The Pennsylvania State University - ETDA

The Pennsylvania State University

The Graduate School

School of Labor & Employment Relations

SELECTION AND RETENTION OF CERTIFIED NURSING ASSISTANTS IN

NONPROFIT LONG-TERM HEALTHCARE FACILITIES AND THE IMPACT

ON QUALITY OF CARE

A Thesis in:

Human Resources and Employment Relations

by

Sarah Glei

© 2016 Sarah Glei

Submitted in Partial Fulfillment

of the Requirements

for the Degree of:

Master of Science

May 2016

ii

The thesis of Sarah Glei was reviewed and approved* by the following:

Antone Aboud

Professor of Practice- School of Labor and Employment Relations

Thesis Adviser

Jean Phillips

Professor of Human Resource Management

Stan Gully

Professor of Human Resource Management

Paul Clark

Head of the Department of Labor and Employment Relations

*Signatures on file in the Graduate School

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ABSTRACT

The purpose of this study is to examine existing practices in staffing and selection

methods at nonprofit long-term healthcare facilities in Pennsylvania in order to discover

which are the most viable for optimal hourly certified nursing assistant retention.

Common staffing trends and selection methods used by nonprofit nursing homes have

been examined through a literature review to determine the human resources practices

that may contribute to higher levels of retention. First-hand data was gathered through

surveying 39 human resources professionals at nonprofit long-term care facilities to

reveal information about the potential link between selection methods and retention rates

and then ultimately the connection to quality of care ratings for nonprofit long-term care

organizations. The aim of this thesis is to examine the relationship between the use of

different selection methods and retention rates and quality of care among the

organizations surveyed.

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TABLE OF CONTENTS

List of Tables ..................................................................................................................... vi

Acknowledgements ........................................................................................................... vii

Chapter 1 Introduction ........................................................................................................ 1

Problem Statement .......................................................................................................... 3

Chapter 2 Literature Review ............................................................................................... 5

Certified Nursing Assistants ........................................................................................... 5

The Costs of Turnover .................................................................................................... 6

Person-Environment Fit .................................................................................................. 8

Selection .......................................................................................................................... 8

Hypothesis 1.............................................................................................................. 13

Staffing .......................................................................................................................... 14

Hypothesis 2.............................................................................................................. 15

Quality of Care .............................................................................................................. 16

Hypothesis 3.............................................................................................................. 17

Chapter 3 Methodology .................................................................................................... 18

Sample........................................................................................................................... 18

Measures ....................................................................................................................... 20

Chapter 4 Results .............................................................................................................. 26

Hypothesis 1.................................................................................................................. 26

Hypothesis 1a ............................................................................................................ 26

Hypothesis 1b............................................................................................................ 27

Hypothesis 1c ............................................................................................................ 28

Hypothesis 1d............................................................................................................ 29

Hypothesis 1e ............................................................................................................ 29

Hypothesis 1f ............................................................................................................ 30

Hypothesis 1g............................................................................................................ 31

Hypothesis 1h............................................................................................................ 31

Hypothesis 1i ............................................................................................................ 31

Hypothesis 1j ............................................................................................................ 32

Hypothesis 1k............................................................................................................ 33

Hypothesis 1l ............................................................................................................ 33

Hypothesis 2.................................................................................................................. 34

Hypothesis 3.................................................................................................................. 34

Turnover/Reasons for leaving ....................................................................................... 39

Any Type of Structured Interview ................................................................................ 41

Any Realistic Job Preview ............................................................................................ 42

Strategic Staffing Index ................................................................................................ 42

Competitive Starting Pay .............................................................................................. 43

Exploratory Analysis .................................................................................................... 43

Chapter 5 Conclusion ........................................................................................................ 47

Discussion ..................................................................................................................... 47

Limitations .................................................................................................................... 51

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Opportunities for Future Research ................................................................................ 54

Demographic Information ............................................................................................. 55

Final Remarks ............................................................................................................... 57

Appendix A IRB Exemption Determination Letter .......................................................... 59

Appendix B Letter sent to Pennsylvania Nonprofit Long-Term Care Facility HR

Directors ............................................................................................................................ 60

Appendix C Survey ........................................................................................................... 61

Appendix D Correlation Table with Hypothesized Variables .......................................... 66

Appendix E Correlation Table with all Variables............................................................. 70

Bibliography ..................................................................................................................... 79

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List of Tables

Table 1. Suggested Direct-care Worker to Patient Ratios ......................................................... 14

Table 2. Medicare.gov Quality Measures ....................................................................................... 23

Table 3. Descriptive Statistics for Hypotheses.............................................................................. 35

Table 4. Correlation Table for Hypotheses .................................................................................... 36 Table 5. Reported Involuntary Turnover of CNAs Compared to Estimated Turnover Rate

of CNAs reported by the American Healthcare Association 2010 ................................ 40 Table 6. Reported Involuntary Turnover of CNAs Compared to Turnover Rate of CNAs

reported by Castle et. al 2005 ................................................................................................... 40

Table 7. Descriptive Statistics for Reasons for Leaving ............................................................ 44

Table 8. Correlations for Reasons for Leaving ............................................................................. 44

Table 9. Descriptive Statistics for Recruitment Methods .......................................................... 56

Table 10. Correlation Table for Recruitment Methods ............................................................... 56

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Acknowledgements

I would first like to thank my thesis advisor, Dr. Antone Aboud for his endless

support and guidance throughout the entire process of writing this thesis. As this research

study evolved and matured he was always very reassuring. When I doubted myself, he

encouraged me to continue on and persevere.

Next, I owe much gratitude to my thesis committee members, Dr. Jean Phillips

and Dr. Stan Gully. Both provided significant assistance and guidance in the aspects of

research that I had not yet encountered in my academic career. Dr. Gully provided a great

deal of help in the analysis of results, through SPSS and his knowledge in statistics. Both

Dr. Phillips and Dr. Gully guided me through several challenges I faced in creating an

original research study.

I also owe a great deal of gratitude to the School of Labor and Employment

Relations for providing me with so many opportunities to grow throughout my time at

Penn State. The faculty and staff have given me the tools for success and have greatly

assisted me in achieving this important milestone.

Additionally, I would like to thank my family and friends for all of their support

and positive reinforcement. It has been an arduous journey to complete this thesis but I

am grateful for their faith in me in this endeavor and in all that I wish to accomplish.

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Chapter 1 Introduction

As a great proportion of the U.S. population is aging, the American healthcare system

must be well equipped to provide care for senior adults. By 2030 around 20% of the U.S.

population will be age 65 and older and, while some baby boomers may evade retirement and

remain in the work force longer than previous generations, in the coming years many may find

themselves residing in or receiving medical care in long-term healthcare facilities such as

nursing homes, assisted living centers or rehabilitation centers (Eliopoulos, 2013, p.4). In

selecting a long-term care facility there are many factors that individuals and their families, or

primary caregivers, typically consider and a multitude of these factors are directly influenced by

the direct-care staff of a long-term care facility. In a study that examined the factors most highly

considered in the selection of a nursing home, residents ranked supervision and safety as most

important, followed by personal assistance, medical care, cost of care, location/neighborhood,

homelike atmosphere, flexible routine, organized activities and physical rehabilitation (Castle,

2003, p. 228-229).

Nursing home ownership is also another factor taken into account during the selection

process of a facility. Nonprofit nursing homes are often accompanied with positive reputations

for high quality of care because they have a greater ability to use funds to provide better services

and amenities for patients, enhancing the patient experience, which contrasts with for-profit

nursing homes that typically must maximize profits for shareholders (Santerre and Vernon, 2007,

p. 382). A majority of hospitals in the U.S. as well as a significant number of nursing homes are

considered nonprofit. While one study examining nonprofit and for-profit hospitals found no

differences in efficiency, it did highlight that ownership has an impact on operations. More

specifically, for-profit hospitals tended to spend more than they were able to collect in

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comparison to nonprofit hospitals that were able to manage the cash flow more effectively

(Plante, 2009, p.16). Nonprofit nursing homes often have higher employee retention rates in

comparison to for-profit nursing homes and, although, this study does not seek to compare

ownership categories, this makes nonprofit nursing homes important to examine in considering

retention factors (Donoghue, 2010, p.103).

Many employees are attracted to a nonprofit organization because they believe in the

cause but in the long run, “doing good” may not be enough to attract and retain the best-fit

employees. It is essential that nonprofit organizations, especially in the healthcare industry, adopt

human resources best practices in the areas of selection and retention because employees are the

lifeblood of the nonprofit, working hard to sustain and further the cause of the organization and

accomplishing many of the charitable activities that better society. Having the best recruitment

and selection practices will yield nonprofit nursing home facilities staffed with certified nursing

assistants (CNAs) that are the best fit for the organization and this will lead to increased quality

of care for patients and increased employee retention.

Overall the nonprofit sector employs more than 10 million Americans and has been

experiencing great expansion in the past few years (2013 Nonprofit Employment Trends Survey,

2013). The nonprofit sector represents more than 10.3% of all private sector employment and a

vast majority of nonprofit jobs exist in the healthcare and social assistance sectors. In the

healthcare industry about 60% of community hospitals are nonprofit while approximately 30% of

nursing homes are nonprofit (The Value of Nonprofit Healthcare, p.1).

Economically nonprofit organizations have many positive advantages. Those that are

covered under section 501(c)3 of the Internal Revenue Code are exempt from federal income tax

and they have fared very well in comparison with for-profit organizations in times of economic

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hardships. According to a John Hopkins University Center for Civil Society Studies’ Nonprofit

Economic Data Project report, “Holding the Fort: Nonprofit Employment During a Decade of

Turmoil,” the U.S. nonprofit sector showed a 10 year record of job growth in spite of two

recessions, reaching an average annual growth rate of 2.1 percent from 2000 to 2010, as for-

profit jobs decreased by an average of minus 0.6 percent per year (Salamon et al., 2015). During

these recessions the nonprofit sector as a whole, especially health care, education and social

assistance, has created jobs and has made a positive impact on the nation economically.

Problem Statement

The importance of geriatric care will continue to increase as the baby boomer generation

ages. As previously mentioned, by 2030 there will be 61 million adults aged 66-84, which will

put significant stress on the healthcare system (Knickman and Snell, 2002, p. 850). Long-term

healthcare providers such as nursing homes must anticipate this influx and reduce high turnover

rates for direct care staff, more specifically certified nursing assistants, by developing and

maintaining solid staffing procedures. Turnover of certified nursing assistants is a major problem

for long-term care facilities and annual turnover rates range anywhere from 14% to 346%

according to one study (Castle, 2006). Another study that defines high turnover of certified

nursing assistants as any rate higher than 25.3%, citing 46.6% as the estimated annual certified

nursing assistant rate, found high certified nursing assistant turnover to be related to higher

numbers of quality of care deficiencies (Lerner et al., 2014). Implementing more extensive hiring

selection methods to supplement the state-mandated certification exams can ultimately lead to

higher retention of certified nursing assistants by establishing a greater person-environment fit

which, in turn, causally affects job satisfaction, a leading predictor of turnover intention.

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Creating a precedent of hiring for the best organizational fit instead of hiring to rapidly

fill positions not only affects the quality of new hires but can also build morale among existing

healthcare staff by demonstrating that the organization is willing to invest in its staff. Having the

proper number of nurses on staff is also an important factor because it reduces job burnout and

alleviates some of the stress placed on direct care staff. It is important for organizations to

understand that staffing practices impact not only quality outcomes but also organizational

commitment (Bowers, 2003).

The costs of certified nursing assistant turnover in nursing homes are high, not only

monetarily, but also in the way that turnover affects quality of care for patients and residents.

One study found that lower levels of CNA and Registered Nurse staffing hours were correlated

with more citations from the Centers for Medicare and Medicaid services for poor quality of care

which might include a higher percentage of patients with falls with injury, a higher percentage of

high-risk residents with pressure ulcers and other adverse health effects that could be a result of

poor long-term care (Hyer et al., 2011). Certified nursing assistants work closely with nursing

home residents building relationships, trust, and knowledge of their unique needs and high

turnover does not allow certified nursing assistants the opportunity to build these important

connections (Rantz et al., 2004, p.36). Studies have shown that certified nursing assistants are

innately driven to provide a high degree of quality of care and can develop job dissatisfaction

when they are unable to do so. One study even found that frontline nursing home workers care

more about their ability to provide care for residents than rewards such as pay and benefits

(Bishop et. al., 2008). Selecting certified nursing candidates that are a good fit for the

organization from the start could eliminate some of the unnecessary negative effects of high

turnover.

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Chapter 2 Literature Review

Certified Nursing Assistants

Certified Nursing Assistants (CNAs) provide as much as 80-90% of the direct care

received by patients in nursing homes on a day-to-day basis (Peterson et al., 2002, p. 154).

According to O*NET, Occupational Information Network, there were 1,480,000 CNAs

employed in 2012. Working alongside Registered Nurses (RNs) and/or Licensed Practical

Nurses (LPNs), CNAs complete tasks such as taking vital signs, providing personal care such as

bathing residents, dressing or assisting residents eat meals, and communicating with patients

directly. With limited training, typically low salaries and often low levels of respect and regard,

turnover rates are very high (Ersek et al., 1999, p. 574). “Pennsylvania’s Long-term Care

System: Building Careers, Enhancing Quality Resident Care” a report released March 2013,

states that turnover among CNAs in Pennsylvania is especially high, and while turnover data for

CNAs is not collected by the state of Pennsylvania, two other studies were cited finding turnover

rates of 43% and greater than 100% (Pennsylvania’s Long Term Care System, 2013, p. 1).

In order to become a CNA, one must obtain a certification, and, although many critics

argue that the mandated training prior to certification is not stringent enough, this ensures that

CNAs have the correct skills to provide care to nursing home residents. Those interested in

becoming a CNA typically have a high school diploma or a GED. In Pennsylvania a state-

approved training program with a minimum of 120 hours of instruction must be completed prior

to becoming eligible to take the nursing assistant certification exam. The exam tests ability and

knowledge through a skills test and written portion (“Becoming a CNA”, n.d.). Once CNAs enter

the field they are, in many instances, exposed to a high stress environment where they must react

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quickly and use instinct and natural intuition in addition to applying what was learned through

training whether from the certification training or on-the-job training.

The average pay of CNAs in nursing homes varies based on location but in Pennsylvania

it ranges from about $22,000 to $23,000 which is around $10.00-$12.00 per hour (CNA Salary in

Pennsylvania, 2013). According to the Bureau of Labor Statistics, in 2014 the average wage for

nursing assistants in skilled nursing facilities was $25,160 and O*NET reported the median wage

in 2014 to be $25,100 and $12.07 hourly (31-1014.00 - Nursing Assistants).

The Costs of Turnover

According to a study by Castle and Engberg, replacing a CNA can cost an organization

up to $2,200 (Castle and Engberg, 2006, p. 62). The costs associated with high CNA turnover are

not only those that come with having to recruit and train a replacement, but as previously

mentioned they also include a decreased quality of care for patients. High turnover results in

lower quality of care for several reasons. The relationships that CNAs develop with nursing

home residents can be very potent due to the nature of the care provided by CNAs. With high

levels of turnover CNAs may not be able to form bonds with and know the preferences and

needs of residents (Wiener et al., 2009, p. 198). With unpredictable staffing due to high turnover,

sometimes nurses must work consecutive shifts, rotate from day to night shifts or work

unexpected overtime shifts in addition to their typically long shifts. This can lead to burnout, job

dissatisfaction and ultimately further turnover along with issues with the quality of care provided

to patients. When nurses have been working seemingly endless hours and are fatigued they are

not in the best state to provide optimal patient care and this impacts patient satisfaction. A 2012

study examined the effects of shift length for nurses and found that when more nurses worked

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shifts of 13 or more hours patient satisfaction was lower and the nurses were more dissatisfied,

experienced greater burnout and had a greater intention to leave compared with nurses working

shorter shifts (Stimpfel et al., 2012).

CNAs do not typically earn a very high salary, but pay is not the only important factor in

the retention of healthcare workers. One study found that recognition, especially by managers,

led to employee satisfaction and that work environment and intrinsic rewards played a greater

role in retaining employees (Maguire et al., 2003 p. 43). In a study that surveyed CNAs, those

that stayed in their organizations were referred to as “stayers”, those that switched to other

organizations but still performed a direct-care role, “switchers”, and those that completely left

the direct-care workforce were termed “leavers.” It found that leavers left not only for job factors

such as pay and benefits but also for issues such as emotional distress, less supervisor respect

and lower job satisfaction (Rosen et al., 2011).

Taken from Mansfield‘s 1997 article appearing in the Nursing Management Review, the

circular model of factors explains some of the causes of turnover in nursing homes. Turnover is

causal with several stages and this model depicts the convergence of work-related factors and

personal factors that create a good or poor person-job fit with satisfaction or dissatisfaction as a

mediator in the decision to leave. (Mansfield, 1997, p. 60). Figure 1 displays the causal

relationship.

Sainfort et al.’s 2005 study that examined commitment, satisfaction and turnover

intentions found that there is a strong correlation, .931, between intrinsic satisfaction and total

satisfaction. Some of the questions answered by respondents included “chance to use my

abilities” or “freedom to use my own abilities.” Intrinsic satisfaction has much to do with a

person-job fit because it connects personal factors such as skills and work-related factors like the

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work content or job structure (Sainfort et. al., 2005). Although this study does not examine

person-job fit or person-environment fit, both are a component of the causal relationship between

selection of candidates and turnover.

Person-Environment Fit

There is a causal relationship where person-environment fit is a factor associated with

turnover. In this relationship, organizational characteristics and job characteristics lead to

positive or negative job satisfaction, which then affects turnover. Both organizational and job

characteristics lead to person-environment fit. Person-environment fit is described as the extent

to which the person matches the environment and the extent to which the environment matches

the person (Hardin & Donaldson, 2014 p. 634). An individual may match with an environment in

terms of values, personality, career aspirations and possibilities, etc. Higher person-environment

fit leads to greater job satisfaction so promoting a person-environment fit in the selection process

is beneficial (Kristof-Brown et al., 2005). Having an organization clearly convey its culture and

values system may be a way for prospective candidates to determine whether or not a match

might exist before getting too far in the selection process.

Selection

Selection is described by a Society for Human Resource Management publication as “the

process of choosing from a group of applicants the individual best suited for a particular position

and for the organization” (Gusdorf, 2008 p. 7). Having selection methods that identify certain

traits or values a successful CNA possesses is one way that organizations might be able to

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determine a good fit. Even though the state administered CNA exam demonstrates that a nurse

will have the right knowledge, skills and abilities, different selection methods such as behavioral

interviews or reference checks can be indicators of a person-organization match that is much

more specific than simply focusing on person-job fit. This is imperative for organizations to

perfect in a fairly wage-stable market such as that of direct-care workers.

One recruiting and selection technique, the realistic job preview, has been associated with

lower turnover and higher employee performance (Haden, 2012). A realistic job preview occurs

when a job candidate is provided with a more representative view of what they might expect

from the organization if employed. The organization gives the candidate all of the information

without distortion; the positive aspects and the negative (Baur et al., 2014). The realistic job

preview provides an opportunity for a candidate to opt out of the selection process early on

before too much time and effort is invested. Many organizations may try to provide more

attractive descriptions of jobs that may not be completely accurate or holistic and when

candidates’ expectations are not met, entry shock may ensue and the employee will quit (Oxford

University Press, Realistic Job Preview, 2009). It could be argued that realistic job previews

reduce voluntary turnover by showing prospective employees a true preview of what they might

encounter on the job if hired. Although this may turn some candidates away, it can communicate

more accurately what a certified nursing assistant might find himself or herself doing,

eliminating any misaligned expectations. One study noted that the realistic job preview is an

ethical practice that can lower expectations reducing the disillusionment of nurses, especially

those just entering the field (Crow et al., 2006). Realistic job previews can also further the

psychological contract, the implicit agreement between the employer, representing the

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organization, and employee by promoting honesty and upfront behaviors (Henderson et al.,

2008).

On-the-job shadowing, in-person presentations and written materials can provide accurate

previews and may be best used in conjunction with other realistic job previews. Informal realistic

job previews such as conversations between a candidate and a friend already working for the

organization are very effective in increasing performance and retention rates (Baur, 2014). For

the nursing profession, in particular, realistic job previews are not commonly used but are an

approach that can affect clinical quality, patient safety and practice excellence (Gilmartin, et al.,

2013). In a paper by Baur et al. (2014), it is stated that realistic job previews may not be as

successful as they are often hypothesized to be, however this is likely due to initial unrealistic

expectations or a desire for positive first impressions for both the organization and the candidate,

and the inability to self-select out of the process. Many CNAs may have worked in similar

positions and likely know what to expect from the job itself, so making the realistic job preview

such that it gives exposure to organizational factors is important. The types of realistic job

previews examined in this paper are employee testimonials, job shadowing, tours of the facility

and meet-and greet with current staff. All of these may give prospective employees a glimpse of

the position as well as the organization and its culture, and, even though there is still room for

influencing positive expectations or creating false impressions the prospective employee may be

able to perceive more accurately whether or not he or she would be a good fit. This allows for a

candidate to self-select out of the process, saving both parties time, effort and, for the

organization, the costs associated with turnover.

Long-term care facilities should also utilize an interview process that uses specific

questions to reveal the attitudes and values of candidates. A case study of Geisinger

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Mountainview Care Center in Scranton, PA describes its hiring process that attempts to mitigate

attrition through selection practices. In an interview structured to identify a strong person-

environment fit the interviewer asks questions such as, “What do you find most rewarding when

you are out there in the trenches and taking care of the elderly on a daily basis?” This question

may evoke a response that involves a story or a personal sentiment that goes beyond

qualifications and basic skills (Parker-Bell, 2013). This is an intentional practice as it is a

selection tool designed for the purpose of revealing individual values for comparison with those

of the organization.

Additionally interviews may reveal candidates’ personality attributes in a less invasive

and inappropriate manner than a paper and pencil test. Assessing for the big-five personality

traits of extroversion, conscientiousness, openness to experience, low neuroticism and

agreeableness can predict job performance, leadership, training success and can identify the

potential for counterproductive work behaviors. Although assessing this through interviews is

typically more accurate than self-reports there is still the opportunity for candidates to inflate

information (Van Iddekinge, 2005 pp. 536- 537).

Reference checking is another way to gather information about a candidate’s past

experience. By requesting external sources, past managers or co-workers, the organization can

gather more information that may not be conveyed directly by the candidate. Although there is

still a chance for the candidate to be portrayed more positively than negatively with this method,

especially through letters of recommendation, it is still a good indicator of potential performance,

areas of improvement or a way to learn more about employee traits and competencies. It is noted

in a study about web-based multisource-reference checking, that having structured questions to

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which references respond, is fair and more accurate in comparison to unstructured reference

checks (Hedricks, 2013, p.100).

In addition to the reference check, many organizations also use honesty tests or integrity

tests to predict future behaviors. However the accuracy is often questionable which poses

questions about the ethics of using such tests. One article examining the legality of honesty tests,

proposes a balancing test for organizations to use in determining whether or not this type of

testing should be used. The three factors are, “the potential harm posed by a dishonest employee

in a particular job, the linkage between the test and the assessment process and the accuracy and

validity of the honesty test” (Brody, 2015, p. 551). For a CNA the potential harm could be great

because of the close relationships these direct care workers have with patients.

Another way for organizations to assess an employee, before being permanently hired is

through probationary periods. This can give the organization an idea of whether or not the new

employee will be able to complete job tasks and if they will get along with co-workers, managers

and be a good fit for the organization (Probationary Periods, 2007, p. 889). It is important to

understand, however, that the employee cannot be evaluated too stringently as they are still new

and learning.

Opportunities for career advancement within the organization are something that a

candidate may seek in looking for a position. One study found that among other variables

associated with reducing quitting behavior of nurses, training opportunities and promotion

prospects were important (Shields, 2001). Although career advancement and professional

development may not take place until an employee has been with an organization for a period of

time, letting candidates know the opportunities that exist may attract interested individuals. For

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prospective CNAs offering CNA certification training may be a way to bring in candidates and

build organizational commitment by offering something that will build them professionally.

A study conducted for the International Journal of Business and Management delves into

the concepts of predictive and face validity. Face validity deals with a candidates’ perception of

the selection process, whether it is equitable, too invasive or if it leaves an employee with a

negative attitude about the organization. The recruiting and selection experience can affect a

candidate’s perception of the organization and, if he or she is hired, can be a factor in either job

satisfaction or dissatisfaction. Predictive validity can also be impacted by the face validity of the

process. The predictive validity of a particular selection tool is the measured effectiveness of the

selection tool in predicting job performance of an employee. This can be quantified through a

correlation coefficient. If a selection tool is perfect, or 100% accurate, in determining

performance a correlation coefficient of 1 would be found (Ekuma, 2012). Such strong

relationship are never observed in actual selection contexts. A very strong predictive validity

would be above .35 and a weak validity is considered below .11 (Understanding Test Quality-

Concepts of Reliability and Validity, 1999).

Hypothesis 1: There is a direct relationship between realistic job previews (job shadowing, meet-

and-greet with current staff, employee testimonials, tours of the facility), structured interviews,

interviews with behavioral questions, reference checks, physical ability tests, honesty tests, a

probationary period, organizations offering a CNA certification program, organizations offering

opportunities for career advancement and further professional development and CNA turnover

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Staffing

Nurse staffing pertains to the composition, size, characteristics and competencies,

organization of, and administration of nursing home staff (Unruh & Wan, p. 202). The size and

number of nursing home staff is a common issue for many nursing homes as often the number of

staff to residents and number of hours per resident makes a difference in the quality of care a

patient receives. Facilities with lower levels of staffing for CNAs report higher turnover because

of greater workload and work stressors (Castle & Engberg, p.71). It is recommended in The

Gerontologist that the ratio for a day shift be 1 direct-care worker, which includes CNAs, to 5

residents, 1 direct-care worker to 10 for evening shifts and for night shifts 1 to 15 residents

(Experts Recommend, 2000, p. 10). An expert panel of nurse educators, researchers and

administrators in long-term care, consumer advocates, health services researchers and health

economists gathered at New York University at the John A. Hartford Institute for Geriatric

Nursing for a one day conference where they considered many questions associated with staffing

and quality of care. One of the questions pertained to minimum standards for nurse staffing

levels in long-term care facilities. Using previous studies as well as other pertinent sources of

information, the panel created the suggested standards in the table below.

Table 1. Suggested Direct-care Worker to Patient Ratios

Shift

Number of Direct-Care Workers

(RN, CNA or LPN)

Number of Patients

Day 1 5

Evening 1 10

Night 1 15

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Low staffing levels can lead to high turnover but some of the ways that organizations

seek to remedy low staffing levels can actually cause more harm. One study involving interviews

with CNAs found that many of the techniques commonly used to fix low staffing ratios such as

rotating staff to cover shortages in other areas or constantly recruiting for potential replacements

are viewed as professionally dismissive by current CNAs, and this does not contribute to

organizational commitment. Not finding the best-fit CNA for a position has implications for

current CNAs as it also revealed that current CNAs within an organization felt management did

not value them as much when they would “take just anyone off the street” (Bowers et al., 2003).

According to the American Nurses’ Association, Pennsylvania is not a state that has

adopted or enacted legislation that regulates staffing for nurses. Many states have adopted

legislation to either set specific nurse-to-patient ratios or ensure that hospitals have a nurse

staffing committee to generate staffing plans that take into account patient need and nurse skills

or that require facilities to publically reveal staffing levels to either the public or a regulatory

body. At the Federal level, regulation exists, but it is very ambiguously worded and not very

specific in its requirements. The Federal regulation, 42 Code of Federal Regulations (42CFR

482.23(b), mandates that hospitals certified to participate in Medicare must "have

adequate numbers of licensed registered nurses, licensed practical (vocational) nurses, and other

personnel to provide nursing care to all patients as needed" (American Nurses Association,

2016).

Hypothesis 2: Staffing levels for employees will affect turnover rates.

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Quality of Care

Quality of care can be measured through Donabedian’s structure-process-outcome (SPO)

framework. Outcomes are the results for patients, which may include positive or negative

changes in health. This is often measured through the prevalence of bedsores or pressure ulcers,

bladder or bowel incontinence and other health decline factors. Process refers to how the staff of

the nursing home provides care through interactions with patients or residents. This can entail

social or emotional care, which is very important for nursing homes to consider as they provide

long-term care. Structure pertains to the organization itself, which can include demographic

information about residents, size and ownership of the facility and staffing (Spilsbury et al.,

2011).

There are many other frameworks that have either expanded upon the SPO framework or

that have rejected it, however, staffing is a common theme throughout. One framework proposed

by Mueller solely focuses on staffing. The goal of this framework is to help managers as they

identify nursing needs and are hiring new nurses or redistributing assignments. First, an

organization creates standards and develops its own philosophy of care. Next, it matches resident

need with quality and quantity of nursing staff and then placement of nurse. Finally, the

organization meets the needs of the patients or residents through the previously developed

philosophy of care and standards (Mueller, 2000). This framework identifies the values and the

beliefs of nursing staff, which an organization then uses in making staffing decisions such as the

number of new staff to recruit, the types of new staff to recruit, how to allocate tasks, etc.

For-profit nursing homes have higher levels of turnover which may be due to several

factors. Lower quality of care is a major issue for many for-profit nursing homes that can be

reflected through higher turnover rates. CNA turnover intention may be reflective of this factor

17

as it has been found that nurses typically prefer to care for patients in a setting where high quality

of care is valued (Castle and Engberg, 71). This may relate to a nonprofit’s philosophy of care

which is linked to a charitable cause.

Hypothesis 3: Higher turnover rates will positively correlate with lower levels of quality of care.

18

Chapter 3 Methodology

Sample

I administered a survey to collect data regarding recruitment, selection methods and

staffing practices used by nonprofit nursing homes as well as information about turnover rates. I

created the survey, which contained 27 open-ended and multiple-choice questions, using a Penn

State Qualtrics account. The questions were not designed to elicit a forced-response so

participants could answer some questions and skip questions they were unable or unwilling to

answer. Forty-nine surveys were completed, but because some respondents did not answer all of

the questions, all data points could not be used in the data analysis. In question 2, items 1-17 of

the Likert scale matrix listed potential reasons nurses might quit their jobs and asked respondents

to indicate the frequency with which certified nursing assistants quit for each listed reason. The

items were based on measures used from a summary of “Why People Quit Assessments” by

Maertz et al. (2007), pertaining to reasons for leaving (Schmitt, 2012, p. 575).

To establish my sample I began with a list of all nursing homes in Pennsylvania, 714

facilities, which identified non-profit or for-profit status, the organizations’ addresses and phone

numbers along with several other pertinent pieces of information. I then created a spreadsheet in

Excel containing only non-profit organizations, which contained 266 long-term care facilities.

Because the spreadsheet did not contain email addresses, which may have been the most accurate

way to communicate with the human resources directors, I used U.S. Mail to send information

and instructions for my survey.

I first called every organization in an attempt to acquire the name of the human resources

director of each organization so that I would be able to address the survey to the individual most

19

likely to complete the survey. I also asked for the email addresses of the human resources

directors if the organizations were willing to provide them so that I might be able to follow-up

more quickly and send out reminders. I then created a letter, which used Penn State School of

Labor and Employment Relations letterhead, explaining the purpose of my study that contained a

link which could be copied and pasted into a web browser for the survey to be taken online. I

shortened the link using the website, Bitly.com, to make it easier and less difficult for the human

resources director to type the link into the web browser. The letter, featured in Appendix B,

emphasized the confidentiality of information gathered in the survey. I also created an incentive

for participation through the opportunity to win one of five $50.00 Amazon gift cards. I used

envelopes provided by the Penn State School of Labor and Employment Relations and

handwrote all addresses to further increase the chance that the letter would be opened and read. I

sent the survey to 266 nonprofit long-term care facilities on December 22, 2015. I was unable to

get the names of 84 Human Resources Directors so I addressed those “ATTN: Human Resources

Director.”

I sent a reminder email to those who had not yet participated on January 7, 2016 and

again January 13, 2017. The emails contained much of the same information presented in the

mailed letter in the event that the human resources directors had not received or read the initial

letter. I selected the winners of the Amazon gift cards Monday, January 18, 2016 using the

RANDBETWEEN function in Excel in order to ‘pick winners out of a hat.’ I notified the

winners individually requesting an address to which I might send a gift card along with thanking

them for participating. I then sent an email to all those who had responded to the survey but had

not won thanking them for their participation and giving them notice that I would send my

analysis in the near future.

20

Measures

For the first hypothesis, 10 multiple linear regression tests were performed. The

dependent variable was CNA voluntary turnover and the independent variables were all of the

selection tools mentioned in the hypothesis. Two of the independent variables of interest in

Hypothesis 1 were constants because all respondents answered in the same way. For the

questions, “Does this organization ask for references and conduct reference checks?” and “Is

there a probationary period for recently hired CNAs?” all respondents answered with “yes.” As a

result the regression analysis could not be performed. Given that all of the organizations

conducted reference checks and utilized probationary periods, these appear to be important

practices but the relationship between the practices and CNA voluntary turnover cannot be

evaluated.

Because the number of cases, or responses, was low, several analytic challenges arose.

First, with a small sample size it is not reasonable to include all possible staffing practices as

separate independent variables, due to overfitting, low degrees of freedom, and because there

were fewer than ten times the number of cases per independent variable (Liu, 2016). Instead of

completing a single linear regression analysis with all predictors included, which may have

yielded more accurate and complete information about the data, 10 separate linear regression

tests were completed. More of the limitations of this method of analysis will be discussed later.

For each regression, two control variables were used, type of facility and whether or not

the organization has a religious affiliation or a charitable mission. The possible responses for

type of facility were: Independent Living, Skilled Nursing, Rehabilitation or Other with an

option to describe what ‘other’ might entail. Most facilities surveyed were categorized as either

skilled nursing or other, which most elaborated as CCRC, an acronym for ‘Continuing Care

21

Retirement Facility.’ For this control, a dummy variable was used to signify skilled nursing,

(scored 1), and CCRC, (scored 0). No organizations responded to the question measuring this

control variable, ‘Independent Living’ and one responded for ‘Rehabilitation.’ Fifty-eight

percent of respondents categorized their organization as ‘Skilled nursing’ and 38% as ‘Other,’

which signified CCRC.

The other control variable, religious affiliation or charitable mission required a ‘yes/no’

response. This was coded using a dummy variable coded 0 or 1, (1 was used for ‘yes,’ and 0 was

used for ‘no’). Seventy-seven percent of respondents marked ‘yes,’ this organization has a

religious affiliation or charitable mission and 23% responded that it did not. Both control

variables were entered into the regression analysis in Excel along with the independent variable

tested for each linear regression test performed. The use of these control variables is important

because they might provide an alternative explanation for why relationships may or may not

exist and why certain selection tools may be more applicable to particular types of organizations.

It also may reveal more accurate information about turnover rates. Since this survey captured

responses from the nonprofit long-term healthcare industry in Pennsylvania, all different types of

long-term care facilities and their different attributes were not controlled for in the collection of

data. Using these controls in the data analysis process is essential.

For hypothesis two a multiple regression was conducted to investigate the relationship

between the dependent variable turnover and the predictor variable, staffing level which was

measured through the descriptive question using a Likert scale, ‘Regarding staffing levels this

organization is:’ which potential answers ‘Overstaffed,’ ‘Adequately staffed,’ ‘Understaffed,’ or

‘Severely understaffed.’ No organization responded with ‘Severely Understaffed.’ The responses

were then reverse coded. The responses were originally coded such that lower numbers

22

corresponded with higher staffing levels. Reverse coding associated lower numbers with lower

staffing

Cases that did not respond to turnover or staffing levels were not used in the regression

analysis so list wise deletion was necessary. The control variables used in the Hypothesis 1

regressions were also used in this test.

Hypothesis 3 required information about quality of care. I used five-star ratings from

Medicare.gov to measure quality of care for each nursing home surveyed. The ratings are based

on long-stay and short-stay measures listed in the table below. I matched the ratings found on

Medicare.gov to the long-term care facilities that reported CNA voluntary turnover rates for

2014. The possible ratings received on Nursing Home Compare were “Much Below Average” (1

star), “Below Average” (2 stars), “Average” (3 stars), “Above Average” (4 stars) and “Much

Above Average” (5 stars). None were considered “Much Below Average” but several received

“Below Average” ratings.

23

Table 2. Medicare.gov Quality Measures

Medicare.gov Quality Measures

Long-stay residents: Percent of residents whose need for help with activities of daily living

has increased

Percent of high risk residents with pressure ulcers (sores)

Percent of residents who have/had a catheter inserted and left in their

bladder

Percent of residents who were physically restrained

Percent of residents with a urinary tract infection

Percent of residents who self-report moderate to severe pain

Percent of residents experiencing one or more falls with major injury

Percent of residents who received an antipsychotic medication

Short-stay residents: Percent of residents with pressure ulcers (sores) that are new or

worsened

Percent of residents who self-report moderate to severe pain

Percent of residents who newly received an antipsychotic medication

The dependent variable in Hypothesis 3 was quality of care and the independent variable

was turnover. A multiple regression was used to test this hypothesis and the control variables

were included with the independent variable in the regression. As was true for Hypothesis 2,

listwise deletion was used, so cases that did not respond to questions either measuring the

independent or dependent variable were not included in this test.

24

A correlation table was created to capture the correlations present among all independent

and dependent variables tested. This displays the strength and direction of the relationship.

Means and standard deviation were also calculated to provide information about the averages of

variables among those surveyed and also the amount of variation present in the responses. The

complete correlation table is located in Appendix D.

It is important to point out that several outliers emerged which was confirmed with an

outlier analysis. One case reported 0.25%, and 0.0025 is a very low turnover rate, which may be

attributable to a reporting error. It is possible that a respondent meant to enter 25%. As a result

this case was removed from all data analysis as to not skew the data. Additionally 1.12%, 0.0112

and 69%, 0.69, are two more extreme values. It is possible that 69% is legitimate. Although an

outlier in comparison to the other turnover rates collected in this survey, because of the evidence

found in the literature review, citing turnover rates that exceeded 100% this turnover rate may be

accurate. 1.12% is low but it is not quite as unrealistic as a turnover rate under 0.25%. Because

of this and the low number of cases measured, these two cases remained a part of the data

analysis while 0.25% was removed.

Because of the small sample size collected the statistical power is very low meaning that

analysis is less likely to reveal an effect, even if there may be one present in the data (Cohen,

1992). In research there is the possibility of two types of decision errors. There is the possibility

of a Type I error, rejecting the null hypothesis when it is not actually rejected, where false

positives could be seen. There is also a Type II error where effects are not detected even though

an effect might exist, and the opposite error occurs. There are many factors that contribute to

these issues which include sample size, population effect size, significance criterion and

statistical power. According to an article by Cohen addressing this issue, it is possible to adjust

25

the alpha significance criterion to compensate for low statistical power due to a small sample

size (Cohen, 1992, pp. 155-159). Typically the widely accepted p-value which supports

statistical significance is one where p <.05. For my study it is necessary to use a higher p-value

of p <.10.

Additionally several index measures were used to provide information about

organizations that might use more than one selection tool or staffing practice. Regression tests

were performed for these and they were used to support or not support findings made in the

hypotheses. According to an article by Huselid, the importance of using human resources

management systems is emphasized and evidence is provided that some organizations use

effective staffing practices in conjunction with each other (Huselid, 1995, p.636). The strategic

staffing index takes into account the combination of all the staffing practices addressed with

hypothesis 1. Any structured interview and any realistic job preview encompass all of the

individually measured selection tools in those categories. Competitive pay is another variable

that is measured in the survey and so a regression test was performed to test its significance with

CNA voluntary turnover. The results sections for these additional regression tests also include

further explanation for their uses, and, like the regression tests for the hypotheses, they use the

same controls. Additionally an exploratory analysis pertaining to several additional relationships

discovered will be mentioned later in the results and discussion.

26

Chapter 4 Results

Hypothesis 1

Hypothesis 1 involves many different separate relationships between the dependent

variable turnover and the various selection tools tested. Because I could not enter all of the

selection practices as a block, it is not possible to make a conclusion regarding the overall effect

of ‘selection tools’ as a group comprising one independent variable on CNA voluntary turnover

rates, which was the initial intention of this hypothesis and this study, or the effect of a particular

selection tool on turnover, holding all other variables constant. In the coming section I will

describe the relationship found in each regression test and whether or not Hypothesis 1 is

supported or unsupported based upon the information provided in my survey.

Hypothesis 1a: There is a direct relationship between structured interviews and CNA turnover.

According to the t-test for the significance of the slope of the regression coefficient, the

p-value is 0.191 which is greater than 0.10, the alpha level used for this study. The coefficient for

the use of structured interviews -0.074, t(34)= -1.33, p>.10, and according to the p-value, is not

significantly different from 0 so a relationship is not detected. Changes in structured interviews,

or the use of or lack of use of structured interviews are not directly associated with higher or

lower levels of turnover and so the hypothesis is rejected. Additionally the control variables

reported a p-value that is not significant. Control 1, type of facility and Control 2, religious

affiliation or charitable mission had a non-significant p-value of 0.560. Hypothesis 1a is not

supported. Additionally the correlation found between use of a structured interview and CNA

voluntary turnover for 2014 is -0.227(p>.10), which shows that the correlation is not significant.

27

The correlation implies a weak, negative relationship where, as use of structured interviews

increases, turnover percentages decrease which shows that it should be noted that the mean of

use of structured interviewing was 0.82, which is very close to 1, the dummy variable for yes.

This indicates that 82% of organizations use this selection tool. The restriction in variance in the

positive use of structured interviews may limit the ability to detect relationship with turnover.

Overall the statistical power is too low to establish a definite, existing relationship but the

regression coefficient, -0.074 is consistent with the notion presented by the hypothesis that an

increase in the use of this selection tool will result in a decrease in CNA voluntary turnover. This

is a test for the single predictor, use of structured interview. Use of any structured interview

entails the use of any type of structured interview which could include those that ask behavioral

questions as well as those that ask behavioral questions involving a healthcare-related situation.

This will be addressed later in the analyses. Below is a table summarizing some of the relevant

values contained in this description of results.

Predictor B (Regression

Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Structured

Interview

-0.074 0.055 -1.33 34 0.191 > 0.10

Hypothesis 1b: There is a direct relationship between the use of behavioral interview questions in

the interview process and CNA turnover.

The regression for the use of behavioral interview question is .068, t(34)= .914, p >.10, so

the regression coefficient is not significantly different than 0 and the hypothesis is rejected.

There is not a relationship found between the independent variable, behavioral interviews, and

the dependent variable, CNA voluntary turnover but the slope of the predictor and outcome,

28

0.068 is not consistent than what was hypothesized because a positive relationship would exist if

a relationship was found. This would mean that increase in the use of behavioral interviews

would result in an increase in CNA voluntary turnover which is not consistent with findings from

the literature review. The control variables were also not considered significant with a p-value of

0.560.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Behavioral

Interviews

0.068 0.075 0.914 34 0.367 > 0.10

Hypothesis 1c: There is a direct relationship between the use of employee testimonials and CNA

turnover.

This hypothesis is rejected as a relationship between the use of employee testimonials

and CNA voluntary turnover is not found. The regression coefficient was .046, t(34)= 0.708, p >

.10, so the slope, or the regression coefficient, is not significantly different than 0. Control 1 and

Control 2 are not statistically significant with a p-value of 0.560. The correlation coefficient for

the use of employee testimonials and CNA voluntary turnover is .142 (p > .10), which indicates a

weak positive but non-significant relationship between employee testimonials and CNA

involuntary turnover for 2014.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Employee

Testimonials

0.046 0.065 0.708 34 0.484> 0.10

29

Hypothesis 1d: There is a direct relationship between the use of job shadowing and CNA

turnover.

The t-test, with a t of 0.281 yields a p-value of 0.781 which is greater than 0.10 and so

there is not a relationship between job shadowing and the CNA voluntary turnover. The

regression coefficient is not significantly different than 0 and the hypothesis is not supported.

The regression coefficient is 0.013, t(34)= .281, p >.10.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Job

Shadowing

0.013 0.047 0.281 34 0.781> 0.10

Hypothesis 1e: There is a direct relationship between the use of a meet-and-greet with current

staff and CNA turnover.

The regression coefficient is -0.036, t(34)= -.759, p >.10 and is not significantly different

than 0. Although the regression does follow the hypothesized pattern, where, as the use of meet

and greet with current staff increases, CNA voluntary turnover decreases. However, there is no

statistical support for the hypothesis. Again, the p-value for Control 1 and Control 2 is 0.560.

The correlation coefficient for the use of a meet-and-greet for current staff was -0.063,

which is a weak negative relationship that is not significant (p > .10). The mean of a meet-and-

greet with current staff is 0.63 and the standard deviation is 0.489.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Meet and

Greet with

Current Staff

-0.036 0.048 -0.759 34 0.453> 0.10

30

Hypothesis 1f: There is a direct relationship between the use of tours of the facility for

prospective CNA candidates and CNA turnover.

This hypothesis is supported. The regression coefficient is -0.128, t(34)= -2.003, p >.10,

and is significantly different from 0, with alpha set at .10. Thus, there is a statistically significant

relationship between the use of tours of the facility and CNA voluntary turnover. Additionally

the direction of the relationship is consistent with the hypothesis, where, as the use of tours of the

facility increases, CNA voluntary turnover decreases. The change is R-squared is 0.102 which

signifies that 10.2% of the variance in CNA voluntary turnover is associated with the use of tours

of the facility for prospective CNA candidates, taking into account the controls used in this test .

The control variables were found to be statistically insignificant because they yielded a p-

value of 0.560. The correlation coefficient was -0.250 (p > .10), which indicates a negative but

non-significant relationship. The use of controls removed extraneous variance which accounted

for the difference in conclusions from significance tests for the correlation and the regression

coefficients. The mean for the use of tours of the facility was 0.884 and the standard deviation is

0.343.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Tours of the

Facility

-0.128 0.064 -2.003 34 0.053< 0.10

31

Hypothesis 1g: There is a direct relationship between the use of references and reference conduct

checks and CNA turnover.

Because all respondents answered the question measuring references and reference

conduct checks with the response ‘yes,’ this predictor is a constant and running a regression

analysis is impossible because there is no variance in the predictor.

Hypothesis 1h: There is a direct relationship between the use of physical ability tests and the

likelihood that an organization will hire CNAs that will stay with an organization.

This hypothesis is not supported. The regression coefficient, 0.000, and the t-test yields a

p-value of 0.994 which is not less than 0.10. There is no relationship between the use of physical

ability tests and CNA voluntary turnover.

The correlation coefficient for the use of physical ability tests is -0.033 so there is a weak

negative and nonsignificant relationship between the predictor, physical ability tests, and the

outcome, CNA voluntary turnover rates. The two-tailed significance test showed a significance

of 0.845. The mean for use of physical ability tests is 0.68 and the standard deviation is 0.471.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Physical

Ability Tests

0.000 0.048 -0.008 34 0.994< 0.10

Hypothesis 1i: There is a direct relationship between the use of honesty tests and CNA turnover.

The regression coefficient is -.027, t(34)= -.440, p> .10, so no significant relationship is

detected and the regression coefficient is not significantly different from 0. The correlation

32

coefficient is -0.011 for honesty tests highlighting a very weak, insignificant relationship (p>

.10). The mean for this selection tool is 0.18 and the standard deviation is 0.393.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Honesty Tests -0.027 0.061 -0.440 34 0.663< 0.10

Hypothesis 1j: There is a direct relationship between the organizations that offer a CNA

certification program for interested individuals and CNA turnover.

The hypothesis of a significant relationship is supported but not in the predicted direction.

The regression coefficient is 0.086, t(34)= 2.068, p <.10, which is significantly different from 0

because the p-value is smaller than 0.10. The only anomaly is that the relationship is positive,

meaning that as the organizations that offer a CNA certification program for interested

individuals increases, CNA voluntary turnover would also increase. The change in R-squared is

0.108 so 10.8% of the variance in CNA voluntary turnover is associated with the use of this

selection tool, controlling for the type of facility. The p-value for the controls is 0.560. Both

controls are statistically insignificant.

The correlation coefficient for organizations offering a CNA certification program is

0.348, which implies a moderate, positive relationship (p > .10). The mean for organizations

offering a CNA certification program for interested individuals is 0.55 and the standard deviation

is 0.504, which indicates a larger spread compared to some of the other selection tools tested.

Explanations for the unexpected positive relationship are considered in the discussion.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Offer CNA

Certification

Program

0.086 0.042 2.068 34 0.046 < 0.10

33

Hypothesis 1k: There is a direct relationship between the organizations that provide opportunities

for career advancement or further professional training and CNA turnover.

The regression coefficient is 0.112, t(34)= 1.967, p < .10, is significantly different from 0

because the p-value is smaller than 0.10. Once again the regression coefficient is positive which

does not seem consistent with trends predicted. The p-value for the controls is 0.560 so they are

not statistically significant.

The change in R-squared value for this regression is 0.097 which indicates that, with the

controls in place, 9.7% of the variance in CNA voluntary turnover for 2014 is associated with

organizations providing opportunities for career advancement or further professional training.

The correlation coefficient is 0.319, which is a moderate, positive relationship and the two-tailed

significance reveals a p-value of 0.051, which is significant. The mean for the predictor variable

is 0.84 and the standard deviation is 0.370.


Coefficient)

Standard

Error

t Degrees of

Freedom

Significance

(p-value)

Career

Advancement

0.112 0.057 1.967 34 0.057< 0.10

Hypothesis 1l: There is a direct relationship between the organizations that have probationary

periods and CNA turnover.

This hypothesis also could not be tested with a regression analysis because the predictor

is a constant.

34

Hypothesis 2

Hypothesis 2 examined staffing levels and their relationship with CNA involuntary

turnover rates. For the multiple linear regression, the change in R-Squared is 0.176 signifying

that, taking into account the control variables, 17.6% of the variance in CNA involuntary

turnover for 2014 is associated with staffing level. The regression coefficient is -.120, t(34)= -

2.433, p > .10. The regression coefficient of -.120 is statistically significantly different from 0, so

the findings support a statistically significant relationship. Hypothesis 2 is supported because

there is a relationship detected demonstrating that as staffing levels increase, turnover is

diminished. Neither of the controls were statistically significant.

The correlation coefficient for staffing level and CNA involuntary turnover rate for 2014

is -.329 (p > .10) which indicates a significant negative relationship between staffing level and

turnover. Consistent with the regression results, as staffing increases, CNA voluntary turnover

rates go down. The mean for staffing level is 2.84 and the scale for possible responses was

between 1 and 4. All responses, once reverse coded, fell in the 2-3 range with the exception of

one staffing level of 4. To provide further clarification, 2 represented understaffed, 3 was

adequately staffed and 4 represented severely overstaffed. For this regression analysis N=37 and

the degrees of freedom is 34. In the overall survey, which includes more cases than were

measured for this regression test, 74% of respondents selected adequately staffed, 21%

understaffed and only 5%, or two respondents, reported being severely overstaffed.

Hypothesis 3

Hypothesis 3 states that higher turnover rates will positively correlate with lower levels

of quality of care. The regression coefficient is -1.027, t(27)= -.713, p > .10. The regression

35

coefficient, -1.027, is not significantly different from 0. There is no observed relationship present

between CNA voluntary turnover and quality of care. The R-squared change is 0.017, which

means that 1.7% of the variance in level of quality of care is explained by CNA voluntary

turnover, controlling for the type of facility.

***The correlation coefficient for CNA voluntary turnover for 2014 and level of quality

of care is -0.049, which signifies that as CNA voluntary turnover increases, the quality of care

ratings decrease, but this is a weak and insignificant relationship. The mean for CNA voluntary

turnover for 2014 was 24.4% and the mean for quality of care was 3.81. The scale used for

quality of care was 1-5, 1 indicating 1 star which represents ‘Much below Average,’ to 5

indicating 5 star which represents ‘Much above Average.’ For this hypothesis N=30 and so the

degrees of freedom is 27, after including controls.

Table 3. Descriptive Statistics for Hypotheses

Descriptive Statistics

N Mean Std. Deviation

Turnover Rate 2014 38 .244 .131

Quality of Care 31 3.806 1.038

Strategic Staffing Index 38 6.421 1.287

Any Structured Interview 38 .974 .162

Structured Interview 38 .816 .393

Behavioral Interview 38 .895 .311

Any RJP Type 38 .895 .311

RJP Employee Testimony 38 .132 .343

RJP Job Shadowing 38 .526 .506

RJP Tour 38 .868 .343

RJP Meet and Greet 38 .632 .489

Reference Check 38 1.000 .000

Physical Abilities Test 38 .684 .471

36

Honesty Test 38 .184 .393

Offering CAN Certification 38 .553 .504

Career Advancement 38 .842 .370

Probation 38 1.000 .000

Staffing Level 38 2.842 .437

Starting Pay 34 12.069 1.326

Skilled Nursing Facility 38 .579 .500

Religious or Charitable

Organization 38 .684 .471

Valid N (listwise) 31

Table 4. Correlation Table for Hypotheses

Correlations

Turnover 2014 Quality of Care

Strategic Staffing Index Pearson

Correlation .036 .242

Sig. (2-

tailed) .830 .190

N 38 31

Any Structured

Interview

Pearson

Correlation -.284 .323

Sig. (2-

tailed) .085 .076

N 38 31

Structured Interview Pearson


Sig. (2-

tailed) .170 .314

N 38 31

Behavioral Interview Pearson


Sig. (2-

tailed) .366 .536

N 38 31

37

Any RJP Type Pearson

Correlation .021 .365*

Sig. (2-

tailed) .900 .043

N 38 31

RJP Employee

Testimony

Pearson


Sig. (2-

tailed) .397 .520

N 38 31

RJP Job Shadowing Pearson


Sig. (2-

tailed) .496 .661

N 38 31

RJP Tour Pearson


Sig. (2-

tailed) .130 .053

N 38 31

RJP Meet and Greet Pearson


Sig. (2-

tailed) .707 .736

N 38 31

Reference Check Pearson

Correlation .b .b

Sig. (2-

tailed) . .

N 38 31

Physical Abilities Test Pearson


Sig. (2-

tailed) .845 .644

N 38 31

Honesty Test Pearson


Sig. (2-

tailed) .947 .353

38

N 38 31

Offering CNA

Certification

Pearson

Correlation .348* .141

Sig. (2-

tailed) .032 .449

N 38 31

Career Advancement Pearson

Correlation .319 -.093

Sig. (2-

tailed) .051 .619

N 38 31

Probation Pearson

Correlation .b .b

Sig. (2-

tailed) . .

N 38 31

Staffing Level Pearson

Correlation -.329* .227

Sig. (2-

tailed) .044 .220

N 38 31

Starting Pay Pearson

Correlation -.369* .025

Sig. (2-

tailed) .032 .893

N 34 31

Skilled Nursing Facility Pearson

Correlation -.119 -.108

Sig. (2-

tailed) .477 .565

N 38 31

Religious or Charitable

Organization

Pearson

Correlation -.148 -.260

Sig. (2-

tailed) .375 .157

N 38 31

*. Correlation is significant at the 0.05 level (2-tailed).

b. Cannot be computed because at least one of the variables is constant.

39

Turnover/Reasons for leaving

The lowest reported voluntary turnover rate is less than 1%, which, as previously

mentioned, was dropped from the analysis, and the highest is 69%. For the 39 organizations that

answered the first question regarding CNA voluntary turnover for 2014, the average voluntary

turnover rate is 23.76%, including the extreme outlier. 0.25%. Without the outlier, the average

voluntary turnover rate is 24.4%. The standard deviation of all the turnover rates reported is

0.134, and without the outlier of 0.25% it is 0.131, meaning there is a standard deviation

variation of approximately 13% in turnover across different facilities.

According to the figures presented by “Pennsylvania’s Long-term Care System: Building

Careers, Enhancing Quality Resident Care” CNA turnover rates could be as high as 43% or even

over 100%. I am only measuring voluntary turnover rates of nonprofit long-term care facilities,

which represents a narrower view of turnover. However, as the figures presented by the

aforementioned publication are consistent with other studies pertaining to CNA turnover and

encompass a broad range of turnover rates they will be used as a point of comparison for my

study. According to the data, the nonprofit long-term care facilities responding to the survey

reported much lower voluntary turnover than the average.

Below are two graphs comparing the turnover rates found in the survey I conducted and

the other studies cited by “Pennsylvania’s Long-term Care System: Building Careers, Enhancing

Quality Resident Care,” 43% reported by the American Healthcare Association 2010 and over

100% reported by a study conducted by Castle et. al 2005. Below are two graphs that compare

the data points for turnover percentages in my study to constants in, first the low end of the

turnover rates listed in the publication, 43% and next the high end, 100%. As can be seen the

40

turnover rates I collected are, on average much lower than even the low end from the figures

from the publication minus 69% which is also an outlier in this sample but in comparison to

these turnover rates from the other publication it seems like a logical value. Theses graphs

include the outlier, 0.25% which may be due to a repsonse error.

Table 5. Reported Involuntary Turnover of CNAs Compared to Estimated Turnover Rate of CNAs reported by the American

Healthcare Association 2010

Table 6. Reported Involuntary Turnover of CNAs Compared to Turnover Rate of CNAs reported by Castle et. al 2005

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Per

cen

tag

e C

NA

IIn

vo

lun

tary

Tu

rno

ver

Reported Involuntary Turnover of CNAs

Turnover Rates of CNAs, 2014

Estimated Turnover Rate of

CNAs, American Healthcare

Association 2010

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Per

cen

tag

e C

NA

In

vo

lun

tary

Tu

rno

ver

Reported Involuntary Turnover of CNAs

Turnover Rates of CNAs, 2014

Turnover Rate of CNAs, Castle

et. al 2005

41

Any Type of Structured Interview

In addition to testing for the structured interview, on its own, as a predictor for CNA

voluntary turnover, any type of structured interview was tested in a regression analysis. The

rationale for the creation of this index measure is that most organizations used some type of

structured interview and I chose to test types of structured interviews in Hypothesis 1 with

structured interview and behavioral interview. In the regression analysis, the t-test revealed a p-

value of 0.085, which is less than 0.10 and so a relationship is present. The regression

coefficient, -0.238 is significantly different from 0 and so the index measure helps to support the

sub-hypotheses in hypothesis 1 pertaining to structured interviews; any type. The R-square

change is 0.082 and so 8.2% of the variance in CNA voluntary turnover can be associated with

the use of any structured interview, taking into account the controls for the type of facility. The

p-value of the controls is 0.560 which is not significant.

Additionally it is important to note the direction of the relationship which is indicated

with the regression coefficient, -0.238 which supports the idea that as the use of structured

interview increases, CNA voluntary turnover will decrease. This is an interesting finding because

it is important to understand that most of these organizations use a combination of different

selection tools and may use structured interviews including behavioral question or different types

of structured interviews. Overall, it is interesting to know that the use of any type of structure

interview is significant because so many organizations reported using them.

42

Any Realistic Job Preview

Several of the selection tools measured in hypothesis 1 were types of realistic job

previews. The use of Employee testimonials, tours of the facility, job shadowing, meet and greet

with current staff and job shadowing are all types of job previews. An index measure was created

for the use of any of these selection tools and their impact/effect on CNA voluntary turnover. A

regression was run and a t-test with a p-value of 0.795 demonstrated that the regression

coefficient, -0.020 was not significantly different from 0 and so no relationship can be detected.

This does not support hypothesis 1 although in the earlier results the use of RJP tours was

significantly negatively associated with turnover. Taking into account the controls in place, 0.2%

of the variance in CNA voluntary turnover is associated with the use of any type of realistic job

preview. Additionally, in purely exploratory analyses the use of any type of realistic job preview

was found to be related to quality of care (B= 1.09, t(27)= 1.70, p > .10.

Strategic Staffing Index

The strategic staffing index, which is an index which includes all of the selection tools

through the sum of their scores. This accounts for organizations that use more than one of these

selection tools, which is highly likely. The t-test has a p-value of 0.953 and this is greater than

0.10 and so no relationship is present between the combination of staffing practices and CNA

voluntary turnover. The regression coefficient, 0.001 is not significantly different from 0 and the

R-squared change is also 0 so 0% of the variance in CNA voluntary turnover is associated with

the use of a combination of these staffing practices.

43

Competitive Starting Pay

The t-test associated with the regression analysis for starting pay revealed a relationship

between starting pay and CNA voluntary turnover. The regression coefficient for starting pay

which is -0.049 is significantly different from 0 (p > .10), which is less than 0.10. Not only is

there a relationship but there is a negative relationship: As starting pay increases, CNA voluntary

turnover decreases. The p-value for the controls is 0.422 so it is not significant. The change in R-

squared is 0.178 and so 17.8% of the variance in CNA voluntary turnover is associated with

starting pay.

Exploratory Analysis

Relationships among frequency of rating for key determinants of turnover and turnover

rates were explored to determine if there were patterns between the causes of turnover and

turnover rates. Another item measured in my survey, but not tested for my hypotheses are

reasons for leaving and associations with turnover and quality of care. The question, “To the best

of your knowledge, how frequently do CNAs voluntarily leave for the following reasons?” gave

17 reasons and contained a Likert scale for each reason where respondents could answer ‘Very

Infrequently (1),’ Infrequently (2),’ ‘Somewhat Infrequently (3),’ ‘Sometimes (4),’ ‘Somewhat

Frequently (5),’ ‘Frequently (6),’ ‘Very Frequently(7).’ From the descriptive statistics below it

seems that the most common reason CNAs voluntarily leave is for wanting better pay and the

least common reason is dangerous work conditions. Better work schedule, better benefits, more

time with family and less stress at work were also more common. It is important to consider the

response bias possible as CNAs who left the organizations were not asked these questions, but

human resources directors. There is the potential for overly positive responses. The correlations

44

between these reasons for leaving and CNA voluntary turnover and quality of care are also

below.

Table 7. Descriptive Statistics for Reasons for Leaving


Mean Std. Deviation N

Life or career change 3.05 1.598 37

Attraction to other jobs 3.25 1.296 36

Wanted different/better job tasks 2.57 1.482 37

Wanted better opportunity for

advancement/growth 2.95 1.452 37

Wanted better work schedule 3.95 1.615 37

Wanted more time with family than job

allows/ family pressure to quit 3.68 1.617 37

Fear of job loss 1.43 .867 37

Family-related relocation 2.65 1.438 37

Nonspecific desire to relocate or avoid

relocation 1.72 .944 36

Co-worker problems/issues 3.19 1.630 37

Wanted less commute or travel 2.86 1.337 37

Management problems/issues 3.05 1.311 37

Wanted less stress at work 3.27 1.627 37

Dangerous work conditions 1.22 .417 37

Harassment 1.51 .901 37

Wanted better pay 4.35 1.719 37

Wanted better benefits 3.56 1.576 36

Voluntary Turnover 2014 .24357 .130707 38

Quality of Care 3.7419 1.02365 31

Table 8. Correlations for Reasons for Leaving

Correlations

TO2014 QltyCare

Life or career change Pearson Correlation .132 .428*

Sig. (2-tailed) .436 .018

N 37 30

45

Attraction to other jobs Pearson Correlation .164 .106

Sig. (2-tailed) .340 .576

N

36 30

Wanted different/better job

tasks

Pearson Correlation .074 .084

Sig. (2-tailed) .663 .658

N 37 30

Wanted better opportunity for

advancement/growth


Sig. (2-tailed) .667 .695

N

37 30

Wanted better work schedule Pearson Correlation .317 -.056

Sig. (2-tailed) .056 .770

N 37 30

Wanted more time with family

than job allows/ family

pressure to quit

Pearson Correlation -.043 -.109

Sig. (2-tailed) .800 .565

N

37 30

Fear of job loss Pearson Correlation -.027 -.097

Sig. (2-tailed) .876 .608

N 37 30

Family-related relocation Pearson Correlation -.404* .235

Sig. (2-tailed) .013 .211

N 37 30

Nonspecific desire to relocate

or avoid relocation

Pearson Correlation -.082 -.080

Sig. (2-tailed) .636 .672

N

36 30

Co-worker problems/issues Pearson Correlation .213 -.290

Sig. (2-tailed) .205 .121

N 37 30

Wanted less commute or travel Pearson Correlation -.152 -.116

Sig. (2-tailed) .369 .543

N 37 30

46

Management problems/issues Pearson Correlation .274 -.218

Sig. (2-tailed) .101 .248

N

37 30

Wanted less stress at work Pearson Correlation .359* -.248

Sig. (2-tailed) .029 .187

N 37 30

Dangerous work conditions Pearson Correlation -.081 -.262

Sig. (2-tailed) .632 .163

N 37 30

Harassment Pearson Correlation .043 -.294

Sig. (2-tailed) .799 .115

N 37 30

Wanted better pay


Sig. (2-tailed) .466 .554

N 37 30

Wanted better benefits Pearson Correlation .065 -.139

Sig. (2-tailed) .704 .473

N 36 29

It can be seen that turnover rates are highest when CNAs leave for reasons of finding better work

schedules, leaving management problems, and reducing stress. Turnover rates are lowest when

CNAs leave for reasons of family relocation. It is interesting to note that quality of care is

negatively associated with leaving due to co-worker problems and leaving to reduce stress. These

exploratory results suggest there may be many types of issues that create stress, scheduling

issues, and they may be associated with management problems. Similar issues appear to affect

quality of care. It should be stressed, however, that these are exploratory findings and that future

research should be conducted to investigate alternative explanations for quality of care and

turnover associated with workplace culture, management, and stress.

47

Chapter 5 Conclusion

Discussion

Of all the regression tests, several were statistically significant revealing relationships

between the predictors and outcomes. For Hypothesis 1; Hypothesis 1f- Tours of the Facility,

Hypothesis 1j- Offering a CNA Certification Program and Hypothesis 1k- Career Advancement

Opportunities all had a p-value lower than the level of significance against which it was tested,

0.10. A relationship was found for all three, where the regression coefficient or slope was found

to be significantly different than 0, but the only that indicated that an increase in the use of the

selection tool of interest that was associated with a in a decrease in CNA voluntary turnover,

based on the regression coefficient’s sign, was tours of the facility.

Tours of the facility had a regression coefficient of -0.128, offering a CNA certification

had 0.086 and career advancement 0.112. The change in R-squared, which predicts the variance

in turnover associated with the selection tool tested, was 10.2% for tours of the facility, 10.8%

for offering a CNA certification test and 9.7% for career advancement. Overall, these figures do

not signify an extremely strong relationship.

Tours of the facility was a part of the measures of realistic job previews. Giving the

candidate a look around the facility to get a better idea of what they might expect on the job and

a glimpse of future co-workers and patients could deter candidates that would not stay from even

going through with the selection process. It can also identify which candidates see themselves as

being a good fit based on their initial perception of the organization. This is also a selection

method that gives more selection power to the job candidate. Approximately 97% of the

organizations surveyed reported using tours of the facility in the selection process, so this has

48

likely already been considered useful. It can also be argued, that of the realistic job previews

studied, this is the most cost-effective and is not difficult to implement.

It is not surprising that organizations offering CNA certification program had a

relationship with CNA voluntary turnover but it was not in the predicted direction. Offering such

programs actually increased turnover. Although it is likely that a program such as this builds

organizational commitment and creates a better person-environment or person-organization fit

because the employees receive training in the realm of where they will work, it also means they

will be more mobile and able to find alternative jobs. It also shows more investment in interested

individuals. It is possible that this is part of the HR strategy for firms that offer lower starting

pay. Some firms may offer lower starting pay but in return also offer development and

certification programs. This can be seen in the significant negative correlations between offering

CNA certification programs and starting pay (r = -.352, p > .10). Thus, some firms may accept

higher turnover from CNA certification because they offer lower starting pay, and to attract

applicants they provide such development. Of the organizations surveyed 53% of respondents

reported that they offer such training programs. This is only a little over half of those surveyed,

and while this is not very representative of the total population, it may show that this is an

underutilized recruitment tool or benefit provided by the employer. Organizations may be more

competitive in the job market for CNAs if they offer this selection tool. Opportunities for career

advancement and further professional development is likely associated with CNA voluntary

turnover for some of the same reasons. Also, organizations offering CNA certification programs

or opportunities for career advancement may anticipate a return on investment in the newly hired

CNAs and, while candidates may still end up leaving the organization, there may be an impact

49

for quality of care because the CNAs that are well trained and invested in by the organization

may be more capable or inspired to provide high quality of care.

These selection tools that revealed statistically significant relationships, compared to

some of the others studied, deal more with the selection of the organization by the individual job

candidate. Other selection tools may give more of the ‘selection’ power to the organizations

hiring the CNAs. This is a very interesting finding and provides valuable information for

nonprofit long-term healthcare facilities because the selection tools that were shown to have a

relationship with CNA voluntary turnover because it supports the idea that giving the candidate

an accurate perception of the job and organization and also investing in the candidate, once hired

can reduce turnover intention. These organizations offering certification programs, other

opportunities for career advancement and further professional development should advertise

these benefits in order to attract candidates.

Overall with such low statistical power, it is not surprising that few relationships were

found between the selection tools and CNA voluntary turnover but many of the regression

coefficients revealed trends that were consistent with the theories proposed where, as the use of

the given selection tool increases, CNA voluntary turnover decreases. The use of structured

interviews, meet and greet with current staff, tours of the facility and honesty tests all had

negative slopes. One finding that should also be noted is that physical ability tests have no

relationship with CNA voluntary turnover. No relationship was found because the p-value was

higher than 0.10 and the regression coefficient actually was 0.000. Organizations attempting to

reduce CNA voluntary turnover and increase retention, according to the results, should not use

physical ability tests as a predictor of CNA voluntary turnover, but such tests may have

relationships with other outcomes such as on-the-job injuries.

50

Any Type of Structured Interview, one of the index measures used to support the

hypothesis, found no relationship and did not support the hypothesis but the negative direction of

the slope was consistent with the theory. Any Realistic Job Preview, another index measure, also

did not support the hypothesis but had a negative regression coefficient. The Strategic Staffing

Index, surprisingly did not present any findings to supplement the hypotheses when predicting

CNA voluntary turnover. Not surprisingly starting pay and CNA voluntary turnover were related.

The change in R-squared, 17.8%, showed the greatest amount of variance in CNA voluntary

turnover associated with a predictor. It is very logical that pay is an incentive not to leave, and as

noted earlier, some firms may accept higher turnover rates because they offer lower pay, but

attract candidates by offering career development.

The hypothesis that staffing levels will affect turnover rates, Hypothesis 2, was supported

because the p-value was level than 0.000, which is less than 0.10. The regression coefficient is

negative (-.120) and significant. Approximately 14.3% of the variance in CNA voluntary

turnover is related to staffing levels after controlling for type of organization. As staffing levels

increase, CNA voluntary turnover decreases, there is great evidence through other studies that

having appropriate staffing levels leads to decreased turnover.

Hypothesis 3 shows no relationship (B= -1.027, p > .482) between CNA voluntary

turnover, which is the predictor, and quality of care ratings. Even though there was no significant

relationship, it is important for these organizations to address issues associated with quality of

care because it has a demonstrated relationship with CNA voluntary turnover. It was noted in the

exploratory analysis that leaving due to co-worker problems and to reduce stress was associated

with lower quality of care. Staffing challenges can create both co-worker problems and work unit

stress. Such factors can be reduced through the selection process and in maintaining the

51

appropriate staffing levels. Although all three hypotheses do not fully support the theories

presented, there are certain aspects of each that human resource professionals can adjust of

implement in order to better the organizations in which they work through staffing of CNAs.

Limitations

The biggest limitation of this study was the low response rate and sample size. While for

a survey delivered via U.S. mail the response rate is not totally inadequate, for what this study

sought to accomplish it presented significant challenges. For example, the hypotheses were

found to be statistically insignificant. The responses gathered were likely not numerous enough

to generate strong statistical power. For Hypothesis 1, however some findings were made so this

study yielded some value.

Response bias is another issue that must be addressed. Because some of the questions

asked dealt with sensitive subjects for a human resources director to reveal, such as turnover

rates and selection methods used, the responses could be overly optimistic and possibly

misleading. The turnover rates gleaned were much lower than expected based upon turnover

rates found in the literature review that ranged, based upon the figures presented by

“Pennsylvania’s Long-term Care System: Building Careers, Enhancing Quality of Care” that

ranged from 43% to 100%. The average CNA voluntary turnover rate reported in my survey was

23.8% which is very low. Another possibility is that some organizations recognized they might

have poor human resources practices, turnover rates and quality of care and as a result, chose not

to participate. There was a high dropout rate for this survey as around 61 surveys were started

and a little under 49 were completed.

52

Causality is also an important factor that must be addressed. It is not always possible to

determine if a mediator or moderator variable is present in the hypothesized relationships. Within

the models explained in the section of the literature review discussing person-environment fit

and person-organization fit, there are other factors that are present in the relationship between

selection and turnover intention. It is also likely that there are other factors present, not

accounted for, in hypotheses two and three as well. Additionally it is possible that within the

hypotheses the causal relationships are such that the predictors can actually serve as outcomes

simultaneously. For example, staffing levels could predict turnover and turnover can predict

staffing levels.

In order to reduce nonresponse and to limit missing responses within the survey I made it

very clear in all communications with the organizations asked to participate, that the results of

the survey would remain highly confidential. I also created an incentive for the human resources

directors that chose to take the survey. Every respondent had the chance to win gift cards, which

was described in detail in the methodology section, and also the chance to see the report of the

results. I also reached out to the organizations using various modes of communication through

email and the mailing to make sure that it was easy for respondents to complete the survey.

As described in the methodology section there were several statistical limitations. In the

regression analysis for Hypothesis 1 I could not use multiple regression because I had too many

variables relative to sample size and so the design requirements for multiple regression were not

met. The benefits of using multiple linear regression are that predictions can be improved, it

controls for confounding variables and it can test for interactions between the predictors. Had I

been able to use multiple regression I would have likely gained more descriptive information

about the selection tools. The confounding variables could not be accounted for which likely

53

impacted my analysis greatly. This also means that I potentially increased Type I error by

lowering alpha and running so many separate analyses.

Another limitation is simply the scope of my study. I chose to research Pennsylvania

nonprofit long-term healthcare facilities. The population for this sector is not huge. There were

263 possible respondents and I received a little under 49 cases with the ability to use 38- 40 per

question. I attempted to control for the independent variables in my regression analysis with the

type of facility and whether or not the organization had a religious affiliation or charitable

mission but neither were ever found to be statistically significant factors. The ideology was that

maybe these controls also play a role in CNA turnover rates or in the other factors measured

because of the nature of the organization.

Additionally respondents had the option to choose not to answer all questions since the

survey was not created for forced responses. The implication for this is that some of the

respondents answered for the x values of my hypothesis and not y values and vice versa. In

testing my hypotheses if a respondent did not answer all questions linked to the hypotheses, this

eliminated that organization from that regression test, or in the case of hypothesis 1, those that

did not respond to certain measures were assigned a ‘no’ response.

Because there are so many limitations in my statistical analysis I have provided a general

overview of the relevant demographic information collected by my survey. This can allow for

some inferences of trends among those surveyed and the potential areas where organizations may

seek to implement selection tools that are underutilized or those that my study may have

identified as potentially successful in predicting lower CNA voluntary turnover rates.

For all of the variables tested it is important to keep in mind the small sample size, which

makes the findings very unstable. Because it is so small, even one data point could skew the data

54

and alter the results. In interpreting my findings it is important to realize the low statistical power

present.

Opportunities for Future Research

While this study, in and of itself, may not have provided much further information about

selection and retention and the impact of quality of care for nonprofit long-term healthcare

facilities in Pennsylvania it has generated some ideas for further research. Either facilitating a

higher response rate or expanding the scope of the study to either other regions or other sectors

of the long-term healthcare industry may yield more information to be analyzed. Even a

comparison of nonprofit and for-profit long-term healthcare facilities may provide interesting

information. This study relied on information provided in the literature review about the

comparison between the two.

Selection tools as predictors of voluntary turnover were studied in this thesis, however

much information in the literature review process was discovered on other factors that occur after

the hiring and selection process, such as motivation and job enrichment, in impacting voluntary

turnover rates. The ideology behind studying selection methods are that it is a way for

organizations to cut costs associated with turnover by selecting the best-fit before investing in

employees that will ultimately leave because of issues of fit. There are definitely many other

reasons that employees leave and this is just one idea behind preventing unnecessary turnover.

Additionally only voluntary turnover was studied as opposed to involuntary turnover. It would be

interesting to study the effects of selection tools on involuntary turnover, which would entail

dismissal for cause. The honesty tests, reference checks and probationary periods may also be

very applicable in a study pertaining to involuntary turnover.

55

Had multiple regression been used I could have learned more about organizations that use

more than one of the selection tools. It would have been interesting and useful to know what

combinations of selection tools result in lower voluntary turnover rates. Focusing this thesis on

one of the hypotheses and developing many measures around that would have been another

opportunity for more in-depth research on either selection tools and CNA voluntary turnover,

staffing levels and CNA involuntary turnover and CNA involuntary turnover and quality of care

ratings.

Demographic Information

This information was compiled through the Qualtrics survey results report along with

information used in the data analysis. Below I have summarized some of the most pertinent

information and then I have also provided several tables to depict the information as well.

In addition to the information gathered supporting my hypotheses, I also included several

other questions pertaining to nonprofit long-term care facility demographics. The average

starting pay for a CNA among the organizations surveyed is $12.04. This is fairly consistent with

the figure presented by the Bureau of Labor Statistics in 2014. There was a fairly large spread of

facilities of different sizes but the average number of CNAs presently authorized including

vacant positions, for those surveyed is 83.38285714.

In recruiting prospective CNAs the use of references/word-of-mouth is the most prevalent

technique for 93% of those surveyed and 80% use online job postings such as Monster or Career

Builder. 78% marked the organization’s website as a means of searching for prospective CNAs

and 76% use job postings in the newspaper. 63% recruit through nursing certification programs,

56% go to career fairs to recruit and 24% marked “other.” Some of the responses for “other”

56

include the radio, direct mailing to homes, social media, signs posted outside the facility,

postcards and application tracking systems. Below are tables of descriptive statistics and

correlations between each recruiting method and both CNA voluntary turnover and quality of

care. Recruitment of CNAs was not specifically addressed through my hypotheses but is an

important factor in the selection process. Recruitment and turnover and the effect on quality of

care might be another area of potential future research although the correlations present in my

study between the types of recruitment methods and both turnover and quality of care were not

particularly strong or significant at the p <.10 level.

Table 9. Descriptive Statistics for Recruitment Methods


Mean Std. Deviation N

Online Job Sites .79 .413 38

Career Fairs .53 .506 38

Job Postings in Newspaper .68 .471 38

References/Word of Mouth .87 .343 38

Nursing Certification Programs .61 .495 38

Other .26 .446 38

Voluntary Turnover 2014 .24357 .130707 38

Quality of Care 3.7419 1.02365 31

Table 10. Correlation Table for Recruitment Methods

Correlations

TO2014 QltyCare

Online Job Sites Pearson Correlation .011 -.126

Sig. (2-tailed) .946 .501

N 38 31

Career Fairs Pearson Correlation .396* .172

Sig. (2-tailed) .014 .356

57

N

38 31

Job Postings in

Newspaper

Pearson Correlation .001 -.062

Sig. (2-tailed) .996 .741

N

38 31

References/Word

of Mouth


Sig. (2-tailed) .241 .736

N 38 31

Nursing

Certification

Programs

Pearson Correlation .383* -.245

Sig. (2-tailed) .018 .183

N 38 31

Other Pearson Correlation .086 .129

Sig. (2-tailed) .608 .490

N 38 31

Final Remarks

It is important that long-term healthcare facilities provide high levels of care, creating an

environment for patients where they can feel safe, comfortable and well cared for. Being

equipped with the right staff is essential for every organization, but is especially vital when it

affects the well-being and quality of life for a patient. This study, taking into account the

statistical limitations, identified that selection processes that allow for a candidate to get a

realistic idea of what it is like to work at an organization through a job tour, the opportunity to

participate in a CNA certification program offered by the organization and the opportunity for

career advancement and further professional development are most associated with voluntary

turnover among CNAs. Identifying whether or not the employee will be a fit, by both the

58

candidate and the organization, before too much is invested can be beneficial and reduce the

rates of turnover in some cases. Additionally having the right staffing levels for CNAs plays a

role in voluntary turnover so organizations should ensure that they are adequately staffed.

Although this study did not find a relationship between turnover and quality of care, the literature

review revealed that there likely is a relationship present. Overall, identifying turnover and

seeking ways to remedy turnover when it is above the desired rate through maintaining effective

staffing practices can contribute to a better patient experience. By ensuring that the staff feels

comfortable and well suited to be a part of the long-term care facility, it is more possible that

patients will feel more comfortable as well.

59

Appendix A IRB Exemption Determination Letter

IRB Program Office for Research Protections

Vice President for Research The Pennsylvania State University 205 The 330 Building University Park, PA 16802

Phone : (814) 865-1775 Fax: (814) 863-8699 Email : [email protected] Web : www.research.psu.edu/orp

NOT HUMAN RESEARCH

Date: December 16, 2015

From: Tracie Kahler, IRB Analyst

To: Sarah Glei

Type of Submission: Initial Study

Title of Study: Selection and Retention of Certified Nursing Assistants

in Nonprofit Long-term Healthcare Facilities and the Impact on Quality of Care

Principal Investigator: Sarah Glei

Study ID: STUDY00003935

Submission ID: STUDY00003935

Funding: Not Applicable

The Office for Research Protections determined that the proposed activity, as described in the

above-referenced submission, does not meet the definition of human subject research as defined in 45 CFR 46.102(d) and/or (f). Institutional Review Board (IRB) review and approval is not

required.

The IRB requires notification and review if there are any proposed changes to the activities

described in the IRB submission that may affect this determination. If changes are being considered and there are questions about whether IRB review is needed, please contact the

Office for Research Protections.

This correspondence should be maintained with your records.

60

Appendix B Letter sent to Pennsylvania Nonprofit Long-Term Care Facility HR Directors

College of the Liberal Arts An Equal Opportunity University

School of Labor and Employment Relations (814) 865-5425

Fax: (814) 867-4169 The Pennsylvania State University Website: http://lser.la.psu.edu 506 Keller Building

University Park, PA 16802-2800

My name is Sarah Glei and I am a student pursing a M.S. in Human Resources and Employment Relations at Penn State. As a graduate student and also a part of the Schreyer’s Honors College I am

conducting my thesis research on Selection and Retention of Certified Nursing Assistants in Nonprofit

Long-term Healthcare Facilities and the Impact on Quality of Care. As the number of aging Americans

is increasing it is crucial for long-term healthcare facilities to have the best direct-care staff. Certified

nursing assistant turnover is a major issue for many long-term healthcare facilities that I hypothesize might be impacted, and even reduced, by staffing practices.

I invite you to take my survey and contribute to my study so that I might examine the relationship

between turnover and selection tools, recruitment practices and staffing and possibly find some

meaningful information that might contribute to higher retention of certified nursing assistants coupled with higher quality of care for patients. Participants in this study will be provided with a complete

report of my analysis and will have the opportunity to be entered to win one of 5 $50 Amazon gift

cards! The drawing for the gift cards will take place on Monday, January 15th

.

Here is the link to complete the survey: http://bit.ly/CNAsurvey

If you choose to participate in this study all information about your organization will be kept completely

confidential and will not be analyzed by anyone but myself. No personally identifiable information

will be requested and organizational information will remain anonymous and will be deleted once

the data has been analyzed. If you have any questions please feel free to contact me using the information below and thank you very much for taking the time to consider participating in this study.

Best Regards,

Sarah Glei

Human Resources Graduate Student

[email protected] (717) 609-3312

61

Appendix C Survey

Thesis Survey

This survey is a part of a study examining the effects of turnover, staffing practices, selection and

recruitment tools and quality of care in nonprofit long-term healthcare organizations. The results

of this survey will be kept completely confidential and all relevant information will only be used

by the researcher to identify trends across the nonprofit long-term healthcare sector. No

personally identifiable information will be collected and all information regarding this

organization will be kept anonymous. Participants in this study will be provided with a complete

report of my analysis and will have the opportunity to be entered to win one of 5 $50 Amazon

gift cards!

Q1 What is this organization’s certified nursing assistant voluntary turnover rate for 2014?

Please provide a close estimate. Voluntary turnover can be calculated by dividing the number of

voluntary employee separations during the year by the average number of certified nursing

assistants employed during the year. Voluntary separation entails employees leaving the

organization on their own accord.

62

Q2 To the best of your knowledge, how frequently do certified nursing assistants voluntarily

leave this organization for the following reasons?

Very

Infrequently

(1)

Infrequently

(2)

Somewhat

Infrequently

(3)

Sometimes

(4)

Somewhat

frequently

(5)

Frequently

(6)

Very

frequently

(7)

Wanted life or

career change (1)

Nonspecific

attraction to other

jobs (2)

Wanted

different/better job

tasks at the company

(3)

Wanted better

opportunity for

advancement/growth

(4)

Wanted better work

schedule (5)

Wanted more time

with family than job

allows or family

pressure to quit (6)

Feared job loss

and/or company

failure (7)

Family-related

relocation (8)

Nonspecific desire

to relocate or avoid

relocation (9)

Co-worker

problems/issues (10)

Wanted less

commute or travel

(11)

Management

problems/issues (12)

Wanted less stress at

work (13)

Dangerous work

conditions (14)

Harassment (15)

Wanted better pay

(16)

Wanted better

benefits (17)

63

Q3 Does this organization use a structured interview process in the selection of certified nursing

assistant candidates? A structured interview process is one where every prospective candidate

goes through and answers the same set of questions.

Yes (1)

No (2)

Q4 Does this organization use behavioral interview questions in the interview process? This

includes questions about how candidates dealt with past situations at work or in their personal

lives.

Yes (1)

No (2)

Q5 If this organization uses behavioral interview questions, do questions ask candidates about

healthcare-related situations?

Yes (1)

No (2)

Q6 Does this organization provide any of the items listed below for new hire CNA candidates?

Employee testimonials- written materials, videos, in-person (1)

Job shadowing (2)

Tours of the facility (3)

Meet-and-greet with current staff (4)

Q7 Does this organization ask for references and conduct reference checks?

Yes (1)

No (2)

Q8 Does this organization require physical ability tests? This includes tests to ensure that

candidates can complete the basic physical requirements of the job such as lifting patients, etc.

Yes (1)

No (2)

Q9 Does this organization require honesty tests? An honesty test asks candidates questions about

experiences and attitudes related to theft.

Yes (1)

No (2)

Q10 Does this organization offer a CNA certification program for interested individuals?

Yes (1)

No (2)

64

Q11 Does this organization provide opportunities for career advancement or further professional

training?

Yes (1)

No (2)

Q12 Is there a probationary period for recently hired CNAs?

Yes (1)

No (2)

Q13 How frequently do CNAs typically seem motivated after having worked with this

organization for 90 days?

Always (1)

Most of the Time (2)

Sometimes (3)

Rarely (4)

Never (5)

Q14 Regarding staffing levels, this organization is:

Overstaffed (1)

Adequately staffed (2)

Understaffed (3)

Severely understaffed (4)

Q15 What is the typical patient to direct-care worker for day shifts? (Example: 5 patients for 1

direct-care worker, 5:1)

Q16 What is the typical patient to direct-care worker for evening shifts? (Example: 10 patients

for 1 direct-care worker, 10:1)

Q17 What is the typical patient to direct-care worker for night shifts? (Example:15 patients for 1

direct-care worker, 15:1)

Q18 How does this organization search for prospective CNAs? Please select all that apply.

Online job sites (Monster, Career Builder, etc.) (1)

Career fairs (2)

Job postings in the newspaper (3)

References/ word of mouth (4)

Nursing certification programs (5)

Organization's website (6)

Other: (7) ____________________

Q19 What recruitment strategy is the most effective for this organization?

65

Q20 What is the total number of CNAs presently authorized? (including vacant positions)

Q21 How many CNA positions are presently vacant?

Q22 What is the name of this organization?

Q23 Which best describes this facility?

Independent living (1)

Assisted living (2)

Skilled nursing (3)

Rehabilitation (4)

Other: (5) ____________________

Q24 Does this organization have a religious affiliation or charitable mission?

Yes- please describe: (1) ____________________

No (2)

Q25 CNA starting pay rate (hourly):

Q26 For how many patients does this organization currently provide care?

Q27 Please indicate, by providing your email address below, if you would be willing to answer

any further questions.

66

Appendix D Correlation Table with Hypothesized Variables

Correlations

Tu

rno

ver

201

4

Qu

alit

y C

are

Str

ateg

ic S

taff

ing

In

dex

An

y S

tru

ctu

re I

nte

rvie

w

Str

uct

ure

d I

nte

rvie

w

Beh

avio

ral

Inte

rvie

w

An

y R

JP T

yp

e

RJP

Em

plo

yee

Tes

tim

on

RJP

Jo

b S

had

ow

RJP

To

ur

of

Fac

ilit

y

RJP

Mee

t an

d G

reet

Ref

eren

ce c

hec

k

Ph

ysi

cal

Abil

ity

Tes

t

Ho

nes

ty T

est

CN

A C

erti

fica

tio

n

Car

eer

Ad

van

cem

ent

Pro

bat

ion

ary

Per

iod

Sta

ffin

g L

evel

Sta

rtin

g P

ay

Sk

ille

d N

urs

ing

Fac

ilit

y

Rel

igio

us

or

Ch

arit

able

Org

aniz

atio

n

Tu

rno

ver

201

4

Pearson

Correlation 1 -.049 .036 -

.284

-

.22

7

.15

1

.0

21

.14

2

.1

14

-

.25

0

-

.0

63

.a -.033 -

.011 .348* .319 .a

-

.329*

-

.369

*

-

.1

19

-

.14

8

Sig. (2-

tailed) .794 .830 .085

.17

0

.36

6

.9

00

.39

7

.4

96

.13

0

.7

07 . .845 .947 .032 .051 . .044 .032

.4

77

.37

5

N

38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Qu

alit

y C

are

Pearson

Correlation -.049 1 .242 .323 .18

7

.11

5

.3

65

*

.12

0

.0

82

.35

1

.0

63 .a .087 .173 .141

-

.093 .a .227 .025

-

.1

08

-

.26

0

Sig. (2-

tailed) .794 .190 .076

.31

4

.53

6

.0

43

.52

0

.6

61

.05

3

.7

36 . .644 .353 .449 .619 . .220 .893

.5

65

.15

7

N 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31 31

Str

ateg

ic S

taff

ing

In

dex

Pearson

Correlation .036 .242 1 .055 .21

1

-

.02

1

.2

49

.30

0

.1

07

.19

0

.1

67 .a

.671*

*

.591

**

.674*

*

.485

** .a .266 .100

-

.1

37

-

.08

7

Sig. (2-

tailed) .830 .190 .745

.20

3

.89

9

.1

32

.06

7

.5

22

.25

2

.3

15 . .000 .000 .000 .002 . .107 .574

.4

12

.60

4

N

38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

An

y S

tru

ctu

red

Inte

rvie

w

Pearson

Correlation -.284 .323 .055 1 .34

6*

.47

9**

-

.0

56

.06

4

-

.1

56

-

.06

4

.2

15 .a -.112 .078 -.148

-

.071 .a -.060 .143

.1

93

-

.11

2

Sig. (2-

tailed) .085 .076 .745

.03

3

.00

2

.7

37

.70

3

.3

50

.70

3

.1

94 . .504 .641 .375 .671 . .719 .421

.2

46

.50

4

67

N

38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Str

uct

ure

d I

nte

rvie

w

Pearson

Correlation -.227 .187 .211 .346

* 1

.05

8

.0

58

-

.01

6

.0

93

.21

7

.3

41

*

.a .115 .226 -.018 .167 .a .141 .032 .1

45

-

.03

1

Sig. (2-

tailed) .170 .314 .203 .033

.72

8

.7

28

.92

5

.5

79

.19

1

.0

36 . .491 .173 .915 .318 . .398 .860

.3

86

.85

5

N

38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Beh

avio

ral

Inte

rvie

w

Pearson

Correlation .151 .115 -.021 .479

**

.05

8 1

-

.1

18

-

.12

0

-

.1

54

-

.13

4

.0

94 .a -.233 .163 .036 .087 .a -.126

-

.215

.2

29

-

.23

3

Sig. (2-

tailed) .366 .536 .899 .002

.72

8

.4

82

.47

2

.3

57

.42

4

.5

76 . .159 .328 .829 .605 . .452 .223

.1

68

.15

9

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

An

y R

JP T

yp

e

Pearson

Correlation .021 .365* .249 -

.056

.05

8

-

.11

8

1 .13

4

.3

62

*

.88

1**

.4

49

**

.a -.049 .163 .036 .087 .a .272 -

.020

-

.2

93

-

.23

3

Sig. (2-

tailed) .900 .043 .132 .737

.72

8

.48

2

.42

4

.0

26

.00

0

.0

05 . .772 .328 .829 .605 . .098 .909

.0

75

.15

9

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

RJP

Em

plo

yee

Tes

tim

on

ial

Pearson

Correlation .142 .120 .300 .064

-

.01

6

-

.12

0

.1

34 1

.2

13

-

.07

9

.1

36 .a .264 .217 .350* .169 .a .143

-

.131

-

.1

41

-

.07

1

Sig. (2-

tailed) .397 .520 .067 .703

.92

5

.47

2

.4

24

.1

98

.63

8

.4

16 . .109 .191 .031 .312 . .393 .459

.3

98

.67

4

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

RJP

Jo

b S

had

ow

ing

Pearson

Correlation .114 .082 .107 -

.156

.09

3

-

.15

4

.3

62

*

.21

3 1

.41

0*

.4

77

**

.a .036 .043 .100 .023 .a .386* .132

-

.2

75

-

.30

4

Sig. (2-

tailed) .496 .661 .522 .350

.57

9

.35

7

.0

26

.19

8

.01

1

.0

02 . .831 .798 .549 .892 . .017 .455

.0

94

.06

3

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

RJP

To

ur

of

Fac

ilit

y

Pearson

Correlation -.250 .351 .190 -

.064

.21

7

-

.13

4

.8

81

**

-

.07

9

.4

10

*

1

.5

10

**

.a -.097 .185 -.037 .045 .a .399* .018

-

.1

74

-

.26

4

68

Sig. (2-

tailed) .130 .053 .252 .703

.19

1

.42

4

.0

00

.63

8

.0

11

.0

01 . .563 .266 .825 .789 . .013 .921

.2

95

.10

9

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

RJP

Mee

t an

d G

reet

Pearson

Correlation -.063 .063 .167 .215 .34

1*

.09

4

.4

49

**

.13

6

.4

77

**

.51

0** 1 .a -.167 .081 -.139

.417

** .a .353* .167

-

.3

20

-

.16

7

Sig. (2-

tailed) .707 .736 .315 .194

.03

6

.57

6

.0

05

.41

6

.0

02

.00

1 . .317 .627 .407 .009 . .030 .345

.0

50

.31

7

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Ref

eren

ce C

hec

k

Pearson

Correlation .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a

Sig. (2-

tailed) . . . . . . . . . . . . . . . . . . . .

N

38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Ph

ysi

cal

Abil

ity

Tes

t

Pearson

Correlation -.033 .087 .671*

*

-

.112

.11

5

-

.23

3

-

.0

49

.26

4

.0

36

-

.09

7

-

.1

67

.a 1 .323

*

.413*

* .016 .a .145 .065

.1

09

.14

7

Sig. (2-

tailed) .845 .644 .000 .504

.49

1

.15

9

.7

72

.10

9

.8

31

.56

3

.3

17 . .048 .010 .922 . .384 .717

.5

16

.37

7

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Ho

nes

ty T

est

Pearson

Correlation -.011 .173 .591*

* .078

.22

6

.16

3

.1

63

.21

7

.0

43

.18

5

.0

81 .a .323* 1 .291 .206 .a .174

-

.196

-

.0

07

-

.40

7*

Sig

.

(2-

tail

ed)

.947 .353 .000 .641 .17

3

.32

8

.3

28

.19

1

.7

98

.26

6

.6

27 . .048 .076 .215 . .296 .265

.9

66

.01

1

N

38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

CN

A C

erti

fica

tio

n

Pearson

Correlation .348* .141 .674*

*

-

.148

-

.01

8

.03

6

.0

36

.35

0*

.1

00

-

.03

7

-

.1

39

.a .413*

* .291 1

.336

* .a .162

-

.352

*

-

.0

17

-

.15

6

Sig. (2-

tailed) .032 .449 .000 .375

.91

5

.82

9

.8

29

.03

1

.5

49

.82

5

.4

07 . .010 .076 .039 . .332 .041

.9

20

.35

0

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Car

eer

Ad

van

cem

ent

Pearson

Correlation .319 -.093 .485*

*

-

.071

.16

7

.08

7

.0

87

.16

9

.0

23

.04

5

.4

17

**

.a .016 .206 .336* 1 .a .009 -

.049

-

.0

77

.01

6

69

Sig. (2-

tailed) .051 .619 .002 .671

.31

8

.60

5

.6

05

.31

2

.8

92

.78

9

.0

09 . .922 .215 .039 . .958 .785

.6

46

.92

2

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Pro

bat

ion

ary

PE

rio

d

Pearson

Correlation .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a .a

Sig. (2-

tailed) . . . . . . . . . . . . . . . . . . . .

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Sta

ffin

g L

evel

Pearson

Correlation -

.329* .227 .266

-

.060

.14

1

-

.12

6

.2

72

.14

3

.3

86

*

.39

9*

.3

53

*

.a .145 .174 .162 .009 .a 1 .235

-

.3

13

.01

4

Sig. (2-

tailed) .044 .220 .107 .719

.39

8

.45

2

.0

98

.39

3

.0

17

.01

3

.0

30 . .384 .296 .332 .958 . .180

.0

56

.93

4

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Sta

rtin

g p

ay

Pearson

Correlation -

.369* .025 .100 .143

.03

2

-

.21

5

-

.0

20

-

.13

1

.1

32

.01

8

.1

67 .a .065

-

.196

-

.352*

-

.049 .a .235 1

-

.3

92

*

.28

4

Sig. (2-

tailed) .032 .893 .574 .421

.86

0

.22

3

.9

09

.45

9

.4

55

.92

1

.3

45 . .717 .265 .041 .785 . .180

.0

22

.10

4

N 34 31 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34

Sk

ille

d N

urs

ing

Fac

ilit

y

Pearson

Correlation -.119 -.108 -.137 .193 .14

5

.22

9

-

.2

93

-

.14

1

-

.2

75

-

.17

4

-

.3

20

.a .109 -

.007 -.017

-

.077 .a -.313

-

.392

*

1 .10

9

Sig. (2-

tailed) .477 .565 .412 .246

.38

6

.16

8

.0

75

.39

8

.0

94

.29

5

.0

50 . .516 .966 .920 .646 . .056 .022

.51

6

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

Rel

igio

us

or

Ch

arit

able

Org

aniz

atio

n

Pearson

Correlation -.148 -.260 -.087 -

.112

-

.03

1

-

.23

3

-

.2

33

-

.07

1

-

.3

04

-

.26

4

-

.1

67

.a .147

-

.407

*

-.156 .016 .a .014 .284 .1

09 1

Sig. (2-

tailed) .375 .157 .604 .504

.85

5

.15

9

.1

59

.67

4

.0

63

.10

9

.3

17 . .377 .011 .350 .922 . .934 .104

.5

16

N 38 31 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 38 34 38 38

70

Appendix E Correlation Table with all Variables

Correlations

Pro

bat

ion

ary

Per

iod

Co

mp

etit

ive

Sta

rtin

g

Pay

Sta

rtin

g p

ay

90

day

moti

vat

ion

Sta

ffin

g L

evel

Pat

ien

t R

atio

Day

Pat

ien

t R

atio

Ev

e

Pat

ien

t R

atio

Nig

ht

Rec

ruit

On

lin

e

Rec

ruit

Car

eer

Fai

rs

Rec

ruit

New

spap

er

Rec

ruit

Ref

eren

ce

Rec

ruit

NC

ert

Pro

gr

Rec

ruit

Oth

er

Bst

SrR

efs

Bst

SrO

nli

ne

Bst

SrN

wsA

d

# C

NA

Au

tho

rize

d

CN

A P

osi

tion

Vac

ant

# P

atie

nts

CN

A V

ol

Tu

rnov

er 2

014

Pearson

Correlation

.b

-.456

-

.36

9 .220 .329*

.34

4

.22

2

-

.00

8

.01

1 .396* .001

.19

5

.383

*

.08

6

.18

5

.05

8

-

.13

0

-

.24

4

-

.059

.003

Sig. (2-

tailed)

.

.007

.03

2 .183 .044

.05

8

.22

9

.96

6

.94

6 .014 .996

.24

1 .018

.60

8

.28

1

.73

7

.45

0

.17

8 .739

.988

N 38

34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Qu

alit

y C

are

Pearson

Correla

tion

.b

.040

.02

7

--

.247 -.199

-

.04

1

.07

6

.20

7

-

.12

6 .172 -.062

.06

3

-

.245

.12

9

-

.20

1

-

.29

7

-

.01

5

-

.24

6 .081

-

.163

Sig. (2-

tailed) .

.832

.88

5 .181 .284

.83

9

.70

5

.31

1

.50

1 .356 .741

.73

6 .183

.49

0

.27

9

.10

5

.93

7

.20

6 .676 .388

N 31

31 31 31 31 27 27 26 31 31 31 31 31 31 31 31 31 28 29 30

Sk

ille

d N

urs

ing

Fac

ilit

y

Pearson

Correla

tion

.b -.361

-

.39

2*

.216 .313 .07

2

.12

3

.18

3

.08

3 .258 .223

.14

1 .184

-

.09

6

.11

4

-

.08

9

.24

8

.11

3 .251

-

.198

Sig. (2-

tailed) . .036

.02

2 .192 .056

.70

1

.51

1

.33

4

.62

2 .117 .178

.39

8 .270

.56

8

.50

8

.60

5

.14

5

.53

9 .153 .262

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Rel

igio

us

or

Ch

arit

able

Org

aniz

atio

n

Pearson

Correla

tion

.b .272 .28

4 .172 -.014

-

.04

8

-

.13

0

-

.15

4

.34

4* .263 .026

.23

8 .146

.02

0

.37

2*

-

.19

4

-

.00

9

.13

2 .111 .116

Sig. (2-

tailed) . .119

.10

4 .301 .934

.79

7

.48

7

.41

6

.03

5 .111 .879

.15

0 .381

.90

4

.02

5

.25

7

.96

0

.47

0 .530 .514

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Lif

e

chan

ge

Pearson

Correla

tion

.b -.130

-

.20

9

.180 -.170

-

.10

2

.06

3

-

.19

3

-

.14

9

.135 .135 .16

4 .027

.24

9

-

.10

5

-

.28

1

.18

8

-

.18

8

.123 -

.142

71

Sig. (2-

tailed) . .471

.24

4 .286 .314

.58

6

.73

8

.30

8

.38

0 .426 .426

.33

2 .875

.13

7

.54

8

.10

2

.28

1

.30

3 .496 .429

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Att

ract

ed t

o O

ther

Jo

bs Pearson

Correla

tion

.b -.104 .05

7 -.024 .074

.29

3

.31

7

.05

7

.26

2 .316 .319

.26

7

-

.022

.38

9*

-

.11

5

.03

8

.00

4

.22

5 .264 .312

Sig. (2-

tailed) . .566

.75

4 .890 .668

.12

3

.09

4

.77

5

.12

3 .061 .058

.11

5 .897

.01

9

.51

7

.83

1

.98

4

.23

1 .144 .082

N 36 33 33 36 36 29 29 28 36 36 36 36 36 36 34 34 34 30 32 32

Bet

ter

Job

Tas

ks

Pearson

Correla

tion

.b -.020

-

.13

1

-.029 .152 .04

2

-

.04

6

-

.22

9

.06

9 -.087 -.031

-

.00

9

.036 .05

5

.10

7

-

.17

6

-

.01

0

.19

1 .251 .087

Sig. (2-

tailed) . .911

.46

9 .863 .368

.82

2

.80

4

.22

3

.68

4 .608 .857

.95

9 .832

.74

6

.54

1

.31

1

.95

7

.29

6 .158 .630

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Op

po

rtun

ity C

ar A

dv

Pearson

Correla

tion

.b .058

-

.03

6

-.115 .317 .46

5**

.33

1

-

.02

1

.39

3* .230 -.066

.04

0 .126

.27

8

.31

5

-

.03

9

-

.19

6

.38

3* .262 .301

Sig. (2-

tailed) . .749

.84

2 .498 .056

.00

8

.06

9

.91

4

.01

6 .170 .699

.81

3 .457

.09

6

.06

6

.82

2

.25

8

.03

0 .140 .089

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Bet

ter

Wo

rk S

ched

ule

Pearson

Correla

tion

.b -.216

-

.07

8

.196 .246 .24

1

.23

0

-

.16

2

.39

4* .309 .052

.08

6 .218

.09

7

.32

9

.07

6

-

.06

4

.19

8 .227 .125

Sig. (2-

tailed) . .227

.66

7 .244 .142

.19

1

.21

4

.39

3

.01

6 .063 .759

.61

3 .194

.56

8

.05

3

.66

3

.71

6

.27

8 .203 .490

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Mo

re T

ime

wit

h F

amil

y

Pearson

Correla

tion

.b -.036 .03

3 -.245 .076

.09

6

.09

1

-

.11

5

.38

7* .289 .090

-

.08

0

.156 .08

6

.13

7

.12

7

.00

0

.34

8

.375

* .290

Sig. (2-

tailed) . .841

.85

7 .144 .656

.60

8

.62

8

.54

3

.01

8 .083 .596

.63

6 .357

.61

4

.43

4

.46

7

1.0

00

.05

1 .031 .102

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Fea

r Jo

b L

oss

Pearson

Correla

tion

.b -.018 .26

9 .329* -.188

-

.24

0

-

.23

5

-

.25

2

.11

2 -.105 .052

-

.07

7

-

.127

-

.23

6

.04

7

-

.05

6

-

.16

4

-

.03

5

.337 .051

Sig. (2-

tailed) . .921

.13

0 .047 .265

.19

3

.20

4

.17

9

.50

9 .538 .759

.64

9 .455

.15

9

.78

9

.74

8

.34

7

.85

1 .055 .778

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

72 F

amil

y R

elo

cati

on

Pearson

Correla

tion

.b .383* .45

0** -.263 -.258

-

.07

7

-

.21

6

-

.08

3

.37

9* .116 -.036

-

.09

8

-

.115

.02

2

-

.04

6

-

.13

8

-

.05

0

.31

8

.351

*

.344

*

Sig. (2-

tailed) . .028

.00

9 .116 .124

.68

2

.24

2

.66

2

.02

1 .495 .832

.56

4 .499

.89

7

.79

4

.42

9

.77

5

.07

7 .045 .050

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

No

nsp

ecif

ic d

esir

e R

elo

Pearson

Correla

tion

.b .029

-

.05

4

.063 -.004 .06

3

-

.07

5

-

.15

9

.12

8 -.087 .081

-

.10

5

.086

-

.01

5

.27

9

-

.17

2

-

.16

5

.08

8 .134 .130

Sig. (2-

tailed) . .871

.76

7 .716 .981

.73

9

.69

2

.41

0

.45

9 .615 .637

.54

0 .617

.93

2

.11

0

.33

2

.35

2

.63

8 .464 .471

N 36 33 33 36 36 30 30 29 36 36 36 36 36 36 34 34 34 31 32 33

Co

wo

rker

Pro

ble

ms

Pearson

Correla

tion

.b -.082

-

.18

2

.247 .303 .20

0

-

.20

1

-

.17

9

.34

8* .176 .113

.24

3

.369

*

.00

4

.54

9**

-

.00

4

.09

7

-

.05

3

.112 .075

Sig. (2-

tailed) . .648

.30

9 .141 .068

.28

1

.27

8

.34

4

.03

5 .298 .504

.14

7 .025

.98

1

.00

1

.98

2

.58

1

.77

3 .536 .677

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Les

s o

f a

com

mu

te

Pearson

Correla

tion

.b .177 .04

4 -.025 -.009

.02

1

.01

1

-

.01

6

.49

4** .070 .023

.01

9 .089

.10

9

.17

4

.08

4

-

.14

2

.28

6 .312 .306

Sig. (2-

tailed) . .326

.80

6 .882 .958

.91

2

.95

4

.93

1

.00

2 .680 .892

.90

9 .600

.52

3

.31

9

.63

2

.41

6

.11

2 .077 .084

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Man

agem

ent

Pro

ble

ms Pearson

Correla

tion

.b -.144

-

.03

6

.342* .272 .06

4

-

.25

1

-

.36

1

.32

7* .080 .073

.13

9

.334

*

.06

9

.35

3*

.13

9

-

.04

4

.07

9 .174 .152

Sig. (2-

tailed) . .424

.84

2 .038 .103

.73

2

.17

3

.05

0

.04

9 .636 .668

.41

3 .043

.68

6

.03

7

.42

4

.80

3

.66

7 .332 .397

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Les

s S

tres

s

Pearson

Correla

tion

.b -.255

-

.22

8

.165 .285 .30

8

.13

0

-

.05

8

.25

2 .189 -.001

.01

7 .236

.20

1

.25

3

.12

4

.20

3

.04

8 .060 .208

Sig. (2-

tailed) . .152

.20

3 .328 .087

.09

2

.48

7

.76

3

.13

2 .262 .995

.91

9 .160

.23

3

.14

3

.47

6

.24

3

.79

4 .741 .245

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Dan

gW

rk

Pearson

Correla

tion

.b -.181

-

.08

6

.245 -.045

-

.06

6

-

.29

4

-

.41

4*

-

.04

3

-.043 .054

-

.17

6

.139

-

.02

4

.08

6

-

.02

9

-

.07

1

.16

1 .075

-

.004

73

Sig. (2-

tailed) . .314

.63

3 .143 .792

.72

3

.10

8

.02

3

.80

0 .802 .749

.29

6 .412

.88

8

.62

4

.87

0

.68

3

.37

9 .679 .982

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Har

assm

ent

Pearson

Correla

tion

.b -.104

-

.07

1

.169 .134 .21

7

.01

2

-

.11

7

.15

6 .106 -.023

-

.12

7

.325

*

-

.07

8

.33

2

-

.01

4

-

.04

7

.25

8 .179 .196

Sig. (2-

tailed) . .565

.69

5 .316 .430

.24

1

.94

7

.54

0

.35

8 .534 .891

.45

2 .049

.64

8

.05

2

.93

4

.78

7

.15

4 .319 .275

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Bet

ter

Pay

Pearson

Correla

tion

.b -.274

-

.29

1

.079 .142 .16

5

.16

6

-

.11

8

.34

1* .063 -.074

-

.10

5

-

.003

.23

3

.32

8

-

.02

9

-

.33

7*

.13

9 .171 .171

Sig. (2-

tailed) . .122

.10

0 .641 .401

.37

6

.37

1

.53

5

.03

9 .711 .661

.53

8 .988

.16

6

.05

5

.86

9

.04

8

.44

8 .343 .340

N 37 33 33 37 37 31 31 30 37 37 37 37 37 37 35 35 35 32 33 33

Bat

ter

Ben

efit

s

Pearson

Correla

tion

.b -.267

-

.17

2

-.078 -.135 .26

8

.39

7*

-

.01

1

.13

1 .159 -.073

-

.21

8

.065 .09

8

.03

8

.10

1

-

.24

3

.29

2 .089

.360

*

Sig. (2-

tailed) . .139

.34

6 .651 .432

.15

2

.03

0

.95

4

.44

8 .354 .671

.20

1 .706

.57

1

.83

1

.57

1

.16

6

.11

2 .630 .043

N 36 32 32 36 36 30 30 29 36 36 36 36 36 36 34 34 34 31 32 32

Str

ateg

ic S

taff

ing

In

dex

Pearson

Correla

tion

.b .289 .10

0 .008 -.266

-

.05

8

-

.14

6

-

.17

9

-

.03

2

-.100 .002 .12

9

-

.114

.13

1

-

.02

2

-

.28

6

.20

3

.00

8 .120 .027

Sig. (2-

tailed) . .097

.57

4 .962 .107

.75

8

.43

3

.34

3

.84

8 .548 .989

.44

0 .496

.43

2

.90

0

.09

0

.23

6

.96

6 .500 .880

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

An

y S

tru

ctu

red

Inte

rvie

w

Pearson

Correla

tion

.b .120 .14

3 -.141 .060

-

.42

0*

-

.29

7

-

.17

6

-

.08

5

-.156 -.112

-

.06

4

-

.133

.09

8

-

.16

9

.15

1

.08

3

.00

3 .051

-

.481

**

Sig. (2-

tailed) . .498

.42

1 .398 .719

.01

9

.10

4

.35

2

.61

2 .350 .504

.70

3 .427

.55

7

.32

4

.37

9

.63

0

.98

7 .773 .004

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Str

uct

ure

d I

nte

rvie

w

Pearson

Correla

tion

.b .041 .03

2 -.106 -.141

-

.44

9*

-

.34

3

-

.42

2*

-

.24

5

-.179 -.031

-

.18

5

-

.106

-

.17

9

-

.07

0

-

.26

7

.24

1

.03

9 .165

-

.176

Sig. (2-

tailed) . .817

.86

0 .526 .398

.01

1

.05

9

.02

0

.13

8 .283 .855

.26

6 .527

.28

4

.68

4

.11

6

.15

6

.83

4 .351 .319

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

74 B

ehav

iora

l In

terv

iew

Pearson

Correla

tion

.b -.138

-

.21

5

-.295 .126 .13

3

.28

4

.29

9

-

.17

7

.190 -.233

-

.13

4

.074 .01

0

-

.17

7

.13

8

.17

4

.04

9 .124

-

.252

Sig. (2-

tailed) . .437

.22

3 .073 .452

.47

6

.12

2

.10

8

.28

7 .254 .159

.42

4 .659

.95

1

.30

2

.42

1

.31

1

.78

9 .486 .150

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Ask

Hea

lth

Car

e

Qu

esti

on

Pearson

Correla

tion

.b -.068

-

.09

4

-

.334* .143

.31

3

.35

1

.31

7

-

.01

0

.254 -.264

-

.15

2

.163

-

.12

1

-

.24

1

.03

6

-

.00

6

.11

5 .179 .063

Sig. (2-

tailed) . .703

.59

8 .040 .393

.08

7

.05

3

.08

7

.95

2 .123 .109

.36

4 .327

.46

9

.15

7

.83

5

.97

4

.53

0 .311 .722

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

An

y R

JP T

yp

e

Pearson

Correla

tion

.b -.138

-

.02

0

-.040 -.272

-

.16

3

-

.00

8

-

.00

8

-

.17

7

.018 -.233

-

.13

4

-

.277

-

.18

4

-

.35

4*

-

.21

7

-

.05

0

-

.06

7

.108 -

.027

Sig. (2-

tailed) . .437

.90

9 .811 .098

.38

2

.96

6

.96

5

.28

7 .914 .159

.42

4 .092

.26

8

.03

4

.20

3

.77

4

.71

4 .544 .880

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

RJP

Em

plo

yee

Mee

t an

d

Gre

et

Pearson

Correla

tion

.b -.253

-

.13

1

.103 -.143

-

.08

3

-

.22

4

-

.28

1

.20

1 .057 -.238

-

.07

9

-

.004

.12

1

.08

0

-

.03

6

-

.19

7

.00

6

.374

* .300

Sig. (2-

tailed) . .150

.45

9 .537 .393

.65

7

.22

6

.13

2

.22

6 .732 .150

.63

8 .980

.46

9

.64

1

.83

5

.24

9

.97

5 .029 .085

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

RJP

Jo

b S

had

ow

ing

Pearson

Correla

tion

.b -.063 .13

2 .202

-

.386*

-

.30

7

-

.00

8

-

.27

7

-

.10

2

-.161 -.078

-

.21

3

-

.335

*

-

.27

1

-

.11

1

.00

0

-

.49

1**

.02

5 .181 .069

Sig. (2-

tailed) . .724

.45

5 .225 .017

.09

3

.96

6

.13

8

.54

2 .334 .643

.19

8 .040

.10

0

.51

9

1.0

00

.00

2

.89

0 .306 .697

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

RJP

To

ur

Pearson

Correla

tion

.b -.068 .01

8 -.103

-

.399*

-

.26

1

-

.06

1

-

.01

7

-

.20

1

-.057 -.097

-

.15

2

-

.314

-

.29

8

-

.40

2*

-

.28

7

-

.00

6

-

.02

1

.111 -

.027

Sig. (2-

tailed) . .703

.92

1 .537 .013

.15

7

.74

5

.93

0

.22

6 .732 .563

.36

4 .055

.06

9

.01

5

.08

9

.97

4

.91

1 .531 .880

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

RJP

Mee

t Pearson

Correla

tion

.b .068 .16

7 -.089

-

.353*

-

.40

2*

-

.50

8**

-

.49

3**

-

.12

7

-.178 -.284

-

.13

6

-

.170

-

.41

1*

.00

0

-

.20

4

-

.32

8

.12

3 .204

-

.023

75

Sig. (2-

tailed) . .704

.34

5 .593 .030

.02

5

.00

3

.00

6

.44

8 .284 .084

.41

6 .307

.01

0

1.0

00

.23

3

.05

1

.50

2 .247 .897

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Ref

eren

ce c

hec

k

Pearson

Correla

tion

.b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b

Sig. (2-

tailed) . . . . . . . . . . . . . . . . . . . .

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Ph

ysi

cal

Abil

ity

Tes

t

Pearson

Correla

tion

.b .130 .06

5 .256 -.145

.16

0

-

.01

4

.01

4

.06

6 .036 .269

.23

8 .030

.27

7

.12

4

-

.19

4

.14

8

.13

6 .154 .135

Sig. (2-

tailed) . .464

.71

7 .120 .384

.39

1

.93

9

.94

0

.69

5 .831 .102

.15

0 .856

.09

2

.47

1

.25

7

.38

9

.45

8 .383 .446

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Ho

nes

ty T

est

Pearson

Correla

tion

.b -.155

-

.19

6

-.196 -.174

-

.02

2

.05

6

-

.14

7

-

.08

8

-.093 -.115

-

.21

7

-

.172

.02

4

-

.07

0

-

.15

7

.11

3

.09

9 .072 .181

Sig. (2-

tailed) . .381

.26

5 .238 .296

.90

6

.76

7

.43

7

.60

1 .579 .491

.19

1 .302

.88

5

.68

4

.36

1

.51

1

.59

1 .684 .307

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Off

er C

NA

Cer

tifi

cati

on

Pearson

Correla

tion

.b -.145

-

.35

2*

.248 -.162 .09

7

.01

4

-

.06

4

-

.20

5

-.006 .072 .11

9 .140

.17

7

.00

0

-

.21

2

.29

8

-

.26

8

.033 -

.049

Sig. (2-

tailed) . .412

.04

1 .133 .332

.60

2

.93

9

.73

8

.21

7 .973 .668

.47

5 .403

.28

7

1.0

00

.21

3

.07

7

.13

9 .851 .785

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Car

eer

Ad

van

cem

ent

Pearson

Correla

tion

.b -.010

-

.04

9

-.158 -.009 .07

1

-

.18

0

-

.24

3

.13

0 .167 -.139

.04

5 .093

-

.06

9

.14

9

-

.20

0

.03

1

.05

2 .213 .118

Sig. (2-

tailed) . .957

.78

5 .344 .958

.70

3

.33

4

.19

6

.43

5 .315 .406

.78

9 .578

.68

1

.38

6

.24

2

.85

6

.77

7 .227 .506

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Pro

bat

ion

ary

Per

iod

Pearson

Correla

tion

.b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b .b

Sig. (2-

tailed)

. . . . . . . . . . . . . . . . . . .

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

76 C

om

pet

itiv

e S

tart

ing

Pay

Pearson

Correla

tion

.b 1 .74

5** -.197 -.197

-

.22

4

-

.20

2

-

.07

5

.15

5

-

.443*

*

-.061 .17

3

-

.328

-

.03

2

.06

3

-

.02

2

-

.04

1

.07

8

-

.195

-

.066

Sig. (2-

tailed) .

.00

0 .265 .265

.24

2

.29

4

.70

4

.38

1 .009 .732

.32

8 .058

.85

5

.72

4

.90

1

.81

7

.68

0 .286 .717

N 34 34 34 34 34 29 29 28 34 34 34 34 34 34 34 34 34 30 32 33

Sta

rtin

g p

ay

Pearson

Correla

tion

.b .745** 1 -.110 -.235

-

.24

3

-

.24

0

-

.23

3

.26

0 -.166 .015

.13

2

-

.398

*

-

.08

2

-

.14

8

.22

5

-

.10

8

.43

5* .096 .111

Sig. (2-

tailed) . .000 .536 .180

.20

4

.20

9

.23

2

.13

7 .347 .935

.45

7 .020

.64

6

.40

3

.20

1

.54

2

.01

6 .599 .540

N 34 34 34 34 34 29 29 28 34 34 34 34 34 34 34 34 34 30 32 33

90

day

moti

vat

ion

Pearson

Correla

tion

.b -.197

-

.11

0

1 -.138

-

.18

4

-

.19

6

-

.35

4

-

.15

6

.045 .340* .24

3

.345

*

-

.10

7

.16

2

-

.17

2

.09

1

-

.14

2

-

.110

-

.086

Sig. (2-

tailed) . .265

.53

6

.408 .32

2

.29

2

.05

5

.34

9 .787 .037

.14

1 .034

.52

1

.34

5

.31

5

.59

7

.44

0 .537 .628

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Sta

ffin

g L

evel

Pearson

Correla

tion

.b -.197

-

.23

5

-.138 1 .34

7

.13

9

.35

4

.18

9 .225 .118

.32

3* .296

.33

6*

.12

6

.29

6

.13

3

.03

8 .057

-

.047

Sig. (2-

tailed) . .265

.18

0 .408

.05

5

.45

6

.05

5

.25

5 .174 .482

.04

8 .071

.03

9

.46

4

.08

0

.44

1

.83

7 .747 .792

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Pat

ien

t R

atio

Day

Pearson

Correla

tion

.b -.224

-

.24

3

-.184 .347 1 .64

7**

.54

5**

.23

2

.487*

* -.040

.13

3 .228

.39

9*

.19

3

.13

1

.03

3

.32

2 .156

.510

**

Sig. (2-

tailed) . .242

.20

4 .322 .055

.00

0

.00

2

.20

9 .005 .831

.47

6 .217

.02

6

.29

8

.48

2

.85

9

.08

2 .411 .005

N 31 29 29 31 31 31 31 30 31 31 31 31 31 31 31 31 31 30 30 29

Pat

ien

t R

atio

Ev

e

Pearson

Correla

tion

.b -.202

-

.24

0

-.196 .139 .64

7** 1

.67

4**

.09

3 .434* -.014

-

.18

3

.018 .29

0

-

.22

1

.23

1

-

.00

6

.14

2

-

.029 .301

Sig. (2-

tailed) . .294

.20

9 .292 .456

.00

0

.00

0

.61

9 .015 .939

.32

4 .923

.11

3

.23

3

.21

2

.97

6

.45

4 .878 .112

N 31 29 29 31 31 31 31 30 31 31 31 31 31 31 31 31 31 30 30 29

Pat

Rat

Nt Pearson

Correla

tion

.b -.075

-

.23

3

-.354 .354 .54

5**

.67

4** 1

-

.09

2

.360 .183 .08

6 .045

.21

8

-

.25

2

.20

4

.16

7

.00

7

-

.055 .072

77

Sig. (2-

tailed) . .704

.23

2 .055 .055

.00

2

.00

0

.62

8 .050 .333

.65

0 .814

.24

6

.17

9

.28

1

.37

9

.97

0 .778 .715

N 30 28 28 30 30 30 30 30 30 30 30 30 30 30 30 30 30 29 29 28

Rec

ruit

Po

st J

ob O

nli

ne Pearson

Correla

tion

.b .155 .26

0 -.156 .189

.23

2

.09

3

-

.09

2

1 .286 -.073 .18

1 .111

.30

9

.29

8

.25

0

-

.34

5*

.20

6 .221 .326

Sig. (2-

tailed) . .381

.13

7 .349 .255

.20

9

.61

9

.62

8

.082 .663 .27

7 .506

.05

9

.07

7

.14

1

.03

9

.25

7 .210 .060

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Rec

ruit

Car

eer

Fai

rs

Pearson

Correla

tion

.b -.443**

-

.16

6

.045 .225 .48

7**

.43

4*

.36

0

.28

6 1 .263

.25

4

.420

**

.20

8

-

.11

2

.12

5

.15

7

.24

4 .288

.350

*

Sig. (2-

tailed) . .009

.34

7 .787 .174

.00

5

.01

5

.05

0

.08

2

.111 .12

3 .009

.21

0

.51

6

.46

8

.36

1

.17

8 .098 .042

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Rec

ruit

Jo

b P

ost

On

lin

e

Pearson

Correla

tion

.b -.061 .01

5 .340* .118

-

.04

0

-

.01

4

.18

3

-

.07

3

.263 1 .57

3**

.494

**

.27

7

-

.37

2*

.30

5

.30

5

.10

7

-

.208 .026

Sig. (2-

tailed) . .732

.93

5 .037 .482

.83

1

.93

9

.33

3

.66

3 .111

.00

0 .002

.09

2

.02

5

.07

0

.07

1

.56

0 .237 .883

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Rec

ruit

Ref

eren

ces

Pearson

Correla

tion .b .173

.13

2 .243 .323*

.13

3

-

.18

3

.08

6

.18

1 .254

.573*

* 1

.323

*

.23

3

.10

1

.06

7

.14

8

-

.12

2

-

.049

-

.096

Sig. (2-

tailed) . .328

.45

7 .141 .048

.47

6

.32

4

.65

0

.27

7 .123 .000 .048

.16

0

.56

0

.69

6

.38

9

.50

7 .782 .591

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Rec

ruit

N C

erti

fica

tion Pearson

Correla

tion .b -.328

-

.39

8* .345* .296

.22

8

.01

8

.04

5

.11

1

.420*

*

.494*

*

.32

3* 1

.23

8

-

.05

8

.20

7

.22

3

.04

8

-

.123 .169

Sig. (2-

tailed) . .058

.02

0 .034 .071

.21

7

.92

3

.81

4

.50

6 .009 .002

.04

8

.15

0

.73

8

.22

6

.19

1

.79

5 .488 .339

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

Rec

ruit

Oth

er

Pearson

Correla

tion .b -.032

-

.08

2 -.107 .336*

.39

9*

.29

0

.21

8

.30

9 .208 .277

.23

3 .238 1

-

.12

4

.44

4**

.16

5

.10

5 .009 .179

Sig. (2-

tailed) . .855

.64

6 .521 .039

.02

6

.11

3

.24

6

.05

9 .210 .092

.16

0 .150

.47

1

.00

7

.33

5

.56

8 .958 .311

N 38 34 34 38 38 31 31 30 38 38 38 38 38 38 36 36 36 32 34 34

78

Rec

ruit

Ref

eren

ce

Pearson

Correla

tion .b .063

-

.14

8 .162 .126

.19

3

-

.22

1

-

.25

2

.29

8 -.112

-

.372*

.10

1

-

.058

-

.12

4 1

-

.44

7**

-

.35

1*

-

.14

6 .075 .046

Sig. (2-

tailed) . .724

.40

3 .345 .464

.29

8

.23

3

.17

9

.07

7 .516 .025

.56

0 .738

.47

1

.00

6

.03

6

.42

7 .673 .796

N 36 34 34 36 36 31 31 30 36 36 36 36 36 36 36 36 36 32 34 34

Rec

ruit

On

lin

e

Pearson

Correla

tion .b -.022

.22

5 -.172 .296

.13

1

.23

1

.20

4

.25

0 .125 .305

.06

7 .207

.44

4**

-

.44

7** 1

-

.01

6

.32

3 .012 .159

Sig. (2-

tailed) . .901

.20

1 .315 .080

.48

2

.21

2

.28

1

.14

1 .468 .070

.69

6 .226

.00

7

.00

6

.92

8

.07

2 .947 .369

N 36 34 34 36 36 31 31 30 36 36 36 36 36 36 36 36 36 32 34 34

Rec

ruit

New

spap

er A

d Pearson

Correla

tion .b -.041

-

.10

8 .091 .133

.03

3

-

.00

6

.16

7

-

.34

5* .157 .305

.14

8 .223

.16

5

-

.35

1*

-

.01

6 1

-

.04

2

-

.066

-

.175

Sig. (2-

tailed) . .817

.54

2 .597 .441

.85

9

.97

6

.37

9

.03

9 .361 .071

.38

9 .191

.33

5

.03

6

.92

8

.81

9 .710 .323

N 36 34 34 36 36 31 31 30 36 36 36 36 36 36 36 36 36 32 34 34

# C

NA

Po

sito

on

s A

uth

Pearson

Correla

tion .b .078

.43

5* -.142 .038

.32

2

.14

2

.00

7

.20

6 .244 .107

-

.12

2 .048

.10

5

-

.14

6

.32

3

-

.04

2 1

.398

*

.679

**

Sig. (2-

tailed) . .680

.01

6 .440 .837

.08

2

.45

4

.97

0

.25

7 .178 .560

.50

7 .795

.56

8

.42

7

.07

2

.81

9 .024 .000

N 32 30 30 32 32 30 30 29 32 32 32 32 32 32 32 32 32 32 32 30

CN

AV

acan

t P

osi

tion

s

Pearson

Correla

tion .b -.195

.09

6 -.110 .057

.15

6

-

.02

9

-

.05

5

.22

1 .288 -.208

-

.04

9

-

.123

.00

9

.07

5

.01

2

-

.06

6

.39

8* 1 .260

Sig. (2-

tailed) . .286

.59

9 .537 .747

.41

1

.87

8

.77

8

.21

0 .098 .237

.78

2 .488

.95

8

.67

3

.94

7

.71

0

.02

4 .151

N 34 32 32 34 34 30 30 29 34 34 34 34 34 34 34 34 34 32 34 32

# o

f P

atie

nts

Pearson

Correla

tion .b -.066

.11

1 -.086 -.047

.51

0**

.30

1

.07

2

.32

6 .350* .026

-

.09

6 .169

.17

9

.04

6

.15

9

-

.17

5

.67

9** .260 1

Sig. (2-

tailed) . .717

.54

0 .628 .792

.00

5

.11

2

.71

5

.06

0 .042 .883

.59

1 .339

.31

1

.79

6

.36

9

.32

3

.00

0 .151

N 34 33 33 34 34 29 29 28 34 34 34 34 34 34 34 34 34 30 32 34

79

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