History Embedded in Physical Spaces - Study 3: West Complex and Gilmer

Purpose and Research question

study3_pretest = merge(read.csv('Prescreen and Pretest Data/F24 Pretest.csv'),
                                        read.csv('Prescreen and Pretest Data/F24 Prescreen.csv'), 
                                        by = "computID") %>% 
  mutate(semester = 'Fall 24') %>% 
  mutate(across(matches(c("pretest_UVaHistory_3","pretest_UVaHistory_6","pretest_UVaHistory_8")), ~ 8 - .), #reverse code items 3, 6, 8 in historical continuity scale
         race = as.factor(ifelse(multieth==1, 'Multiracial', #recode race
                            ifelse(black==1, 'Black',
                              ifelse(hispanic==1, 'Latinx',
                                     ifelse(asian==1, 'Asian',
                                            ifelse(mideast==1, 'Middle Eastern',
                                                   ifelse(white==1, 'White', NA)))))))) %>% 
  mutate(pretest_institutionalContinuity = rowMeans(select(.,matches("pretest_UVaHistory")), na.rm=TRUE),
         pretest_institutionalContinuity_cultural = rowMeans(select(.,c('pretest_UVaHistory_1', 'pretest_UVaHistory_2', 'pretest_UVaHistory_3')), na.rm=TRUE),
         pretest_institutionalContinuity_temporal = rowMeans(select(.,c('pretest_UVaHistory_4', 'pretest_UVaHistory_5', 'pretest_UVaHistory_6')), na.rm=TRUE),
         pretest_institutionalContinuity_diversity = rowMeans(select(.,c('pretest_UVaHistory_7', 'pretest_UVaHistory_8', 'pretest_UVaHistory_9')), na.rm=TRUE),
         pretest_ingroupIdentification = rowMeans(select(.,matches("pretest_IngroupID")), na.rm=TRUE)) 

The purpose of this study is to investigate how people interact with knowledge about an institution’s dark history, in particular when they identify with the institution.

More specifically, we expect that learning about their school’s dark history will threaten students’ positive regard about their school, which will motivate them to employ a coping strategy. People can mitigate threats by creating psychological distance between their ingroup and its wrongdoings (see Peetz et al 2010 and Knowles et al 2014). So what will people do when they have to confront those wrongdoings with little room to create physical or temporal distance?

One possibility is that being in close physical proximity to the location of the institution’s wrongdoings (i.e. when they are in the same physical building where wrongdoings took place) will produce greater threat, which people will respond to by perceiving institutional discontinuity. That is, when people don’t have reasonable space to distance themselves from their ingroup’s wrongdoings physically, they will turn to institutional continuity as a way of creating space between their ingroup and their ingroup’s history. This could look like: “My institution is not the same institution it was in the past. My institution has done things since then that make it a new institution.” In turn, this psychological mechanism gives space for people to create temporal distance between their institution and its past. ’

Another possibility is that being in close physical proximity to the location of an institution’s wrongdoings will force people to confront the institution’s dark history. Rather than creating psychological space between the institution and its past, people will perceive the past as being closer and the institution as having a stronger continuous identity that connects its past and present.

Method

We tested this question by confronting participants - all UVA students - with information about UVA’s past as a leader of eugenics research. Participants encountered this information either while sitting in the building where the eugenics research was housed (the West Complex and UVA’s first medical school) or while in a building that was not involved in eugenics research.

Participants then responded to a number of measures to capture their perceptions of UVa’s institutional continuity and the temporal and physical distances between UVa’s past and present.

DVs

• Familiarity:
  • have you heard of this history before? [yes/no/not sure]
  • how familiar were you with this history? [1 = not familiar, 7 = very familiar]
• Physical distance: sum of the standardized scores on two items
  1. Think about the location where eugenics was practiced on UVa grounds. How far away does that feel to you? Use the slider below to indicate how far away where eugenics was practiced on UVa Grounds feels to you currently.
  2. On the below scale, please indicate how far away where eugenics was practiced on UVA Grounds feels to you currently. [1 = very near, 7 = very far]
• Physical continuity [1 = totally disagree, 7 = totally agree]
  1. The university’s aesthetic has endured over time
  2. The University of Virginia has a hallmark buildings and aesthetic has remained constant over time
  3. UVa has changed its architecture throughout history reversed
  4. UVa has changed the way it looks throughout history reversed
• Institutional continuity [1 = totally disagree, 7 = totally agree]
  1. The university’s founding values and worldview have endured over time.
  2. The University of Virginia will always be characterized by specific traditions and beliefs
  3. UVa has changed its traditions and customs throughout history reversed
  4. There is a causal link between different events in UVa history
  5. Major phases in UVa history are linked to one another
  6. There is no connection between past, present, and future events at the university reversed
  7. The value UVa places on diverse people and ideas has been stable over time
  8. The rules about who can enroll as a student are different now than when the university was founded reversed
  9. Institutional messaging about who is welcome at UVa has been continuous throughout its history
• Ingroup identification [1 = not at all, 7 = very much so]
  1. How important is UVa to your own personal identity?
  2. How similar do you feel in attitudes and opinions to other UVa students?
  3. How strongly do you identify as a UVa student?
• Ingroup regard [1 = strongly disagree, 7 = strongly agree]
  1. I am proud to be a UVa student
  2. I am happy to be a UVa student
  3. I feel good about UVa
• Temporal distance
  1. The past may feel quite close or far away, regardless of the amount of time that has actually passed. Think about when UVa’s eugenics practices began. How far away in time does the practice of eugenics at UVA feel to you? Use the slider below to indicate how far away in time the practice of eugenics at UVa feels from the present.
  2. On the below scale, please indicate how far away in time the practice of eugenics at UVa feel to you. [1 = feels like yesterday, 7 = feels very far away]
• Centrality of wrongdoing
  • Consider the history described in the UVa today article about the West Complex that you just read. Compared to other events in UVa’s long history, how important do you feel that this particular history is for understanding the story of UVa as an institution? [0 = this is the least important history for understanding the story of UVA. 100= this is the most important history for understanding the story of UVA]
• Institutional responsibility for historical wrongdoing [1 = totally disagree, 7 = totally agree]
  1. The University, today, is absolutely not responsible for wrongdoings it has committed in the past reversed
  2. The University of Virginia is obligated to atone for wrondgoings it has committed in the past
  3. UVa has a duty to educate current and prospective UVA students about the history of UVa’s involvement in past eugenics practices
  4. UVa has a duty to financially compensate (e.g. offer scholarships, student loan forgiveness) members of the local Black community it historically harmed
  5. UVa is responsible for taking action to ensure that Black Americans and other victim groups of past UVA eugenics practices are compensated for their past suffering
  6. UVa can be trusted to never commit race based, group based, faith based, or identity based wrongdoings again reversed
  7. UVa has atoned for the wrongdoings it has committed in the past reversed
• Familiarity with the West Complex
  • Before this study, how familiar were you with the West Complex building? [i had never been to the West Complex, I have been 1 - 2 times, I have been several times, I regularly work in or attend classes]
• Free response reactions
  • What stood out to you the most about the article you read today?
• Exploratory measure: public regard [1 = strongly disagree, 7 = strongly agree]
  1. Overall, UVa is considered good by others.
  2. In general, others respect UVa.
  3. Most people consider UVa, on average, to be less respected than other schools. reversed
  4. UVa is not respected by the broader society. reversed
  5. In general, people view UVa in a positive manner.
  6. Society views UVa as an asset.
• Core affect: How much do you feel ___ at this time? [1 = not at all, 5 = very much so]
  1. Excited
  2. Happy
  3. Calm
  4. Relaxed
  5. Depressed
  6. Lethargic
  7. Tense
  8. Nervous
  9. Disgusted

Procedure

Participants were recruited from the psychology department participant pool beginning on 11/12. We only recruited participants who identified as not being Black. We also screened for participants who had completed the insitutional continuity and ingroup identificaiton scales on the psychology department’s pretest at the beginning of the semester. The pretest scores on the continuity measure will be used to analyze pre-post differences between conditions.

Participants signed up for time slots themselves but did not know the location of the time slot ahead of time. The morning before their time slot, they were emailed the location of their session, which was either the West Complex (for those in the proximal condition) or Gilmer (for those in the distal condition). The emails included pictures of the building they were participating in, which served to give all participants imagery of the building as being identical to the building they view in the historical account for participants in the West Complex, or very different from the building in the article for participants in Gilmer.

Participants arrived at their session location at the time of their scheduled time slot and were directed to a single testing room to complete a Qualtrics survey on a computer. As much as 6 participants participated at a time. They read a summary of a UVA Today article then responded to the measures listed above.

Analyses

Preliminary analyses

Difference scores: ingroup identification and institutional continuity (3 subscales) changes across time. (Plot by condition)

Check for differences by year

Correlation between physical, temporal distance, & everything

• make one for white participants, one for non-white participants

Main analyses

institutional continuity

• linear regression: institutional continuity predicted by condition + pre-test score

temporal distance

• t-test: temporal distance (composite score?) predicted by condition

institutional repair

• t-test: institutional repair (composite score) predicted by condition

  • (higher impressions of institutional repair should represent perceptions of institutional discontinuity)

Exploratory analyses

Ingroup identification as a moderator

institutional continuity moderated by ingroup pretest score

• linear regression: institutional continuity predicted by condition + pre-test continuity score * pre-test ingroup identification

temporal distance moderated by ingroup pretest score

• linear regression: temporal distance predicted by condition * pre-test ingroup identification

institutional repair moderated by ingroup pretest score

• linear regression: institutional repair predicted by condition * pre-test ingroup identification

Geospatial distance as a manip check or continuous measure of assigned condition

First, look at differences in perceptions of geospatial distance by condition. Should be obviously different. Then, the following analyses:

institutional continuity

• linear regression: institutional continuity predicted by geospatial distance + pre-test continuity score

temporal distance

• linear regression: temporal distance predicted by geospatial distance

institutional repair

• linear regression: institutional repair predicted by geospatial distance

Familiarity with history

• does familiarity with history predict

Conceptual distinction of DVs

• Perform PCA on the items from the three main DVs (inst. continuity, temporal distance, institutional repair) to explore potential for combining under one to two umbrella concepts

Do primary analyses, then use the familiarity and affect measures as robustness checks

Results

study3_raw = read.csv('study 3 - in lab study/study3.csv', stringsAsFactors = TRUE) %>% 
  mutate(across(c("physicalContinuity_3","physicalContinuity_4", #reverse score relevant items
                  "institutionContinuit_3", "institutionContinuit_6", "institutionContinuit_8",
                  'institutionRepair_1', 'institutionRepair_6', 'institutionRepair_7',
                  'Public_Regard_3', 'Public_Regard_4'), ~ 8 - .)) %>%  
  mutate(physicalDistance = (as.vector(scale(spatialDist1_1)) + as.vector(scale(spatialDist2))/2), #
         physicalContinuity = rowMeans(select(.,matches("physicalContinuity")), na.rm=TRUE),
         institutionalContinuity = rowMeans(select(.,matches("institutionContinuit")), na.rm=TRUE),
         institutionalContinuity_cultural = rowMeans(select(.,c('institutionContinuit_1', 'institutionContinuit_2', 'institutionContinuit_3')), na.rm=TRUE),
         institutionalContinuity_temporal = rowMeans(select(.,c('institutionContinuit_4', 'institutionContinuit_5', 'institutionContinuit_6')), na.rm=TRUE),
         institutionalContinuity_diversity = rowMeans(select(.,c('institutionContinuit_7', 'institutionContinuit_8', 'institutionContinuit_9')), na.rm=TRUE),
         ingroupIdentification = rowMeans(select(.,matches("ingroupIdenti")), na.rm=TRUE),
         ingroupRegard = rowMeans(select(.,matches("ingroupUVA_regard")), na.rm=TRUE),
         temporalDistance = (as.vector(scale(TempDist1_1)) + as.vector(scale(TempDist2))/2),
         institutionalResponsibility = rowMeans(select(.,matches("institutionRepair"))),
         institutionalResponsibility_culpability = rowMeans(select(.,c('institutionRepair_1', 'institutionRepair_2', 'institutionRepair_3','institutionRepair_4','institutionRepair_5')), na.rm=TRUE),
         institutionalResponsibility_distrust = rowMeans(select(.,c('institutionRepair_6', 'institutionRepair_7')), na.rm=TRUE),
         publicRegard = rowMeans(select(.,matches("Public_Regard")), na.rm=TRUE),
         condition = factor(condition, labels=c('Distant','Proximate')))

study3 = merge(study3_raw %>% select(c('computID','condition','centralityOfWrongdoing','physicalDistance':ncol(study3_raw))),
               study3_pretest %>% select(c('computID','race':ncol(study3_pretest))), by='computID')

Participants

8 participants opened the pretest more than once, resulting in duplicate values. Those duplicates were resolved in the excel file by deleting the redundant rows. In most cases, one of the redundant rows was empty. In some cases (kvr2ze & rzq9wc & Ukm5vj) there were values in both submissions. We need to decide how to handle these two cases’ pretest data (either delete one submission or average the two submissions together). One person mistyped their computing ID (cr6zur should be crz6ur). I verified this by checking against the pretest and then checking Sona for their timeslot. I fixed it in the Excel datafile “study3.csv”.

Data decisions:

• kvr2ze: had 3 total rows. one was blank. one submission was opened at 11:20AM but had 3 missing values. the last submission was opened at 12:18PM and had no missing values. I kept the latest submission and deleted the other two.

• rzq9wc: two total rows, only one had no missing values.

• Ukm5vj: three total rows, only one had no missing values.

We collected a total of 216 responses

Power: N = 216 (n = 121 and n = 95) affords us 80% power to detect an effect size of at least d = 0.39 in a two-tailed t-test.

nice_table(study3 %>% group_by(condition)  %>%  count(), title = 'N per condition')
nice_table(study3 %>% group_by(race)  %>%  count(), title = 'Participant Race') %>%fontsize(size=30, part='body') %>% fontsize(size=30, part='header')
nice_table(study3_raw %>% group_by(familiarWest)  %>%  count(), title = "Participants' familiarity with the West complex")
nice_table(study3_raw %>% group_by(heardBefore)  %>%  count(), title = "Participants' familiarity with the history of the West Complex")

N per condition
condition	n
Distant	121
Proximate	95

Participant Race
race	n
Asian	21
Latinx	5
Middle Eastern	5
Multiracial	25
White	160

Participants' familiarity with the West complex
familiarWest	n
	84
been once or twice	40
been several times	6
never been	84
regularly go to the West Complex	3

Participants' familiarity with the history of the West Complex
heardBefore	n
no	129
not sure	11
yes	77

Preliminary Analyses

ggcorrplot(round(cor(study3 %>% select(-condition,-computID,-race), use='complete.obs'), 3),
           hc.order = TRUE, type = "upper", outline.color = "white", ggtheme = ggplot2::theme_gray, colors = c("#E46726", "white","#6D9EC1" ), lab=FALSE, digits=2)

Main analyses

Institutional continuity

#institutional continuity pre v post 

institutionalContinuityplot = study3 %>% select(condition, matches('institutionalContinuity')) %>% select(1,2,6) %>% rename(pre = 'pretest_institutionalContinuity', post = 'institutionalContinuity') %>% mutate(PID = 1:nrow(.)) %>% 
pivot_longer(., 2:3, names_to = "time", values_to="institutionalContinuity") %>% mutate(time = factor(time, levels=c('pre', 'post'))) %>% 
  ggerrorplot(x = 'time', y = 'institutionalContinuity', color='condition',  alpha=0.25) +
  geom_line(alpha=0.15, aes(group = PID, color=condition)) +
  labs(color='Condition', x = ' ',y = "Institutional Continuity")+
  facet_wrap(~condition)
ggsave(institutionalContinuityplot, filename='SPSSI talk/study2_continuity.png', width = 8, height = 6, dpi=300)

nice_table(tidy(lm(data=study3, institutionalContinuity~condition+pretest_institutionalContinuity)), title='institutionalContinuity ~ condition + pretest')
#mixed effects model
colnames(study3)
insitutionalCont_long = study3 %>% 
  select(computID, condition, institutionalContinuity, pretest_institutionalContinuity) %>% 
  pivot_longer(
    cols = c(pretest_institutionalContinuity, institutionalContinuity),
    names_to = "time",
    values_to = "institutionalContinuity") %>% 
  mutate(time = case_when(
      time == "pretest_institutionalContinuity" ~ "pre",
      time == "institutionalContinuity" ~ "post"),
    time = factor(time, levels = c("pre", "post")))

nice_table(tidy(lmer(data = insitutionalCont_long, institutionalContinuity ~ time * condition + (1 | computID))) %>% filter(effect=='fixed') %>% select(-effect, -group, -statistic), title='Mixed effects model: Institutional Continuity ~ Time * Condition')

institutionalContinuity ~ condition + pretest
Term	estimate	std.error	statistic	p
(Intercept)	2.40	0.27	8.99	< .001***
conditionProximate	-0.03	0.07	-0.39	.700
pretest_institutionalContinuity	0.29	0.07	4.43	< .001***

 [1] "computID"                                 
 [2] "condition"                                
 [3] "centralityOfWrongdoing"                   
 [4] "physicalDistance"                         
 [5] "physicalContinuity"                       
 [6] "institutionalContinuity"                  
 [7] "institutionalContinuity_cultural"         
 [8] "institutionalContinuity_temporal"         
 [9] "institutionalContinuity_diversity"        
[10] "ingroupIdentification"                    
[11] "ingroupRegard"                            
[12] "temporalDistance"                         
[13] "institutionalResponsibility"              
[14] "institutionalResponsibility_culpability"  
[15] "institutionalResponsibility_distrust"     
[16] "publicRegard"                             
[17] "race"                                     
[18] "pretest_institutionalContinuity"          
[19] "pretest_institutionalContinuity_cultural" 
[20] "pretest_institutionalContinuity_temporal" 
[21] "pretest_institutionalContinuity_diversity"
[22] "pretest_ingroupIdentification"            

Mixed effects model: Institutional Continuity ~ Time * Condition
Term	estimate	std.error	df	p
(Intercept)	3.96	0.05	394.72	< .001***
timepost	-0.40	0.06	214.00	< .001***
conditionProximate	0.05	0.08	394.72	.531
timepost × conditionProximate	-0.06	0.09	214.00	.494

Temporal distance

#temporal distance 

temporalDistance_plot = ggerrorplot(study3,x = 'condition', y = 'temporalDistance', color='condition', ylim=c(-1, 1))+
  labs(y='Temporal Distance', x='')


ggsave(temporalDistance_plot, filename='SPSSI talk/study2_temporalDistance.png', width = 4, height = 6, dpi=300)

nice_table(study3 %>% 
  t_test(temporalDistance ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(study3, temporalDistance~condition, paired=FALSE)) %>%  
  select(statistic, df, p, effsize),
  title="t test: temporal distance predicted by condition")

t test: temporal distance predicted by condition
statistic	df	p	effsize
0.09	214	.931	0.01

Institutional Responsibility for Historical Wrongdoings

#institutional responsibility
ggerrorplot(study3,x = 'condition', y = 'institutionalResponsibility', color='condition', ylim=c(4, 5))
nice_table(study3 %>% 
  t_test(institutionalResponsibility ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(study3, institutionalResponsibility~condition, paired=FALSE))%>%  
  select(statistic, df, p, effsize),
  title="t test: institutional responsibility predicted by condition")

t test: institutional responsibility predicted by condition
statistic	df	p	effsize
1.06	214	.289	0.15

Centrality of Wrongdoing

#centrality of wrongdoing
ggerrorplot(study3,x = 'condition', y = 'centralityOfWrongdoing', color='condition', ylim=c(50, 100))
nice_table(study3 %>% 
  t_test(centralityOfWrongdoing ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(study3, centralityOfWrongdoing~condition, paired=FALSE))%>%  
  select(statistic, df, p, effsize),
  title="t test: icentralityOfWrongdoing predicted by condition")

t test: icentralityOfWrongdoing predicted by condition
statistic	df	p	effsize
-0.39	214	.698	-0.05

Exploratory Analyses

PCA on institutional responsibility

*note that reverse scored items have been reversed

institutionalResponsibilityPCA = select(study3_raw, starts_with("institutionRepair")) %>% 
  rename_with(~gsub('institutionRepair','responsible',.x)) %>% 
  filter(!if_any(everything(), is.na)) %>% 
  princomp(.)

institutionalResponsibilityPCA$loadings

Loadings:
              Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7
responsible_1  0.417  0.302  0.730  0.399  0.180         0.102
responsible_2  0.397         0.177 -0.618         0.511 -0.394
responsible_3  0.275         0.109 -0.578 -0.176 -0.501  0.540
responsible_4  0.544 -0.252 -0.420  0.278         0.389  0.481
responsible_5  0.515 -0.239 -0.213  0.169        -0.573 -0.522
responsible_6  0.140  0.794 -0.447         0.374              
responsible_7  0.107  0.379         0.110 -0.888        -0.192

               Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7
SS loadings     1.000  1.000  1.000  1.000  1.000  1.000  1.000
Proportion Var  0.143  0.143  0.143  0.143  0.143  0.143  0.143
Cumulative Var  0.143  0.286  0.429  0.571  0.714  0.857  1.000

biplot(institutionalResponsibilityPCA, col=c('white','red'))

plot(institutionalResponsibilityPCA, type='l')

cat('MEANS and SEs BY ITEM')

MEANS and SEs BY ITEM

institutionalRespLong = study3_raw %>% select(starts_with("institutionRepair"),condition) %>% 
  rename_with(~gsub('institutionRepair','responsible',.x)) %>% 
  pivot_longer(starts_with("responsible"),names_to='item') 

ggerrorplot(institutionalRespLong, x = 'item', y = 'value', color='item')+
  geom_jitter(aes(color=item), alpha=0.15)+
  theme(legend.position = 'none', axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
  facet_wrap('condition')

nice_table(institutionalRespLong %>% group_by(item) %>%  
  t_test(value ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(institutionalRespLong %>% group_by(item), value~condition, paired=FALSE))%>%  
  select(item, statistic, df, p, effsize),
  title="t tests: items by condition")

t tests: items by condition
item	statistic	df	p	effsize
responsible_1	0.79	215	.432	0.11
responsible_2	-0.05	215	.962	-0.01
responsible_3	0.91	215	.364	0.13
responsible_4	0.16	215	.873	0.02
responsible_5	0.14	215	.888	0.02
responsible_6	1.39	215	.166	0.19
responsible_7	1.17	215	.243	0.16

Given PCA results, I decided to create two sub scales of institutional responsibility:

institutional culpability for historical wrongdoings:

1. The University, today, is absolutely not responsible for wrongdoings it has committed in the past reversed
2. The University of Virginia is obligated to atone for wrondgoings it has committed in the past
3. UVa has a duty to educate current and prospective UVA students about the history of UVa’s involvement in past eugenics practices
4. UVa has a duty to financially compensate (e.g. offer scholarships, student loan forgiveness) members of the local Black community it historically harmed
5. UVa is responsible for taking action to ensure that Black Americans and other victim groups of past UVA eugenics practices are compensated for their past suffering

Institutional distrust:

6. UVa can be trusted to never commit race based, group based, faith based, or identity based wrongdoings again reversed
7. UVa has atoned for the wrongdoings it has committed in the past reversed

institutionalResp_subscales = study3 %>% select(starts_with("institutionalResponsibility_"),condition) %>% 
  rename_with(~gsub('institutionalResponsibility_','',.x)) %>% 
  pivot_longer(1:2,names_to='subscale') 

ggerrorplot(institutionalResp_subscales, x = 'subscale',y='value', color='condition', ylim=c(3,6), ylab="",xlab="")
nice_table(institutionalResp_subscales %>% group_by(subscale) %>% 
  t_test(value ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(institutionalResp_subscales %>% group_by(subscale), value~condition, paired=FALSE)) %>%  select(subscale, statistic, df, p, effsize))

subscale	statistic	df	p	effsize
culpability	0.60	214	.551	0.08
distrust	1.58	214	.116	0.22

Physical Continuity

#physical continuity
ggerrorplot(study3,x = 'condition', y = 'physicalContinuity', color='condition', ylim=c(4, 6))
nice_table(study3 %>% 
  t_test(physicalContinuity ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(study3, physicalContinuity~condition, paired=FALSE))%>%  
  select(statistic, df, p, effsize),
  title="t test: physical continuity predicted by condition")

t test: physical continuity predicted by condition
statistic	df	p	effsize
-1.56	214	.121	-0.22

Geospatial distance

#geospatial distance

physicalDistance_plot = ggerrorplot(study3,x = 'condition', y = 'physicalDistance', color='condition', ylim=c(-1, 1))+
  labs(x = '',y='Physical Distance')

ggsave(physicalDistance_plot, filename='SPSSI talk/study2_physicallDistancey.png', width = 4, height = 6, dpi=300)

nice_table(study3 %>% 
  t_test(physicalDistance ~ condition, paired = FALSE, var.equal=TRUE) %>% mutate(p = round(p, 3)) %>% 
  inner_join(cohens_d(study3, physicalDistance~condition, paired=FALSE))%>%  
  select(statistic, df, p, effsize),
  title="t test: geospatial distance predicted by condition")
library('interactions')
interaction_model = lm(temporalDistance~condition*physicalDistance, data=study3)
interact_plot(interaction_model, pred = 'physicalDistance', modx='condition', interval=TRUE, plot.points=TRUE)
nice_table(tidy(lm(temporalDistance~physicalDistance*condition, data=study3))) #0.23, p = 0.005

t test: geospatial distance predicted by condition
statistic	df	p	effsize
3.11	214	.002**	0.42

Term	estimate	std.error	statistic	p
(Intercept)	-0.11	0.13	-0.80	.423
physicalDistance	0.42	0.09	4.43	< .001***
conditionProximate	0.11	0.20	0.53	.598
physicalDistance × conditionProximate	-0.38	0.13	-2.79	.006**

Ingroup Regard

#temporal distance ~ingroup regard * condition
#physical distance ~ingroup regard * condition

Subscales of institutional continuity

study3 %>% select(condition, matches('institutionalContinuity_')) %>% 
  mutate(PID = 1:nrow(.)) %>% 
  pivot_longer(., -c(1,8), 
               names_to = c("time", "variable"),
               names_pattern="(.*)_(.*)") %>% 
  mutate(time = factor(time, labels=c('pre', 'post'), levels=c('pretest_institutionalContinuity','institutionalContinuity'))) %>% 
  ggerrorplot(x = 'time', y = 'value', color='condition',  alpha=0.25, position=position_dodge(0)) +
  geom_line(alpha=0.15, aes(group = PID, color=condition)) +
  facet_wrap(~variable)
nice_table(as.data.frame(
  summary(lm(data=study3, institutionalContinuity_cultural~condition+pretest_institutionalContinuity_cultural))$coefficients) %>% 
    slice(-1)%>% cbind(row.names(.), .) %>% rename(term = 'row.names(.)'),
  title='CULTURAL subscale of institutional continuity ~ condition + pretest')

CULTURAL subscale of institutional continuity ~ condition + pretest
Term	Estimate	Std. Error	t value	Pr(>|t|)
conditionProximate	-0.12	0.11	-1.05	0.29
pretest_institutionalContinuity_cultural	0.30	0.06	4.55	0.00

Ingroup identification

Does ingroup id change as a result of the manipulation? i.e. test for pre v post changes in ingroup id

study3 %>% select(condition, contains('ingroupIdentification')) %>% rename(pre = 'pretest_ingroupIdentification', post = 'ingroupIdentification') %>% mutate(PID = 1:nrow(.)) %>% 
pivot_longer(., 2:3, names_to = "time", values_to="ingroupIdentification") %>% mutate(time = factor(time, levels=c('pre', 'post'))) %>% 
  ggerrorplot(x = 'time', y = 'ingroupIdentification', color='condition',  alpha=0.25) +
  geom_line(alpha=0.15, aes(group = PID, color=condition)) +
  facet_wrap(~condition)
nice_table(as.data.frame(
  summary(lm(data=study3, ingroupIdentification~condition+pretest_ingroupIdentification))$coefficients) %>% 
    slice(-1)%>% cbind(row.names(.), .) %>% rename(term = 'row.names(.)'),
  title='ingroupIdentification ~ condition + pretest')

ingroupIdentification ~ condition + pretest
Term	Estimate	Std. Error	t value	Pr(>|t|)
conditionProximate	-0.11	0.10	-1.14	0.25
pretest_ingroupIdentification	0.65	0.05	13.62	0.00

Does ingroup id moderate any of the main effects of interest? For these tests, I use pretest ingroup ID scores

Institutional continuity

for institutional continuity, I calculated difference score between pre, post and plugged that into a regression

study3 = study3 %>% mutate(institutionalContinuity_diffScore = institutionalContinuity-pretest_institutionalContinuity)

ggscatter(study3, x='pretest_ingroupIdentification', y = 'institutionalContinuity_diffScore', color='condition', position='jitter',add = 'reg.line')
nice_table(as.data.frame(
  summary(lm(data=study3, institutionalContinuity_diffScore~condition*pretest_ingroupIdentification))$coefficients) %>% 
    slice(-1)%>% cbind(row.names(.), .) %>% rename(term = 'row.names(.)'),
  title='institutionalContinuity difference score ~ condition * pretest ingroup identification')

institutionalContinuity difference score ~ condition * pretest ingroup identification
Term	Estimate	Std. Error	t value	Pr(>|t|)
conditionProximate	0.00	0.41	0.01	0.99
pretest_ingroupIdentification	-0.06	0.06	-1.03	0.30
conditionProximate × pretest_ingroupIdentification	-0.01	0.09	-0.15	0.88

Temporal distance

ggscatter(study3, x='pretest_ingroupIdentification', y = 'temporalDistance', color='condition', position='jitter',add = 'reg.line')
nice_table(as.data.frame(
  summary(lm(data=study3, temporalDistance~condition*pretest_ingroupIdentification))$coefficients) %>% 
    slice(-1)%>% cbind(row.names(.), .) %>% rename(term = 'row.names(.)'),
  title='temporalDistance ~ condition * pretest ingroup identification')

temporalDistance ~ condition * pretest ingroup identification
Term	Estimate	Std. Error	t value	Pr(>|t|)
conditionProximate	0.64	0.94	0.68	0.50
pretest_ingroupIdentification	0.10	0.14	0.73	0.47
conditionProximate × pretest_ingroupIdentification	-0.14	0.20	-0.72	0.47

Institutional responsibility for wrongdoings

Culpability

ggscatter(study3, x='pretest_ingroupIdentification', y = 'institutionalResponsibility_culpability', color='condition', position='jitter',add = 'reg.line')
nice_table(as.data.frame(
  summary(lm(data=study3, institutionalResponsibility_culpability~condition*pretest_ingroupIdentification))$coefficients) %>% 
    slice(-1)%>% cbind(row.names(.), .) %>% rename(term = 'row.names(.)'),
  title='institutional culpability ~ condition * pretest ingroup identification')

institutional culpability ~ condition * pretest ingroup identification
Term	Estimate	Std. Error	t value	Pr(>|t|)
conditionProximate	0.26	0.74	0.35	0.73
pretest_ingroupIdentification	0.23	0.11	2.13	0.03
conditionProximate × pretest_ingroupIdentification	-0.08	0.15	-0.50	0.62

Distrust

ggscatter(study3, x='pretest_ingroupIdentification', y = 'institutionalResponsibility_distrust', color='condition', position='jitter',add = 'reg.line')
nice_table(as.data.frame(
  summary(lm(data=study3, institutionalResponsibility_distrust~condition*pretest_ingroupIdentification))$coefficients) %>% 
    slice(-1)%>% cbind(row.names(.), .) %>% rename(term = 'row.names(.)'),
  title='institutional distrust ~ condition * pretest ingroup identification')

institutional distrust ~ condition * pretest ingroup identification
Term	Estimate	Std. Error	t value	Pr(>|t|)
conditionProximate	-1.01	0.72	-1.39	0.17
pretest_ingroupIdentification	-0.23	0.11	-2.13	0.03
conditionProximate × pretest_ingroupIdentification	0.16	0.15	1.08	0.28

Race

centrality of wrondgoing predicted by change in instititutional continuity

summary(lm(data=study3, centralityOfWrongdoing~institutionalContinuity_diffScore*condition))

Call:
lm(formula = centralityOfWrongdoing ~ institutionalContinuity_diffScore * 
    condition, data = study3)

Residuals:
    Min      1Q  Median      3Q     Max 
-64.423  -7.047   2.810  13.272  36.141 

Coefficients:
                                                     Estimate Std. Error
(Intercept)                                           65.4455     2.0626
institutionalContinuity_diffScore                      4.7610     2.7125
conditionProximate                                    -0.5499     3.1830
institutionalContinuity_diffScore:conditionProximate  -4.0528     4.0693
                                                     t value Pr(>|t|)    
(Intercept)                                           31.729   <2e-16 ***
institutionalContinuity_diffScore                      1.755   0.0807 .  
conditionProximate                                    -0.173   0.8630    
institutionalContinuity_diffScore:conditionProximate  -0.996   0.3204    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 19.28 on 212 degrees of freedom
Multiple R-squared:  0.01527,   Adjusted R-squared:  0.001335 
F-statistic: 1.096 on 3 and 212 DF,  p-value: 0.3519

Ingroup interaction

library(interactions)
colnames(study3)

 [1] "computID"                                 
 [2] "condition"                                
 [3] "centralityOfWrongdoing"                   
 [4] "physicalDistance"                         
 [5] "physicalContinuity"                       
 [6] "institutionalContinuity"                  
 [7] "institutionalContinuity_cultural"         
 [8] "institutionalContinuity_temporal"         
 [9] "institutionalContinuity_diversity"        
[10] "ingroupIdentification"                    
[11] "ingroupRegard"                            
[12] "temporalDistance"                         
[13] "institutionalResponsibility"              
[14] "institutionalResponsibility_culpability"  
[15] "institutionalResponsibility_distrust"     
[16] "publicRegard"                             
[17] "race"                                     
[18] "pretest_institutionalContinuity"          
[19] "pretest_institutionalContinuity_cultural" 
[20] "pretest_institutionalContinuity_temporal" 
[21] "pretest_institutionalContinuity_diversity"
[22] "pretest_ingroupIdentification"            
[23] "institutionalContinuity_diffScore"        

study3_institutionalCont_long = study3 %>% 
  select(computID, condition, institutionalContinuity, pretest_institutionalContinuity, pretest_ingroupIdentification) %>% 
  pivot_longer(
    cols = c(pretest_institutionalContinuity, institutionalContinuity),
    names_to = "time",
    values_to = "institutionalContinuity") %>% 
  mutate(time = case_when(
      time == "pretest_institutionalContinuity" ~ "pre",
      time == "institutionalContinuity" ~ "post"),
    time = factor(time, levels = c("pre", "post")))

institutionalCont_ingroupID_model = lm(data=study3_institutionalCont_long |> mutate(time=as.numeric(time)), institutionalContinuity ~ time * condition + pretest_ingroupIdentification)

interact_plot(model = institutionalCont_ingroupID_model, pred = time, modx = pretest_ingroupIdentification, mod2 = condition)

Study 3.2

Changes:

Institutional Physical Continuity - pre and post:

1. The physical aesthetic of UVA has remained constant over time
2. UVa’s hallmark buildings have endured over time
3. UVa has significantly changed its architecture throughout its history
4. UVa has changed the way it looks throughout history 
5. The people who run UVA today have a different vision for the university than people who have run UVA in the past
6. UVA leadership has largely taken the same approach to running the university throughout the university’s history

Temporal distance - pre and post

• Think about when UVA was founded (1819). How far away in time does the founding of UVA feel to you? Use the slider below to indicate how far away in time the founding of UVA feels from the present [0 - 100 pt slider]
• On the below scale, please indicate how far away in time the founding of UVA feels to you. [likert: 1 = feels like yesterday, 7 = feels very far away]
• Think about when UVA hospital first opened (1901). How far away in time does the opening of UVA hospital feel to you? Use the slider below to indicate how far away the opening of UVa hospital feels from the present [0 - 100 pt slider]
• On the below scale, please indicate how far away in time the opening of UVA hospital feels to you. [likert: 1 = feels like yesterday, 7 = feels very far away]
• Think about when Gilmer hall first opened (1963). How far away in time does the opening of Gilmer hall feel to you? Use the slider below to indicate how far away the opening of Gilmer hall feels from the present [0 - 100 pt slider]
• On the below scale, please indicate how far away in time the opening of Gilmer hall feels to you. [likert: 1 = feels like yesterday, 7 = feels very far away]

Institutional responsibility for historical wrongdoing [1 = totally disagree, 7 = totally agree]

1. The University, today, is absolutely not responsible for wrongdoings it has committed in the past reversed
2. The University of Virginia is obligated to atone for wrondgoings it has committed in the past
3. UVa has a duty to educate current and prospective UVA students about the history of UVa’s involvement in past eugenics practices
4. UVa has a duty to financially compensate (e.g. offer scholarships, student loan forgiveness) members of the local Black community it historically harmed
5. UVa is responsible for taking action to ensure that Black Americans and other victim groups of past UVA eugenics practices are compensated for their past suffering
6. UVa can be trusted to never commit race based, group based, faith based, or identity based wrongdoings again reversed
7. UVa has atoned for the wrongdoings it has committed in the past reversed

Moved public regard up before final demogs

Physical continuity: added following items

5. The people who run UVA today have a different vision for the university than people who have run UVA in the past
6. UVA leadership has largely taken the same approach to running the university throughout the university’s history

FINAL NOTES 05/01/2025

Due to very slow signups, we stopped collecting in lab participants on 4/11 and switched to a Bagling study, or “study 4”

Notes

Current trends are pointing to threat response

1. historical continuity and discontinuity and threat

2. From Abstract to Concrete: Subjective Reading of UrbanSpace