Terrence’s Master Thesis Analysis Replication
Terrence Calistro
7/15/2020
Abstract
Abstract: Developing a vocational identity is a significant developmental task for emerging adults (Arnett, 2000; Porfeli, Lee, Vondracek, & Weigold, 2011). During the ages of 18-29 emerging adults are at a development point in their lives where they start contemplating and planning for careers they want to pursue for the rest of their lives (Arnett, 2015). For emerging adults, developing a understanding of personal long term occupational goals primarily comes from work experience (Arnett, 2015). However, most emerging adults usually have menial jobs while trying to develop themselves for their future careers (Arnett, 2015). The purpose of this study was to identify if emerging adults who currently had jobs that were related towards their future occupational goals would have stronger vocational identities and more proactive coping behaviors compared to emerging adults who either had jobs that did not relate towards their future occupational goals and those who were unemployed. The results showed that students with jobs related to their future careers possessed stronger vocational identity than those whose jobs did not relate and unemployed students. However, there was no difference in coping strategies based on type of employment. The results of the current study can be used to provide information to the development of future employment and academic placement setting for future college students.
Definitions of Main Study Variables
Vocational Identity: The degree to which an individual identifies with their career goals, career interests, and career-related abilities.
Identity Formation: The process of building a representation of one’s sense of self and identity through self-reflecting on ideals, principals, and personal goals.
Identification with Commitment: The process in which people can identify with their choices and decisions.
Commitment Making: The degree to which people make commitments that reflects on their values and goals.
Exploration in-Breadth: The degree in which people explore alternative options that co-align with their respective goals before making commitments.
Exploration in Depth: The process in which people explore and evaluate commitments that are aligned with their values and ideals.
Ruminative Exploration: A negative form of identity exploration that is described as a near-constant state of uncertainty and issues making long term identity commitments.
Problem-Focused Coping: Coping strategies or behaviors that are used to solve problems that are causing distress.
Emotion-Focused Coping: Coping strategies or behaviors that are used to reduce negative emotional responses that occur due to exposure to stressors.
Avoidant Coping: Coping strategies or behaviors that are used to avoidant negative thoughts, feelings, or situations.
Academic Stress: Stress levels due to difficulties with academic related problems such as exams and coursework.
Social Stress: Stress levels due to difficulties with social relationships with social peers, friends, family, family, faculty member etc.
Financial Stress: Stress levels due to difficulties with financial responsibilities such as rent, food, school fees etc.
Import Packages
library(tidyverse)## -- Attaching packages -------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 3.0.3 v dplyr 0.8.5
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ----------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggiraphExtra)
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(summarytools)## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')##
## Attaching package: 'summarytools'## The following object is masked from 'package:tibble':
##
## viewlibrary(foreign)Import Data
df <- read.spss("Thesis Data Set Analyse Mode 18-29.sav", use.value.labels = TRUE, to.data.frame = TRUE)## re-encoding from UTF-8attach(df)Demographics Descriptive Analysis
descr(Age)## Descriptive Statistics
## Age
## N: 145
##
## Age
## ----------------- --------
## Mean 23.03
## Std.Dev 5.09
## Min 18.00
## Q1 20.00
## Median 21.00
## Q3 24.00
## Max 57.00
## MAD 2.97
## IQR 4.00
## CV 0.22
## Skewness 3.05
## SE.Skewness 0.21
## Kurtosis 14.15
## N.Valid 136.00
## Pct.Valid 93.79descr(GPA)## Descriptive Statistics
## GPA
## N: 145
##
## GPA
## ----------------- --------
## Mean 3.24
## Std.Dev 0.45
## Min 2.10
## Q1 3.00
## Median 3.20
## Q3 3.60
## Max 4.00
## MAD 0.44
## IQR 0.60
## CV 0.14
## Skewness -0.28
## SE.Skewness 0.21
## Kurtosis -0.47
## N.Valid 132.00
## Pct.Valid 91.03descr(Avg_Wrk_Hrs_Perweek)## Descriptive Statistics
## Avg_Wrk_Hrs_Perweek
## N: 145
##
## Avg_Wrk_Hrs_Perweek
## ----------------- ---------------------
## Mean 25.82
## Std.Dev 11.53
## Min 5.00
## Q1 19.00
## Median 25.00
## Q3 35.00
## Max 68.00
## MAD 14.83
## IQR 16.00
## CV 0.45
## Skewness 0.58
## SE.Skewness 0.24
## Kurtosis 0.71
## N.Valid 105.00
## Pct.Valid 72.41freq(Sex)## Frequencies
## Sex
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ------------ ------ --------- -------------- --------- --------------
## Male 19 13.10 13.10 13.10 13.10
## Female 126 86.90 100.00 86.90 100.00
## <NA> 0 0.00 100.00
## Total 145 100.00 100.00 100.00 100.00freq(Aca_Year)## Frequencies
## Aca_Year
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## --------------- ------ --------- -------------- --------- --------------
## Freshman 6 4.14 4.14 4.14 4.14
## Sophomore 32 22.07 26.21 22.07 26.21
## Junior 43 29.66 55.86 29.66 55.86
## Senioir 53 36.55 92.41 36.55 92.41
## Graduate 11 7.59 100.00 7.59 100.00
## <NA> 0 0.00 100.00
## Total 145 100.00 100.00 100.00 100.00freq(Resident_STS)## Frequencies
## Resident_STS
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------------------------------------------------- ------ --------- -------------- --------- --------------
## On Campus (Dormitory or Hotel) 20 13.89 13.89 13.79 13.79
## Off Campus with parents and/or family. 91 63.19 77.08 62.76 76.55
## Off Campus independent of parents and/or family 33 22.92 100.00 22.76 99.31
## <NA> 1 0.69 100.00
## Total 145 100.00 100.00 100.00 100.00freq(Employment)## Frequencies
## Employment
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## -------------------------------------------------------------- ------ --------- -------------- --------- --------------
## Unemployed 34 23.45 23.45 23.45 23.45
## Community Service? Internship 5 3.45 26.90 3.45 26.90
## Part-Time Employment 72 49.66 76.55 49.66 76.55
## Full-Time Employment 27 18.62 95.17 18.62 95.17
## Part/Full-Time Employment & Internship/Community Service 7 4.83 100.00 4.83 100.00
## <NA> 0 0.00 100.00
## Total 145 100.00 100.00 100.00 100.00freq(Career_Related_Employment)## Frequencies
## Career_Related_Employment
## Type: Factor
##
## Freq % Valid % Valid Cum. % Total % Total Cum.
## ----------------------------------- ------ --------- -------------- --------- --------------
## Career-related-employment 45 31.25 31.25 31.03 31.03
## Non Career-related-employment 65 45.14 76.39 44.83 75.86
## Unemployed 34 23.61 100.00 23.45 99.31
## <NA> 1 0.69 100.00
## Total 145 100.00 100.00 100.00 100.00Demographics Summary: Measures of central tendency and variability were used to examine the data. The mean age of the sample was 21.81 (SD = 2.56), The average GPA in the study sample was 3.25 (SD =.43) and the average weekly working hours was 25.40 (SD = 11.76). The majority of the sample was female (87.9%), where male participants consisted of the minority of the population (12.1%). Participants’ ethnicity was identified as: Latino/Latina (37.8%), Caucasian (26.6%), African American (21.8%), Asian (4.8%) and Other (8.9%). Participants’ academic year was identified as: Freshman (4.8%), Sophomore (20.2%), Junior (31.5%), Senior (38.7%) and Graduate (4.8%). Participants residential status was identified as: On Campus (Dormitory or Hotel) (15.3%), Off Campus with Parents and/or Family (66.9%) and Off Campus Independent of Parents and/or Family (17.7%). Participants’ employment status was identified as: Unemployed (21.0%), Community Service/Internship (4.0%), Part-Time Employment (53.2%), Full-Time Employment (16.9%) and Part/Full-Time Employment and Internship/Community Service (5.6%). Participants’ Career/Non-Career Related Employed was identified as: Career-Related Employment (31.5%), Non-Career Related Employment (46.8%) and Unemployed (21.0%)
RQ1:
Research Question: How does different types of employment (Unemployed, Non career-related employment, and career-related employment) affects vocational identity levels?
model1 <- aov(VIM_TOTAL~Career_Related_Employment, df, na.action = na.omit)
summary(model1)## Df Sum Sq Mean Sq F value Pr(>F)
## Career_Related_Employment 2 1400 699.8 3.992 0.0206 *
## Residuals 140 24543 175.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingnesspairwise.t.test(VIM_TOTAL,Career_Related_Employment, p.adjust.method = "bonferroni")##
## Pairwise comparisons using t tests with pooled SD
##
## data: VIM_TOTAL and Career_Related_Employment
##
## Career-related-employment
## Non Career-related-employment 1.000
## Unemployed 0.024
## Non Career-related-employment
## Non Career-related-employment -
## Unemployed 0.063
##
## P value adjustment method: bonferronidf %>% drop_na(Career_Related_Employment)%>%
group_by(Career_Related_Employment)%>%
get_summary_stats(VIM_TOTAL, type = "mean_sd")## # A tibble: 3 x 5
## Career_Related_Employment variable n mean sd
## <fct> <chr> <dbl> <dbl> <dbl>
## 1 Career-related-employment VIM_TOTAL 45 85.2 11.8
## 2 Non Career-related-employment VIM_TOTAL 64 83.7 12.2
## 3 Unemployed VIM_TOTAL 34 77.1 16.6df %>% drop_na(Career_Related_Employment)%>%
ggBoxplot(aes(VIM_TOTAL, color = Career_Related_Employment))## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.## Warning in ggBoxplot(., aes(VIM_TOTAL, color = Career_Related_Employment)): At
## least three variables are required## Warning: `fun.y` is deprecated. Use `fun` instead.## Warning: Removed 1 rows containing non-finite values (stat_boxplot).## Warning: Removed 1 rows containing non-finite values (stat_summary).
Statistical Results Summary: A one-way ANOVA test was used to compare vocational identity scores between the three types of employment. A significant difference was found among the types of employment (F(2,119) = 3.37, p < .05). Bonferroni post hoc was used to determine differences in vocational identity among participant types of employment. Results show that unemployed students (M = 77.00, SD = 16.02) had scored significantly lower in vocational identity than in career-related employment (M = 84.90, SD = 11.79). The was no significant difference between career-related employment (M = 84.90, SD = 11.79), and non-career related employment (M = 84.18, SD = 12.42)
General Summary: A one-way ANOVA test was used to compare vocational identity scores between the three types of employment. The results show that unemployed students had scored significantly lower in vocational identity than in career-related employment. The was no significant difference between career-related employment and non-career related employment.
RQ2
Research Question: How does different types of employment (Unemployed, Non career-related employment, and career-related employment affect students different forms of coping strageties (problem-focused coping, emotion focused coping, and avoidant coping)?
model2 <- manova(cbind(Problem_Focused_Coping,Emotion_Focused_Coping,Avoidant_Coping)~Career_Related_Employment, na.action = na.omit)
summary(model2)## Df Pillai approx F num Df den Df Pr(>F)
## Career_Related_Employment 2 0.041757 0.98089 6 276 0.4384
## Residuals 139summary.aov(model2)## Response Problem_Focused_Coping :
## Df Sum Sq Mean Sq F value Pr(>F)
## Career_Related_Employment 2 53.23 26.613 1.175 0.3119
## Residuals 139 3148.27 22.649
##
## Response Emotion_Focused_Coping :
## Df Sum Sq Mean Sq F value Pr(>F)
## Career_Related_Employment 2 295.9 147.963 2.1474 0.1206
## Residuals 139 9577.5 68.903
##
## Response Avoidant_Coping :
## Df Sum Sq Mean Sq F value Pr(>F)
## Career_Related_Employment 2 76.6 38.297 0.8768 0.4184
## Residuals 139 6071.0 43.676
##
## 3 observations deleted due to missingnessaggregate(cbind(Problem_Focused_Coping,Emotion_Focused_Coping,Avoidant_Coping)~Career_Related_Employment,df,mean)## Career_Related_Employment Problem_Focused_Coping Emotion_Focused_Coping
## 1 Career-related-employment 15.58140 25.46512
## 2 Non Career-related-employment 16.95385 27.86154
## 3 Unemployed 16.82353 29.29412
## Avoidant_Coping
## 1 18.27907
## 2 19.47692
## 3 20.23529df %>% drop_na(Career_Related_Employment)%>%
ggBoxplot(aes(c(Problem_Focused_Coping,Avoidant_Coping, Emotion_Focused_Coping), color = Career_Related_Employment))## Warning: `fun.y` is deprecated. Use `fun` instead.## Warning: Removed 6 rows containing non-finite values (stat_boxplot).## Warning: Removed 6 rows containing non-finite values (stat_summary).
Statistical Results Summary: One-Way MANOVA was conducted to examine the differences in coping strategies (problem-focused coping, emotion-focused coping and avoidant-coping) based on types of employment. No significant effect was found (Lambda (2,120) = .954, p> .05). There was no significant difference in coping strategies based on participants’ type of employment.
General Summary: One-Way MANOVA was conducted to examine the differences in coping strategies (problem-focused coping, emotion-focused coping and avoidant-coping) based on types of employment. There was no significant difference in coping strategies based on participants’ type of employment.
Other Analysis
Research Question: How can identity formation factors(commitment making, identification with commitment, exploration in breadth, exploration in depth, and ruminative exploration ) predict vocational identity levels?
#Regression Model 1
model3 <- lm(VIM_TOTAL~Identification_With_Commitment+Commit_Making+Exploration_In_Breadth+Exploration_In_Depth+Ruminative_Exploration+Work_Level)
summary(model3)##
## Call:
## lm(formula = VIM_TOTAL ~ Identification_With_Commitment + Commit_Making +
## Exploration_In_Breadth + Exploration_In_Depth + Ruminative_Exploration +
## Work_Level)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6202 -4.5268 -0.3333 4.5743 23.2132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.0329 5.5855 6.630 7.52e-10
## Identification_With_Commitment 0.7125 0.2643 2.695 0.00793
## Commit_Making 1.7572 0.2868 6.128 9.31e-09
## Exploration_In_Breadth -0.2960 0.2060 -1.437 0.15310
## Exploration_In_Depth 0.4453 0.2319 1.920 0.05697
## Ruminative_Exploration -0.7510 0.1337 -5.617 1.08e-07
## Work_LevelNon Career Related Employment 0.8883 1.5272 0.582 0.56175
## Work_LevelCareer Related Employment 2.6599 1.6433 1.619 0.10789
##
## (Intercept) ***
## Identification_With_Commitment **
## Commit_Making ***
## Exploration_In_Breadth
## Exploration_In_Depth .
## Ruminative_Exploration ***
## Work_LevelNon Career Related Employment
## Work_LevelCareer Related Employment
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.02 on 134 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.7431, Adjusted R-squared: 0.7297
## F-statistic: 55.38 on 7 and 134 DF, p-value: < 2.2e-16cor.data1 = data.frame(VIM_TOTAL,Identification_With_Commitment,Commit_Making,Exploration_In_Breadth,Exploration_In_Depth,Ruminative_Exploration)
ggCor(cor.data1, label = 2)
VIM_TOTAL_Observed1 = model3$model[,1]
VIM_TOTAL_predicted1 = fitted(model3)
rs1 = data.frame(VIM_TOTAL_Observed1,VIM_TOTAL_predicted1)
ggPoints(rs1, aes(VIM_TOTAL_Observed1,VIM_TOTAL_predicted1), method = 'lm', title = "Multiple R value for Regression Model")
Statistical Results Summary: Multiple linear regression analysis was used to test if identity formation factors was a significant predictor of participants’ vocational identity. A significant regression was found (F (5,117) = 60.97, p < .001) with an R2 of .72. Participants’ vocational identity was significantly predicted by their commit making (b = .45, p < .001), identification with commitment (b = .28, p < .01) and ruminative exploration (b = -.66, p < .001), while exploration in breath (b = -.08, p >.05) and exploration in depth (b = .06, p > .05) were not significant predictors of vocational identity
General Summary Multiple linear regression analysis was used to test if identity formation was a significant predictor of participants’ vocational identity. The results show that identification with commitment and commitment making are significant positive predictors of vocational identity, whereas ruminative exploration is a significant negative predictor.
Research Question: How can different coping strageties (problem-focused, emotion-focused coping and avoidant coping) predict vocational identity levels?
#Regression Model 2
model4 <- lm(VIM_TOTAL~Problem_Focused_Coping+Emotion_Focused_Coping+Avoidant_Coping)
summary(model4)##
## Call:
## lm(formula = VIM_TOTAL ~ Problem_Focused_Coping + Emotion_Focused_Coping +
## Avoidant_Coping)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.135 -9.154 0.988 10.781 25.157
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.23130 4.40530 20.482 <2e-16 ***
## Problem_Focused_Coping 0.07808 0.39293 0.199 0.8428
## Emotion_Focused_Coping 0.01909 0.27100 0.070 0.9439
## Avoidant_Coping -0.49999 0.25141 -1.989 0.0487 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.58 on 138 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.04788, Adjusted R-squared: 0.02718
## F-statistic: 2.313 on 3 and 138 DF, p-value: 0.07871cor.data2 = data.frame(VIM_TOTAL,Problem_Focused_Coping,Emotion_Focused_Coping,Avoidant_Coping)
ggCor(cor.data2, label = 2)
VIM_TOTAL_Observed2 = model4$model[,1]
VIM_TOTAL_predicted2 = fitted(model4)
rs2 = data.frame(VIM_TOTAL_Observed2,VIM_TOTAL_predicted2)
ggPoints(rs2, aes(VIM_TOTAL_Observed2,VIM_TOTAL_predicted2), method = 'lm', title = "Muliple R value for Regression Model")
Statistical Results Summary: Multiple linear regression analysis was used to test if coping strategies was a significant predictor of participants’ vocational identity. A significant regression was found (F (3,119) = 3.05 p < .05) with an R2 of .48. Particpants’ vocational identity was significantly predicted by avoidant coping (b = -.28, p >.05). Problem-focused coping (b = .10, p> .05) and emotion-focused coping (b = -.05, p > .05) were not significant predictors of vocational identity.
General Summary Multiple linear regression analysis was used to test if coping strategies was a significant predictor of participants’ vocational identity. The results show that avoidant coping is a signficiant negative predictor of vocational identity.
Research Question: Hpw can college stress factors (academic stress, financial stress, and social stress) predict vocational identity levels?
# Regression Model 3
model5 <- lm(VIM_TOTAL~Academic_Stress+Social_Stress+Financial_Stress)
summary(model5)##
## Call:
## lm(formula = VIM_TOTAL ~ Academic_Stress + Social_Stress + Financial_Stress)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.055 -8.343 0.752 10.679 22.261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 89.0690 2.2766 39.124 <2e-16 ***
## Academic_Stress -0.4207 0.2371 -1.774 0.0782 .
## Social_Stress -0.4642 0.2672 -1.737 0.0846 .
## Financial_Stress 0.1813 0.1978 0.917 0.3608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.27 on 138 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.09097, Adjusted R-squared: 0.0712
## F-statistic: 4.603 on 3 and 138 DF, p-value: 0.004206cor.data3 = data.frame(VIM_TOTAL,Academic_Stress,Social_Stress,Financial_Stress)
ggCor(cor.data3, label = 2)
VIM_TOTAL_Observed3 = model5$model[,1]
VIM_TOTAL_predicted3 = fitted(model5)
rs3 = data.frame(VIM_TOTAL_Observed3,VIM_TOTAL_predicted3)
ggPoints(rs3, aes(VIM_TOTAL_Observed3,VIM_TOTAL_predicted3), method = 'lm', title = "Mutiple R value for Regression Model")
Statistical Results Summary: Multiple linear regression was used to test if participants’ sources of college stress predicted their vocational identity. A significant regression was found (F (3,119) = 5.21, p < .01) with an R2 of .12. Participants’ vocational identity was significantly predicted by their social stress (b = -.27, p <.05), however academic stress (b = -.19, p > .05), and financial stress (b = .21,p>.05) were not significant predictors of vocational identity.
General Summary Multiple linear regression was used to test if participants’ sources of college stress predicted their vocational identity. The results show that only social stress is a signficant negative predictor