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Rehabilitation Counseling Bulletin

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 DOI: 10.1177/0034355210368432

published online 19 May 2010Rehabil Couns BullK. Brigid Flannery, Michael R. Benz, Paul Yovanoff, Mary McGrath Kato and Lauren Lindstrom

Predicting Employment Outcomes for Consumers in Community College Short-Term Training Programs  

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Rehabilitation Counseling BulletinXX(X) 1 –12© Hammill Institute on Disabilities 2010Reprints and permission: http://www. sagepub.com/journalsPermissions.navDOI: 10.1177/0034355210368432http://rcb.sagepub.com

Predicting Employment Outcomes for Consumers in Community College Short-Term Training Programs

K. Brigid Flannery1, Michael R. Benz2, Paul Yovanoff1,Mary McGrath Kato1, and Lauren Lindstrom1

Abstract

Postsecondary education has been linked to improved access to employment opportunities for individuals with and without disabilities. The purpose of this study was to determine factors associated with increased employment outcomes for Vocational Rehabilitation consumers enrolled in community college short term occupational skill training programs. Findings indicate that certain student and program factors were associated with more positive program and employment outcomes. Females, older participants, and those who received financial support were more likely to complete a certificate, be employed at exit, and maintain employment for 90 days. Individuals with psychiatric disabilities and other skill barriers were less likely to obtain positive program outcomes. Being female or a member of an ethnic minority group was associated with lower annual wages, while completion of all program outcomes predicted higher total annual wages. Implications for rehabilitation practice are discussed.

Keywords

career/vocational employment, education, career/vocational development, collaboration

There is widespread agreement that education can change lives. Over the past 25 years, changes in the labor market have rendered postsecondary education essential for people to attain even a moderate level of financial stability. For persons with and without disabilities, completion of postsecondary education improves the chances of securing employment and achieving greater levels of financial independence (Carnevale, 2007; Flannery, Yovanoff, Benz, & Kato, 2008; Madaus, 2006; National Council on Disability, 2003).

The relationship between postsecondary education and improved employment outcomes holds true for consumers of vocational rehabilitation (VR) services as well. Nationwide analyses of VR services (Gilmore, Schuster, Zafft, & Hart, 2001; Hayward & Schmidt-Davis, 2003; Schmidt-Davis, Kay, & Hayward, 2000) found that consumers who received postsecondary education services through VR had higher rates of competitive employment than consumers who did not receive such services. Specifically, in one analysis, indi-viduals who received any postsecondary education services through VR (32.6% of consumers) were almost twice as likely to earn high wages as low wages (30.8% vs. 16.0%), and both receipt of postsecondary services through VR and completion of some level of postsecondary education sig-nificantly predicted higher earnings (Schmidt-Davis et al., 2000). Yet, Gilmore, Schuster, et al. (2001) found that less than a quarter (21%) of all VR consumers participated in any form of postsecondary education, ranging from short-term occupational training programs to 4-year colleges.

Individuals with disabilities need to be prepared for and succeed in postsecondary education to compete in the work-force and obtain living wage jobs. A living wage is defined as one that would allow workers and their families to meet their basic needs (e.g., housing, health care, child care, food) without resorting to public assistance. For a single adult, 70% of job openings that pay a living wage require moderate-to-long-term postsecondary education, training, or both (Employment Policies Institute, 2000; Northwest Federa-tion of Community Organizations, 2008; Northwest Policy Center, 2001). Even those who initially enter the labor market earning minimum wage will see significant wage gains (14.5% in the 1st year) toward a living wage with some post-secondary education (Employment Policies Institute, 2000).

Community college short-term training programs are designed to meet workforce development demands. These programs furnish individuals with training that is occupationally specific, work-related, and short-term (usu-ally less than 1 year) and that results in entry into careers that offer a living wage (Flannery et al., 2008; Grubb, 2001; Northwest Policy Center, 2001). Short-term training

1University of Oregon, Eugene, USA2Texas A&M University, College Station, USA

Corresponding Author:K. Brigid Flannery, Educational and Community Supports, 1235 University of Oregon, Eugene, OR 97403-1235Email: brigidf@uoregon.edu

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2 Rehabilitation Counseling Bulletin XX(X)

programs serve the needs of both local employers and job seekers by focusing on specific occupations and offering hands-on instruction and worksite-based training.

In this article, we examine the outcomes for participants in a partnership developed between four community college short-term skills training programs and a state Office of Vocational Rehabilitation Services. This partnership, called Career Workforce Skills Training (CWST), began in 1998 in one pilot site and has since expanded to four community colleges. CWST was established to provide a postsecondary training option that would result in improved employment outcomes for VR consumers. It is important to note that these short-term training programs were not new. The CWST partnership, however, provided a mechanism to support existing programs to meet the needs of individuals with dis-abilities, thereby using existing resources and expertise and increasing capacity to serve a broader range of participants. This partnership was successful in assisting students to obtain certificates and employment in preferred career areas and demonstrated that the consumers who completed had significantly higher employment outcomes in the areas of wages, hours worked, and quarters worked than those who did not complete the program (Flannery et al., 2008).

This study expands on this initial research in two important ways. First, the analysis includes data from four sites instead of just one, as in the previous study. Second, it extends existing research by looking at consumers’ outcomes for 1 year post-exit, rather than taking a one-time snapshot of their outcome status post-service. This provides valuable information on the durability of the outcomes achieved by VR consumers in this partnership. The value of evidence-based practice is not new to the rehabilitation community (Corthell & VanBoskirk, 1988; Houser, Hampton, & Carriker, 2000; McAlees & Menz, 1992). Expanding the initial research, we hope to strengthen the research base on the use of postsecondary education to achieve positive outcomes for VR consumers. The following research questions were explored in this study:

Research Question 1: What student and program fac-tors are associated with program completion?

Research Question 2: What factors are associated with higher employment outcomes after complet-ing the program?

To establish a context for our research, we briefly describe the organizational structure and service delivery pattern of the Career Workforce Skills Training partnership and present descriptive information on the employment outcomes achieved by participating VR consumers.

Career Workforce Skills TrainingIn Oregon, where this study was conducted, short-term occupational skills training programs are community college

programs that provide opportunities for individuals to learn marketable job skills. The types of occupational skill areas are quite broad and include occupations such as wastewater treat-ment technician, auto body mechanic, meat cutter/wrapper, clerical support staff, surgical tools technician, and veterinary assistant. These programs are approved by the Oregon Depart-ment of Education and offer an opportunity for students to earn college credit as well as state-approved certificates.

Although these college short-term occupational skills training programs had key components that were potentially appropriate for individuals with disabilities (e.g., individu-ally designed work-based learning tied to the local economy), consumers served by Oregon’s Office of Vocational Reha-bilitation Services (OVRS) accessed these training options on a limited basis due to lack of information about the options and limited college support services available. To modify these programs to successfully address the instruction and support needs of VR consumers, a partnership was formed between OVRS, local community colleges, the employment community, and the University of Oregon (UO) that included administrative, financial, and programmatic components (Flannery, Lindstrom, & Toricellas, 2009; Flannery, Slovic, Benz, & Levine, 2007). This resulted in a coordinated effort by four key personnel who work directly with the VR con-sumer: (a) a skills training faculty member who develops and evaluates progress on the individualized training plan, (b) an employment specialist who develops the training worksite in the community and provides monitoring and support as needed on the worksite and at the community college (co-funded by OVRS and community college), (c) a worksite supervisor (at local worksite) who provides the direct occu-pational skills training and evaluation, and (d) a VR counselor who provides overall case management and develops the Individualized Plan for Employment (IPE).

CWST services are offered on an open entry/open exit basis, where consumers can enter and complete their training at any point in the term. A curriculum for the specific occupa-tion, including specific objectives for the training site and necessary college coursework, is determined jointly by the consumer, VR counselor, the employment specialist, other faculty at the community college, and training site employ-ers. Although the majority of the curriculum and training is delivered at the approved employment training site, most consumers enroll in some related classroom instruction (e.g., academic or professional technical education) at the commu-nity college as a part of their approved training plan.

Method

Participants

There were four community colleges in the CWST part-nership. The four colleges were reflective of colleges across Oregon that serve the range of rural and nonrural

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Flannery et al. 3

communities in the state. The first community college served a three-county region, with a main campus located in a major population center and small satellite campuses in surrounding rural communities. The CWST staff at this col-lege worked with four OVRS branch offices. The second community college served a three-county rural region of the state with three small campuses and worked with the OVRS branch office located in each rural county. The third com-munity college served one rural county that was adjacent to an urban community and worked with the one OVRS branch office in the county. The fourth community college was located in one county with a main campus that served a metropolitan area made up of two communities, as well as smaller adjacent rural communities, and worked with two OVRS branch offices located in the metropolitan area.

At the time of this study, data were available on 963 con-sumers who had been or who were being served across the four CWST sites. All individuals (a) were eligible for ser-vices through VR, (b) had a vocational career goal that was obtainable through CWST, and (c) agreed, along with their VR counselor and CWST staff, that CWST was the mecha-nism to achieve this goal. Because we were interested in examining the effect of program services on post-program employment outcomes, we included only CWST participants who had exited the program (successfully or unsuccessfully) and who were eligible for 1 year (four quarters) of post-program earnings. These inclusion criteria reduced our sample to 465 former CWST participants.

Of the 465 participants in the analysis, 238 (51.3%) were male and 226 (48.7%) were female; 1 participant was missing information on gender. With regard to age, 112 (25.4%) of the participants were 25 years of age or younger, 83 participants (18.8%) were 26 to 35 years of age, and 246 participants (55.8%) were 36 years of age or older. Information on age was missing on 24 participants. The distribution of racial ethnic groups in the sample was as follows: 87.1% Caucasian (n = 405), 6.5% Hispanic (n = 30), 3.4% American Indian/Alaska Native (n = 16), 1.5% Asian/Pacific Islander (n = 7), 1.3% African American (n = 6), and <1% missing (n = 1).

Approximately half of the former participants entered the CWST program with only one disability (50.8%) com-pared to having two or more identified disabilities (49.2%), as documented by Vocational Rehabilitation. The primary disability of participants in the sample for whom disability information was available (N = 393) was distributed as fol-lows: 50.6% (n = 199) Physical/Orthopedic (including multiple sclerosis, spinal cord injury, muscular dystrophy), 40.5% (n = 159) Psychiatric (including schizophrenia, anxi-ety disorder, mental illness), 33.3% (n = 131) Cognitive (including learning disability, mental retardation, traumatic brain injury, attention deficit/hyperactivity disorder), 20.6% (n = 81) Physical/Medical (including epilepsy, HIV/AIDS, diabetes mellitus, respiratory disorders), and 11.5% (n = 45) Substance Abuse (including drug and alcohol abuse).

Data Collection

Data for this study were obtained from three databases. Access to the data in these three databases was authorized by the evaluation contract between OVRS and the UO. All procedures were reviewed and approved by the Institutional Review Board at the UO.

Much of our data were obtained from a database that was maintained at each CWST partnership site. The CWST database contained information on student characteristics, program services received, employment outcomes achieved at exit, and employment outcomes 90 days after exit. We followed several procedures to ensure the accuracy of the CWST data collection activities. First, local staff were pro-vided with a standardized data entry computer application that contained graphic-user interface features such as pull-down menus, check boxes, mandatory fields, and automated incorrect range filters. Second, local staff received written materials with variable definitions, annual training, and on-site technical assistance related to data collection. Third, data were reviewed quarterly with college staff, VR branch managers, and technical assistance staff, and all outcome data were submitted quarterly and reviewed for accuracy and completeness by a technical assistance staff person at the University of Oregon. Any questions about the data were resolved with local VR and college staff prior to entry into the final database for this study.

Additional data were obtained from the Oregon Vocational Rehabilitation Services Division (participant demographic and disability information) and the Oregon State Employ-ment Division (employment outcomes). Information was requested only for the individuals included in the Univer-sity of Oregon database. Electronic data sets were transferred through secure means from each state agency as defined by the university’s contract with OVRS. All data were merged into a single data set following examination and cleaning of the individual state data sets. After merging, the unique identifiers were deleted from the merged data set. Finally, all data were stored on a secure, restricted server that was firewall protected.

Research Question 1: Predictors of Program Completion

Dependent variable. The CWST program was established to provide VR consumers with occupationally specific classroom and jobsite training related to their identified career goal and their Individualized Plan for Employment. Participants were considered formally admitted to the pro-gram once they had developed an IPE with the VR counselor; had a goal approved by the consumer, VR counselor, and CWST staff; and had begun receiving services related to the CWST training plan. The intended program outcomes at program completion included (a) receipt of an occupational

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4 Rehabilitation Counseling Bulletin XX(X)

certificate documenting completion of all approved require-ments, (b) employment at exit in a career-related job, and (c) maintenance of employment at 90 days follow-up. Simi-lar to all postsecondary education students, it was possible for participants to (a) start the program but drop out prior to program completion, (b) complete the program having achieved at least one, but not all, intended outcomes (e.g., obtain a career-related position but choose not to complete requirements for the certificate), or (c) complete the program having achieved all three intended outcomes (i.e., certifi-cate, employment at exit, and employment at follow-up). We created a dependent variable to capture these three cate-gories of possible program completion: (a) no positive

outcomes (n = 197; 42.4%), (b) some positive outcomes (n = 136; 29.2%), and (c) all positive outcomes (n = 132; 28.4%). These categories formed an ordered structure of program outcomes coded 0, 1, and 2, respectively.

Independent variables. Based on program evaluation information and pilot research (Flannery et al., 2008), we examined three categories of theoretically relevant predictors of program completion: participant demo-graphics, participant barriers to program completion, and program services received. A series of preliminary univari-ate contingency table analyses was completed, testing the univariate relation of each predictor variable to the dependent variable. Theoretically relevant variables with

Table 1. Comparison of Predictor Variables on Program Success Outcome Variable

Dropped Out Completed Completed All Total (n = 197) Some (n = 136) (n = 132) (N = 465)

Predictor Variable n % n % n % n %

Participant demographics Gender 1 Male 115 48.3 59 24.8 64 26.9 238 51.3 2 Female 81 35.8 77 34.1 68 30.1 226 48.7 Ethnic minority 1 No 133 43.9 80 26.4 90 29.7 303 67.5 2 Yes 55 37.7 52 35.6 39 26.7 146 32.5 Age at program entry 1 ≤25 years 56 50.0 34 30.4 22 19.6 112 25.4 2 26–35 years 45 54.2 18 21.7 20 24.1 83 18.8 3 36+ years 87 35.4 74 30.1 85 34.5 246 55.8Participant barriers Physical/orthopedic disabilitya

0 No 86 44.3 61 31.4 47 24.2 194 49.4 1 Yes 88 44.2 53 26.6 58 29.2 199 50.6 Psychiatric disabilitya

0 No 93 39.7 74 31.6 67 28.6 234 59.5 1 Yes 81 50.9 40 25.2 38 23.9 159 40.5 Skill barriers 0 No 107 37.2 88 30.6 93 32.3 288 61.9 1 Yes 90 50.8 48 27.1 39 22.1 177 38.1 Unusual work accommodations 0 No 104 38.7 84 31.2 81 30.1 269 62.9 1 Yes 81 50.9 40 25.2 38 23.9 159 37.1Career Workforce Skills Training program services Financial support 0 No 156 49.4 91 28.8 69 21.8 316 68.0 1 Yes 41 27.5 45 30.2 63 42.3 149 32.0 Career planning services 0 No 154 50.0 82 26.6 72 23.4 308 66.2 1 Yes 43 27.4 54 34.4 60 38.2 157 33.8 Vocational courses re: plan 0 No 156 47.9 85 26.1 85 26.1 326 70.1 1 Yes 41 29.5 51 36.7 47 33.8 139 29.9 Other college coursework 0 No 165 47.3 95 27.2 89 25.5 349 75.1 1 Yes 32 27.6 41 35.3 43 37.1 116 24.9

a. Primary disability came from the Office of Vocational Rehabilitation Services data set. Total N for physical/orthopedic disability and psychiatricdisability was 393.

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statistically significant univariate relationships (α ≤ .05) were selected for the multivariate model. The independent variables that we examined are summarized in Table 1 and described below.

Information on independent variables was obtained from the CWST and OVRS databases. Information in the OVRS database was collected by VR staff during the VR eligibility determination process. Information in the CWST database was collected by college staff from participating commu-nity colleges quarterly following the procedures described earlier. Participant barriers and program services were coded dichotomously (no = 0, yes = 1). Skill barriers included the presence of one or more potential barriers to employment: no prior work experience, poor social skills, low academic placement test scores, or unclear career goal. Program services included (a) financial support, including receipt of government-sponsored financial aid; (b) career planning services provided by the college; (c) completion of vocational courses at the college related to the training plan; and (d) completion of other college courses (e.g., developmental courses in reading, math, and writing).

Analyses. We used multinomial logistic regression (Hosmer & Lemeshow, 2000) to investigate Research Question 1. Multinomial logistic regression is an extension of logistic regression and is used when the dependent variable of inter-est is coded into three or more ordered categories, as was the case in our study (see earlier description of dependent vari-able). As noted above, our dependent variable was scored 0, 1, and 2. We set the lowest category (0 = no positive out-comes) as the baseline, which in effect results in logistic regression models estimating the log (odds) of being in cat-egory “1” compared to category “0” (i.e., achieving some positive outcomes vs. none), and then the log (odds) of being in category “2” compared to category “0” (i.e., achieving all positive outcomes vs. none). These analyses helped to understand which independent variables were most effec-tive for increasing the odds of moving to increasingly more positive outcomes relative to no positive outcomes.

Research Question 2: Predictors of Total Annual Wages Earned 1 Year Post-Exit

Dependent variable. The dependent variable for this research question, total annual wages earned 1 year post-exit, was based on data obtained by OVRS from the Oregon Employ-ment Division (OED). OED provided quarterly earnings reports for participants by employer. Some participants may have had multiple employers within a single quarter. Quar-terly reports for the first four quarters after the exit date were identified and wages from those reports were summed to compute the total wages earned within the 1st year post-exit from the CSWT program.

One challenge with computation of this variable per-tained to the absence of quarterly reports for some partici pants. In essence, these are missing data. Three

plausible explanations of the “missing” quarterly report were (a) the person was unemployed during this period; (b) the person was employed, but not in the state of Oregon; or (c) the person was employed in Oregon but in a job that did not require reporting employment data to OED (e.g., supported employment). Therefore, it was not reasonable to automatically impute the value $0.00 earned when reports were absent for each of the four quarters. We tested to see if a person had an OED report within the 2nd year, and only then were $0.00 imputed for the absent 1st year of earnings. Otherwise, the data were treated as missing.

Independent variables. The independent variables for pre-dicting earnings were the same as those listed above for predicting level of program completion. In addition, for this research question, we included the program outcome vari-able as a predictor. Our interest was in determining the predictive value that achieving different positive program outcomes had on post-exit earnings.

Analyses. A multiple linear ordinary least squares regres-sion model was used to estimate the regression coefficients of all independent variables. The model was estimated in two steps. The first step included all variables with the exception of the level of positive outcome. Then, in a second step, the positive outcome variable was included. This sequence was used to obtain a clearer understanding of the additional value associated with obtaining higher levels of the positive outcome.

Dependent variable distributional assumptions underly-ing this analysis were considered, including the presence of outliers. The mean, standard deviation, and skew of total wages were approximately $12,371.00, $13,980.00, and 1.74, respectively. The positive skew is attributable to the impu-tation of zero earnings when justifiable (see discussion above). The maximum total wages $82,616.00 is not considered an outlier for this population. Although the non-normal distri-bution of total wages does not necessarily prevent use of the regression model, it does require cautious interpretation of unexpected results. In addition to assumptions about nor-mality, homogeneity of variance across levels of predictor variables is assumed, also. For all predictors, statistical tests indicated homogeneity of variance.

Missing DataFor both research questions, participants were included in the analyses only if they had complete information on all variables. As noted in Table 1, data on primary disability from the OVRS data set were available for only 393 of the 465 participants. As a result of the listwise deletion proce-dures employed during the regression analyses, the final models were based on 363 participants. A series of chi-square tests were conducted for all predictor and dependent variables to evaluate whether the 363 included participants were different from the 102 participants excluded based on missing data. Results indicated no statistically significant

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6 Rehabilitation Counseling Bulletin XX(X)

differences for the vast majority of variables. Differences were found for ethnic minority status and presence of skill barriers. Compared to excluded participants, participants with complete data were (a) less likely to include ethnic minorities (28% of included participants were ethnic minor-ities vs. 52% of excluded participants) and (b) more likely to have one or more skill barriers (42% of included partici-pants had 1 or more skill barriers vs. 23% of excluded participants). The disproportionate numbers of participants associated with these two independent variables should be taken into consideration in the interpretation of results.

Nested DataFinally, this study involved a nested design (i.e., participants were nested within community colleges). Nested designs have the potential to violate the assumption of independence of observations, an assumption of the analyses employed in the study, and in turn inflate Type I error rates. To account for the dependency among the observations, each of the four sites was identified with a dummy coded (0, 1) variable (i.e., one site was selected as the reference site and three dummy coded variables were created to uniquely identify each site). These site indicator variables were included in the model to control for site effects.

ResultsWe present our findings in terms of our two research questions.

Research Question 1: Predictors of Program CompletionWe examined predictors associated with different levels of successful program completion. Descriptive information on

how each predictor is related to the dependent variable is presented in Table 1. The results of the multinomial logistic regression are presented in Table 2.

Referring to the multivariate findings in Table 2, three variables were associated with staying in the program (vs. dropping out) and completing some positive outcomes (i.e., obtaining a certificate, being employed at exit or follow-up). The odds of the female participants staying in the program were two times greater than the odds of the male partici-pants. Gender was not associated with obtaining all positive outcomes (i.e., it does not appear as a predictor in the bottom part of Table 2). Looking at the descriptive findings in Table 1, it can be seen that 48% of males dropped out of the program versus 36% of females; conversely, 34% of females stayed in the program and completed some level of positive program outcome versus 25% of males.

Similarly, identification of a psychiatric disability at the time of program entry and identification of skill barriers at program entry were each strong negative predictors of stay-ing in the program and completing some level of positive outcome. Potential barriers to employment were noted by OVRS and CWST staff upon participants’ entry into the program and included barriers in one or more of the follow-ing skill areas: no prior work experience, poor social skills, low placement test scores, or unclear career goal. Descrip-tively, about 51% of participants with psychiatric disabilities or skill barriers dropped out of the program compared with 40% and 37% of participants without psychiatric disabili-ties and skill barriers, respectively. These variables were also strong predictors of whether participants stayed in the program and obtained all positive program outcomes.

Finally, participant age and financial support were both strong predictors of staying in the program and completing all positive outcomes (i.e., obtaining a certificate and being employed in a career-related position at exit and at follow-up). The odds of older participants staying in the program

Table 2. Summary of Multinomial Logistic Regression for Variables Predicting Levels of Positive Program Outcome

Predictor Variable B SE Odds Ratio (95% CI)

Certified or employed (exit or follow-up) Gender (female) .70** .280 2.01 (1.16, 3.48) Psychiatric disability (yes) -.67* .306 0.51 (0.28, 0.94) Skill barriers (yes) -.74** .303 0.48 (0.26, 0.86) Constant -1.39* .717 Certified and employed (exit & follow-up) Age at entry (older age range) .55** .201 1.74 (1.17, 2.58) Psychiatric disability (yes) -.91** .335 0.40 (0.21, 0.78) Skill barriers (yes) -.99** .333 0.37 (0.19, 0.71) Unusual work accommodations (yes) -.74** .342 0.48 (0.25, 0.94) Received financial support (yes) 1.32* .342 3.74 (1.98, 7.06) Constant -1.60* .78

*p ≤ .05. **p ≤ .01.

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and obtaining all positive outcomes were 1.74 times greater than the odds for younger participants. For participants who received financial support while in the program, the odds of staying in the program and obtaining all positive outcomes were 3.74 times greater than the odds for those who didn’t receive financial support.

Research Question 2: Predictors of Total Annual WagesMultiple linear regression was used to predict total annual wages during the 1st year after program exit. The results of our analysis are presented in Table 3. Two demographic variables—gender (1 = male, 2 = female) and ethnic minority status (1 = no, 2 = yes)—were significant predictors of total annual wages earned during the 1st year post-exit. These two demographic variables accounted for 2.8% of the vari-ance in total annual wages, F = 3.05, df = 5, 395, p ≤ .01. After Step 2 (entering positive program outcome as a pre-dictor), 13.2% of the variance was explained. The change in R2 (10.4%) was statistically significant, F = 42.002, df = 1, 394, p ≤ .001. With regard to the amount of variance accounted for in the model, it is worth noting that total annual wages was a highly variable outcome (min. = $0.00, max. = $82,616.40), and furthermore, the variable was skewed in the direction of lower wage earners. Multiple linear

regression models tend to be sensitive to this skew, which is likely to attenuate the R2 estimate. In fact, our model tended to predict poorly participants at the upper extreme with earn-ings of more than $50,000.00 a year. The prediction model underestimates these individuals, leading to an attenuated R2.

With regard to important predictors, gender and ethnic minority had negative regression coefficients, suggesting that being female or being a member of an ethnic minority group had a negative effect on projected earnings. Women, on average, earned approximately $10,994.15 in total annual earnings during the 1st year post-exit compared to $13,719.20 for men (see Table 4). Looking further at the post-exit employment data in Tables 4 and 5, it can be seen that the differences between women and men in total annual earnings were due largely to differences in the average hours worked per quarter (i.e., women worked an average of 268.60 hours per quarter compared to 321.03 hours per quarter for males). There were slight differences between women and men in average hourly earnings and few differences in the percent-age of quarters with earnings during the 1st year after leaving the program. Similarly, differences in total annual earnings between Caucasian and ethnic minority participants appeared largely to be a function of differences in average hours worked per quarter during the 1st year after leaving the program.

In contrast, the regression findings presented in Table 3 document that obtaining positive program outcomes was

Table 3. Summary of Regression Analysis for Variables Predicting Total Annual Wages 1 Year Post-Exit

Predictor Variable B SE β

Gender (female) -3596.67** 1346.16 -.13Ethnic minority (yes) -4469.81** 1455.28 -.15Positive program outcome 5452.05*** 829.27 .32Constant 14240.12 3020.39

**p ≤ .01. ***p ≤ .001.

Table 4. Comparison of Program Participants on Employment Earnings During 1st Year After Leaving Program

Hours Worked Total Annual Wages Hourly Wage per Quarter

Predictor Variable M SD M SD M SD

Gender Male $13,719.20 $15,200.55 $10.06 $4.33 321.03 303.03 Female $10,994.15 $12,442.55 $9.84 $3.36 268.60 280.68Ethnic minority No $13,901.94 $15,215.55 $10.11 $3.72 326.15 312.83 Yes $ 9,759.58 $11,027.72 $9.69 $4.36 242.75 249.22Program completion Dropped out $8,484.74 $11,817.56 $9.49 $4.67 216.56 262.37 Completed some $10,364.49 $12,002.11 $9.39 $3.07 265.27 289.78 Completed all $20,178.57 $15,682.32 $11.07 $3.23 441.40 289.70

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8 Rehabilitation Counseling Bulletin XX(X)

associated with increased total annual wages. Participants who successfully completed all program outcomes (i.e., obtained a certificate and were employed at exit and follow-up) averaged total annual wages of $20,178.57 in the 1st year after leaving the program compared to $10,364.49 for par-ticipants who completed some but not all outcomes (i.e., they obtained a certificate or employment) and $8,484.74 for participants who dropped out of the program (see Table 4). Looking further at the employment outcomes in Tables 4 and 5, it can be seen that the substantially higher total annual wages earned by participants who completed all program outcomes were a function of (a) higher average hourly wages ($11.07 per hour for participants that completed all outcomes vs. $9.39 per hour for those who complete some and $9.49 per hour for participants who dropped out), (b) higher average hours worked per quarter (441.40 vs. 265.27 and 216.56, respectively), and (c) a higher percentage of quarters with earnings (61.4% of participants who completed all outcomes had earnings in all four quarters vs. 36.8% of participants who completed some but not all outcomes and 24.6% of participants who dropped out of the program).

DiscussionThe primary goal of vocational rehabilitation is to assist indi-viduals in overcoming barriers so they can obtain competitive employment. Postsecondary education has been linked to increasing employment opportunities and higher wages. However, little research is available about the effect of postsecondary short-term training programs, especially for individuals with disabilities. The purpose of this study was to examine employment outcomes for vocational rehabilita-tion consumers who participated in Career Work Skills Training, a program that was delivered collaboratively by community college and vocational rehabilitation personnel at four community colleges in one state.

Our findings indicate that participants who stay in the program and obtain an occupational certificate and career-related employment at exit and follow-up earn total annual wages that are double the wages of participants who either drop out or fail to complete all three education and employ-ment outcomes. But, how do these annual wages compare to an external standard such as living wage rates? A living wage is defined as one that allows workers and their families to meet their basic needs (e.g., housing, food, medical care, child care, and transportation) without resorting to public assistance. Participants who successfully complete all edu-cation and employment outcomes in the CWST earn on average $20,179 during their 1st year post-exit. Using the Living Wage Calculator (http://www.livingwage.geog.psu .edu), the average annual earnings of successful CWST par-ticipants are above the annual wages needed by an individual to meet his or her basic needs without using public assis-tance ($17,035) but below the living wage needed for a single worker who is supporting two adults ($26,541).

It is worth noting that this study used a conservative approach to calculating annual wage data, which may under-estimate the positive employment effect of the program relative to living wage standards. As noted earlier, the calcu-lation of total annual wages was based on actual wages reported to the Employment Division by employers. If a participant was missing data in the 1st year post-exit but had wage data in the following year, $0.00 was entered as his or her wage amount for the 1st year post-exit. This approach results in more individuals with $0.00 annual earnings being included in the calculation. This study’s approach to calculat-ing annual wage data also is more conservative than approaches typically used in other studies on employment outcomes, which often calculate annual wages based on consumer report, or hourly/weekly wages within a short time period post-exit (Gilmore & Bose, 2005; Gilmore, Bose, & Hart, 2001; Newman, Wagner, Cameto, & Knokey, 2009).

Table 5. Comparison of Program Participants on Percentage of Quarters With Earnings During 1st Year After Leaving Program

Percentage of Quarters With Earnings

Predictor Variable 0% 1%–25% 26%–50% 51%–75% 76%–100%

Gender (n = 462) Male 8.0 12.2 20.6 20.6 38.7 Female 12.9 14.7 18.8 14.7 38.8Ethnic minority (n = 463) No 9.6 13.6 19.9 16.9 39.9 Yes 10.3 13.7 20.5 19.9 35.6Program completion (n = 463) Dropped out 14.4 18.5 22.1 20.5 24.6 Completed some 13.2 14.0 19.9 16.2 36.8 Completed all 2.3 5.3 15.9 15.2 61.4

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Estimates of total annual wages based on hourly or weekly data assume that the person is working full time through-out a year. The findings of our study indicate that even the most successful participants on average do not work full-time throughout the entire 1st year post-exit (see Tables 4 & 5).

In our study, the strongest predictor of staying in the program and completing all education and employment outcomes was whether the participant received financial support. The issue of financial support for postsecondary education is a national concern and there is a new focus at the federal level to make college funding more accessible. Financial aid is critical for vocational rehabilitation consum-ers because many individuals are living in poverty at the time of referral. The need for funds to be used for living expenses makes it difficult to finance the other costs associated with returning to school such as transportation, books, tuition, tools, and materials. To address these concerns, vocational rehabili-tation counselors need to work closely with college financial aid offices, other federal programs (e.g., WIA, TANF), and within their own vocational rehabilitation funds to create financial supports needed for consumers to successfully complete their training programs.

The findings of our study also indicate that three sub-groups of individuals—women, ethnic minority participants, and individuals with psychiatric disabilities—may be at risk of poorer employment outcomes. The outcomes for female participants are mixed. Although gender is related to stay-ing in the program (women have greater odds of staying in than men), it is unrelated to obtaining a certificate and career-related employment (meaning technically, there is no statistically significant relationship between gender—being female vs. male—and obtaining all outcomes). On average, women earn lower total annual wages in the 1st year post-exit than men. This appears to be due largely to gender differences in the average number of hours worked per quarter, with women working significantly fewer hours on average than men. Previous research has documented that women with disabilities typically experience lower rates of employment than either men with disabilities or women without disabilities (Benz, Doren, & Yovanoff, 1998; Doren & Benz, 1998; O’Day & Foley, 2008). A national study indicated that young women with disabilities who were out of high school for up to 2 years worked fewer hours per week (23 vs. 32 hours) and earned significantly lower aver-age wages ($6.40 vs. $7.70) than their male peers (Wagner, Newman, Cameto, Garza, & Levine, 2005).

Much of the extant literature has focused on differences between men and women in their hourly and annual wages. Our study highlights the need also to examine the number of hours worked as well as patterns of employment over time as important variables that may explain these differences in

outcomes. In addition, for women with disabilities, it could be important to examine the types of jobs they are obtaining and the relationship to annual wages, as well as family responsibility. More research is needed to further under-stand these gender differences and develop strategies to reduce these discrepancies in outcomes.

Being a member of an ethnic minority group (vs. being Caucasian) is unrelated to staying in the program and successfully completing all education and employment out-comes. Nevertheless, participants who are members of an ethnic minority group have lower annual wages in the 1st year post-exit than Caucasian participants. Closer examina-tion indicates that these differences are due primarily to differences in the average number of hours worked per quarter; there are few differences in average hourly wages or quarters with earnings for minority and nonminority par-ticipants. These findings should be interpreted cautiously in light of the small proportion of ethnic minorities enrolled in the program overall and in the disproportionately higher percentage of ethnic minority participants excluded from final analyses due to missing data. However, previous stud-ies also have documented differences in employment rates as well as hourly wages for adults with disabilities from culturally diverse groups compared to their majority culture peers (Rehabilitation Research and Training Center on Dis-ability Demographics, 2007; Wagner et al., 2005). As a result, this is an area that needs further study with a larger sample of ethnically diverse participants.

In our study, participants identified as having a psychiat-ric disability are much more likely to drop out of the program and those who stay in are much less likely to complete all education and employment outcomes. It is interesting that this study did not identify a direct relationship between having a psychiatric disability and lower total annual wages 1 year post-exit. However, given the much lower probability that these participants stay in and successfully complete the pro-gram, and the strong relationship between successful program completion and higher annual earnings, it is likely that par-ticipants identified with psychiatric disabilities experience poorer employment outcomes through their low program completion rates. Findings from other research have shown that individuals with psychiatric disabilities have numerous barriers to obtaining employment and have less positive vocational outcomes when compared with other disability groups who are served by the state vocational rehabilitation system (Marshak, Bostick, & Turton, 1990; Rogers, Anthony, Lyass, & Penk, 2006; Skelley, 1980). The Longitudinal Study of the Vocational Rehabilitation Services Program reported that consumers with mental illness require more assistance in selecting a vocational goal and need more edu-cational or employment development services than others (Hayward & Schmidt-Davis, 2003). Further study is needed

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10 Rehabilitation Counseling Bulletin XX(X)

of participants with psychiatric disabilities to identify the specific barriers for these individuals so that college staff can become more diligent in identifying and removing barriers early in the program for these individuals. This dif-ferential success rate for individuals with psychiatric disabilities and the support strategies necessary for reten-tion should be specifically examined in further research.

Study LimitationsIn addition to the recommendations for further research identified above, there are two general limitations to this study, which also suggest the need for further research. First, although the data collected in this study are from four different colleges located in different geographic regions of a state with different labor markets, they may not be repre-sentative of the nation. It is important to examine the outcomes from different states as the program requirements (college and rehabilitation), laws, and labor markets could affect the outcomes. Second, the participants in this study were selected from the general caseloads of participating counselors in the state’s public vocational rehabilitation program. As noted earlier, participants were selected by mutual consent of the consumer, the counselor, and community college staff based on the conclusion that the CWST was appropriate for achieving the employment goals in the IPE. With the excep-tion of participants with psychiatric disabilities, we did not find any differences in program or employment outcomes based on disability. Nevertheless, the findings should be used cautiously when making conclusions about any specific impairment. Similarly, although as a group those identified as having a psychiatric disability were less successful, some of these individuals were able to achieve successful program outcomes. More research is needed to understand the spe-cific supports and services that may be needed by vocational rehabilitation consumers with different disabilities to succeed in postsecondary education options that will lead to improved employment outcomes.

Practical ImplicationsThese findings provide a number of important implications for rehabilitation practice. As discussed earlier, participants who had financial support were more likely to succeed in this type of postsecondary training program. Therefore, financial supports should be clearly addressed in individu-alized employment planning for postsecondary education. Rehabilitation counselors also need to attend to and develop individualized supports for certain vulnerable subgroups of individuals with disabilities, including women, individuals from ethnic minority groups, and those with psychiatric dis-abilities. Although some of the variables identified as barriers in our study are not modifiable through rehabilita-tion services (e.g., age, gender, being a member of an ethic

minority group), knowledge of these as risks to successful outcomes can heighten counselors’ attention to these vari-ables when working with consumers and developing an IPE that includes postsecondary education short-term training options.

One barrier that can stand in the way of consumers’ will-ingness to attend these short-term training programs is lack of information and stereotypes about postsecondary educa-tion. Counselors and college staff need to be able to change consumers’ perceptions of who can attend college and pro-vide information to help these individuals be more open to postsecondary education and training as an option. As these hands-on training programs resulted in positive outcomes for many vocational rehabilitation consumers, rehabilita-tion counselors and managers need to explore the availability of these programs in their local community college and work together to increase access to and retention in these programs. Postsecondary programs should be marketed to adult consumers as well as to high school students with disabilities.

ConclusionThrough a partnership with a state Office of Vocational Reha-bilitation, four community colleges were able to expand their services to increase the success of participants with disabili-ties in postsecondary short-term programs. The study has demonstrated that completion of specific short-term training programs can lead to higher wage employment opportunities. The timing is right as there are emerging federal initiatives focused on increasing individuals’ access to and completion of community colleges as well as developing partnerships between community colleges and employers to create work-force training opportunities (Obama, 2009). For individuals with disabilities, access to postsecondary education, includ-ing short-term training, is more critical than ever. It is simply not enough to complete high school. By developing partner-ships with short-term training programs and providing postsecondary education services, vocational rehabilitation counselors can increase the odds that their consumers will be employed and have access to higher wage opportunities when they complete rehabilitation services.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Financial Disclosure/Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article:

U.S. Department of Education, Rehabilitation Services Admin-istration (Grant No. H235M010108)

Interagency Agreement with the Oregon Office of Vocational Rehabilitation

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Flannery et al. 11

Authors’ Note

Preparation of this article was supported, in part, by a federal grant from the U.S. Department of Education, Rehabilitation Services Administration (Grant No. H235M010108) and an Interagency Agreement with the Oregon Office of Vocational Rehabilitation. Opinions and positions do not necessarily reflect the positions or policies of these federal or state agencies, and no official endorse-ment should be inferred.

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Bios

K. Brigid Flannery, PhD, is a senior research associate/associate professor at the University of Oregon. She serves as research fac-ulty in education and community supports, focusing on transition, postsecondary education, and work force training opportunities, and as the director of the special education major.

Michael R. Benz, PhD, is a professor of special education and director of the Center on Disability and Development at Texas A&M

University. His interests include secondary, transition, and post-secondary services for individuals with disabilities.

Paul Yovanoff, PhD, is a senior research associate/associate professor at the University of Oregon, College of Education. With a PhD in educational psychology, his primary interests pertain to large-scale and clinical measurement scaling, and multivariate research methodology.

Mary McGrath Kato, MS, is a research assistant at educational and community supports at the University of Oregon. She cur-rently does research and development in the area of secondary special education and transition.

Lauren Lindstrom, PhD, is an associate professor of counseling psychology and human services and the director of the Secondary Special Education and Transition Research Unit at the University of Oregon. Her areas of expertise include career development, transition services, and post school outcomes for youth with disabilities.

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