Content uploaded by Hansapani Rodrigo
Author content
All content in this area was uploaded by Hansapani Rodrigo on May 07, 2020
Content may be subject to copyright.
The Impact of Vocational Rehabilitation Services and Demographic Factors
Towards the Successful Employment Outcomes Among Hard-of-Hearing
Individuals
Sergio Cuevas ∗Hansapani Rodrigo†Sandra Hansmann‡Shawn Saladin§
Barbara Schoen ¶
Abstract
Hard-of-hearing (HoH) individuals are encouraged to utilize the numerous vocational rehabilitation
services provided by the state-federal vocational rehabilitation (VR) services to enhance their qual-
ity of life and employment opportunities. Despite the importance of these services, limited studies
have been conducted to identify the most meaningful VR services for the HoH population. This
cross-sectional retrospective study on 24,983 evaluated consumers with and without successful em-
ployment outcome who were drawn from the U.S. Department of Education Rehabilitation Service
Administration Case Service Report for the Fiscal Year 2014. The main goal of this study was to
assess the impact of existing VR services in achieving a successful employment outcome. Addition-
ally, we evaluated the effect of various demographic factors (gender, race and ethnicity, age, level
of education, and secondary disability) on obtaining a successful employment outcome. Chi-square
Automatic Interaction Detector (CHAID) and Logistic regression were used to analyze the data.
Among all consumers, 69.7% of HoH consumers reached successful employment outcomes. The
most significant VR services related to successful employment outcomes included assessment, diag-
nosis, and treatment of impairments, rehabilitation technology, vocational rehabilitation counseling
and guidance, information and referral services, job placement assistance, job search assistance,
transportation, maintenance, and other services.
Key Words: Hard-of-Hearing population, Employment outcomes, CHAID analysis, Logistic re-
gression
1. Introduction
According to the National Center for Health Statistics (2015), approximately 37.2 million
Americans live with hearing loss. About 2-3 of every 1,000 children in the U.S. are born
with a hearing loss in one or both ears (Centers for Disease Control and Prevention, 2010).
Data from the Committee on Accessible and Affordable Hearing Health Care for Adults
(2016) as well as other sources, demonstrated that hearing loss may develop at any point
during the life course, and the onset can be sudden from a variety of causes (e.g., trauma,
infection, genetic syndromes, aging, or excessive noise exposure), where one or both ears
can be affected problems (Hasson, Theorell, Wall´
en, Leineweber, & Canlon, 2011).
Due to a variety physical and psychosocial barriers, employment rates among the hard-
of-hearing (HoH) individuals are lower than deaf individuals (Luft, Vierstra, Copeland, &
Resh, 2009), and they earned lower than those in the general population (Walter & Dirmyer,
2013). Utilization of proper accommodations at the workplace may help reduce the em-
ployment barriers for HoH individuals, although the size and corporatization of the em-
ployer can significantly affect the provision of such accommodations (Bowe et al., 2005).
∗School of Rehabilitation Services and Counseling, UT Rio Grande Valley, Edinburg, TX 78539
†School of Mathematical and Statistical Sciences, UT Rio Grande Valley, Edinburg, TX 78539
‡School of Rehabilitation Services and Counseling, UT Rio Grande Valley, Edinburg, TX 78539
§School of Rehabilitation Services and Counseling, UT Rio Grande Valley, Edinburg, TX 78539
¶School of Rehabilitation Services and Counseling, UT Rio Grande Valley, Edinburg, TX 78539
1221
JSM 2019 - Social Statistics Section
As a consequence, HoH individuals have faced higher rates of unemployment and under-
employment than people who are not hard-of-hearing (Danermark, 2005; Punch, 2016;
Punch et al., 2004) throughout the past years.
The State-Federal Vocational Rehabilitation (VR) Program under the Department of
Education is crucial in providing rehabilitation services that can help people with disabili-
ties increase their employment rates (Huang, et al., 2016). Hard-of-hearing individuals can
benefit from numerous vocational rehabilitation services to enhance their quality of life
and employment opportunities. These include developing self-advocacy, obtaining infor-
mation and referral services to community resources, and receiving job placement services,
to name just a few options.
But despite the availability of important vocational rehabilitation services, earlier stud-
ies have found that individuals with hearing loss are less likely to seek assistance from VR
professionals (Glass & Elliott, 1993; Jennings & Shaw, 2008). For instance, one early study
has found that younger persons who are HoH had less chance of successful employment
than older persons who are HoH (Lafitte, 1978) while a more recent study has found that an
early onset of hearing loss is related to employment difficulties later (Hogan et al. 2009).
Much later Hayward and Schmidt-Davis (2003) still found that only 15% out of 75,117
consumers with hearing loss had obtained job placement services between the years 1995
and 2000. Moreover, they discovered that consumers with hearing loss had received assis-
tive devices (i.e. hearing aid) and other services relatively at a lower rate than the persons
with other disabilities (Bradley et al., 2013).
In addition, compelling evidence exists to support the claim that there are significant
disparities among gender, race, and ethnicity, level of education and impact from a sec-
ondary disability of the individuals in receiving the VR services (Boutin & Wilson, 2009;
Nakaji 2014; Feist-Price,1995; Lafitte, 1978; Olney & Kennedy, 2002). Previous studies
demonstrate slightly more women with hearing loss were served under the VR program,
differences among genders who receive VR services create an interest since men are more
likely to be HoH than women (Cruickshanks et al., 2015).
Previous studies also suggest access to VR services is more difficult for minorities than
for service-seekers from non-minority groups (Wilson, 1999; Wilson & Senices, 2005; Wil-
son, Harley, McCormick, Jolivette, & Jackson, 2001; Wilson, Jackson, & Doughty, 1999).
Somewhat unsurprisingly, Feist-Price (1995) found White individuals were accepted for
rehabilitation services more often than Black or African Americans and were success-
fully rehabilitated more frequently than their Black or African American counterparts with
higher-paid positions. Similarly, Moore (2001) has found that Hispanic and Latino con-
sumers who are HoH possessed a lower rate of success in closures in VR service programs
than non-Latinos who are HoH. Overall, minorities have had less success in becoming em-
ployed under the VR system when compared with White individuals or other racial/ethnic
groups (Olney & Kennedy, 2002), possibly due to limited knowledge of rehabilitation ser-
vices and its benefits and expressing a cultural mistrust of rehabilitation practitioners and
potential employers (Moore, Ningning, Eugene-Cross, & Washington, 2016). And while
education clearly plays a role in awareness of services and service-seeking, Boutin (2010)
demonstrated that most consumers who are HoH with higher levels of education are still
typically underemployed.
A large percentage of people served under the VR program with a documented sec-
ondary disability (Nakaji, 2014) exists, yet previous studies have not explored individuals
who are HoH with a secondary disability regarding VR services and employment outcomes.
One previous study by Hogan et al. (2009) found when the main condition was a hearing
loss, 66.2% of these individuals were employed full-time, but when the main condition was
not a hearing loss, only 46.4% were employed full-time.
1222
JSM 2019 - Social Statistics Section
Despite the rich literature of the studies on individuals with hearing loss, there is limited
knowledge about issues relevant to rehabilitation counseling services related to individuals
who are HoH regarding gender, race and ethnicity, age, level of education, and those with
a secondary disability (Dammeyer & Chapman, 2017). As a result of these factors, identi-
fying VR service areas that effectively address the needs of individuals who are HoH may
help reduce concerning problems between VR services and employment outcomes for this
population and maximize the possibility for a successful employment outcome.
Thus, the purpose of the present study was to evaluate the importance of VR services on
reaching successful employment and identify the impact from various demographic factors
like gender, race and ethnicity, age, level of education, and secondary disability among the
hard-of-hearing individuals.
2. Materials and Methods
2.1 Data
We conducted a cross-sectional retrospective study using data from the RSA-911 service re-
port for the year 2014. RSA-911 report includes information regarding demographic char-
acteristics, type of disability, interventions or services provided, the reason for case closure,
employment status, and sources of financial support (Dowden, Ethridge, & Brooks, 2016).
The study included 24,983 consumers who were HoH, including both with and without a
successful employment outcome. The dataset contains five consumers’ demographic vari-
ables (Table 1) and the details of 28 VR services. All of them were categorical variables
with age at application and level of education attained at closure being ordinal.
The study population consists of 50.2% female consumers, and the majority of the
consumers were non-Hispanic White (76.7%). Most consumers, 45.1%, were ages 25-
54, and 47.8% had no formal schooling or had a high school diploma/GED. The majority
of consumers, 72.9%, did not have a secondary disability. According to Table 1, the top
five VR services consumers who are HoH received were assessment (60.3%), vocational
rehabilitation counseling and guidance (55.2%), rehabilitation technology (55%), diagnosis
and treatment of impairments (51.4%), and information and referral services (17.4%). The
five VR services consumers did not receive or least received were apprenticeship training
(0%), reader services (0%), personal attendant services (0%), basic academic remedial or
literacy training (0.2%), and customized employment services (0.4%).
The response variable for this study was the VR employment outcome, either success-
ful or unsuccessful. According to the RSA-911 Reporting Manual (Rehabilitation Services
Administration, 2013), a “successful rehabilitation” outcome as occurs after VR consumers
have been accepted for services, developed and signed a written Individualized Plan for
Employment, and obtained and maintained employment for a minimum of 90 days, and an
“unsuccessful rehabilitation” outcome occurs after a consumer has been accepted for and
provided with VR services, but was not able to make it to the point of obtaining and main-
taining employment for at least 90 days. After receiving VR services, 69.7% of consumers
reached successful employment outcomes.
2.2 Statistical methods
We used a Chi-square Automatic Interaction Detection (CHAID) (Kass, 1980) analysis to
find the most influential VR services and the impact from demographic variables in find-
ing successful employment outcomes among the hard-of-hearing consumers. CHAID is a
decision tree model which helps in discovering the relationship between a set of predictor
variables and the response variable. This model helps to break down the overall population
1223
JSM 2019 - Social Statistics Section
into homogeneous groups which share similar characteristics among each other. It builds
a predictive decision tree model by determining the optimal ways to merge predictor vari-
ables to explain the relationship between the response and the predictor variables using
chi-square tests. The process repeats to find the predictor variable on each leaf that is most
significantly related to the response, branch by branch until no further predictors are found
to have a statistically significant effect on the response. A Bonferroni adjusted p-value was
utilized in order to account for multiple comparisons (Statistics Solutions, 2019).
Variable Category Count(n) Percentage(%)
Gender Male n=12,435 49.80%
Female n=12,548 50.20%
Race and Ethnicity Non-Hispanic White n=19,155 76.70%
Non-Hispanic
Black n=2,585 10.30%
Hispanic n=2,360 9.40%
Other n=883 3.50%
Age at Application 14-24 n=3,088 12.40%
25-54 n=11,277 45.10%
55+ n=10,618 42.50%
Level of Education
Attained at Closure
No formal schooling,
Elementary education
(grades 1-8), Secondary
education, no high school
diploma (grades 9-12), Special
education certificate of
completion/diploma
or in attendance,
High school graduate
or equivalency
certificate (GED)
n=11,933 47.80%
Vocational/Technical
Certificate or License n=962 3.90%
Post-secondary education,
no degree or certificate,
Post-secondary
academic degree,
Associate degree,
Bachelor’s degree,
Occupational credential
beyond undergraduate
degree work
n=10,537 42.20%
Master’s degree,
Any degree above a Master’s
-e.g. Ph.D., Ed.D., J.D.,
Occupational credential
beyond graduate
degree work
n=1,551 6.20%
1224
JSM 2019 - Social Statistics Section
Secondary Disability Mental n=2,391 9.60%
Physical n=3,406 13.60%
Other n=962 3.90%
None n=18,224 72.90%
Assessment No n=9,930 39.70%
Yes n=15,053 60.30%
Diagnosis and
Treatment of
Impairments
No n=11,636 46.60%
Yes n=12,848 51.40%
Missing n=499 2.00%
Vocational Rehabilitation
Counseling and Guidance No n=9,964 39.90%
Yes n=13,796 55.20%
Missing n=1,223 4.90%
Graduate College or
University Training No n=23,666 94.70%
Yes n=104 0.40%
Missing n=1,213 4.90%
Four-Year College or
University Training No n=22,919 91.70%
Yes n=850 3.40%
Missing n=1,214 4.90%
Junior or
Community College
Training
No n=23,432 93.80%
Yes n=361 1.40%
Missing n=1,190 4.80%
Occupational or
Vocational Training No n=23,068 92.30%
Yes n=700 2.80%
Missing n=1,215 4.90%
On-the-job
Training No n=23,581 94.40%
Yes n=156 0.60%
Missing n=1,246 5.00%
Apprenticeship
Training No n=23,733 95.00%
Yes n=6 0.00%
Missing n=1,244 5.00%
Basic Academic
Remedial or
Literacy Training
No n=23,684 94.80%
Yes n=58 0.20%
Missing n=1,241 5.00%
Job Readiness
Training No n=23,062 92.30%
Yes n=679 2.70%
Missing n=1,242 5.00%
1225
JSM 2019 - Social Statistics Section
Disability-Related
Skills Training No n=23,599 94.50%
Yes n=141 0.60%
Missing n=1,243 5.00%
Miscellaneous
Training No n=23,132 92.60%
Yes n=564 2.30%
Missing n=1,287 5.20%
Job Search
Assistance No n=21,759 87.10%
Yes n=2,033 8.10%
Missing n=1,191 4.80%
Job Placement
Assistance No n=21,409 85.70%
Yes n=2,483 9.90%
Missing n=1,091 4.40%
On-the-job
Supports-Short
Term
No n=22,965 91.90%
Yes n=776 3.10%
Missing n=1,242 5.00%
On-the-job
Supports-Supported
Employment
No n=23,258 93.10%
Yes n=489 2.00%
Missing n=1,236 4.90%
Transportation No n=22,004 88.10%
Yes n=1,853 7.40%
Missing n=1,126 4.50%
Maintenance No n=22,411 89.70%
Yes n=1,378 5.50%
Missing n=1,194 4.80%
Rehabilitation Technology No n=10,880 43.50%
Yes n=13,749 55.00%
Missing n=354 1.40%
Reader Services No n=23,734 95.00%
Yes n=5 0.00%
Missing n=1,244 5.00%
Interpreter Services No n=22,952 91.90%
Yes n=876 3.50%
Missing n=1,155 4.60%
Personal Attendant
Services No n=23,732 95.00%
Yes n=7 0.00%
Missing n=1,244 5.00%
Technical Assistance
Services No n=23,580 94.40%
Yes n=165 0.70%
Missing n=1,238 5.00%
1226
JSM 2019 - Social Statistics Section
Information and
Referral Services No n=19,495 78.00%
Yes n=4,340 17.40%
Missing n=1,148 4.60%
Benefits Counseling No n=23,555 94.30%
Yes n=192 0.80%
Missing n=1,236 4.90%
Customized Employment
Services No n=23,690 94.80%
Yes n=108 0.40%
Missing n=1,185 4.70%
Other Services No n=20,652 82.70%
Yes n=3,230 12.90%
Missing n=1,101 4.40%
Type of Closure Not Successful n=7,578 30.30%
Successful n=17,405 69.70%
Table 1: Summary of the Study Population
Subsequently, a binary logistic regression analysis was performed to quantify the ef-
fect and identify the impact of each predictor towards a successful employment outcome.
The stepwise model selection method was utilized based on the Akaike information crite-
rion (Akaike, 1973), in developing the binary logistic regression models. Potential multi-
collinearity effects and were addressed successfully. Out of several candidates models with
two-way interactions, the best model was selected, based on both statistical significance
and personal expertise through the fieldwork experience. The goodness of fit tests was
used including the Omnibus and the likelihood ratio tests to do the model validation.
The corresponding odds ratios were calculated by controlling for other factors. The
odds ratio identifies the likelihood of a successful employment outcome for individuals
with certain consumer characteristics and vocational rehabilitation services compared to
those who did not. Statistical analyses were conducted using R version 3.6.1. All statistical
tests were 2 -sided and has a significance (alpha) level of 0.05.
3. Results
Similar to the population with hearing impairments (Boutin, 2010), non-Hispanic whites
(76.7%) comprised the largest group of HoH consumers in this study served in the VR
program followed by non-Hispanic Black HoH consumers (10.4%), Hispanic consumers
who are hard-of-hearing (9.5%), and American Indian or Alaska Native, Asian, Native
Hawaiian or Other Pacific Islander, or Multiracial individuals made up the least amount of
consumers (3.5%).
1.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments
= 0 and Job Placement Assistance is either 0 or 2 and Maintenance is either 0
or 2, and Other Services is either 0 or 2 and Secondary Disability = 4 and Age
at Application=1, then the predicted outcome for this category would be 0, not
successfully employed. There were 601 subjects in this category, and the error
rate was 18.64%..
1227
JSM 2019 - Social Statistics Section
2.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments =
0 and Job Placement Assistance is either 0 or 2, and Maintenance is either 0 or
2, and Other Services is either 0 or 2 and Secondary Disability = 4 and Age at
Application is either 2 or 3, and Level of Education Attained at Closure is either
1 or 3, then the predicted outcome for this category would be 0, not successfully
employed. There were 1,939 subjects in this category, and the error rate was
30.17%.
3.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments
= 0 and Job Placement Assistance is either 0 or 2, and Maintenance is either 0
or 2, and Other Services is either 0 or 2, and Secondary Disability = 4 and Age
at Application is either 2 or 3, and Level of Education is either 2 or 4, then the
predicted outcome for this category would be0, not successfully employed. There
were 220 subjects in this category, and the error rate was 18.64%.
4.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments =
0 and Job Placement Assistance are either 0 or 2 and Maintenance is either 0 or 2,
and Other Services is either 0 or 2, and Secondary Disability is either 1 or 3, then
the predicted outcome for this category would be 0, not successfully employed.
There were 735 subjects in this category, and the error rate was 18.91%.
5.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments =
0 and Job Placement Assistance are either 0 or 2 and Maintenance is either 0 or 2,
and Other Services is either 0 or 2 and Secondary Disability=2, then the predicted
outcome for this category would be 0, not successfully employed. There were 782
subjects in this category, and the error rate was 14.96%.
6.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments =
0 and Job Placement Assistance is either 0 or 2 and Maintenance is either 0 or 2,
and Other Services is 1, then the predicted outcome for this category would be
1, successfully employed. There were 185 subjects in this category, and the error
rate was 44.32%.
7.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments =
0 and Job Placement Assistance is either 0 or 2 and Maintenance is 1, then the
predicted outcome for this category would be 1, successfully employed. There
were 131 subjects in this category, and the error rate was 26.72%.
8.) If Rehabilitation Technology=0 and Diagnosis and Treatment of Impairments = 0
and Job Placement Assistance = 1, then the predicted outcome for this category
would be 1, successfully employed. There were 419 subjects in this category, and
the error rate was 1.26%.
9.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments
is either 1 or 2 and Age at Application = 1, then the predicted outcome for this
category would be 1, successfully employed. There were 419 subjects in this
category, and the error rate was 39.86%.
10.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments
is either 1 or 2 and Age at Application = 2, then the predicted outcome for this
category would be 1, successfully employed. There were 1,742 subjects in this
category, and the error rate was 29.22%.
11.) If Rehabilitation Technology = 0 and Diagnosis and Treatment of Impairments
is either 1 or 2 and Age at Application = 3, then the predicted outcome for this
category would be 1, successfully employed. There were 1,576 subjects in this
category, and the error rate was 20.05%.
1228
JSM 2019 - Social Statistics Section
12.) If Rehabilitation Technology = 1 and Transportation=0 and Secondary Disability
= 4 and Age at Application=1, then the predicted outcome for this category would
be 1, successfully employed. There were 425 subjects in this category, and the
error rate was 23.06%.
13.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either
0 or 2, and Job Placement Assistance is either 0 or 2, and Interpreter Services is
either 0 or 2, and Diagnosis and Treatment of Impairments is either 0 or 2 and
Maintenance is either 0 or 2 and Race = 1, then the predicted outcome for this
category would be 1, successfully employed. There were 2,714 subjects in this
category, and the error rate was 3.8%.
14.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3 and Job Search Assistance is either
0 or 2, and Job Placement Assistance is either 0 or 2, and Interpreter Services is
either 0 or 2, and Diagnosis and Treatment of Impairments is either 0 or 2 and
Maintenance is either 0 or 2 and Race is either 2 or 3 or 4, then the predicted
outcome for this category would be 1, successfully employed. There were 401
subjects in this category, and the error rate was 7.23%.
15.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3 and Job Search Assistance is either
0 or 2, and Job Placement Assistance is either 0 or 2, and Interpreter Services
is either 0 or 2, and Diagnosis and Treatment of Impairments is either 0 or 2
and Maintenance = 1, then the predicted outcome for this category would be 1,
successfully employed. There were 54 subjects in this category, and the error rate
was 16.67%.
16.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either
0 or 2, and Job Placement Assistance is either 0 or 2, and Interpreter Services is
either 0 or 2, and Diagnosis and Treatment of Impairments = 1 and Vocational Re-
habilitation Counseling and Guidance is either 0 or 2, then the predicted outcome
for this category would be 1, successfully employed. There were 615 subjects in
this category, and the error rate was 11.38%.
17.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either 0 or
2, and Job Placement Assistance is either 0 or 2, and Interpreter Services is either
0 or 2 and Diagnosis and Treatment of Impairments = 1 and Vocational Rehabil-
itation Counseling and Guidance = 1 and Race is either 1 or 3 and Information
and Referral Services is either 0 or 2, then the predicted outcome for this category
would be 1, successfully employed. There were 2,003 subjects in this category,
and the error rate was 6.29%.
18.) If Rehabilitation Technology = 1 and Transportation=0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either 0 or
2, and Job Placement Assistance is either 0 or 2, and Interpreter Services is either
0 or 2 and Diagnosis and Treatment of Impairments=1 and Vocational Rehabil-
itation Counseling and Guidance = 1 and Race is either 1 or 3 and Information
and Referral Services=1, then the predicted outcome for this category would be
1, successfully employed. There were 784 subjects in this category, and the error
rate was 4.21%.
1229
JSM 2019 - Social Statistics Section
19.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either
0 or 2, and Job Placement Assistance is either 0 or 2, and Interpreter Services
is either 0 or 2 and Diagnosis and Treatment of Impairments = 1 and Vocational
Rehabilitation Counseling and Guidance = 1 and Race is either 2 or 4, then the
predicted outcome for this category would be 1, successfully employed. There
were 253 subjects in this category, and the error rate was 11.86%.
20.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either 0
or 2, and Job Placement Assistance is either 0 or 2, and Interpreter Services = 1,
then the predicted outcome for this category would be 1, successfully employed.
There were 80 subjects in this category, and the error rate was 21.25%.
21.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance is either 0
or 2, and Job Placement Assistance = 1, then the predicted employment outcome
would be 1, successfully employed. There were 270 subjects in this category, and
the error rate was 14.44%.
22.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 4 and Age at Application is either 2 or 3, and Job Search Assistance = 1, then the
predicted employment outcome would be 1, successfully employed. There were
224 subjects in this category, and the error rate was 25%.
23.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 1, then the predicted employment outcome would be 1, successfully employed.
There were 492 subjects in this category, and the error rate was 21.95%.
24.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 2, then the predicted employment outcome would be 1, successfully employed.
There were 1,045 subjects in this category, and the error rate was 16.36%.
25.) If Rehabilitation Technology = 1 and Transportation = 0 and Secondary Disability
= 3, then the predicted employment outcome would be 1, successfully employed.
There were 340 subjects in this category, and the error rate was 11.47%.
26.) If Rehabilitation Technology = 1 and Transportation is either 1 or 2, then the
predicted employment outcome would be 1, successfully employed. There were
1,260 subjects in this category, and the error rate was 31.98%.
27.) If Rehabilitation Technology = 2, then the predicted employment outcome would
be 0, not successfully employed. There were 278 subjects in this category, and the
error rate was 26.62%.
Table 2: CHAID Interactions 1
Through the univariate analysis performed using Chi-square tests and Fisher’s exact
tests (when the cell counts are small), it was found that there exists a significant associa-
tion between the employment outcome with each predictor variable except gender (p-value
1Footnote: The code significance each variable were: gender: male=male and female=female, race and
ethnicity: Non-Hispanic White=1, Non-Hispanic Black=2, Hispanic=3, and Other=4, age at application: 14-
24=1, 25-54=2, 55+=3, level of education attained at closure: no formal schooling or had a high school
diploma/GED=1, vocational/technical certificate or license=2, post-secondary education to occupational cre-
dential beyond undergraduate degree work=3, and master’s degree to occupational credential beyond grad-
uate degree work=4, secondary disability: mental=1, physical=2, other=3, and none=4, VR services: not
received=0, received=1, and missing=2, and employment outcome: not successful=0, successful=1, and miss-
ing=2.
1230
JSM 2019 - Social Statistics Section
.084).
A CHAID model was developed to identify the factors with the greatest impact on the
likelihood of response. The first sets of splits of the final CHAID model, hence the most
significant predictor variables towards the response, was whether or not several vocational
rehabilitation services were utilized by the consumers. We have listed down some of the
interesting interactions that we have found through our analysis. The rest of the interactions
are listed in Table 2.
•HoH consumers with any level of education attained at closure from any age groups
that only received diagnosis and treatment of impairments as a VR service were able
to achieve a successful employment outcome.
•Regardless of the race and ethnicity, for consumers ages 25-54 or 55+, when they
received diagnosis and treatment of impairments, vocational rehabilitation counsel-
ing and guidance, information and referral services, and rehabilitation technology,
successful employment outcome was guaranteed.
•If consumers had a mental, physical, or other secondary disability and only received
rehabilitation technology as a VR service, they were still predicted to achieve a suc-
cessful employment outcome.
•If consumers did not receive any VR services, an unsuccessful employment outcome
was predicted.
Additionally, we were able to score the relative importance of the explanatory variable in
the CHAID based on mean decrease in accuracy
a) rehabilitation technology (0.264)
b) diagnosis and treatment of impairments (0.090)
c) job placement assistance (0.016)
d) transportation (0.016)
e) secondary disability (0.010)
f) age at application (0.010)
g) maintenance (0.006)
h) other services (0.005)
i) job search assistance (0.003)
j) race and ethnicity (0.001)
k) vocational rehabilitation counseling and guidance (0.001)
l) level of education attained at closure (0.001)
m) interpreter services (<0.001)
n) information and referral services (<0.001)
1231
JSM 2019 - Social Statistics Section
The most significant VR services identified through the likelihood ratio test after con-
trolling for other variables are as follows: maintenance (p-value 1.393e-05), assessment
(p-value 2.2e-16), on-the-job supports-supported employment (p-value 2.2e-16), other ser-
vices (p-value 2.898e-07), transportation (p-value 3.293e-07), diagnosis and treatment of
impairments (p-value 0.0000), vocational rehabilitation counseling and guidance (p-value
0.0000), job search assistance (p-value 0.0000), job placement assistance (p-value 0.0000),
information and referral services (p-value 0.0005), rehabilitation technology (p-value 0.0000),
job readiness training (p-value 0.0010), miscellaneous training (p-value 0.0034), interpreter
services (p-value 0.0166), benefits counseling (p-value 0.0298), and junior or community
college training (p-value 0.0793). The impact of demographic predictors include, level of
education attained at closure (p-value 0.0000), secondary disability (p-value 0.0000), race
and ethnicity (p-value 0.0000), and age at application (p-value 0.0000).
Moreover, the following interactions were found to be significant regarding success-
ful outcomes from the binary logistic regression model based on the likelihood ratio test:
secondary disability and diagnosis and treatment of impairments (p-value 4.68e-7), sec-
ondary disability and job placement assistance (p-value 0.0006), secondary disability and
age at application (p-value 1.366e-05), level of education attained at closure and vocational
rehabilitation counseling and guidance (p-value 4.438e-06), level of education attained at
closure and rehabilitation technology (p-value 0.0006), level of education attained at clo-
sure and age at application (p-value 2.2e-16), diagnosis and treatment of impairments and
age at application (p-value 0.0294), job search assistance and age at application (p-value
6.03e-12), job placement assistance and age at application (p-value 0.0004), rehabilitation
technology and age at application (p-value 1.357e-07), and rehabilitation technology and
race and ethnicity (p-value 0.0018).
For a HoH consumer who had a level of education attained at closure of master’s de-
gree or higher, the estimated odds of achieving a successful employment outcome is 11.71
(95% CI 3.21-42.66) times as great as the estimated odds for a consumer who had no formal
schooling up to a high school graduate or equivalency certificate (GED) after controlling
for other factors in the model (Table 3). When an HoH consumer had received rehabilita-
tion technology as a VR service, the estimated odds of achieving a successful employment
outcome is 4.55 (95% CI 3.51-5.89) times the estimated odds for a consumer who had not
receive this VR service after controlling for other factors in the model. When an HoH con-
sumer had received diagnosis and treatment of impairments as a VR service, the estimated
odds of achieving a successful employment outcome is 3.15 (95% CI 2.51-3.95) times the
estimated odds for a consumer who had not receive this VR service.
Variable Odds Ratio Lower Upper
Level of Education Attained at Closure2 2.0438 1.1125 3.7546
Level of Education Attained at Closure3 2.2190 1.7664 2.7875
Level of Education Attained at Closure4 11.7085 3.2134 42.661
Secondary Disability1 0.8055 6.1492 1.0552
Secondary Disability2 0.6927 4.7048 1.0198
Secondary Disability3 0.9015 5.7233 1.4200
Assessment1 0.6799 6.2354 7.4145
Diagnosis and Treatment of Impairments1 3.1515 2.5147 3.9495
Diagnosis and Treatment of Impairments2 3.9667 1.3443 1.1705
Vocational Rehabilitation Counseling and Guidance1 1.4099 1.2470 1.5941
Vocational Rehabilitation Counseling and Guidance2 0.0887 1.4826 5.3063
Information and Referral Services1 0.9779 8.6882 1.1006
Information and Referral Services2 0.2734 1.3794 5.4201
1232
JSM 2019 - Social Statistics Section
Junior or Community College Training1 0.7012 5.0281 9.7792
Junior or Community College Training2 0.4769 1.8380 1.2374
Job Readiness Training1 0.6949 5.5019 8.7758
Job Readiness Training2 41.3634 9.6182 1.7789
Job Search Assistance1 2.0077 1.4696 2.7428
Job Search Assistance2 0.7009 1.5303 3.2097
Job Placement Assistance1 2.7433 1.9482 3.8628
Job Placement Assistance2 0.4432 1.1994 1.6380
Transportation1 0.7187 6.0965 8.4717
Transportation2 1.9621 1.1560 3.3303
Maintenance1 1.5704 1.2975 1.9006
Maintenance2 1.2545 6.0609 2.5965
Rehabilitation Technology1 4.5493 3.5122 5.8926
Rehabilitation Technology2 0.8248 2.5933 2.6230
Other Services1 1.4123 1.2354 1.6145
Other Services2 0.8499 5.2749 1.3695
Miscellaneous Training1 0.6743 5.2794 8.6122
Miscellaneous Training2 1.3929 7.1479 2.7143
On-the-job Supports-Short Term1 5.5831 4.2409 7.3501
On-the-job Supports-Short Term2 2.4185 1.7767 3.2922
On-the-job Supports-Supported Employment1 6.3910 4.4049 9.2727
On-the-job Supports-Supported Employment2 0.0140 1.0726 1.8210
Benefits Counseling1 0.7232 4.7279 1.1063
Benefits Counseling2 17.7254 4.7401 6.6283
Interpreter Services1 0.7824 6.3299 9.6709
Interpreter Services2 1.3727 7.2529 2.5980
Race and Ethnicity2 0.6460 5.5531 7.5146
Race and Ethnicity3 0.7985 6.7298 9.4753
Race and Ethnicity4 0.8528 6.7971 1.0699
Age at Application2 3.08011 2.4921 3.8069
Age at Application3 3.8530 3.0966 4.7941
Secondary Disability1:
Diagnosis and Treatment of Impairments1 0.5329 4.1153 6.8995
Secondary Disability2:
Diagnosis and Treatment of Impairments1 0.8128 6.5167 1.0139
Secondary Disability3:
Diagnosis and Treatment of Impairments1 1.0874 7.2510 1.6307
Secondary Disability1:
Diagnosis and Treatment of Impairments2 0.9041 3.1707 2.5778
Secondary Disability2:
Diagnosis and Treatment of Impairments2 4.3989 1.7905 1.0807
Secondary Disability3:
Diagnosis and Treatment of Impairments2 1.0420 1.9823 5.4778
Secondary Disability1: Job Placement Assistance1 2.1763 1.5302 3.0952
Secondary Disability2: Job Placement Assistance1 1.3593 9.5451 1.9357
Secondary Disability3: Job Placement Assistance1 0.7497 4.1613 1.3507
Secondary Disability1: Job Placement Assistance2 0.8697 4.8232 1.5681
Secondary Disability2: Job Placement Assistance2 0.8803 4.9349 1.5703
Secondary Disability3: Job Placement Assistance2 0.7342 3.6019 1.4966
Secondary Disability1: Age at Application2 0.4791 3.5418 6.4813
1233
JSM 2019 - Social Statistics Section
Secondary Disability2: Age at Application2 0.5463 3.6172 8.2514
Secondary Disability3: Age at Application2 0.8978 5.3739 1.4998
Secondary Disability1: Age at Application3 0.7561 5.2388 1.0912
Secondary Disability2: Age at Application3 0.6467 4.2782 9.7771
Secondary Disability3: Age at Application3 0.6949 4.0808 1.1831
Level of Education Attained at Closure2:
Vocational Rehabilitation Counseling and Guidance1 2.2703 1.4301 3.6041
Level of Education Attained at Closure3:
Vocational Rehabilitation Counseling and Guidance1 1.11 9.44 1.3052
Level of Education Attained at Closure4:
Vocational Rehabilitation Counseling and Guidance1 2.3565 1.632 3.4025
Level of Education Attained at Closure2:
Vocational Rehabilitation Counseling and Guidance2 2.1683 9.9182 4.7402
Level of Education Attained at Closure3:
Vocational Rehabilitation Counseling and Guidance2 1.5456 1.0034 2.3808
Level of Education Attained at Closure4:
Vocational Rehabilitation Counseling and Guidance2 1.6331 6.8027 3.9203
Level of Education Attained at Closure2:
Rehabilitation Technology1 1.1129 6.8473 1.8089
Level of Education Attained at Closure3:
Rehabilitation Technology1 0.8322 7.0165 9.87
Level of Education Attained at Closure4:
Rehabilitation Technology1 1.9659 1.2969 2.979
Level of Education Attained at Closure2:
Rehabilitation Technology2 1.3615 3.0283 6.1208
Level of Education Attained at Closure3:
Rehabilitation Technology2 1.5591 6.775 3.5877
Level of Education Attained at Closure4:
Rehabilitation Technology2 0.8114 9.6977 6.7893
Level of Education Attained at Closure2:
Age at Application2 0.3762 2.055 6.8853
Level of Education Attained at Closure3:
Age at Application2 0.4681 3.6962 5.9274
Level of Education Attained at Closure4:
Age at Application2 0.0799 2.1538 2.9618
Level of Education Attained at Closure2:
Age at Application3 0.2741 1.4295 5.2575
Level of Education Attained at Closure3:
Age at Application3 0.362 2.8371 4.6202
Level of Education Attained at Closure4:
Age at Application3 0.0401 1.0891 1.4778
Diagnosis and Treatment of Impairments1:
Age at Application2 0.8322 6.5182 1.0625
Diagnosis and Treatment of Impairments2:
Age at Application2 0.5898 1.8266 1.9045
Diagnosis and Treatment of Impairments1:
Age at Application3 1.0455 8.1125 1.3474
Diagnosis and Treatment of Impairments2:
Age at Application3 0.4145 1.2539 1.3702
Job Search Assistance1: Age at Application2 0.4286 2.9597 6.2063
1234
JSM 2019 - Social Statistics Section
Job Search Assistance2: Age at Application2 1.8098 4.139 7.9138
Job Search Assistance1: Age at Application3 0.1988 1.2947 3.0518
Job Search Assistance2: Age at Application3 1.7959 3.4194 9.4325
Job Placement Assistance1: Age at Application2 0.5535 3.76 8.1488
Job Placement Assistance2: Age at Application2 0.3619 7.7861 1.6822
Job Placement Assistance1: Age at Application3 0.3696 2.3905 5.7136
Job Placement Assistance2: Age at Application3 0.2783 4.9729 1.5576
Rehabilitation Technology1: Age at Application2 1.9399 1.4907 2.5245
Rehabilitation Technology2: Age at Application2 0.5762 1.7029 1.9493
Rehabilitation Technology1: Age at Application3 2.2489 1.7155 2.9481
Rehabilitation Technology2: Age at Application3 0.6948 1.9157 2.5198
Rehabilitation Technology1: Race and Ethnicity2 0.8566 6.6718 1.0997
Rehabilitation Technology2: Race and Ethnicity2 1.3251 5.0438 3.481
Rehabilitation Technology1: Race and Ethnicity3 0.6517 5.015 8.47
Rehabilitation Technology2: Race and Ethnicity3 0.4795 1.3548 1.6973
Rehabilitation Technology1: Race and Ethnicity4 0.5266 3.5989 7.7042
Rehabilitation Technology2: Race and Ethnicity4 1.3373 2.1083 8.4824
Table 3: Estimated Odds Ratio and 95% Confidence Intervals 2
The following odds ratios unveil some hidden contributions of different VR services
towards the successful employment outcome that have not been commonly explored in pre-
vious literature. If an HoH consumer had received on-the-job supports-supported employ-
ment as a VR service, the estimated odds of achieving a successful employment outcome
is 6.39 (95% CI 4.40-9.27) times the estimated odds for a consumer who did not receive
this VR service after controlling for other factors in the model. When a consumer who
is hard-of-hearing received on-the-job supports-short term as a VR service, the estimated
odds of achieving a successful employment outcome is 5.58 (95% CI 4.24-7.35) times the
estimated odds for a consumer who did not receive this VR service after controlling for
other factors in the model.
4. Discussion
As one of the relatively few studies focused on the HoH consumer population served by the
State-Federal VR service program, we anticipate our findings may open new insight into the
field of service management. Our results confirmed that the minority HoH groups continue
to receive VR services at a lesser rate than non-Hispanic White HoH. This deserves the
attention of the VR personnel in finding more effective, culturally responsive strategies for
serving this community. For example, more meaningful strategies might include address-
ing the previously identified barriers like limited knowledge of VR services and cultural
mistrust of rehabilitation professionals (Moore et al., 2016). Other areas of improvement
may be found in addressing problems associated with transportation and possible language
barriers among American Indians (Martin et al., 1988; Saravanabhavan, 1991). Further
strategies could better respond to the more collectivist values of group harmony and famil-
ial pride among Asian Americans (Ghosh & Fouad, 2016; Millner & Kim, 2017; Sue &
Sue, 2012).
2Footnote: The reference (base) levels for each explanatory variable were: gender=“male”, race and
ethnicity=“Non-Hispanic White”, age at application=“14-24”, level of education attained at closure=“no for-
mal schooling or had a high school diploma/GED”, secondary disability= “none”, and all VR services=“did
not receive VR service”.
1235
JSM 2019 - Social Statistics Section
As expected based on prior research results (Bainbridge & Ramachandran, 2014), non-
Hispanic White HoH consumers received rehabilitation technology at a higher rate (57.0%)
than all other groups. However, significant results were found between the rates in which
each group received these VR services, particularly among Hispanic consumers. However,
it was surprising to find this group received assessment (76%), diagnosis and treatment
of impairments (63.5%), vocational rehabilitation counseling and guidance (62.6%), and
information and referral services (29.4%) at higher rates than all other groups. Hispanic
consumers were also at a close rate (55.9%) to Non-Hispanic White consumers (57%)
when receiving rehabilitation technology.
The majority of HoH consumers were from the prime working-age group, 25-54, (45.1%)
followed by consumers who were in the older age group, 55+, (42.5%). While it is signif-
icant how consumers in the age group 55+ were found to be assisted at a higher rate in
the VR program than previous research indicates, it is also significant how consumers in
the transition age group continue to be under-served, indicating further attention needed to
HoH consumers in the transition age group.
The majority of consumers in this study (72.9%) did not have a secondary disabil-
ity. A noteworthy finding among HoH consumers with secondary disabilities of other sen-
sory/communicative impairments was how these consumers utilized the majority of the top
VR services more frequently than consumers with a physical, mental, or no secondary dis-
ability did. Consumers who had a vocational/technical certificate or license had received
assessment (70.1%), diagnosis and treatment and impairments (59.3%), vocational rehabil-
itation counseling and guidance (61.1%), rehabilitation technology (60.4%), and informa-
tion and referral services (24.7%) at higher rates than all other consumers with other levels
of education did. The HoH consumers looking into post-secondary opportunities may ben-
efit from discussing career exploration with VR counselors to assess personal levels of
motivation, interest in exploring higher education, and self-advocacy skills, as these can
play an important role in pursuing higher education (Albertini, Kelly, & Matchett, 2011;
Hyde, Nikolaraizi, Powell, & Stinson, 2016).
In terms of gender, there were no statistically noteworthy differences found among the
top VR services received, which included assessment, diagnosis, and treatment of impair-
ments, vocational rehabilitation counseling and guidance, rehabilitation technology, and
information and referral services. On the other hand, notable findings were found between
males and females who are hard-of-hearing regarding females utilizing certain VR services
at slightly higher rates than males.
Overall, the most significant VR services that contributed towards successful employ-
ment outcomes include assessment, diagnosis, and treatment of impairments, rehabilitation
technology, vocational rehabilitation counseling and guidance, information and referral ser-
vices, job placement assistance, job search assistance, transportation, maintenance, and
other services. These VR services turned out to be as expected and similar to previous
research.
There were also several interesting combinations of demographic variables and VR
services found which predicted successful employment outcomes among HoH consumers.
We encourage administrative personnel in the VR program to examine these relationships
in providing a better service which is well adjusted to the majority of HoH consumers
served by each service centers. These include focusing on providing the VR service; reha-
bilitation technology to HoH individuals in any ethnic group who is in between the ages,
25-54 or 55+, to achieve a successful employment outcome. In fact, if those individuals
can be served by a combination of VR services; diagnosis and treatment of impairments,
vocational rehabilitation counseling and guidance, and rehabilitation technology, this will
help them in achieving their employment outcomes. For the HoH consumers who are in
1236
JSM 2019 - Social Statistics Section
the transition-age group 14-24, if they were served by both VR services; rehabilitation
technology and diagnosis & treatment of impairments, then they are highly likely to get
a successful employment outcome. VR counselors are encouraged to focus on provid-
ing a better service in rehabilitation technology, specifically for HoH individuals with a
secondary disability who, if they had received rehabilitation technology as a VR service,
would have been much more likely to have a successful outcome.
In closing, we would like to mention about a few limitations in our study. Although we
had a sample size of 24,983, there were several predictor categories that didn’t have more
than five consumers. Second, only data from FY 2014 was explored, so if results are com-
pared to previous or future years, they may differ. Third, due to low population numbers
for American Indian or Alaska Native, Asian, Native Hawaiian or Other Pacific Islander, or
Multiracial groups, these consumers were not analyzed individually, and instead they were
generalized.
REFERENCES
Akaike, H. (1973), “Information theory and an extension of the maximum likelihood principle.” In B. N.
Petrov & F. Csaki, (Eds.), 2nd International Symposium on Information Theory, Tsahkadsor, Armenia,
USSR, September 2-8, 1971, 267–281. Budapest: Akademiai Kiado.
Albertini, J. A., Kelly, R. R., & Matchett, M. K. (2011), “Personal factors that influence deaf college students’
academic success,” Journal of Deaf Studies and Deaf Education, 17, 85-101.
Bainbridge, K. E., & Ramachandran, V. (2014), “Hearing aid use among older United States adults: The
national health and nutrition examination survey, 2005–2006 and 2009–2010,” Ear and Hearing, 35(3),
289.
Boutin, D. L., (2010), “Occupational outcomes for vocational rehabilitation consumers with hearing impair-
ments,” Journal of Rehabilitation, 76(3), 40.
Boutin, D. L., and Wilson, K. (2009), “An analysis of vocational rehabilitation services for consumers with
hearing impairments who received college or university training,” Rehabilitation Counseling Bulletin,
52(3), 156–166.
Bowe, F., McMahon, B., Chang, T., and Louvi, I. (2005), “Workplace discrimination, deafness and hearing
impairment: The national EEOC ADA research project,” Work, 25(1), 19–25.
Bradley, C. F., Geyer, P. D., and Ebener, D. J. (2013), “Contributors to successful VR outcomes among non-
Latino (Caucasian) and Latino consumers with hearing loss,” Journal of Rehabilitation, 79(2), 24.
Centers for Disease Control and Prevention (2010), Identifying infants with hearing loss-United States, 1999-
2007. Morbidity and Mortality Weekly Report, 59(8), 220–223.
Committee on Accessible and Affordable Hearing Health Care for Adults. (2016). 2 Hearing loss: Extent,
impact, and research needs. In D.G.,Blazer, S.Domnitz, and C.T.,Liverman (Eds.). Hearing Health Care
for Adults: Priorities for Improving Access and Affordability,” Washington (DC): National Academies
Press (US).
Cruickshanks, K. J., Dhar, S., Dinces, E., Fifer, R. C., Gonzalez, F., Heiss, G., Hoffman, H. J., Lee, D. J.,
Newhoff, M., Tocci, L., Torre, P., and Tweed, T. S. (2015), “Hearing impairment prevalence and associated
risk factors in the Hispanic Community Health Study/Study of Latinos,” JAMA Otolaryngology-Head and
Neck Surgery, 141(7), 641–648.
Dammeyer, J., and Chapman, M. (2017), “Prevalence and characteristics of self-reported physical and mental
disorders among adults with hearing loss in Denmark: A national survey,” Social Psychiatry and Psychi-
atric Epidemiology, 52(7), 807–813.
Danermark, B. (2005), “A review of the psychsocial effects of hearing impairment in the working-age popu-
lation” In D. Stephens and L. Jones (Eds.), The impact of genetic hearing impairment, London, England:
Whurr, 106—136.
Dowden, A. R., Ethridge, G., and Brooks, M. (2016), “The impact criminal history has on the employability
of African American and Latino populations with disabilities receiving state vocational rehabilitation ser-
vices: Implications for adding a criminal history variable to the RSA-911 data.” Journal of Vocational
Rehabilitation, 45(2), 213–224.
Feist-Price, S. (1995), “African Americans with disabilities and equity in vocational rehabilitation services:
One state’s review.” Rehabilitation Counseling Bulletin, 39(2), 119–129.
Ghosh, A., & Fouad, N. A. (2016), “Family influence on careers among Asian parent–child dyads,” Journal of
Career Assessment, 24(2), 318–332.
Glass, L. E., and Elliot, H. (1993), “Work place success for persons with adult-onset hearing impairment,” The
1237
JSM 2019 - Social Statistics Section
Volta Review, 95(4), 403-415.
Hasson, D., Theorell, T., Wall´
en, M. B., Leineweber, C., and Canlon, B. (2011), “Stress and prevalence of
hearing problems in the Swedish working population,” BMC Public Health, 11(1), 130.
Hayward, B. J. and Schmidt-Davis, H. (2003), ‘Longitudinal study of the vocational rehabilitation services
program (Final report: VR services and outcomes). Research Triangle Park, C: Research Triangle Institute
International.
Hogan, A., O’Loughlin, K., Davis, A., and Kendig, H. (2009), “Hearing loss and paid employment: Australian
population survey findings,” International Journal of Audiology, 48(3), 117–122.
Huang, I., Cheing, G., Rumrill, P., Bengtson, K., Chan, F., Telzlaff, J., and Snitker, M. (2016), “Characteristics
of people with disabilities receiving assistive technology services in vocational rehabilitation: A logistic
regression analysis,” Journal of Vocational Rehabilitation, 45(1), 63–72.
Hyde, M., Nikolaraizi, M., Powell, D., & Stinson, M. (2016), “Critical factors toward the inclusion of deaf
and hard-of-hearing students in higher education,” In M. Marschark, V. Lampropoulou, & E. K. Skordilis
(Eds.), Diversity in deaf education, 441–471, New York, NY: Oxford University Press.
Jennings, M. B., and Shaw, L. (2008), “Impact of hearing loss in the workplace: Raising questions about
partnerships with professionals,” Work, 30(3), 289–295.
Kass, Gordon V. (1980), “An exploratory technique for investigating large quantities of categorical data,” Ap-
plied Statistics, 29(2), 119–127.
Lafitte, J. A. (1978), “Rehabilitation potential of hearing-impaired clients,” American Annals of the Deaf,
123(5), 580–587.
Luft, P., Vierstra, C., Copeland, C., and Resh, N. (2009), “How staffing patterns impact employment outcomes
across diverse d/hh consumers,” Journal of the American Deafness and Rehabilitation Association, (2009
Conference Issue Supplement), 210–219.
Moore, C. L. (2001), “Racial and ethnic members of under-represented groups with hearing loss and VR
services: Explaining the disparity in closure success rates,” Journal of Applied Rehabilitation Counseling,
32(1), 15–23.
Moore, C. L., Ningning, W., Eugene-Cross, K., and Washington, A. L. (2016), “Immigration trends’ impacts
on state vocational rehabilitation agency minority application rates: An empirical forecast model demon-
stration study.,” Journal of Vocational Rehabilitation, 45(2), 197–212.
Nakaji, D. (2014), Vocational rehabilitation outcomes for deaf and hard of hearing consumers who were sup-
ported by the rehabilitation services administration to pursue postsecondary education (Doctoral disser-
tation). Retrieved from ProQuest Dissertations and Theses Global. (Order No. 3632592).
National Center for Health Statistics, (2015), Disability and functioning (noninstitutionalized adults aged 18
and over), Retrieved January 29, 2018, from http://www.cdc.gov/nchs/fastats/disability.htm
Olney, M. F. and Kennedy, J. (2002), “Racial disparities in VR use and job placement rates for adults with
disabilities,” Rehabilitation Counseling Bulletin, 45(3), 177–185.
Punch, R. (2016), “Employment and adults who are deaf or hard of hearing: Current status and experiences of
barriers, accommodations, and stress in the workplace,” American Annals of the Deaf, 161(3), 384–397.
Punch, R., Hyde, M., and Creed, P. A. (2004), “Issues in the school-to-work transition of hard of hearing
adolescents,” American Annals of the Deaf, 149(1), 28–38.
Rehabilitation Services Administration, (2013), Reporting manual for the case service report (RSA-911) (RSA-
PD-14-01), Washington, DC: United States Department of Education. Office of Special Education and
Rehabilitative Services.
Saravanabhavan, R. C. (1991), The fundamentals of vocational rehabilitation: A guide for vr counselors work-
ing with American Indian clients, The Fundamentals of Vocational Rehabilitation: A Guide for VR Coun-
selors Working with American Indian Clients, 171.
Statistics Solutions. (2019). CHAID. Retrieved from https://www.statisticssolutions.com/non-parametric-
analysis-chaid/
Stika, C. and Trybus, R. (2002), “Working effectively with persons who are hard of hearing, late-deafened, or
deaf,” Employment and Disability Institute Collection, 11.
Walter, G. G., and Dirmyer, R. (2013), “The effect of education on the occupational status of deaf and hard of
hearing 26-to-64-year-olds,” American Annals of the Deaf, 158(1), 41–49.
Wilson, K. B. (1999), “Vocational rehabilitation acceptance: A tale of two races in a large midwestern state,”
Journal of Applied Rehabilitation Counseling, 30, 25–31.
Wilson, K. B., Harley, D. A., McCormick, K., Jolivette, K., and Jackson, R. (2001), “A literature review of
vocational rehabilitation acceptance and explaining bias in the rehabilitation process,” Journal of Rehabil-
itation, 32, 24–35.
Wilson, K. B., Jackson, R., and Doughty, J. (1999), “What a difference a race makes: Reasons for unsuccessful
closures within the vocational rehabilitation system,” American Rehabilitation, 25, 16–24.
Wilson, K. B., and Senices, J. (2005), “Exploring the vocational rehabilitation acceptance rates for Hispanics
versus non-Hispanics in the United States,” Journal of Counseling and Development, 83(1), 86–96.
1238
JSM 2019 - Social Statistics Section
