Psych 80 Survey
Julie Cachia
1/10/2023
Load Libraries
Read Data
# fetch surveys from Qualtrics
df <- fetch_survey(surveyID = "SV_eo2QXlH2FyUuKZE", force_request = TRUE, label = FALSE, convert = FALSE)##
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## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## StartDate = col_datetime(format = ""),
## EndDate = col_datetime(format = ""),
## IPAddress = col_character(),
## RecordedDate = col_datetime(format = ""),
## ResponseId = col_character(),
## RecipientLastName = col_logical(),
## RecipientFirstName = col_logical(),
## RecipientEmail = col_logical(),
## ExternalReference = col_logical(),
## DistributionChannel = col_character(),
## UserLanguage = col_character(),
## SUID = col_character(),
## gender_3_TEXT = col_character(),
## ethnicity_10_TEXT = col_character(),
## schoolyear_5_TEXT = col_character(),
## birthorder_5_TEXT = col_character(),
## GPA = col_character(),
## dates = col_character(),
## hoursstudy = col_character(),
## magazine_text = col_character()
## # ... with 14 more columns
## )
## ℹ Use `spec()` for the full column specifications.#write.csv(df, file = "./data/assignment1_raw.csv")df <- df %>%
dplyr::filter(Progress == 100) %>% # only complete responses
rename("mtbi_introvert" = "MyersBriggs_1",
"mtbi_sensing" = "MyersBriggs_2",
"mtbi_thinking" = "MyersBriggs_3",
"mtbi_judging" = "MyersBriggs_4",
"mtbi_extravert" = "MyersBriggs_5",
"mtbi_intuition" = "MyersBriggs_6",
"mtbi_feeling" = "MyersBriggs_7",
"mtbi_perceiving" = "MyersBriggs_8")
mtbi_summary <- df %>%
dplyr::summarise(n = n(),
introvert_mean = mean(mtbi_introvert, na.rm = T),
introvert_stdv = sd(mtbi_introvert, na.rm = T),
sensing_mean = mean(mtbi_sensing, na.rm = T),
sensing_stdv = sd(mtbi_sensing, na.rm = T),
thinking_mean = mean(mtbi_thinking, na.rm = T),
thinking_stdv = sd(mtbi_thinking, na.rm = T),
judging_mean = mean(mtbi_judging, na.rm = T),
judging_stdv = sd(mtbi_judging, na.rm = T),
extravert_mean = mean(mtbi_extravert, na.rm = T),
extravert_stdv = sd(mtbi_extravert, na.rm = T),
intuition_mean = mean(mtbi_intuition, na.rm = T),
intuition_stdv = sd(mtbi_intuition, na.rm = T),
feeling_mean = mean(mtbi_feeling, na.rm = T),
feeling_stdv = sd(mtbi_feeling, na.rm = T),
perceiving_mean = mean(mtbi_perceiving, na.rm = T),
perceiving_stdv = sd(mtbi_perceiving, na.rm = T))
mtbi_table <- pander(mtbi_summary)
mtbi_table| n | introvert_mean | introvert_stdv | sensing_mean | sensing_stdv |
|---|---|---|---|---|
| 134 | 25.74 | 23.68 | 21.73 | 21.19 |
| thinking_mean | thinking_stdv | judging_mean | judging_stdv | extravert_mean |
|---|---|---|---|---|
| 26.84 | 25.48 | 27.1 | 21.11 | 37.19 |
| extravert_stdv | intuition_mean | intuition_stdv | feeling_mean | feeling_stdv |
|---|---|---|---|---|
| 24.26 | 30.39 | 22.94 | 33.5 | 20.32 |
| perceiving_mean | perceiving_stdv |
|---|---|
| 27.77 | 24.91 |
MBTI
mtbi_summary <- df %>%
summarise(n = n(),
introvert_mean = mean(mtbi_introvert, na.rm = T),
introvert_stdv = sd(mtbi_introvert, na.rm = T),
sensing_mean = mean(mtbi_sensing, na.rm = T),
sensing_stdv = sd(mtbi_sensing, na.rm = T),
thinking_mean = mean(mtbi_thinking, na.rm = T),
thinking_stdv = sd(mtbi_thinking, na.rm = T),
judging_mean = mean(mtbi_judging, na.rm = T),
judging_stdv = sd(mtbi_judging, na.rm = T),
extravert_mean = mean(mtbi_extravert, na.rm = T),
extravert_stdv = sd(mtbi_extravert, na.rm = T),
intuition_mean = mean(mtbi_intuition, na.rm = T),
intuition_stdv = sd(mtbi_intuition, na.rm = T),
feeling_mean = mean(mtbi_feeling, na.rm = T),
feeling_stdv = sd(mtbi_feeling, na.rm = T),
perceiving_mean = mean(mtbi_perceiving, na.rm = T),
perceiving_stdv = sd(mtbi_perceiving, na.rm = T))
pander(mtbi_summary)| n | introvert_mean | introvert_stdv | sensing_mean | sensing_stdv |
|---|---|---|---|---|
| 134 | 25.74 | 23.68 | 21.73 | 21.19 |
| thinking_mean | thinking_stdv | judging_mean | judging_stdv | extravert_mean |
|---|---|---|---|---|
| 26.84 | 25.48 | 27.1 | 21.11 | 37.19 |
| extravert_stdv | intuition_mean | intuition_stdv | feeling_mean | feeling_stdv |
|---|---|---|---|---|
| 24.26 | 30.39 | 22.94 | 33.5 | 20.32 |
| perceiving_mean | perceiving_stdv |
|---|---|
| 27.77 | 24.91 |
# save mean and stdv
introvert_mean <- mtbi_summary$introvert_mean
introvert_stdv <- mtbi_summary$introvert_stdv
sensing_mean <- mtbi_summary$sensing_mean
sensing_stdv <- mtbi_summary$sensing_stdv
thinking_mean <- mtbi_summary$thinking_mean
thinking_stdv <- mtbi_summary$thinking_stdv
judging_mean <- mtbi_summary$judging_mean
judging_stdv <- mtbi_summary$judging_stdv
extravert_mean <- mtbi_summary$extravert_mean
extravert_stdv <- mtbi_summary$extravert_stdv
intuition_mean <- mtbi_summary$intuition_mean
intuition_stdv <- mtbi_summary$intuition_stdv
feeling_mean <- mtbi_summary$feeling_mean
feeling_stdv <- mtbi_summary$feeling_stdv
perceiving_mean <- mtbi_summary$perceiving_mean
perceiving_stdv <- mtbi_summary$perceiving_stdv
# plot introvert
introvert_plot <- ggplot(df, aes(mtbi_introvert)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = introvert_mean,
color = "red",
size = 1.5) +
xlab("introversion")
introvert_plot## Warning: Removed 52 rows containing non-finite values (stat_bin).
# plot sensing
sensing_plot <- ggplot(df, aes(mtbi_sensing)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = sensing_mean,
color = "red",
size = 1.5) +
xlab("sensing")
sensing_plot## Warning: Removed 71 rows containing non-finite values (stat_bin).
# plot thinking
thinking_plot <- ggplot(df, aes(mtbi_thinking)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = thinking_mean,
color = "red",
size = 1.5) +
xlab("thinking")
thinking_plot## Warning: Removed 84 rows containing non-finite values (stat_bin).
# plot judging
judging_plot <- ggplot(df, aes(mtbi_judging)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = judging_mean,
color = "red",
size = 1.5) +
xlab("judging")
judging_plot## Warning: Removed 47 rows containing non-finite values (stat_bin).
# plot extravert
extravert_plot <- ggplot(df, aes(mtbi_extravert)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = extravert_mean,
color = "red",
size = 1.5) +
xlab("extraversion")
extravert_plot## Warning: Removed 44 rows containing non-finite values (stat_bin).
# plot intuition
intuition_plot <- ggplot(df, aes(mtbi_intuition)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = intuition_mean,
color = "red",
size = 1.5) +
xlab("intuition")
intuition_plot## Warning: Removed 42 rows containing non-finite values (stat_bin).
# plot feeling
feeling_plot <- ggplot(df, aes(mtbi_feeling)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = feeling_mean,
color = "red",
size = 1.5) +
xlab("feeling")
feeling_plot## Warning: Removed 23 rows containing non-finite values (stat_bin).
# plot perceiving
perceiving_plot <- ggplot(df, aes(mtbi_perceiving)) +
geom_histogram(color = "black",
fill = "white",
bins = 20) +
geom_vline(xintercept = perceiving_mean,
color = "red",
size = 1.5) +
xlab("perceiving")
perceiving_plot## Warning: Removed 70 rows containing non-finite values (stat_bin).
NEO
Neuroticism
Q1 I am not a worrier (R)
Q6 I often feel inferior to others
Q11 When I’m under a great deal of stress, sometimes I feel
like going to pieces
Q16 I rarely feel lonely or blue (R)
Q21 I often feel tense and jittery
Q26 Sometimes I feel completely worthless
Q31 I rarely feel fearful or anxious (R)
Q36 I often get angry at the way people treat me
Q41 Too often, when things go wrong, I get discouraged and feel
like giving up
Q46 I am seldom sad or depressed (R)
Q51 I often feel helpless and want someone else to solve my
problems
Q56 At times I have been so ashamed I just wanted to hide
# reverse code items
df$NEO1r <- 6 - df$NEO1
df$NEO16r <- 6 - df$NEO16
df$NEO31r <- 6 - df$NEO31
df$NEO46r <- 6 - df$NEO46
# calculate descriptives
df$neuroticism <-
rowMeans(subset
(df,
select =
c("NEO1r", "NEO6", "NEO11", "NEO16r", "NEO21", "NEO26",
"NEO31r", "NEO36", "NEO41", "NEO46r", "NEO51", "NEO56"),
na.rm = T))
# save mean and stdv
neuroticism_mean <- mean(df$neuroticism, na.rm = T)
neuroticism_mean## [1] 3.050254neuroticism_stdv <- sd(df$neuroticism, na.rm = T)
neuroticism_stdv## [1] 0.716762# plot
neuroticism_plot <- ggplot(df, aes(neuroticism)) +
geom_histogram(color = "black",
fill = "white",
binwidth = 0.2) +
geom_vline(xintercept = neuroticism_mean,
color = "red",
size = 1.5)
neuroticism_plot## Warning: Removed 3 rows containing non-finite values (stat_bin).
Extraversion
Q2 I like to have a lot of people around me
Q7 I laugh easily
Q12 I don’t consider myself especially light-hearted (R)
Q17 I really enjoy talking to people
Q22 I like to be where the action is
Q27 I usually prefer to do things alone (R)
Q32 I often feel as if I’m bursting with energy
Q37 I am a cheerful, high-spirited person
Q42 I am not a cheerful person (R)
Q47 My life is fast paced
Q52 I am a very active person
Q57 I would rather go my own way than be a leader of others
(R)
# reverse code items
df$NEO12r <- 6 - df$NEO12
df$NEO27r <- 6 - df$NEO27
df$NEO42r <- 6 - df$NEO42
df$NEO57r <- 6 - df$NEO57
# calculate descriptives
df$extraversion <-
rowMeans(subset
(df,
select =
c("NEO2", "NEO7", "NEO12r", "NEO17", "NEO22", "NEO27r",
"NEO32", "NEO37", "NEO42r", "NEO47", "NEO52", "NEO57r"),
na.rm = T))
# save mean and stdv
extraversion_mean <- mean(df$extraversion, na.rm = T)
extraversion_mean## [1] 3.583333extraversion_stdv <- sd(df$extraversion, na.rm = T)
extraversion_stdv## [1] 0.5301016# plot
extraversion_plot <- ggplot(df, aes(extraversion)) +
geom_histogram(color = "black",
fill = "white",
binwidth = 0.2) +
geom_vline(xintercept = extraversion_mean,
color = "red",
size = 1.5)
extraversion_plot## Warning: Removed 4 rows containing non-finite values (stat_bin).
Openness
Q3 I don’t like to wast time daydreaming (R)
Q8 Once I find the right way to do something, I stick to it
(R)
Q13 I am intrigued by the patterns I find in art and
nature
Q18 I believe in letting students hear controversial speakers
(R)
Q23 Poetry has little or no effect on me (R)
Q28 I often try new and foreign foods
Q33 I seldom notice the moods or feelings that different
environments produce (R)
Q38 I believe we should look to our religious authorities for
decisions on moral issues (R)
Q43 Sometimes when I am reading poetry or looking at a work of
art, I feel a chill or wave
Q48 I have little interest in speculating on the nature of the
universe or the human condition (R)
Q53 I have a lot of intellectual curiosity
Q58 I often enjoy playing with theories or abstract ideas
# reverse code items
df$NEO3r <- 6 - df$NEO3
df$NEO8r <- 6 - df$NEO8
df$NEO18r <- 6 - df$NEO18
df$NEO23r <- 6 - df$NEO23
df$NEO33r <- 6 - df$NEO33
df$NEO38r <- 6 - df$NEO38
df$NEO48r <- 6 - df$NEO48
# calculate descriptives
df$openness <-
rowMeans(subset
(df,
select =
c("NEO3r", "NEO8r", "NEO13", "NEO18r", "NEO23r", "NEO28",
"NEO33r", "NEO38r", "NEO43", "NEO48r", "NEO53", "NEO58"),
na.rm = T))
# save mean and stdv
openness_mean <- mean(df$openness, na.rm = T)
openness_mean## [1] 3.543928openness_stdv <- sd(df$openness, na.rm = T)
openness_stdv## [1] 0.4330224# plot
openness_plot <- ggplot(df, aes(openness)) +
geom_histogram(color = "black",
fill = "white",
binwidth = 0.2) +
geom_vline(xintercept = openness_mean,
color = "red",
size = 1.5)
openness_plot## Warning: Removed 5 rows containing non-finite values (stat_bin).
Agreeableness
Q4 I try to be courteous to everyone I meet
Q9 I often get into arguments with my family and co-workers
(R)
Q14 Some people think I’m selfish or egotistical (R)
Q19 I would rather cooperate with others than compete with
them
Q24 I tend to be cynical and skeptical of others’ intentions
(R)
Q29 I believe that most people will take advantage of you if
you let them (R)
Q34 Most people I know like me
Q39 Some people think of me as cold and calculating (R)
Q44 I’m hard-headed and tough-minded in my attitudes (R)
Q49 I generally try to be thoughtful and considerate
Q54 If I don’t like people, I let them know it (R)
Q59 If necessary, I am willing to manipulate people to get what
I want (R)
# reverse code items
df$NEO9r <- 6 - df$NEO9
df$NEO14r <- 6 - df$NEO14
df$NEO24r <- 6 - df$NEO24
df$NEO29r <- 6 - df$NEO29
df$NEO39r <- 6 - df$NEO39
df$NEO44r <- 6 - df$NEO44
df$NEO54r <- 6 - df$NEO54
df$NEO59r <- 6 - df$NEO59
# calculate descriptives
df$agreeableness <-
rowMeans(subset
(df,
select =
c("NEO4", "NEO9r", "NEO14r", "NEO19", "NEO24r", "NEO29r",
"NEO34", "NEO39r", "NEO44r", "NEO49", "NEO54r", "NEO59r"),
na.rm = T))
# save mean and stdv
agreeableness_mean <- mean(df$agreeableness, na.rm = T)
agreeableness_mean## [1] 3.747455agreeableness_stdv <- sd(df$agreeableness, na.rm = T)
agreeableness_stdv## [1] 0.5167017# plot
agreeableness_plot <- ggplot(df, aes(agreeableness)) +
geom_histogram(color = "black",
fill = "white",
binwidth = 0.2) +
geom_vline(xintercept = agreeableness_mean,
color = "red",
size = 1.5)
agreeableness_plot## Warning: Removed 3 rows containing non-finite values (stat_bin).
Conscientiousness
Q5 I keep my belongings clean and neat
Q10 I’m pretty good about pacing myself so as to get things
done on time
Q15 I am not a very methodical person (R)
Q20 I try to perform all the tasks assigned to me
conscientiously
Q25 I have a clear set of goals and work toward them in an
orderly fashion
Q30 I waste a lot of time before settling down to work
(R)
Q35 I work hard to accomplish my goals
Q40 When I make a commitment, I can always be counted on to
follow through
Q45 Sometimes I’m not as dependable or reliable as I should be
(R)
Q50 I am a productive person who always gets the job done
Q55 I never seem to be able to get organized (R)
Q60 I strive for excellence in everything I do
# reverse code items
df$NEO15r <- 6 - df$NEO15
df$NEO30r <- 6 - df$NEO30
df$NEO45r <- 6 - df$NEO45
df$NEO55r <- 6 - df$NEO55
# calculate descriptives
df$conscientiousness <-
rowMeans(subset
(df,
select =
c("NEO5", "NEO10", "NEO15r", "NEO20", "NEO25", "NEO30r",
"NEO35", "NEO40", "NEO45r", "NEO50", "NEO55r", "NEO60"),
na.rm = T))
# save mean and stdv
conscientiousness_mean <- mean(df$conscientiousness, na.rm = T)
conscientiousness_mean## [1] 3.668561conscientiousness_stdv <- sd(df$conscientiousness, na.rm = T)
conscientiousness_stdv## [1] 0.5232574# plot
conscientiousness_plot <- ggplot(df, aes(conscientiousness)) +
geom_histogram(color = "black",
fill = "white",
binwidth = 0.2) +
geom_vline(xintercept = conscientiousness_mean,
color = "red",
size = 1.5)
conscientiousness_plot## Warning: Removed 2 rows containing non-finite values (stat_bin).
Correlations with MBTI
Introversion (MBTI) and NEO
# intraversion (MBTI) and openness (NEO)
cor_introvertMBTI_opennessNEO <-
cor.test(df$mtbi_introvert,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_introvertMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_introvert and df$openness
## t = -0.81641, df = 78, p-value = 0.4168
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3055841 0.1303048
## sample estimates:
## cor
## -0.09204744# intraversion (MBTI) and conscientiousness (NEO)
cor_introvertMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_introvert,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_introvertMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_introvert and df$conscientiousness
## t = -0.85732, df = 79, p-value = 0.3939
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3079047 0.1249589
## sample estimates:
## cor
## -0.09601008# intraversion (MBTI) and extraversion (NEO)
cor_introvertMBTI_extraversionNEO <-
cor.test(df$mtbi_introvert,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_introvertMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_introvert and df$extraversion
## t = -3.2701, df = 77, p-value = 0.001609
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5294341 -0.1388099
## sample estimates:
## cor
## -0.3492025# intraversion (MBTI) and agreeableness (NEO)
cor_introvertMBTI_agreeablenessNEO <-
cor.test(df$mtbi_introvert,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_introvertMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_introvert and df$agreeableness
## t = -0.18831, df = 78, p-value = 0.8511
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2399105 0.1993332
## sample estimates:
## cor
## -0.02131733# intraversion (MBTI) and neuroticism (NEO)
cor_introvertMBTI_neuroticismNEO <-
cor.test(df$mtbi_introvert,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_introvertMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_introvert and df$neuroticism
## t = -0.16055, df = 78, p-value = 0.8729
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2369463 0.2023494
## sample estimates:
## cor
## -0.01817562Sensing (MBTI) and NEO
# sensing (MBTI) and openness (NEO)
cor_sensingMBTI_opennessNEO <-
cor.test(df$mtbi_sensing,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_sensingMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_sensing and df$openness
## t = -0.080465, df = 58, p-value = 0.9361
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2637820 0.2440143
## sample estimates:
## cor
## -0.010565# sensing (MBTI) and conscientiousness (NEO)
cor_sensingMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_sensing,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_sensingMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_sensing and df$conscientiousness
## t = -0.15783, df = 60, p-value = 0.8751
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2687720 0.2305701
## sample estimates:
## cor
## -0.0203713# sensing (MBTI) and extraversion (NEO)
cor_sensingMBTI_extraversionNEO <-
cor.test(df$mtbi_sensing,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_sensingMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_sensing and df$extraversion
## t = -0.14691, df = 58, p-value = 0.8837
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2718801 0.2357925
## sample estimates:
## cor
## -0.01928693# sensing (MBTI) and agreeableness (NEO)
cor_sensingMBTI_agreeablenessNEO <-
cor.test(df$mtbi_sensing,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_sensingMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_sensing and df$agreeableness
## t = -0.38094, df = 60, p-value = 0.7046
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2952651 0.2031411
## sample estimates:
## cor
## -0.04911938# sensing (MBTI) and neuroticism (NEO)
cor_sensingMBTI_neuroticismNEO <-
cor.test(df$mtbi_sensing,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_sensingMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_sensing and df$neuroticism
## t = -0.41872, df = 59, p-value = 0.6769
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3021114 0.2001323
## sample estimates:
## cor
## -0.05443168Thinking (MBTI) and NEO
# thinking (MBTI) and openness (NEO)
cor_thinkingMBTI_opennessNEO <-
cor.test(df$mtbi_thinking,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_thinkingMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_thinking and df$openness
## t = -0.85788, df = 46, p-value = 0.3954
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3955193 0.1645135
## sample estimates:
## cor
## -0.1254869# thinking (MBTI) and conscientiousness (NEO)
cor_thinkingMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_thinking,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_thinkingMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_thinking and df$conscientiousness
## t = -0.33489, df = 47, p-value = 0.7392
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3255215 0.2356385
## sample estimates:
## cor
## -0.04879102# thinking (MBTI) and extraversion (NEO)
cor_thinkingMBTI_extraversionNEO <-
cor.test(df$mtbi_thinking,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_thinkingMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_thinking and df$extraversion
## t = -0.10482, df = 45, p-value = 0.917
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3014379 0.2727671
## sample estimates:
## cor
## -0.01562348# thinking (MBTI) and agreeableness (NEO)
cor_thinkingMBTI_agreeablenessNEO <-
cor.test(df$mtbi_thinking,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_thinkingMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_thinking and df$agreeableness
## t = -0.57628, df = 46, p-value = 0.5672
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3601343 0.2043886
## sample estimates:
## cor
## -0.08466283# thinking (MBTI) and neuroticism (NEO)
cor_thinkingMBTI_neuroticismNEO <-
cor.test(df$mtbi_thinking,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_thinkingMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_thinking and df$neuroticism
## t = -1.583, df = 46, p-value = 0.1203
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.48040186 0.06076683
## sample estimates:
## cor
## -0.2272923Judging (MBTI) and NEO
# judging (MBTI) and openness (NEO)
cor_judgingMBTI_opennessNEO <-
cor.test(df$mtbi_judging,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_judgingMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_judging and df$openness
## t = -0.049206, df = 83, p-value = 0.9609
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2182738 0.2079625
## sample estimates:
## cor
## -0.005400986# judging (MBTI) and conscientiousness (NEO)
cor_judgingMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_judging,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_judgingMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_judging and df$conscientiousness
## t = 2.3168, df = 85, p-value = 0.02292
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03486026 0.43217944
## sample estimates:
## cor
## 0.2437188# judging (MBTI) and extraversion (NEO)
cor_judgingMBTI_extraversionNEO <-
cor.test(df$mtbi_judging,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_judgingMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_judging and df$extraversion
## t = 0.75098, df = 83, p-value = 0.4548
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1333063 0.2901950
## sample estimates:
## cor
## 0.08215183# judging (MBTI) and agreeableness (NEO)
cor_judgingMBTI_agreeablenessNEO <-
cor.test(df$mtbi_judging,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_judgingMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_judging and df$agreeableness
## t = -0.14442, df = 84, p-value = 0.8855
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2268737 0.1967764
## sample estimates:
## cor
## -0.01575577# judging (MBTI) and neuroticism (NEO)
cor_judgingMBTI_neuroticismNEO <-
cor.test(df$mtbi_judging,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_judgingMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_judging and df$neuroticism
## t = -1.2559, df = 84, p-value = 0.2126
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.33791637 0.07836854
## sample estimates:
## cor
## -0.135761Extraversion (MBTI) and NEO
# extraversion (MBTI) and openness (NEO)
cor_extravertMBTI_opennessNEO <-
cor.test(df$mtbi_extravert,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_extravertMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_extravert and df$openness
## t = -0.81629, df = 84, p-value = 0.4166
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2950427 0.1255216
## sample estimates:
## cor
## -0.08871307# extraversion (MBTI) and conscientiousness (NEO)
cor_extravertMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_extravert,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_extravertMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_extravert and df$conscientiousness
## t = 0.77984, df = 87, p-value = 0.4376
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1271461 0.2866007
## sample estimates:
## cor
## 0.08331681# extraversion (MBTI) and extraversion (NEO)
cor_extravertMBTI_extraversionNEO <-
cor.test(df$mtbi_extravert,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_extravertMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_extravert and df$extraversion
## t = 4.5086, df = 85, p-value = 2.073e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2519798 0.5949056
## sample estimates:
## cor
## 0.4393098# extraversion (MBTI) and agreeableness (NEO)
cor_extravertMBTI_agreeablenessNEO <-
cor.test(df$mtbi_extravert,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_extravertMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_extravert and df$agreeableness
## t = -1.4646, df = 87, p-value = 0.1466
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.35200700 0.05490865
## sample estimates:
## cor
## -0.1551219# extraversion (MBTI) and neuroticism (NEO)
cor_extravertMBTI_neuroticismNEO <-
cor.test(df$mtbi_extravert,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_extravertMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_extravert and df$neuroticism
## t = -1.5454, df = 86, p-value = 0.1259
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.36137722 0.04667286
## sample estimates:
## cor
## -0.1643762Intuition (MBTI) and NEO
# intuition (MBTI) and openness (NEO)
cor_intuitionMBTI_opennessNEO <-
cor.test(df$mtbi_intuition,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_intuitionMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_intuition and df$openness
## t = 0.33489, df = 88, p-value = 0.7385
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1726908 0.2409868
## sample estimates:
## cor
## 0.03567616# intuition (MBTI) and conscientiousness (NEO)
cor_intuitionMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_intuition,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_intuitionMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_intuition and df$conscientiousness
## t = 0.23141, df = 90, p-value = 0.8175
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1813372 0.2280643
## sample estimates:
## cor
## 0.02438596# intuition (MBTI) and extraversion (NEO)
cor_intuitionMBTI_extraversionNEO <-
cor.test(df$mtbi_intuition,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_intuitionMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_intuition and df$extraversion
## t = 1.9993, df = 88, p-value = 0.04866
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.001410813 0.398337398
## sample estimates:
## cor
## 0.208441# intuition (MBTI) and agreeableness (NEO)
cor_intuitionMBTI_agreeablenessNEO <-
cor.test(df$mtbi_intuition,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_intuitionMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_intuition and df$agreeableness
## t = -0.99084, df = 89, p-value = 0.3245
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3038623 0.1037220
## sample estimates:
## cor
## -0.1044541# intuition (MBTI) and neuroticism (NEO)
cor_intuitionMBTI_neuroticismNEO <-
cor.test(df$mtbi_intuition,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_intuitionMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_intuition and df$neuroticism
## t = -2.5857, df = 89, p-value = 0.01134
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.44599980 -0.06175122
## sample estimates:
## cor
## -0.2643344Feeling (MBTI) and NEO
# feeling (MBTI) and openness (NEO)
cor_feelingMBTI_opennessNEO <-
cor.test(df$mtbi_feeling,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_feelingMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_feeling and df$openness
## t = 1.2208, df = 106, p-value = 0.2249
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.0728492 0.3000434
## sample estimates:
## cor
## 0.1177457# feeling (MBTI) and conscientiousness (NEO)
cor_feelingMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_feeling,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_feelingMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_feeling and df$conscientiousness
## t = -0.08361, df = 109, p-value = 0.9335
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1941113 0.1786515
## sample estimates:
## cor
## -0.008008099# feeling (MBTI) and extraversion (NEO)
cor_feelingMBTI_extraversionNEO <-
cor.test(df$mtbi_feeling,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_feelingMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_feeling and df$extraversion
## t = 2.623, df = 107, p-value = 0.009989
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06048788 0.41472050
## sample estimates:
## cor
## 0.245793# feeling (MBTI) and agreeableness (NEO)
cor_feelingMBTI_agreeablenessNEO <-
cor.test(df$mtbi_feeling,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_feelingMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_feeling and df$agreeableness
## t = 2.708, df = 108, p-value = 0.007872
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06812993 0.41958594
## sample estimates:
## cor
## 0.2521547# feeling (MBTI) and neuroticism (NEO)
cor_feelingMBTI_neuroticismNEO <-
cor.test(df$mtbi_feeling,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_feelingMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_feeling and df$neuroticism
## t = -1.1497, df = 108, p-value = 0.2528
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.29120144 0.07891081
## sample estimates:
## cor
## -0.1099553Perceiving (MBTI) and NEO
# perceiving (MBTI) and openness (NEO)
cor_perceivingMBTI_opennessNEO <-
cor.test(df$mtbi_perceiving,
df$openness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_perceivingMBTI_opennessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_perceiving and df$openness
## t = -0.4959, df = 59, p-value = 0.6218
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.311199 0.190484
## sample estimates:
## cor
## -0.06442713# perceiving (MBTI) and conscientiousness (NEO)
cor_perceivingMBTI_conscientiousnessNEO <-
cor.test(df$mtbi_perceiving,
df$conscientiousness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_perceivingMBTI_conscientiousnessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_perceiving and df$conscientiousness
## t = 0.77068, df = 61, p-value = 0.4439
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1532967 0.3377457
## sample estimates:
## cor
## 0.09819805# perceiving (MBTI) and extraversion (NEO)
cor_perceivingMBTI_extraversionNEO <-
cor.test(df$mtbi_perceiving,
df$extraversion,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_perceivingMBTI_extraversionNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_perceiving and df$extraversion
## t = 0.51819, df = 59, p-value = 0.6063
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1876920 0.3138118
## sample estimates:
## cor
## 0.06731015# perceiving (MBTI) and agreeableness (NEO)
cor_perceivingMBTI_agreeablenessNEO <-
cor.test(df$mtbi_perceiving,
df$agreeableness,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_perceivingMBTI_agreeablenessNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_perceiving and df$agreeableness
## t = 0.056918, df = 61, p-value = 0.9548
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2409126 0.2545927
## sample estimates:
## cor
## 0.007287394# perceiving (MBTI) and neuroticism (NEO)
cor_perceivingMBTI_neuroticismNEO <-
cor.test(df$mtbi_perceiving,
df$neuroticism,
method = "pearson",
conf.level = .95,
na.action = na.omit)
cor_perceivingMBTI_neuroticismNEO##
## Pearson's product-moment correlation
##
## data: df$mtbi_perceiving and df$neuroticism
## t = -0.01789, df = 60, p-value = 0.9858
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2519325 0.2476014
## sample estimates:
## cor
## -0.002309625NEO Correlations with Outcomes
# convert character responses to numeric
df$hoursstudy <- as.numeric(df$hoursstudy)## Warning: NAs introduced by coerciondf$GPA <- as.numeric(df$GPA)## Warning: NAs introduced by coerciondf$friends <- as.numeric(df$friends)
df$dates <- as.numeric(df$dates)## Warning: NAs introduced by coercioncor_conscientious_study <-
cor.test(df$conscientiousness,
df$hoursstudy,
method = "pearson",
conf.level = .95)
cor_conscientious_study##
## Pearson's product-moment correlation
##
## data: df$conscientiousness and df$hoursstudy
## t = 0.2228, df = 121, p-value = 0.8241
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.157348 0.196580
## sample estimates:
## cor
## 0.02025041df %>%
ggplot(aes(x = conscientiousness, y = hoursstudy, na.rm = T)) +
geom_point() +
stat_smooth(method = "lm") +
theme_bw()## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 11 rows containing non-finite values (stat_smooth).## Warning: Removed 11 rows containing missing values (geom_point).
cor_conscientious_gpa <-
cor.test(df$conscientiousness,
df$GPA,
method = "pearson",
conf.level = .95)
cor_conscientious_gpa##
## Pearson's product-moment correlation
##
## data: df$conscientiousness and df$GPA
## t = 2.7613, df = 129, p-value = 0.006597
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06744664 0.39188504
## sample estimates:
## cor
## 0.2362391cor_neurotic_study <-
cor.test(df$neuroticism,
df$hoursstudy,
method = "pearson",
conf.level = .95)
cor_neurotic_study##
## Pearson's product-moment correlation
##
## data: df$neuroticism and df$hoursstudy
## t = 0.0050004, df = 120, p-value = 0.996
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1773188 0.1782029
## sample estimates:
## cor
## 0.0004564693cor_neurotic_gpa <-
cor.test(df$neuroticism,
df$GPA,
method = "pearson",
conf.level = .95)
cor_neurotic_gpa##
## Pearson's product-moment correlation
##
## data: df$neuroticism and df$GPA
## t = -1.8234, df = 128, p-value = 0.07058
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32246506 0.01344074
## sample estimates:
## cor
## -0.1591136cor_extravert_friends <-
cor.test(df$extraversion,
df$friends,
method = "pearson",
conf.level = .95)
cor_extravert_friends##
## Pearson's product-moment correlation
##
## data: df$extraversion and df$friends
## t = 3.2549, df = 127, p-value = 0.001454
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1098972 0.4297237
## sample estimates:
## cor
## 0.2774805cor_extravert_dates <-
cor.test(df$extraversion,
df$dates,
method = "pearson",
conf.level = .95)
cor_extravert_dates##
## Pearson's product-moment correlation
##
## data: df$extraversion and df$dates
## t = 1.9128, df = 124, p-value = 0.05808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00578337 0.33430274
## sample estimates:
## cor
## 0.1692948cor_agreeable_friends <-
cor.test(df$agreeableness,
df$friends,
method = "pearson",
conf.level = .95)
cor_agreeable_friends##
## Pearson's product-moment correlation
##
## data: df$agreeableness and df$friends
## t = 2.2383, df = 128, p-value = 0.02693
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02264447 0.35441648
## sample estimates:
## cor
## 0.1940739cor_agreeable_dates <-
cor.test(df$agreeableness,
df$dates,
method = "pearson",
conf.level = .95)
cor_agreeable_dates##
## Pearson's product-moment correlation
##
## data: df$agreeableness and df$dates
## t = -0.037594, df = 125, p-value = 0.9701
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1774732 0.1709523
## sample estimates:
## cor
## -0.00336253