The Gerontologist
Vol. 42, No. 2, 159–168
Copyright 2002 by The Gerontological Society of America
An Exploration of Job, Organizational, and
Environmental Factors Associated With High
and Low Nursing Assistant Turnover
Diane Brannon, PhD,1 Jacqueline S. Zinn, PhD,2 Vincent Mor, PhD,3
and Jullet Davis, PhD4
Purpose: This article examines factors that distinguish
nursing facilities with very high and very low nursing assistant turnover rates from a middle referent group, exploring the possibility that high and low turnover are
discrete phenomena with different antecedents. Design
and Methods: Data from a stratified sample of facilities in
eight states, with directors of nursing as respondents (N 5
288), were merged with facility-level indicators from the
On-Line Survey Certification of Automated Records and
county-level data from the Area Resource File. Multinominal logistic regression was used to identify factors associated with low (less than 6.6% in 6 months) and high (more
than 64% in 6 months) turnover rates. Results: With the
exception of registered nurse turnover rate, low turnover
and high turnover were not associated with the same
factors. Implications: Future studies of facility turnover
should avoid modeling turnover as a linear function of a
single set of predictors in order to provide clearer recommendations for practice.
Cohen-Mansfield, 1997; Helmer, Olson, & Heim,
1993; Mesirow, Klopp, & Olson, 1998). This trend,
coupled with increased regulatory and market pressures to improve quality, has prompted industry observers to list maintaining a high-quality nonprofessional staff as a major strategic challenge as the
demand for various levels of long-term care continues
to rise (Kodner, 1993). Nursing assistant wages and
benefits continue to reflect the marginality of chronic
caregiving work within the health care system and
within society at large (Crown, 1994), and the turnover problem appears to be as salient as it has ever
been.
In the United States, interventions to improve the
quality of the nonprofessional long-term care workforce have focused on training. Since 1991, certification training has been in place, presumably raising the
minimum standard of care in nursing homes. It is also
possible that an unintended consequence of this certification has been enhanced occupational definition
that extends beyond the organizational boundaries of
the employer, making these certified staff attractive to
other, better paying health care employers. More recently, specialized training (Grant, Kane, Potthoff, &
Ryden, 1996; McCallion, Toseland, Lacey, & Banks,
1999), improved preemployment screening (Kettlitz,
Zbib, & Motwani, 1997), and a primary-care job design approach (Teresi et al., 1993) have shown promising results. Overall, however, the accumulated findings of research on staff turnover in nursing homes
have produced little by way of empirically reliable
guidance on remedying the problem.
The findings reported in this article are from a multistate study of organizational and environmental factors that are associated with aggregate resident outcomes in a representative sample of 308 nursing
facilities. Although not designed to explain individual
turnover decisions, the data do allow for a novel, categorical exploration of very high and very low facility
turnover, drawing on factors identified in prior work
to be correlated with facility turnover rates. The purpose of the analysis was to explore factors from previously established lists of turnover correlates that
Key Words: Nursing assistant turnover, Staff turnover
Despite two decades of industry and policy research focus on them, persistent staff shortages and
turnover rates several times the national average continued to be reported throughout the 1990s (Anderson, Issel, & McDaniel, 1996; Banaszak-Holl &
Hines, 1996; Bayer, 1994; Caudill & Patrick, 1991;
This work was supported by National Institute on Aging Grants
AG11624 and AG00048.
Address correspondence to Diane Brannon, Department of Health Policy and Administration, The Pennsylvania State University, 116 Henderson
Building, University Park, PA 16802. E-mail: [email protected]
1Department of Health Policy and Administration, The Pennsylvania
State University, University Park.
2Department of Risk, Insurance and Healthcare Management, Temple
University, Philadelphia, Pennsylvania.
3Bio Med Gerontology Health, Brown University, Providence, Rhode
Island.
4Department of Marketing and Management, University of Alabama,
Tuscaloosa.
Vol. 42, No. 2, 2002
159
mix, and the ratio of administrative costs to clinical
costs. Both of these studies and the previous studies
summarized by Cohen-Mansfield (1997) indicated
that in addition to larger labor market contexts, organizational and job factors, some of which are controllable by management, are influential vis á vis nonprofessional staff turnover.
Similarly, research on turnover in general and in
the health professions in particular has identified
three broad categories of factors that influence turnover (Cotton & Tuttle, 1986)—environmental, job
and organizational, and personal factors. Environmental factors include the availability of alternative
employment, the local unemployment rate, and general economic conditions. Job and organizational factors include pay (although this appears to be more influential among managerial/professional employees
than with others), job stress, role clarity and job design, and leadership effectiveness, all of which influence turnover through job satisfaction. Personal factors shown to be reliably related to turnover behavior
include age, education, marital status, and number of
dependents supported.
distinguish low and high facility turnover levels from
a middle referent group, an approach that may open
new avenues of inquiry regarding potential solutions
to this persistent problem.
Prior Research
Although extensive research has been conducted in
testing both individual-level, behavioral models of the
intent to leave and turnover (cf. Mobley, Griffith,
Hand, & Meglino, 1979) and aggregate industrylevel models reflecting labor economic approaches (cf.
Terborg & Lee, 1984), the role of the organization itself and its immediate environment in influencing
turnover has been explored only more recently.
Sheridan, White, and Fairchild (1992), for example,
estimated that differences in organizational culture
explained more than $6 million difference in turnover-related costs among six accounting firms. The
application of these models in pink-collar industries—that is, predominantly female and nonprofessional—is rare even though turnover rates tend to be
higher among women (see Cotton & Tuttle, 1986, for
a meta-analytic review). One notable exception is the
study by Schaefer and Moos (1996), which examined
the role of work stressors and environment on longterm care staff. Although there have been several
studies of staff turnover in nursing homes (Caudill &
Patrick, 1991; Wagnild, 1988; Waxman, Carner, &
Berkenstock, 1984), two recent ones are noteworthy
for their sample sizes and for examining this phenomenon using multivariate analyses to test theoretically
derived hypotheses.
Banaszak-Holl and Hines (1996) analyzed survey
data from 250 facilities in 10 states for the purpose of
examining which job design and other organizational
factors affected turnover, controlling for local economic conditions. They observed that turnover was
higher in for-profit homes, confirming earlier findings
(Waxman et al., 1984), and that facilities that involved nursing assistants in resident care planning, a
core clinical organizing task in nursing homes, experienced lower turnover rates. Training and workload
issues, case mix severity of the residents, payor source
mix, and facility size were not significantly related to
nursing assistant turnover, but labor market conditions were significant predictors.
Anderson and colleagues (1996) reported on a
study of differential predictors of registered nurse
(RN), licensed practical nurse (LPN), and nursing assistant turnover in 467 Texas facilities. Consistent
with findings from studies in service industries (Cotton & Tuttle, 1986), including Halbur’s work in nursing homes (Halbur & Fears, 1986), although in contrast to Banaszak-Holl and Hines (1996), nursing
assistant turnover was not significantly affected by
labor market conditions, whereas turnover of professional nurses was. Rather, the nonprofessional staff
turnover rates were positively related to organizational factors including for-profit status, facility occupancy rate, and workload, and negatively related to
factors including profit margin, nursing staff skill
Costs and Benefits of Turnover
The disadvantages of turnover from the organizational perspective include the following: replacement
costs (including training), lost productivity, compromised quality, and lowered morale. In nursing homes,
these costs may well be reflected in the quality of care
that residents receive (Burnfeind & O’Connor, 1992;
Cohen-Mansfield, 1997; Wagnild, 1988; Waxman et
al., 1984). Burnfeind and O’Connor (1992) reported
that facilities with high turnover rates had more code
violations resulting from the state survey process. It is
argued that providing consistent care in a setting that
demands intense levels of staff resident interaction
places a premium on staff retention (Banaszak-Holl
& Hines, 1996; Cohen-Mansfield, 1997).
Although excessive turnover is clearly a problem
that has continued to plague the long-term care sector, another view found in the literature, although
rarely cited in studying this problem, is that not all
turnover is bad. Accordingly, a 1% increase in turnover in a facility with very few staff terminations is
likely not the same phenomenon—in terms of either
antecedents or consequences—as a 1% increase in a
high-turnover facility.
Abelson (1986) and Iglehart (1990) have argued
for health and human services managers to seek their
optimal level of turnover rather than assuming all
turnover is equally dysfunctional. They have each argued that organizations have optimal aggregate turnover rates that reflect necessary involuntary as well
as voluntary terminations and retention costs as well as
turnover costs. They further argued that the optimal
rate for a given organization or type of organization is
almost always greater than zero, given imperfect employee selection practices. Selection ratios and practices, Shaw, Delery, Jenkins, and Gupta (1998) argued, have a disproportionate impact on involuntary
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Methods
turnover, whereas human resources practices have
more influence on voluntary turnover. Given that
most nursing facilities recruit from very small pools of
applicants, creating unfavorable selection ratios,
some turnover is likely needed to weed out hiring mistakes. From the point of view of organizational performance in general, functional aspects of turnover
can include the opportunity to reduce costs by replacing higher paid workers with lower paid workers,
renewing the workforce through increased demographic diversity, weeding out poor performers, and
avoiding retention costs related to promotion, transfers, and so forth.
The financial benefits to the nursing facility can
quickly be discounted from the perspective of continuity and quality of care, however. Frail and often
disoriented elders need consistent care. Furthermore,
it has been argued that there are “group learning
curves” (Argote et al., 1995) that are adversely affected by turnover. Stryker (1982), however, argued
two decades ago that quality of care in nursing facilities would likely not be well served by zero or nearzero rates of turnover. She viewed workforce renewal
as an investment in good care. In addition, Halbur
and Fears (1986) found that RN turnover positively
predicted nursing home resident discharge rates, and
they questioned the presumed negative consequences
of personnel turnover. Kettlitz and colleagues (1997)
observed that poor selection practices are a significant source of nurse’s aide turnover. Logically, there
are individuals hired by facilities as nursing assistants, either before or after completion of the mandated training, who are not well suited to this demanding work. In these cases, turnover may be
positive in terms of quality. If it takes a facility 10
hires from a limited applicant pool to find a committed and skilled caregiver, does that make this facility
less effective than one that retains the first available
“warm body”? Thus, the question arises, is the workforce stability implied by very low turnover rates necessarily a desirable goal for nursing facility management? Can a functional range of turnover rates be
identified for industry benchmarking purposes that
will enable managers to plan for what Haveman
(1995) referred to as a “demographic metabolism of
organizations” (p. 586) that balances industry dynamics, turnover, and tenure distributions? This
question has particular salience in today’s nursing
home industry. After several decades of stable funding and relatively little environmental turbulence,
clinical and administrative innovation and management of complexity are now survival skills for nursing homes. Personnel who are wedded to established,
more custodial practices may be a liability as nursing
facilities must respond to new clinical demands and
prove the cost effectiveness of their services. Ignoring
these questions about the need for workforce renewal
and quality through maintenance of some level of attrition or, in research terms, assuming that turnover
rate is a linear function of a given set of predictors,
may be clouding our understanding of how to manage this problem.
Vol. 42, No. 2, 2002
Sample
In this article, we report data from a project (National Institute on Aging Grant AG11624-01A1) designed to examine how organizational structure and
administrative and clinical processes affect nursing
home outcomes. A sample of 360 facilities, stratified
by ownership, size, and urban location, was drawn
from eight states that were universal in their computerization of the mandated nursing home Resident
Assessment Instrument/Minimum Data Set during
1995–1996. An 80% response rate was achieved.
Data Sources
Three sources of information, including primary
and secondary data, were merged to form the analytic
database for this analysis: The 1995 On-Line Survey
Certification of Automated Records (OSCAR) file
maintained by the Health Care Financing Administration, the most closely matched county-level Area
Resource File (ARF), and a cross-sectional survey of
directors of nursing (DON) of the sample facilities.
The OSCAR data and the ARF were used to obtain
background characteristics of participating homes.
The OSCAR contains facility and aggregated resident
data routinely collected through the nursing home
certification process and includes data on staffing,
resident mix, sex, ownership, and average census.
The ARF describes the health-related resources in the
environment in which the facility is located, summarizing a large array of census, health, and social resource data.
DON in all homes were interviewed by telephone
during late 1995 and early 1996 to characterize the
internal management structure, participation patterns, and responses to changes in the environment as
well as to characterize the nursing care processes in
place to guide staff regarding care planning and service delivery. Those job, organizational, and environmental factors in the data set that had been shown to
influence turnover in the previous literature were examined in bivariate analyses, and those that showed
some correlation (p 5 .20) with turnover rate were included in the multivariate analyses.
The Low/High Turnover Model
As the overall study was not designed to study
turnover, no one theoretical model could be applied.
Rather, we examined job, organizational, and environmental factors associated with both low and high
turnover rates among nonprofessional nursing assistants at the facility level in a multistate sample. The
Cotton and Tuttle (1986) organizing framework of
individual, job, and organizational and environmental factors that are associated with turnover is invoked. No individual-level data were included in this
facility-level data set, therefore personal, demographic,
and wage rate factors were not included. Table 1 describes the variables listed in conceptual configurations
under the broad categories of job, organizational,
161
Table 1. Variables, Data Sources, and Their Measurement and Means
Variable
Turnover Categories
Low (DON)
Referent (DON)
High (DON)
Job Factors
Leadership
Close supervision (DON)
Trained supervisors (DON)
Performance rewards (DON)
Integration
Involvement in assessment (DON)
Involvement in care planning (DON)
Rotating assignments (DON)
Workload
No. aides/100 beds (OSCAR)
Organizational Factors
Clinical resources
NP (DON)
No. aides/RNs & LPNs (OSCAR)
RN turnover rate (DON)
DON tenure (DON)
Nurse aide training (DON)
Administrative resources
Hospital relationship (DON)
Training site (DON)
Investor-owned vs. nonprofit (OSCAR)
Chain membership (OSCAR)
% Medicaid (OSCAR)
Administrator span of control (DON)
Nurse’s aide union (DON)
Recent owner change (OSCAR)
Facility size (OSCAR)
Investor-owned by nurse’s aide union
Investor-owned by chain membership
Clinical demands
% bladder incontinent (OSCAR)
% ambulatory (OSCAR)
Emergency room admit rate (OSCAR)
Market Environment Factors
Log co. unemployment rate (1.8–12.9) (ARF)
Co. per capita income (ARF)
Nursing facility market concentration (ARF)
Measures
0–10th percentile
11th–75th percentile
75th–100th percentile
Charge nurse disciplines, 1 5 yes
Any management training, 1 5 yes
Bonus, 1 5 yes
M (SD)
#6.6%a
7.0–63%a
64–300%a
.73 (.44)
.74 (.44)
.41 (.49)
Nine domains (3-point scale)—3–27 (low)
Yes 5 1
Same resident ,1 month to 51 month
19.33 (3.18)
.40 (.49)
.43 (.49)
Aide FTEs/100 beds
41.10 (33.0)
NP on site, 1 5 yes
Aide FTEs/RN FTEs 1 LPN FTEs
No. terminations (6 months)/No. current RN positions
,2 years 5 1
Log hours
.08 (.28)
2.49 (1.3)
21.7 (32.9)
.31 (.46)
2.37 (.92)
Formal relationship 5 1
A clinical training site 5 1
Investor owned 5 1
Chain 5 1
% residents Medicaid
No. departments reporting to administration
Any aides union 5 1
Since last survey 5 1
Four categories (60, 90, 120, 120)
.59 (.49)
.64 (.47)
.59 (.49)
.44 (.49)
66.23 (21.61)
7.08 (2.13)
.19 (.39)
.18 (.38)
2.63 (1.12)
% current residents
% current residents
No. resident emergency room admits/100 residents
50.69 (16.92)
45.99 (20.45)
9.77 (9.3)
1995 %
1995 $ (10 intervals: $10,653–$49,197)
Herfindahl index
6.78 (2.11)
18,740 (45.05)
.18 (.22)
Note: DON 5 directors of nursing; OSCAR 5 On-Line Survey Certification of Automated Records; FTE 5 full-time equivalent; NP 5
nurse practitioner; RN 5 registered nurse; LPN 5 licensed practical nurse; ARF 5 area resource file.
aIndicates range rather than mean.
differ from those associated with very high turnover,
ordinary least squares regression would obscure
such differences.
Consequently, we opted to treat turnover as a
three-category discrete variable with low and high
categories compared against a middle referent category. This approach is exploratory in that there is no
prior body of work to guide the development of directional hypotheses for very low versus very high turnover. Here we assumed that neither very high nor very
low turnover is desirable and used multinomial logit
analysis to identify which predictor variables from a
set of six configurations distinguish very low and very
high turnover facilities from those in the middle referent category.
and market environment factors. These configurations were not intended to represent underlying constructs (i.e., leadership); rather, they served as an organizing framework for this exploratory analysis.
Although this work builds on previous conceptualizations in terms of using conceptual configurations
of independent variables (Shaw et al., 1998) and on
recent work in the nursing home industry by Banaszak-Holl and Hines (1996) and Anderson and associates (1996), the primary new contribution of
this study is in the way turnover is operationalized
and modeled. If both very low turnover and very
high turnover are potentially problematic, then linear regression models can be uninformative. Similarly, if the factors associated with very low turnover
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Job Factors.—A limited number of job factors
known to influence turnover were available in this
data set. These were configured as leadership, integration, and workload. Leader behavior has been consistently shown to affect turnover (Cotton & Tuttle,
1986). As no direct measures of perceived leadership
attributes were available, we chose several indicators
from the DON questionnaire that we believed distinguished facilities that had a well-articulated structure
for supervision of aides from those that did not. The
lack of middle management capacity in nursing has
been previously noted (Smyer, Brannon, & Cohn,
1992). Lines of authority are often unclear, and
poorly managed reward systems can lead to perceptions of inequity and dissatisfaction (Brannon &
Streit, 1991). The indicators chosen in this study were
whether the charge nurses (the closest supervisory
personnel to the aides) applied discipline when it was
warranted, whether any of supervising nursing staff
had received any management training, and whether
performance-based bonus rewards were given to individuals. Again, it is useful to note that these were indicators of managerial structure rather than of leadership effectiveness per se.
administrative resources on aggregate facility turnover
rates, with clinical resources being more influential in
predicting nurses’ turnover than that of nurse’s aides.
Several items were included here in measuring clinical
resources. These included whether the facility had a
nurse practitioner. This clinical resource is viewed as
needed support for maintaining role clarity, setting
standards, and providing guidance for nurse’s aides
(Anderson et al., 1996; Monahan & McCarthy,
1992). Also, following Anderson and colleagues’
(1996) logic, we examined the skill mix (the ratio of
aides to RNs and LPNs). Professional staff turnover
has been linked to nonprofessional staff turnover in
nursing facilities (Anderson et al., 1996; Halbur &
Fears, 1986); consequently, we included the turnover
rate for RNs for the facility and whether the DON
had been hired within the past 2 years. Finally, we included the measure of the number of inservice training programs offered by the facility for its nursing assistants during the previous year.
Hypothesis 4: Facilities with a nurse practitioner,
with a lower aide-to-professional-staff ratio,
with lower RN turnover, with a longer tenured
director of nursing, or with more inservice offering for aides will be less likely to report very
high or very low turnover.
Hypothesis 1: Facilities reporting close supervision
of nursing assistants, management training for supervisors, or performance-based rewards will be
less likely to report very high or very low turnover.
Administrative resources served as proxy for the
pay, benefits, and organizational culture variables that
are influential in turnover but were not measured in
this study. Larger organizations have been reported to
have higher turnover rates (Cotton & Tuttle, 1986), as
have investor-owned nursing facilities (Anderson et
al., 1996; Banaszak-Holl & Hines, 1996). Union contracts, probably through their effects on pay, benefits,
and job security, tend to be associated with lower
turnover (Cotton & Tuttle, 1986). Cotton and Tuttle
also noted, however, that the union effect was moderated by the nature of the firm, with service organizations’ turnover rates being less consistently influenced
by the presence of a union. Whether the facility has a
formal relationship with a hospital or a multifacility
chain and whether it is a clinical training site speak to
its reputation and legitimacy, factors also shown to influence individual turnover decisions (Cotton & Tuttle,
1986). Banaszak-Holl and Hines found that the proportion of residents whose care was paid for at least in
part by Medicaid was a factor in predicting aggregate
turnover rates, presumably due to financial constraints imposed by lower per resident reimbursement.
Finally, we viewed administrative span of control, or
the number of departments reporting directly to the
top administrator, as an inverse indicator of managerial resources. The assumption here was that the traditional flat bureaucracy that once typified the industry
may be inadequate for managing such organizations
in the contemporary environment. Because human resource management, for example, falls within the purview of the administrator in most nursing facilities,
this function has a better chance of being reasonably
well developed when the administrator is not directly
overseeing all or most departments. Finally, we were
Integration into decision making has been shown to
affect turnover (Cotton & Tuttle, 1986) and has been
reported as a reason why nurse’s aides quit (Caudill &
Patrick, 1991). Job designs that promote nurse’s aide
integration or participation in clinical decision making
have been suggested for years as a potentially powerful way to improve the quality of their work (Smyer et
al., 1992; Waxman et al., 1984). Banaszak-Holl and
Hines (1996), however, found that only one of four aspects of job design, participation in care planning, was
a significant predictor of turnover for nurse’s aides.
Hypothesis 2: Facilities that involve nursing assistants in resident assessments and care planning
or provide stable resident assignments (not rotating) will be less likely to report very high or
low turnover.
Workload was the third job factor included in these
analyses. Staffing ratios have become a common process-of-care measure in nursing homes. We presumed
that caregiving staff are more susceptible to emotional and physical burnout when they have higher
patient loads (Banaszak-Holl & Hines, 1996; Wagnild, 1988).
Hypothesis 3: Facilities that maintain higher nursing assistant-to-bed ratios will be less likely to
report very high or very low turnover.
Organizational Factors.—Organizational factors
were configured as clinical and administrative resources and clinical demands. Anderson and associates (1996) found differential effects of clinical and
Vol. 42, No. 2, 2002
163
Results
interested in whether a change of ownership would
influence turnover because ownership turnover has
been considerable in this industry and may result in
disruptions in collective bargaining agreements and
other personnel policies.
A wide range of turnover rates was reported by this
sample. Extreme cases, those of more than 100% for
the 6-month period, were discussed with the DON in
the telephone interviews and were assumed to be accurate. The mean for the sample was 51% for a
6-month period, with a standard deviation of almost
54%. This mean rate is higher than the mean 6month rate of 32% reported by Banaszak-Holl and
Hines (1996), but not as high as the annual mean rate
of 179% observed by Anderson and associates (1996)
in Texas facilities. The distribution of facility turnover rates was skewed, with half the facilities reporting 33% or less turnover in a 6-month period.
Twenty-five percent of the homes reported 15% or
less turnover among their nursing assistants during
the previous 6-month period, and 18% reported 10%
or less. At the high end, 25% of the facilities experienced more than 63% turnover, and 15% reported
more than 90% in 6 months.
As no guidelines for establishing cut-off points for
dysfunctional aggregate turnover rates were found in
the literature or in consultation with industry trade
associations, the creation of categories of high and
low turnover with a middle referent category was necessarily arbitrary. As the argument for very low turnover being dysfunctional is somewhat novel and thus
exploratory, we used the relatively extreme cut-off
point of the 10th percentile of the sample turnover
rates to create the low category. The result was that
this category included 30 facilities with a range of reported turnover rates from 0% to 6.6% during the
previous 6 months. Thus, although the group was arbitrarily defined, it was unequivocally composed of
low-turnover nursing facilities.
Because the distribution was considerably more
spread out at the high end, however, we opted for a
more inclusive cut-off point and used the 75th percentile. This resulted in a grouping of those 74 facilities that reported greater than 63% turnover in the
previous 6 months. The range of turnover rates for
these facilities was 64% to 300%. As with the low
group, although the cut-off point was arbitrary and
reasonable arguments could be made for setting it
lower than the 75th percentile, the selected group
clearly represented facilities with very high turnover.
The multinominal logit analysis then compared the
low and high groups with the remaining middle
group, which consisted of 189 facilities with reported
rates from 6.7% to 62.5%. It should be noted that
this middle group cannot be claimed to have productive or even moderate turnover. The categories are arbitrary and the analyses exploratory. Given the arbitrary definitions of the categories, we assessed
whether observed findings were robust across alternative—higher and lower— turnover category cutoff points by running several models using alternative cut-off points and groupings for the dependent
variable. Although variation in the relative significance of different factors was observed, no substantive or directional changes in the results were observed
Hypothesis 5: Facilities with formal relationships
with hospitals, those that serve as training sites,
are nonprofit, are part of multifacility chains,
are smaller in size, are less reliant on Medicaid
funding, have smaller administrator span of
control, have a union contract, or have no recent
ownership change will be less likely to report
very high or very low turnover.
Few of the resident status variables, or clinical demands, showed any notable correlations with turnover in preliminary analyses. To provide case mix
control variables, we chose three variables that reflected somewhat different potentially stressful aspects of caregiving work. Bladder incontinence requires considerable patience and may be distasteful.
Monitoring residents who are ambulatory, although
less physically demanding, requires vigilance. Emergency room admissions represent negative, unplanned
events that disrupt workflow and increase uncertainty
and stress.
Hypothesis 6: Facilities with fewer proportions of
residents who are bladder incontinent, that have
fewer ambulatory residents, or that experience
fewer emergency room admissions will be less
likely to report very high or very low turnover.
Market Environment Factors.—Local economic conditions have been shown to be associated with turnover,
reflecting the perception of alternative employment
opportunities as employees periodically evaluate their
current situation (Abelson, 1986). Generally, higher
per capita income and lower unemployment rates
suggest the availability of job alternatives and, hence,
higher turnover rates, although Cotton and Tuttle
(1986) pointed to evidence that these economic factors may be less important for service workers than
for those in manufacturing jobs. Recently, these two
variables have been shown to be significant predictors
of nursing home turnover (Banaszak-Holl & Hines,
1996), and thus we included them in these analyses.
Banaszak-Holl and Hines also used the number of
nursing facility beds in the county as a measure of competition and found that, indeed, turnover was lower
in counties with fewer beds. We used a Herfindahl index of market concentration. This index was calculated as the sum of the squared market shares of the
nursing homes in the county. Lower market concentration suggests more facilities competing for staff, which
in turn likely raises wages in the long-term care sector
and reduces overall turnover of nursing assistants.
Hypothesis 7: Facilities located in counties with
relatively high unemployment and low per capita income and those in less concentrated nursing home markets will be less likely to report
very high or very low turnover.
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Table 2. Weighted Multinomial Logit Results for Low- and
High-Turnover Groups Compared with Moderate-Turnover
Group: Relative Risks
as a result of changing the high and low category cutoff points.
The sample was stratified to be nationally representative in terms of size, ownership, and rural versus
urban location. Staffing and participation patterns
and case mix variables were similar to those reported
elsewhere (Anderson et al., 1996; Banaszak-Holl &
Hines, 1996). With regard to the variables not examined in these prior studies, 19% of facilities reported
union representation for nursing assistants, and 41%
used a bonus reward system for performance. That on
average seven departments reported directly to the
top administrator (administrative span of control)
suggests that nursing facilities continue to operate as
relatively flat bureaucracies, with minimal managerial
capacity. Mean per capita income and unemployment
rates in the sample facilities’ counties were similar to
those observed by Banaszak-Holl and Hines. The relative risk associated with each predictor for the facility’s being in the low or high turnover groups (using
the middle category as the referent) is shown in Table
2. The analyses were conducted using STATA 5.0
(StataCorp, 1997).
Variable
Job Factors
Leadership
Close supervision (0,1)
Trained supervisors (0,1)
Performance rewards (0,1)
Integration
Involvement in assessment (3–27)
Involvement in care planning (0,1)
Rotating assignments (0,1)
Workload
No. aides/100 beds
Organizational Factors
Clinical resources
Nurse practitioner (0,1)
No. Aides/RNs and LPNs
RN turnover rate (%)
DON tenure (0,1)
Nurse’s aide training
Administrative resources
Hospital relationship (0,1)
Training site (0,1)
Investor-owned vs. nonprofit (0,1)
Chain membership (0,1)
% Medicaid (0–100)
Administrator span of control
(2–14)
Nurse’s aide union (0,1)
Recent owner change (0,1)
Facility size (4 categories)
Investor-owned by nurse’s aide
union (1,1)
Investor-owned by chain
membership (1,1)
Clinical demands
% bladder incontinent (0–100)
% ambulatory (0–100)
Emergency room admit rate (0–48)
Job Factors
Table 2 shows that the job factors, for the most
part, bore little relationship to the likelihood of being
either a high- or a low-turnover facility when other
factors in the model were controlled for. The exception was that one of the leadership variables (Hypothesis 1), having supervisors trained in management,
was associated with lower likelihood of being in the
low-turnover group of facilities. That is, facilities
with supervisors trained in management were more
likely to be in the referent group than in the very low
turnover group. This makes sense if one assumes that
much of what is missing in very low turnover facilities
is productive, possibly involuntary, turnover. Extreme
staff stability may reflect an absence of functional
management and resulting low performance expectations. Trained supervisors, by contrast, are more
likely to set expectations and provide feedback to
staff about performance, thereby promoting desirable
attrition among poor performers. It is also possible
that facilities with trained supervisors also have more
effective hiring and human resources practices and
thus experience very low turnover. Neither nurse’s
aides per 100 beds (Hypothesis 3) nor any of the job
integration variables (Hypothesis 2) were significantly associated with either high or low turnover,
consistent with Banaszak-Holl and Hines’s (1996)
observation that only one of four job design variables
affected turnover rate.
Market Environment Factors
Log co. unemployment rate
Co. per capita income
Nursing facility market concentration
High
0.53 (33) 1.69 (.20)
0.25 (.05) 1.09 (.84)
0.87 (.83) 1.36 (.39)
1.10 (.48) 1.07 (.36)
1.34 (.74) 1.07 (.89)
1.43 (.59) 0.86 (.69)
1.01 (.79) 1.00 (.83)
0.21 (.37)
0.59 (.25)
0.94 (.03)
0.41 (.24)
1.17 (.69)
2.31 (.16)
0.89 (.57)
1.02 (.01)
0.66 (.27)
0.92 (.71)
2.02 (.28)
1.29 (.70)
1.15 (.88)
0.75 (.82)
1.01 (.55)
0.55 (.09)
3.06 (.01)
6.31 (.00)
3.88 (.09)
1.00 (.70)
1.41 (.03)
10.68 (.02)
0.47 (.43)
0.79 (.49)
1.50 (.76)
1.13 (.14)
3.54 (.12)
1.40 (.43)
1.01 (.97)
0.18 (.07)
0.54 (.67) 0.16 (.04)
1.00 (.89) 1.00 (.67)
1.50 (.76) 1.01 (.21)
1.02 (.56) 1.04 (.09)
0.32 (.28) 1.37 (.61)
1.00 (.17) 1.00 (.99)
0.75 (.30) 0.79 (.13)
Note: p levels are given in parentheses. DON 5 directors of
nursing; RN 5 registered nurse; LPN 5 licensed practical nurse.
n 5 257; x2(58) 5 104.51, p , .001; Psuedo R2 5 .23.
of being in the very low aide turnover group and
higher risk of being in the high aide turnover group
(Hypothesis 4). None of the other clinical resource
variables was significant.
Administrative Resources
Organizational Factors
As for the administrative resources identified in
Hypothesis 5, only the administrative span of control
and the nurse’s aides having a union contract were associated with being in the low-turnover group. The
likelihood of being in the high-turnover group rather
than the referent group was associated with being a
training site, for-profit ownership status, and although
Consistent with prior work, the logit analysis supported a positive linear association between RN
turnover and nurse’s aide turnover in that the likelihood of being in the low- or the high-turnover group
was significant in the expected directions. That is,
higher RN turnover was associated with lower risk
Vol. 42, No. 2, 2002
Low
165
in the high-turnover category. The excessive turnover rates observed in the for-profit sector in this
sample, then, were disproportionately attributable
to the independent or freestanding proprietary nursing facilities.
not significant at the standard .5 level, being part of a
multifacility firm.
The wider the administrator’s span of control—
that is, the greater the number of people who reported
directly to the administrator—the more likely the facility was to be in the low-turnover group. Thus, flatter organizations with less middle management were
more likely to have very low turnover. This is consistent with the earlier finding that the likelihood of
being in the very low turnover group was reduced by
having supervisors trained in management.
Having a union contract was associated with a 10fold increase in the likelihood that a facility would be
in the very low turnover category. The union impact
on the risk of being in the high-turnover group was
not significant, although positive, when controlling
for other factors in the model. Previous studies from
other industries (Cotton & Tuttle, 1986) have suggested that the presence of the union reduces turnover
by improving aspects of the job that we did not measure here, such as pay, benefits, and job security. As
the logit analysis shows, however, the union presence
was significant only in distinguishing facilities with
very low turnover; it did not reduce the likelihood of
being in the high-turnover group. In fact, there was a
trend (relative risk ratio 5 3.53, p 5 .12) for the
union factor to be associated with increased risk of
very high turnover. We sought additional insight regarding the effect of unionization by examining the
interaction between having a union and ownership
status. Although not significant at the .05 level, the interaction term suggests that the investor-owned facilities with union contracts were less likely to be in the
very high turnover group than were the nonprofit facilities with union contracts.
Having a formal relationship with a hospital, financial constraints as measured by the percentage of
Medicaid reimbursement, and facility size were not
significantly associated with the high- and low-turnover
categories defined here. Facilities that served as clinical training sites, however, were more than three
times as likely to be in the high-turnover group than
in the referent group. It seems likely that these facilities were hiring, providing certification training, and
then losing to other health care facilities a disproportionate number of nursing assistants. What is not
known, of course, is whether these training sites were
more likely to lose their strongest or their weakest
trainees and whether the turnover was voluntary or
involuntary. At any rate, it likely represents a substantial financial burden. It is also possible, given the
cross-sectional nature of the data, that facilities initiated training programs in response to high turnover.
As shown in previous studies, investor-owned facilities were significantly more vulnerable to very
high staff turnover than were nonprofits. An interaction term analysis indicated, however, that not all investor-owned facilities were equally vulnerable. The
interaction of investor-owned status and chainmembership showed that the investor-owned chain
facilities were more likely than investor-owned independent facilities to be in the referent category than
Clinical Demands
Clinical demands, as identified in Hypothesis 6,
were not predictive of the high- and low-turnover
groups. Neither of the “bed and body” clinical demands, the percentage of residents incontinent and
the percentage ambulatory, was significantly associated with different turnover categories. The emergency room admission rate for the facility showed
some (nonsignificant) effect on increased likelihood
of high turnover, however. What cannot be shown
from these cross-sectional analyses, however, is
whether clinical acuity and stress led to turnover in
these facilities or vice versa.
Market Environment Factors
None of the variables stated in Hypothesis 7 was
significant in distinguishing either high- or low-turnover facilities. Neither the unemployment rate nor the
per capita income in the county in which the facility
was located appeared to be a factor in distinguishing
extreme turnover rates among nursing assistants. Examination of the distribution of unemployment rate
by turnover category, however, showed that the variances in unemployment were not similar across the
three categories, making it difficult to compare effects. Market concentration of nursing home beds in
the county, another typical indication of competition
for employees, was not a significant factor in this
analysis.
In sum, the factors that significantly distinguished
low-turnover facilities from the referent group of facilities, with the exception of RN turnover rate, were
not the same factors associated with very high turnover. Further, RN turnover rate was the only factor
that suggested evidence of a somewhat linear and
monotonic relationship with turnover in that it was
both negatively associated with being in the lowturnover group and positively associated with being
in the high-turnover group.
Summary and Discussion
One limitation of this research is that although it
drew on previous inquiries regarding the correlates of
staff turnover, it did not test a single theoretical perspective, and the overall predictive validity of the
model tested should not be used for comparison purposes. Rather than testing a well-defined model of
turnover, our intent was to explore an alternative empirical approach to viewing turnover in light of
known predictors. Although these known predictors
are organized in conceptual configurations and stated
as hypotheses for conceptual clarity, we view the findings presented here as exploratory. The approach of
166
The Gerontologist
manage the long-term care staff retention problem.
The findings presented here offer a starting point.
Furthermore, human resources professionals from the
nursing home industry should address with researchers
the measurement of turnover and provide guidance
regarding productive and nonproductive levels of
turnover, both high and low.
These findings should be interpreted in light of the
fact than an optimal turnover rate for nursing homes
has not been established and the categorical outcome
variable used in the logit analysis was arbitrarily defined. Nevertheless, the logit analysis, in contrast with
the traditional ordinary least squares modeling approach, is instructive. Specifically, it gives some credence to the argument that simple analysis of turnover
rates expressed as percentages may not be desirable.
Accordingly, a goal of reducing turnover by 10%
where turnover is very high would be a very different
challenge than reducing turnover by 10% where it is
already very low. Managing voluntary and involuntary
terminations of chronically poor performers requires a
different approach than stemming a significant outflow
of good staff to better paying organizations. Facilities
that have a collective bargaining agreement may struggle to avoid both high- and very low-turnover situations. Having a union, again, does not prevent high
turnover. Further, it may promote very low turnover in
some facilities. This is a question that warrants further
research. Having a collective bargaining agreement generally promotes a more stable staff, but it also reduces
management’s discretion, affects organizational culture, and may prevent workforce renewal through job
security measures. Given the cross-sectional nature of
the data, it should be noted, union presence may be endogenous with high turnover in that both high turnover
itself and the conditions that lead to it to may increase
the likelihood of a union contract. Unionization, on the
other hand, would not likely be caused by low turnover.
Some of the reports of very high turnover are apparently from facilities that have their own training
programs. Some of these may be serving as entry
points to the occupation for nurse’s aides who leave
after becoming certified for employment in other
nursing facilities or hospitals. Reimbursement incentives for providing nurse’s aide certification may need
to be examined. If nurse’s aides are, in fact, the backbone of long-term care, then policy-making bodies
which have historically addressed shortages of health
personnel through education financing and other
strategies urgently need to work with the industry to
increase the supply of these caregiving staff.
The finding from the introduction of the Investor
Owned 3 Chain Membership interaction also suggests
that a stronger partnership between public policy
makers and industry leaders may be productive. Although each of these separate factors predicted the likelihood of high turnover (although chain membership
was not significant at .05 in the model), the interaction
term showed that being a for-profit, chain-owned facility significantly reduced the chances of being in
either extreme group compared with being a for-profit
independent. The expertise of the investor-owned
comparing high- and low-turnover facilities against a
middle referent group is itself exploratory and not intended to test well-conceived theoretical models. Our
intent was to reintroduce to the turnover literature the
possibility that not all staff turnover is equally damaging to nursing facilities and their residents and, consequently, to show that turnover should not necessarily
be viewed as a linear function of a set of predictors.
The findings presented here push this argument
one step further in that they suggest that different sets
of predictors operate at the low end of the turnover
continuum (fewer than 6.6% in 6 months) than at the
high end (more than 64% in 6 months). Table 2
shows four predictors to be significant at the .05 level
in distinguishing low-turnover facilities from the referent group: not having supervisors trained in management, having lower RN turnover, having a flatter
management structure in which many people report
directly to the administrator, and having a union contract governing nursing assistant/management relations. As a group, these predictors are somewhat suggestive of the simple, stable nursing home that was
once the norm when one-size-fits-all, custodial care
was all that was expected. Does this model still work
well in some communities and with some resident
populations? A question for future research is whether
this simple and stable model is truly still identifiable
and, if so, whether residents are receiving good care.
The likelihood of being in the high-turnover category was increased by three factors significant at the
.05 level—having higher RN turnover, being a training site, and being investor owned rather than nonprofit. Interaction analysis revealed that investorowned chain facilities were less likely to be in the
high-turnover group than were investor-owned independents. This set of predictors is suggestive of a facility type with quite permeable borders and much inward and outward movement of employees between
the facility and its environment. Future research
should address the question of whether there are circumstances where this instability works well to produce good nursing home care or whether this level of
instability is categorically incompatible with good
long-term care.
Beyond these statistically significant findings, the
reader is encouraged to examine the relative risk
ratios and significance levels throughout Table 2 in
building future models because, as has been discussed, the direction of the findings was stable across
alternative groupings of the dependent variable, although the significance levels of some predictors did
vary depending on the cut-off points for the dependent variable categories.
Researchers attempting to model the causes of high
turnover, we now argue, may produce more clear and
precise findings if they omit very low turnover facilities from their analyses. Studies are needed that evaluate models predicting turnover in low- to moderateturnover facilities and those that evaluate separate,
distinct models to predict turnover in moderate- to
high-turnover facilities. This next step of inquiry is
needed to move forward our understanding of how to
Vol. 42, No. 2, 2002
167
chains in hiring and retaining staff should not be underestimated in the search for replicable solutions.
These findings suggest that practicing managers in
nonunionized facilities may want to consider how
they can provide alternative support structures to fill
the role a labor contract plays without the loss of renewing turnover that may accompany the collective
bargaining agreement. On the other hand, the nursing
home industry has been a major focus of unionization
efforts within the health care system for some time,
and the commitment of the unions to quality-of-worklife issues in long-term care has been substantial.
RN turnover is closely tied to turnover among nonprofessional staff, suggesting the need for progressive
human resource management at all levels. Professional leadership instability may indicate a lack of
commitment to the facility that trickles down to nursing assistants. Further analysis may reveal that local
labor market conditions other than per capita income
do affect nursing assistant turnover indirectly through
their impact on professional staff turnover. The insignificant role of market factors evidenced here is somewhat puzzling, although it has been noted elsewhere
that these environmental factors tend to be less influential in service employment turnover than in manufacturing (Cotton & Tuttle, 1986). Further, although
it is not possible to establish causal order between
emergency room admissions and staff turnover from
these cross-sectional data, a relationship between resident and caregiver instability is suggested.
From an analytic perspective, these findings raise
questions about the use of linear regression as the best
approach to modeling nursing facility turnover. Both
in terms of the antecedents and presumed consequences of turnover, very low turnover cannot be assumed to be less of a problem than, or even assumed
to be less of the same substantive problem as, very
high turnover. In addition, although aggregate, organizational turnover is important to understand regardless of the individual events, quits and terminations are different and combining them does create
“noise” from a conceptual perspective. This study,
like many others, does not distinguish between voluntary and involuntary turnover.
Future analysis of these data will enable explorations of the shape of the relationship between levels of
staff turnover and resident outcomes. What this analysis has accomplished is to deconstruct a series of relationships that may influence turnover. Future research should identify a range of facility turnover
rates that is neither too high nor too low in terms of
quality of care and financial outcomes.
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Received December 5, 2000
Accepted October 12, 2001
Decision Editor: Laurence G. Branch, PhD
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