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Before delving deeper into the data, I will first give an overview of the data, so that you might get a better “feel” for the basic structure and nature of the responses (statements) that people provided. I will do this by going through each of the six questions to which people responded, and showing a sample set of responses for each question.

Packages

Before loading in any of the data, I will first load some very useful packages in R that will faciliated analysis and cleaning of the data.

# this is actually a series of packages, highly useful for cleaning and visualizing data
library(tidyverse)

Import

Next, I will import the statements, which are stored in a csv file. I will import and then store these statements in an object called “stats_raw” (i.e. raw statements). (Note I have already done some pre-procesing, which I decided to spare you from here. For example, I already eliminated responses from anyone who did not respond to all six questions with both a true statement and a false statement. I also eliminated some empty columns and renamed columns to make their meaning clearer.)

[Note, that you can toggle the display of the code, by clicking the button labeled “hide”/“code” on the right, above each code block,]

# First, load in the statements
stats_raw <- read.csv(file = "statements_final.csv")

Basics

First, let’s look at some very basic features of the data set, to get a better understanding of its basic structure and organization.

To begin, let’s look at the basic dimensions of the dataset – that is the number of rows and columns.

dim(stats_raw)
## [1] 5004   13

Above, we see that the dataset has 5004 rows, and 13 columnns.

Let’s examine what those columns are.

# See the names of the columns
colnames(stats_raw)
##  [1] "per_id"      "stat_id"     "q_num"       "grd_truth"   "statement"  
##  [6] "stat_method" "stat_type"   "order_first" "sex"         "age"        
## [11] "race"        "rand"        "rand_order"

As we see, we have the following columns

Column Name Content
per_id unique identification number for each participant
stat_id unique identification number for each statement
q_num the question (of the six) to which this statement is a response
grd_truth “ground truth”, a column indicating whether the statment is a lie or a truth
statement this contains the entire text of the participants response
stat_method for each lie, the participants written explanation for how they generated that lie (empty for truths)
stat_type for each lie, the participant’s multiple choice response for whether the response is based more on fantasy or reality (empty for truth)
order_first the order in which participants completed the statements (lie followed by truths, or truths followed by lies)
sex the participants self-reported sex
age the participants self-reported age in years
race the participants self-reported race/ethnicity
rand a random number (previously used to sort the statements)
rand_order a number ranking each statement by the previous random number (i.e. lowest random # gets 1, second lowest gets 2, etc)

Finally, let’s take a little preview at a few full rows of the data, to also get a better sense of the structure. (Note: the columns appear in the order “per_id”, “stat_id”, “q_num”, etc. You can view more columns, by clicking the right pointing arrow following at the right of the right-most column name.)

head(stats_raw)

Statements

Okay, now let’s look at some of the responses provided by participants, to each of the six questions.

Question 1: Meeting

The first question instructed participants to “Describe how you met [person you know well].” Let’s look at some of the responses participants gave.

Truthful Responses (Q1)

Here is a sample of 5 truthful responses. (Note: I’ve re-arranged the columns here, so that the statement text appears as the firsct column.)

# display 5 true responses to question 1, by pre-computed random number
stats_raw %>%
  filter(q_num == 1,
         grd_truth == "truth") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Untruthful Responses, i.e. lies (Q1)

And here is a sample of 5 lies.

# display 5 untruthful responses to question 1, by pre-computed random number
stats_raw %>%
  filter(q_num == 1,
         grd_truth == "lie") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Question 2: Regret

The second question asked participants: “What is something in your life that you regret (i.e. wish you had done differently)?”

Truthful Responses (Q2)

Here is a sample of 5 truthful responses.

# display 5 true responses to question 2, by pre-computed random number
stats_raw %>%
  filter(q_num == 2,
         grd_truth == "truth") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Untruthful Responses, i.e. lies (Q2)

And here is a sample of 5 lies.

# display 5 untruthful responses to question 2, by pre-computed random number
stats_raw %>%
  filter(q_num == 2,
         grd_truth == "lie") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Question 3: Yesterday

The third question instructed participants to: “Please describe what you did yesterday.”

Truthful Responses (Q3)

Here is a sample of 5 truthful responses.

# display 5 true responses to question 3, by pre-computed random number
stats_raw %>%
  filter(q_num == 3,
         grd_truth == "truth") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Untruthful Responses, i.e. lies (Q3)

And here is a sample of 5 lies.

# display 5 untruthful responses to question 3, by pre-computed random number
stats_raw %>%
  filter(q_num == 3,
         grd_truth == "lie") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Question 4: Liking

The fourth question instructed participants to: “Give some reason why you like [person you like/dislike]” (where the name of a person they listed as as person they like was piped in when they were asked for a truthful response, and the name of a person they listed as a person they disliked was piped in when they were asked for a un untruthful response).

Truthful Responses (Q4)

Here is a sample of 5 truthful responses.

# display 5 true responses to question 4, by pre-computed random number
stats_raw %>%
  filter(q_num == 4,
         grd_truth == "truth") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Untruthful Responses, i.e. lies (Q4)

And here is a sample of 5 lies.

# display 5 untruthful responses to question 4, by pre-computed random number
stats_raw %>%
  filter(q_num == 4,
         grd_truth == "lie") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Question 5: Strength

The fifth question instructed participants: “Describe a personal strength of yours (i.e. something that you are good at).”

Truthful Responses (Q5)

Here is a sample of 5 truthful responses.

# display 5 true responses to question 5, by pre-computed random number
stats_raw %>%
  filter(q_num == 5,
         grd_truth == "truth") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Untruth Responses, i.e. lies (Q5)

And here is a sample of 5 lies.

# display 5 untruthful responses to question 5, by pre-computed random number
stats_raw %>%
  filter(q_num == 5,
         grd_truth == "lie") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Question 6: Hobby

In the sixth question, participants were asked: “What is a hobby of yours or something that you enjoy doing in your free time? And why do you enjoy it?”

Truthful Responses (Q6)

Here is a sample of 5 truthful responses.

# display 5 true responses to question 6, by pre-computed random number
stats_raw %>%
  filter(q_num == 6,
         grd_truth == "truth") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

Untruth Responses, i.e. lies (Q6)

And here is a sample of 5 lies.

# display 5 untruthful responses to question 6, by pre-computed random number
stats_raw %>%
  filter(q_num == 6,
         grd_truth == "lie") %>%
  arrange(rand_order) %>%
  select(statement,
         everything()) %>%
  top_n(5)
## Selecting by rand_order

END