – 0.15	0.063
Observations	1599
R² / R² adjusted	0.097 / 0.093

Regression Models Summary - Model 101
	anti_imm_att
Predictors	Estimates	CI	p
(Intercept)	1.11	-0.45 – 2.66	0.163
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.06	-0.49 – 2.61	0.182
Sex [Male]	1.04	-0.51 – 2.59	0.187
ladder latino zscore	-0.04	-0.13 – 0.04	0.293
binary var	0.11	-0.00 – 0.22	0.050
ladder white zscore	-0.21	-0.30 – -0.13	<0.001
ladder latino zscore × binary var	0.16	0.05 – 0.28	0.005
binary var × ladder white zscore	0.07	-0.05 – 0.18	0.250
Observations	1599
R² / R² adjusted	0.057 / 0.052

Summary:

all_anti_policies and anti_imm_att interaction with latino ladder is significant, suggesting that whites may feel more threat when latino ethnicity is asked as a race question

Testing ladder difference measure

merged_data <- merged_data %>%
  mutate(ladder_difference_zscore = ladder_white_zscore - ladder_latino_zscore)

model_102 <- lm(all_anti_policies ~ Age + Sex + ladder_difference_zscore*binary_var, data = merged_data)
model_103 <- lm(anti_imm_att ~ Age + Sex + ladder_difference_zscore*binary_var, data = merged_data)

Regression Models Summary - Model 102
	all_anti_policies
Predictors	Estimates	CI	p
(Intercept)	-1.26	-2.30 – -0.21	0.019
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	0.60	-0.44 – 1.65	0.257
Sex [Male]	0.73	-0.32 – 1.77	0.173
ladder difference zscore	-0.09	-0.13 – -0.04	<0.001
binary var	0.03	-0.04 – 0.10	0.423
ladder difference zscore × binary var	-0.01	-0.08 – 0.05	0.669
Observations	1599
R² / R² adjusted	0.083 / 0.080

Regression Models Summary - Model 103
	anti_imm_att
Predictors	Estimates	CI	p
(Intercept)	1.07	-0.49 – 2.64	0.178
Age	0.01	0.01 – 0.02	<0.001
Sex [Female]	1.06	-0.50 – 2.62	0.182
Sex [Male]	1.06	-0.50 – 2.62	0.183
ladder difference zscore	-0.08	-0.15 – -0.01	0.017
binary var	0.11	-0.00 – 0.22	0.059
ladder difference zscore × binary var	-0.05	-0.14 – 0.04	0.290
Observations	1599
R² / R² adjusted	0.041 / 0.038

Summary:

No significance

Overall Summary

Models with no interaction

-Significance for crime threat. Crime threat increases with new census latino race question.

-Significance for Imm_amvalue - with new question people think we should shut the door on immigration more.

-Significance with imm_strength_ - people think that less that immigration strengthens our country.

-Significance for DACA - people who see race question think we should charge DACA students higher tuition.

Interaction with Latino Ladder

-Significance for undoc_imm_2 - Meaning that people who view latinos as higher on the social ladder and see the new latino race question there is a 3.7% increase in the percent of Latinx people they think are undocumented.

-Significance for imm_amvalue - meaning that people who see latinos as higher on the social ladder are more likely to want to close the doors on immigration when shown the new latino race question.

-Significance for imm_urban - For people who see latinos as high on the social ladder, there is a decrease of wanting to promote immigration in urban areas for people who see the Latino race question.

-Significance for imm_develop - For those who see latinos as higher on the social ladder there is a lower likelihood in thinking we should increase the number of immigrants for bussiness development for people who see the latino race question.

-Significance for imm_burden - people who see the latino race question and think that they are higher on the ladder think immigrants are more of a burden.

-Significance for imm_strength - people who see the latino race question and think that they are higher on the ladder think less than immigrants strengthen our country.

-Significance for DACA - people who see the latino race question and think that they are higher on the ladder think we should charge DACA students more for college.

Testing aggregate policy attitudes

-Significance with all_anti_policies - when people think Latinos or whites are high on the ladder, they are more anti-immigrant when seeing the new latino race question

-Significance with anti_imm_att - when people think Latinos or whites are high on the ladder, they are more anti-immigrant when seeing the new latino race question

Latino Race Survey Preliminary Results

Angelita Repetto

2024-07-26

Data Cleaning

Guide to Variables

Baseline Models - testing if our experimental condiiton is assocaited with all relevant outcomes

Testing two interactions

Creating Aggregate Policy Measures

Interaction with anti immigrant attitudes and experimental group

Testing interaction with all_anti_policies and binary_var

Models testing association between aggreggate measures and experimental condition

Models testing interaction of White/Latino ladder and experimental binary variable

Overall Summary