NURSING STUDENT STRESS AND DEMOGRAPHIC FACTORS. A Thesis. Presented to the faculty of the School of Nursing. California State University, San Marcos

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1 A Thesis Presented to the faculty of the School of Nursing California State University, San Marcos Submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in Nursing Public Health Clinical Nurse Specialist by Mary Lelia Baker SPRING 2012

2 2012 Mary Lelia Baker ALL RIGHTS RESERVED ii

3 CALIFORNIA STATE UNIVERSITY SAN MARCOS THESIS SIGNATURE PAGE THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE MASTER OF SCIENCE IN NURSING THESIS TITLE: Nursing Student Stress and Demographic Factors 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. iii

4 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 iv

5 Abstract of Nursing Student Stress and Demographic Factors 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. v

6 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. I would also like to thank my spouse, Debra Kossler who has been my rock in the storm. I could not have done this without you.. vi

7 TABLE OF CONTENTS Page Preface... i Abstract...v Acknowledgments...vi List of Tables...x List of Figures... xi Chapter 1. INTRODUCTION...1 Background and Significance...1 The Problem...2 Purpose of the Research...2 Research Question...3 Research Variables LITERATURE REVIEW...4 Introduction...4 Major Variables Defined...7 Theoretical Framework...8 Summary METHODOLOGY...13 Introduction...13 Research Question...13 Identification of Setting...13 Research Design...14 Population and Sample...15 Measurement Methods...16 Data Collection Process...18 vii

8 Coding and Scoring...19 Data Analysis...19 Bias...21 Ethical Considerations...22 Summary RESULTS Introduction...24 Sample...25 Data Collection and Preparation...30 Instruments...31 Results by Research Question...31 Summary DISCUSSION...37 Introduction...37 Major Findings...37 Limitations...38 Generalizability...39 Implications for Research...39 Recommendations for Future Research...39 Summary...40 Appendix A. Student Nurse Stress Index...41 Appendix B. Marlowe-Crowne Social Desirability Index...44 Appendix C. Online Survey...45 Appendix D. IRB Approval Letter...54 Appendix E. Student Recruitment Flyer...55 Appendix F. Sample Didactic Instructor Request...57 Appendix G. Permission to Use SNSI...58 Appendix H. Permission to Use Graphic...59 viii

9 References...60 ix

10 LIST OF TABLES Tables Page 1. Relationship between the Transactional Model and variables Comparison of Available Demographic Factors of Sample and Population Frequency of Participants by Student Type Frequency of Participants by Semester Frequency of Participants by GPA Frequency of Participants by Gender Frequency of Participants by Age Frequency of Participants by Marital Status Frequency of Participants by Household Living Situation Frequency of Participants by Number of Children Frequency of Participants by Number of Children Residing in Household Frequency of Participants by Employment Status Frequency of Participants by Household Income Frequency of Participants by Ethnic or Cultural Background Frequency of Participants by Amount of Daily Study Time Frequency of Participants by Weekly Hours Spent in Class or Clinical Setting Frequency of Participants by Daily Hours of Sleep Model Summary ANOVA Coefficients...35 x

11 Figures LIST OF FIGURES Page 1. Lazarus and Folkman s model of psychological stress Power analysis prior to data collection Power analysis post hoc Distribution of SNSI total stress score xi

12 1 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 selfreported 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

13 2 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).

14 3 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.

15 4 CHAPTER TWO: LITERATURE REVIEW Introduction 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 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 healthrelated programs (medicine, pharmacy, and social work). The significant findings indicated that baccalaureate nursing students, regardless of year in program or university

16 5 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

17 6 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. Pulido-Martos, Augusto-Landa, and Lopez-Zafra (2012) published a systematic 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).

18 7 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

19 8 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

20 9 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).

21 10 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).

22 11 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.

23 12 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

24 13 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

25 14 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%), years of age (19%), years of age (13%), 36 or older years of age (4%). Graduate students age was distributed as 22 or younger years of age (5%), years of age (24%), 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).

26 15 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.

27 16 Figure 2 Power analysis prior to data collection (Faul, et al, 2009) Measurement Methods 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).

28 17 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 selfreported 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).

29 18 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.

30 19 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.

31 20 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

32 21 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.

33 22 Table 2. Comparison of available demographic factors of sample and population Sample Population Gender Female 86.4% 82.5% Male 13.6% 17.5% Age years 37.7% 33.5% years 24.7% 33.5% years 20.1% 21.5% % 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 #: 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

34 23 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 s 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.

35 24 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 (R 2 ) effect size in a multiple regression analysis with a significance level of.05 and a power of.86. Figure 3. Power analysis post hoc. (Faul, & et al., 2009)

36 25 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 of Participants by Student Type Frequency Percent Generic-San Marcos ABSN-Temecula ABSN-San Marcos MSN LVN to BSN 1.6 RN to BSN Total 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 (Table 5). The student s gender (Table 6) was reported as female (n=133) (86.4%) and male (n=21)

37 26 (13.6%). The participant s age (Table 7) ranged between 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 Frequency Percent First Second Third Fourth Fifth Sixth Seventh Eighth Total Table Frequency of Participants by GPA Frequency Percent or above Total Table Frequency Percent Male Female Total Total Frequency of Participants by Gender

38 27 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%). Table 8 Frequency of Participants by Marital Status Frequency Percent Never Married Married Divorced Widowed 1.6 Domestic partner/same sex Marriage Total Table 9 Frequency of Participants by Household Living Situation Frequency Percent Live by myself Live with roommate(s) Live with spouse or significant other Live with spouse or significant other and children Live with parents Live with children only Total 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).

39 28 Table 10 Frequency of Participants by Number of Children Frequency Percent None One Two Three Four More than five Total Table 11 Frequency of Participants by Number of Children Residing in the Household Frequency Percent None One Two Three Four Total Table 12 Frequency of Participants by Employment Status Frequency Percent None Less than 10 hours a week Between hours per week Between hours per week Between hours per week More than 40 hours per week Total 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

40 29 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 of Participants by Household Income Frequency Percent $15,000/year or less $15,001-$30,000/year $30,001-$45,000/year $45,001-$60,000/year $61,001-$75,000/year $75,001-$90,000/year $90,000/year or over Decline to state Total Table 14 Frequency of Participants by Ethnic or Cultural Background Frequency Percent Caucasian African American Asian/Pacific Islander Mexican American/Latino/Hispanic Native American 1.6 Other Decline to state Total 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

41 30 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 of Participant by Amount of Daily Study Time Frequency Percent Less than 2 hours hours hours More than 7 hours Total Table 16 Frequency of Participants by Weekly Hours Spent in Class or Clinical Setting Frequency Percent Less than 10 hours per week hours per week hours per week hours per week More than 26 hours per week Total Table 17 Frequency of Participants by Daily Hours of Sleep Frequency Percent Less than 4 hours hours hours Total 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

42 31 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 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?

43

44 33 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 Summary b Model R R Square Adjusted R Square Std. Error of the Estimate a 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.

45 34 Table 19. ANOVA ANOVA a Model Sum of Squares df Mean Square F Sig. Regression b 1 Residual Total 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 (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).

46 35 Table 20. Coefficients Coefficients a Model Unstandardized Standard. t Sig. 95.0% Confidence Coefficients Coefficient Interval for B B Std. Beta Lower Upper Error Bound Bound (Constant) Student Type Semester GPA Gender Age Marital Status Housing Situation No. of Children Child. Lvg. Home Employment Household Income Culture Study time Hours in class Hours of Sleep MC Total a. Dependent Variable: SNSI_Total

47 36 Summary The independent variables entered into the regression model simultaneously 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.

48 37 CHAPTER 5: DISCUSSION Introduction 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 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? 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,

49 38 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. Limitations 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.