Untitled
Michael
2024-05-08
#Read the dataset into R
library (tidyverse)## Warning: package 'ggplot2' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dlookr)## Warning: package 'dlookr' was built under R version 4.3.3## Registered S3 methods overwritten by 'dlookr':
## method from
## plot.transform scales
## print.transform scales
##
## Attaching package: 'dlookr'
##
## The following object is masked from 'package:tidyr':
##
## extract
##
## The following object is masked from 'package:base':
##
## transformlibrary(caret)## Warning: package 'caret' was built under R version 4.3.3## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:purrr':
##
## liftlibrary(flextable)## Warning: package 'flextable' was built under R version 4.3.3##
## Attaching package: 'flextable'
##
## The following object is masked from 'package:purrr':
##
## composelibrary(gtsummary)## Warning: package 'gtsummary' was built under R version 4.3.3##
## Attaching package: 'gtsummary'
##
## The following objects are masked from 'package:flextable':
##
## as_flextable, continuous_summarylibrary(factoextra)## Warning: package 'factoextra' was built under R version 4.3.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(FactoMineR)## Warning: package 'FactoMineR' was built under R version 4.3.3library(readxl)
calculateddata <- read_excel("C:/Users/Michael/Desktop/calculateddata.xlsx", skip = 1)
#head(calculateddata)
#dim(calculateddata)Data consists of 756 rows and 82 columns
#The first column 'JakesNEWCODE' is a row column. Convert the column to row
df <- calculateddata %>% column_to_rownames (var = "JakesNEWCODE")
#head(df)#look at the colnames
#colnames(df)Explore the demographic part of the data
The demographic data begins from column 1 to column 44
demo <- df %>% select(1:44)
head(demo)## A "mentor" is someone who supports, guides, or otherwise helps colleagues in an organization.\r\n\r\n\r\n\r\nDo you currently have a mentor?
## 1 Yes I do
## 2 No I don't
## 3 Yes I do
## 4 Maybe / not sure
## 5 Yes I do
## 6 Yes I do
## As of today, do you currently serve as a mentor to someone else?
## 1 Yes I do
## 2 No I don't
## 3 Yes I do
## 4 Yes I do
## 5 Yes I do
## 6 Yes I do
## Have you EVER in your professional career served as a mentor to someone else?
## 1 Yes I have
## 2 No I have not
## 3 Yes I have
## 4 Yes I have
## 5 Yes I have
## 6 Yes I have
## Which of the following best describes your primary job function (where you spend the majority of your time) within Facility Management or related field? - Selected Choice
## 1 Facility Operations
## 2 Facility Operations
## 3 Facility Operations
## 4 Facility Operations
## 5 Facility Operations
## 6 Facility Operations
## Which of the following best describes your primary job function (where you spend the majority of your time) within Facility Management or related field? - Other - Text
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following industries best describes the INSTITUTION that you represent? - Selected Choice
## 1 Educational (Training Center, K-12, College / University)
## 2 Educational (Training Center, K-12, College / University)
## 3 Federal Government
## 4 Educational (Training Center, K-12, College / University)
## 5 Insurance (Health, Life, Auto, Mutual, Casualty, Flood)
## 6 Educational (Training Center, K-12, College / University)
## Which of the following industries best describes the INSTITUTION that you represent? - Other Institution: - Text
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Do you consider your current job on the managerial or technical path? - Selected Choice
## 1 Managerial (e.g. supervising people, budgets, and/or projects)
## 2 Managerial (e.g. supervising people, budgets, and/or projects)
## 3 Managerial (e.g. supervising people, budgets, and/or projects)
## 4 Managerial (e.g. supervising people, budgets, and/or projects)
## 5 Managerial (e.g. supervising people, budgets, and/or projects)
## 6 Managerial (e.g. supervising people, budgets, and/or projects)
## Do you consider your current job on the managerial or technical path? - Other - Text
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Are you employed directly by the facility owner (in-house) or a third-party contractor (outsourced) that provides services to the facility owner?
## 1 In-house - I am employed directly by the facility owner
## 2 In-house - I am employed directly by the facility owner
## 3 In-house - I am employed directly by the facility owner
## 4 In-house - I am employed directly by the facility owner
## 5 Outsourced - I am employed by a contractor to provide services to the facility owner
## 6 In-house - I am employed directly by the facility owner
## Which of the following best describes your current position level within your organization?
## 1 Level 3 - Manage supervisor who manage others
## 2 Level 4 - Manage two or more levels of supervisors
## 3 Level 4 - Manage two or more levels of supervisors
## 4 Level 4 - Manage two or more levels of supervisors
## 5 Level 2 - Manage employees but do not manage supervisors
## 6 Level 4 - Manage two or more levels of supervisors
## How many total years of management experience do you have that are directly related to working in Facility Operations / the built environment?
## 1 11-15 years
## 2 More than 30 years
## 3 16-20 years
## 4 More than 30 years
## 5 <NA>
## 6 26-30 years
## About how many years until you retire?
## 1 More than 5 years
## 2 More than 5 years
## 3 More than 5 years
## 4 More than 5 years
## 5 3-5 years
## 6 More than 5 years
## This is the last page! Can you tell us about yourself?\r\n\r\n\r\nWhen were you born?
## 1 1979 - 1997
## 2 1946 - 1964
## 3 1979 - 1997
## 4 1965 - 1978
## 5 1965 - 1978
## 6 1965 - 1978
## How do you describe yourself? - Selected Choice
## 1 Male
## 2 Male
## 3 Male
## 4 Male
## 5 Female
## 6 Female
## How do you describe yourself? - Prefer to self-describe - Text
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## What is the highest level of education you have attained?
## 1 Bachelor's degree
## 2 Bachelor's degree
## 3 Bachelor's degree
## 4 Bachelor's degree
## 5 Bachelor's degree
## 6 Bachelor's degree
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - AIA
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - ARM (IREM)
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - AssocRICS (RICS)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CEM (AEE)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CEFM (APPA)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CFM (IFMA)
## 1 <NA>
## 2 CFM (IFMA)
## 3 <NA>
## 4 CFM (IFMA)
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CHFM (ASHE)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CIWFM
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CPM (IREM)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - CPMM (AFE)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - FIWFM
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - FMA (BOMI)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - FMP (IFMA)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - LEAN/Six Sigma
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - LEED AP or GA (USGBC)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - MCR (CoreNet)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - WIWFM
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - MRICS (RICS)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - PE
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - PMP (PMI)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - RPA (BOMI)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - SHRM
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - SFP (IFMA)
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## Which of the following professional designations and credentials, if any, do you hold? (Select all that apply) - Other
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 Other
## 6 Other
## In which continent do you currently reside? 50 States, D.C. and Puerto Rico
## 1 Africa <NA>
## 2 South America <NA>
## 3 Asia <NA>
## 4 Asia <NA>
## 5 North America Georgia
## 6 North America Texas
## In which country do you reside?
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>#Replace the column names for the demographics with these
colnames(demo) <- c("C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9", "C10", "C11", "C12", "C13", "C14", "C15", "C16", "C17", "C18", "C19", "C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29", "C30", "C31", "C32", "C33", "C34", "C35", "C36", "C37", "C38", "C39", "C40", "C41", "C42", "C43", "C44")#Diagnose the demographic data
flextable(diagnose(demo))variables | types | missing_count | missing_percent | unique_count | unique_rate |
|---|---|---|---|---|---|
C1 | character | 0 | 0.0000000 | 4 | 0.005291005 |
C2 | character | 0 | 0.0000000 | 4 | 0.005291005 |
C3 | character | 0 | 0.0000000 | 3 | 0.003968254 |
C4 | character | 0 | 0.0000000 | 13 | 0.017195767 |
C5 | character | 717 | 94.8412698 | 39 | 0.051587302 |
C6 | character | 0 | 0.0000000 | 33 | 0.043650794 |
C7 | character | 686 | 90.7407407 | 68 | 0.089947090 |
C8 | character | 1 | 0.1322751 | 4 | 0.005291005 |
C9 | character | 718 | 94.9735450 | 35 | 0.046296296 |
C10 | character | 38 | 5.0264550 | 3 | 0.003968254 |
C11 | character | 95 | 12.5661376 | 6 | 0.007936508 |
C12 | character | 304 | 40.2116402 | 9 | 0.011904762 |
C13 | character | 3 | 0.3968254 | 5 | 0.006613757 |
C14 | character | 4 | 0.5291005 | 6 | 0.007936508 |
C15 | character | 0 | 0.0000000 | 5 | 0.006613757 |
C16 | character | 755 | 99.8677249 | 2 | 0.002645503 |
C17 | character | 1 | 0.1322751 | 9 | 0.011904762 |
C18 | character | 751 | 99.3386243 | 2 | 0.002645503 |
C19 | logical | 756 | 100.0000000 | 1 | 0.001322751 |
C20 | character | 747 | 98.8095238 | 2 | 0.002645503 |
C21 | character | 743 | 98.2804233 | 2 | 0.002645503 |
C22 | character | 749 | 99.0740741 | 2 | 0.002645503 |
C23 | character | 523 | 69.1798942 | 2 | 0.002645503 |
C24 | character | 750 | 99.2063492 | 2 | 0.002645503 |
C25 | character | 752 | 99.4708995 | 2 | 0.002645503 |
C26 | character | 749 | 99.0740741 | 2 | 0.002645503 |
C27 | character | 752 | 99.4708995 | 2 | 0.002645503 |
C28 | logical | 756 | 100.0000000 | 1 | 0.001322751 |
C29 | character | 736 | 97.3544974 | 2 | 0.002645503 |
C30 | character | 563 | 74.4708995 | 2 | 0.002645503 |
C31 | character | 706 | 93.3862434 | 2 | 0.002645503 |
C32 | character | 706 | 93.3862434 | 2 | 0.002645503 |
C33 | character | 740 | 97.8835979 | 2 | 0.002645503 |
C34 | character | 754 | 99.7354497 | 2 | 0.002645503 |
C35 | character | 744 | 98.4126984 | 2 | 0.002645503 |
C36 | character | 732 | 96.8253968 | 2 | 0.002645503 |
C37 | character | 699 | 92.4603175 | 2 | 0.002645503 |
C38 | character | 742 | 98.1481481 | 2 | 0.002645503 |
C39 | character | 753 | 99.6031746 | 2 | 0.002645503 |
C40 | character | 678 | 89.6825397 | 2 | 0.002645503 |
C41 | character | 621 | 82.1428571 | 2 | 0.002645503 |
C42 | character | 1 | 0.1322751 | 7 | 0.009259259 |
C43 | character | 192 | 25.3968254 | 54 | 0.071428571 |
C44 | character | 750 | 99.2063492 | 6 | 0.007936508 |
Some columns in the data contains missing values
Select columns that contains little missing data.
subset <- demo %>% select(C1, C2, C3, C4, C6, C8, C10, C11, C12, C13, C14, C15, C17, C42)#Summarise the subsetted data.
-99 has to be removed
subset %>% tbl_summary()| Characteristic | N = 7561 |
|---|---|
| C1 | |
| -99 | 3 (0.4%) |
| Maybe / not sure | 67 (8.9%) |
| No I don't | 468 (62%) |
| Yes I do | 218 (29%) |
| C2 | |
| -99 | 7 (0.9%) |
| Maybe / not sure | 93 (12%) |
| No I don't | 272 (36%) |
| Yes I do | 384 (51%) |
| C3 | |
| Maybe / not sure | 53 (7.0%) |
| No I have not | 118 (16%) |
| Yes I have | 585 (77%) |
| C4 | |
| Architecture | 2 (0.3%) |
| Construction/Project Management | 49 (6.5%) |
| Consulting | 38 (5.0%) |
| Education | 13 (1.7%) |
| Engineering | 18 (2.4%) |
| Environmental Health and Safety | 1 (0.1%) |
| Facility Operations | 545 (72%) |
| Information Technology | 6 (0.8%) |
| Interior Design/Space Planning | 8 (1.1%) |
| Janitorial | 4 (0.5%) |
| Other | 44 (5.8%) |
| Real Estate | 20 (2.6%) |
| Sales | 8 (1.1%) |
| C6 | |
| Aircraft/Industrial (industrial Equipment, Aerospace) | 9 (1.2%) |
| Association (Association, Federation, Non-Profit Foundation, Society) | 25 (3.3%) |
| Banking (Consumer, Commercial, Savings, Credit Unions) | 40 (5.3%) |
| Building/Construction (Building, Construction Materials) | 21 (2.8%) |
| Charitable Foundation | 11 (1.5%) |
| Chemical/Pharmaceutical (Chemical, Pharmaceutical, Biotech) | 21 (2.8%) |
| City/County Government (Law Enforcement, Library, Parks / Public Open Space) | 56 (7.4%) |
| Computer (Computer hardware or software) | 8 (1.1%) |
| Consumer Products (Food, Paper, or related) | 9 (1.2%) |
| Corrections (private, state, federal, city, county) | 1 (0.1%) |
| Cultural Facilities (Private, Institutions, Government) | 28 (3.7%) |
| Educational (Training Center, K-12, College / University) | 85 (11%) |
| Electronics (Electronics, Telecommunications Equipment) | 7 (0.9%) |
| Energy (Energy related, mining, or distribution) | 12 (1.6%) |
| Federal Government | 30 (4.0%) |
| Health Care | 43 (5.7%) |
| Hospitality (Hotel, Restaurants, Hospitality-Related) | 23 (3.0%) |
| Information Services (Data Processing, Information Services, E-Commerce) | 18 (2.4%) |
| Insurance (Health, Life, Auto, Mutual, Casualty, Flood) | 25 (3.3%) |
| Investment Services (Securities and Investment Services) | 5 (0.7%) |
| Media (Broadcasting, Entertainment, Gaming, Media, Publishing) | 12 (1.6%) |
| Military | 2 (0.3%) |
| Motor Vehicles | 6 (0.8%) |
| Other Institution: | 76 (10%) |
| Professional Services (Legal, Accounting, Consulting, Engineering, Architecture) | 80 (11%) |
| Religious | 12 (1.6%) |
| Research | 17 (2.2%) |
| Special Districts/ Quasi-government (Transportation Authorities, School Boards) | 3 (0.4%) |
| State/Provincial Government | 13 (1.7%) |
| Telecommunications (Telecommunication, Internet Services/Products) | 16 (2.1%) |
| Trade (Wholesale, Retail) | 15 (2.0%) |
| Transportation (Transportation, Freight) | 9 (1.2%) |
| Utilities (Water, Gas, Electric, Energy Management) | 18 (2.4%) |
| C8 | |
| Managerial (e.g. supervising people, budgets, and/or projects) | 675 (89%) |
| Other | 40 (5.3%) |
| Technical (e.g. mechanical or systems focus - do not manage people of budgets) | 40 (5.3%) |
| Unknown | 1 |
| C10 | |
| In-house - I am employed directly by the facility owner | 580 (81%) |
| Outsourced - I am employed by a contractor to provide services to the facility owner | 138 (19%) |
| Unknown | 38 |
| C11 | |
| Level 1 - Professional specialist (manage no employees) | 55 (8.3%) |
| Level 2 - Manage employees but do not manage supervisors | 136 (21%) |
| Level 3 - Manage supervisor who manage others | 174 (26%) |
| Level 4 - Manage two or more levels of supervisors | 167 (25%) |
| Level 5 - Senior executive | 129 (20%) |
| Unknown | 95 |
| C12 | |
| 1-2 years | 19 (4.2%) |
| 11-15 years | 90 (20%) |
| 16-20 years | 74 (16%) |
| 21-25 years | 67 (15%) |
| 26-30 years | 30 (6.6%) |
| 3-5 years | 38 (8.4%) |
| 6-10 years | 85 (19%) |
| More than 30 years | 49 (11%) |
| Unknown | 304 |
| C13 | |
| 1-2 years | 31 (4.1%) |
| 3-5 years | 106 (14%) |
| More than 5 years | 580 (77%) |
| Within the next year | 36 (4.8%) |
| Unknown | 3 |
| C14 | |
| 1946 - 1964 | 193 (26%) |
| 1965 - 1978 | 319 (42%) |
| 1979 - 1997 | 232 (31%) |
| 1998 or later | 7 (0.9%) |
| Prior to 1946 | 1 (0.1%) |
| Unknown | 4 |
| C15 | |
| Female | 224 (30%) |
| Male | 524 (69%) |
| Non-binary / third gender | 3 (0.4%) |
| Prefer not to say | 4 (0.5%) |
| Prefer to self-describe | 1 (0.1%) |
| C17 | |
| Associate's degree | 43 (5.7%) |
| Bachelor's degree | 289 (38%) |
| Doctorate degree | 12 (1.6%) |
| High school graduate or equivalent | 30 (4.0%) |
| Master's degree | 239 (32%) |
| Other | 5 (0.7%) |
| Some college, no degree | 87 (12%) |
| Vocational certificate, no degree | 50 (6.6%) |
| Unknown | 1 |
| C42 | |
| Africa | 35 (4.6%) |
| Asia | 80 (11%) |
| Australia | 5 (0.7%) |
| Europe | 48 (6.4%) |
| North America | 568 (75%) |
| South America | 19 (2.5%) |
| Unknown | 1 |
| 1 n (%) | |
Demographic data summary based on gender
subset %>%
tbl_summary(by = C15) | Characteristic | Female, N = 2241 | Male, N = 5241 | Non-binary / third gender, N = 31 | Prefer not to say, N = 41 | Prefer to self-describe, N = 11 |
|---|---|---|---|---|---|
| C1 | |||||
| -99 | 0 (0%) | 3 (0.6%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Maybe / not sure | 19 (8.5%) | 46 (8.8%) | 0 (0%) | 2 (50%) | 0 (0%) |
| No I don't | 128 (57%) | 335 (64%) | 2 (67%) | 2 (50%) | 1 (100%) |
| Yes I do | 77 (34%) | 140 (27%) | 1 (33%) | 0 (0%) | 0 (0%) |
| C2 | |||||
| -99 | 0 (0%) | 7 (1.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Maybe / not sure | 22 (9.8%) | 70 (13%) | 0 (0%) | 1 (25%) | 0 (0%) |
| No I don't | 97 (43%) | 170 (32%) | 2 (67%) | 2 (50%) | 1 (100%) |
| Yes I do | 105 (47%) | 277 (53%) | 1 (33%) | 1 (25%) | 0 (0%) |
| C3 | |||||
| Maybe / not sure | 16 (7.1%) | 37 (7.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| No I have not | 45 (20%) | 71 (14%) | 0 (0%) | 2 (50%) | 0 (0%) |
| Yes I have | 163 (73%) | 416 (79%) | 3 (100%) | 2 (50%) | 1 (100%) |
| C4 | |||||
| Architecture | 0 (0%) | 2 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Construction/Project Management | 14 (6.3%) | 35 (6.7%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Consulting | 10 (4.5%) | 28 (5.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Education | 3 (1.3%) | 10 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Engineering | 1 (0.4%) | 17 (3.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Environmental Health and Safety | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Facility Operations | 152 (68%) | 385 (73%) | 3 (100%) | 4 (100%) | 1 (100%) |
| Information Technology | 1 (0.4%) | 5 (1.0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Interior Design/Space Planning | 7 (3.1%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Janitorial | 0 (0%) | 4 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Other | 23 (10%) | 21 (4.0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Real Estate | 10 (4.5%) | 10 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Sales | 3 (1.3%) | 5 (1.0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| C6 | |||||
| Aircraft/Industrial (industrial Equipment, Aerospace) | 3 (1.3%) | 6 (1.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Association (Association, Federation, Non-Profit Foundation, Society) | 10 (4.5%) | 15 (2.9%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Banking (Consumer, Commercial, Savings, Credit Unions) | 12 (5.4%) | 28 (5.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Building/Construction (Building, Construction Materials) | 3 (1.3%) | 18 (3.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Charitable Foundation | 1 (0.4%) | 8 (1.5%) | 1 (33%) | 1 (25%) | 0 (0%) |
| Chemical/Pharmaceutical (Chemical, Pharmaceutical, Biotech) | 9 (4.0%) | 12 (2.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| City/County Government (Law Enforcement, Library, Parks / Public Open Space) | 18 (8.0%) | 38 (7.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Computer (Computer hardware or software) | 2 (0.9%) | 6 (1.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Consumer Products (Food, Paper, or related) | 4 (1.8%) | 5 (1.0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Corrections (private, state, federal, city, county) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Cultural Facilities (Private, Institutions, Government) | 8 (3.6%) | 18 (3.4%) | 1 (33%) | 1 (25%) | 0 (0%) |
| Educational (Training Center, K-12, College / University) | 14 (6.3%) | 71 (14%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Electronics (Electronics, Telecommunications Equipment) | 1 (0.4%) | 6 (1.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Energy (Energy related, mining, or distribution) | 2 (0.9%) | 10 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Federal Government | 5 (2.2%) | 25 (4.8%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Health Care | 8 (3.6%) | 35 (6.7%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Hospitality (Hotel, Restaurants, Hospitality-Related) | 4 (1.8%) | 18 (3.4%) | 0 (0%) | 1 (25%) | 0 (0%) |
| Information Services (Data Processing, Information Services, E-Commerce) | 8 (3.6%) | 10 (1.9%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Insurance (Health, Life, Auto, Mutual, Casualty, Flood) | 13 (5.8%) | 12 (2.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Investment Services (Securities and Investment Services) | 3 (1.3%) | 2 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Media (Broadcasting, Entertainment, Gaming, Media, Publishing) | 11 (4.9%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Military | 1 (0.4%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Motor Vehicles | 2 (0.9%) | 4 (0.8%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Other Institution: | 25 (11%) | 50 (9.5%) | 0 (0%) | 1 (25%) | 0 (0%) |
| Professional Services (Legal, Accounting, Consulting, Engineering, Architecture) | 29 (13%) | 51 (9.7%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Religious | 1 (0.4%) | 11 (2.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Research | 5 (2.2%) | 12 (2.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Special Districts/ Quasi-government (Transportation Authorities, School Boards) | 1 (0.4%) | 2 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| State/Provincial Government | 5 (2.2%) | 7 (1.3%) | 1 (33%) | 0 (0%) | 0 (0%) |
| Telecommunications (Telecommunication, Internet Services/Products) | 4 (1.8%) | 12 (2.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Trade (Wholesale, Retail) | 4 (1.8%) | 11 (2.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Transportation (Transportation, Freight) | 3 (1.3%) | 6 (1.1%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Utilities (Water, Gas, Electric, Energy Management) | 4 (1.8%) | 13 (2.5%) | 0 (0%) | 0 (0%) | 1 (100%) |
| C8 | |||||
| Managerial (e.g. supervising people, budgets, and/or projects) | 197 (88%) | 471 (90%) | 2 (100%) | 4 (100%) | 1 (100%) |
| Other | 16 (7.1%) | 24 (4.6%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Technical (e.g. mechanical or systems focus - do not manage people of budgets) | 11 (4.9%) | 29 (5.5%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Unknown | 0 | 0 | 1 | 0 | 0 |
| C10 | |||||
| In-house - I am employed directly by the facility owner | 168 (81%) | 406 (81%) | 2 (67%) | 3 (75%) | 1 (100%) |
| Outsourced - I am employed by a contractor to provide services to the facility owner | 40 (19%) | 96 (19%) | 1 (33%) | 1 (25%) | 0 (0%) |
| Unknown | 16 | 22 | 0 | 0 | 0 |
| C11 | |||||
| Level 1 - Professional specialist (manage no employees) | 28 (15%) | 27 (5.8%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Level 2 - Manage employees but do not manage supervisors | 45 (24%) | 89 (19%) | 0 (0%) | 1 (25%) | 1 (100%) |
| Level 3 - Manage supervisor who manage others | 54 (28%) | 117 (25%) | 1 (100%) | 2 (50%) | 0 (0%) |
| Level 4 - Manage two or more levels of supervisors | 36 (19%) | 130 (28%) | 0 (0%) | 1 (25%) | 0 (0%) |
| Level 5 - Senior executive | 28 (15%) | 101 (22%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Unknown | 33 | 60 | 2 | 0 | 0 |
| C12 | |||||
| 1-2 years | 6 (5.1%) | 12 (3.7%) | 1 (50%) | 0 (0%) | 0 (0%) |
| 11-15 years | 15 (13%) | 74 (23%) | 0 (0%) | 1 (25%) | 0 (0%) |
| 16-20 years | 21 (18%) | 52 (16%) | 0 (0%) | 1 (25%) | 0 (0%) |
| 21-25 years | 16 (14%) | 51 (16%) | 0 (0%) | 0 (0%) | 0 (0%) |
| 26-30 years | 6 (5.1%) | 24 (7.3%) | 0 (0%) | 0 (0%) | 0 (0%) |
| 3-5 years | 17 (15%) | 21 (6.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| 6-10 years | 28 (24%) | 54 (16%) | 1 (50%) | 2 (50%) | 0 (0%) |
| More than 30 years | 8 (6.8%) | 40 (12%) | 0 (0%) | 0 (0%) | 1 (100%) |
| Unknown | 107 | 196 | 1 | 0 | 0 |
| C13 | |||||
| 1-2 years | 6 (2.7%) | 24 (4.6%) | 0 (0%) | 0 (0%) | 1 (100%) |
| 3-5 years | 36 (16%) | 70 (13%) | 0 (0%) | 0 (0%) | 0 (0%) |
| More than 5 years | 174 (78%) | 399 (76%) | 3 (100%) | 4 (100%) | 0 (0%) |
| Within the next year | 6 (2.7%) | 30 (5.7%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Unknown | 2 | 1 | 0 | 0 | 0 |
| C14 | |||||
| 1946 - 1964 | 52 (23%) | 140 (27%) | 0 (0%) | 0 (0%) | 1 (100%) |
| 1965 - 1978 | 96 (43%) | 219 (42%) | 2 (67%) | 2 (67%) | 0 (0%) |
| 1979 - 1997 | 70 (32%) | 160 (31%) | 1 (33%) | 1 (33%) | 0 (0%) |
| 1998 or later | 4 (1.8%) | 3 (0.6%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Prior to 1946 | 0 (0%) | 1 (0.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Unknown | 2 | 1 | 0 | 1 | 0 |
| C17 | |||||
| Associate's degree | 12 (5.4%) | 31 (5.9%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Bachelor's degree | 97 (43%) | 188 (36%) | 2 (67%) | 1 (25%) | 1 (100%) |
| Doctorate degree | 1 (0.4%) | 10 (1.9%) | 0 (0%) | 1 (25%) | 0 (0%) |
| High school graduate or equivalent | 9 (4.0%) | 21 (4.0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Master's degree | 64 (29%) | 173 (33%) | 1 (33%) | 1 (25%) | 0 (0%) |
| Other | 2 (0.9%) | 3 (0.6%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Some college, no degree | 28 (13%) | 58 (11%) | 0 (0%) | 1 (25%) | 0 (0%) |
| Vocational certificate, no degree | 11 (4.9%) | 39 (7.5%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Unknown | 0 | 1 | 0 | 0 | 0 |
| C42 | |||||
| Africa | 8 (3.6%) | 27 (5.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Asia | 9 (4.0%) | 69 (13%) | 0 (0%) | 2 (50%) | 0 (0%) |
| Australia | 0 (0%) | 5 (1.0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Europe | 19 (8.5%) | 29 (5.5%) | 0 (0%) | 0 (0%) | 0 (0%) |
| North America | 185 (83%) | 377 (72%) | 3 (100%) | 2 (50%) | 1 (100%) |
| South America | 2 (0.9%) | 17 (3.2%) | 0 (0%) | 0 (0%) | 0 (0%) |
| Unknown | 1 | 0 | 0 | 0 | 0 |
| 1 n (%) | |||||
#Lets explore the Hexaco data**
hexaco <- df %>% select(49,54,59,64,69,74)
head(hexaco)## Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1 4.42 3.17 3.42 4.67 4.54
## 2 4.25 2.54 4.04 3.38 4.71
## 3 4.29 1.96 3.71 4.13 3.92
## 4 3.79 2.96 3.21 3.38 3.42
## 5 4.08 2.71 3.67 3.58 4.42
## 6 3.79 2.13 3.29 3.58 4.33
## Openness to Experience
## 1 3.33
## 2 3.88
## 3 4.04
## 4 2.54
## 5 2.58
## 6 2.71#Summary of hexaco data
flextable(describe(hexaco) %>% select(1,2,3,4,5))described_variables | n | na | mean | sd |
|---|---|---|---|---|
Honesty/Humility | 756 | 0 | 3.907831 | 0.4800359 |
Emotionality | 756 | 0 | 2.782368 | 0.5519061 |
Extraversion | 756 | 0 | 3.568135 | 0.5464286 |
Agreeableness | 756 | 0 | 3.412407 | 0.5211523 |
Conscientiousness | 756 | 0 | 4.063968 | 0.4541863 |
Openness to Experience | 756 | 0 | 3.184894 | 0.4951469 |
hexaco %>% tbl_summary(statistic = list(
all_continuous() ~ "{mean} ({sd})"))| Characteristic | N = 7561 |
|---|---|
| Honesty/Humility | 3.91 (0.48) |
| Emotionality | 2.78 (0.55) |
| Extraversion | 3.57 (0.55) |
| Agreeableness | 3.41 (0.52) |
| Conscientiousness | 4.06 (0.45) |
| Openness to Experience | 3.18 (0.50) |
| 1 Mean (SD) | |
#Segmentation of respondents based on the hexaco personality traits using principal component analysis
hexaco <- df %>% select(49,54,59,64,69,74)# Compute PCA with ncp = 3
res.pca <- PCA(hexaco, ncp = 3, graph = FALSE)# Compute hierarchical clustering on principal components
res.hcpc <- HCPC(res.pca, graph = FALSE)
fviz_dend(res.hcpc,
cex = 0.7, # Label size
palette = "jco", # Color palette see ?ggpubr::ggpar
rect = TRUE, rect_fill = TRUE, # Add rectangle around groups
rect_border = "jco", # Rectangle color
labels_track_height = 0.8 # Augment the room for labels
)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
To display the original data with cluster assignments
head(res.hcpc$data.clust)## Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1 4.42 3.17 3.42 4.67 4.54
## 2 4.25 2.54 4.04 3.38 4.71
## 3 4.29 1.96 3.71 4.13 3.92
## 4 3.79 2.96 3.21 3.38 3.42
## 5 4.08 2.71 3.67 3.58 4.42
## 6 3.79 2.13 3.29 3.58 4.33
## Openness to Experience clust
## 1 3.33 3
## 2 3.88 3
## 3 4.04 3
## 4 2.54 1
## 5 2.58 3
## 6 2.71 3To display quantitative variables that describe the most each cluster
#res.hcpc$desc.var$quanti#Comparison between male and female personality traits.
hexaco_gender <- df %>% select(49,54,59,64,69,74)
head(hexaco_gender)## Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1 4.42 3.17 3.42 4.67 4.54
## 2 4.25 2.54 4.04 3.38 4.71
## 3 4.29 1.96 3.71 4.13 3.92
## 4 3.79 2.96 3.21 3.38 3.42
## 5 4.08 2.71 3.67 3.58 4.42
## 6 3.79 2.13 3.29 3.58 4.33
## Openness to Experience
## 1 3.33
## 2 3.88
## 3 4.04
## 4 2.54
## 5 2.58
## 6 2.71hexaco_gender$gender <- demo$C15 # add gender column to the hexaco data
head(hexaco_gender)## Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1 4.42 3.17 3.42 4.67 4.54
## 2 4.25 2.54 4.04 3.38 4.71
## 3 4.29 1.96 3.71 4.13 3.92
## 4 3.79 2.96 3.21 3.38 3.42
## 5 4.08 2.71 3.67 3.58 4.42
## 6 3.79 2.13 3.29 3.58 4.33
## Openness to Experience gender
## 1 3.33 Male
## 2 3.88 Male
## 3 4.04 Male
## 4 2.54 Male
## 5 2.58 Female
## 6 2.71 Femalehexaco_gender %>%
tbl_summary(
by = gender,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"
),
digits = all_continuous() ~ 2,
) %>% add_p()| Characteristic | Female, N = 2241 | Male, N = 5241 | Non-binary / third gender, N = 31 | Prefer not to say, N = 41 | Prefer to self-describe, N = 11 | p-value2 |
|---|---|---|---|---|---|---|
| Honesty/Humility | 3.91 (0.47) | 3.91 (0.48) | 4.60 (0.15) | 3.57 (0.46) | 4.04 (NA) | 0.055 |
| Emotionality | 3.03 (0.59) | 2.67 (0.50) | 3.24 (0.39) | 2.93 (0.26) | 2.92 (NA) | <0.001 |
| Extraversion | 3.56 (0.56) | 3.58 (0.54) | 3.03 (0.88) | 3.36 (0.54) | 3.79 (NA) | 0.7 |
| Agreeableness | 3.36 (0.48) | 3.44 (0.54) | 3.07 (0.19) | 3.36 (0.56) | 2.83 (NA) | 0.13 |
| Conscientiousness | 4.10 (0.44) | 4.05 (0.46) | 4.27 (0.63) | 3.89 (0.37) | 3.79 (NA) | 0.3 |
| Openness to Experience | 3.17 (0.52) | 3.19 (0.48) | 3.75 (0.26) | 3.07 (0.65) | 2.83 (NA) | 0.2 |
| 1 Mean (SD) | ||||||
| 2 Kruskal-Wallis rank sum test | ||||||
consider only male and female
hexaco_genderFilter <- hexaco_gender %>% filter(gender %in% c("Female","Male")) #select only female and males. exclude the rest
hexaco_genderFilter %>%
tbl_summary(
by = gender,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"
),
digits = all_continuous() ~ 2,
) %>% add_p()| Characteristic | Female, N = 2241 | Male, N = 5241 | p-value2 |
|---|---|---|---|
| Honesty/Humility | 3.91 (0.47) | 3.91 (0.48) | 0.8 |
| Emotionality | 3.03 (0.59) | 2.67 (0.50) | <0.001 |
| Extraversion | 3.56 (0.56) | 3.58 (0.54) | 0.8 |
| Agreeableness | 3.36 (0.48) | 3.44 (0.54) | 0.071 |
| Conscientiousness | 4.10 (0.44) | 4.05 (0.46) | 0.12 |
| Openness to Experience | 3.17 (0.52) | 3.19 (0.48) | 0.7 |
| 1 Mean (SD) | |||
| 2 Wilcoxon rank sum test | |||
#Impact of Hexaco on Emotional intelligence
Use multiple linear regression.
hexaco_EQ <- df %>% select(49,54,59,64,69,74,78)
head(hexaco_EQ)## Honesty/Humility Emotionality Extraversion Agreeableness Conscientiousness
## 1 4.42 3.17 3.42 4.67 4.54
## 2 4.25 2.54 4.04 3.38 4.71
## 3 4.29 1.96 3.71 4.13 3.92
## 4 3.79 2.96 3.21 3.38 3.42
## 5 4.08 2.71 3.67 3.58 4.42
## 6 3.79 2.13 3.29 3.58 4.33
## Openness to Experience Overall EQ
## 1 3.33 87
## 2 3.88 88
## 3 4.04 86
## 4 2.54 79
## 5 2.58 84
## 6 2.71 72model <- lm(hexaco_EQ$`Overall EQ` ~., data = hexaco_EQ)summary(model)##
## Call:
## lm(formula = hexaco_EQ$`Overall EQ` ~ ., data = hexaco_EQ)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.5467 -3.6502 0.1886 3.9322 16.5754
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.8951 3.0876 8.387 2.47e-16 ***
## `Honesty/Humility` 0.4646 0.4632 1.003 0.31618
## Emotionality -1.2038 0.3884 -3.099 0.00201 **
## Extraversion 4.8599 0.4182 11.621 < 2e-16 ***
## Agreeableness 3.9132 0.4341 9.014 < 2e-16 ***
## Conscientiousness 4.2833 0.4843 8.844 < 2e-16 ***
## `Openness to Experience` 1.8047 0.4360 4.139 3.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.716 on 749 degrees of freedom
## Multiple R-squared: 0.4544, Adjusted R-squared: 0.45
## F-statistic: 104 on 6 and 749 DF, p-value: < 2.2e-16Analysis from chatgpt
The results from this multiple linear regression model summarize the relationships between several personality traits (predictors) and a dependent variable (not specified here, but it’s what the model is trying to predict). Here’s a breakdown of each component in the regression table:
Coefficients (Estimate): These values represent the estimated effect of each predictor variable on the dependent variable. For instance, a one-unit increase in Extraversion is associated with a 4.8599 unit increase in the dependent variable, holding all other variables constant. Std. Error: The standard error of the coefficient estimates indicates the average distance that the estimated values fall from the actual value. A smaller standard error suggests more precise estimates. t value: This is the ratio of the coefficient to its standard error. It’s used to test the null hypothesis that the coefficient is equal to zero (no effect). A larger absolute value of the t-statistic indicates more evidence against the null hypothesis. Pr(>|t|): This is the p-value associated with the t-statistic. It indicates the probability of observing any value equal to or more extreme than the t-statistic under the null hypothesis. Smaller p-values (< 0.05, typically) suggest that the effect of the predictor on the dependent variable is statistically significant. *** indicates p < 0.001 (highly significant). ** indicates p < 0.01. * indicates p < 0.05. No stars indicate a lack of statistical significance (p >= 0.05). Interpretation of the Individual Coefficients: (Intercept): The estimated value of the dependent variable when all predictors are zero is 25.8951. The intercept is highly significant (p < 0.001). Honesty/Humility: The coefficient is 0.4646, but it’s not statistically significant (p = 0.31618), suggesting that changes in this trait might not predict changes in the dependent variable in this model. Emotionality: A one-unit increase in Emotionality is associated with a decrease of 1.2038 in the dependent variable, and this effect is statistically significant (p = 0.00201). Extraversion: This trait has a significant positive impact on the dependent variable, with a coefficient of 4.8599 (p < 0.001). Agreeableness: This also has a significant positive effect on the dependent variable, with a coefficient of 3.9132 (p < 0.001). Conscientiousness: Like Extraversion and Agreeableness, Conscientiousness significantly positively affects the dependent variable, with a coefficient of 4.2833 (p < 0.001). Openness to Experience: This trait significantly increases the dependent variable by 1.8047 for each unit increase (p < 0.001). Conclusion: The model suggests that Extraversion, Agreeableness, Conscientiousness, and Openness to Experience have significant positive effects on the dependent variable, while Emotionality has a significant negative effect. Honesty/Humility does not significantly impact the dependent variable according to this model.