Intro
The survey presented some questions concerning the benefits of doing outreach, both to the researcher themself and to wider society. Here, we take a look at the ratings for these benefits in a bit of detail.
# NOTE: This dataset is still private and so this data frame is generated
# from within the storage environment of the file in question.
dat <- read.csv(file = "../../quant_analysis/final_dataset.csv",
header = TRUE)
library(dplyr, warn.conflicts = FALSE)
library(gridExtra, warn.conflicts = FALSE)
library(ggplot2, warn.conflicts = FALSE)
library(magrittr, warn.conflicts = FALSE)
# for long form conversion of data frame
library(reshape2)
The responses are split into two categories based on whether the respondents had NOT participated in outreach previously (“No”) or whether they had (“Yes”). Responses with “Not applicable” have been removed from the datasets plotted below.
Benefits to society
Below, we see responses to questions concerning one of the benefits to society: namely, “The public gets better knowledge/understanding of science.”
ggplot(dat, aes(x = factor(better_knowledge_science), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“The public gets better knowledge/understanding of science.”")
Similarly, we see the responses for the remaining eight questions concerning societal benefits.
ggplot(dat, aes(x = factor(judge_scientific_issues), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It enables the public to judge scientific issues for themselves.”")
ggplot(dat, aes(x = factor(lives_informed_decisions), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It enables the public to make informed decisions about their lives.”")
ggplot(dat, aes(x = factor(improves_understanding_scientists), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It helps improve the understanding of what scientists do.”")
ggplot(dat, aes(x = factor(less_opposition), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“There is less opposition to scientific research.”")
ggplot(dat, aes(x = factor(more_study_science), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“More people enter science education/science careers as a result.”")
ggplot(dat, aes(x = factor(policy_making_equipped), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“Policy-makers are better equipped to make decisions.”")
ggplot(dat, aes(x = factor(non_specialists), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“Non-specialists can participate in scientific research.”")
ggplot(dat, aes(x = factor(informed_decisions), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“Society can make informed decisions about research funding.”")
Benefits to self
We now examine – breaking, as before, the responses down to those who have never participated in outreach before (“No”) and those who have (“Yes”) – the ratings provided for benefits to oneself from doing outreach.
ggplot(dat, aes(x = factor(experience_communications), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It gives me experience in communicating my research.”")
ggplot(dat, aes(x = factor(name_known), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It gets my name known.”")
ggplot(dat, aes(x = factor(attracts_funding), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It attracts research funding.”")
ggplot(dat, aes(x = factor(advances_career), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It advances my career.”")
ggplot(dat, aes(x = factor(feeling_enjoyment), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“I get a feeling of enjoyment.”")
ggplot(dat, aes(x = factor(job_satisfaction), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It gives me job satisfaction.”")
ggplot(dat, aes(x = factor(collaboration_opportunities), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It is an opportunity for others to contact me for collaborative purposes.”")
ggplot(dat, aes(x = factor(research_new_ways), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It shapes the direction of my research or makes me think about it in new ways.”")
ggplot(dat, aes(x = factor(makes_better_scientist), y = ..count..)) +
geom_bar(stat = "count") +
geom_text(stat = "count", aes(label = ..count..,
y=..count..),
vjust=-0.5) +
facet_grid(. ~ previous_participation) +
scale_x_discrete(name = NULL,
limits = c(1:5),
drop = FALSE,
labels = c("1" = "1\nStrongly\nDisagree",
"2" = "2", "3" = "3", "4" = "4",
"5" = "5\nStrongly\nAgree")) +
ylab("Count") +
labs(title = "“It makes me a better scientist.”")
---
title: "Benefits of doing outreach"
output: 
  html_notebook: 
    code_folding: hide
    fig_caption: yes
    highlight: tango
    theme: united
    toc: yes
---

## Intro

The survey presented some questions concerning the benefits of doing outreach, both to the researcher themself and to wider society.
Here, we take a look at the ratings for these benefits in a bit of detail.

```{r}
# NOTE: This dataset is still private and so this data frame is generated
# from within the storage environment of the file in question.
dat <- read.csv(file = "../../quant_analysis/final_dataset.csv", 
                header = TRUE)

library(dplyr, warn.conflicts = FALSE)
library(gridExtra, warn.conflicts = FALSE)
library(ggplot2, warn.conflicts = FALSE)
library(magrittr, warn.conflicts = FALSE)

# for long form conversion of data frame
library(reshape2)
```

The responses are split into two categories based on whether the respondents had NOT participated in outreach previously ("No") or whether they had ("Yes").
Responses with "Not applicable" have been removed from the datasets plotted below.

## Benefits to society

Below, we see responses to questions concerning one of the benefits to society: namely, "The public gets better knowledge/understanding of science."

```{r}
ggplot(dat, aes(x = factor(better_knowledge_science), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“The public gets better knowledge/understanding of science.”")
```

Similarly, we see the responses for the remaining eight questions concerning societal benefits.

```{r}
ggplot(dat, aes(x = factor(judge_scientific_issues), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It enables the public to judge scientific issues for themselves.”")
```

```{r}
ggplot(dat, aes(x = factor(lives_informed_decisions), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It enables the public to make informed decisions about their lives.”")
```

```{r}
ggplot(dat, aes(x = factor(improves_understanding_scientists), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It helps improve the understanding of what scientists do.”")
```

```{r}
ggplot(dat, aes(x = factor(less_opposition), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“There is less opposition to scientific research.”")
```

```{r}
ggplot(dat, aes(x = factor(more_study_science), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“More people enter science education/science careers as a result.”")
```

```{r}
ggplot(dat, aes(x = factor(policy_making_equipped), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“Policy-makers are better equipped to make decisions.”")
```

```{r}
ggplot(dat, aes(x = factor(non_specialists), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“Non-specialists can participate in scientific research.”")
```

```{r}
ggplot(dat, aes(x = factor(informed_decisions), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“Society can make informed decisions about research funding.”")
```

## Benefits to self

We now examine -- breaking, as before, the responses down to those who have never participated in outreach before ("No") and those who have ("Yes") -- the ratings provided for benefits to oneself from doing outreach.

```{r}
ggplot(dat, aes(x = factor(experience_communications), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It gives me experience in communicating my research.”")
```

```{r}
ggplot(dat, aes(x = factor(name_known), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It gets my name known.”")
```

```{r}
ggplot(dat, aes(x = factor(attracts_funding), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It attracts research funding.”")
```

```{r}
ggplot(dat, aes(x = factor(advances_career), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It advances my career.”")
```

```{r}
ggplot(dat, aes(x = factor(feeling_enjoyment), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“I get a feeling of enjoyment.”")
```

```{r}
ggplot(dat, aes(x = factor(job_satisfaction), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It gives me job satisfaction.”")
```

```{r}
ggplot(dat, aes(x = factor(collaboration_opportunities), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It is an opportunity for others to contact me for collaborative purposes.”")
```

```{r}
ggplot(dat, aes(x = factor(research_new_ways), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It shapes the direction of my research or makes me think about it in new ways.”")
```

```{r}
ggplot(dat, aes(x = factor(makes_better_scientist), y = ..count..)) + 
    geom_bar(stat = "count") + 
    geom_text(stat = "count", aes(label = ..count.., 
                                  y=..count..), 
              vjust=-0.5) + 
    facet_grid(. ~ previous_participation) + 
    scale_x_discrete(name = NULL, 
                     limits = c(1:5), 
                     drop = FALSE, 
                     labels = c("1" = "1\nStrongly\nDisagree",
                                "2" = "2", "3" = "3", "4" = "4", 
                                "5" = "5\nStrongly\nAgree")) + 
    ylab("Count") + 
    labs(title = "“It makes me a better scientist.”")
```

