prep data

# load packages
if (!require(tidyverse)) {
  install.packages('tidyverse')
}
if (!require(knitr)) {
  install.packages('knitr')
}
if (!require(kableExtra)) {
  install.packages('kableExtra')
}
if (!require(DT)) {
  install.packages('DT')
}
if (!require(brms)) {
  install.packages('brms')
}
if (!require(tidybayes)) {
  install.packages('tidybayes')
}

# define palettes
palette = wesanderson::wes_palette("Zissou1", n = 3, type = "continuous")
palette_message = ggsci::pal_simpsons()(15)

# load and tidy data
data = read.csv("../covid19_study1/covid19_study1_clean_long.csv", stringsAsFactors = FALSE) %>%
  bind_rows(read.csv("../covid19_study1_pilot2/covid19_study1_pilot2_clean_long.csv", stringsAsFactors = FALSE)) %>%
  bind_rows(read.csv("../covid19_study1_pilot/covid19_study1_pilot_clean_long.csv", stringsAsFactors = FALSE)) %>%
  mutate(condition = ifelse(condition == "message control", "control", condition),
         group = ifelse(is.na(group), condition, group)) %>%
  filter(!grepl("time", survey_name)) %>%
  mutate(SID = sprintf("%s_%s", study, SID))

# load survey item info and merge with data
items = read.csv("item_text.csv", stringsAsFactors = FALSE)

# subset and tidy message data
messages = data %>%
  mutate(value = as.numeric(value)) %>%
  filter(grepl("msg", survey_name)) %>%
  filter(!grepl("time", survey_name)) %>%
  extract(item, "item", "msg_.*_(.*)") %>%
  spread(survey_name, value) %>%
  mutate(msg_favorability = ((msg_positive - msg_negative) / 2) + 4) %>%
  select(-msg_negative, -msg_positive) %>%
  gather(survey_name, value, contains("msg")) %>%
  mutate(item = sprintf("%s_%s", survey_name, item)) %>%
  extract(item, "message", "msg_.*_([0-9]{2})", remove = FALSE)

merged = messages %>%
  left_join(., items, by = "item") %>%
  mutate(value = as.numeric(value),
         text = ifelse(survey_name == "msg_familiarity", filter(items, item == "msg_familiarity")$text,
                ifelse(survey_name == "msg_rel_self", filter(items, item == "msg_rel_self")$text,
                ifelse(survey_name == "msg_rel_social", filter(items, item == "msg_rel_social")$text,
                ifelse(survey_name == "msg_motiv_self", filter(items, item == "msg_motiv_self")$text,
                ifelse(survey_name == "msg_motiv_other", filter(items, item == "msg_motiv_other")$text,
                ifelse(survey_name == "msg_share", filter(items, item == "msg_share")$text, text)))))),
         measure = ifelse(survey_name == "msg_familiarity", filter(items, item == "msg_familiarity")$measure,
                   ifelse(survey_name == "msg_rel_self", filter(items, item == "msg_rel_self")$measure,
                   ifelse(survey_name == "msg_rel_social", filter(items, item == "msg_rel_social")$measure,
                   ifelse(survey_name == "msg_motiv_self", filter(items, item == "msg_motiv_self")$measure,
                   ifelse(survey_name == "msg_motiv_other", filter(items, item == "msg_motiv_other")$measure,
                   ifelse(survey_name == "msg_favorability", "Message favorability",
                   ifelse(survey_name == "msg_share", filter(items, item == "msg_share")$measure, measure)))))))) %>%
  filter(!is.na(message)) ######## CHECK THIS ##########

# subset studies
pilot1 = merged %>%
  filter(study == "study1_pilot1")

pilot2 = merged %>%
  filter(study == "study1_pilot2")

study1 = merged %>%
  filter(study == "study1")

# tidy data for brm models
data_pred = merged %>%
  filter(!survey_name == "msg_familiarity") %>%
  group_by(survey_name) %>%
  mutate(value_scaled = scale(value, center = TRUE, scale = TRUE)) %>%
  select(study, condition, group, SID, survey_name, message, value, value_scaled)

message information

In this report, we present descriptives for the COVID-19 health messages used in this project. This information is collapsed across message framing conditions. For reports related to specific framing conditions, please see the individual condition reports. For an overview of the project as a whole, please see the project overview reports.

In each study, participants were randomly assigned to either a message framing intervention group (using autonomy-supportive language, encouraging or mocking humor, or descriptive norms, a fact-based control), or a group that saw no messages. Each participant in the intervention and fact-based message control groups saw a series of 5 messages about social distancing related to COVID-19 randomly sampled from a pool of 15 messages previously normed for argument strength (M = 4.16, SD = 0.18, possible range = 1-5). For more information about message norming, please view the reports on argument stength and humor norming.

Each message was created to look like an instagram post that included a visual message (collected online) about COVID-19 accompanied by a “post” about the message. For each message, the post began with the same stem (e.g., “Staying home protects our community by stopping the spread of #covid19.”). The message control condition contained this stem only, whereas the experimental conditions contained additional text framing the messages. Participants then completed various outcome and individual differences measures.

Below are the control messages used in this project to illustrate the range of images included; all message stimuli in this project used the same main images, and condition specific variants can be viewed by clicking on the links under each study (below the “message framing conditions” sub-heading).

These 15 stimuli focused on social distancing were selected from a larger pool health messages, which included additional behaviors related to COVID-19 (e.g. hand washing, flattening the curve, combating misperceptions). For more information about these messages, see the argument strength norming report.

message_info = read.csv("message_info.csv", stringsAsFactors = FALSE) %>%
  rename("message_text" = text) %>%
  mutate(condition = gsub(" ", "", condition),
         condition = tolower(condition),
         condition = ifelse(condition == "control", "control",
                     ifelse(condition == "descriptivenorm", "norm",
                     ifelse(condition == "humor(mocking)", "mocking", 
                     ifelse(condition == "humor(encouraging)", "encouraging", condition))))) %>%
  mutate(message = sprintf("%02d", as.numeric(image_number))) %>%
  filter(message %in% c("06", "07", "08", "09", "10", 
                        "12", "15", "17", "18", "20", 
                        "23", "24", "25", "31", "32")) %>%
  mutate(behavior = "social_distancing") %>%
  arrange(message) %>%
  select(condition, message, message_text, behavior)

pilot 1 (Mar 26, 2020)

message framing conditions

message_num = messages %>%
  filter(study == "study1_pilot1") %>%
  extract(item, "message", "msg_.*_([0-9]{2})", remove = FALSE)

message_info %>% 
  filter(message %in% message_num$message & condition %in% message_num$group) %>%
  select(message, condition, message_text) %>%
  arrange(message, condition) %>%
  DT::datatable(rownames = FALSE, extensions = 'FixedColumns', 
                    options = list(scrollX = TRUE,
                                   scrollY = TRUE),
                caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left;',
                                                  "Message text by condition"))

pilot 2 (Mar 29-30, 2020)

message framing conditions

message_num = messages %>%
  filter(study == "study1_pilot2") %>%
  extract(item, "message", "msg_.*_([0-9]{2})", remove = FALSE)

message_info %>% 
  filter(message %in% message_num$message & condition %in% message_num$group) %>%
  select(message, condition, message_text) %>%
  arrange(message, condition) %>%
  DT::datatable(rownames = FALSE, extensions = 'FixedColumns', 
                    options = list(scrollX = TRUE,
                                   scrollY = TRUE),
                caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left;',
                                                  "Message text by condition"))

full study 1 (Apr 4-6, 2020)

message framing conditions

message_num = messages %>%
  filter(study == "study1") %>%
  extract(item, "message", "msg_.*_([0-9]{2})", remove = FALSE)

message_info %>% 
  filter(message %in% message_num$message & condition %in% message_num$group) %>%
  select(message, condition, message_text) %>%
  arrange(message, condition) %>%
  DT::datatable(rownames = FALSE, extensions = 'FixedColumns', 
                    options = list(scrollX = TRUE,
                                   scrollY = TRUE),
                caption = htmltools::tags$caption(style = 'caption-side: top; text-align: left;',
                                                  "Message text by condition"))


descriptives and distributions

Below are descriptive statistics and response distributions for the message questions in this project.

FIGURE LEGEND

For each message question, individual responses, the density distribution, and a box and whisker plot are shown for each health message.

Each dot represents the response for one individual person.

The box and whisker plots depicted below represent the following statistics: the vertical line in the middle is the median, the box encompasses the inter-quartile range (25th to 75th percentile), and the whiskers capture +/- 1.5 times the inter-quartile range from the box hinge. Points outside the whiskers are likely outliers.

These plots are generated across studies, as well as for each study individually.

plot_desc = function(data, survey, item=TRUE, 
                     palette=palette, min=1, max=7,
                     text_size=3.5, alpha=.1) {

  source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")

if (item == TRUE) {
  labels = data %>%
    filter(grepl(!!(survey), survey_name)) %>%
    group_by(survey_name, text, message) %>%
    summarize(y = (max + min) / 2) %>%
    ungroup() %>%
    mutate(text = sprintf("message %s", message))

  data %>%
    filter(grepl(!!(survey), survey_name)) %>%
    ggplot(aes(message, value)) +
    geom_flat_violin(position = position_nudge(x = .1, y = 0), color = FALSE, fill = palette[1]) +
    geom_point(position = position_jitter(width = .05), size = .5, alpha = alpha, color = palette[1]) +
    geom_boxplot(width = .1, outlier.shape = NA, alpha = 0) +
    geom_text(data = labels, aes(x = message, y = y, label = text), nudge_x = .55, size = text_size) +
    coord_flip() +
    scale_x_discrete(expand = expansion(add = c(0, .75))) +
    labs(x = "", y = "rating") +
    theme_minimal() +
    theme(axis.text.y = element_blank(),
            legend.position = "top")
    
  } else {

  labels = data %>%
    filter(grepl(!!(survey), survey_name)) %>%
    group_by(survey_name, text) %>%
    summarize(y = (max + min) / 2) %>%
  
  data %>%
    filter(grepl(!!(survey), survey_name)) %>%
    ggplot(aes(survey_name, value)) +
    geom_flat_violin(position = position_nudge(x = .1, y = 0), color = FALSE, fill = palette[1]) +
    geom_point(position = position_jitter(width = .05), size = .5, alpha = alpha, color = palette[1]) +
    geom_boxplot(width = .1, outlier.shape = NA, alpha = 0) +
    geom_text(data = labels, aes(x = survey_name, y = y, label = text), nudge_x = .55, size = text_size) +
    coord_flip() +
    scale_x_discrete(expand = expansion(add = c(0, .75))) +
    labs(x = "", y = "rating") +
    theme_minimal() +
    theme(axis.text.y = element_blank(),
          legend.position = "top")
  }
}

table_desc = function(data, survey, condition=FALSE) {
  
  if (condition == FALSE) {
      
      data %>%
        filter(grepl(!!(survey), survey_name)) %>%
        filter(!is.na(value)) %>%
        group_by(survey_name, message) %>%
        summarize(n = n(),
                  min = min(value, na.rm = TRUE),
                  max = max(value, na.rm = TRUE),
                  mean = mean(value, na.rm = TRUE),
                  median = median(value, na.rm = TRUE),
                  sd = sd(value, na.rm = TRUE)) %>%
      mutate_if(is.numeric, round, 2) %>%
      DT::datatable(rownames = FALSE, extensions = 'FixedColumns', 
                    options = list(scrollX = TRUE,
                                   scrollY = TRUE,
                                   fixedColumns = list(leftColumns = 2)))
    
  } else {
  
    data %>%
      filter(grepl(!!(survey), survey_name)) %>%
      filter(!is.na(value)) %>%
      group_by(survey_name, message, group) %>%
      summarize(n = n(),
                min = min(value, na.rm = TRUE),
                max = max(value, na.rm = TRUE),
                mean = mean(value, na.rm = TRUE),
                median = median(value, na.rm = TRUE),
                sd = sd(value, na.rm = TRUE)) %>%
      mutate_if(is.numeric, round, 2) %>%
      DT::datatable(rownames = FALSE, extensions = 'FixedColumns', 
                    options = list(scrollX = TRUE,
                                   scrollY = TRUE,
                                   fixedColumns = list(leftColumns = 2)))
  }
}

familiarity

question = "msg_familiarity"

measure
Message familiarity

question
Have you seen content like this before?

scale
1 = yes, 0 = no

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette, min = 0, max = 1)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette, min = 0, max = 1)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette, min = 0, max = 1)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette, min = 0, max = 1)


favorability

question = "msg_favorability"

measure
Message favorability

questions
1. The message put thoughts in my mind about wanting to avoid physical contact with others.
2. The message put thoughts in my mind about not wanting to avoid physical contact with others.

Message favorability was calculated as follows to convert the difference between items to a 7-point scale:
((item 1 - item 2) / 2) + 4

scale
1 = strongly disagree, 7 = strongly agree

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette)


self-relevance

question = "msg_rel_self"

measure
Message self-relevance

question
This message is relevant to me

scale
1 = strongly disagree, 7 = strongly agree

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette)


social relevance

question = "msg_rel_social"

measure
Message social relevance

question
This message is relevant to other people I know

scale
1 = strongly disagree, 7 = strongly agree

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette)


self motivation

question = "msg_motiv_self"

measure
Message self motivation to social distance

question
This message motivates me to avoid physical contact with others.

scale
1 = strongly disagree, 7 = strongly agree

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette)


other motivation

question = "msg_motiv_other"

measure
Message others motivation to social distance

question
This message would motivate people I know to avoid physical contact with others.

scale
1 = strongly disagree, 7 = strongly agree

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette)


sharing intentions

question = "msg_share"

measure
Message sharing intention

question
I would share this message on social media.

scale
1 = strongly disagree, 7 = strongly agree

all studies

table

table_desc(merged, question)

figure

plot_desc(merged, question, item = TRUE, palette = palette)

pilot 1

table

table_desc(pilot1, question)

figure

plot_desc(pilot1, question, item = TRUE, palette = palette)

pilot 2

table

table_desc(pilot2, question)

figure

plot_desc(pilot2, question, item = TRUE, palette = palette)

study 1

table

table_desc(study1, question)

figure

plot_desc(study1, question, item = TRUE, palette = palette)


correlations

Below are correlation matrices for the message outcomes for each message separately, collapsed across studies.

Please refer to the message information section for the message key.

FIGURE LEGEND

Correlations below are Pearson correlations using pair-wise removal for missing data. These correlations are generated across studies, using the raw (un-winsorized data).


measures
Message sharing intention
Message self-relevance
Message social relevance
Message self motivation to social distance
Message others motivation to social distance
Message favorability

scale
1 = strongly disagree, 7 = strongly agree

plot_corr = function(data, message, text_size=3) {
  
  data %>%
    select(-value_scaled) %>%
    filter(grepl(!!(message), message)) %>%
    unite("survey_name", survey_name, message) %>%
    spread(survey_name, value) %>%
    select(-c(SID, study)) %>%
    GGally::ggcorr(., hjust = 1, size = text_size, 
                   label = TRUE, label_size = 3, label_round = 2) +
    scale_x_discrete(expand = expansion(add = c(3, 0))) +
    theme(legend.position = "none",
          axis.title.x = element_blank(),
          axis.text.x = element_blank(),
          axis.ticks.x = element_blank())
  
}

message 06

data_pred %>%
  plot_corr(., "06")

message 07

data_pred %>%
  plot_corr(., "07")

message 08

data_pred %>%
  plot_corr(., "08")

message 09

data_pred %>%
  plot_corr(., "09")

message 10

data_pred %>%
  plot_corr(., "10")

message 12

data_pred %>%
  plot_corr(., "12")

message 15

data_pred %>%
  plot_corr(., "15")

message 17

data_pred %>%
  plot_corr(., "17")

message 18

data_pred %>%
  plot_corr(., "18")

message 20

data_pred %>%
  plot_corr(., "20")

message 23

data_pred %>%
  plot_corr(., "23")

message 24

data_pred %>%
  plot_corr(., "24")

message 25

data_pred %>%
  plot_corr(., "25")

message 31

data_pred %>%
  plot_corr(., "31")

message 32

data_pred %>%
  plot_corr(., "32")


scatterplots

Below are scatterplots between a single message question and the other message questions in this project, collapsed across studies.

Please refer to the message information section for the message key.

FIGURE LEGEND

Each individual plot shows the association between the ratings for a single measure (x-axis) and the ratings for another measures (y-axis). These associations are depicted across studies for each message separately. These data were not winsorized.

plot_scatter = function(data, survey, alpha=.1) {
  
  data %>%
    select(-value_scaled) %>%
    ungroup() %>%
    mutate(study = ifelse(study == "study1", "full study 1",
                   ifelse(study == "study1_pilot1", "pilot 1", "pilot 2"))) %>%
    spread(survey_name, value) %>%
    gather(survey_name, value, -SID, -study, -group, -condition, -message, -!!(survey)) %>%
    ggplot(aes(get(survey), value, color = message)) +
      geom_point(alpha = alpha, size = .25, position = position_jitter(width = .2, height = .2)) +
      geom_smooth(method = "lm", alpha = 0, size = .5, fullrange = TRUE) +
      facet_wrap(~survey_name, scales = "free") +
      scale_color_manual(values = palette_message, guide = guide_legend(direction = "horizontal",
                                                                        title.position = "top",
                                                                        nrow = 5)) +
      labs(x = sprintf("\n%s", survey), y = "rating\n") +
      theme_minimal(base_size = 12) +
      theme(legend.position = c(.83, .225),
            legend.direction = "horizontal")

}

message favorability

measure
Message favorability

questions
1. The message put thoughts in my mind about wanting to avoid physical contact with others.
2. The message put thoughts in my mind about not wanting to avoid physical contact with others.

Message favorability was calculated as follows to convert the difference between items to a 7-point scale:
((item 1 - item 2) / 2) + 4

scale
1 = strongly disagree, 7 = strongly agree

plot_scatter(data_pred, "msg_favorability")

message self-relevance

measure
Message self-relevance

question
This message is relevant to me

scale
1 = strongly disagree, 7 = strongly agree

plot_scatter(data_pred, "msg_rel_self")

message social relevance

measure
Message social relevance

question
This message is relevant to other people I know

scale
1 = strongly disagree, 7 = strongly agree

plot_scatter(data_pred, "msg_rel_social")

message self motivation

measure
Message self motivation to social distance

question
This message motivates me to avoid physical contact with others.

scale
1 = strongly disagree, 7 = strongly agree

plot_scatter(data_pred, "msg_motiv_self")

message other motivation

measure
Message others motivation to social distance

question
This message would motivate people I know to avoid physical contact with others.

scale
1 = strongly disagree, 7 = strongly agree

plot_scatter(data_pred, "msg_motiv_other")

message sharing intentions

measure
Message sharing intention

question
I would share this message on social media.

scale
1 = strongly disagree, 7 = strongly agree

plot_scatter(data_pred, "msg_share")


predictor comparison

Below are plots comparing the effect sizes of each message predictor for each message outcome, collapsed across studies. In other words, we examined the effect of exposure to each message variant on message-level outcomes. More positive coefficients indicate higher than average effectiveness; negative coefficients indicate lower than average effectiveness, for each outcome.

Please refer to the message information section for the message key.

FIGURE LEGEND

Using Bayesian multilevel modeling, we estimated the effect of each message predictor (y-axis) on the measure specified in the tab. These plots depict the posterior distribution for each predictor, as well as the mean standardized parameter estimate and probability intervals. The thicker line represents the 66% probability interval and the thiner line represents the 95% probability interval. The mean estimate and 95% probability interval are also listed on the left side of the plot.

run_brm = function(data, x, y, survey,
                   x_diff=.2, text_size=3, palette) {
  
  data = data %>%
    select(SID, survey_name, !!(x), !!(y)) %>%
    filter(survey_name %in% !!(survey)) %>%
    rename("x" = !!(x),
           "y" = !!(y))
  
  if (file.exists(sprintf("model_message_%s.RDS", survey))) {
    model = readRDS(sprintf("model_message_%s.RDS", survey))
  } else {
    model = brms::brm(y ~ 0 + (1 | x), data, cores = 8, iter = 1000, silent = TRUE)
    saveRDS(model, sprintf("model_message_%s.RDS", survey))
  }

  model_data = model %>%
    spread_draws(r_x[condition,term]) %>%
    ungroup() %>%
    mutate(condition_mean = r_x,
           condition = sprintf("%02d", condition)) %>%
    group_by(condition) %>%
    mutate(mean = mean(condition_mean, na.rm = TRUE))

  labels = model %>%
    spread_draws(r_x[condition,term]) %>%
    ungroup() %>%
    mutate(condition_mean = r_x,
           condition = sprintf("%02d", condition)) %>%
    group_by(condition) %>%
    tidybayes::mean_qi(condition_mean) %>%
    mutate(label = sprintf("%.2f [%.2f, %.2f]", condition_mean, .lower, .upper)) %>%
    group_by(.point) %>%
    mutate(x = min(.lower) - x_diff)
  
  plot = model_data %>%
    ggplot(aes(x = condition_mean, y = reorder(condition, mean))) +
    geom_vline(xintercept = 0, alpha = .5) +
    stat_halfeyeh(fill = palette[1]) +
    geom_text(data = labels, aes(label = label, x = x, y = condition), size = text_size) +
    scale_x_continuous(expand = expansion(add = c(.12, 0))) +
    labs(x = "\nstandardized parameter estimate", y = "message\n") +
    theme_minimal() +
    theme(legend.position = "top")
    
  
  return(list(model = model, plot = plot))
}

# define common variables
x = "message"
y = "value_scaled"

favorability

measure
Message favorability

questions
1. The message put thoughts in my mind about wanting to avoid physical contact with others.
2. The message put thoughts in my mind about not wanting to avoid physical contact with others.

Message favorability was calculated as follows to convert the difference between items to a 7-point scale:
((item 1 - item 2) / 2) + 4

scale
1 = strongly disagree, 7 = strongly agree

results = run_brm(data_pred, x, y, survey = "msg_favorability", palette = palette)
results$plot

self-relevance

measure
Message self-relevance

question
This message is relevant to me

scale
1 = strongly disagree, 7 = strongly agree

results = run_brm(data_pred, x, y, "msg_rel_self", palette = palette)
results$plot

social relevance

measure
Message social relevance

question
This message is relevant to other people I know

scale
1 = strongly disagree, 7 = strongly agree

results = run_brm(data_pred, x, y, "msg_rel_social", palette = palette)
results$plot

self motivation

measure
Message self motivation to social distance

question
This message motivates me to avoid physical contact with others.

scale
1 = strongly disagree, 7 = strongly agree

results = run_brm(data_pred, x, y, "msg_motiv_self", palette = palette)
results$plot

other motivation

measure
Message others motivation to social distance

question
This message would motivate people I know to avoid physical contact with others.

scale
1 = strongly disagree, 7 = strongly agree

results = run_brm(data_pred, x, y, "msg_motiv_other", palette = palette)
results$plot

sharing intentions

measure
Message sharing intention

question
I would share this message on social media.

scale
1 = strongly disagree, 7 = strongly agree

results = run_brm(data_pred, x, y, "msg_share", palette = palette)
results$plot