Predictors of employment outcomes for transition-age state-federal vocational rehabilitation consumers with attention-deficit/hyperactivity disorder

Abstract

BACKGROUND:

Relationships among consumer demographic variables, services, and employment outcomes for 7,776 16 to 19 year old and 2,183 20 to 24 year old consumers with ADHD were examined.

OBJECTIVE:

To examine employment outcomes, relationships among demographic and case service variables, and weekly earnings for African American, White, and Hispanic consumers with ADHD ages 16–19 and 20–24.

METHODS:

For research question one the criterion variable was successful employment or not employed. The predictor variables included consumer demographic and vocational rehabilitation service variables. Participants in both the 16–19 and 20–24 age groups were randomly split for cross validation.

RESULTS:

Demographic variables of Hispanic and African American, high school graduation and postsecondary education, public support at application, and case service variables of college training, on the job training, job search assistance, and job placement were related to successful employment. White consumers earned significantly more than African American and Hispanic consumers in the 16–19 group, and significantly more than African American consumers in the 20–24 group.

CONCLUSIONS:

Implications for practice include: characteristics identified by ethnically diverse parents of professionals who made a positive difference in the life of their child and guidelines for collaboration identified by ethnically diverse parents.

Keywords

Transition ethnically diverse youth AD/HD

1 Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most commonly diagnosed neurobiological developmental disorders in the United States (Sigelman & Ryder, 2011; Wilens, Biederman & Spencer, 2002). According to the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-V), ADHD is characterized by “a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development” (American Psychological Association [APA], 2013, p. 59). Although ADHD has historically been viewed as a childhood disorder, it has been increasingly recognized to continue into adolescence and adulthood, and therefore has become a growing issue throughout the lifespan (Weyand & DuPaul, 2008).

Two central domains of transitioning to adulthood are entering postsecondary education and obtaining gainful employment. A number of studies have reported graduation rates and postsecondary education participation rates for youth with ADHD and youth from culturally and/or linguistically diverse backgrounds. For example, Kuriyan et al. (2012) reported that participants with ADHD in comparison to control participants had completed fewer years of school and lower occupational attainment. Job loss was positively related by a higher rate of academic problems and diagnosis of ADHD.

In contrast, in an older study Barkley et al. (1996) compared a group of young adults with ADHD with control participants matched on gender, education, and ethnic background but found that the two groups did not differ significantly on graduation from high school, college attendance, or college graduation.

In examining the National Longitudinal Transition Study-2 outcomes for individuals categorized under Other Health Impairment (OHI) four years out of high school, Wagner et al. (2009) found that 55% had enrolled in some type of postsecondary education. However, only 13.2% enrolled in a four-year college, more common was community college at 43%, or vocational or technical school at 32%. (It should be noted that ADHD is under the umbrella category of OHI in the NLTS studies that includes disabilities such as diabetes and epilepsy; however, according to parental reports 76% of students under the OHI category had a primary disability of ADHD). In examining postsecondary outcomes at six years out of high school using the NLTS-2 data, Sanford et al. (2011) had similar findings with 57% of individuals with OHI enrolling in postsecondary education, 43% in community college, 28% in vocational or technical school, and 19.5% in a four-year college. In terms of employment outcomes, the NLTS-2 data indicated that 68% of participants in the OHI category were employed at both four and six years out of high school.

In reporting on participation rates in post-secondary education settings for youth across all disability categories, Newman et al. (2011) found that participation varied by race/ethnicity with 33.1% of White youth having been enrolled in a two year or community college program, 16.2% enrolled in a four year college setting, and 20.7% enrolled in a vocational business or technical school. For African American youth, 34.5% had enrolled in a two year or community college program, whereas 5.3% had enrolled in a four year college setting, and 28% had been in a vocational business or technical school. For Hispanic youth, 27.2% had enrolled in a two year or community college program, 15.4% had enrolled in a four year college setting, and 25% had been in a vocational business or technical school.

Although high school dropout rates for youth with ADHD have been estimated at around 32%, the Alliance for Excellent Education (2012) reported high school graduation rates for students in the general population at 78% for White students, 57% for African American students, and 58% for Hispanic students (Alliance for Excellent Education, 2012).

In examining employment outcomes, Mannuzza, Klein, Bessler, Malloy, and Hynes (1997) found that 85% of participants with ADHD were employed compared to 79% of control participants, although participants with ADHD tended to have a lower occupational rank. Barkley et al. (1996) also reported on employment but found no significant differences between groups in the number employed, annual salary, number of jobs held, rate of quitting/being terminated, or occupational status. However, participants with ADHD held jobs for a significantly shorter period of time. Babinski et al. (2011) examined late adolescent and young adult outcomes for females with ADHD and a control group matched on demographic features but found no group differences in employment status and self-reported job impairment.

In examining demographic characteristics and case service variables predictive of successful employment (indicating that the individual had been employed successfully for a minimum of 90 days) for transition age youth with ADHD, Schaller, Yang, and Trainor (2006) found that for males predictor variables included age, vocational counseling and guidance, job search assistance and job placement assistance. For females, the only predictor variable was job search assistance. The rates of successful employment were not statistically significantly different between males and females. However, the study did not include other racial/ethnic groups than white consumers due to the small number of participants.

In examining ADHD and adult labor market outcomes, Fletcher (2014) analyzed a longitudinal national sample from the AddHealth study and found that, when separated by race, Black and Hispanic individuals had lower levels of employment (14 and 17 percentage points respectively). As for earnings, those with ADHD earned 33% less. With regard to receipt of public assistance, individuals with childhood ADHD were found to have a 15% increase in receipt of social assistance.

Finally, the Bureau of Labor Statistics (BLS) provides labor force statistics from the Current Population Survey (CPS). The September 2014 labor force statistics on the civilian non-institutional population of 16–24 year olds reported the following: an employment rate of 21% for 16–19 year olds and 42% for 20–24 year olds; females were employed at a higher rate than males (32% versus 28% respectively); 33% of White individuals aged 16–24 years old were employed in comparison to 28% of Hispanic individuals, and 23% of African American individuals (U.S. Bureau of Labor Statistics, 2014).

Although there is variation in rates of participation in post-secondary education and employment for youth with disabilities across the preceding studies, in general African American youth with disabilities had lower rates of high school completion and participation in college, and lower rates of employment, in comparison to White and Hispanic youth. In response to these findings for ethnically diverse youth with disabilities a number of issues have appeared in the literature including engaging culturally linguistically diverse (CLD) individuals in the VR process and system (Anderson & Smart, 2010; Rosenthal & Bervin, 1999; Taylor-Ritzler et al., 2010), transition planning with ethnically diverse parents and families (Alston, Gayles, Rucker & Hobson, 2007; Feist-Price & Harris, 1994; Irving & Hudley, 2005; Kim & Morningstar, 2005; Landmark, Zhang & Montoya, 2007), guidelines for collaboration in family and professional partnerships (Blue-Banning, Summers, Frankland, Nelson & Beegle, 2004; Kalyanpur & Harry, 1997), and gender issues for youth with disabilities (Gil-Kashiwabara, Geenen & Powers, 2012; Hegewisch, Williams & Zhang, 2012; Linstrom, Benz & Doren, 2004). Although research on education and employment outcomes for transition age youth with ADHD has been reported, research specific to CLD youth with ADHD who received VR services that examined relationships between demographic characteristics, VR services, and education and employment outcomes has been limited.

Therefore, the purpose of this study was to examine education and employment outcomes, and relationships among demographic and VR case service variables for African American, White, and Hispanic consumers with ADHD ages 16–19 and 20–24 who exited the state-federal vocational rehabilitation system in FY 2012. There were very few consumers in the Asian, American Indian or Alaskan Native, Native American or Pacific Islander groups (less than 1% of consumers for each age group); therefore, they were not included in this study. The age range was limited to 16–24 years and broken down to 16–19 and 20–24 years to match the age ranges used by the Bureau of Labor Statistics in reporting labor force participation for young adults in the general civilian population (U.S. Bureau of Labor Statistics, 2014).

Research Question 1: Are demographic variables and VR case services statistically significantly related to employment outcomes at closure for African American, White, and Hispanic consumers with ADHD, ages 16–19 and 20–24?

Research Question 2: Are there statistically significant differences in weekly earnings at closure for African American, White, and Hispanic consumers ages 16–19 and 20–24 with ADHD by race/ethnicity and gender?

2 Method

The design of this study was correlational (Shadish, Cook & Campbell, 2002). The Rehabilitation Services Administration (RSA) national 911 data file for 2012 was used. The RSA national data file is an annual data file on all individuals with disabilities who applied for services from the state/federal VR system.

2.1 Participants

Participants in this study were consumers who exited the state-federal VR program in the U.S. in FY 2012. The sample was derived from the Rehabilitation Service Administration Case Service Report (RSA, 2012) and included those individuals with a primary cause of disability of ADHD; who were White, Hispanic, or African American; who fell within the age range of 16–24; and who exited the program with successful or unsuccessful employment after signing an individual plan for employment (IPE) and receiving services.

2.1.1 Consumers aged 16–19

There was a total of 7,776 consumers with ADHD in the 16–19 age range. This age group was 72% male (n = 5,599) and 28% female (n = 2,177). The mean age was 17.63 (SD = 0.867). The majority of consumers were White (72.3%, n = 5,805) followed by African American (14.4%, n = 1,153), and Hispanic (10.2%, n = 818). Across all categories of race/ethnicity the successful employment rate was 55.8% for male and 54.6% for female consumers. The successful employment rate was 56.7% (n = 3,290) for White consumers, 63.1% (n = 516) for Hispanic, and 45.2% (n = 521) for African American consumers.

The source of referral for VR services accounting for the largest number of consumers in the 16–19 group was secondary or primary educational institutions (71.4%, n = 5,556), followed by self-referral (9.6%, n = 759) and post-secondary educational institutions (4.4%, n = 344). These three sources of referral represented 85.4% of all consumers in this age group.

The source of support at application for services accounting for the largest number of consumers was family/friends (86.3%, n = 6,716), followed by public support (7.6%, n = 592) and personal income (3.5%, n = 276). These three sources of income made up 97.4% of all consumers in this age group.

Table 1 includes education level by race and gender for consumers aged 16–19 at application and closure. As can be seen, White male and females had the highest percentages of less than high school education at application (69.4% and 64.72% respectively), with African American males and females next (62.2% and 58.6% respectively), and Hispanic males and females having the lowest percentages (55.7% and 48.92% respectively). Although there was variation in percentages of consumers with an associate’s degree at application, females had the highest percentages of an associate’s degree across all races. At closure, Hispanic consumers had the highest rates of an associate’s degree, with White consumers next, and African American consumers having the lowest rates.

Table 1
Education by Race and Gender for 16–19 Year Old Consumers with ADHD

White African American Hispanic

Male Female Male Female Male Female

n % n % n % n % n % n %

Level of Education at Application

Special Education 367 8.89 151 8.98 153 18.34 51 15.99 110 17.41 25 13.44

Less than High School 2,864 69.45 1,088 64.72 519 62.23 187 58.62 352 55.70 91 48.92

High School Graduate 776 18.82 359 21.36 141 16.90 69 21.63 155 24.52 62 33.33

Associate Degree 113 2.74 82 4.89 21 2.51 12 3.76 15 2.37 8 4.30

College or Graduate Degree 4 0.00001 1 0.00001 0 0.00 0 0.00 0 0.00 0 0.00

Level of Education at Closure

Special Education 351 8.51 131 7.79 163 19.54 54 16.93 58 9.18 16 8.60

Less than High School 702 17.02 242 14.40 208 24.94 64 20.06 97 15.35 21 11.29

High School Graduate 1842 44.67 696 41.40 340 40.77 126 39.50 207 32.75 54 29.03

Associate Degree 1075 26.07 517 30.76 109 13.07 67 21.00 237 37.5 76 40.86

College or Graduate Degree 154 3.73 95 5.65 14 1.68 8 2.50 33 5.22 19 10.22

	White	African American	Hispanic
Level of Education at Application
Special Education	367	8.89	151	8.98	153	18.34	51	15.99	110	17.41	25	13.44
Less than High School	2,864	69.45	1,088	64.72	519	62.23	187	58.62	352	55.70	91	48.92
High School Graduate	776	18.82	359	21.36	141	16.90	69	21.63	155	24.52	62	33.33
Associate Degree	113	2.74	82	4.89	21	2.51	12	3.76	15	2.37	8	4.30
College or Graduate Degree	4	0.00001	1	0.00001	0	0.00	0	0.00	0	0.00	0	0.00
Level of Education at Closure
Special Education	351	8.51	131	7.79	163	19.54	54	16.93	58	9.18	16	8.60
Less than High School	702	17.02	242	14.40	208	24.94	64	20.06	97	15.35	21	11.29
High School Graduate	1842	44.67	696	41.40	340	40.77	126	39.50	207	32.75	54	29.03
Associate Degree	1075	26.07	517	30.76	109	13.07	67	21.00	237	37.5	76	40.86
College or Graduate Degree	154	3.73	95	5.65	14	1.68	8	2.50	33	5.22	19	10.22

White male and female consumers had the highest mean weekly earnings at closure with $315.65 and $293.69, respectively. Hispanic males and females were next with $285.42 and $271.20 respectively, and African American males and females the lowest with $272.29 and $268.98, respectively.

2.1.2 Consumers aged 20–24

There was a total of 2,183 consumers with ADHD in the 20–24 age range. This age group was 71% male (n = 1,549) and 29% female (n = 633). The mean age was 21.5 (SD = 1.39). With regard to race/ethnicity, the majority of consumers were White (77.5%, n = 1,693) followed by African American (14%, n = 307) and Hispanic (8.4%, n = 183). Across all categories of race/ethnicity, 59.4% of male and 60.8% of female consumers were employed successfully in this age group. Hispanic consumers had the highest rate of successful employment (63.9%), followed by White consumers (60.4%), and African Americans (54.4%).

The source of referral for VR services accounting for the largest number of consumers in the 20–24 age group was self-referral (39%, n = 852), followed by other sources that include disability advocacy organizations or professional organizations (28.5%, n = 623), and educational institutions (9.5%, n = 207). These three sources of referral made up 77% of all consumers in this age group.

The source of support at application for services accounting for the largest number of consumers was family/friends (68%, n = 1,486), followed by public support (14.3%, n = 312), and personal income (13.1%, n = 287). These three sources of income made up 95.4% of all consumers in this age group.

Table 2 includes education level by race and gender for consumers aged 20–24 at application and closure. White male and females had the lowest percentages of less than high school at application (14.75% and 11.11% respectively), Hispanic males and females had the next lowest percentages (19.3% and 12.73% respectively), and African American males and females the highest percentages (22.02% and 24.72% respectively). Although there was variation in percentages of consumers with an associate degree’s at application, White consumers had the highest percentages of having an associate’s degree, and females had the highest percentages of an associate’s degree across all races. At closure, of male consumers White males had the highest rate of having an associate’s degree, with Hispanic females having a slightly higher percentage of having an associate’s degree than White females. Of female consumers, African American females had the lowest percentage of having an associate’s degree.

Table 2
Education by Race and Gender for 20–24 Year Old Consumers with ADHD

White African American Hispanic

Male Female Male Female Male Female

n % n % n % n % n % n %

Level of Education at Application

Special Education 107 8.86 33 6.79 28 12.84 12 13.48 15 11.72 4 7.27

Less than High School 178 14.75 54 11.11 48 22.02 22 24.72 25 19.53 7 12.73

High School Graduate 629 52.11 223 45.88 99 45.41 33 37.08 67 52.34 31 56.36

Associate Degree 281 23.28 169 34.77 41 18.81 21 23.60 21 16.41 12 21.82

College or Graduate Degree 12 0.99 7.00 1.44 2 0.91 1 1.12 0 0.00 1 1.82

Level of Education at Closure

Special Education 94 7.79 29 5.97 25 11.47 12 13.48 13 10.16 3 5.45

Less than High School 123 10.19 34 6.99 32 14.68 15 16.85 17 13.28 4 7.27

High School Graduate 553 45.82 178 36.63 100 45.87 33 37.08 54 42.19 15 27.27

Associate Degree 365 30.24 199 40.94 56 25.69 25 28.09 31 24.22 23 41.82

College or Graduate Degree 72 5.97 46 9.47 5 2.29 4 4.49 13 10.16 10 18.18

	White	African American	Hispanic
Level of Education at Application
Special Education	107	8.86	33	6.79	28	12.84	12	13.48	15	11.72	4	7.27
Less than High School	178	14.75	54	11.11	48	22.02	22	24.72	25	19.53	7	12.73
High School Graduate	629	52.11	223	45.88	99	45.41	33	37.08	67	52.34	31	56.36
Associate Degree	281	23.28	169	34.77	41	18.81	21	23.60	21	16.41	12	21.82
College or Graduate Degree	12	0.99	7.00	1.44	2	0.91	1	1.12	0	0.00	1	1.82
Level of Education at Closure
Special Education	94	7.79	29	5.97	25	11.47	12	13.48	13	10.16	3	5.45
Less than High School	123	10.19	34	6.99	32	14.68	15	16.85	17	13.28	4	7.27
High School Graduate	553	45.82	178	36.63	100	45.87	33	37.08	54	42.19	15	27.27
Associate Degree	365	30.24	199	40.94	56	25.69	25	28.09	31	24.22	23	41.82
College or Graduate Degree	72	5.97	46	9.47	5	2.29	4	4.49	13	10.16	10	18.18

Of consumers aged 20–24, White male and female consumers had the highest mean weekly earnings at closure with $344.25 and $321.85, respectively. Hispanic males and females were next with $317.88 and $315.36 respectively, and African American males and females the lowest with $288.02 and $290.04, respectively.

2.2 Variables and data analyses

For research question one on demographic and VR case services significantly related to employment outcomes at closure for African American, White, and Hispanic males and females with ADHD ages 16–19 and 20–24, the criterion variable was successful employment or not employed. Successful employment was defined as employment in a competitive, integrated setting for at least 90 days. The predictor variables included four consumer demographic and ten VR service variables. Consumer demographic variables included race, gender, level of education at application, and primary source of support at application.

Vocational rehabilitation service variables included vocational rehabilitation counseling, college training, occupational training, job search assistance, job placement, job readiness training, on the job training, assessment, diagnosis and treatment, and maintenance. According to the RSA (2012) reporting manual, vocational rehabilitation counseling includes discrete therapeutic counseling and guidance services that are necessary for an individual to achieve an employment outcome that are distinct from general counseling and guidance provided to individuals during the rehabilitation process. College training is academic training leading to a degree, certificate, or other recognized educational credential. Occupational training is provided by a community college and/or business, vocational/trade, or technical school to prepare an individual for gainful employment that does not lead to an academic degree or certification. Job search assistance includes assistance with resume writing, making contacts with employers, developing interview skills, and identifying job opportunities. Job placement assistance is a referral to a specific job, resulting in an interview whether or not the person obtained a job. Job readiness training is training to prepare an individual to work including appropriate work behaviors, dress, and grooming. On the job training consists of training in specific skills by a prospective employer and could include apprenticeship training. Assessment includes services provided to determine eligibility for VR services and to determine VR services to be included in the IPE. Diagnosis and treatment can include physical and/or occupational therapy. Maintenance can include cost of short term expenses such as clothing required for a particular job or costs of activities related to the person’s training program. Services received by consumers 16–19 and 20–24 are presented in Tables 3 and 4, respectively.

Table 3
Vocational Rehabilitation Services 16–19 Year Old Group

Case Service Type of Closure

Unsuccessful Successful Total

n % n % n %

Assessment Yes 2055 43.7 2651 56.3 4706 100

No 1521 45.8 1797 54.2 3318 100

Diagnosis & Treatment Yes 924 45.1 1123 54.9 2047 100

No 2652 44.4 3325 55.6 5977 100

Vocational Rehabilitation Yes 2166 44.0 2758 56.0 4924 100

Counseling & Guidance

No 1410 45.5 1690 54.5 3100 100

College Training Yes 698 37.0 1190 63.0 1888 100

No 2878 46.9 3258 53.1 6136 100

Occupational Training Yes 394 29.4 947 70.6 1341 100

No 3182 47.6 3501 52.4 6683 100

On-the-job Training Yes 66 21.6 240 78.4 306 100

No 3510 45.5 4208 54.5 7718 100

Basic Academic Remedial Yes 70 46.7 80 53.3 150 100

or Literacy Training

No 3506 44.5 4368 55.5 7874 100

Job Readiness Training Yes 554 38.1 899 61.9 1453 100

No 3022 46.0 3549 54.0 6571 100

Miscellaneous Training Yes 422 38.3 681 61.7 1103 100

No 3154 45.6 3767 54.4 6921 100

Job Search Assistance Yes 579 29.9 1356 70.1 1935 100

No 2997 49.2 3092 50.8 6089 100

Job Placement Assistance Yes 780 28.8 1933 71.2 2713 100

No 2796 52.6 2515 47.4 5311 100

On-the-job Supports Yes 371 27.3 986 72.7 1357 100

No 3205 48.1 3462 51.9 6667 100

Maintenance Yes 373 29.6 888 70.4 1261 100

No 3203 47.4 3560 52.6 6763 100

Rehabilitation Technology Yes 86 35.0 160 65.0 246 100

No 3490 44.9 4288 55.1 7778 100

Information and Referral Yes 591 40.5 869 59.5 1460 100

No 2985 45.5 3579 54.5 6564 100

Case Service		Type of Closure
Assessment	Yes	2055	43.7	2651	56.3	4706	100
	No	1521	45.8	1797	54.2	3318	100
Diagnosis & Treatment	Yes	924	45.1	1123	54.9	2047	100
	No	2652	44.4	3325	55.6	5977	100
Vocational Rehabilitation	Yes	2166	44.0	2758	56.0	4924	100
Counseling & Guidance
	No	1410	45.5	1690	54.5	3100	100
College Training	Yes	698	37.0	1190	63.0	1888	100
	No	2878	46.9	3258	53.1	6136	100
Occupational Training	Yes	394	29.4	947	70.6	1341	100
	No	3182	47.6	3501	52.4	6683	100
On-the-job Training	Yes	66	21.6	240	78.4	306	100
	No	3510	45.5	4208	54.5	7718	100
Basic Academic Remedial	Yes	70	46.7	80	53.3	150	100
or Literacy Training
	No	3506	44.5	4368	55.5	7874	100
Job Readiness Training	Yes	554	38.1	899	61.9	1453	100
	No	3022	46.0	3549	54.0	6571	100
Miscellaneous Training	Yes	422	38.3	681	61.7	1103	100
	No	3154	45.6	3767	54.4	6921	100
Job Search Assistance	Yes	579	29.9	1356	70.1	1935	100
	No	2997	49.2	3092	50.8	6089	100
Job Placement Assistance	Yes	780	28.8	1933	71.2	2713	100
	No	2796	52.6	2515	47.4	5311	100
On-the-job Supports	Yes	371	27.3	986	72.7	1357	100
	No	3205	48.1	3462	51.9	6667	100
Maintenance	Yes	373	29.6	888	70.4	1261	100
	No	3203	47.4	3560	52.6	6763	100
Rehabilitation Technology	Yes	86	35.0	160	65.0	246	100
	No	3490	44.9	4288	55.1	7778	100
Information and Referral	Yes	591	40.5	869	59.5	1460	100
	No	2985	45.5	3579	54.5	6564	100

Table 4

Vocational Rehabilitation Services 20–24 Year Old Group

Case Service		Type of Closure
		Unsuccessful		Successful		Total
		n	%	n	%	n	%
Assessment	Yes	543	38.2	877	61.8	1420	100
	No	362	43.6	468	56.4	830	100
Diagnosis & Treatment	Yes	329	43.3	431	56.7	760	100
	No	576	38.7	914	61.3	1490	100
Vocational Rehabilitation	Yes	560	38.6	891	61.4	1451	100
Counseling & Guidance
	No	345	43.2	454	56.8	799	100
College Training	Yes	137	33.2	276	66.8	413	100
	No	768	41.8	1069	58.2	1837	100
Occupational Training	Yes	92	29.4	221	70.6	313	100
	No	813	42.0	1124	58.0	1937	100
On-the-job Training	Yes	23	25.8	66	74.2	89	100
	No	882	40.8	1279	59.2	2161	100
Basic Academic Remedial	Yes	9	29.0	22	71.0	31	100
or Literacy Training
	No	896	40.4	1323	59.6	2219	100
Job Readiness Training	Yes	124	35.3	227	64.7	351	100
	No	781	41.1	1118	58.9	1899	100
Miscellaneous Training	Yes	86	40.0	129	60.0	215	100
	No	819	40.2	1216	59.8	2035	100
Job Search Assistance	Yes	198	28.7	491	71.3	689	100
	No	707	45.3	854	54.7	1561	100
Job Placement Assistance	Yes	278	29.3	670	70.7	948	100
	No	627	48.2	675	51.8	1302	100
On-the-job Supports	Yes	119	22.8	403	77.2	522	100
	No	786	45.5	942	54.5	1728	100
Maintenance	Yes	146	34.0	283	66.0	429	100
	No	759	41.7	1062	58.3	1821	100
Rehabilitation Technology	Yes	13	26.0	37	74.0	50	100
	No	892	40.5	1308	59.5	2200	100
Information and Referral	Yes	112	28.1	287	71.9	399	100
	No	793	42.8	1058	57.2	1851	100

For research question one, participants in both the 16–19 and 20–24 age groups were randomly split into Sample A and Sample B for cross validation, resulting in a Sample A and B for each age group. Each random sample consisted of half the White, African American, and Hispanic consumers with ADHD for that age group. Cross validation was used due to the concern that findings from logistic regression for a given sample may not generalize to a larger population (Cohen, Cohen, West & Aiken, 2003). Cross-validation with a second sample is recommended with only the variables that are statistically significant for both random samples reported and interpreted (Cohen et al., 2003). The random sample feature of SPSS 19 was used to randomly split the samples, and there were no significant differences on demographic variables between Sample A and Sample B for either 16–19 year old or 20–24 year old groups.

A separate forward logistic regression was run for participants in Sample A and another in Sample B in both age groups. Forward stepwise entry of the variables was chosen as it may be used as a hypothesis generating technique for statistically examining relationships among variables (Tabachnick & Fidell, 2001). Indicator coding was used for the logistic regression analyses in which the first category for a given variable was represented by a zero (Field, 2009). For example, for the racial/ethnic variable White was coded as 0 and used as the reference group for the other racial/ethnic groups (African American coded as 1 and Hispanic coded as 2). Pearson and Kendall’s tau-b correlations were run on all predictor variables and correlations among predictor variables ranged from 0.29 to 0.36, indicating that values were low enough to preclude issues of multicollinearity.

For research question two as to whether there were statistically significant differences in weekly earnings at closure for African American, White, and Hispanic consumers ages 16–19 and 20–24 with ADHD by race/ethnicity and gender, a univariate analysis of variance (ANOVA) was used. ANOVA was conducted to test for between-subjects differences. Post-hoc comparisons using Scheffe were conducted for both the 16–19 and 20–24 age groups.

3 Results

3.1 Research Question One for 16–19 year olds

A total of fourteen consumer demographic and VR service variables were entered for both Sample A and Sample B for 16–19 year old consumers. Ten predictor variables were found to be significant in both random samples A and B, and per Cohen et al. (2003) only these statistically significant predictor variables are reported. Significant demographic predictors included the status of African American, which was negatively related to successful employment, Hispanic status, which was positively related to successful employment, and high school graduation and postsecondary education, which were both positively related to successful employment. Public support at application was negatively related to successful employment.

Five case service variables were significantly related to successful employment: college training, on the job training, job search assistance, job placement assistance, and on the job supports. Significant predictor variables of successful employment for Sample A and B for 16–19 year olds are in Table 5.

Table 5
Predictors of Successful Employment Significant in Both Sample A and Sample B 16–19 Age Group

Variable β SE Wald p Exp(β)

Race-African American Sample A –0.323 0.104 9.579 0.002 0.724

Sample B –0.539 0.101 28.470 0.000 0.584

Race-Hispanic/Latino Sample A 0.387 0.115 11.276 0.001 1.473

Sample B 0.529 0.123 18.391 0.000 1.698

High School Graduate Sample A 0.353 0.134 6.989 0.008 1.424

Sample B 0.307 0.133 5.322 0.021 1.36

Postsecondary Education Sample A 0.570 0.246 5.389 0.020 1.769

Sample B 0.438 0.223 3.855 0.050 1.549

Public Support at Application Sample A –1.037 0.241 18.487 0.000 0.354

Sample B –1.025 0.228 20.277 0.000 0.359

College Training Sample A 0.486 0.094 26.919 0.000 0.354

Sample B 0.207 0.090 5.304 0.021 1.230

On the Job Training Sample A 0.550 0.224 6.004 0.014 1.733

Sample B 0.614 0.216 8.066 0.005 1.848

Job Search Assistance Sample A 0.459 0.094 24.089 0.000 1.583

Sample B 0.427 0.092 21.535 0.000 1.533

Job Placement Assistance Sample A 0.846 0.085 99.847 0.000 2.331

Sample B 0.808 0.084 93.089 0.000 2.244

On the Job Supports Sample A 0.442 0.109 16.566 0.000 1.555

Sample B 0.533 0.107 24.807 0.000 1.705

Variable		β	SE	Wald	p	Exp(β)
Race-African American	Sample A	–0.323	0.104	9.579	0.002	0.724
	Sample B	–0.539	0.101	28.470	0.000	0.584
Race-Hispanic/Latino	Sample A	0.387	0.115	11.276	0.001	1.473
	Sample B	0.529	0.123	18.391	0.000	1.698
High School Graduate	Sample A	0.353	0.134	6.989	0.008	1.424
	Sample B	0.307	0.133	5.322	0.021	1.36
Postsecondary Education	Sample A	0.570	0.246	5.389	0.020	1.769
	Sample B	0.438	0.223	3.855	0.050	1.549
Public Support at Application	Sample A	–1.037	0.241	18.487	0.000	0.354
	Sample B	–1.025	0.228	20.277	0.000	0.359
College Training	Sample A	0.486	0.094	26.919	0.000	0.354
	Sample B	0.207	0.090	5.304	0.021	1.230
On the Job Training	Sample A	0.550	0.224	6.004	0.014	1.733
	Sample B	0.614	0.216	8.066	0.005	1.848
Job Search Assistance	Sample A	0.459	0.094	24.089	0.000	1.583
	Sample B	0.427	0.092	21.535	0.000	1.533
Job Placement Assistance	Sample A	0.846	0.085	99.847	0.000	2.331
	Sample B	0.808	0.084	93.089	0.000	2.244
On the Job Supports	Sample A	0.442	0.109	16.566	0.000	1.555
	Sample B	0.533	0.107	24.807	0.000	1.705

Note: Constant is included in the model.

The overall accuracy of the model to predict successful employment for the 16–19 year old group was 67.8 % for Sample A and 67.4 % for Sample B. The Hosmer and Lemeshow goodness of fit test for Sample A was significant (χ2 (8) = 17.78, p = 0.023) whereas it was not significant in Sample B (χ2 (8) = 15.42, p = 0.051), indicating that Sample B was a better fit of the data to the model. For Sample A, the Cox and Snell R2 was 0.162 and the Negelkerke R2 was 0.217. The Cox and Snell R2 was 0.160, and the Negelkerke R2 was 0.215 for Sample B.

3.2 Research Question One for 20–24 year olds

Five predictor variables were found to be significant in both Sample A and Sample B for 20–24 year olds. These included two types of support at application and three service variables. The significant demographic predictor variables were receipt of support from family and friends at application and receipt of public support at application, both of which were negatively related to successful employment. Three case services were related to successful employment: job search assistance; job placement assistance, and on the job supports. Predictors of successful employment for Sample A and B for 20–24 year olds are presented in Table 6.

Table 6
Predictors of Successful Employment Significant in Both Sample A and Sample B 20–24 Age Group

Variable β SE Wald p Exp(β)

Primary Source of Support at Sample A –0.851 0.223 14.579 0.000 0.427

Application- Family and Friends

Sample B –0.581 0.220 6.962 0.008 0.559

Primary Source of Support at Sample A –1.247 0.278 20.162 0.000 0.287

Application- Public Support

Sample B –1.171 0.278 17.735 0.000 0.310

Job Search Assistance Sample A 0.623 0.166 14.128 0.000 1.865

Sample B 0.373 0.166 5.045 0.025 1.452

Job Placement Assistance Sample A 0.520 0.154 11.349 0.001 1.682

Sample B 0.621 0.150 17.165 0.000 1.861

On the Job Supports Sample A 0.663 0.193 11.737 0.001 1.940

Sample B 0.957 0.182 27.481 0.000 2.603

Variable		β	SE	Wald	p	Exp(β)
Primary Source of Support at	Sample A	–0.851	0.223	14.579	0.000	0.427
Application- Family and Friends
	Sample B	–0.581	0.220	6.962	0.008	0.559
Primary Source of Support at	Sample A	–1.247	0.278	20.162	0.000	0.287
Application- Public Support
	Sample B	–1.171	0.278	17.735	0.000	0.310
Job Search Assistance	Sample A	0.623	0.166	14.128	0.000	1.865
	Sample B	0.373	0.166	5.045	0.025	1.452
Job Placement Assistance	Sample A	0.520	0.154	11.349	0.001	1.682
	Sample B	0.621	0.150	17.165	0.000	1.861
On the Job Supports	Sample A	0.663	0.193	11.737	0.001	1.940
	Sample B	0.957	0.182	27.481	0.000	2.603

Note: Constant is included in the model.

The overall accuracy of the model to predict successful employment for the 20–24 year old group Sample A was 70.9 %; for Sample B it was 67.6%. The Hosmer and Lemeshow goodness of fit test for Sample A was not significant (χ2 (8) = 11.06, p = 0.243) whereas it was significant in Sample B (χ2 (8) = 16.12, p = 0.041). This indicates that in this age group Sample A was a better fit of the data to the model. The Cox and Snell R2 was 0.179 and the Negelkerke R2 was 0.243 for Sample A; the Cox and Snell R2 was 0.175 and the Negelkerke R2 was 0.236 for Sample B.

3.3 Research Question Two for 16–19 year olds

Analysis of variance (ANOVA) was conducted to test for between-subjects differences and identified significant differences across racial/ethnic groups (F(2, 4,321) = 16.77, p < 0.01) and across gender groups (F(1, 4,321) = 6.00, p < 0.05), as well as a significant interaction between race and gender (F(2, 4,321) = 4.42, p < 0.05). Mean weekly earnings for White males and females combined ($304.67) were statistically higher than African American males and females combined ($270.63) (mean difference = 63.56, p < 0.01) and Hispanic males and females combined ($278.31) (mean difference = 52.62, p < 0.01).

Post-hoc comparisons using Scheffe were conducted, and the analyses revealed that significant differences existed between racial/ethnic groups. White males and females earned higher mean weekly earnings than (a) African American males and females and (b) Hispanic males and females. However, there were no significant differences between African American male and female earnings and Hispanic male and female earnings, respectively.

There was also a significant interaction between race and gender. Pairwise comparisons showed that race and gender interacted for males and race however not for females and race. White males had statistically significantly higher earnings than African American and Hispanic males, while White females’ earnings were not significantly higher than African American or Hispanic females’ earnings. Table 7 presents pairwise comparisons for gender and racial groups.

Table 7
Pairwise Comparison Race and Gender Interaction 16–19 Age Group

Gender (I) Race (J) Race Mean Standard Sig.

/Ethnicity /Ethnicity Difference Error

Male White African American 79.357* 11.196 0.000

Hispanic or Latino 66.226* 0.000

African American Hispanic or Latino –13.131 14.491 1.00

Female White African American 24.717 18.671 0.557

Hispanic or Latino 22.496 20.426 0.812

African American Hispanic or Latino –2.22 26.42 1.00

Gender	(I) Race	(J) Race	Mean	Standard	Sig.
Male	White	African American	79.357*	11.196	0.000
		Hispanic or Latino	66.226*		0.000
	African American	Hispanic or Latino	–13.131	14.491	1.00
Female	White	African American	24.717	18.671	0.557
		Hispanic or Latino	22.496	20.426	0.812
	African American	Hispanic or Latino	–2.22	26.42	1.00

*p < 0.05.

3.4 Research Question Two for 20–24 year olds

For the 20–24 age group, a univariate ANOVA was run. The ANOVA results for weekly earnings indicated significant differences between racial/ethnic groups only (F(2, 1,301) = 3.08, p < 0.05). Weekly earnings between males and females were not significantly different for this age group. Post hoc comparisons indicated that the only statistically significant difference was mean weekly earnings for White males and females combined ($330.05) in comparison to African American males and females combined ($289.03) (mean difference = 49.03, p < 0.05).

4 Discussion

The descriptive findings of this study indicate that 16–19 year old consumers with ADHD had a successful employment rate of 55.2%, which is comparable to the overall successful employment rate for VR consumers across all disability categories in FY 2012 (55.5%). The employment rate for 20–24 year old consumers was slightly higher at 60.1%. However, these rates are lower than the 68% employment rate for transition age youth with OHI in the NLTS-2 reports (Newman, Wagner, Cameto & Knokey, 2009; Sanford et al., 2011). In comparing employment rates for the general population, the Bureau of Labor Statistics (2014) reported the employment rate for 16–19 year olds as 21% versus 42% for 20–24 year olds. When broken down by gender the U.S. Bureau of Labor Statistics (2014), reported that females were employed at a higher rate than males (32% versus 28% of males).

The present study also found that Hispanic consumers had the highest employment rate in both the 16–19 and 20–24 age groups (63.1% and 63.9% respectively), which conflicts with existing studies on labor market outcomes for individuals with ADHD (Fletcher, 2014) where White participants had the highest employment rate and Hispanic participants had an employment rate 14 percentage points lower. In comparing the results of this study to the general population without disabilities, the Bureau of Labor Statistics (2014) indicated that employment rates for transition age White individuals were 33%, 25% for Hispanic, and 23% for African Americans. The finding of Hispanic consumers having the highest employment rates in both age groups could be an artifact of the samples for this study. Without additional research it is difficult to say if the finding of Hispanic consumers with ADHD having the highest employment rates in comparison to White and African American consumers with ADHD would replicate with other samples across other studies.

4.1 Research Question One

For research question one, on demographic and case service variables related to successful employment, results indicated that the demographic variables of Hispanic and African American ethnicity, high school graduation and postsecondary education, and public support at application, as well as the case service variables of college training, on the job training, job search assistance, job placement, and on the job supports were significantly related to successful employment.

Race/ethnicity was only found to be a significant predictor of employment outcome in the 16–19 year old age group with Hispanic consumers approximately 1.4 times more likely to be successfully employed than African American and White consumers in this study. This is in contrast to the Fletcher (2014) labor market study, which found that Hispanic individuals with ADHD had a 14-point reduction in employment rate in comparison to White individuals. However, two studies in the VR literature, Gonzalez (2011) and Giesen and Cavenaugh (2012) found Hispanic ethnicity to be a predictor of successful employment among transition age individuals.

Conversely, being African American was negatively related to successful employment in this study. This is a new finding in that race had not been previously explored in the context of ADHD in VR. However, it is consistent with studies in the VR literature among other disability categories where African American status was negatively related to successful employment (Capella, 2002; Olney & Kennedy 2002; Moore et al., 2009; Mwachofi, Broyles & Khaliq, 2009) and in one study specifically on transition age consumers (Giesen & Cavenaugh, 2012). Further, this finding is aligned with those of the NLTS and NLTS-2 studies (Blackorby & Wagner, 1996; Newman et al., 2009), which reported lower employment rates for transition age African Americans across all disabilities.

There could be several possible explanations for this troubling finding, including longstanding social and attitudinal barriers that result in inequities in employment, postsecondary educational attainment, and levels of poverty for African American consumers (Roessler & Rubin 2006). Also, as Feist-Price & Harris, 1994) suggested, African American consumers may bring long-standing mistrust and expectations that the VR process will not be responsive to their needs, and will be an uncaring bureaucracy. Further, research by Rosenthal and Berven (1999) suggested that White rehabilitation counselors may make assumptions about African American consumers during the rehabilitation process. These researchers attributed counselor racial bias as a factor for inequitable treatment of people with disabilities from culturally diverse backgrounds, including decisions about eligibility for services, plan development, and service provision. Matrone and Leahy (2005), in examining relationships between vocational rehabilitation consumer outcomes and counselor multicultural counseling competencies, found that consumer race (defined as White or Non-White) was important in explaining consumer outcomes, with Non-White consumers having a lower successful employment rate than White consumers.

High school graduation and postsecondary educational attainment were statistically significant predictors for successful employment only for the 16–19 age group. Consumers who were high school graduates and those who had attained an associate’s degree or higher were about 1.5 times more likely to be successfully employed than consumers with less than a high school education or a special education degree. These findings are consistent with previous studies that found level of education as a predictor of successful employment for individuals with ADHD, where postsecondary education was the strongest predictor of employment success (Halmoy, Fasmer, Gillberg & Haavik, 2009; Kurian et al., 2013).

Public support was a significant predictor in both age groups whereas support by family and friends was significant in the 20–24 age group only, and both the receipt of public support and having family and friends as the primary source of support at application were negatively related to successful employment. Consumers who received public support (e.g. Social Security Disability Insurance -SSDI, Supplemental Security Income -SSI, or Temporary Assistance for Needy Families -TANF) at application were less likely to be successfully employed. This finding is consistent with not only VR studies examining RSA-911 data across disability categories (Bolton, Bellini & Brookings, 2000; Dutta, Gervey, Chan, Chou & Ditchman, 2008) but also studies specifically examining outcomes of individuals with ADHD (Fletcher, 2014) and transition age consumers (Gonzalez, 2011). Gonzalez (2011) found that public support was the strongest predictor of unsuccessful VR outcome, more than any other demographic or case service variable.

Support from family and friends was also a negative predictor of successful employment in the 20–24 age group, which may relate to an issue that has been identified in the general population. It has been suggested that it may take longer for today’s young adults to become self sufficient versus earlier generations (Konstam, 2007). Therefore, with ongoing financial support from family perhaps individuals in the 20–24 group were not feeling as much urgency to obtain employment than 16–19 year old consumers.

The three case service variables that were statistically significant in both age groups were: (a) job placement assistance, (b) job search assistance and (c) on the job supports. Those who received these three case services were more likely to be successfully employed with the strongest predictors being job placement assistance for the 16–19 year old group and on the job supports in the 20–24 age group. The significant findings for job search assistance and job placement assistance are consistent with a study on transition age consumers with ADHD (Schaller et al., 2006), which also found these two case service variables to be significant predictors of successful employment. Job placement assistance has consistently been identified as a predictor of employment throughout the VR literature across disability categories (Bolton et al., 2000; Gamble & Moore, 2003; Johnstone, Vessell, Bounds, Hoskins & Sherman, 2003) as well as in one of the few other studies specifically on transition age consumers (Geisen & Cavenaugh, 2012). However, although job placement assistance was a significant predictor of success in this study as well as in the VR literature, it is concerning that less than half (34% of the 16–19 year olds and 42% of the 20–24 year olds) of consumers in this study were provided with this case service as part of their Individual Plan for Employment.

College training and on the job training were only found to be statistically significant predictors of successful employment in the 16–19 age group. Consumers in the 16–19 age group who received these case services were more likely to be successfully employed. However, college training with regard to successful employment in the VR literature has had mixed results. Gilmore, Shuster, Zafft & Hart (2001) found across disability categories that consumers’ receipt of postsecondary education services from VR had little effect on successful employment. Another study examining VR consumers with learning disabilities found that college training was negatively correlated with successful employment (Dunham et al., 1996). Bolton et al. (2001) found that college training was a significant predictor of successful employment for individuals with learning disabilities. Finally, Boutin and Accordino (2011) in examining outcomes of VR consumers with psychiatric disabilities who received college training had a 33% increase in successful employment.

4.2 Research Question Two

Research question two results indicated statistically significant differences between racial groups for both age groups, with White consumers earning significantly more than African American and Hispanic consumers in the 16–19 group, and significantly more than African American consumers in the 20–24 group. Also in the 16–19 age group, gender was found to be a statistically significant predictor with males earning significantly more than females. There was also a significant interaction between race and gender. White males earned significantly more than the other racial groups, however there were no significant differences among the females across race.

The results for the 16–19 age group offer different findings than Fletcher (2014) and Schaller et al. (2006) where no statistically significant differences in weekly earnings between males and females were found. However, the findings of this study are consistent with data for the general population in 2012 where it was reported that on average, women earned about 81% of what males earned (U.S. Bureau of Labor Statistics, 2013). Also, data for the general population indicate that Whites have the highest earnings, followed by African Americans, and then Hispanics, unlike the finding in this study where Hispanics had slightly higher wages than African Americans in both age groups (U.S. Bureau of Labor Statistics, 2013).

In comparing the results of this study to the NLTS-2, average hourly wages for those with OHI was $10.70 ($374.50 per week and $19,747 per year based on 35 hour work week). However, the NLTS-2 found no significant differences on demographic variables of race and gender although overall participants in the NLTS-2 studies earned less than the general population (Newman et al. 2011).

The poverty threshold for individuals in 2012 was $11,170 (U.S. Department of Health and Human Services, 2012). Although it appears that consumers in this study were on average earning above the individual poverty level (calculated average yearly earnings in this study ranged from $15,082 –$17,901) these wages are lower than the figures reported for young adults where young adults with no high school credential earned a median yearly salary of $22,000, with a high school credential $30,000, and with a bachelor’s degree $46,900 (U.S. Department of Education, 2014). As such, these findings support previously reported findings that individuals with ADHD tend to have a reduction in earnings (Fletcher, 2014).

4.3 Implications for practice and future research

For both age groups, African American males and females had the lowest rates of successful employment, the lowest rates of having an associate’s degree or college/graduate degree at closure (except for Hispanic males with an associate’s degree in the 20–24 age group), and the lowest mean wages at closure in comparison to White and Hispanic consumers. This is in contrast to Hispanic males and females in both age groups who had the highest rates of successful employment, the highest rates of having an associate’s degree or college/graduate degree at closure, but slightly lower wages than White consumers. White males and females in both age groups had successful employment rates in between African American and Hispanic males and females, lower rates of having an associate’s degree or college/graduate degree at closure than Hispanic males and females in the 16–19 age group (with similar rates to Hispanic consumers for the 20–24 age group), but had higher mean wages at closure than African American or Hispanic consumers in both age groups. Finally, in the 16–19 age group, despite having higher rates of having an associate’s or college/graduate degree at closure than males in their respective racial/ethnic category, females had lower wages.

Given these findings, four implications for practice emerge: characteristics identified by ethnically diverse parents of professionals who made a positive difference in the life of their child (Kim & Morningstar, 2005; Landmark et al., 2007); guidelines for collaboration identified by ethnically diverse parents (Blue-Banning et al., 2004); counselor’s use of an empowerment philosophy in counseling (Taylor-Ritzler et al. (2010); and gender issues in wages (Hegewisch et al., 2012). These implications are discussed next.

4.3.1 Characteristics of professionals identified by ethnically diverse parents

Professionals who were honest, clear, and knowledgeable were viewed by ethnically diverse parents as making a positive difference in their lives and the life of their child (Kim & Morningstar, 2005; Landmark et al., 2007). Professionals who welcomed parents and family members and felt that their perspective had value were important. Parents need information, and professionals who addressed strengths of their son or daughter and shared information about services pertaining to transition, education, and employment were considered positively by parents (Kim & Morningstar, 2005). Counselors took into consideration a family’s decision making process and viewed family involvement in the transition process as a continuum from uninvolved to very involved to represent the range of differences between families, but also changing dynamics of an individual family as part of the process. If a family chooses minimal participation, professionals should ensure that the choice is not due to lack of information, but is a free and informed choice (Landmark et al., 2007).

Professionals who incorporate informal supports such as community organizations, community centers, and religious institutions in combination with formal supports may promote family involvement, as families may feel best supported and understood when combinations of formal and informal supports are used (Kim & Morningstar, 2005; Landmark et al., 2007). Specifically, counselors can ask parents how they access resources in their communities, how and where information is obtained and exchanged, which church or churches support them, what services are offered by schools in their community, and other families that may be a resource on services and supports in their community.

4.3.2 Guidelines for collaboration identified by ethnically diverse parents

Ethnically diverse parents have identified guidelines for collaboration (Blue-Banning et al., 2004). Parents commented on professionals being open, with provision of information about resources that was free of jargon. Professionals mentioned the need for checking tactfully to see if parents and youth understood the information that was provided, both verbally and in reports or other documents.

Trust was identified by parents as critical in relationships with professionals in that people were trustworthy and could be depended upon to follow through with what they said they would do. Trust also meant parents could feel that their child would be safe in the community and treated with dignity. Trust also meant discretion, as professionals could be trusted with information about the family and that their confidence would not be violated.

Respect was intermingled with trust. For parents, professionals showing respect meant valuing the child as a person and demonstrating courtesy by acknowledging parent’s contributions and efforts. Respect intermingled with equality, or a working relationship that had a sense of harmony or ease. Parents noted professionals who did not treat people as beneath them, and who empowered family members by encouraging family members to express opinions, within family guidelines, and who helped family members to participate fully in the process (Blue-Banning et al., 2004).

With regard to the findings from this study, while it is important for counselors to build trust and respect in their counseling relationships with all consumers, it may be especially so when working with consumers and families from culturally and/or linguistically diverse backgrounds. As mentioned previously, for both age groups in this study African American males and females had the lowest rates of successful employment, the lowest rates of having an associate’s degree or college/graduate degree at closure, and the lowest mean wages at closure in comparison to White and Hispanic consumers. Building trust and respect may assist consumers and families from culturally and/or linguistically diverse backgrounds to share their perceptions and concerns on education, working, roles for individual family members within the family, developing a plan for the future, and steps needed for getting there. Counselors, in turn, can listen empathically, share information and discuss educational options such as community college programs or four year university degrees, educational requirements and local employment rates for different jobs, different time frames for earning a certification or degree, earnings from different jobs, and supports provided by community colleges or universities for students. Counselors can do this in a non-judgmental fashion, respecting family boundaries while recognizing and responding respectfully to consumer and/or family concerns or anxiety over issues of education and employment.

4.3.3 Use of an empowerment philosophy in counseling

Taylor-Ritzler et al. (2010) noted that one of the most significant contributors to successfully engaging culturally and/or linguistically diverse consumers in the VR system was an empowerment philosophy in counseling (Taylor-Ritzler et al., 2010). This empowerment philosophy overlaps with and complements the guidelines for collaboration identified by ethnically diverse parents above. The empowerment philosophy includes three dimensions.

The first dimension is assisting consumers and/or family members to understand their responsibilities and role in the rehabilitation process and in employment. This includes talking with consumers and/or family members about the VR process of assessment, eligibility, planning, service delivery, job placement, and follow-up. Counselors can work with consumers to clarify what occurs in each step of the VR process, what responsibilities the consumer and/or their family and the counselor has at each step, concerns or anxiety a consumer may have in working with a government agency, and work to ensure consumers have real choices and are taking control of their rehabilitation by personally engaging with each step of the VR process.

The next dimension of the empowerment philosophy is working with consumers to develop a vision of the future. Taylor-Ritzler et al. (2010) noted that some consumers may struggle to develop and/or implement an Individualized Plan of Employment immediately after eligibility determination, and consumers may need support to develop an understanding and appreciation of their capacity for success in education and employment. In turn, counselors may need to assist consumers to envision a future that includes education and employment, but at the same time, be mindful that consumers may have had little experience with future planning and may have come to the VR process with a short time horizon that consisted of securing employment now so as to generate an income as quickly as possible.

The third dimension is assisting consumers to advocate for themselves. This could start with assisting consumers to assess and identify skills required for empowerment, encourage and facilitate an ongoing evaluation of the counseling process, assisting consumers to process their experiences over time to gain self-awareness, advocate for consumer self-direction with others involved with the consumer’s case (while still being cognizant of family values and roles of individuals within the family) including vendors and other providers of services, and work with consumers to identify how they would map out taking control at each step of the VR process.

4.3.4 Gender issues in wages

Wages of consumers in this study may reflect gender differences in society as a whole, but closing the wage gap is important given how women’s earnings contribute to family incomes and self-sufficiency for women (Hegewisch et al., 2012). Young women may retain responsibility for children regardless of working outside the home and may make work choices to allow flexibility in working to accommodate those responsibilities. Rehabilitation professionals can assist transition age women with ADHD to identify their personal strengths and interests in order to consider educational and occupational choices in traditional and nontraditional occupations (Gil-Kashiwabara et al., 2012). Structured opportunities including job shadows or site visits may give young women more options for consideration, even if they choose greater flexibility in employment over higher wages (Linstrom et al., 2004).

5 Limitations

There are a number of limitations to the findings of this study in regard to internal, external, and construct validity. First, this study employed a correlational research design which precludes the ability to infer cause and effect relationships between the criterion and predictor variables (Shadish et al., 2002). Non-probability sampling was used which limits generalization of these findings to other consumers. Also, even with cross validation the use of logistic regression may limit inferences to other populations as findings from logistic regression can be population specific (Cohen et al., 2003). Another limitation lies in the fact that the RSA-911 data set may include data input errors. Also, the RSA-911 data set does not differentiate the severity or subtype of ADHD symptoms; thus it does not capture the variability of functioning that exists among people with ADHD.

6 Conclusion

Given the dearth of research on ADHD in the VR literature and the mixed findings of this study vis a vis previous studies, there is a need for additional research on the postsecondary educational and employment outcomes of transition age consumers with ADHD in order to build a repertoire of results to inform best practice. Because this study only utilized data from a single fiscal year, future research using multiple fiscal years of RSA-911 data should be conducted in order to obtain a more accurate understanding of this population and to assess whether results are sustained over time. Also, the present study is the only one in the VR literature thus far that has examined outcomes for culturally diverse consumers with ADHD, therefore more research is needed to better understand outcomes for all consumers with ADHD.

Conflict of interest

None to report.

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