Empirical Research Project

Mental Health Report of University Students

Abstract: 4/10 marks

An unsatisfactory results section here, that is oddly incorrect about your
findings. When looking at your appendix, I can see that you've only run
two regressions, one with just self-esteem, and one with self-esteem and
risk-taking, which is unable to answer your assessment question. The rest
of the abstract is ok, but we could have seen more setup around context
and implications or future research.

Abstract

This study aimed to investigate the effect of personal factors on the mental health status of
university students. The personal factors examined were self-efficacy, self-esteem, risk-
taking willingness, and personal growth initiative. An expo-facto methodology was
employed, using a convenience sampling technique to gather data from 84 participants, 72 of
whom were female and 12 were male. The mean age of participants was 35.17, with a
standard deviation of 9.951. Data was collected using four legal instruments that measured
self-efficacy, self-esteem, personal growth, risk-taking behaviors, and mental health status.
Correlation tests and multiple regression analysis were used to analyze the data. Results
revealed that self-esteem significantly predicted the mental health of university students (F1,
80 = 4.733, p = .033 < 0.05). There was no correlation between education level and mental
health (r = -0.133, p = .232 > 0.05). Other personal factors (risk-taking, self-efficacy, and
personal growth initiative) did not significantly predict mental health. The findings suggest
that boosting students' self-esteem could be an effective primary preventive measure for
mitigating adverse mental effects and improving academic performance among university
students.

Keywords: Mental health, self-esteem, self-efficacy, personal growth initiative, university

students.

Introduction

Ensuring that university students thrive in their personal and academic lives is crucial for
both individual and societal well-being. Mental health issues have been shown to be a
significant factor that can impact academic success among university students (Macaskill,
2013; Lo et al., 2020). However, many studies have not yet identified the primary causes of
poor mental health among these students (Macaskill, 2013; Lo et al., 2020). Factors such as
the environment, genetics, and lack of emotional and social support have been proposed as
potential causes (Wahlstrom & Owens, 2017). Understanding the underlying causes of poor
mental health among university students is essential to develop effective interventions at both
the individual and institutional level. Previous research has identified personal factors such as
self-efficacy and self-esteem as being related to mental health (Shorey & Lopez, 2021).
Additionally, risk-taking behaviors and personal growth initiative have also been found to
impact mental health (Wahlstrom & Owens, 2017; DURSUN, 2021). This study aims to
investigate the relationship between self-efficacy, self-esteem, risk-taking behavior, and
personal growth initiative and mental health among university students from different
countries.

Mental health is a crucial aspect of overall well-being and it encompasses emotional, social,
and psychological well-being, which affects a person's ability to learn, cope with stress, and
reach their potential (Macaskill, 2013). University students are particularly at risk for mental
health issues such as anxiety and depression (Wang et al., 2020; Liu et al., 2019). The
development of mental health is influenced by a wide range of structural, social, biomedical,
and personal factors (Macaskill, 2013). The Diathesis-Stress theory posits that a combination
of psychological, genetic, cultural, and biological vulnerabilities and stressors increases an
individual's susceptibility to poor mental health outcomes (Kendler, 2020). However,
personal factors can act as protective factors that determine an individual's ability to cope
with stressors and prevent the onset of poor mental health outcomes (Kendler, 2020).
Personal attributes such as self-efficacy, self-esteem, and intelligence levels have been
identified as protective factors related to mental health (Macaskill, 2013; Hiçdurmaz et al.,
2017; Shorey & Lopez, 2021). Huffman et al. (2021) also suggest that the broad experiences
and knowledge that students gain at the university level can contribute to personal growth,
leading to a better understanding of one's purpose, direction, and goals.

One personal factor that has been found to have a significant impact on mental health is self-
efficacy. Self-efficacy is an individual's belief in their ability to accomplish tasks or goals
(Bandura, 1997). People with high self-efficacy are confident in their ability to control factors
that influence their behavior, social environment, and motivations, and are better able to cope
with depression or anxiety-inducing events such as academic failure, adapting to new
lifestyles, and pessimism (Bandura, 1997). In a study of doctoral students, Liu et al. (2019)
found that self-efficacy was directly related to overall life satisfaction and higher ratings of
subjective health and well-being. Low levels of self-efficacy have also been linked to poor
mental health outcomes, such as depression and anxiety (Liu et al., 2019). Therefore, self-
efficacy plays a crucial role in the mental health outcomes of university students.

Another important personal factor that affects mental health is self-esteem. Self-esteem is a
subjective feeling of value or worth (Orth et al., 2018). As people age, self-esteem typically
increases as they gain more life experiences and develop a better understanding of themselves
(Orth et al., 2018). University students with a strong sense of belonging, identity, and
competence, as a result of high self-esteem, have been found to have greater academic
achievement (Visier-Alfonso et al., 2022). Though self-esteem is not a mental health
condition, it greatly influences mental well-being. For example, students with low self-esteem
may have difficulty adapting to change and may experience anxiety or depression (Visier-
Alfonso et al., 2022). These students also tend to avoid challenging situations and have low
confidence in their abilities, which may lead to poor academic performance. The results by
Visier-Alfonso et al. (2022) suggest a correlation between self-esteem and self-efficacy, but
further research is needed to quantify this correlation.
On the other hand, students with high self-esteem tend to be happier and more successful
academically and in daily life (Visier-Alfonso et al., 2022). People with low self-esteem often
have a negative perception of themselves and may experience social isolation, which can lead
to depression and other mental health issues (Guarneri et al., 2019). Additionally, a study by
Yang et al. (2022) found that children and young adults with low self-esteem are at a greater
risk of developing addictive behaviors in later life, which is linked to poor mental health
outcomes such as depression and anxiety (Yang et al., 2022). Other studies have also linked
low self-esteem to a range of mental health problems such as somatization, suicidal ideation,
melancholy, anxiety, aggression, paranoia, psychosis, and eating disorders (Campbell et al.,
2022; Bridge et al., 2019; Orth et al., 2018; Keane & Loades, 2017). However, these studies
have not explored the predictability of mental health using self-esteem, but only revealed the
correlation. Overall, the research suggests that self-esteem is a positive predictor of mental
health and is correlated with self-efficacy.

Risk-taking can be a formative experience for university students, providing an opportunity

to learn valuable lessons (Crone & van Duijvenvoorde, 2021). However, when students take
adverse risks and fail, they may experience mental health problems such as pessimism,
trauma, and post-traumatic depression (Crone & van Duijvenvoorde, 2021). Many studies
have found that university students engage in risky behaviors that can have negative
implications on their mental and physical health (Scalese et al., 2017; Campbell et al, 2022;
Gidi et al., 2021). Additionally, research has shown that risk-taking can lead to addictive
behaviors and ultimately, mental deterioration (Hiçdurmaz et al., 2017). As a result, risk-
taking can limit personal growth in areas such as self-esteem and personal growth initiatives
(PGI).

Personal growth is particularly important for university students as the world is constantly
changing and curricula are frequently adjusted to meet modern challenges (Erstad & Voogt,
2018). PGI is a multi-faceted concept that encompasses people's intentions to achieve specific
goals and actively make positive life changes (Costa Jr. et al., 2019). PGI has been
recognized as an essential construct for improving well-being as it encompasses diverse
components such as risk-taking behavior and psychological well-being (Hurwich, 1993). A
widely used tool for measuring PGI is the Personal Growth Initiative Scale (PGIS).

Despite a lack of research on PGI, recent studies indicate that individuals with high PGI
scores are more committed to self-improvement and better able to change their lives in the
direction they desire (Ryff, 2017). Furthermore, there is a growing body of research linking
PGI to self-efficacy. Studies have found that people's belief in their ability to accomplish
their goals can lead to designated achievements and resolutions over factors that affect their
lives, thus promoting personal growth (Harmon & Venta, 2022). Additionally, people's
understanding of their capabilities determines their growth initiation process.

In summary, this study aims to investigate the extent to which various predictor variables,
specifically risk-taking behaviors, personal growth initiative, self-efficacy, and self-esteem,
influence mental health wellness among university students. It also aims to explore if there
exists a correlation between mental health and level of education. The research aims to shed
light on how personal factors can influence the mental health outcomes of university students
and provide insight into how interventions at the personal and institutional level can be
developed to improve their mental health.
The research question of this study is to investigate whether self-efficacy, self-esteem,
personal growth initiative, and risk-taking predict the mental health of university students.
The study aims to answer this question by testing the following hypotheses:

H01 - Personal factors (i.e., self-efficacy, self-esteem, risk-taking, and PGI) jointly do not
influence the mental health of university students.
H02 - Self-efficacy does not significantly influence mental health.
H03 - Self-esteem does not significantly influence mental health.
H04 - Risk-taking does not significantly influence mental health.
H05 - PGI does not significantly predict mental health.
H06 - Educational level does not correlate with students’ mental health.

These hypotheses will be tested using statistical methods such as correlation tests and
multiple regression analysis to determine the relationship between the predictor variables and
mental health among university students. The results will provide insight into whether
personal factors such as self-efficacy, self-esteem, personal growth initiative, and risk-taking
can predict mental health outcomes among university students and if there is any correlation
between educational level and mental health.

Methodology

Participants
The study included 84 volunteers (72 females and 12 males) who participated after seeing an
advertisement for the survey. All participants were university students from various
countries, pursuing different levels of education, and fluent in English. The minimum age of
participants was 18, and the maximum was 65. The mean age was 35.73, and the standard
deviation (SD) was 10.51. The psychology faculty at Arden University collected the data and
provided it to the students for analysis. Participants signed an informed consent form before
participating in the study.

Design
This study used an expo-facto design. This design examines how the explanatory variables of
a sample that existed before the study affect the dependent variable without manipulation of
the independent/explanatory variable. In this study, the explanatory variables were self-
efficacy, self-esteem, personal growth initiative (PGI), and risk-taking behavior, while the
dependent variable was mental health. All the variables were continuous.

Measures
The study collected data using four credible and standard instruments.

Mental Health: The Mental Health Inventory questionnaire (MHI-5) was used to assess
mental health in the study. This scale was developed by Veit and Ware (1983) and comprises
five items in a Likert format, where 1 represents the lowest score and 6 represents the highest
score. The questionnaire evaluates the period in which individuals have experienced specific
mental health-related problems such as pessimism and low self-control. The scale has an
optimal score of 30 and the least score is 5. High scores indicate that individuals have been
experiencing more psychological distress and mental health problems in the past month. The
MHI-5 has been found to be closely related to the General Health Questionnaire (GHQ-12) in
a study by Hoeymans et al. (2004). The kappa statistics for the two scales was 0.49, showing
a moderate correlation. The MHI-5 was also found to be a reliable scale compared to MHI-18
and the somatic symptom inventory (SSI-28) in a study by Vilca et al. (2021). The reliability
of the MHI-5 scale accounts for its extensive use in population, psychology, and clinical
studies (Vilca et al., 2021; Atlantis & Sullivan, 2012).

Self-efficacy: The study used the General Perceived Self-Efficacy Scale (GPSS) to measure
self-efficacy. This scale, developed by Shortridge-Baggett (2000), assesses individuals'
perceptions of their ability to accomplish specific tasks or goals using a Likert scale format
with ten questions. Responses range from "totally not true" (1) to "exactly true" (4). The
GPSS has a high Cronbach's alpha coefficient of .75 to .95 (Shortridge-Baggett, 2000),
indicating high reliability. Shortridge-Baggett also found that the scale is culturally sensitive
and positively correlates with optimism and self-esteem, but negatively with symptoms of
stress, depression, or anxiety. High scores on the GPSS indicate a high level of self-efficacy.

Self-esteem: The Rosenberg Self-Esteem Scale (RSES) was used to measure self-esteem in
the study. The scale comprises ten items in a 4-point Likert format, with scores ranging
between 10 and 40. A high score on the RSES scale indicates high self-esteem, while a low
score indicates low self-esteem. The reliability and validity coefficients of the RSES are 0.71
and 0.75, respectively (Gnambs et al., 2018). Therefore, the RSES is a valid and reliable scale
for measuring self-esteem in students. High scores on the RSES indicate high levels of self-
esteem.

Personal Growth Initiative (PGI): The Personal Growth Initiative Scale (PGIS) was used to
measure PGI in this study. Developed by Robitschek (1999), the PGIS is a reliable tool that
consists of nine items rated on a 6-point Likert scale, ranging from "definitely disagree" (1) to
"definitely agree" (6). The scores of the nine items are combined to yield a final score, with a
minimum score of nine and a maximum score of 54. A high score on the PGIS indicates a
high level of personal growth initiative. The scale has been found to have an internal
consistency of .78 to .90 and a reliability of .74 (Robitschek, 1999).

Risk-taking: The primary scale used to measure students' risk-taking behavior was the
International Personality Pool Scale (IPPS), developed by Goldberg (1999). The scale
contains ten items in a Likert scale format, with each item scored on a 5-point scale. The
Likert scale measures the range of accuracy of the results, with "very inaccurate" (1) being
the lowest score and "very accurate" (5) being the highest score. A high score on the IPPS
indicates better emotional stability. The IPPS questionnaire contains both positive and
negative statements, such as "knowing how to observe rules" and "never able to partake in
risky environments." Research reveals that the coefficient consistency for IPPS is .78, making
it a reliable scale (Goldberg, 1999).

Procedure
Participants were recruited for the study through an advertisement. The psychology faculty at
Arden University sent a link to the survey, which provided general information about the
study, including its purpose, potential risks, inclusion criteria, an overview, and data
protection initiatives. Participants then consented to participate in the study by signing an
informed consent form and generating a short form code to be used as a recovery link for
their account.

Next, participants were asked to provide their demographic information and country of
origin. They then completed Likert-type questionnaires assessing mental health, self-efficacy,
self-esteem, PGI, and risk-taking behavior.
Finally, participants completed a debrief section to confirm the accuracy of the information
provided in the survey. The data collection process was conducted by the psychology faculty
at Arden University.

Results

Socio-demographic Characteristics of Sample Population

The socio-demographic characteristics of the sample are presented in Table 1 (in the
appendices). The results show that out of the 82 participants, 71 (86.6%) were female and 11
(13.4%) were male students. The mean age of the participants was 35.73 years, with a
standard deviation of 10.51. The minimum age was 21, and the maximum age was 65. Two
of the participants (2.4%) were undergraduate students; 58 of the participants (69%) were
from graduate school, while the remaining 24 (28.6%) were postgraduate students (Table 2).
As per Table 2, 2.4% of the participants were from undergraduate school, 69% were from
graduate school, and 28.6% were postgraduate students. The results from Table 3 reveal that
participants from graduate school recorded the highest mean for mental health (M = 48.70,
SD = 11.517). The postgraduate students recorded the lowest Mean for mental health (M =
45.22, SD = 9.38).

Correlational Analysis
The correlational analysis compares if there is a significant relationship between the level of
education and mental health. The results are presented in tabular form in Table 4. The results
indicate that Pearson's correlation analysis, which assessed the linear relationship between
education level and mental health outcomes, revealed a weak and non-significant correlation
between the level of education and mental health in the sample population (r = -0.133, p
= .232 > .05). This suggests that there is little to no relationship between the level of
education and mental health in the sample population. Additionally, it suggests that mental
health amongst the different levels of education was similar.

Multiple Linear Regression

Multiple regression analysis was conducted to compare the influence of each personal factor
on the student’s mental health and predict the effects of all four personal factors on students’
mental health. However, before conducting a multiple regression, it is important to ensure
that the assumptions of linearity, independence of errors, normality of errors, and
homoscedasticity are met.
Linearity
A change in predictor variables (self-esteem, self-efficacy, PGI, and risk-taking) should cause
a change in the dependent variable (Mental Health). There are various linearity tests, such as
comparing means or analysis of correlations. However, this study confirmed linearity by
examining the collinearity matrix in table 6. The results showed that PGI, self-efficacy, and
risk-taking did not significantly correlate with mental health. Their r values indicated whether
they positively or negatively correlated with mental health, indicating a linear relationship
between the variables and mental health.

Multicollinearity
Collinearity is the tendency of the variable used in regression to be highly correlated, which
can cause overfitting problems and difficulty interpreting the data. When collinearity is high,
changes in one independent variable can cause changes in others, resulting in inconsistent and
unstable results in the final model. This makes it difficult to determine which variables are
significant and can also make interpreting the model more complex.

One way to check for collinearity is by examining the tolerance levels. Tolerance is a
coefficient that measures how much other predictors do not explain the variability in a
predictor variable in the regression model. If the tolerance is less than 0.1, it suggests that the
specific predictor is highly collinear. From the results in Table 5, the tolerance coefficient for
PGI, risk-taking, self-efficacy, and self-esteem are 0.568, 0.976, 0.549, and 0.493,
respectively. All the values are above 0.1, indicating that there is no evidence of
multicollinearity in the data. The lack of multicollinearity indicates that the predictor
variables are independent of each other.

Homoscedasticity
Homoscedasticity is a key assumption in regression analysis that refers to the homogeneity of
variances of the predictor variables. When this assumption is not met, the results of the
regression analysis may be skewed or biased. It is important to test the variances of predictor
variables in any parametric tests, as variations in variances can reduce the accuracy of the test
(Mishra et al., 2019).

To ensure homoscedasticity, the medians of the data set used should be constant and their
mean-variance should be zero. In order to test for homoscedasticity in regression, a scatter
plot of Z-residuals against Z-predictors can be reviewed and a loess line can be drawn. The
loess line should be smooth and parallel to the x-axis. According to Figure 1, the Z-residuals
vs. Z-predictors fulfill the assumption for homoscedasticity as the loess line is smooth and
equally distributed, indicating that the variation in means is zero.

Normality
In this regression analysis, normality is a concern because the sample size is large. However,
it is important to note that normality is only checked for the errors and not the predictor
variables (Mishra et al., 2019). The histogram for standardized residuals, shown in Figure 2,
indicates that they are normally distributed. This suggests that the assumption of normality in
multiple regression is met. As seen in Figure 2, the histogram is bell-shaped, which confirms
that the residuals are normally distributed.

Multiple Linear Regression

Table 6 shows no significant relationship between a person's ability to try new things (risk-
taking) and all the other interpersonal factors (self-efficacy, self-esteem, and PGI) and the
mental wellness of individuals. Risk taking and PGI (r = -0.33, p = .383 > .05). However, the
Pearson correlation coefficient (r) is negative, indicating risk-taking reduces PGI, and only
3.3% of individuals' risk-taking can influence their personal growth. For risk-taking and self-
efficacy, r = .092, p = .206 > .05. For risk-taking and self-esteem, r = -.029, p = .397 > .05.
However, the negativity of r indicated that risk-taking reduces self-esteem. For risk-taking
and mental health, r = .020, p = .468 > .05. There was a strong positive correlation between
PGI and self-efficacy (r = .552, p = .000 < .01). The strongest positive correlation was
between PGI and self-esteem (r = .662, p = .000 < .01), which the Pearson's coefficient
revealed that 66.2 % of individuals' self-esteem influenced personal growth. There was a
slightly insignificant relationship between PGI and mental health (r = .157, p = .079 > .05).
Self-efficacy positively and strongly correlated with self-esteem (r = .629, p = .000 < .01).
However, there was an insignificant correlation between self-efficacy and mental health (r
= .183, p = .050 > .01). Self-esteem negatively correlated with mental health (r = .236, p
= .016 > .05).

The results in Table 7 indicated that the regression of independent variables (PGI, self-
efficacy, self-esteem, and risk-taking) yielded a regression coefficient (R) of 0.236 and an R
squared of .056. The results indicated that the four predictor/independent variables influenced
5.6% of the participants’ total variance in mental health. Table 7 also shows that the analysis
yielded a significant variance of the multiple regression since the F value was substantial
(F(1, 80) = 4.733, p = .033 < 0.05). By entering the predictors in a stepwise pattern, the
analysis results arranged the regression results depending on the independent variable
correlation strength with the dependable variable. The first independent variable strongly
correlated with mental health, and the hierarchy trails down. In this case, stepwise regression
analysis yielded the result in Table 7, where only self-esteem had a significant relationship
with mental health.

Table 8 shows the predictability of self-esteem on mental health, which can be quantified by
F(1, 80) = 4.733; R = .236, R2 = .236, p < .05). The result shows that self-esteem alone
predicts 23.6% of the variation in the mental health of the university students. The other
factors did not meet the significant amount of predictability of mental health, which is why
the model excluded them from regression analysis. The efficiency of the self-esteem
predicting mental health of university students can further be proved by the beta coefficient
as shown in Table 9 (β = -.236; t = ; p < .05).

Discussion

The results of this study indicate that there is no significant association between individuals'
level of education and their mental health (r = - 0.133, p = .232 > 0.05). This supports the null
hypothesis that there is no correlation between mental health and education level. The
negativity of the Pearson's coefficient suggests a negative association, but it is not statistically
significant to be generalized. This aligns with previous research, such as Silva et al. (2016),
which found that higher levels of education may be associated with common mental
problems due to the resources available to graduates to address psychological issues. The use
of a random sampling technique and an increased sample size could have increased the
generalizability of the results.
The analysis also revealed that the four personal factors examined were not successful in
predicting the mental health of university students. The study employed a stepwise regression
model, which only includes independent variables with a significant relationship with the
dependent variable. From Table 6, self-esteem had the strongest correlation with mental
health (r = - 23.6, p = 0.00< 0.05), and was therefore the first variable included in the
analysis. The second variable to be considered, based on collinearity strength, was self-
efficacy (r = - 18.3, p = 0.05= 0.05), but it did not meet the criteria for inclusion in the
regression model (p < 0.05). The other two variables (PGI and Risk-taking) also did not meet
the inclusion criteria.

Therefore, out of the four factors, only self-esteem was found to have a significant
predictability on mental health. The F-ratio (F) of 4.733 was significant at .05, which
confirms the effectiveness of the predictability of self-esteem on mental health. The
prediction did not develop by chance, as self-esteem had to meet the inclusion criteria for the
stepwise regression model (p < .05).

The strong collinearity between self-esteem and mental health, as revealed in Table 6, serves
as one of the proofs of self-esteem's ability to predict mental health. The results from Table 6
confirm that self-esteem had a significant negative correlation with mental health (r = -.236, p
= .016 <0.05). Thus, self-esteem can predict 23.6% of mental health outcomes. These results
reveal that as self-esteem increases, mental health reduces, and vice versa. This aligns with
the mental health inventory scale, as high scores indicate high anxiety. Therefore, the more
people have high self-esteem, the more they suppress their anxious selves, as they have a
positive attitude towards themselves, can recognize their worth, and are satisfied with their
self-image and the life they live. This means they are less likely to worry about other people's
perceptions of them, their current lifestyle, career choices, and plans. Studies such as Visier-
Alfonso et al. (2022) have also found that students with high self-esteem have better
academic achievement and are less likely to worry about failing exams. Guarneri et al. (2019)
revealed that students with high self-esteem tend to be more socially active, leading to better
mental health outcomes as they are better able to manage stress and anxiety. Additionally,
Yang et al. (2022) found that individuals with higher self-esteem also have less addictive
behaviors and better mental health outcomes. These findings support the rejection of the null
hypothesis that self-esteem does not influence mental health, and align with previous research
linking self-esteem to better mental health outcomes. For example, Gidi et al. (2020) found
that low self-esteem prevalence among medical school students accounted for 19.7% of
mental health problems. Similarly, Saleh et al. (2017) found a significant relationship
between self-esteem and stress among university students. The current study improves upon
these studies by quantifying the predictability of self-esteem on mental health outcomes at
23.6%.

Most studies identify the main predictors of mental health outcomes as self-efficacy, self-
esteem, PGI, and risk-taking behaviors (Yang et al., 2022; Scalese et al., 2017). These
predictors affect students' mental health differently, depending on a variety of factors, such as
the structural setup where the university is located. For instance, in a study by Gidi et al.,
most students' mental health was influenced by high risk-taking, which was associated with
the low-socio-economic background in which the students thrive. In our current study, which
entailed most students from first-world countries, students are less likely to have high risk-
taking behaviors due to economic and structural stability. There is less likelihood of the
students having low PGI since the governments in the respective countries have developed a
curriculum that progressively guides students to achieve the future. Although the current
result has revealed that self-esteem significantly influences mental health outcomes, there is a
need for more research to evaluate the specific factors that affect self-esteem. Further
research on how universities can best intervene in the factors affecting self-esteem can help
improve students' mental stability.

Although the other variables could not fit the criterion for the stepwise regression analysis,
their keen evaluation of their correlation from table 6 reveals that self-efficacy is a negative
but slightly insignificant relationship with mental health (r = -.183, p = .05). Although the
insignificance is very small, the model excluded self-efficacy from the stepwise regression
analysis, which incorporates only the variables whose p < 0.05. Since the other three personal
factors (self-efficacy, risk-taking, and PGI) did not influence mental health, hypotheses H2,
H4, and H5 were true. That is, self-efficacy, Risk-taking behavior, and PGI, respectively,
could not predict mental health.

The Implications of the Study

The current study highlights the importance of self-esteem in relation to mental health
outcomes among university students. The findings suggest that universities can develop
interventions to improve students' self-esteem, such as offering individual and group
counseling and guidance services, providing opportunities for social interaction, and
organizing motivational talks on self-esteem improvement. Additionally, the use of therapies
such as Cognitive Behavioral Therapy, which has been shown to be effective in improving
self-esteem (Gautam et al., 2020), may also be beneficial.

These findings also have implications for policy development related to university students'
mental health. However, further research is needed to better understand the specific factors
that affect self-esteem and how they can be effectively addressed. Improving students' self-
esteem can not only enhance their mental health outcomes but also improve their academic
performance. Therefore, universities should prioritize the development and implementation
of strategies that aim to improve self-esteem among their students.
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 Appendices

Appendix 1: Participant Information Sheet.

Appendix 2: Informed Consent Form.
Appendix 3: Materials & procedure.
Appendix 4: Debrief sheet.
Appendix 5: SPSS Output.

Appendix 1: Participant Information Sheet

Participant Information Sheet

You are invited to participate in research being conducted by Matthew Copeman at Arden
University on understanding what factors affect mental health in Students.
This sheet provides you with information about the research and what you will be required to
do. You should read this sheet in full, and ask any questions you have, before consenting to
participate.
What is the purpose of the research?
The aim of the research is to investigate which factors could help predicting mental health in
students.
Why have I been chosen to participate in the research?
You have been chosen to participate in the research as you responded to the advert
volunteering to take part in the study. You should be over the age of 18 and be fluent in
speaking English.
What are the benefits of participating in the research?
By participating in the research, you will be assisting Matthew Copeman in providing
teaching data for the MSc Psychology students at Arden University in understanding which
factors affect mental health.
What are the risks associated with my participation?
There are no significant risks associated with participating in this research. The research has
been reviewed and approved by Arden University’s ethics process.
Is my participation voluntary?
Your participation in the research is entirely voluntary. The data may be used in the
production of research reports. Please only consent to participation if you are happy to do so.
If you wish to withdraw, please close the browser window as the data is anonymised. On the
next page, you will be asked to enter a memorable code. If you wish to withdraw your data up
to two weeks after completing the study, you will need to quote this code.
What will participation in the research involve?
You will be shown three questionnaires on topics such as your self-esteem and mental health,
personal growth, self-efficacy and risk taking behaviours. You will be asked to indicate how
much you either agree or disagree with various statements, or how much a feeling or
statement is appropriate to you. The task will take approximately 15 minutes.
Is my data protected?
Your data will be used in accordance with the General Data Protection Regulation 2016
(GDPR) and the Data Protection Act 2018. All data will be treated confidentially until data is
anonymised. All electronic data will be stored on a password protected computer. Consent to
participate will be kept separately to the data.
What will happen to the results from the research?
The anonymised results of the research will be used for the MSc Psychology students at
Arden University to write up a research report on the factors that could predict mental health.
What do I do if I have concerns with the research or want to make a complaint?
If you have concerns regarding the research please, in the first instance contact the researcher
(Matthew Copeman) at mcopeman@arden.ac.uk. If your concerns/complaint is not resolved,
please contact the Postgraduate Programme Team Leader (Anthony Thompson) at
athompson@arden.ac.uk. In your email, please provide the research title, the researcher’s
name, and an outline of your concerns/complaint.
Thank you for taking the time to read this participation information sheet.

Appendix 2: Informed Consent Form

Appendix 3: Materials & procedure

Mental health in University Students materials and procedure

Data file code book (what each column represents)
Gender: what gender did the participant report? (1=female 2=male 3=transgender)
Age: what age did the participant report?
Native-English: Did the participant report to be a native English speaker? (1=Yes 2=no)
Country: What country of origin did the participant report?
Education: What level of education has the participant completed? (1 = Less than secondary
school / high school / equivalent 2 = Graduated secondary school / high school / equivalent 3
= Some university / college 4 = Graduated university / college 5 = Postgraduate (Masters or
Doctorate))
Truthful: Were your answers truthful? (1 = yes / 2 = no)
First-time: Was this the first time completing the experiment? (1 = yes / 2 = no)
PGI-Total: The averaged score from the Personal Growth Inventory, with higher scores
indicating higher levels of personal growth
Risk-Taking: The averaged score from the Risk-Taking Inventory, with higher scores
indicating more proneness to risk taking behaviour
Self-Efficacy: The averaged score from the Self-Efficacy Scale with higher scores indicating
higher levels of self-efficacy
Self-esteem: The total summed score of the Self-Esteem Scale with higher scores indicating
higher levels of self-esteem
Mental-Health: The percentile score of mental health inventory from the Mental Health
Inventory (Short Form) 5 with higher scores indicating participants are more likely to be at
risk of anxiety symptoms.
Youtube recording of the experiment to help with the method section:

https://youtu.be/eIil-dUWfnw
Mental Health Inventory Questionnaire

Personal Growth Questionnaire

Risk Taking Behaviour Questionnaire

Self-Efficiacy Scale
Self-Esteem Questionnairre

Appendix 4: Debrief sheet

Appendix 5: SPSS Output.

Appendices
SPSS Tables Output
Table 1
Gender
Cumulative
Frequency Percent Valid Percent Percent
Valid female 71 86.6 86.6 86.6
male 11 13.4 13.4 100.0
Total 82 100.0 100.0

Table 2
Education
Valid Cumulative
Frequency Percent Percent Percent
Valid College 2 2.4 2.4 2.4
graduate school 57 69.5 69.5 72.0
postgraduate 23 28.0 28.0 100.0
Total 82 100.0 100.0

Table 3

Comparison Of Mental Health Mean Across Different Education Levels

education Mean N Std. Deviation

College 48.00 2 5.657
graduate school 48.70 57 11.517
postgraduate 45.22 23 9.376
Total 47.71 82 10.883

Table 4
Correlations
education MentalHealth
education Pearson Correlation 1 -.133
Sig. (2-tailed) .232
N 82 82
MentalHealth Pearson Correlation -.133 1
Sig. (2-tailed) .232
N 82 82
Table 5
Multicollinearity coefficientsa
Collinearity Statistics
Model Tolerance VIF
1 PGITotal .568 1.761
RiskTaking .976 1.025
SelfEfficacy .549 1.821
selfesteem .493 2.028
a. Dependent Variable: MentalHealth

Table 6
Correlations
MentalHealth PGITotal RiskTaking SelfEfficacy selfesteem
Pearson MentalHealth 1.000 -.157 .020 -.183 -.236
Correlation PGITotal -.157 1.000 -.033 .552 .622
RiskTaking .020 -.033 1.000 .092 -.029
SelfEfficacy -.183 .552 .092 1.000 .629
selfesteem -.236 .622 -.029 .629 1.000
Sig. (1- MentalHealth . .079 .428 .050 .016
tailed) PGITotal .079 . .383 .000 .000
RiskTaking .428 .383 . .206 .397
SelfEfficacy .050 .000 .206 . .000
selfesteem .016 .000 .397 .000 .
N MentalHealth 82 82 82 82 82
PGITotal 82 82 82 82 82
RiskTaking 82 82 82 82 82
SelfEfficacy 82 82 82 82 82
selfesteem 82 82 82 82 82

Table 7
Model Summaryb
Adjusted R Std. Error of
Model R R Square Square the Estimate
a
1 .236 .056 .044 10.640
a. Predictors: (Constant), selfesteem
b. Dependent Variable: MentalHealth
 Table 8
ANOVAa
Sum of
Model Squares df Mean Square F Sig.
1 Regression 535.792 1 535.792 4.733 .033b
Residual 9057.184 80 113.215
Total 9592.976 81
a. Dependent Variable: MentalHealth
b. Predictors: (Constant) self-esteem

Table 9

Coefficients of regression analysis

Standardiz
Unstandardiz ed
ed Coefficient Collinearity
Coefficients s Correlations Statistics
Zero
-
Std. Sig orde Parti Par Toleran
Model B Error Beta t . r al t ce VIF
1 (Constan 60.79 6.130 9.91 .00
t) 5 8 0
selfestee -.373 .171 -.236 - .03 -.23 -.236 -.2 1.000 1.00
m 2.17 3 6 36 0
5
a. Dependent Variable: MentalHealth

Table 10

Excluded Variablesa
Collinearity Statistics
Partial Minimum
Model Beta In t Sig. Correlation Tolerance VIF Tolerance
1 PGITotal -.016b -.117 .907 -.013 .613 1.632 .613
b
RiskTaking .014 .124 .902 .014 .999 1.001 .999
b
SelfEfficacy -.058 -.409 .683 -.046 .605 1.653 .605
a. Dependent Variable: MentalHealth
b. Predictors in the model: (Constant), selfesteem
 Figures

Figure 1: a scatter plot of regression standardized residuals against regression standardized

predictors with a loess line.

Figure 2: A histogram for regression standard residuals.