job-preference and job-matching assessment results...

Job-Preference and Job-Matching Assessment Results andTheir Association with Job Performance and Satisfaction

among Young Adults with Developmental Disabilities

Julie HallDavis School District, Farmington, UT.

Robert L. Morgan and Charles L. SalzbergUtah State University

Abstract: We investigated the effects of preference and degree of match on job performance of four 19 to20-year-old young adults with developmental disabilities placed in community-based job conditions. Weidentified high-preference, high-matched and low-preference, low-matched job tasks using a video web-basedassessment program. The job matching assessment provided index scores on degree of match between jobrequirements and participant skill levels. Each participant was subsequently placed in a randomized sequenceof 30-min sessions on one high-preference, high-matched job and one low-preference, low-matched job. A jobcoach instructed individual participants to perform jobs, teaching tasks when necessary. We collected data on(a) productivity, (b), accuracy of tasks performed, (c) job satisfaction and (d) an independent observer’sjudgment. We found higher productivity, accuracy and job satisfaction across all participants on the high-preference, high-matched jobs. We discuss results in terms of assessment of preference and matching tofacilitating self-determination.

In recent years, professionals working in spe-cial education and vocational rehabilitationhave placed increased emphasis on providingyoung adults with developmental disabilities,such as intellectual disability and autism spec-trum disorder, with opportunities to identifypreferences that serve to shape their careerpaths. Making choices and identifying prefer-ences is one component of self-determination,which Wehmeyer (2005) defines broadly as“volitional actions that enable one to act as theprimary causal agent in one’s life and to main-tain or improve one’s quality of life” (p. 117).The Individuals with Disabilities EducationImprovement Act (IDEIA, 2004) mandatesthat transition planning be based on the indi-vidual’s needs, while accounting for strengths,preferences, and interests. To be empoweredas primary causal agents to determine their

futures, young adults with developmental dis-abilities in transition must, at minimum, statetheir preferences to begin directing activitiesleading towards selected employment, career,and life goals.

Research has shown that identifying pre-ferred job tasks increases productivity for par-ticipants with developmental disabilities(Bambara, Ager, & Koger, 1994; Morgan &Horrocks, 2011; Parsons, Reid, Reynolds, &Bumgarner, 1990). For example, Parsons et al.found that work performance of participantswith significant intellectual disability almostdoubled when given their choice of job task orwhen assigned a preferred job task. Morganand Horrocks investigated the effect of jobpreference on on-the-job performance in acommunity setting among adults with intellec-tual disability and concluded that working in achosen job increased both on-task behaviorand satisfaction, at least for two of three par-ticipants.

Findings from these studies compel practi-tioners to acknowledge and support an indi-vidual’s preferences for employment. Prefer-

Correspondence concerning this article shouldbe addressed to Robert L. Morgan, Department ofSpecial Education and Rehabilitation, Utah StateUniversity, 2865 Old Main Hill, Logan, UT. 84322-2865. E-mail: [email protected]

Education and Training in Autism and Developmental Disabilities, 2014, 49(2), 301–312© Division on Autism and Developmental Disabilities

Preference and Matching Assessment / 301

ences are important both for determininginitial job sampling and long-term careerpathways (Field, Martin, Miller, Ward, &Wehmeyer, 1998). However, selection of apreferred job may not predict increased per-formance if the job tasks require skills beyondone’s skill level, or skills different from one’srepertoire. Morgan and Horrocks (2011)worked with one participant who more fre-quently chose the low-preference job as “theone I liked better today.” Researchers notedthat even though this participant selected thejob as highly preferred, he struggled with thetask requirements and demonstrated higherproductivity on a low-preference job. The re-searchers speculated that a participant whoworks in a high-preference job who does nothave the prerequisite skills may alter his/herpreferences, which could result in lower pro-ductivity and reduced job satisfaction. Al-though these speculations were generatedfrom only one participant’s data, they suggestthat practitioners may consider assessing fre-quently to update an individual’s preferences,and examining a companion assessment: de-gree of job match with preferred jobs.

Job matching has been defined as an assess-ment of job seeker knowledge and skills inrelation to job requirements (Ellis, Rusch, Tu,& McCaughrin, 1990). Roessler (2002) notedthat job matching should not only involvefinding an entry-level job in the short-term,but also, exploring career opportunities in thelong-term. Many job matching services areavailable on the Internet. Most services in-clude an assessment of one’s ideal job, workexperience, and self-reported skills (e.g.,hiringhive.com). The outcome is usually a listof occupations that are best-matched to inter-ests, experience, and skills. Although web-based job assistance is available for individualswithout disabilities (e.g., www.careeronestop.org; www.peopleresources.org), only one sitewas found providing comprehensive assess-ment of preferences and job matching specif-ically designed for individuals with develop-mental disabilities (i.e., www.yesjobsearch.com). Transition teachers, rehabilitationcounselors, and other stakeholders can helpindividuals with developmental disabilities totake advantage of Internet job matching ser-vices, but a website enabling a young adult to“self-determine” one’s career pathway might

be more desirable. A potential drawback tosome Internet sites is that they require highlevels of reading (e.g., responding to ques-tions and rating scales, decoding vocationallanguage) that may inhibit understanding forsome users with disabilities.

On a web-based site for individuals withdevelopmental disabilities, Morgan (2008)first assessed preferences, then degree ofmatch to preferred jobs. To assess job match-ing, the Occupational Information Network(O*NET: National Center for O*NET Devel-opment, 2010) was used to identify 106 di-mensions associated with jobs from the pref-erence assessment. A rater familiar with theparticipant’s skills, such as a teacher, parent,or job coach, rated the participant’s skill levelfor each of 106 dimensions. On this web site,the computer program first multiplied the rat-ing for each job dimension times the O*NETimportance score, then summed all scoresacross 106 dimensions. Summed scores weredivided by the total possible score to yield aproportion score ranging from 0.00 to 1.00.Second, the program ranked the preferredjobs in order from high to low match. Theoutcome was a list of job match index scoresfor each preferred job.

Research is needed to investigate whetherpreferred, well-matched jobs result in higherproductivity and greater satisfaction regardingcommunity jobs than low preference, low-matched jobs. Although the relationship isintuitive, very little data are available for indi-viduals with developmental disabilities. Thecombination of a job preference and jobmatching assessment may serve as a usefulpredictor of productivity.

The purpose of this study was to determinewhether level of preference and degree ofmatch were associated with job performanceand satisfaction for young adults with develop-mental disabilities.

Method

Participants

The researchers selected four 19–20-year-oldyoung adults with mild to moderate intellec-tual disability from among 100 students en-rolled in a post-high school transition pro-gram. A common component of the post-high

302 / Education and Training in Autism and Developmental Disabilities-June 2014

school program was for students to rotatethrough community jobs. In collaborationwith transition program teachers, the re-searchers selected students who (a) were ableto identify job and job task preferences usingverbal English, pointing responses, or otherdiscriminable behaviors; (b) exhibited basicvocational skills, including adequate behav-ioral control for working in a public settingand following simple verbal or modeled in-structions; (c) possessed at least 6 monthscombined part-time employment training ex-perience across two or more community jobsor job tasks; and (d) attended the program ona regular basis.

Kristin was a 19-year-old Caucasian femalewith Down syndrome. Based on recent testingresults, she had obtained a full-scale IQ of 47with academic scores ranging from 1st to 3rdgrade performance. She was able to verballyexpress her basic needs. Kristin struggled withchoosing realistic careers. She learned newtasks quickly and completed them accurately;however, she did not consistently follow in-structions or accept feedback from supervi-sors.

Mark was a Caucasian 20-year-old male withtraumatic brain injury. Test results indicated afull-scale IQ of 69, an adaptive behavior levelscore of 37, and academic scores ranging from4th to 6th grade performance. He verbally ex-pressed needs. Mark learned new skills rela-tively quickly, but required repetition orprompts to retain the skills. Without close su-pervision, Mark was easily distracted by co-workers and by conversations around him.

Natalie was a Caucasian 20-year-old femalewith autism spectrum disorder and a visualimpairment. No IQ score was available inavailable records. She verbally expressedneeds. She read at about the 5th grade leveland performed math at the 3rd to 4th gradelevel. With glasses, Natalie read books andaccessed a computer screen at 25 cm withoutadditional magnification. Natalie’s depth per-ception was compromised; however, she accu-rately completed activities requiring hand/eyecoordination, fine motor skills and depth per-ception such as cutting on a line, lacing orputting templates on a ring.

Conner was a Caucasian 20-year-old malewith autism spectrum disorder. Test resultsindicated a full scale IQ score of 58, with

academic scores indicating 1st to 3rd gradeperformance. Conner verbally expressed hisneeds. He required supervision to stay on taskor move to a new task.

Settings

Participants completed the web-based jobpreference assessments using a computer in a4 m by 4 m office located at the participants’transition program. A job coach familiar withthe participant completed the job matchingassessment. Participants worked in communi-ty-based job settings including (a) a deli lo-cated within a supermarket; (b) a carpentryshop in an industrial strip mall; (c) a Mexicanrestaurant; (d) an electronics lab located at atechnology college; (e) an elementary schoollibrary; and (f) a dining room located in anassisted living center.

Dependent Variables and Measurement

Productivity. For a given job task, produc-tivity was defined as the number of tasks orsubtasks completed to criteria specified by theemployer or supervisor as compared to thequantity completed by a typical adult em-ployee in the same amount of time. Each taskwas analyzed to produce a sequence of sub-tasks and task steps and criteria required tocomplete the task correctly (for task analyses,contact the first author). The first researcherdeveloped the subtasks as well as task stepsbased on the standard expected by the em-ployer at the deli, carpentry, restaurant, din-ing room and library. At the electronics lab,the first researcher developed productivityguidelines based on supervisor input and in-dustry standards from the University of Cali-fornia at Berkeley’s Basic Semiconductor Cir-cuits Manual (2010).

Productivity was measured using a per op-portunity measure of the number of discretetasks independently completed to criteria di-vided by the total number of tasks completedby a typical adult employee and multiplied by100. Trend function lines were calculated inExcel® for productivity and accuracy. A trendfunction line is a straight line running fromthe first to last session on a graph, formedusing the method of least squares (i.e., a linethat minimizes variability on each of two


sides) (Jiang, 1998), to capture the directionand slope of the numeric variable.

The first researcher and a job coach mea-sured productivity in 10-min probes. For eachparticipant, there were at least four 10-minproductivity probes conducted per job sitethroughout the study. Productivity probeswere conducted either at the beginning or theend of the session. Scheduling and timing ofthe probes remained consistent across jobsites. During the probes, the job coach toldthe participant to “work on your own. I willnot be able to help you.” If the participant wasunable to correctly complete a task or had aquestion, he/she was told to solve the prob-lem or to move on to the next task. Theresearcher and job coach recorded the num-ber of tasks or subtasks completed indepen-dently based on specified criteria during theprobe.

Accuracy of tasks performed. Accuracy was de-fined as the number of tasks completed cor-rectly compared to the total number of taskscompleted during a set amount of time. Accu-racy was measured by dividing tasks com-pleted correctly by total tasks completed andmultiplying by 100 to obtain a percentage.Trend function lines were calculated for eachset of data.

Choice/Satisfaction. Choice was defined asthe response to the question: “Which job didyou work better at today?” Satisfaction wasdefined as the participant’s response to thequestion: “Which job did you like better to-day?” The job coach asked individual partici-pants these two questions after leaving thesecond and final job site each day. For bothmeasures, a choice was scored if the partici-pant pointed to a picture of one job site orverbally stated the name of one job. The totalnumber of sessions that the high-preference,high-match job was identified by a participantas the job he/she “worked better at” and“liked better” was divided by the total numberof sessions a selection was offered to obtain apercentage score for the participant’s choiceand satisfaction.

As an additional measure of job matching,the first researcher asked the job coach at theend of the study to identify which job she feltwas the better match for each participant. Thejob coach was naı̈ve to the purpose of the

study, so this provided an unbiased measureof job match.

Procedure

Web-based job preference assessment. Duringcompletion of the job preference portionof the assessment, individual participantswatched selected videos of jobs, followed anarrator’s instructions to “choose the job youwant”, then selected an icon (thumbs up orthumbs down) to indicate their preference.High preference was defined as selectingthumbs up. The participant reduced the list ofhigh preference jobs to three, after which theparticipant selected preferred tasks involvedin the jobs for each job on the website. A“more info” tab showed photos and print de-scriptions of critical job tasks for each job asidentified by O*NET. The first researcher an-swered questions, assisted with technology is-sues, and printed out a list of preferred jobsand preferred tasks.

Thereafter, the first researcher and individ-ual participants repeated the job preferenceassessment to identify low preference jobs andthen low preference job tasks. Low preferencejobs and tasks were defined as those least pre-ferred by the participant. The first researcherdirected the participant to “choose the jobsyou don’t like” and participants watched vid-eos. If participants identified jobs they did notwant, the researcher selected that job. Thethree least-preferred jobs were used in thelow-preference job-match assessment.

Web-based job matching assessment. Jobcoaches who were familiar with the job skills ofthe participants completed the job-matchingassessment. They rated a participant’s skills aspoor, fair or good on each of 106 job dimen-sions in the assessment. Dimensions includedknowledge and skill constructs listed inO*NET, such as judgment and decision-mak-ing, critical thinking, money skills, time-tell-ing, reading comprehension, and speech clar-ity, among others. Each dimension name wasaccompanied by a brief definition. A propor-tion score from 0.00 to 1.00 was computed toindicate degree of job match for each of threehigh-preference and each of three low-prefer-ence jobs. A higher proportion score denotesa better match.


Development of job sites. Based on the resultsof the job-preference and job-matching assess-ment, the first researcher developed two jobconditions for each participant. A high-pref-erence, high-match job condition consisted ofone for which the participant’s skills mostclosely matched those required for the job. Alow-preference, low-match job was one forwhich the participant indicated a low prefer-ence and for which his or her skills were a lowmatch with job requirements. For the pur-poses of this study, high-match jobs were thosewith the highest proportion score and low-match jobs were those with the lowest propor-tion score.

The first researcher met with employers togain permission to perform jobs in the worksite. She researched industry standards of pro-ductivity for each job task. For example, incarpentry, measurements for sanding woodwere required to be within 1/32 in (0.79 mm).The first researcher and job coach practicedthe tasks, as needed, in order to perform thetask at the level required by the standards.The first researcher or job coach then per-formed the task for 10 min to determine thestandard against which each participant’s per-formance would be compared. In each case,the employer confirmed the standard set bythe researchers.

Work sessions. We arranged for individualparticipants to perform one high- preference,high match task and one low-preference, low-match task each day in community job settingsaccording to a randomized schedule as shownin Table 1.

Participants worked on each job for 30 min.Sessions were randomly scheduled by toss of acoin for a participant in the first week. As

shown in Table 1, the schedule for the secondweek was counterbalanced to reduce potentialreactivity to sequencing of jobs.

Prior to beginning the first session, the firstresearcher informed participants that theywould have a job coach check their work andanswer any questions. If participants made amistake, their job coach prompted them tocomplete the task correctly. If participantshad questions, they were told to ask the jobcoach.

The first researcher explained to partici-pants they could request a 1-min break at anytime, but afterwards, would need to resumework. Participants were told they could re-quest as many breaks as they wanted. Somebreak activities were prevented (e.g., makingphone calls). After working at least 10 min,the participants were told they could requestto stop work altogether. Requesting breaks orstopping work did not result in negative con-sequences. If the participant requested abreak, the job coach/data collector begantiming for 1 min. After 1 min, the job coach/data collector asked, “Are you ready to worknow?” If the participant said no, or refused toanswer or to restart work, the job coach begantiming again. If the participant refused to an-swer or work for more than 3 min, and if theparticipant had been at that work site formore than 10 min, the job coach asked theparticipant if he or she wanted to stop work-ing. If the participant said yes, or otherwiserequested to stop working, the job coach ter-minated the session.

Teaching tasks. The job coach taught jobtasks to individual participants in an effort tocomplete all steps of the task to the employ-er’s standard. If the participant did not com-

TABLE 1

Randomized Schedule of Low Preference/Low Match and High-Preference/High Match Job Conditions forIndividual Participants

Monday Tuesday Wednesday Thursday Friday

Week 1 (1) Low(2) High

(1) High(2) Low

(1) High(2) Low

(1) Low(2) High

(1) High(2) Low

Week 2 (1) High(2) Low

(1) Low(2) High

(1) Low(2) High

(1) High(2) Low

(1) Low(2) High

Additional (as needed) (1) High(2) Low

(1) Low(2) High

(1) Low(2) High

(1) High(2) Low


plete a task, the job coach used verbal, ges-tural or partial physical prompts as well asmodeling to teach job tasks. The job coachremained in close enough proximity to theparticipant to visually determine if the partic-ipant was performing the job correctly.

Interobserver Agreement (IOA)

The first researcher trained the job coach tocollect data by providing and discussing be-havior definitions and recording behaviors(see Dependent Variables above) in commu-nity-based work settings. The first researcherand job coach conducted observations of pro-ductivity and accuracy probes until theyachieved 90% IOA on each behavior. IOA wascollected in 98% of productivity and accuracyprobes. Total count IOA was calculated bydividing the agreements on task steps com-pleted to specifications by agreements plusdisagreements and multiplying by 100. Pro-ductivity IOA ranged from 73.68% to 100%with a mean of 97.35%. The low score oc-curred in the first productivity probe at theelectronics lab with Conner.

Treatment Integrity

Independent observers recorded data basedon a checklist on how the first researcher

conducted activities (e.g., presents scripted in-structions, answers participant’s questions, re-directs participant if participant asks for help,and so forth). A percentage score was calcu-lated by dividing the number of items markedcorrectly by the total number of items andmultiplying by 100. Observers collected datain 43% of total sessions. Observer data indi-cated that the first researcher completed100% of steps correctly. In a second measureof integrity, the first researcher recorded databased on the job coach’s activities to ensurethat she followed procedures as outlined. Achecklist was constructed with items similar tothose for the first researcher (e.g., instructedparticipants on tasks, allowed breaks when re-quested, redirected participants if conversingon topics not related to work, and so forth).Data were recorded in 29% of total sessionsacross all conditions. Job coach integrity dataranged from 87.50% to 100% with a mean of98.70%.

Results

Data from the job preference and matchingassessments are shown in Table 2. Job match-ing index scores were higher for high-prefer-ence, high-matched jobs than low-preference,low-matched jobs. Differences between index

TABLE 2

Job Preference and Matching Assessment Results

Participant High-preference JobsProportion

Score Low-preference JobsProportion

Score

Kristin Restaurant CookPrepare, season, cook foodFollow food ordersClean food prep area

0.52 CarpenterSand surfacesMeasure and mark locationsMeasure and cut material

0.36

Mark Restaurant CookPrepare, season, cook foodFollow food ordersCut vegetables, meat, etc.

0.54 Electronics AssemblerCheck functions of electrical partsConnect wires

0.39

Natalie Library WorkerCheck out booksScan booksUse barcode scanners

0.43 CarpenterInstall door frameworkMeasure and cut materialSand surfaces

0.39

Conner Dining Room AttendantServe food and drinkReplenish food at tablesSet tables

0.58 Electronics AssemblerConnect wiresCheck functions of electrical partsDry parts with an air hose

0.47


scores ranged from 0.16 (Kristin) to 0.04 (Nat-alie). In the latter case, the assessment sug-gests that Natalie’s skills for the high-prefer-ence job were only slightly better matchedthan skills for the low-preference job.

Participants worked between eight and 20total sessions. Numbers of sessions for someparticipants were cut short due to schedulingissues (e.g., job coach schedule, family vaca-tion, etc.). Performance of individual partici-pants is described below.

Kristin’s Performance

Kristin worked in a deli preparing mini-sand-wiches and in a carpentry shop measuring andmarking wood for garden trellises. Kristin’sjob matching score for her high-preference,high-matched job (deli) was 0.52. Tasks in-cluded (a) preparing, seasoning, and cookingfood; (b) following food orders; and (c) clean-ing food prep area as her preferred tasks. Thescore for her low-preference, low-matched job(carpenter’s helper) was 0.36. Tasks included(a) sanding surfaces, (b) measuring and mark-ing locations, and (c) measuring and cuttingmaterial.

Productivity data for Kristin are presentedin Figure 1. Productivity on her high-prefer-ence, high-matched job increased from28.57% to 35.71% on the fourth and finalprobe. Productivity on her low-preference,low-matched job was 0% for all four sessions.Prior to the last productivity probe at the car-pentry shop, Kristin commented, “I just want

to get my work done so I can just get out ofhere and not come back.”

Accuracy data (i.e., % of tasks completedcorrectly) for Kristin are presented in Figure2. From Sessions 1 to 4, accuracy on her high-preference, high-matched job trended up-ward from 80% to 85.71%. Accuracy on herlow-preference, low-matched job was 0% forall four sessions.

In terms of choice (“Which job did youwork better at today?”) and satisfaction(“Which job did you like better today?”), Kris-tin selected the high-preference, high-matched job in all opportunities. When ques-tioned at the conclusion of the study, Kristinindicated she preferred and worked better atthe deli because she liked working with foodand “the sandwiches are delicious.” The jobcoach identified the deli (high-preference,high-matched) as Kristin’s better-matchedjob.

Kristin, unlike other participants, requestedbreaks. She requested a total of 78 min ofbreaks (18.57% of total work time), all duringthe low-preference, low-match job.

Mark’s Performance

Mark worked in a Mexican restaurant prepar-ing fried ice cream balls and in an electronicslab at a technical college soldering wires toposts. Mark’s job matching score for his high-preference, high-matched job (restaurantcook) was 0.54. Tasks included (a) preparing,seasoning and cooking food; (b) followingfood orders; and (c) cutting vegetables and

Figure 1. Percentage of productivity for Kristin ascompared to the quantity completed by atypical adult employee in the sameamount of time.

Figure 2. Percent of tasks completed accurately byKristin.


meats. The score for his low-preference, low-matched job (electronics assembler) was 0.39.Tasks included (a) checking functions of elec-trical parts, (b) connecting wires, and (c) dry-ing parts with an air hose.

Productivity data for Mark are presented inFigure 3. Productivity on his high-preference,high-matched job increased across sessionsfrom 0% to 57.14%. Productivity on his low-preference, low-matched job was 0% for allsessions.

Accuracy data for Mark are presented inFigure 4. From Sessions 1 to 4, accuracy on hishigh-preference, high-matched job increasedfrom 0% to 100%. Accuracy on his low-pref-erence, low-matched job was 0% for all foursessions.

Mark’s choice of his preferred and better-

matched job varied throughout the study.Mark chose and was more satisfied with thehigh-preference, high-matched job on 61.54%of choice opportunities. On remaining occa-sions, he chose the low-preference, low-matched job. On 76.92% of choice opportu-nities, Mark chose the last job site he hadvisited, suggesting a potential recency effect.At the conclusion of the study, Mark indicatedhe sometimes chose electronics assembly be-cause it was ”new” and “fun.” He indicated hesometimes chose the restaurant because it was“more fun” and he “loved making fried icecream.” The job coach identified restaurantcook (high-preference, high-matched) asMark’s better-matched job.

Natalie’s Performance

Natalie worked in a library and as a carpen-ter’s helper. Natalie’s job matching score forher high-preference, high-matched job (li-brary) was 0.43. Tasks included (a) checkingout books, (b) scanning books, and (c) usingbarcode scanners. The score for her low-pref-erence, low-matched job (carpenter’s helper)was 0.39. Tasks included (a) installing doorframework, (b) measuring and cutting mate-rial, and (c) sanding surfaces.

Productivity data for Natalie are presentedin Figure 5. Productivity on her high-prefer-ence, high-matched job increased across ses-sions from 61.76% to 69.61%. Productivity onher low-preference, low-matched job variedfrom 0 to 20%.

Figure 3. Percentage of productivity for Mark ascompared to the quantity completed by atypical adult employee in the sameamount of time.

Figure 4. Percent of tasks completed accurately byMark.

Figure 5. Percentage of productivity for Natalie ascompared to the quantity completed by atypical adult employee in the sameamount of time.


Accuracy data for Natalie are presented inFigure 6. Accuracy on her high-preference,high-matched job maintained at 100% acrossall sessions. Accuracy on her low-preference,low-matched job fluctuated from 0% to 50%with a mean of 19.44%. She ended with 0%accuracy on Session 6 and generally struggledwith sanding, having difficulties completing afull stroke when sanding an edge of wood.

In regard to choice and satisfaction, Nataliechose the library (high-preference, high-matched) in all opportunities. At the conclu-sion of the study, Natalie indicated she chosethe library because she liked “to read andhandle books.” She also indicated she was fa-miliar with libraries and that carpentry was“not her thing.” Natalie commented “I hopethat it (the carpentry shop) burns down be-fore we get there” and “Maybe it (the shop)will be locked”. The job coach identified thelibrary work (high-preference, high-matched)as Natalie’s better-matched job.

Conner’s Performance

Conner worked as a dining room attendantand in an electronics lab at a technical collegesoldering wires to posts. Conner’s job match-ing score for his high-preference, high-matched job (dining room attendant) was0.59. Tasks included (a) serving food anddrink, (b) replenishing food at tables, and (c)setting tables. The score for his low-prefer-ence, low-matched job (electronics lab) was0.47. Tasks included (a) connecting wires, (b)checking functions of electrical parts, and (c)drying parts with an air hose.

Productivity data for Conner are presentedin Figure 7. Although at relatively low levelsthroughout, productivity on his high-prefer-ence, high-matched job increased from 6.25%to 31.25%. Productivity on his low-preference,low-matched job varied from 0% to 7.14%.

Accuracy data for Conner are presented inFigure 8. From Sessions 1 to 6, accuracy on hishigh-preference, high-matched job trendedupward from 0% to 100%. Accuracy in Ses-sions 7 through 10 averaged 85%. Accuracy onhis low-preference, low-matched job rangedfrom 0% to 50%, averaging 12.5%.

Conner’s performance improved over thecourse of 10 sessions. Productivity trended up-ward on both jobs, but with a steeper slope onthe high-preference, high-matched job. Hisaccuracy increased similarly on both jobs, withthe steeper slope and much higher levels onthe high-preference, high-matched job.

Figure 6. Percent of tasks completed accurately byNatalie.

Figure 7. Percentage of productivity for Conner ascompared to the quantity completed by atypical adult employee in the sameamount of time.

Figure 8. Percent of tasks completed accurately byConner.


In terms of choice and satisfaction, Connerselected the high-preference, high-matchedjob in all opportunities. Conner indicated hechose the dining room because he preferredthe job and stated that he was “a professionalat setting up the dining room.” However, thejob coach selected the low-preference, low-matched job of connecting wires as Conner’sbetter-matched job, citing slow work speedwhen setting up dining room tables and fasterwork speed at the electronics lab as her ratio-nale.

Discussion

This study sought to determine whether levelof preference and degree of match were asso-ciated with job performance and satisfactionfor young adults with developmental disabili-ties. Results indicate that high-preference,high-matched jobs were associated withhigher productivity, accuracy and satisfactionin all but a few cases. For all four participants,productivity was higher, although it rangedfrom high to low in terms of comparison tothe employer standard set for a typical adultemployee. Additionally, accuracy of tasks per-formed was higher in all four cases. Positivetrends were evidenced in both productivityand accuracy for high-preference, high-matched jobs for all participants. High-prefer-ence, high-matched jobs were associated withchoice and higher satisfaction for three out offour participants. The single exception wasMark, who occasionally chose the low-prefer-ence, low-match job (electronics assembler),even though his productivity and accuracywere far higher in the high-preference, high-matched job (restaurant cook). The jobcoach, naı̈ve to preferences and degree ofmatch in the study, selected high-preference,high-matched jobs for three out of four par-ticipants. Generally, the findings support thehypothesis that high-preference and high-matched jobs are associated with improvedperformance and satisfaction.

Results from the current study suggest thatpreferred jobs that are also well-matched maypredict higher productivity and task comple-tion accuracy for young adults with develop-mental disabilities. Further, results suggestthat the job-matching component of a web-based assessment may be a useful tool in pre-

dicting degree of match between skills and jobrequirements. Mark’s results in particular sug-gest that job match, as predicted by the assess-ment, may have accounted for his increasedproductivity on the high-preference, high-matched job more than job preference, as hispreference fluctuated throughout the study.

The concept of a job match index score forone’s preferred job is relatively new in transi-tion and special education. The results of thisstudy support the importance of a job matchassessment for successful employment for in-dividuals with developmental disabilities.While the impact of job preference plus de-gree of match has been explored to someextent (Morgan, 2008), the research is in itsinfancy. It would seem logical that an individ-ual’s preference for a job or a set of job tasksmight enhance the probability of success, butit also seems unlikely that preference would,of itself, be sufficient. After all, one might likea job but not have the skills to perform itadeptly. The combination, however, of highpreference and high match would seem likelyto more strongly predict job productivity. Cer-tainly more research on this topic would bebeneficial.

One caveat is noteworthy regarding thefindings in this study: identifying high-prefer-ence, well-matched jobs does not mean thatyoung adults, transition specialists, and otherstakeholders have reached a final destinationin career planning. Obtaining a high prefer-ence, high-matched job should only be thefirst step on a stairway of career path oppor-tunities. Indeed, it may be detrimental to “stayput” indefinitely in a preferred, matched jobbecause to do so would preclude advance-ment and the development of what might oth-erwise become future career directions.

There are several limitations to this study.First, with only four participants, generaliz-ability of the results is severely limited. Repli-cation will be needed to more fully establish apositive correlation between job match andjob productivity, as well as the usefulness of aweb-based job-matching program. Research-ers may want to consider including partici-pants with more and less severe cognitive dis-ability and from varied cultural and ethnicbackgrounds. Second, only four high-prefer-ence and four low-preference sessions wereconducted for two participants. Increased


numbers of sessions may have providedclearer patterns of data for productivity, accu-racy, and satisfaction. Job preference, match,and satisfaction may, in some cases, vary as afunction of amount of work experience (Szy-manski, Enright, Hershenson, & Ettinger,2003). Third, tasks performed by participantsmay not realistically represent the range oftasks in an actual job. Therefore, preferencesand degree of match for job tasks do notnecessarily equate to preferences and matchfor the complete jobs. For example, connect-ing electrical wires to posts, although part ofan electronics assembler’s job description,hardly represented the complexity of tasks re-quired for the entire job. Future researchersshould include a more extensive variety oftasks in each job condition to more realisti-cally characterize jobs.

Findings from this study underscore the im-portance of frequent assessment of preferenceand job match. In transition from school towork, only one assessment must be adminis-tered each year starting at age 16 (IDEA,2004). Yet, we believe that this is inadequatebecause preferences and degree of skill matchmay change during the performance of a job.As Mark became more familiar with an elec-tronics job, he became more interested in it.On a related note, young adults with develop-mental disabilities need exposure to many dif-ferent types of job situations with job coacheswho are well-trained in increasing the work-er’s skills and who are able to support theworker during the skill acquisition process.

This study was designed to allow partici-pants to identify job preferences. As such, itwas consistent with one element of the philos-ophy of self-determination. These findingslend credence to the importance of identify-ing high-preference, well-matched jobs bydemonstrating an association with increasedperformance on the job. Paying close atten-tion to self-determined job preferences ap-pears to be associated with increased produc-tivity, accuracy, and satisfaction, which in turn,may relate to improved quality of life.

Future longitudinal research should deter-mine if job satisfaction or performance re-mains high among young adults with disabili-ties placed on jobs identified as preferred andhigh-matched following several weeks ormonths on the job. Finally, future research

would be useful to ascertain if preferred, high-matched jobs provide more opportunity for ad-vancement in pay or status in an organization.

References

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Received: 23 August 2012Initial Acceptance: 20 October 2012Final Acceptance: 15 January 2013


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