Survey 01

Trust in science and scientists

Purpose

This page documents the data processing steps involved with Survey-01 in PSYCH 490.012 Fall 2024.

The survey questions were adapted from those discussed in (Krumrei-Mancuso & Rouse, 2016; Nadelson et al., 2014; Plohl & Musil, 2023).

Survey

Direct link: https://forms.gle/kpwGFo2qpsKmNLya9

Preparation

First, we load the external packages (groups of R commands) that we will be using.

library('ggplot2')
library('dplyr')

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Gathering

Next, we download the data from the Google Sheet where it is collected. Dr. Gilmore has stored his Google account credentials in a special environment file that can be accessed by the R command Sys.getenv("GMAIL_SURVEY").

Tip

It’s vital to be very careful when creating and sharing code like this that involves sensitive information like login credentials.

Gilmore likes to put credentials in an .Renviron file that lives in his home directory. This is a recommended practice. On Mac OS and Linux, that’s ~/.Renviron. You can use the usethis::edit_r_profile() command at the R console (not the Terminal) to open your own .Renviron file. In Gilmore’s case, he has added the following line to that file:

GMAIL_SURVEY="<my-google-account>"

Here, he has substituted his Google account with credentials/access to the required files for <my-google-account>. Then, when the R code below calls Sys.getenv("GMAIL_SURVEY"), the value of those credentials is returned as a text string.

Make sure to close and save the .Renviron file and restart your R session before testing this yourself.

if (!dir.exists('csv')) {
  message("Creating missing `csv/`.")
  dir.create("csv")
}

if (params$update_data) {
  options(gargle_oauth_email = Sys.getenv("GMAIL_SURVEY"))
  googledrive::drive_auth()

  googledrive::drive_download(
    "PSYCH 490.012: Survey 01: Openness and Trust in Science (Responses)",
    path = file.path("csv", params$fn),
    type = "csv",
    overwrite = TRUE

  )
  message("Data updated.")
} else {
  message("Using stored data.")
}

The data file has been saved as a comma-separated value (CSV) format data file in a special directory called csv/.

Note

Because these data might contain sensitive or identifiable information, we only keep a local copy and do not share it publicly via GitHub. This is achieved by adding the name of the data directory to a special .gitignore file.

Cleaning

Next we load the saved data file and then proceed to clean it.

survey_01 <-
  readr::read_csv(file.path("csv", params$fn), show_col_types = FALSE)

There are 17 responses. That’s great for a small class!

These are the column/variable names.

# Google Forms puts the full question in the top row of the data file.
# We use the names() function to extract and print the original questions.
survey_01_qs <- names(survey_01)
survey_01_qs

 [1] "Timestamp"                                                                                                                                             
 [2] "I have at times changed opinions that were important to me, when someone showed me I was wrong."                                                       
 [3] "I am willing to change my position on an important issue in the face of good reasons."                                                                 
 [4] "I am open to revising my important beliefs in the face of new information."                                                                            
 [5] "I am willing to change my opinions on the basis of compelling reason."                                                                                 
 [6] "I’m willing to change my mind once it’s made up about an important topic."                                                                             
 [7] "When scientists change their mind about a scientific idea it diminishes my trust in their work."                                                       
 [8] "Scientists ignore evidence that contradicts their work."                                                                                               
 [9] "Scientific theories are weak explanations."                                                                                                            
[10] "Scientists intentionally keep their work secret."                                                                                                      
[11] "We can trust scientists to share their discoveries even if they don't like their findings."                                                            
[12] "Scientists don't value the ideas of others."                                                                                                           
[13] "I trust that the work of scientists is to make life better for people."                                                                                
[14] "Scientists don't care if laypersons understand their work."                                                                                            
[15] "We should trust the work of scientists."                                                                                                               
[16] "We should trust that scientists are being honest in their work."                                                                                       
[17] "We should trust that scientists are being ethical in their work."                                                                                      
[18] "Scientific theories are trustworthy."                                                                                                                  
[19] "When scientists form a hypothesis they are just guessing."                                                                                             
[20] "People who understand science more have more trust in science."                                                                                        
[21] "We can trust science to find the answers that explain the natural world."                                                                              
[22] "I trust scientists can find solutions to our major technological problems."                                                                            
[23] "We cannot trust scientists because they are biased in their perspectives."                                                                             
[24] "Scientists will protect each other even when they are wrong."                                                                                          
[25] "We cannot trust scientists to consider ideas that contradict their own."                                                                               
[26] "Today's scientists will sacrifice the well being of others to advance their research."                                                                 
[27] "We cannot trust science because it moves too slowly."                                                                                                  
[28] "If you wish to comment about the questions in this survey, you may do so here. You are not required to comment. Your comments might be seen by others."

For plotting and analyses, it’s usually easier to shorten the questions by creating a short name that reflects the underlying idea or construct. We’ll use the rename() function from the dplyr package for this.

We first rename the variables from the “Openness to Revising One’s Viewpoint” subscale from the Comprehensive Intellectual Humility Scale (Krumrei-Mancuso & Rouse, 2016).

survey_01_clean <- survey_01 |>
  dplyr::rename(
    timestamp = "Timestamp",
    when_shown_wrong = "I have at times changed opinions that were important to me, when someone showed me I was wrong.",
    good_reason = "I am willing to change my position on an important issue in the face of good reasons." ,
    new_info = "I am open to revising my important beliefs in the face of new information.",
    compelling_reason = "I am willing to change my opinions on the basis of compelling reason.",
    mind_made_up = "I’m willing to change my mind once it’s made up about an important topic.",
    comments = "If you wish to comment about the questions in this survey, you may do so here. You are not required to comment. Your comments might be seen by others."
  )

Note

Notice how the rename() function puts the ‘new’ name on the left and the ‘old’ name on the right.

Keep in mind that the shorter names involve non-arbitrary choices. It’s good to keep the original questions close at-hand if you really want to understand what question people were asked.

Now, we rename the variables from the (Nadelson et al., 2014) trust in science and scientists survey.

survey_01_clean <- survey_01_clean |>
  dplyr::rename(
    ignore_contradictory_evidence = "Scientists ignore evidence that contradicts their work.",
    theories_are_weak = "Scientific theories are weak explanations.",
    keep_work_secret = "Scientists intentionally keep their work secret.",
    dont_value_others_ideas = "Scientists don't value the ideas of others.",
    dont_care_laypeople_understand = "Scientists don't care if laypersons understand their work.",
    should_trust_work = "We should trust the work of scientists.",
    should_trust_honesty = "We should trust that scientists are being honest in their work.",
    should_trust_ethical = "We should trust that scientists are being ethical in their work.",
    more_understanding_more_trust = "People who understand science more have more trust in science.",
    trust_explain_natural_world = "We can trust science to find the answers that explain the natural world.",
    cant_trust_biased = "We cannot trust scientists because they are biased in their perspectives.",
    protect_each_other_when_wrong = "Scientists will protect each other even when they are wrong.",
    wont_consider_contradictory_ideas = "We cannot trust scientists to consider ideas that contradict their own.",
    sacrifice_others_to_advance = "Today's scientists will sacrifice the well being of others to advance their research.",
    cant_trust_moves_slowly = "We cannot trust science because it moves too slowly.",
    change_minds_undermines_trust = "When scientists change their mind about a scientific idea it diminishes my trust in their work.",
    share_findings_dont_like = "We can trust scientists to share their discoveries even if they don't like their findings.",
    make_life_better = "I trust that the work of scientists is to make life better for people.",
    theories_trustworthy = "Scientific theories are trustworthy.",
    hypotheses_just_guesses = "When scientists form a hypothesis they are just guessing.",
    trust_find_tech_solutions = "I trust scientists can find solutions to our major technological problems."
  )

Now, let’s look at the names to confirm they all got changed.

names(survey_01_clean)

 [1] "timestamp"                         "when_shown_wrong"                 
 [3] "good_reason"                       "new_info"                         
 [5] "compelling_reason"                 "mind_made_up"                     
 [7] "change_minds_undermines_trust"     "ignore_contradictory_evidence"    
 [9] "theories_are_weak"                 "keep_work_secret"                 
[11] "share_findings_dont_like"          "dont_value_others_ideas"          
[13] "make_life_better"                  "dont_care_laypeople_understand"   
[15] "should_trust_work"                 "should_trust_honesty"             
[17] "should_trust_ethical"              "theories_trustworthy"             
[19] "hypotheses_just_guesses"           "more_understanding_more_trust"    
[21] "trust_explain_natural_world"       "trust_find_tech_solutions"        
[23] "cant_trust_biased"                 "protect_each_other_when_wrong"    
[25] "wont_consider_contradictory_ideas" "sacrifice_others_to_advance"      
[27] "cant_trust_moves_slowly"           "comments"                         

Data dictionary

We’ll pause here to start building a data dictionary, a file that explains the origin, format, and usage of our dataset.

survey_01_data_dictionary <-
  tibble::tibble(
    question = survey_01_qs,
    short_name = names(survey_01_clean),
    reference = c(
      NA,
      rep("krumrei-mancuso-2016", 5),
      rep("nadelson-2014", 21),
      NA
    )
  )

We’ll add other items to the data dictionary later.

Filtering out irrelevant responses

We should omit the first response in the dataset. That was the one Dr. Gilmore used to generate a Google Sheet, and isn’t real data. He put “test” in the comments field to make it easy to detect.

n_responses <- dim(survey_01_clean)[1]

if (n_responses > 1) {
  survey_01_clean <- survey_01_clean[2:n_responses,]
  test_phase_no_print <- FALSE
} else {
  message("No 'non-test' responses yet. Leaving data file unchanged.")
  test_phase_no_print <- TRUE
}

Visualizations

Openness questions

Remember, this is the relationship between the numbers and the verbal ratings:

Figure 1: Survey 01 response options

Here are the questions:

survey_01_data_dictionary[2:6,1:2] |>
  knitr::kable(format = "html") |>
  kableExtra::kable_classic()

question	short_name
I have at times changed opinions that were important to me, when someone showed me I was wrong.	when_shown_wrong
I am willing to change my position on an important issue in the face of good reasons.	good_reason
I am open to revising my important beliefs in the face of new information.	new_info
I am willing to change my opinions on the basis of compelling reason.	compelling_reason
I’m willing to change my mind once it’s made up about an important topic.	mind_made_up

Plot

Here is a dotplot of the data:

survey_01_openness <- survey_01_clean[, 1:6] |>
  tidyr::pivot_longer(cols=2:6, names_to = "question", values_to = "rating")

survey_01_openness |>
  ggplot() +
  aes(x = rating) +
  geom_dotplot(dotsize = .2) +
  xlim(1,5) +
  xlab("strongly disagree <-----> strongly agree   ") +
  theme(axis.title.y = element_blank()) +                    
  theme(axis.text.y = element_blank()) +
  theme(axis.ticks = element_blank()) +
  facet_wrap(facets = vars(question), ncol = 1)

Figure 2: Reponses to Openness to Revising One’s Viewpoint subscale questions from (Krumrei-Mancuso & Rouse, 2016)

See the Fall 2023 results here: Figure 6.

Statistical summary

survey_01_openness |>
  dplyr::group_by(question) |>
  dplyr::summarise(mean_rating = mean(rating), sd_rating = sd(rating))

Table 1

# A tibble: 5 × 3
  question          mean_rating sd_rating
  <chr>                   <dbl>     <dbl>
1 compelling_reason        4.06     0.748
2 good_reason              4.47     0.717
3 mind_made_up             3.53     0.874
4 new_info                 4.18     0.393
5 when_shown_wrong         4.18     0.529

Trust in science questions

Note

It would be even better to create a function that generates the plot and shows the long and short question names. Any time I repeat myself, I should remember this acronym:

Don’t Repeat Yourself

Write It Down

There are a number of these, so we break them into smaller groups for visualization.

Remember, we use the same response options as for the ‘openness’ questions, see Figure 1.

Here are the questions:

survey_01_data_dictionary[7:27, 1:2] |>
  knitr::kable(format = "html") |>
  kableExtra::kable_classic()

question	short_name
When scientists change their mind about a scientific idea it diminishes my trust in their work.	change_minds_undermines_trust
Scientists ignore evidence that contradicts their work.	ignore_contradictory_evidence
Scientific theories are weak explanations.	theories_are_weak
Scientists intentionally keep their work secret.	keep_work_secret
We can trust scientists to share their discoveries even if they don’t like their findings.	share_findings_dont_like
Scientists don’t value the ideas of others.	dont_value_others_ideas
I trust that the work of scientists is to make life better for people.	make_life_better
Scientists don’t care if laypersons understand their work.	dont_care_laypeople_understand
We should trust the work of scientists.	should_trust_work
We should trust that scientists are being honest in their work.	should_trust_honesty
We should trust that scientists are being ethical in their work.	should_trust_ethical
Scientific theories are trustworthy.	theories_trustworthy
When scientists form a hypothesis they are just guessing.	hypotheses_just_guesses
People who understand science more have more trust in science.	more_understanding_more_trust
We can trust science to find the answers that explain the natural world.	trust_explain_natural_world
I trust scientists can find solutions to our major technological problems.	trust_find_tech_solutions
We cannot trust scientists because they are biased in their perspectives.	cant_trust_biased
Scientists will protect each other even when they are wrong.	protect_each_other_when_wrong
We cannot trust scientists to consider ideas that contradict their own.	wont_consider_contradictory_ideas
Today’s scientists will sacrifice the well being of others to advance their research.	sacrifice_others_to_advance
We cannot trust science because it moves too slowly.	cant_trust_moves_slowly

Trust in science questions from (Nadelson et al., 2014)

Plot

Here is are plots of your ratings:

survey_01_trust <- survey_01_clean |>
  tidyr::pivot_longer(cols=7:27, names_to = "question", values_to = "rating")

survey_01_trust |>
  ggplot() +
  aes(rating) +
  geom_dotplot(dotsize = .4) +
  xlim(1, 5) +
  theme(axis.title.y = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(axis.ticks = element_blank()) +
  facet_wrap(facets = vars(question), ncol = 3)

Figure 3: Responses to trust in science questions from (Nadelson et al., 2014)

See the Fall 2023 results here: Figure 7.

Summary statistics

Here are summary statistics about the ratings sorted in descending order:

survey_01_trust |>
  dplyr::group_by(question) |>
  dplyr::summarise(mean_rating = mean(rating), sd_rating = sd(rating)) |>
  dplyr::arrange(desc(mean_rating)) |>
  kableExtra::kable(format = 'html') |>
    kableExtra::kable_classic()

question	mean_rating	sd_rating
more_understanding_more_trust	4.000000	0.6123724
trust_explain_natural_world	3.764706	0.6642112
make_life_better	3.705882	0.9195587
trust_find_tech_solutions	3.529412	0.7998162
theories_trustworthy	3.411765	1.0036697
should_trust_work	3.352941	0.9963167
should_trust_ethical	3.176471	0.9510057
should_trust_honesty	3.176471	0.9510057
share_findings_dont_like	3.058823	0.8993462
dont_care_laypeople_understand	3.000000	0.7905694
ignore_contradictory_evidence	2.764706	0.9034249
protect_each_other_when_wrong	2.764706	1.0914103
change_minds_undermines_trust	2.470588	0.8744746
keep_work_secret	2.411765	1.0641207
cant_trust_biased	2.235294	0.9034249
hypotheses_just_guesses	2.235294	0.8313702
sacrifice_others_to_advance	2.235294	1.0914103
wont_consider_contradictory_ideas	2.117647	0.6002450
dont_value_others_ideas	1.823529	0.8089572
theories_are_weak	1.823529	0.8089572
cant_trust_moves_slowly	1.705882	0.9851844

Comments

survey_01_clean |>
  dplyr::select(comments) |>
  dplyr::filter(!is.na(comments)) |>
  knitr::kable(format="html") |>
  kableExtra::kable_classic()

comments
Emily Acosta PSYCH 490 SEC 012

No one seemed to have had any comments.

Aggregate openness and trust questions

Next, we calculate aggregate “openness” and “trust” scores to look at the relationship between these variables.

survey_01_clean <- survey_01_clean |>
  dplyr::mutate(openness_comp = sum(survey_01_clean[, 2:6]))

Some of the “trust” variables are reverse-coded, so we have to address that. We’ll start by adding a variable to our data dictionary that indicates the “sign” of the weight we should apply to that variable.

If the sign is reversed, then we must assign 1 to 5, 2 to 4, 3 to 3, 4 to 2, and 5 to 1.

Here is a table of the weights to apply to the questions:

survey_01_data_dictionary <- survey_01_data_dictionary |>
  dplyr::mutate(sign_wt = c(0, 
                            rep(1, 5),
                            rep(-1, 4), 
                            1, 
                            -1, 
                            1, -1, 
                            rep(1, 3), 
                            1,
                            -1,
                            rep(1, 3), 
                            rep(-1, 5),
                            0))

survey_01_data_dictionary[,c(1,2,4)] |>
  knitr::kable(format = "html") |>
  kableExtra::kable_classic()

question	short_name	sign_wt
Timestamp	timestamp	0
I have at times changed opinions that were important to me, when someone showed me I was wrong.	when_shown_wrong	1
I am willing to change my position on an important issue in the face of good reasons.	good_reason	1
I am open to revising my important beliefs in the face of new information.	new_info	1
I am willing to change my opinions on the basis of compelling reason.	compelling_reason	1
I’m willing to change my mind once it’s made up about an important topic.	mind_made_up	1
When scientists change their mind about a scientific idea it diminishes my trust in their work.	change_minds_undermines_trust	-1
Scientists ignore evidence that contradicts their work.	ignore_contradictory_evidence	-1
Scientific theories are weak explanations.	theories_are_weak	-1
Scientists intentionally keep their work secret.	keep_work_secret	-1
We can trust scientists to share their discoveries even if they don't like their findings.	share_findings_dont_like	1
Scientists don't value the ideas of others.	dont_value_others_ideas	-1
I trust that the work of scientists is to make life better for people.	make_life_better	1
Scientists don't care if laypersons understand their work.	dont_care_laypeople_understand	-1
We should trust the work of scientists.	should_trust_work	1
We should trust that scientists are being honest in their work.	should_trust_honesty	1
We should trust that scientists are being ethical in their work.	should_trust_ethical	1
Scientific theories are trustworthy.	theories_trustworthy	1
When scientists form a hypothesis they are just guessing.	hypotheses_just_guesses	-1
People who understand science more have more trust in science.	more_understanding_more_trust	1
We can trust science to find the answers that explain the natural world.	trust_explain_natural_world	1
I trust scientists can find solutions to our major technological problems.	trust_find_tech_solutions	1
We cannot trust scientists because they are biased in their perspectives.	cant_trust_biased	-1
Scientists will protect each other even when they are wrong.	protect_each_other_when_wrong	-1
We cannot trust scientists to consider ideas that contradict their own.	wont_consider_contradictory_ideas	-1
Today's scientists will sacrifice the well being of others to advance their research.	sacrifice_others_to_advance	-1
We cannot trust science because it moves too slowly.	cant_trust_moves_slowly	-1
If you wish to comment about the questions in this survey, you may do so here. You are not required to comment. Your comments might be seen by others.	comments	0

Warning

I found some mistakes in the code I used last year to reverse-code variables when I revisited it this year. Only when I went through each question one-by-one did I find the errors. I’m very glad I’d shown the variables in a table.

Note

This next section of code reflects my attempt to make a function that categorizes the weights that should be applied to the variables. I think this is more reliable and more transparent. In other words, I think it’s easier to see what’s actually going on.

What do you think?

# Note: this function seems to be equivalent to the manual coding above.
# So, I did not change the rest of the code to make use of it.
assign_trust_wt <- function(var_name) {
  wt <- if_else(var_name %in% c('when_shown_wrong',
                                'good_reason',
                                'new_info',
                                'mind_made_up',
                                'compelling_reason',
                                'should_trust_work',
                          'should_trust_honesty',
                          'should_trust_ethical',
                          'more_understanding_more_trust',
                          'trust_explain_natural_world',
                          'share_findings_dont_like',
                          'make_life_better',
                          'theories_trustworthy',
                          'trust_find_tech_solutions'), 1,
                if_else(var_name %in% c('ignore_contradictory_evidence',
                                        'theories_are_weak',
                                        'keep_work_secret',
                                        'dont_value_others_ideas',
                                        'dont_care_laypeople_understand',
                                        'cant_trust_biased',
                                        'protect_each_other_when_wrong',
                                        'wont_consider_contradictory_ideas',
                                        'sacrifice_others_to_advance',
                                        'cant_trust_moves_slowly',
                                        'change_minds_undermines_trust',
                                        'hypotheses_just_guesses'
                                        ), -1, 0))
  wt
}

survey_01_data_dictionary <- survey_01_data_dictionary |>
  dplyr::mutate(sign_wt_new = assign_trust_wt(short_name))
                
survey_01_data_dictionary[,c(1,2,4,5)] |>
  knitr::kable(format = "html") |>
  kableExtra::kable_classic()

question	short_name	sign_wt	sign_wt_new
Timestamp	timestamp	0	0
I have at times changed opinions that were important to me, when someone showed me I was wrong.	when_shown_wrong	1	1
I am willing to change my position on an important issue in the face of good reasons.	good_reason	1	1
I am open to revising my important beliefs in the face of new information.	new_info	1	1
I am willing to change my opinions on the basis of compelling reason.	compelling_reason	1	1
I’m willing to change my mind once it’s made up about an important topic.	mind_made_up	1	1
When scientists change their mind about a scientific idea it diminishes my trust in their work.	change_minds_undermines_trust	-1	-1
Scientists ignore evidence that contradicts their work.	ignore_contradictory_evidence	-1	-1
Scientific theories are weak explanations.	theories_are_weak	-1	-1
Scientists intentionally keep their work secret.	keep_work_secret	-1	-1
We can trust scientists to share their discoveries even if they don't like their findings.	share_findings_dont_like	1	1
Scientists don't value the ideas of others.	dont_value_others_ideas	-1	-1
I trust that the work of scientists is to make life better for people.	make_life_better	1	1
Scientists don't care if laypersons understand their work.	dont_care_laypeople_understand	-1	-1
We should trust the work of scientists.	should_trust_work	1	1
We should trust that scientists are being honest in their work.	should_trust_honesty	1	1
We should trust that scientists are being ethical in their work.	should_trust_ethical	1	1
Scientific theories are trustworthy.	theories_trustworthy	1	1
When scientists form a hypothesis they are just guessing.	hypotheses_just_guesses	-1	-1
People who understand science more have more trust in science.	more_understanding_more_trust	1	1
We can trust science to find the answers that explain the natural world.	trust_explain_natural_world	1	1
I trust scientists can find solutions to our major technological problems.	trust_find_tech_solutions	1	1
We cannot trust scientists because they are biased in their perspectives.	cant_trust_biased	-1	-1
Scientists will protect each other even when they are wrong.	protect_each_other_when_wrong	-1	-1
We cannot trust scientists to consider ideas that contradict their own.	wont_consider_contradictory_ideas	-1	-1
Today's scientists will sacrifice the well being of others to advance their research.	sacrifice_others_to_advance	-1	-1
We cannot trust science because it moves too slowly.	cant_trust_moves_slowly	-1	-1
If you wish to comment about the questions in this survey, you may do so here. You are not required to comment. Your comments might be seen by others.	comments	0	0

Note

I’m on the fence about whether “People who understand science more have more trust in science.” should be in the composite. What do you think? What did (Plohl & Musil, 2023) do?

# Recode variables with "reverse" indicator (sign_x == -1)
recode_reverse_vars <- function(x, sign_x) {
  if (sign_x == -1) {
    switch(x,
           5,
           4,
           3,
           2,
           1)
  } else {
    x
  }
}

# Recode a specific variable based on its column index
recode_var <- function(var_i, df_vars = survey_01_clean, df_dict = survey_01_data_dictionary) {
  vals <- unname(unlist(df_vars[, var_i]))
  wts <- unname(unlist(rep(df_dict[var_i, 4], length(vals))))

  purrr::map2(vals, wts, recode_reverse_vars) |>
    unlist()
}

# Recode the entire dataset and create a new data frame/tibble
recode_survey_01 <- function() {
  x <- purrr::map(1:28, recode_var)
  var_names <- survey_01_data_dictionary[,2] |>
    unlist() |>
    unname()
  names(x) <- var_names
  as_tibble(x)
}

# Run the recode_survey_01() function
survey_01_recoded <- recode_survey_01()

# Calculate the composite scores as mean values across rows (within participants)
survey_01_recoded <- survey_01_recoded |>
  dplyr::mutate(openness_comp = rowMeans(survey_01_recoded[, 2:6]),
                trust_comp = rowMeans(survey_01_recoded[, 7:27]))

Plots

Here’s a plot to ensure our recoded data about trust in science make sense.

survey_01_recoded |>
  tidyr::pivot_longer(cols=7:27, names_to = "question", values_to = "rating") |>
  ggplot()+
  aes(rating) +
  geom_dotplot(dotsize = .4) +
  xlim(1, 5) +
  theme(axis.title.y = element_blank()) +
  theme(axis.text.y = element_blank()) +
  theme(axis.ticks = element_blank()) +
  facet_wrap(facets = vars(question), ncol = 3)

Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.

Figure 4: Recoded ‘trust’ scores

Here we evaluate the relationship between a person’s openness and their trust in science.

survey_01_recoded |>
  dplyr::select(openness_comp, trust_comp) |>
  ggplot() +
  aes(openness_comp, trust_comp) +
  geom_point() +
  geom_smooth(method = "lm",
              formula = y ~ x) +
  xlim(1,5) +
  ylim(1,5) +
  coord_fixed()

Figure 5: Composite ‘openness’ and ‘trust’ scores

with(survey_01_recoded, stats::cor.test(openness_comp, trust_comp))

    Pearson's product-moment correlation

data:  openness_comp and trust_comp
t = 2.6603, df = 15, p-value = 0.01782
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.1175296 0.8228953
sample estimates:
      cor 
0.5661903 

Here are the Fall 2023 results: Figure 8.

Comparison with Fall 2023 results

Figure 6: Fall 2023

Figure 7

Figure 8

References

Krumrei-Mancuso, E. J., & Rouse, S. V. (2016). The development and validation of the comprehensive intellectual humility scale. Journal of Personality Assessment, 98(2), 209–221. https://doi.org/10.1080/00223891.2015.1068174

Nadelson, L., Jorcyk, C., Yang, D., Jarratt Smith, M., Matson, S., Cornell, K., & Husting, V. (2014). I just don’t trust them: The development and validation of an assessment instrument to measure trust in science and scientists. School Science and Mathematics, 114(2), 76–86. https://doi.org/10.1111/ssm.12051

Plohl, N., & Musil, B. (2023). Assessing the incremental value of intellectual humility and cognitive reflection in predicting trust in science. Personality and Individual Differences, 214, 112340. https://doi.org/10.1016/j.paid.2023.112340