History Embedded in Physical Spaces Study 1

Purpose & Background

show code
study1_raw <- read.csv('~/Library/CloudStorage/Box-Box/History Embedded in Physical Spaces/study1_data.csv') %>% filter(Progress==100)
### re-evaluate without likely to attend
study1 = study1_raw %>% 
  mutate(across(starts_with("MDSSQ.") & ends_with("1"), ~ 8 - .), #reverse score "MDSSQ - tired", "MDSSQ - bored", "MDSSQ - relaxed", "MDSSQ - confident"
         across(starts_with("MDSSQ.") & ends_with("2"), ~ 8 - .),
         across(starts_with("MDSSQ.") & ends_with("5"), ~ 8 - .),
         across(starts_with("MDSSQ.") & ends_with("6"), ~ 8 - .)) %>% 

  mutate(familiarity_west = rowMeans(select(.,"Direct.West", "Find.West","Familiar.West"),  na.rm=TRUE),
         familiarity_CC = rowMeans(select(.,"Direct.CC", "Find.CC","Familiar.CC"),  na.rm=TRUE),
         familiarity_madHouse = rowMeans(select(.,"Direct.Mad", "Find.Mad","Familiar.Mad"),  na.rm=TRUE),
         
         belonging_west = rowMeans(select(.,"Like.West", "Comf.West","Class.West"),  na.rm=TRUE),
         belonging_CC = rowMeans(select(.,"Like.CC", "Comf.CC","Class.CC"),  na.rm=TRUE),
         
         taskEngagement_west = rowMeans(select(.,"MDSSQ.West_1", "MDSSQ.West_2","Attn.West"),  na.rm=TRUE),
         taskEngagement_CC = rowMeans(select(.,"MDSSQ.CC_1", "MDSSQ.CC_2","Attn.CC"),  na.rm=TRUE),
         
         distress_west = rowMeans(select(.,"MDSSQ.West_3", "MDSSQ.West_4","MDSSQ.West_5","MDSSQ.West_6"),  na.rm=TRUE),
         distress_CC = rowMeans(select(.,"MDSSQ.CC_3", "MDSSQ.CC_4","MDSSQ.CC_5","MDSSQ.CC_6"),  na.rm=TRUE),
         
         worry_west = rowMeans(select(.,"MDSSQ.West_7", "MDSSQ.West_8","MDSSQ.West_9","MDSSQ.West_10"),  na.rm=TRUE),
         worry_CC = rowMeans(select(.,"MDSSQ.CC_7", "MDSSQ.CC_8","MDSSQ.CC_9","MDSSQ.CC_10"),  na.rm=TRUE),
         
         gender = factor(gender, levels=c('1','2','1,2','5'), labels=c('Woman','Man','Woman Man','Rather not say')),
         Race = factor(Race, levels=c("1","1,3,5","1,5","2","4,5","5","5,6","6"), labels=c('Asian','Asian, Native Hawaiian/Pacific Islander, White','Asian, White','Black','Am. Indian/Alaskan Native, White','White','White, not listed','Not listed')))

The purpose of this project is twofold:

  • Practical goal: How will the West Complex location affect the quality of data collection?

  • Research goal: What are the antecedents & consequences of perceiving institutions like UVa as continuous or discontinuous over time?

    • The purpose of this research is to investigate how the history embedded in valued institution’s physical spaces is perceived and may impact people’s thoughts and behaviors (e.g., Cheryan et al., 2009). We are particularly interested in understanding this process in institutions with physical spaces that hold rich and nuanced histories (e.g., freedom, discrimination).

    • In this project, we will document 1) UVA students’ working knowledge about the historical context of specific physical spaces around Grounds, 2) their responses to that historical information (e.g., feelings of belonging, like or dislike, disgust), 3) how UVA students conceptualize UVA as a continuous or discontinuous entity over time, and 4) how the interaction between those cognitive-emotional elements affects their tendency to approach or avoid history-laden spaces around Grounds. Importantly, we intend to explore how UVA students’ racial identity shapes the way they interact with historical information about the spaces they inhabit as measured by the 4 factors we outlined.

Study 1

  • 59 responses collected on Psychology Participant Pool from 04/19/2024 - 04/30/2024
  • Qualtrics title: “West Complex Study - S1”
  • Rough overview of survey

Participants

show code
library('rempsyc')
nice_table(study1 %>% group_by(gender) %>%  count() %>% arrange(desc(n)))

gender

n

Man

30

Woman

27

Woman Man

1

Rather not say

1

show code
nice_table(study1 %>% group_by(Race) %>%  count() %>% arrange(desc(n)))

Race

n

White

31

Asian

10

Black

8

Not listed

6

Asian, Native Hawaiian/Pacific Islander, White

1

Asian, White

1

Am. Indian/Alaskan Native, White

1

White, not listed

1

Results

Results are categorized into those that inform the practical goals and those that inform the research goals.

Practical goal

Familiarity with West Complex vs Madison House vs Contemplative Commons

4 questions. Scale 1 - 7. Collapsed:

  1. Imagine someone stopped you on Grounds and asked for directions to the [West Complex/Contemplative Commons/Madison House]. How confident are you that you could point them in the right direction (without using Maps)?
  2. If you had to attend an event at [building] how confident are you that you could easily find the building and attend on time?
  3. How familiar are you with [building]?
  4. How likely are you to attend an event if it was hosted at [building]?
show code
ggerrorplot(data=study1 %>% select(contains('familiarity')) %>% pivot_longer(1:3,names_to = 'variable'), x = 'variable', y = 'value', color='variable', size=1)+
  coord_cartesian(ylim=c(2,6))+xlab('')+ ylab("Familiarity")+
  scale_x_discrete(labels=c('West Complex','Contemp. Commons','Madison House'))+theme_classic(base_size=20)+rremove('legend')

Affect - Self Assessment Manakin

Measured using Self Assessment Manakin: Click the number of the figure above that best represents how you would feel in the spaces represented above. (1 = Furthest to the Left; 5 = Furthest to the Right)

show code
ggerrorplot(data=study1 %>% select('Affect.West','Affect.CC') %>% pivot_longer(1:2,names_to = 'variable'), x = 'variable', y = 'value', color='variable', size=1)+
  coord_flip(ylim=c(1,5))+ggtitle('Valence')+
  scale_x_discrete(labels=c('West Complex','Contemp. Commons'))+theme_classic(base_size=20)+rremove('legend')+rremove('xlab')+rremove('ylab')


show code
ggerrorplot(data=study1 %>% select('Arousal.West','Arousal.CC') %>% pivot_longer(1:2,names_to = 'variable'), x = 'variable', y = 'value', color='variable', size=1)+
  coord_flip(ylim=c(1,5))+ggtitle('Arousal')+
  scale_x_discrete(labels=c('West Complex','Contemp. Commons'))+theme_classic(base_size=20)+rremove('legend')+rremove('xlab')+rremove('ylab')


show code
ggerrorplot(data=study1 %>% select('Size.West','Size.CC') %>% pivot_longer(1:2,names_to = 'variable'), x = 'variable', y = 'value', color='variable', size=0.5)+
  coord_flip(ylim=c(1,5))+ggtitle('Dominance')+
  scale_x_discrete(labels=c('West Complex','Contemp. Commons'))+theme_classic(base_size=20)+rremove('legend')+rremove('xlab')+rremove('ylab')

Core Affect

How much would you feel _____ if you were in the space represented in the images above?

Not at all (1) - Very much so (5)

show code
coreAffect = study1 %>% 
  select(contains('Core.Affect')) %>% 
  pivot_longer(everything(), 
               names_to = c("location", "variable"),
               names_prefix="Core.Affect.",
               names_pattern="(.*)_(.*)") %>% 
  mutate(location = factor(location, levels=c('West','CC'), labels=c('West Complex','Contemp. Commons')))
         

ggerrorplot(data=coreAffect, x = 'variable', y = 'value', color='location', size=1, legend='top')+
  coord_flip(ylim=c(1,5))+
  rremove('legend.title')+rremove('xlab')+rremove('ylab')

Belonging

3 Questions collapsed. Inspired by Driskell & Trawalter (2021) on antebellum architecture:

  1. How much do you like this building? dislike a great deal (1) - like a great deal (5)

  2. How comfortable would you be attending an event inside this building? extremely uncomfortable (1) - extremely comfortable (5)

  3. How much would you want to have a class or your study space in this building? None at all (1) - a great deal (5)

show code
ggerrorplot(data=study1 %>% select(contains('belonging')) %>% pivot_longer(1:2,names_to = 'variable'), x = 'variable', y = 'value', color='variable', size=1)+
  coord_cartesian(ylim=c(1,5))+ylab("Belonging")+
  scale_x_discrete(labels=c('West Complex','Contemp. Commons'))+theme_classic(base_size=20)+rremove('legend')+rremove('xlab')

Modified Dundee State Stress Questionnaire

(Matthews et al., 1999; Matthews et al., 2013)

Imagine you are assigned to complete a task in this space. For these questions, imagine you are taking a psychology study in a room in the [West Complex]/[Contemplative Commons].

* = reverse scored

Divided into subscales:

Task engagement:

  1. How confident are you that your attention would be directed towards the task [1 not at all confident - 7 extremely confident] *
  2. I would feel tired [1 strongly disagree - 7 strongly agree] *
  3. I would feel bored [1 strongly disagree - 7 strongly agree] *

Distress: I would feel… [1 strongly disagree - 7 strongly agree]

  1. tense
  2. like I could not deal with the situation effectively
  3. relaxed*
  4. confident about my performance on the task*

Worry/Distraction: I would feel… [1 strongly disagree - 7 strongly agree]

  1. concerned about the impression I was making
  2. reflecting about the space I was in while completing the task
  3. thinking about something else
  4. thinking about personal concerns and interests
show code
MDSSQ = study1 %>% 
  select(contains('task') | contains('distress') | contains('worry')) %>% 
  pivot_longer(everything(), 
               names_to = c("variable", 'location'),
               names_pattern="(.*)_(.*)") %>% 
  mutate(location = factor(location, levels=c('west','CC'), labels=c('West Complex','Contemp. Commons')))
         
ggerrorplot(data=MDSSQ, x = 'variable', y = 'value', color='location', size=1, legend='top')+
  coord_flip(ylim=c(1,7))+
  rremove('legend.title')+rremove('xlab')+rremove('ylab')

Free response

Please write down the first 3 words that come to mind when you view each image. x 3 images per building.

*Note that all words that only appeared once were removed

show code
library('ggwordcloud'); library('stringr')

freeResponseWords = study1 %>% 
  select(contains("FR.") & contains("_")) %>% 
  mutate(across(everything(), str_remove_all, pattern = fixed(" ")),
         across(everything(), tolower)) 
  
freeResponseWords_west = freeResponseWords %>% 
  select(contains("West")) %>% 
  pivot_longer(everything(),names_to="response") %>% 
  count(value) %>% 
  arrange(-n)%>% 
  filter(n>1)

freeResponseWords_CC = freeResponseWords %>% 
  select(contains("CC")) %>% 
  pivot_longer(everything(),names_to="response") %>% 
  count(value) %>% 
  arrange(-n) %>% 
  filter(n>1)

ggplot(freeResponseWords_CC, aes(label = value, size=n, alpha=n, color=n)) +
  geom_text_wordcloud() +
  scale_size_area(max_size = 30) +
  theme_minimal()+
  scale_alpha_continuous(range =c(0.25,1))+
  scale_color_gradient(low = "#F8766D", high = "#F8766D")

show code
ggplot(freeResponseWords_west, aes(label = value, size=n, alpha=n, color=n)) +
  geom_text_wordcloud() +
  scale_size_area(max_size = 30) +
  theme_minimal()+
  scale_alpha_continuous(range =c(0.25,1))+
  scale_color_gradient(low = "#00BFC4", high = "#00BFC4")