Nursing Student Stress and Demographic Factors

Nursing Student Stress and Demographic FactorsA Thesis
California State University, San Marcos
Submitted in partial satisfaction of the requirements for the degree of
MASTER OF SCIENCE
by
THESIS SIGNATURE PAGE
THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
MASTER OF SCIENCE
AUTHOR: Mary Lelia Baker
DATE OF SUCCESSFUL DEFENSE: April 25, 2012
THE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NURSING.
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Student: Mary Lelia Baker
I certify that this student has met the School of Nursing format requirements, and that this thesis
is suitable for shelving in the Library and credit is to be awarded for the thesis.
School of Nursing College of Education, Health, and Human Services California State University San Marcos
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Abstract
of
by
Mary Lelia Baker
Statement of Problem Nursing students experience high levels of stress (Beck, Hackett, Srivastava, McKim, & Rockwell, 1997), have been shown to be at increased risk for physical and psychiatric illnesses (Beck & Srivastava, 1991) and stress has been shown to increase dropout rates (O’Regan, 2005). No prior published studies that focus on the relationship between demographic variables and reported stress levels in nursing students have been published.
Sources of Data Data was collected from a convenience sample of all nursing students currently enrolled at California State University San Marcos. Students completed an online survey that included demographic data, the Student Nurse Stress Index (SNSI) (Jones & Johnston, 1999), and the Marlowe-Crowne Social Desirability Scale (Strahan & Gerbasi, 1972).
Conclusions Reached Two independent variables, GPA (p=.001) and Study Time (p=.013) showed statistical significance in affecting the self-reported stress levels as measured by the SNSI.
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ACKNOWLEDGEMENTS
I have been indebted in the preparation of this thesis to my committee, Dr. JoAnn Daugherty, Dr. Linnea Axman, and Professor Ilene Dunagan of California State University San Marcos. Each
contributed to this project in their own unique way. I wish to thank each one immensely for their support, kindness, and patience.
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Appendix C. Online Survey ......................................................................................45
Appendix F. Sample Didactic Instructor Email Request ..........................................57
Appendix G. Permission to Use SNSI .......................................................................58
Appendix H. Permission to Use Graphic ...................................................................59
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References ...................................................................................................................60
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1. Relationship between the Transactional Model and variables ........................12
2. Comparison of Available Demographic Factors of Sample and Population ...22
3. Frequency of Participants by Student Type ....................................................25
4. Frequency of Participants by Semester ...........................................................26
5. Frequency of Participants by GPA .................................................................26
6. Frequency of Participants by Gender ..............................................................26
7. Frequency of Participants by Age ...................................................................27
8. Frequency of Participants by Marital Status ...................................................27
9. Frequency of Participants by Household Living Situation .............................27
10. Frequency of Participants by Number of Children .........................................28
11. Frequency of Participants by Number of Children Residing in Household ...28
12. Frequency of Participants by Employment Status ..........................................28
13. Frequency of Participants by Household Income ...........................................29
14. Frequency of Participants by Ethnic or Cultural Background ........................29
15. Frequency of Participants by Amount of Daily Study Time ..........................30
16. Frequency of Participants by Weekly Hours Spent in Class or
Clinical Setting ..........................................................................................30
18. Model Summary...............................................................................................33
19. ANOVA ...........................................................................................................34
20. Coefficients ......................................................................................................35
1. Lazarus and Folkman’s model of psychological stress ...................................... 9
2. Power analysis prior to data collection ............................................................ 16
3. Power analysis post hoc ................................................................................... 24
4. Distribution of SNSI total stress score ............................................................. 32
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Nursing Student Stress and Demographic Factors
CHAPTER ONE: INTRODUCTION
Nursing students frequently complain of being “stressed-out” and “overwhelmed”
during their time in nursing school. Previous studies have assessed the stress levels of
nursing students; however, there is a gap in the literature regarding research about the
relationship between demographic factors and self-reported stress levels. This study was
designed to determine if a specific group of demographic variables could explain self-
reported stress levels.
This research study was based on the theoretical framework of Richard Lazarus
and Susan Folkman’s Transactional model (Lazarus & Folkman, 1984). The model has
three themes associated with it: “(1) relationship or transaction, (2) process, (3) a view of
emotional as an interdependent process” (Lazarus & Folkman, 1987, p. 142). This
theorem will be more thoroughly discussed in Chapter Two.
Background and Significance
Baccalaureate nursing students, regardless of year, experience higher levels of
stress and have higher levels of physiological and psychological symptoms than students
in other health fields (Beck, Hackett, Srivastava, McKim, & Rockwell, 1997). Research
has indicated that stress can cause a higher drop out rate for student nurses (O’Regan,
2005). Additionally, previous studies have identified the increased sources of stress such
2 NURSING STUDENT STRESS AND DEMOGRAPHIC FACTORS
as academic load, clinical problems or personal problems (Pulido-Martos, Augusto-
Landa, & Lopez-Zafra, 2012).
There have been few studies examining the relationship between nursing student
demographics and self-perceived stress levels even though other healthcare student
populations have been studied. Prior research has explored demographic factors such as
gender differences (Shih & Eberhart, 2010; American College Health Association, 2011),
financial concerns (Gibbons, Dempster & Moutray, 2008), relationship concerns (Gray,
2011) and age differences (Whitman, 1985). Thus, a gap in research focused on nursing
students was identified. Further study in this area was considered beneficial in order to
identify factors for which interventions could be designed in order to ameloriate the
negative effects of stress on nursing students.
The Problem
Nursing students experiencing high levels of stress (Beck, et al, 1997), have been
shown to be at increased risk for physical and psychiatric illnesses (Beck & Srivastava,
1991) and stress has been shown to increase drop out rates (O’Regan, 2005). At this
writing, there have been no studies that investigate the relationship between demographic
variables and reported stress levels in nursing students.
Purpose of the Research
The purpose of the study is to evaluate the relationship between selected student
demographic variables and the sources of stress experienced by students currently
enrolled in any nursing program at California State University San Marcos (CSUSM).
Research Question
The research question was “How much of the variability in stress is explained by
student demographic factors in student nurses currently enrolled in any CSUSM School
of Nursing program?”
Research Variables
The dependent variable in the study is the students self-reported stress levels as
calculated by the Student Nursing Stress Index (SNSI) (Jones & Johnston, 1999). The
independent variables explored were the type of nursing student, current semester,
current grade point average (GPA), gender, age, marital status, household living
arrangement, number of children, number of children residing in the household at least
75% of the time, employment status, household income, cultural background, study time,
class time, and amount of sleep.
CHAPTER TWO: LITERATURE REVIEW
The databases reviewed for this literature review included CINAHL, PubMed,
and Google Scholar. One hundred and twenty-seven (127) article abstracts were reviewed
for inclusion, from that list, 32 were selected for further review, and 14 were included in
this thesis. Literature search terms included nursing student, student, stress, demographic,
demographic factors, age, gender, marital, Marlowe-Crowne, Student Nursing Stress
Index, and SNSI. The search was limited to English, peer-reviewed articles published
after 1980. The researcher focused on journal articles that concentrated on the causes of
student nursing stress rather than coping skills, interventions or curriculum changes.
Beck and Srivastava (1991) conducted a descriptive correlation study (n=94) that
indicated baccalaureate-nursing students had stress levels that put them at increased risk
for physical and psychiatric illnesses. In addition, the researchers indicated that “Overall,
the prevalence of psychiatric symptoms was higher than that found in the general
population” (p. 131). The instruments utilized for this study were the Stress Inventory
(Firth, 1972) and the General Health Questionnaire (GHQ) (Goldberg, 1972). The Beck
Srivastava Stress Inventory (BSSI) (1991) developed from this study.
In 1997, Beck, et al. compared the stress levels nursing students of two
baccalaureate-nursing programs (n=552) to groups of students enrolled in other health-
related programs (medicine, pharmacy, and social work). The significant findings
indicated that “baccalaureate nursing students, regardless of year in program or university
of attendance, experienced higher levels of stress and more health and psychological
symptoms than students in other health related disciplines” (p.184). The instruments used
for this study were the BSSI (Beck & Srivastava, 1991), the GHQ (Goldberg, 1972), and
a demographic section.
In 1997, Jones and Johnston published “Distress, stress and coping in first year
students.” This cross-sectional descriptive study (n=220) focused on the levels and
sources of stress, along with the coping strategies reported by first year nursing students.
The study utilized the GHQ (Goldberg, 1972), the BSSI (Beck & Srivastava, 1991), the
Ways of Coping Questionnaire (Coyne, Aldwin & Lazarus, 1981, adapted by Parkes,
1984) and the Marlowe-Crowne Social Desirability Scale (1960) to survey two cohorts of
nursing students enrolled in a diploma-nursing program that had transitioned into a
university setting. The research indicated that 50.5% of cohort 1 and 67.9% of cohort 2
suffered significant affective distress. Academic sources of stress were the most
frequently reported. “The levels of distress found in these two cohorts of student nurses
are higher than levels of distress found in degree nursing students, fourth-year medical
students and the general female population” (p. 481).
The Student Nurse Stress Index (SNSI) (Jones & Johnston, 1999) (Appendix A)
was developed to provide an improved measure of nursing student stress. The developers
constructed the tool from the BSSI (Beck & Srivastava, 1991) and 15 additional
questions. The final tool contains 22 questions partitioned into four subscales Academic
Load, Clinical Sources, Interface Worries, and Personal Problems. The SNSI (Jones &
Johnson, 1991) has demonstrated and concurrent and discriminant validity. The
researchers have recommended that the SNSI be administered along with the Marlowe-
Crowne Social Desirability Scale (MCSDS) (1960). Jones and Johnston’s (1999) study
indicated that some participants’ answers might be biased toward increased social
desirability. Therefore, the MCSDS (1960) was included in this study to assess for
answer bias.
review of 23 quantitative studies that utilized several, different standardized instruments
for assessing student levels of stress and compiled the results into two major categories.
The majority of the studies were conducted in Europe, had a median sample size of 205,
and 70% were of a cross-sectional design. Only three instruments were used in more than
one study and in some subsequent studies, the instrument was modified. One instrument
was the Lindop (1991) which was modified in two subsequent studies. Three studies
utilized the Perceived Stress Scale (Sheu, et al., 1997) in their research. The Perceived
Stress Scale was originally produced in Chinese. The SNSI (Jones & Johnson, 1999) was
used in two subsequent studies. Many studies assessed the nursing students for increased
stress using other factors such as general personal problems, family and economic issues.
Two studies used instruments not specifically designed for nursing students. In most
studies, the clinical and academic stressors were considered conjointly. Many studies
examined aspects of curriculum and coping strategies employed by nursing students. The
researchers concluded that the two most common categories of increased stress in nursing
students were the academic load (workload, grades) and clinical experiences (fear of the
unknown, harming patients, technical equipment).
Major Variables Defined
Demographic Variables. The variables are defined as follows. The type of
nursing student refers to which nursing program the participant is currently enrolled in at
CSUSM. The semester refers to which semester the participant is currently enrolled it.
The semester can vary by the nursing program the student is enrolled in as the number of
semesters can vary from five to eight depending on the cohort. The grade point average
(GPA) is defined as the student’s current grade point average on a 4-point scale listed in
their university transcript. Gender is defined as self-reported male or female or other.
Other refers to individuals who self-identify as intersex or transgendered. Age refers to
the chronological age of the participant. Marital status denotes the legal relationship
status of the participant as defined by the State of California. The household living
arrangements refers to the participants living arrangements and with whom the
participant resides.
The number of children refers to the number of natural, step, adopted and foster
children that the participant has. The number of children residing in the household
represents to the number of natural, step, adopted and foster children that reside with the
participant more than three-quarters of the time. Employment status refers to whether the
participant is employed outside the home and the number of hours spent working.
Household income signifies the amount of income that is produced by all members of the
household. Cultural background indicates the participants' self-identified ethnic
background. Study time refers to the average number of hours per day that the participant
spends studying for university classes. Class time denotes the number of hours per week
that the participant attends a university class including didactic and clinical time. The
amount of sleep describes the average number of hours that the participants sleep nightly.
Stress. Richard Lazarus and Susan Folkman (1984) defined stress "a particular
relationship between the person and the environment that is appraised by the person as
taxing or exceeding his or her resources and endangering his or her well-being.” This
study utilizes this definition.
Social desirability. Polit and Beck (2008) define social desirability as the
tendency to of respondents to provide biased answers based on perceived expectations or
prevailing social values. This is congruent with the Marlowe and Crown definition of
presenting oneself in a favorable light (1960). This study utilizes this definition.
Theoretical Framework
As introduced in Chapter One, this research study will be based on the theoretical
framework of Richard Lazarus and Susan Folkman’s Transactional model (Lazarus &
Folkman, 1984). The model has three themes associated with it: “(1) relationship or
transaction, (2) process, (3) a view of emotional as an interdependent process” (Lazarus
& Folkman, 1987, p. 142).
The Transactional model (Lazarus & Folkman, 1984) (Figure 1) focuses on the
imbalance between the environmental demands and perceived resources that the
individual has available to meet those demands. If the demands exceed the resources,
stress can occur in the individual. The transaction or relationship between the person and
the environment is what determines the emotional reaction (including stress). The
individual makes a cognitive appraisal of the environmental situation and reacts with an
emotion. The emotion will vary from individual to individual even though the
environmental trigger remains constant. The model suggests that one of three types of
primary appraisals is made. The individual assesses the situation and decides, is it
irrelevant, is it good, or is it stressful? After that initial appraisal, if the individual
determines that situation is stressful than there is a further analysis so determine if there
is any harm or loss, if it a threat, or if this is a challenge (Lazarus & Folkman, 1987).
Concurrently, a secondary appraisal occurs as the individual determines their
capacity to manage the environmental demands (Lazarus & Folkman, 1987). The
individual asks him or herself “Can I handle this?” then determines “No, I will fail.”
“Perhaps I can.” “I might if someone would help me.” “I will try some different ways to
resolve this.” “If I work hard enough it could be possible.” “I can do this.” These are all
responses indicative a secondary appraisal (Krohne, 2002).
Figure 1. Lazarus and Folkman’s model of psychological stress adapted by Lovallo and
Gerin (2003).
Note: Permission to use obtained from author. (Lovallo & Gerin, 2003) (Appendix H).
The process involves the individual adapting to situations over time. Individuals
endeavor to change that which is distressing or unpleasant and learn to process or cope
with environmental demands. The individual must first perceive the environmental
demand as a threat not as a desired outcome or even as a challenge. The individual may
use their coping skills to halt or blunt the development of stress. The theory states that
coping skills may be learned and that stress reduction occurs in individuals who improve
their coping methods. The individuals must change their perception, learn strategies, and
increase their confidence level and this will result in improved coping skills and reduced
stress levels (Lazarus & Folkman, 1987).
Summary
The relationship between the Transactional Model (Lazarus & Folkman, 1984)
and the major variables in this study is illustrated in Table 1. Some variables could be
categorized in both the “Primary: Beliefs and Commitments” and the “Biological
Responses: Autonomic, Endocrine, Immune” (Lovallo & Gerin, 2003). For example,
gender could be described as a strictly biological category but there are certain beliefs
societies hold about gender that could affect stress.
The review of available literature indicates that nursing students are at higher risk
for increased levels of self-reported stress and this stress can have a detrimental effect on
their physical and psychological stress. In addition, information assessing any
relationships between demographic factors and increased self-reported stress are absent
or minimally reported.
Table 1.
Relationship between the Transactional Model (Lazarus & Folkman, 1984) and the major variables in this study.
Primary: Beliefs and Commitments Type of Nursing Student Current Semester GPA Gender Age Marital Status Household Living Arrangements Number of Children Number of Children Residing in Household Employment Status Household Income Cultural Background (Ethnicity) Study Time Class Time
Biological Responses: Autonomic, Endocrine, Immune Age Gender Number of Children Cultural Background (Ethnicity) Hours of Sleep
Primary: Threat or Challenge Stress as measured by the SNSI Total Score
CHAPTER THREE: METHODOLOGY
Introduction
Prior research looking at student nursing stress have used a variety of instruments
to measure self-reported student nurses’ stress levels (Pulido-Martos, et al., 2012). The
SNSI (Jones & Johnston, 1999) (Appendix A) was chosen for this research because it
focused on nursing students and measuring the types of stress particular to the target
population. Jones & Johnston (1999) also recommended that the MCSDS (Crowne &
Marlowe, 1960) (Appendix B) be administered in conjunction with the SNSI and this
research complied with that recommendation.
Additionally, utilizing Lazarus & Folkman (1987) theory regarding how
individuals perceive stress was important in the discussion area of this research. As
previously discussed, Table 1 describes the theoretical relationship between the variable
demographics and perceived stress. The relationship between particular demographics
and the perception of stress will be explored using inferential methods.
Research Question
“How much of the variability in stress is explained by student demographic
factors in student nurses currently enrolled in any CSUSM School of Nursing program?”
Identification of Setting
The setting for the study was California State University San Marcos School of
Nursing in either San Marcos or the satellite campus in Temecula, California. CSUSM is
part of the State of California University system comprised of 23 universities throughout
the state. In the fall semester of 2010, the total enrollment of the university was 9722
students. The main campus consists of 304 acres located 35 miles north of San Diego,
California. The small Temecula satellite campus is located 60 miles north of San Diego.
The most popular undergraduate programs at CSUSM were Business
Administration (n=2056), Liberal Studies (n=670), Psychology (n=624), Nursing
including Pre-Nursing (n=621), Communication (n=549), Human Development (n=444),
Criminology (n=423), Biology (n=324), Sociology (n=321), and Kinesiology (n=315).
The gender of the Fall 2011 students were male (n=3710) (38%) and female
(n=6012) (62%). Ethnicity was African-American (n=258) (3%), Asian/Pacific Islander
(n=984) (10%), Latino/a (n=2670) (28%), Native American (n=65) (<1%), White
(n=4352) (45%), Other (n=1109) (11%), and Multiple races (n=284) (3%).
Undergraduate students’ age was distributed as 22 or younger years of age (64%), 23-25
years of age (19%), 26-35 years of age (13%), 36 or older years of age (4%). Graduate
students’ age was distributed as 22 or younger years of age (5%), 23-25 years of age
(24%), 26-35 years of age (41%), 36 or older years of age (30%) (CSUSM, 2011).
Research Design
The study design used was cross-sectional with the intent to explain any
relationship between student demographic factors and reported stress levels. An online
survey approach (Appendix C) was used to collect self-reported information on student
demographics, and administer the SNSI (Jones & Johnston, 1999) and the MCSDS
(Strahan & Gerbasi, 1972) .
Population and Sample
The participants were recruited using convenience sampling methodology.
Nursing students, currently enrolled in any nursing cohort at CSUSM School of Nursing
during the spring semester of 2012, were approached by the principle investigator at the
end of their didactic class for recruitment. Prior approval to approach students was
obtained from the didactic instructor. The cohorts include Generic Bachelor of Science
Nursing (BSN), Accelerated BSN Temecula, Accelerated BSN San Marcos, Licensed
Vocational Nurse (LVN) to BSN, Registered Nurse (RN) to BSN and Master’s students.
The target population included was 463 student nurses. All students enrolled in any
CSUSM nursing cohort are over the age of 18 and proficient in English.
The required sample size for this study was calculated to be 135 of the 463
currently enrolled students at CSUSM nursing students in the Spring 2012 semester in
order to achieve a power of 0.80 (Faul, Erdfelder, Buchner, & Lang, 2009) (Figure 2).
The calculated sample size (n=135) provided for a .15 effect size in a multiple regression
analysis with a significance level of .05. An additonal 40% was added for loss factors
(e.g. failing to complete the survey). Therefore, the desired number of participants was
set at 189.
Figure 2 Power analysis prior to data collection
(Faul, et al, 2009)
The online survey covered the following demographic information: type of
nursing student (ie ABSN, traditional BSN, LVN-BSN, RN-BSN, Master’s), current
semester enrolled in, current GPA, gender, age, martial status, domicile information,
number of children, employment status, household income, cultural background, study
habits, class time, and hours of sleep and also includes a reliable and validated tool SNSI
(Jones & Johnson, 1999 p.177).
In addition a short version of the the MCSDS MC-1 (10) was included in the
online survey (Strahan & Gerbasi, 1972). The MCSDS is included in order to replicate a
previous study and to assess for answer bias or social response set bias. The MCSDS is in
the public domain.
The SNSI (Jones & Johnston, 1999) and the MCSDS (Strahan & Gerbasi, 1972)
have been previously tested for validity and reliability. The SNSI was developed using
Beck and Srivastava’s (1991) 35 question Stress Inventory and 15 additional questions
selected by Jones and Johnston. The SNSI is a 22-item self-report instrument designed to
measure the sources and levels of stress in student nurses. The authors of the SNSI have
structured the instrument to cover four areas validated as affecting nursing student self-
reported stress levels: 1) Academic load; 2) Clinical concerns; 3) Personal problems; 4)
Interface worries. Responses were rated on a 5-point Likert scale from 1-Not stressful to
5-Extremely stressful. The tool was determined to have “cross-sample factor congruence,
good or acceptable levels of reliability for each of the four subscales, and of the
concurrent and discriminant validity” (Jones & Johnson, 1999, p. 177). Although
predictive validity has not been demonstrated to date in the SNSI, evidence of
discriminate validity has been provided (Jones & Johnson, 1999, p. 178). The Cronbach
alpha exceeds .70 with the exception of personal problems (Cronbach α = .68). The
minimal acceptable level for a new instrument is .70; a preferable level would be .80.
Permission from the author was obtained to use the SNSI in this study (Appendix G).
Data Collection Process
Institutional Review Board (IRB) approval was requested and an approval was
obtained prior to beginning data collection (Appendix D). The didactic instructors for
each of the 10 cohorts were contacted and permission to recruit participants was
requested (Appendix F). The researcher visited cohorts during the last 15 minutes of their
class, explained the study, and requested the student’s participation. An information sheet
(Appendix E) was supplied to each student that explained the study, any risks or benefits
and how to obtain the results. The information sheet was assessed to be at a Flesch-
Kincaid Grade Level of 11.7, which was deemed appropriate for college level
participants. Time allowed for questions and answers during the orientation period.
During the next class period in the following week, the didactic instructor gave the
students a URL address so that they could access and complete an online questionnaire.
No personally identifiable information was collected.
The online survey covered the following demographic information: type of
nursing student, current semester enrolled in, current GPA, gender, age, martial status,
domicile information, number of children, employment status, household income,
cultural background, study habits, class time, and hours of sleep. The demographic
factors listed are commonly included in other research studies and used to describe the
populations being studied but have not been separately evaluated as to determine if there
is a relationship between the demographic factors and the self-reported stress levels of
student nurses.
Coding and Scoring
The SNSI (Jones & Johnston, 1999) and the MCSDS (Strahan & Gerbasi, 1972)
were scored as directed by the authors of these research tools. The SNSI has four
subscales that could be totaled separately for scores in each of the following areas:
Academic load: questions 1, 2, 3, 8, 14, 18, and 20 with totals ranging from 7 to 35
points. Clinical concerns: questions 13, 14, 16, 17, 18, 19, and 20 with totals ranging
from 7 to 35. Personal problems: questions 9, 10, 11, and 12 with totals ranging from 4 to
20. Interface worries: questions 4, 5, 6, 7, 15, 21, and 22 with totals ranging from 7 to 35.
The total score from the SNSI is calculated by summing the answers from each of the 22
questions with the total score possible of 22 to 110. The total score was utilized in data
analysis. A more detailed discussion of scoring can be found in Appendix A.
In the MCSDS, (Strahan & Gerbasi, 1972) one point was given for each socially
desirable response with a total possible score of 0 to 10 as per author’s instruction. A
detailed scoring guide can be found in Appendix B. The total score was utilized in data
analysis.
Data Analysis
IBM SPSS Statistics 20 software (2011) was used to perform the data analysis.
The analyses consisted of descriptive statistics, frequency distributions, bivariate
correlation, and multiple regression analysis using the F-test. Psychometric analysis to
include Cronbach’s alpha (α) of the SNSI (Jones & Johnston, 1999) and the MCSDS
(Strahan & Gerbasi, 1972). The level of significance was set at p≤ .05.
Despite the fact that there is controversy about the level of measurement (ordinal
or interval) when performing the analysis of data obtained from the instruments using
Likert-type scales. The proposed analysis will consider the 5-item Likert scale and scale
score as interval level data. The nominal and ordinal data (primarily from demographic
data) was converted into dummy variables and entered into the regression equation.
Regression analysis was used to determine if there was a relationship between
demographic factors and self-reported stress levels.
Descriptive statistics were used to describe the sample being tested and determine
the mean, median, and mode for each question where appropriate. Frequency distribution
was performed to determine if the data was normally distributed or if the data was
skewed, J-shaped or bimodal. Multiple regression analysis utilizing the F-test was
performed to determine if and/or which student demographic factors had an effect on
self-reported stress levels in the students where effect was a function of the variability in
the dependent variable was explained by the independent variables. Utilizing this
research design allowed the researcher to understand how combinations of factors
influenced the self-reported stress levels.
Regression analysis is used to produce an equation that will predict or explain a
dependent variable using one or more independent variables. In this study, regression
analysis was used to explain the relationship between selected demographic variables and
students nurses reported stress levels. The regression equation was given by:
Y = b1X1 + b2X2 + ...b14X14 + A
Where Y was the dependent variable, Reported Stress and A is the value Y, is predicted
or explained to have when all the independent variables are equal to zero (The Trustees
of Princeton University, 2007)
Bias
The use of a convenience sample was a source of possible bias due to the
participants self-selecting to participate in the research study. The resulting sample may
not be representative of the target population. However, the following statistics (Table 2)
illustrate that the sample group is similar to the population of nursing students at
CSUSM. The sample did not demonstrate any statistically significant differences from
the target population as evaluated using the t-test statistic (p<.05) and therefore
representativeness of the sample can be assumed. All students were encouraged to
participate in the research on several occasions by their didactic instructors.
An additional source of bias may have occurred because the principle investigator
is also a clinical instructor at California State University San Marcos. In order to mitigate
this source of bias the principal investigator was not present when the students completed
the survey to ensure that her presence did not influence or bias the students into feeling
pressured to participate.
The final aspect considered in the original research by Jones & Johnston (1997)
was whether certain participants may slant their responses toward increased social
desirability. This possible bias was addressed in two ways, the surveys do not collect any
personally identifiable information, and the MCSDS was administered to assess for this
bias in the collected data.
Table 2.
Sample Population Gender
Female 86.4% 82.5% Male 13.6% 17.5%
Age 18-25 years 37.7% 33.5% 26-30 years 24.7% 33.5% 31-40 years 20.1% 21.5% 41-50 11.7% 8.7% 51-Over 5.8% 2.8%
Ethnic or Cultural Background Caucasian 66.2% 52.3% African-American 1.9% 2.5% Asian/Pacific Islander 18.8% 29.7% Mexican American/Latino/Hispanic 7.8% 10.9% Native American .6% 0% Other 1.9% 2.8% Decline to State 2.6% 1.8%
Ethical Considerations
The CSUSM Institutional Review Board (IRB) approval was obtained and the
approval number is IRB #: 2011-181.No participants were under the age of 18 were
included in the research. No participants were considered part of an at-risk population,
such as prisoners or mentally disabled, where full, and freely given consent could be
problematic. No incentives were offered for participation. Each possible participant
received a recruitment flyer (Appendix D) one week prior to expected participation. The
online survey’s first page contained a consent form that had to be electronically signed in
order to proceed. The students were informed that the online survey would collect
Internet Protocol (IP) addresses that could be traced back to the location where the
participant completed the survey. The participants were asked to complete the survey on
either the San Marcos or Temecula campus to ensure complete anonymity.
Summary
Data collection proved more challenging than anticipated as some instructors
inadvertently released students from class earlier than anticipated even though they had
received reminder emails the day prior to the principal investigator site visit. Originally,
all recruitment was scheduled to be done within one week but this period was extended to
two weeks to ensure participation from all cohorts. In addition, initial responses were low
and after all classes had been recruited additional recruitment flyers were placed in the
lunchrooms of both campuses to increase participation.
CHAPTER FOUR: RESULTS
Introduction
Chapter Four provides the results for the research question “How much of the
variability in stress is explained by student demographic factors in student nurses
currently enrolled in any CSUSM School of Nursing program?”
An ad hoc power analysis was performed using GPower (Faul, & et al., 2009)
with the total number included participants. The actual sample size (n=154) provided for
a .15 (R2) effect size in a multiple regression analysis with a significance level of .05 and
a power of .86.
(Faul, & et al., 2009)
The data was examined using IBM SPSS Statistics 20 software (2011) for
frequency, mean, median, mode, and distribution if applicable. Following frequency
distribution analysis, data were analyzed for correlations using Pearson’s correlation.
Regression analysis was then performed entering all independent variables into the
module simultaneously.
Sample
All variables were examined for normality using mean, median, and mode. Study
participants were described using frequency distribution. The three most frequent type of
students (Table 3) responding were ABSN Temecula (n=55) (35.7%), Generic (or
traditional) (n=46) (29.8%), and ABSN San Marcos (n=36) (23.4%).
Table 3
Frequency Percent
Generic-San Marcos 46 29.9 ABSN-Temecula 55 35.7 ABSN-San Marcos 36 23.4 MSN 10 6.5 LVN to BSN 1 .6 RN to BSN 6 3.9
Total 154 100.0
The participants were most frequently enrolled (Table 4) in the second semester
(n=48) (31.2%), fifth semesters (n=40) (26%), and fourth semesters (n=36) (23.4%). The
majority of participants (n=140) (90.9%) reported a current GPA of 3.00-3.99 (Table 5).
The student’s gender (Table 6) was reported as female (n=133) (86.4%) and male (n=21)
(13.6%). The participant’s age (Table 7) ranged between 21-59 years of age, with a mean
age of 31 and a median age of 27 years.
Table 4 Table 7
Frequency of Participants by Semester Frequency of Participants by Age
Frequency Percent 21 8 5.2
Frequency Percent 22 10 6.5 First 1 .6 23 12 7.8 Second 48 31.2 24 11 7.1 Third 4 2.6 25 17 11.0 Fourth 36 23.4 26 13 8.4 Fifth 40 26.0 27 8 5.2 Sixth 3 1.9 28 8 5.2 Seventh 3 1.9 29 5 3.2 Eighth 19 12.3 30 4 2.6 Total 154 100.0 31 6 3.9
32 5 3.2
Table 5 33 2 1.3 34 5 3.2
Frequency of Participants by GPA 35 2 1.3 36 4 2.6
Frequency Percent 37 1 .6 38 1 .6
2.00-2.99 1 .6 3.00-3.99 140 90.9
39 2 1.3
4.0 or above 13 8.4 40 3 1.9 41 1 .6
Total 154 100.0 42 2 1.3 43 2 1.3
Table 6 45 3 1.9
Frequency of Participants by Gender 46 47
5 3
3.2 1.9
Male 21 13.6 51 2 1.3
Female 133 86.4 54 3 1.9
Total 154 100.0 56 1 .6 57 1 .6 58 1 .6 59 1 .6
Total 154 100.0
The majority of the participants’ marital status (Table 8) was described as never
married (n=82) (53.2%). The most common household living arrangements (Table 9)
were reported as “live with spouse or significant other” (n=36) (23.4%), “live with
parents” (n=35) (22.7%), and “live with spouse or significant other and children” (n=34)
(22.1%).
Frequency Percent
Never Married 82 53.2 Married 56 36.4 Divorced 12 7.8 Widowed 1 .6 Domestic partner/Same sex Marriage 3 1.9
Total 154 100.0
Frequency Percent
Live by myself 14 9.1 Live with roommate(s) 26 16.9 Live with spouse or significant other 36 23.4 Live with spouse or significant other and children 34 22.1 Live with parents 35 22.7 Live with children only 9 5.8
Total 154 100.0
The majority of respondents (n=104) (67.5%) reported having no children (Table
10). The majority of respondents (n=105) (68.2%) reported no children living in the
household (Table 11). A slight majority of participants (n=78) (50.6%) reported no
employment (Table 12).
Table 10
Frequency Percent
None 104 67.5 One 13 8.4 Two 21 13.6 Three 11 7.1 Four 3 1.9 More than five 2 1.3
Total 154 100.0
Table 11
Frequency of Participants by Number of Children Residing in the Household
Frequency Percent
None 105 68.2 One 18 11.7 Two 20 13.0 Three 9 5.8 Four 2 1.3
Total 154 100.0
Frequency Percent
None 78 50.6 Less than 10 hours a week 20 13.0 Between 10-20 hours per week 28 18.2 Between 21-30 hours per week 12 7.8 Between 31-40 hours per week 12 7.8 More than 40 hours per week 4 2.6
Total 154 100.0
Yearly household income (Table 13) tended to be at either end of the scale with
respondents reporting income under $15,000 (n=36) (23.4%) or reported income $90,000
per year or more (n=26) (16.9%) most frequently. The majority of respondents were
Caucasian (n=102) (66.2%) (Table 14).
Table 13
Frequency Percent
$15,000/year or less 36 23.4 $15,001-$30,000/year 21 13.6 $30,001-$45,000/year 12 7.8 $45,001-$60,000/year 13 8.4 $61,001-$75,000/year 9 5.8 $75,001-$90,000/year 11 7.1 $90,000/year or over 26 16.9 Decline to state 26 16.9
Total 154 100.0
Frequency Percent
Caucasian 102 66.2 African American 3 1.9 Asian/Pacific Islander 29 18.8 Mexican American/Latino/Hispanic 12 7.8 Native American 1 .6 Other 3 1.9 Decline to state 4 2.6
Total 154 100.0
The most frequent response from participants regarding amount of daily study
time (Table 15) was 3-4 hours per day (n=60) (39%). The number of hours in class
(including clinical time) (Table 16) reported by respondents most frequently was 12-25
hours per week (n=56) (36.4%). The vast majority of respondents (n=126) (81.8%)
reported 5-7 hours of sleep nightly (Table 17).
Table 15
Frequency Percent
Less than 2 hours 27 17.5 3-4 hours 60 39.0 5-6 hours 37 24.0 More than 7 hours 30 19.5
Total 154 100.0
Table 16
Frequency of Participants by Weekly Hours Spent in Class or Clinical Setting
Frequency Percent
Less than 10 hours per week 12 7.8 11-15 hours per week 9 5.8 16-20 hours per week 35 22.7 21-25 hours per week 56 36.4 More than 26 hours per week 42 27.3
Total 154 100.0
5-7 hours 126 81.8
8-10 hours 22 14.3
Data Collection and Preparation
After three weeks of data collection, the survey was closed to new input. The data
was exported into an Excel spreadsheet, where all nominal and ordinal data were
converted to numerical values for analysis in SPSS. The data was imported into SPSS 20,
double-checked for accuracy and data analysis performed. All variables were examined
for distribution.
The participants self-selected to be included in the study. Data from any
participant who failed to complete the survey was excluded from data analysis.
Instruments
The SNSI tool has been determined to have cross-sample factor congruence, good
or acceptable concurrent, and discriminant validity in previous studies (Jones & Johnson,
1999). For each of the subscales, the Cronbach alpha has been reported to exceed .70
with the exception of the personal problems subscale (Cronbach α=.68). In this study, the
reliability coefficient for the 22-item SNSI was calculated to have a Cronbach’s alpha of
.89 (n=154).
Strahan and Gerbasi (1972) derived a Kuder-Richardson formula 20 (K-R 20)
reliability coefficients ranging from .59 to .70 for the MCSDS MC-1 (10 item) version
utilized in this study. In this study, the MCSDS-1 was calculated to have a Cronbach
alpha (for dichotomous data) of .57 (n=154). The data was analyzed using IBM SPSS
Statistics 20 software (2011) and the alpha for dichotomous data is equivalent to the
Kuder-Richardson 20 (KR20) coefficient (IBM, 2011).
Results by Research Question
of Nursing program?”
two factors, which suggests that as self-reported stress went up there was a decrease in
social desirability response or conversely as stress went up, social desirability went down.
A linear regression analysis was performed. Two variables GPA (p=.001)
(n=154) and Study Time (p=.013) (n=154) were found to statistically significantly affect
the dependent variable, Self-Reported Stress. For the regression model that included GPA
and Study Time, (the R or Pearson’s Product Moment Correlation was .437.) (Table 18)
This value suggests that there was a moderately strong correlation between the observed
sample values and the predicted values for the dependent variable, Self-Reported Stress.
The effect size (R-squared) for the model was .19. This suggests that the model explains
19% of the variation in stress levels. This means that 81% of the reported stress level is
explained by other unknown factors.
Table 18.
Model Summary
Model Summaryb
Model R R Square Adjusted R Square Std. Error of the Estimate 1 .437a .191 .096 13.079 a. Predictors: (Constant), Crowne_Marlow_Total, GPA, Gender, Household Income, Hours in class, Hours of Sleep, Children Living at Home, Culture, Semester, Marital Status, Study time, Student Type, Housing Situation, Employment, Number of Children, Age b. Dependent Variable: SNSI_Total
When the analysis of variance was performed (Table 19), the F statistic was
significant at .016. This result indicated that the independent variables entered into the
model reliability predicted the reported stress levels.
Table 19.
ANOVA
ANOVAa
Model Sum of Squares df Mean Square F Sig. Regression 5527.493 16 345.468 2.020 .016b
1 Residual 23434.357 137 171.054 Total 28961.851 153
a. Dependent Variable: SNSI_Total
b. Predictors: (Constant), Crowne_Marlow_Total, GPA, Gender, Household Income, Hours in class, Hours of Sleep, Children Living at Home, Culture, Semester, Marital Status, Study time, Student Type, Housing Situation, Employment, Number of Children, Age
The standardized coefficients (b) for the independent variable GPA was -.270 (t
3.267, p.001). This suggested that for every one unit the GPA increased the reported
stress level decreased by .27 units. The standardized coefficient (b) for the independent
variable Study Time was .225 (t 2.514, p. 013). This result suggested that for every one
unit of increase in study time the reported stress level increased by .225 units (Table 20).
Table 20.
B Std. Beta Lower Upper Error Bound Bound
(Constant) 99.002 17.712 5.589 .000 63.977 134.027
Student Type -.973 1.095 -.084 -.888 .376 -3.138 1.193
Semester -.072 .612 -.010 -.118 .906 -1.283 1.138
GPA -12.697 3.886 -.270 -3.267 .001 -20.382 -5.013
Gender 4.999 3.184 .125 1.570 .119 -1.297 11.295
Age -.031 .183 -.021 -.169 .866 -.394 .332
Marital Status -.134 1.438 -.009 -.093 .926 -2.977 2.710
Housing Situation .114 .885 .012 .129 .898 -1.635 1.864
No. of Children -2.704 1.392 -.243 -1.943 .054 -5.457 .048
Child. Lvg. Home 2.407 1.563 .176 1.540 .126 -.683 5.497
Employment 1.833 1.028 .193 1.783 .077 -.200 3.866
Household Income .629 .422 .123 1.491 .138 -.205 1.464
Culture .253 .771 .027 .327 .744 -1.273 1.778
Study time 3.100 1.233 .225 2.514 .013 .662 5.538
Hours in class .317 1.219 .027 .260 .795 -2.095 2.728
Hours of Sleep -3.924 2.675 -.118 -1.467 .145 -9.213 1.366
MC Total -.031 .750 -.003 -.042 .967 -1.515 1.452
a. Dependent Variable: SNSI_Total
Summary
explain 19 percent of the variability the dependent variable, Self-Reported Stress with
GPA being the only independent variable to reach statistical significance. The
relationship between the positive correlation between study time and stress and the
negative correlation between GPA and stress will be discussed in the next section.
CHAPTER 5: DISCUSSION
Only two independent variables, GPA and Study Time, demonstrated a
statistically significant effect on the dependent variable SNSI (Jones & Johnston, 1999)
total stress score. Other factors such as semester, age, marital status, household living
arrangements, culture, or hours in class clearly indicated that they had no statistically
significant relationship to the SNSI total stress score; however, when they were removed
from the model, the model was no longer statistically significant or explained the
dependent variable. This find suggests that there may have been interaction between the
independent variables and the decision was made to leave them in the model. A few
factors such as number of children (p=.054) and employment status (p=.077) were
shown to be close to statistical significance.
Major Findings by Research Question
of Nursing program?” The data indicates that approximately 19% of the self-reported
stress could be explained by the tested demographic factors.
The inverse relationship between GPA and the SNSI total stress score is similar to
what has been reported previously for other health professional students (Stewart, Lam,
Betson, Wong, & Wong, 1999). A study done by Stewart, et al, (1999) reported “In other
words regardless of whether students enter the programme academically weak or strong,
and whether they had high levels of stress when they entered the programme, the quality
of the grades they acquire in medical school independently affects their stress level.”
(p.249.) Other research explored the level of perceived stress in junior medical students
before and after clinical rotation and found that increased stress scores correlated
significantly with poor test scores at the end of the rotation (Linn & Zeppa, 1984). This
research indicates similar results.
The positive relationship between Study Time and the SNSI total stress score
found in this study has also been demonstrated in other studies also. Nicholl and Timmins
(2005) researched the relationship between “trying to balance work commitments and the
required study time” (p. 95) and stress levels in part time undergraduate nursing students.
The study indicated that this individual item had the highest mean score of any item
tested.
In common with previous research (Jones & Johnston, 1999), the SNSI had an
inverse relationship with the MCSDS with defensive students more likely to report fewer
stresses than other students.
Internal validity could be influenced by additional demographic factors not being
tested in this study. Due to the limited number of previous studies, the questions chosen
for this research were specifically designed to cover a broad section of demographic
factors. There are possibly more specific questions or questions focusing on different
demographic factors that could have statistical significance.
A longitudinal repeated measure study design is recommended to enhance the
evaluation of reported stress over time. This design should reduce the bias inherently
associated with a cross sectional study and increase the reliability of the study.
Finally, a larger sample size may allow for the statistical significance of some of
the factors such as number of children and employment status that were close to
significance to demonstrate significance.
The research generalizability is limited to nursing students with similar
demographic populations as students attending CSUSM School of Nursing.
Implications for Nursing Research
There is not sufficient evidence to change policies or to focus on designing an
intervention to reduce the self-reported stress levels of student nurses. This research adds
to the body of knowledge about which broad demographic factors can be correlated to
changes in self-reported stress levels.
Recommendations for Future Research
As of this writing, no other studies have reported results discussing a relationship
between self-reported stress in student nurses and expanded demographic factors. Student
nurses report significantly higher levels of stress (Beck, et al., 1997) than many other
types of students and the general public. Demographic factors other than GPA and study
time do not seem to be related to this increased stress. A research project which includes
time also would add to the body of knowledge if incorporated into a future design
exploring student nurse repeated stress. A research project which includes the time frame
and the amount of material the student is expected to learn as independent variables may
be an appropriate follow-up. Additional factors yet to be identified but which increase
stress levels may require a qualitatative investigation.
Summary
In conclusion, student nurse’s experience stress at higher rates than other types of
students. Many factors influence the amount of stress experienced by the students. Lower
GPA and increased Study Time are two of the factors. Nursing schools tend to have
rigorous programs and students are expected to complete the didactic and clinical
portions simultaneously, while maintaining a minimum level of competency. This can
prove to be difficult.
Appendix A
Student Nurse Stress Index:
Below is list of items that may be associated with stress by students such as yourself.
Think of real events which have occurred in the past month in your role as a student. For each item please circle the rating that applies to YOU. Answer all 22 items.
ITEM NOT STRESSFUL
1 2 3 4 5
2 Difficulty of classwork material to be learned
1 2 3 4 5
3 Examination and/or grades 1 2 3 4 5 4 Peer competition 1 2 3 4 5 5 Attitudes/expectations of other
professionals towards nursing 1 2 3 4 5
6 Lack of free time 1 2 3 4 5 7 College/School response to
student needs 1 2 3 4 5
8 Fear of failing in course 1 2 3 4 5 9 Actual personal health problems 1 2 3 4 5 10 Physical health of other family
members 1 2 3 4 5
11 Relationships with parents 1 2 3 4 5 12 Other personal problems 1 2 3 4 5 13 Relations with other
professionals 1 2 3 4 5
14 Too much responsibility 1 2 3 4 5 15 Lack of timely feedback about
performance 1 2 3 4 5
Answer the following questions from your reflections on your clinical experience:
ITEM NOT STRESSFUL
EXTREMELY STRESSFUL
16 Client attitudes towards me 1 2 3 4 5 17 Client attitudes towards my
profession 1 2 3 4 5
18 Atmosphere created by teaching staff
1 2 3 4 5
19 Relations with staff in the clinical area
1 2 3 4 5
Other academic and related items:
ITEM NOT STRESSFUL
20 I am not sure what is expected of me
1 2 3 4 5
21 I have no time for entertainment 1 2 3 4 5 22 I do not have enough time for
my family 1 2 3 4 5
Scoring instructions for Student Nurse Stress Index (S.N.S.I.)
The S.N.S.I. has a four factor structure (Jones & Johnston, 1997), with “Academic load”, “Clinical concerns”, “Personal problems” and “Interface worries” as underlying variables.
Evidence regarding the factor congruence across independent data sets, and the reliability and validity of the measure can be obtained from Martyn Jones ([email protected]).
The S.N.S.I. subscale and total scores are calculated using the unit weighting method of scoring.
S.N.S.I. Total
Sum scores on items 1-22 to give an overall total, ranging from 22 to 110.
“Academic load”
Sum scores on items 1, 2, 3, 8, 14, 18, 20 to give a subscale total ranging from 7 to 35.
“Clinical concerns”
“Personal problems”
Sum scores on items 9, 10, 11, 12 to give a subscale total ranging from 4 to 20.
“Interface worries”
DO NOT SUM SUBSCALE SCORES TO OBTAIN AN OVERALL S.N.S.I. TOTAL.
Confirmatory factor analysis shows that S.N.S.I. has a less simple factor structure, with several variables loading onto more than one factor, contact Martyn Jones for more details ([email protected]).
N.B. Following validation of the measure, the administration of the S.N.S.I. alongside a measure of social desirability, e.g. (Crown & Marlowe, 1960), is recommended particularly in named reporting conditions.
MCJ Jan 2000
Appendix B
1. I like to gossip at times. Yes No
2. There have been occasions when I took advantage of someone. Yes No
3. I am always willing to admit it when I make a mistake. Yes No
4. I always try to practice what I preach. Yes No
5. I sometimes try to get even rather than forgive and forget. Yes No
6. At times, I have really insisted on having things my own way. Yes No
7. There have been occasions when I felt like smashing things. Yes No
8. I never resent being asked to return a favor. Yes No
9. I have never been irked when people expressed ideas very different from my own.
Yes No
10. I have never deliberately said something to hurt someone’s feelings. Yes No
Scoring
1. No = 1 point, Yes = 0 points 2. No = 1 point, Yes = 0 points 3. Yes = 1 point, No = 0 points 4. Yes = 1 point, No = 0 points 5. No = 1 point, Yes = 0 points 6. No = 1 point, Yes = 0 points 7. No = 1 point, Yes = 0 points 8. Yes = 1 point, No = 0 points 9. Yes = 1 point, No = 0 points 10. Yes = 1 point, No = 0 points
Total number of points is score.
Appendix C
Appendix D
Appendix E
Appendix F
Sample Didactic Instructor Email Request
Dear (Name of Instructor) I am conducting research on student nursing stress levels as part of my Master’s research project. I would like to request 10 minutes at the end of your (level of class) on (day and date) at (time) in (location). If you agree to allow me a few minutes, I will be presenting the project and explaining and distributing the consent form during at that time. I would also request that during the following week you distribute a sheet of paper that has the URL for the students to complete the survey. The survey does not have to be completed during class time. The total time required would be less than 10 minutes. Please let me know if this is acceptable and if I have the correct time and room number. Thanks. Mary Baker
"Be the change you want to see in the world" - Mahatma Gandhi
Mary Baker Nursing Clinical Instructor CSUSM School of Nursing [email protected] 760-822-8264
Appendix G
Appendix H
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Nursing Student Stress and Demographic Factors