Five Factor Personality Traits and Satisfaction With Life

library(tidyverse)

---

Introduction

I asked a convenience sample of respondents (n = 83), recruited via Facebook, to complete a survey with items from the Ten Item Personality Inventory (TIPI) and the Satisfaction With Life Scale (SWLS). The survey is available to view here: https://forms.gle/fev4hh1UnU8q9i886. My goal is to examine how each of the five factors of personality (Openness, Conscientiousness, Extraversion, Agreeableness, and Stability) relate to Satisfaction with Life.

To begin, I must load in my survey responses in CSV format. I will load the responses into a dataset called “my_survey”.

my_survey <- read_csv("https://docs.google.com/spreadsheets/d/1639bF1s4AZQlTO1sM1pYOPA_EJjanYKl2mh3ZwK-iRk/export?format=csv")

---

Organizing the Data

Now I will use glimpse to look at the dataset “my_survey” created by importing the survey responses.

glimpse(my_survey)

Rows: 83
Columns: 17
$ Timestamp                                                                                                                                                                                                                                                                                                                                                <chr> ...
$ `What is your gender?`                                                                                                                                                                                                                                                                                                                                   <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Extraverted, Enthusiastic.]`        <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Critical, Quarrelsome. ]`           <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Dependable, Self-Disciplined.]`     <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Anxious, Easily Upset.]`            <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Open to New Experiences, Complex.]` <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Reserved, Quiet.]`                  <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Sympathetic, Warm.]`                <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Disorganized, Careless.]`           <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Calm, Emotionally Stable.]`         <chr> ...
$ `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Conventional, Uncreative.]`         <chr> ...
$ `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [In most ways my life is close to my ideal. ]`                                                                                                                    <chr> ...
$ `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [The conditions of my life are excellent. ]`                                                                                                                      <chr> ...
$ `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [I am satisfied with my life. ]`                                                                                                                                  <chr> ...
$ `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [So far I have gotten the important things I want in life. ]`                                                                                                     <chr> ...
$ `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [If I could live my life over, I would change almost nothing. ]`                                                                                                  <chr> ...

Looking at the glimpse above, I find a problem: the column names are entirely too long and therefore unuseful.

The code below is designed to fix that problem, making the names shorter and useful.

my_survey <- my_survey %>%
  rename(gender = `What is your gender?`) %>%
  rename(Extraverted = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Extraverted, Enthusiastic.]`) %>%
  rename(Critical = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Critical, Quarrelsome. ]`) %>%
  rename(Dependable = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Dependable, Self-Disciplined.]`) %>%
  rename(Anxious = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Anxious, Easily Upset.]`) %>%
  rename(Open = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Open to New Experiences, Complex.]`) %>%
  rename(Reserved = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Reserved, Quiet.]`) %>%
  rename(Sympathetic = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Sympathetic, Warm.]`) %>%
  rename(Disorganized = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Disorganized, Careless.]`) %>%
  rename(Calm = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Calm, Emotionally Stable.]`) %>%
  rename(Conventional = `Here are a number of personality traits that may or may not apply to you.  Please indicate the extent to which you agree or disagree with each statement. You should rate the extent to which the pair of traits applies to you, even if one characteristic applies more strongly than the other.  [I see myself as Conventional, Uncreative.]`) %>%
  rename(Ideal = `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [In most ways my life is close to my ideal. ]`) %>%
  rename(Excellent = `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [The conditions of my life are excellent. ]`) %>%
  rename(Satisfied = `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [I am satisfied with my life. ]`) %>%
  rename(Important = `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [So far I have gotten the important things I want in life. ]`  ) %>%
  rename(Change = `Below are five statements that you may agree or disagree with. Indicate the extent to which you agree or disagree with each statement. Please be open and honest in your responding.  [If I could live my life over, I would change almost nothing. ]`)

Now that the column names are fixed, there is still an issue with my data. The TIPI scores are derived from ten respective questions, with two questions pertaining to each of the five personality factors. The issue with the data is that, in the TIPI scoring system, all answers should correspond to a number between 1 and 7, and also that half of the ten instrument items are to be manually reverse-scored. As the data currently is, there are phrases used to indicate scores rather than numbers (for instance, where there should be a “7” there is instead “Agree Strongly” in the value). So now I will use the following code to assign the proper scores as numbers while I simultaneously account for the need to reverse-score half of the items (note that in half of the variables, “Disagree Strongly” is changed to “1”, while in the other half, “Disagree Strongly” is changed to “7”, to account for reverse scoring). Note that once I have all of these items (where two subscores will later be consolidated to one score mapping to each of the five personality factors) properly formatted, then I can begin to obtain the actual scores instead of just the subscores, which is important.

my_survey <- my_survey %>%
  mutate(Extraverted = as.numeric(recode(Extraverted,
                                         "Disagree Strongly" = "1",
                                         "Disagree Moderately" = "2",
                                         "Disagree a Little" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "5",
                                         "Agree Moderately" = "6",
                                         "Agree Strongly" = "7"))) %>%
    mutate(Critical = as.numeric(recode(Critical,
                                         "Disagree Strongly" = "7",
                                         "Disagree Moderately" = "6",
                                         "Disagree a Little" = "5",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "3",
                                         "Agree Moderately" = "2",
                                         "Agree Strongly" = "1"))) %>%
    mutate(Dependable = as.numeric(recode(Dependable,
                                         "Disagree Strongly" = "1",
                                         "Disagree Moderately" = "2",
                                         "Disagree a Little" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "5",
                                         "Agree Moderately" = "6",
                                         "Agree Strongly" = "7"))) %>%
    mutate(Anxious = as.numeric(recode(Anxious,
                                         "Disagree Strongly" = "7",
                                         "Disagree Moderately" = "6",
                                         "Disagree a Little" = "5",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "3",
                                         "Agree Moderately" = "2",
                                         "Agree Strongly" = "1"))) %>%
    mutate(Open = as.numeric(recode(Open,
                                         "Disagree Strongly" = "1",
                                         "Disagree Moderately" = "2",
                                         "Disagree a Little" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "5",
                                         "Agree Moderately" = "6",
                                         "Agree Strongly" = "7"))) %>%
    mutate(Reserved = as.numeric(recode(Reserved,
                                         "Disagree Strongly" = "7",
                                         "Disagree Moderately" = "6",
                                         "Disagree a Little" = "5",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "3",
                                         "Agree Moderately" = "2",
                                         "Agree Strongly" = "1"))) %>%
    mutate(Sympathetic = as.numeric(recode(Sympathetic,
                                         "Disagree Strongly" = "1",
                                         "Disagree Moderately" = "2",
                                         "Disagree a Little" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "5",
                                         "Agree Moderately" = "6",
                                         "Agree Strongly" = "7"))) %>%
    mutate(Disorganized = as.numeric(recode(Disorganized,
                                         "Disagree Strongly" = "7",
                                         "Disagree Moderately" = "6",
                                         "Disagree a Little" = "5",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "3",
                                         "Agree Moderately" = "2",
                                         "Agree Strongly" = "1"))) %>%
    mutate(Calm = as.numeric(recode(Calm,
                                         "Disagree Strongly" = "1",
                                         "Disagree Moderately" = "2",
                                         "Disagree a Little" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "5",
                                         "Agree Moderately" = "6",
                                         "Agree Strongly" = "7"))) %>%
    mutate(Conventional = as.numeric(recode(Conventional,
                                         "Disagree Strongly" = "7",
                                         "Disagree Moderately" = "6",
                                         "Disagree a Little" = "5",
                                         "Neither Agree nor Disagree" = "4",
                                         "Agree a Little" = "3",
                                         "Agree Moderately" = "2",
                                         "Agree Strongly" = "1"))) %>%
    mutate(Ideal = as.numeric(recode(Ideal,
                                         "Strongly Disagree" = "1",
                                         "Disagree" = "2",
                                         "Slightly Disagree" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Slightly Agree" = "5",
                                         "Agree" = "6",
                                         "Strongly Agree" = "7"))) %>%
    mutate(Excellent = as.numeric(recode(Excellent,
                                         "Strongly Disagree" = "1",
                                         "Disagree" = "2",
                                         "Slightly Disagree" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Slightly Agree" = "5",
                                         "Agree" = "6",
                                         "Strongly Agree" = "7"))) %>%
    mutate(Satisfied = as.numeric(recode(Satisfied,
                                         "Strongly Disagree" = "1",
                                         "Disagree" = "2",
                                         "Slightly Disagree" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Slightly Agree" = "5",
                                         "Agree" = "6",
                                         "Strongly Agree" = "7"))) %>%
    mutate(Important = as.numeric(recode(Important,
                                         "Strongly Disagree" = "1",
                                         "Disagree" = "2",
                                         "Slightly Disagree" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Slightly Agree" = "5",
                                         "Agree" = "6",
                                         "Strongly Agree" = "7"))) %>%
    mutate(Change = as.numeric(recode(Change,
                                         "Strongly Disagree" = "1",
                                         "Disagree" = "2",
                                         "Slightly Disagree" = "3",
                                         "Neither Agree nor Disagree" = "4",
                                         "Slightly Agree" = "5",
                                         "Agree" = "6",
                                         "Strongly Agree" = "7"))) 

Now that that is done, I would like to take a glimpse at “my_survey” again just to see the difference made.

glimpse(my_survey)

Rows: 83
Columns: 17
$ Timestamp    <chr> "4/14/2020 12:13:24", "4/14/2020 12:14:19", "4/14/2020 12:19:22", "4/14/2020 12:21:20", "4/14/2020 12:23:19", "4/14/2020 12:23:44", "4/14/2020 12:24:17...
$ gender       <chr> "Female", "Male", "Female", "Male", "Female", "Male", "Female", "Female", "Female", "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", "Fe...
$ Extraverted  <dbl> 6, 1, 2, 6, 7, 6, 4, 6, 5, 2, 2, 7, 5, 6, 4, 5, 1, 6, 4, 2, 7, 5, 5, 2, 6, 2, 3, 7, 3, 6, 3, 6, 2, 3, 5, 5, 6, 6, 2, 7, 7, 2, 5, 1, 3, 6, 7, 7, 5, 6, 5...
$ Critical     <dbl> 2, 4, 4, 2, 6, 5, 6, 1, 5, 2, 5, 3, 2, 5, 7, 1, 7, 7, 3, 6, 6, 3, 6, 7, 3, 1, 6, 5, 5, 3, 5, 6, 2, 6, 2, 7, 4, 2, 6, 1, 3, 3, 2, 1, 6, 2, 7, 4, 6, 4, 1...
$ Dependable   <dbl> 7, 3, 6, 6, 6, 7, 5, 5, 6, 3, 7, 6, 5, 7, 5, 7, 7, 6, 6, 5, 7, 6, 6, 7, 5, 7, 6, 7, 6, 7, 6, 2, 5, 6, 2, 5, 5, 6, 5, 7, 5, 5, 5, 6, 2, 6, 6, 6, 6, 5, 5...
$ Anxious      <dbl> 2, 3, 5, 3, 6, 6, 6, 2, 2, 2, 3, 3, 4, 6, 7, 3, 1, 7, 3, 5, 2, 4, 6, 7, 7, 3, 6, 4, 4, 5, 5, 4, 6, 4, 5, 5, 1, 3, 3, 2, 2, 4, 6, 1, 5, 2, 6, 2, 4, 4, 1...
$ Open         <dbl> 5, 5, 6, 5, 7, 6, 6, 6, 7, 5, 6, 7, 2, 7, 5, 6, 7, 5, 6, 5, 6, 5, 6, 7, 4, 7, 6, 5, 7, 5, 7, 4, 4, 5, 5, 1, 7, 5, 7, 6, 7, 6, 6, 7, 6, 7, 6, 7, 6, 6, 7...
$ Reserved     <dbl> 5, 2, 1, 4, 7, 6, 5, 6, 7, 1, 6, 4, 7, 3, 5, 1, 1, 6, 3, 3, 7, 2, 2, 4, 7, 2, 2, 2, 2, 2, 2, 4, 4, 2, 7, 5, 5, 4, 3, 3, 6, 3, 3, 1, 3, 7, 7, 2, 3, 3, 3...
$ Sympathetic  <dbl> 7, 7, 5, 4, 7, 6, 7, 4, 7, 5, 2, 7, 3, 4, 6, 7, 7, 4, 6, 6, 7, 6, 6, 7, 7, 6, 5, 7, 6, 7, 7, 4, 5, 6, 5, 7, 6, 6, 7, 6, 6, 6, 6, 6, 7, 6, 6, 7, 7, 6, 5...
$ Disorganized <dbl> 2, 4, 4, 5, 3, 6, 7, 4, 7, 1, 3, 7, 6, 6, 5, 5, 7, 7, 5, 6, 7, 5, 6, 4, 7, 6, 5, 6, 4, 5, 6, 1, 3, 3, 3, 1, 4, 4, 3, 3, 3, 2, 1, 7, 4, 5, 7, 5, 6, 3, 1...
$ Calm         <dbl> 2, 4, 7, 5, 6, 6, 7, 3, 3, 2, 3, 5, 5, 7, 6, 6, 1, 4, 6, 6, 2, 5, 6, 7, 7, 3, 6, 6, 5, 7, 6, 4, 3, 6, 3, 7, 2, 7, 4, 7, 5, 6, 4, 5, 3, 3, 5, 5, 7, 5, 1...
$ Conventional <dbl> 6, 4, 6, 6, 5, 7, 6, 7, 5, 5, 6, 7, 6, 6, 7, 2, 7, 4, 4, 6, 4, 3, 5, 7, 4, 3, 3, 7, 4, 7, 7, 2, 2, 5, 6, 3, 3, 2, 6, 2, 6, 6, 3, 6, 3, 7, 7, 7, 6, 6, 4...
$ Ideal        <dbl> 6, 1, 2, 5, 5, 2, 2, 3, 3, 2, 5, 3, 5, 6, 5, 6, 1, 6, 6, 7, 3, 2, 5, 7, 2, 5, 6, 5, 4, 3, 7, 6, 6, 2, 2, 6, 6, 5, 2, 5, 5, 1, 5, 4, 2, 4, 7, 5, 5, 2, 5...
$ Excellent    <dbl> 5, 1, 3, 5, 5, 6, 4, 4, 5, 2, 6, 6, 5, 7, 6, 6, 1, 6, 6, 6, 4, 3, 6, 7, 7, 6, 5, 5, 5, 5, 6, 6, 6, 2, 4, 6, 5, 3, 4, 6, 7, 2, 5, 5, 3, 4, 7, 3, 5, 4, 5...
$ Satisfied    <dbl> 3, 1, 5, 6, 5, 3, 4, 4, 5, 2, 6, 6, 6, 7, 6, 6, 1, 6, 7, 6, 5, 6, 6, 7, 7, 7, 6, 5, 6, 6, 3, 6, 6, 3, 3, 7, 6, 5, 4, 6, 6, 1, 6, 5, 2, 5, 7, 7, 6, 2, 6...
$ Important    <dbl> 3, 2, 2, 5, 6, 1, 4, 5, 4, 6, 5, 6, 5, 5, 6, 6, 1, 6, 6, 6, 6, 5, 6, 7, 7, 6, 6, 5, 6, 4, 7, 6, 7, 2, 4, 7, 4, 5, 3, 6, 7, 2, 4, 5, 2, 4, 1, 3, 6, 2, 1...
$ Change       <dbl> 5, 1, 1, 4, 5, 1, 5, 2, 3, 7, 5, 5, 4, 7, 3, 6, 1, 5, 7, 5, 2, 5, 6, 5, 4, 3, 6, 4, 5, 3, 2, 4, 5, 2, 1, 7, 3, 5, 1, 6, 3, 1, 4, 4, 2, 4, 7, 7, 6, 2, 4...

That looks much better! The scores are all now in proper numerical format, which is necessary for the graphing and analysis process ahead.

Because the dataset above contains the ten variables (one per column) corresponding to the five personality factor scores, as well as the five items comprising satisfaction with life, I would like to separate out the ten items that correspond to the TIPI. I will create a new dataset called “tenitem” with just these columns so that I can then use them to create new columns containing the actual item scores instead of just the subscores (as the data is currently formatted). Note that later, I will do the same procedure for the SWLS scores.

tenitem <- select(my_survey, Extraverted:Conventional)

The above code selects the columns Extraverted through Conventional, which will give me the ten subscores in a new dataset of their own. I will use the code below now to take a look at the result.

tenitem

The data table above is looking good. So the next step is to create the actual item scores (one each for Extraversion, Agreeableness, Conscientiousness, Stability, and Openness). This is done by adding the two subscores of each together and then dividing each sum by 2. I will then add these new columns into the “tenitem” dataset with the code below.

tenitem <- mutate(tenitem,
       Extraversion = (Extraverted + Reserved) / 2,
       Agreeableness = (Critical + Sympathetic) / 2,
       Conscientiousness = (Dependable + Disorganized) / 2,
       Stability = (Anxious + Calm) / 2,
       Openness = (Open + Conventional) / 2
       )

Now let’s take a look at what “tenitem” looks like now, with both the subscores and the actual scores.

tenitem

As we can see, the new scores we created are in the five rightmost, newly created columns within the dataset. Because I am only interested in working with the actual five factor item scores, and not interested in the subscores, I will now create a new dataset called “tipiscores” and create it by selecting the appropriate five columns from “tenitem” to give a new dataset with only the actual scores and not the subscores.

tipiscores <- select(tenitem, Extraversion:Openness)

Now let’s take a look to see the result of the above code.

tipiscores

Now, I have the five columns each pertaining to the named item score per response. I will do more work with these scores later in this project, but for now, they are ready and waiting.

I want to do the same thing now for the SWLS items. So I will create a new dataset called “swls” which pulls only the five SLWS score columns from the “my_suvey” dataset.

swls <- select(my_survey, Ideal:Change)

Now let’s take a look at the result.

swls

Because in the SWLS, each item can simply be added to derive an overall score (measuring satisfaction with life in one number), I would like to create a new column containing that number per each response. The code below will accomplish that.

swls <- mutate(swls,
       Satisfaction = Ideal + Excellent + Satisfied + Important + Change,
       )

Now I can view the result of that below.

swls

Since I don’t wish to work any further with the individual SLWS items per response, but rather only the overall “Satisfaction” score per response, I will create yet another new dataset called “swlsscores” which will contain only that one column, which I intend to work with later.

swlsscores <- select(swls, Satisfaction)

Now let’s check the result.

swlsscores

Looks good!

Finally, I want to create one more dataset, this time called “allscores” and containing the columns from both the “tipiscores” and “slwsscores” datasets created earliy.

allscores <- cbind(tipiscores, swlsscores)

Let’s check the results.

allscores

Now that I have all of the actual scores I wish to work with in one dataset, I can begin to plot this data in useful ways.

---

Graphing the Data

To begin with, I would like to graphically look at how Extraversion relates to Satisfaction (with life) using a scatterplot. Extraversion scores are plotted along the X-axis, and Satisfaction scores are plotted along the Y-axis. Extraversion (and all TIPI scores) may range from 1 (low) to 5 (high). Satisfaction scores may range from 5 (extremely dissatisfied) to 35 (extremely satisfied). Additonally, I have added a regression line (using formula y ~ x) with a 95% confidence level interval band.

Then, I will repeat the process to show graphs of how each of the other four personality factors relates, respectively, to Satisfaction with Life.

ggplot(allscores, aes(x = Extraversion, y = Satisfaction)) +
geom_point() +
labs(x = "Extraversion", y = "Satisfaction with Life", title = "How Does Extraversion Relate to Satisfaction with Life?") +  
geom_smooth(method = "lm", formula = y~x) +
theme_light()

ggplot(allscores, aes(x = Agreeableness, y = Satisfaction)) +
geom_point() +
labs(x = "Agreeableness", y = "Satisfaction with Life", title = "How Does Agreeableness Relate to Satisfaction with Life?") +  
geom_smooth(method = "lm", formula = y~x) +
theme_light()

ggplot(allscores, aes(x = Conscientiousness, y = Satisfaction)) +
geom_point() +
labs(x = "Conscientiousness", y = "Satisfaction with Life", title = "How Does Conscientiousness Relate to Satisfaction with Life?") +  
geom_smooth(method = "lm", formula = y~x) +
theme_light()

ggplot(allscores, aes(x = Stability, y = Satisfaction)) +
geom_point() +
labs(x = "Stability", y = "Satisfaction with Life", title = "How Does Stability Relate to Satisfaction with Life?") +  
geom_smooth(method = "lm", formula = y~x) +
theme_light()  

ggplot(allscores, aes(x = Openness, y = Satisfaction)) +
geom_point() +
labs(x = "Openness", y = "Satisfaction with Life", title = "How Does Openness Relate to Satisfaction with Life?") +  
geom_smooth(method = "lm", formula = y~x) +
theme_light()

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Looking at the Correlations

Using a Pearson method, the code below displays a correlations table.

cor(allscores, use="complete.obs", method="pearson") 

                  Extraversion Agreeableness Conscientiousness  Stability   Openness Satisfaction
Extraversion         1.0000000   -0.10300504         0.1582706 0.13304150 0.17591150   0.23914822
Agreeableness       -0.1030050    1.00000000         0.1365336 0.25013012 0.16228276  -0.04847516
Conscientiousness    0.1582706    0.13653358         1.0000000 0.25539867 0.46932719   0.31675103
Stability            0.1330415    0.25013012         0.2553987 1.00000000 0.08045716   0.30147165
Openness             0.1759115    0.16228276         0.4693272 0.08045716 1.00000000   0.15169999
Satisfaction         0.2391482   -0.04847516         0.3167510 0.30147165 0.15169999   1.00000000

Most notably for this dataset, we find a correlation coefficient of approximately .32 for Conscientiousness and Satisfaction with Life, and we find a correlation coefficient of approximately .30 for Stability and Satisfaction with Life.

Two obvious problems with these results are that a very small sample size (n = 83) was used, and that a convenience sample was used. There are certainly other limitations to this study. Therefore more rigorous research methods would be necessary, in order to be able to draw any meaningful conclusions.

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References

Diener, E., Emmons, R.A., Larson, R.J., & Griffin, S. (1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71-75.

Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A Very Brief Measure of the Big Five Personality Domains. Journal of Research in Personality, 37, 504-528.