LINAS Analysis: Extreme Immigration Policy
Kelly Zamudio
December 11, 2025
LINAS Project: Deliverable 2
Objective: This project will give the student the opportunity to apply statistical modeling techniques to real world public opinion data. Each student will estimate and more importantly, interpret, a model they proposed in deliverable 1 using the Latino Immigrant National Attitude Survey. The first deliverable was worth 100 points; this second component will be worth 500 points. This RMD file is critical as it contains recodes of a number of independent variables that will be used by some of you. I will update the recoding section of it to accommodate some of the proposed independent variables, but I will not recode all of the proposed independent variables.
You are to use this RMD to file to produce a final HTML that will be submitted on Canvas by Wednesday, December 10 at 11:59 PM. However, there are a series of extra credit incentives in place in order to induce avoidance of turning it in at the last minute. They are:
Should you submit the HTML for part 2 on Canvas by Friday, Dec. 5, 11:59 PM, you will receive an 8% bonus to your grade. Eight percent of 500 is 40 points.
Should you submit the HTML for part 2 on Canvas by Sunday, Dec. 7, 11:59 PM , you will receive a 5% bonus to your grade. Five percent of 500 is 25 points.
Should you submit the HTML for part 2 on Canvas by Tuesday, Dec. 9, 11:59 PM, you will receive a 3% bonus to your grade. Three percent of 500 is 15 points.
Should you submit the HTML for part 2 on Canvas by Thursday, Dec. 11, 11:59 PM, you will receive a 0% bonus to your grade. This will be the final submission time.
On Canvas, there will be 4 portals for submission, one each for these options.
Accessing LINAS 2025 data
The following chunk of code will access the LINAS data.
linas.1="https://raw.githubusercontent.com/mightyjoemoon/LINAS2025/main/linas_may2025_weighted_csv.csv"
linas.1<-read_csv(url(linas.1))
#summary(linas.1)Dependent variables
The following chunks of code will produce the three dependent variables you selected from. Do not alter this code as it may alter the meaning of the scale (or corrupt it). In deliverable 1, you chose one of these dependent variables for your analysis..
Proactive response to enforcement
The proactive response scale is based on questions q8r1 through q8r9. If you are using this measure as the dependent measure, it is your responsibility to assess what the variable is measuring. The name of this variable is proactive_scale.
linas.1$proactive1 <- ifelse(linas.1$q8r1==1, 1, 0)
linas.1$proactive2 <- ifelse(linas.1$q8r2==1, 1, 0)
linas.1$proactive3 <- ifelse(linas.1$q8r3==1, 1, 0)
linas.1$proactive4 <- ifelse(linas.1$q8r4==1, 1, 0)
linas.1$proactive5 <- ifelse(linas.1$q8r5==1, 1, 0)
linas.1$proactive6 <- ifelse(linas.1$q8r6==1, 1, 0)
linas.1$proactive7 <- ifelse(linas.1$q8r7==1, 1, 0)
linas.1$proactive8 <- ifelse(linas.1$q8r8==1, 1, 0)
linas.1$proactive9 <- ifelse(linas.1$q8r9==1, 1, 0)
#linas.1$proactive10 <- ifelse(linas.1$q8r10==1, 1, 0)
#linas.1$proactive11 <- ifelse(linas.1$q8r11==1, 1, 0)
#linas.1$proactive12 <- ifelse(linas.1$q8r12==1, 1, 0)
linas.1$proactive_scale <- (linas.1$proactive1 + linas.1$proactive2 + linas.1$proactive3 + linas.1$proactive4 + linas.1$proactive5 + linas.1$proactive6 + linas.1$proactive7 + linas.1$proactive8 + linas.1$proactive9)
table(linas.1$proactive_scale)##
## 0 1 2 3 4 5 6 7 9
## 413 275 146 90 41 20 10 3 2Support for punitive policies
I’ve created a summative scale based on survey questions q9, q10, and q11. The concept behind the scale is that the higher one scores on the scale, the more supportive one is of extreme immigration-related policies. Given this is an additive scale, you cannot determine the unique relationship between your independent variables and the individual issues underlying the scale; however, you can say a lot about support or opposition to punitive, extreme policies. To construct the scale, the three component items were rescaled to have a 0 point and then rescaled again to make high scores supportive of these policies. The scale thus ranges from 0 (total opposition) to 12 (full support). If you change the code, you may change the meaning of the scale. I advise to not do this. The name of this variable is support_draconian.
linas.1$birthright<- linas.1$q9
linas.1$alienenemy<- linas.1$q10
linas.1$registry<- linas.1$q11
linas.1$support_draconian <- linas.1$birthright + linas.1$alienenemy + linas.1$registry
linas.1$support_draconian<-12-(linas.1$support_draconian-3)
table(linas.1$support_draconian)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 310 69 101 77 71 65 97 44 45 40 26 17 38Avoidance strategy
The avoidance strategy scale is based on questions q4r1 through q4r9. If you are using this measure as the dependent measure, it is your responsibility to assess what the variable is measuring. The name of this variable is avoid_scale.
linas.1$avoid1 <- ifelse(linas.1$q4r1==1, 1, 0)
linas.1$avoid2 <- ifelse(linas.1$q4r2==1, 1, 0)
linas.1$avoid3 <- ifelse(linas.1$q4r3==1, 1, 0)
linas.1$avoid4 <- ifelse(linas.1$q4r4==1, 1, 0)
linas.1$avoid5 <- ifelse(linas.1$q4r5==1, 1, 0)
linas.1$avoid6 <- ifelse(linas.1$q4r6==1, 1, 0)
linas.1$avoid7 <- ifelse(linas.1$q4r7==1, 1, 0)
linas.1$avoid8 <- ifelse(linas.1$q4r8==1, 1, 0)
linas.1$avoid9 <- ifelse(linas.1$q4r9==1, 1, 0)
linas.1$avoid_scale <- (linas.1$avoid1 + linas.1$avoid2 + linas.1$avoid3 + linas.1$avoid4 + linas.1$avoid5 + linas.1$avoid6 + linas.1$avoid7 + linas.1$avoid8 + linas.1$avoid9)
table(linas.1$avoid_scale)##
## 0 1 2 3 4 5 6 7 8 9
## 582 138 81 54 52 39 16 12 11 15Independent variables
Each student is required to analyze the relationship between gender and party affiliation. In the next two chunks, you will see code producing these two variables.
Gender
Gender is a factor-level variable recorded as “female” and “male”. For purposes of statistical analysis, “male” is the baseline category. It is the student’s responsibility to understan what this means. This variable is called gender.
linas.1$gender <- factor(linas.1$s3,
levels=c("1", "2"),
labels=c("Male", "Female"))
table(linas.1$gender)##
## Male Female
## 485 514Party affiliation
Party affiliation is a three-level factor variable recorded as “Republican” for Republicans, “Democrats” for Democrats, and “Ind./Other” for Independent and other identifiers. This factor variable treats partisan “leaners” as partisans. This will be explained in class. The code is a bit lengthy so don’t alter it. To derive the 3-level factor, I first created a variable to identify the leaners. From this I create the variable for party identification; this variable is called pidthree.
##Coding for party: multi levels
linas.1$pid[linas.1$q65==1 & linas.1$q66==1] <- 1
linas.1$pid[linas.1$q65==1 & linas.1$q66==2] <- 2
linas.1$pid[linas.1$q65==3 & linas.1$q67==1] <- 3
linas.1$pid[linas.1$q65==3 & linas.1$q67==3] <- 4
linas.1$pid[linas.1$q65==3 & linas.1$q67==2] <- 5
linas.1$pid[linas.1$q65==2 & linas.1$q66==2] <- 6
linas.1$pid[linas.1$q65==2 & linas.1$q66==1] <- 7
linas.1$pid[linas.1$q65==3 & linas.1$q67==4] <- 8 #Independent leans other
linas.1$pid[linas.1$q65==4 & linas.1$q67==4] <- 9 #Other leans other
linas.1$pid[linas.1$q65==4 & linas.1$q67==1] <- 3 #Other leans Rep
linas.1$pid[linas.1$q65==4 & linas.1$q67==2] <- 5 #Other leans Dem
linas.1$pid[linas.1$q65==4 & linas.1$q67==3] <- 12 #Other leans Independent
## Note that the code below will exclude: Independents who lean "other"; "Other" identifiers who lean "other"; and "Other that leans Independent"
linas.1$pidseven <- factor(linas.1$pid,
levels=c(1,2,3,4,5,6,7),
labels=c("SR", "R", "LR", "I", "LD", "D", "SD"))
## Coding for party: 3 levels. Note that leaners are treated as partisans. Republicans are baseline category
linas.1$pidthree<- factor(linas.1$pid,
levels=c(1,2,3,4,5,6,7, 8, 9, 12),
labels=c("Republican", "Republican", "Republican", "Ind./Other",
"Democrat", "Democrat", "Democrat", "Ind./Other",
"Ind./Other", "Ind./Other"))
table(linas.1$pidthree)##
## Republican Ind./Other Democrat
## 236 293 471Additional independent variables
Apart from gender and party affiliation, each student selected between 2 and 4 additional independent variables. I have precoded many, but not all, of these items. The following chunks of code will produce several variables, some of which you may have selected.
Age (s2)
Age is coded as a three-level factor variable. The name of this variable is *agecat and categorizes age as 18 to 29, 30 to 49, and greater than 49 years of age.
#table(linas.1$s2)
linas.1$agecat <- factor(linas.1$s2,
levels=c("2","3","4","5","6"),
labels=c("<30", "30-49", "30-49", ">49", ">49"))
table(linas.1$agecat)##
## <30 30-49 >49
## 162 482 356Country-of-origin variable (s6)
This chunk codes country-of-origin as: 1) raw factor by country (21 levels); 2) a 7-level factor variable coded for South American, Central America (excluding Northern Triangle), Northern Triangle, Cuba, Dominican Republic, Mexico, and Other and 3) binary coded as Mexico, not Mexico. It is highly advised to not use the 21-level factor. Either use the variable called *coforigin or MexNotMex**
linas.1$countryfactor <- factor(linas.1$s6,
levels=c("1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11", "12", "13", "14", "15", "16",
"17", "19", "20", "21", "22"),
labels=c("Arg", "Bol", "Brz", "Chl", "Col", "CR", "Cuba",
"DR", "Ecu", "ES", "Gua", "Hon", "Mex", "Nic",
"Pan", "Par", "Pru", "Spn", "Uru", "Ven", "Oth"))
linas.1$coforigin <- factor(linas.1$s6,
levels=c("1", "2", "3", "4", "5", "6", "7", "8",
"9", "10", "11", "12", "13", "14", "15", "16",
"17", "19", "20", "21", "22"),
labels=c("SA", "SA", "SA", "SA","SA", "CA", "Cuba", "DR", "SA", "NT", "NT", "NT", "Mexico", "CA", "CA","SA",
"SA", "Other", "SA", "SA", "Other"))
table(linas.1$coforigin)##
## SA CA Cuba DR NT Mexico Other
## 135 40 85 43 105 573 19#Mexico/non-Mexico
linas.1$MexNotMex <- ifelse(linas.1$s6==13, 1, 0)
table(linas.1$MexNotMex)##
## 0 1
## 427 573Education variables (educat)
The precoded variable “educat” is a good way to account for educational differences. This is a three-level factor variable recorded with the labels shown in the chunk below. This variable is called edulevel.
linas.1$edulevel <- factor(linas.1$educat,
levels=c(1,2,3),
labels=c("HS or less", "Some College", "CD and beyond"))
table(linas.1$edulevel)##
## HS or less Some College CD and beyond
## 496 269 235Immigration status (s10)
All respondents in this survey are immigrants but some have differing immigration statuses. I am creating a factor-level variable for s10 giving it value-labels to denote the status. It is the student’s responsibility to understand what these statuses mean and interpret them properly. The name of this variable is status.
linas.1$status <- factor(linas.1$s10,
levels=c("1", "2","3","4","5","6"),
labels=c("Nat", "LPR", "Visa", "Temp", "NOTA", "PNTS"))
table(linas.1$status)##
## Nat LPR Visa Temp NOTA PNTS
## 462 262 55 101 69 51Time in the US variable (q1)
“What year did you first arrive to live in the United States?” is how we measure time in the United States. This variable is based on q1 which is coded 1=2025, 2=2024, 101=1925. If we subtract 1 from this variable, we have an approximation of the number of years spent in the US. The name of this variable is timefrom2025.
#table(linas.1$q1)
linas.1$timefrom2025 <- linas.1$q1-1
table(linas.1$timefrom2025)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## 7 35 43 33 22 19 19 25 17 16 17 13 19 17 14 45 12 12 18 13 17 14 18 24 29 58
## 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## 24 22 10 11 24 20 9 17 14 28 14 17 7 18 17 14 7 8 8 16 5 7 11 9 10 9
## 52 53 54 55 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 75 76 78 88
## 4 7 9 4 1 4 5 4 2 2 3 4 6 1 1 1 2 1 1 1 1 1 1 1 1Existing family in US? (q2)
To record whether or not a migrant had family already in the United States when he/she arrived, I am reverse scoring q2 to create new variable called NoFamilyHere (1 if no family; 0 if family)
linas.1$NoFamilyHere <- ifelse(linas.1$q2==1, 1, 0)
table(linas.1$NoFamilyHere)##
## 0 1
## 711 289Anxiety about deportation (q46 and q47)
Anxiety about deportation are based on Tquestions q46 and 47. I wrote these questions and ideally, they are meant to be used in conjunction with one another. Some students are using 1 or both of them. The variable personal_anxiety records individual anxiety; the variable ff_anxiety records anxiety for family or friends. High scores reflect greater anxiety.
linas.1$personal_anxiety <- 5-linas.1$q46
linas.1$ff_anxiety <- 5-linas.1$q47
table(linas.1$personal_anxiety)##
## 1 2 3 4
## 348 298 234 120table(linas.1$ff_anxiety)##
## 1 2 3 4
## 230 243 340 187Criminality narrative around deportation and detention (q12 and q13)
The criminality narrative questions are based on q12 and q13. These items were split-sampled so we cannot summate them into a scale. We can consider detention and deportation separately but to do so results in loss of half the data. I’ve created a variable called endorse_narrative which pools these responses. Higher scores reflect higher endorsement of the criminality narrative.
linas.1$detain_criminal <- linas.1$q12
linas.1$deport_criminal <- linas.1$q13
#Rescale criminality such that high scores=endorsement
linas.1$endorse_narrative <- (6-linas.1$criminality)
table(linas.1$endorse_narrative)##
## 1 2 3 4 5
## 253 230 221 185 111Meritocratic beliefs (q17)
Meritocratic beliefs are based on the question q17r1 (in the codebook, it’s just listed as q17). This is a 7-point scale where higher scores denote greater endorsement of meritocratic beliefs. This variable is named meritocratic.
linas.1$meritocratic <- linas.1$q17r1
table(linas.1$meritocratic)##
## 1 2 3 4 5 6 7
## 20 13 33 148 167 195 424Immigrant linked fate (q43)
The variable imm_linked_fate is the measure of immigrant linked fate, which was based on q43. Higher scores reflect greater endorsement.
## Coding for immigrant linked fate: baseline category is "Not at all"
#Rescaling to put DKs at midpoint
linas.1$imm_linked_fate[linas.1$q43==1] <- 5 #A lot
linas.1$imm_linked_fate[linas.1$q43==2] <- 4 #Some
linas.1$imm_linked_fate[linas.1$q43==5] <- 3 #Don't know (non-directional response)
linas.1$imm_linked_fate[linas.1$q43==3] <- 2 #Not Much
linas.1$imm_linked_fate[linas.1$q43==4] <- 1 #Not at all
#Rescaling to put the measure on the unit interval
#linas.1$imm_linked_fate_RS<-round((linas.1$imm_linked_fate-1)/4, digits=2)
table(linas.1$imm_linked_fate)##
## 1 2 3 4 5
## 231 210 105 336 118Immigrant identity (q45)
This chunk of code produces immigrant_identity which is based on q45. High scores reflect greater identity.
## Coding for immigrant identity: Baseline is no identity with immigrants
#Leaving scale as is
linas.1$immigrant_identity<-linas.1$q45
table(linas.1$immigrant_identity)##
## 1 2 3 4 5
## 38 43 235 355 329Latino identity (q44)
This chunk of code produces latino_identity which is based on q44. High scores reflect greater identity.
## Coding for Latino identity
#Leaving scale as is
linas.1$latino_identity<-linas.1$q44
table(linas.1$latino_identity)##
## 1 2 3 4 5
## 28 24 152 387 409Denial of anti-immigrant environment (q41)
Denial of the existence of an anti-immigrant environment (q41) is coded as a binary variable where 1=denial of anti-immigrant sentiment and 0 is all other categories. The variable is named denial.
#[q41]: Some people have said that there seems to be a lot of anti-immigrant, and even anti-Hispanic, sentiments, policies, and attitudes surfacing in recent years. Other people have said that no such anti-immigrant environment exists today. How do you feel?
#Values: 1-4
#1 Definitely anti-Hispanic/anti-immigrant environment
#2 Somewhat anti-Hispanic/anti-immigrant environment
#3 No such anti-Hispanic or anti-immigrant environment exists
#4 Don’t know
linas.1$denial <- linas.1$q41
linas.1$denial[linas.1$q41==1] <- 0 #Definitely
linas.1$denial[linas.1$q41==2] <- 0 #Somewhat
linas.1$denial[linas.1$q41==3] <- 1 #None
linas.1$denial[linas.1$q41==4] <- 0 #Don't know
table(linas.1$denial)##
## 0 1
## 862 138Denial of discrimination (q42)
Question q42 measures beliefs about immigrant discrimination. This variable is named discrim. High scores reflect beliefs that discrimination levels are very low (i.e. denial of discrimination).
#Denial of discrimination
#[q42]: How much discrimination is there in the United States today against immigrants?
#Values: 1-5
#1 A lot
#2 Some
#3 Not much
#4 None
#5 Don't know
linas.1$discrim <- linas.1$q42
linas.1$discrim[linas.1$q42==1] <- 1 #Alot
linas.1$discrim[linas.1$q42==2] <- 2 #Some
linas.1$discrim[linas.1$q42==5] <- 3 #DK
linas.1$discrim[linas.1$q42==3] <- 4 #Not much
linas.1$discrim[linas.1$q42==4] <- 5 #None
table(linas.1$discrim)##
## 1 2 3 4 5
## 493 326 53 94 34Contact with Immigration law enforcement (q49)
This variable is contact1 and is binary coded 1 if the respondent had contact with immigration authorities and 0 if not. This is the original q49.
#[q49]: Have you, or anybody in your household, ever been detained or taken into custody by #Immigration and Customs Enforcement (ICE), Border Patrol, or other immigration law enforcement?
#Values: 1-3
#1 Yes, this has happened within the past 12 months
#2 Yes, this has happened, but more than one year ago
#3 No, this has never happened to me or anyone in my household
linas.1$contact1<-factor(linas.1$q49,
levels=c(3,1,2),
labels=c("No", "Yes", "Yes"))
table(linas.1$contact1)##
## No Yes
## 844 156Health outcomes and Trump (q29)
This is a count of the number of health-related items the survey respondent indicated “got worse” since the election of Trump. Thus is ranges from 0 (no adverse outcomes) to 6 (maximal adverse outcomes). The variable is named health_outcomes.
q29: Since Donald Trump was reelected President in November 2024: Values: 1-3 1 Gotten better 2 Stayed the same 3 Gotten worse [q29r1] Has your physical health: [q29r2] Has your mental health: [q29r3] Has the quality of your sleep: [q29r4] Has your ability to complete daily tasks: [q29r5] Has your use of alcohol or drugs: [q29r6] Have your relationships at home:
linas.1$health1 <- ifelse(linas.1$q29r1==3, 1, 0)
linas.1$health2 <- ifelse(linas.1$q29r2==3, 1, 0)
linas.1$health3 <- ifelse(linas.1$q29r3==3, 1, 0)
linas.1$health4 <- ifelse(linas.1$q29r4==3, 1, 0)
linas.1$health5 <- ifelse(linas.1$q29r5==3, 1, 0)
linas.1$health6 <- ifelse(linas.1$q29r5==3, 1, 0)
linas.1$health_outcomes <- linas.1$health1 + linas.1$health2 + linas.1$health3 + linas.1$health4 + linas.1$health5 + linas.1$health6
table(linas.1$health_outcomes)##
## 0 1 2 3 4 5 6
## 602 152 109 61 49 8 19Achieving the American Dream (q15 and q16)
Question q15 asks about the respondent’s perception that he/she can achieve the American Dream. It is coded as a binary variable such that a 1 denotes people who believe the “American Dream” is out of touch for them. The name of this variable is AD1.
[q15]: The term “The American Dream” can mean different things to people. No matter how you define it, do you believe that: Values: 1-4 1 You have achieved the American Dream 2 You are on your way to achieving the American Dream 3 The American Dream is out of reach for you 4 Don’t know
Question q16 asks about the respondent’s perception that others can achieve the American Dream. It is coded as a binary variable such that a 1 denotes people who believe the “American Dream” is either no longer achievable or was never achievable; 0 otherwise. The name of this variable is AD2.
[q16]: The term “The American Dream” can mean different things to people. No matter how you define it, do you think The American Dream: Values: 1-4 1 Is still possible for people to achieve 2 Was once possible for people to achieve, but it is not anymore 3 Was never possible 4 Don’t know
linas.1$AD1 <- ifelse(linas.1$q15==3, 1, 0)
table(linas.1$AD1)##
## 0 1
## 838 162linas.1$AD2 <- ifelse(linas.1$q16==2 | linas.1$q16==3, 1, 0)
table(linas.1$AD2)##
## 0 1
## 612 388Self-assessed phenotype (skin color; q39)
The variable called phenotype is based on self-assess skin tone. It is coded as given in the codebook (see below) and ranges from 1 (Very light) to 5 (Very dark).
Values: 1-5 1 1. Very light 2 2. Light 3 3. Medium 4 4. Dark 5 5. Very dark
linas.1$phenotype <- linas.1$q39
table(linas.1$phenotype)##
## 1 2 3 4 5
## 107 315 492 73 13State political climate (q40)
The variable called state_laws is coded as a binary variable such that 1=beliefs that state laws are unfavorable towards immigrants and 0 otherwise.
[q40]: Thinking about the immigration laws specifically in your state, would you describe [pipe: STATE] policies as favorable or unfavorable towards immigrants today? Values: 1-3 1 Favorable towards immigrants 2 Unfavorable towards immigrants 3 Don’t know
linas.1$state_laws <- ifelse(linas.1$q40==2, 1, 0)
table(linas.1$state_laws)##
## 0 1
## 612 388Avoid deportation (q48)
The variable called get_out is coded 1 if the respondent indicated they think they could not avoid getting deporting were they detained by immigration authorities, and 0 otherwise.
Values: 1-3 1 Yes 2 No 3 Don’t know
linas.1$get_out <- ifelse(linas.1$q48==2, 1, 0)
table(linas.1$get_out)##
## 0 1
## 823 177Know-your-rights (q52)
This question asks about the respondents knowledge of his/her rights were an immigration enforcement office to show up at his/her residence. Below, I have indicated which of the statements are TRUE and FALSE. Respondents who believe these statements to be true are individuals who believe immigration enforcement officers have more rights than they do.
The count of true rights is called KYR_true and the count of false “rights” is KYR_false. The difference is KYR_true-KYR_false. The maximium number of true rights is 8 and the maximum number of false rights is 6.
q52: If any immigration enforcement officers were to visit your home in search of you or someone who lives in your home, based on what you know, which of the following are your legal rights or actions you may take: (Select all that apply) Values: 0-1 0 Unchecked 1 Checked [q52r1] TRUE: You have the right to ask if they are immigration officers and to state their purpose before you let them inside. [q52r2] TRUE: You have the right to speak to an attorney. [q52r3] FALSE: You must answer all questions asked by an officer. [q52r4] TRUE: You have the right to contact your consulate office. [q52r5] TRUE: You have the right to keep your door closed and do not allow them to enter without a signed search warrant. [q52r6] FALSE: You must allow an officer to enter your home. [q52r7] FALSE: You must allow an officer to enter if they have an arrest warrant signed by a judge. [q52r8] FALSE: You must allow an officer to enter your home but they cannot search your home without a search warrant. [q52r9] FALSE: You must allow an officer to enter your home if they show a warrant of removal or deportation. [q52r10] TRUE:If an officer or agent forces their way into your home, you have the right to state “I do not consent to your entry or to your search of these premises.” [q52r11] TRUE: You have the right to remain silent and refuse to answer questions, even if an officer has a warrant. [q52r12] TRUE: You have the right to request that the officer provide you with all documents related to your immigration case. [q52r13] TRUE: You have the right to refuse to sign anything an officer tells you to sign. [q52r14] FALSE You must sign any document that an officer tells you to sign.
linas.1$KYR_true1 <- ifelse(linas.1$q52r1==1, 1, 0)
linas.1$KYR_true2 <- ifelse(linas.1$q52r2==1, 1, 0)
linas.1$KYR_true3 <- ifelse(linas.1$q52r4==1, 1, 0)
linas.1$KYR_true4 <- ifelse(linas.1$q52r5==1, 1, 0)
linas.1$KYR_true5 <- ifelse(linas.1$q52r10==1, 1, 0)
linas.1$KYR_true6 <- ifelse(linas.1$q52r11==1, 1, 0)
linas.1$KYR_true7 <- ifelse(linas.1$q52r12==1, 1, 0)
linas.1$KYR_true8 <- ifelse(linas.1$q52r3==1, 1, 0)
linas.1$KYR_false1 <- ifelse(linas.1$q52r3==1, 1, 0)
linas.1$KYR_false2 <- ifelse(linas.1$q52r6==1, 1, 0)
linas.1$KYR_false3 <- ifelse(linas.1$q52r7==1, 1, 0)
linas.1$KYR_false4 <- ifelse(linas.1$q52r8==1, 1, 0)
linas.1$KYR_false5 <- ifelse(linas.1$q52r9==1, 1, 0)
linas.1$KYR_false6 <- ifelse(linas.1$q52r14==1, 1, 0)
linas.1$KYR_true<-linas.1$KYR_true1 + linas.1$KYR_true2 + linas.1$KYR_true3 + linas.1$KYR_true4 + linas.1$KYR_true5 + linas.1$KYR_true6 + linas.1$KYR_true7 + linas.1$KYR_true8
linas.1$KYR_false<-linas.1$KYR_false1 + linas.1$KYR_false2 + linas.1$KYR_false3 + linas.1$KYR_false4 + linas.1$KYR_false5 + linas.1$KYR_false6
table(linas.1$KYR_true)##
## 0 1 2 3 4 5 6 7 8
## 57 239 168 124 104 84 104 105 15table(linas.1$KYR_false)##
## 0 1 2 3 4 5 6
## 479 270 158 62 20 3 8######INCOME This question asks about the total combined household income in 2024 before reporting taxes.
table(linas.1$q59)##
## 1 2 3 4 5 6 7 8 9 10 11 12 13
## 132 103 83 88 107 56 79 61 48 91 54 16 82linas.1$income_level[linas.1$q59 <= 4] <- "A. Low"
linas.1$income_level[linas.1$q59 >=5 & linas.1$q59 <=9] <- "B. Medium"
linas.1$income_level[linas.1$q59 >9 & linas.1$q59 <=12] <- "C. High"
table(linas.1$income_level)##
## A. Low B. Medium C. High
## 406 351 161ANALYSIS:
Here is where your story begins. You will describe your data and then estimate and interpret a linear regression model.
Part 1: Overview of research and dependent variable
What is your research question and what are the main features of your dependent variable? You should follow my example but use your own data and language. Do not cut and paste what I write; this will lead you down a path you don’t want to go down. This section is worth 100 points.
Research question:
What is the research question you are addressing?
What demographic of Latino immigrants supports punitive immigration policies?
Why should we care?
Why should anyone care about what it is you’re doing?
The current Presidential Administration has had anti-immigrant rhetoric both during and after the 2025 elections. Although there were various reasons as to why the Latino Immigrant population supported the Trump Administration, one of the most prominent issues has been immigration enforcement policies that have turned punitive and extreme. It is also not important to assume that all Latinos have an interest in immigration policy, which is why it is important to see what the population of Latino immigrants supports the implementation of punitive policies.
Characteristics of your dependent variable
What is your dependent variable measuring and what does the distribution of the variable look like? Below is shell code that produces a barplot using a variable called “endorese_narrative.” You will plot your dependent variable obviously.
table(linas.1$latino_identity) ##
## 1 2 3 4 5
## 28 24 152 387 409table(linas.1$support_draconian) ##
## 0 1 2 3 4 5 6 7 8 9 10 11 12
## 310 69 101 77 71 65 97 44 45 40 26 17 38draconian <-ggplot(linas.1, aes(x=support_draconian, y = after_stat(count/sum(count)))) +
geom_bar(fill = "skyblue3") + scale_y_continuous(labels = percent) +
scale_x_continuous(n.breaks = 10) +
labs(title="About 30 percent of the respondents are in total opposition to extreme immigration related-policies",
y="Percent of sample",
x="Level-of-endorsement of extreme immigration related-policies (0=total opposition/12=full support)") +
theme_classic() +
theme(axis.text.x = element_text(size=7, angle=0, hjust=.5),
axis.ticks = element_blank(),
axis.text.y = element_text(size=8),
plot.title = element_text(size=9),
axis.title.y=element_text(size=8),
axis.title.x=element_text(size=8))
draconian 
#About 48% disagree in some form and about 30% agree in some formProvide an interpretation of this plot here
What are the main features of your plot? The barplot above represents the dependent variable, extreme immigration policies. The plot reveals that a majority of Latino immigrants are in total opposition to punitive related-immigration policies using a 13 point scale. Approximately 557 respondents, or 55%, are in total opposition or mostly opposed, as indicated by the 0-4 scale. The plot also reveals that less than five percent of Latino immigrants are in full support of the extreme immigration policies. This brings the question as to who the Latino immigrants are who are in support of punitive immigration policies.
Part 2: Analysis
In this section you will assess the relationship between your dependent variable and your independent variables. This section is worth 500 points.
Independent variables (100 points)
What are your independent variables. Use natural language and not the literal name of the variable you are interpreting (i.e. if you’re using the variable “endorse_narrative”, do not use the language “endorse_narrative” since no one knows what this means; use substantive language.)
The independent variables that I hypothesize are related to the agreement or disagreement of extreme immigration polices are: gender, party affiliation, Latino identity, and income. The variable of gender is coded in a binary anor were we have both males (485) and females (514). The second variable is party affiliation, which is coded in a three-level factor to designate three party identities: Republicans (236), Democrats (471), and Independent/other party identifications (293). The third independent variable that will be used for this analysis is Latino identity, which reflects the agreement or disagreement using a five-point scale from strongly disagree (1) to strongly agree (5) when it comes to the following statement: “The fact that I am Latino is an important part of my identity.” The final independent variable that will be used for this analysis is income, which was re-coded into three categorical levels: low ($69,999 or lower), medium ($70,000-$99,999), and high (higher or equal to 100K). Given that this question was optional, 82 respondents opted to not respond to this question.
As was discussed in my project proposal, deliberble one, I expect the following:
Men will be more likely to be in agreement with extreme immigration policies. As stated by a study by Pew Research, Voting patterns in the 2024 election, “In 2024, Hispanic women and Hispanic men were divided in their preferences for president. In 2020, Hispanic women were more likely than Hispanic men to vote for the Democratic candidate.”
Those who align with the Republican Party will be in agreement with extreme immigration policies compared to other political party alignments. During his 2024 election, Donald Trump made promises to have extreme polices, which many Republican identifying individuals supported, leading him to win the presidency.
Those who strongly identify with the phrase “The fact that I am Latino is an important part of my identity” will be less likely to agree with extreme immigration policies because they might resonate with the impact it will create in their communities, whereas those whose Latino identity is lesser or non-existent. According to research done by Pew Research, “Of the 42.7 million adults with Hispanic ancestry living in the U.S. in 2015, an estimated 5 million people, or 11%, said they do not identify as Hispanic or Latino.”
Those who are below the low-income threshold, $69,000 or lower, will be in agreement with extreme immigration policies. One of the most consistent arguments I have heard is that Trump had “an excellent economy” during his first presidency. Among Trump’s Latino supporters, the economy (93%), violent crime (73%), and immigration (71%) are the three most-cited issues important to their vote. It is essential not to assume that all Hispanics/Latinos are interested in immigration policy. We can see that the economy is one of the most relevant issues for this demographic.
Regression analysis (400 points)
Here is where you will estimate the linear regression model. The code below is based on my worked example; yours will be based on your deliverable 1 proposal.
reg1 <- lm(support_draconian ~ gender + pidthree + latino_identity + income_level , data=linas.1, weights=weight)
summary(reg1)##
## Call:
## lm(formula = support_draconian ~ gender + pidthree + latino_identity +
## income_level, data = linas.1, weights = weight)
##
## Weighted Residuals:
## Min 1Q Median 3Q Max
## -11.9971 -2.0716 -0.6572 2.0067 10.8797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9039 0.5244 16.980 < 0.0000000000000002 ***
## genderFemale -0.2425 0.2059 -1.178 0.23913
## pidthreeInd./Other -2.3316 0.2939 -7.935 0.00000000000000619 ***
## pidthreeDemocrat -3.4220 0.2654 -12.895 < 0.0000000000000002 ***
## latino_identity -0.7324 0.1142 -6.413 0.00000000022890880 ***
## income_levelB. Medium -0.1830 0.2256 -0.811 0.41752
## income_levelC. High 0.9485 0.3099 3.060 0.00228 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.086 on 910 degrees of freedom
## (83 observations deleted due to missingness)
## Multiple R-squared: 0.2389, Adjusted R-squared: 0.2339
## F-statistic: 47.61 on 6 and 910 DF, p-value: < 0.00000000000000022The results of the linear regression model display that there is no gender gap that is associated with the support of extreme immigration related-policies. Those who identify as male have a score that is about .24 points higher than females on a scale of five points. Although there is difference, it is a very small one.
plot_model(reg1, type = "pred",
terms = c("gender"), ci.lvl = .95,
title="There is no gender gap presence when it comes to the support of \nextreme immigration related-policies"
, axis.title=c("Gender", "Predicted level-of-support"), colors=c("skyblue3")) + geom_line(color="skyblue3", linetype=3, linewidth=.4) +
#ylim(0,4) +
theme_classic() +
theme(axis.text.x = element_text(size=10, angle=0, hjust=.5),
axis.ticks = element_blank())
As shown in the plot given above, the prediction of the level-of-support for extreme immigration related-policies is nearly at a similar confidence interval for both males and females. The plot shows that there is no evidence of a gender gap and that that it disproves the hypothesis in regards to Latino males having a higher level-of-support for extreme immigration related policies.
When if comes to party affiliation, the regression results that there is a substantial difference between party alignment. Those with a Democratic identification support extreme immigration related policies 2.33 lower than Republicans on a 13-point scale, which is a two point difference. When it comes to Independent/Other identifiers, they score 3.4 lower than those who identify as Republican, which is a 3 point difference. These results suggest that the presence of the relationship was consistent with our research expectation.
plot_model(reg1, type = "pred",
terms = c("pidthree"), ci.lvl = .95,
title="Republicans endorse extreme immigration related-policies at rates \nsignificantly higher than other party affiliations", axis.title=c("Party affiliation", "Predicted level-of-support"), colors=c("skyblue3")) + geom_line(color="skyblue3", linetype=3, linewidth=.4) +
#ylim(0,4) +
theme_classic() +
theme(axis.text.x = element_text(size=10, angle=0, hjust=.5),
axis.ticks = element_blank())
The figure above shows that there is a significant partisan gap between Republicans and those who are non-Republican identifying. However, the plot also shows that there is no significant difference when it comes to those who align with the Independent/other and Democratic parties.
The next relationship that will be taken into account is Latino identity, the hypothesis that I made was that those who had a higher lever of Latino identification would be less inclined to be in support of extreme immigration related-policies. The regression data results were consistent with my prediction, as it displays that for every one point increase on Latino identification, the support for extreme policy decreases by about .73 points.
plot_model(reg1, type = "pred",
terms = c("latino_identity"), ci.lvl = .95,
title="High Latino identifiers support extreme immigration related-policies \nat rates significantly lower than those with low Latino identification", axis.title=c("Strength-of-identity (1=low identity; 5=high identity)", "Predicted level-of-support"), colors=c("skyblue3")) + geom_line(color="skyblue3", linetype=3, linewidth=.4) +
#ylim(0,4) +
theme_classic() +
theme(axis.text.x = element_text(size=10, angle=0, hjust=.5),
axis.ticks = element_blank())
The downwardly sloping regression line shows that there is an almost full point gap between those who have a high strength of Latino identity and those with low identification.
Finally, I will consider the role of income level. My hypothesis was that those who had a lower income threshold would be in support of extreme immigration related-policies. The regression results are not fully consistent with the expectation. There is a small increase of support for extreme policies of .20 points for low-income individuals, which in a 13 point scale is very small. However, we can see that there is actually a 0.95 point decrease, almost a whole point, for those who are in a high level income bracket compared to those that are middle income that support extreme immigration related-policies. This finding was opposite of what I predicted.
plot_model(reg1, type = "pred",
terms = c("income_level"), ci.lvl = .95,
title="Those with a higher income support extreme immigration related-policies \nat rates significantly higher than those with a medium and low income", axis.title=c("Income Level", "Predicted level-of-support"), colors=c("skyblue3")) + geom_line(color="skyblue3", linetype=3, linewidth=.4) +
#ylim(0,4) +
theme_classic() +
theme(axis.text.x = element_text(size=10, angle=0, hjust=.5),
axis.ticks = element_blank())
The figure shows that there is a significantly large gap between those in a higher income threshold and those who are at a medium or low income threshold. The figure also shows that there is no significant difference between those who identified as medium income and low income.
Overall, the regression model and plots display that there is a strong relationship with party affiliation, Latino identity, and income that seem to have an impact on support or opposition of extreme immigration related-policies. However, gender is not related to support or opposition of punitive immigration policies.