The Great Divide: Keri L. Heitner, Ph.D. Worker and ...Research Institute The Great Divide: Worker...

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The Great Divide: Worker and Employer Perspectives of Current and Future Workforce Demands

Keri L. Heitner, Ph.D.

Leslie A. Miller, Ph.D., PHR

Table of ConTenTsExecutive Summary 5

Introduction 6

Background 6

Purpose 10

Method 10

Results 13

Discussion 34

Limitations 39

Implications for Further Research 39

Summary 40

References 41

Tables

Table 1. Cronbach’s Alpha – Worker Survey 12

Table 2. Cronbach’s Alpha – Employer Survey 12

Table 3. Crosswalk of Variables in Worker, Working Learner, and Employer Surveys 13

Table 4. Employers’ Median 10-year Demand for Employees with Degrees by Sector 22

Table 5. Current/10-year Difficulty in Finding Skilled Workers 22

Table 6. Median Difficulty in Finding Skilled Workers Now / Next 10 Years 23

Table 7. Current and Projected 10-Year Demand for Business Language Proficiency 24

Table 8. Median Difficulty in Language Demand Now / Next 10 Years 24

Table 9. Demographic Variables by Working Learner Status – Significant 25

Table 10. Working Learner Median Comparisons 26

Table 11. Demographic Variables by Working Learner – Not Significant 26

Table 12. Perceived Demand Variable by Working Learner – Significant 27

Table 13. Perceived Demand Variables by Working Learner – Not Significant 27

Table 14. Working Learner Medians for Demand Variables 28

Table 15. Perceived Skill Variables by Working Learner – Not Significant 28

Table 16. Working Learner Medians for Skill Variables 29

Table 17. Summary of Significant Disparities between Working Learners and Workers 29

Table 18. Perceived Demand Variables by Worker/Employer–Significant 30

Table 19. Employer/Worker Distribution of Education 10-year Demand Responses 31

Table 20. Worker/Employer Median Demand Variables 31

Tables (continued)

Table 21. Worker/Employer Median Skill Level/Difficulty Finding Skilled Workers 32

Table 22. Workers’ Current Language Proficiency by Current Employer Demand 33

Table 23. Worker/Employer Median Skill Level/Future Demand for Workers Proficient in language 33

Table 24. Summary of Significant Disparities between Employers and Workers 34

figures

Figure 1. Growth rate for jobs in occupational categories by educational attainment (based on data from U.S. BLS, 2010b). 8

Figure 2. Unemployment rates and earnings for full-time wage and salary workers aged 25 and over, by educational attainment, 2009 (Source: U.S. BLS, 2010a). 8

Figure 3. Projected changes in workforce share by age (based on data from U.S. BLS, 2009). 9

Figure 4. ZoomPanel study population by gender. 13

Figure 5. ZoomPanel study population by race/ethnicity 13

Figure 6. ZoomPanel study population by worker/working learner status. 14

Figure 7. Gender of worker survey participants. 14

Figure 8. Age of worker survey participants. 14

Figure 9. Intent of worker survey participants to remain in the workforce. 15

Figure 10. Race/ethnicity of worker survey participants. 15

Figure 11. Military status of worker survey participants. 15

Figure 12. Language proficiency of worker survey participants 16

Figure 13. Industry sector of current jobs held. 16

Figure 14. Career clusters of workers’ current occupations. 16

Figure 15. Workers’ years of experience in current field. 17

Figure 16. Workers’ educational attainment. 17

Figure 17. Workers’ current/recent working learner status. 17

Figure 18. Industry sector of hiring managers’ companies. 18

Figure 19. Size of company. 18

Figure 20. Current job status. 18

Figure 21. 10-year demand for skilled and educated employees in field. 19

Figure 22. 10-year demand for skilled and educated employees in occupational cluster. 19

Figure 23. 10-year demand for employees by degree level. 19

Figure 24. Workers’ perceived skill levels. 20

figures (continued)

Figure 25. 5-year likelihood of enrolling in educational offerings. 20

Figure 26. Workers’ perceived increase in value of working learners. 20

Figure 27. 5-year likelihood of moving to a different occupation or industry sector. 21

Figure 28. 10-year likelihood of becoming proficient to conduct business in languages. 21

Figure 29. Demand for skilled and educated employees in the field. 21

Figure 30. Career clusters with current highest demand in company. 22

Figure 31. Demand for skilled and educated employees in three highest current demand career clusters in employers’ organization. 23

Figure 32. 10-year demand for employees by education. 24

Figure 33. Current / 10-year difficulty in finding employees (a-d). 24

Figure 34. Current / 10-year difficulty in finding employees (e-g). 25

Figure 35. Employers’ perceived increase in value of working learners. 26

Figure 36. Current/10-year demand for business language proficiency. 27

Figure 37. Industry sector distribution comparison – employers and workers. 28

Figure 38. Workers’ language proficiency versus current demand for proficient employees 32

exeCuTive summary

background

• The gap between the education of the American workforce and the demand for qualified workers in the emerging economy is growing and is projected to grow during the 2010s.

• Changing jobs and lagging educational attainment are contributing to a growing disparity between current job seekers’ skill sets and employer needs.

Purpose

• The purpose of the quantitative descriptive study was to examine the extent to which disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands.

method

• Surveys were sent to a random sample of more than 30,000 employers and workers within the MarketTools ZoomPanel.

• To narrow the sampled panelists to only those who met the population criteria, screening questions were included at the beginning of each survey and quotas were set to promote receiving responses from population members across specific industries.

• Completed surveys were received from 930 survey respondents meeting the population criteria: 511 workers, of whom 100 were working learners, and 419 employers.

results

• Results provide partial support for an existing disparity between working learners and other workers in terms of multiple demographic characteristics, perceived 10-year workforce demands for employees with bachelor’s and doctoral degrees, and intent to become proficient in Spanish.

• The results do not support existing disparities between working learners and other workers in skills.

• The results support an existing disparity between employers and workers in (a) demand variables, (b) specific skill variables, and (c) future demand versus proficiency in Arabic, Chinese, Russian, and Spanish.

summary

• As the U.S. economy recovers from the recession, employers will seek new hires who have the skills and credentials to fill projected vacancies and new jobs.

• The disparities between workers and employers in the current study are consistent with other research supporting a current and growing skills and education gap in the workforce.

• More research is needed to understand (a) the perceived benefits of postsecondary education for working learners in terms of their current and future employability, (b) the gap between workers’ perceptions of their applied skills and employers’ difficulty finding workers with the needed skill sets, (c) how to bridge the gap between the skills workers acquire from their higher education pursuits and the skills they need to perform well on the job, (d) the ways employers can contribute to make higher education curricula and professional development offerings more responsive to their needs for highly skilled and educated employees, and (e) how employers can contribute to infusing development of high-level applied skills into postsecondary degree programs for working learners. 5


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InTRoducTIon

The gap between the education and training of the American workforce and the demand for qualified persons to fill the types of positions to be available in the emerging economy is growing (Alssid, 2010; Galagan, 2010). Postsecondary education and skills development are essential for workers to be successful in the current and emerging knowledge and innovation economy (Soares & Mazzeo, 2008). Drivers for an increasing demand for postsecondary credentials and associated skills include emerging technologies, technology services, globalization, and changes in the nature of work (Osterman, 2008).

The U.S. Bureau of Labor Statistics (2010) predicted that between 2008 and 2018, occupations in categories of jobs requiring some postsecondary education should grow at a higher rate than occupations in categories of jobs with on-the-job training. Data indicate that between 2006 and 2016 almost half of new jobs created will require employees with postsecondary credentials (Soares & Mazzeo, 2008). The proportion of jobs requiring a postsecondary degree is estimated to increase from 59 to 63% in the next decade (Carnevale, Smith, & Strohl, 2010).

The existing body of research supports the need to examine the perspective of employers and workers about the growing gap between the education and training of the American workforce and the demand for qualified persons to fill the types of positions to be available in the emerging economy. A partnership with MarketTools ZoomPanel provided an opportunity for the University of Phoenix Research Institute to administer surveys to workers, working learners, and employers to examine the growing gap. The purpose was to examine the extent to which disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future labor force demands. Findings, based on analysis of the responses to the surveys and within group and between group comparisons, are discussed in this paper.

The paper begins with background information, placing the research in the context of current literature about the skills deficit, job growth sectors, the need for an educated workforce, the diverse needs of diverse workforce, workforce job and career mobility, and employability. The purpose section includes the research purpose and guiding research questions. The method section includes a description of the population, the sampling frame and sample, and data collection. The results section includes a presentation of the demographics of the worker, working learner, and employer samples; the responses to the worker and employer surveys; and the findings of the statistical comparisons within and between the participant groups. The purpose of the comparisons was to identify what disparities, if any, existed between workers’, working learners’, and employers’ perceptions of current and future skills and demands on a number of variables. Following a discussion of the implications of the findings and where the findings fit within the body of published literature, the paper concludes with a discussion of limitations and implications for further research.

BackgRound

Skills deficit

The United States is experiencing a skills recession (Alssid, 2010). The gap between the education and training of the American workforce and the demand for qualified persons to fill the types of positions to be available in the emerging economy is growing. According to 2009 research conducted by the American Society for Training and Development (ASTD), changing jobs and lagging educational attainment are contributing to a growing disparity between the skill set of current job seekers and employer needs (Galagan, 2010). Mounting evidence indicates that increasing numbers of employees without needed skills, abilities, and knowledge impede business growth and competition (Galagan, 2010).

Employers have voiced concern about the lack of higher-order skills in the current workforce. Seventy-nine percent of employers across industries responding to a 2008 ASTD survey confirmed an existing skills gap (Galagan, 2010). Even before the current economic downturn that began in 2009, employers reported challenges in finding applicants who met job qualifications (Choitz, Soares, & Pleasants, 2010). Hiring managers reported a primary recruiting obstacle was a shortage of persons with the necessary qualifications (Robert Half International, 2007). Employers must compete for highly skilled workers, a situation that is likely to increase in severity with globalization and increased reliance on technology (Choitz et al., 2010). As companies continue to specialize and refine their strategies to remain competitive, they face increasing difficulty finding and attracting prospective employees with the needed experience and skill sets (Choitz et al., 2010; Galagan, 2010).

The growing demand for skills in middle- and high-level positions will exceed supply during and beyond the coming decade (Holzer & Lerman, 2009). Outsourcing of routine work means that in the United States most work available in the future will be knowledge work requiring higher-order skills (Choitz et al., 2010). According to projections from the U.S. Bureau of Labor Statistics (U.S. BLS), occupations necessitating skills at the advanced or specialized level will have the most growth (Galagan, 2010).


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growth Sectors

Employment projections and need for workers will change over time and vary by industry. Experts predict total employment will increase by 10% from 2008 to 2018, with concomitant shifts in the distribution across major industry and occupational groups (U.S. BLS, 2009). About two-thirds of job openings between 2008 and 2018 will involve replacing workers in existing positions (U.S. BLS, 2009).

More than a quarter of all new jobs in the United States will be in health care and social assistance (U.S. BLS, 2009). In addition, the United States will experience a 34% growth in employment in professional, scientific, and technical services by 2018, and an 18% increase in employment in business and financial operations occupations. Professional and related occupations, including several skilled professions, will increase by 17%, and employment for healthcare practitioners and technical occupations will increase by 21% (U.S. BLS, 2009). Projected employment in life, physical, and social science occupations will grow 19%—almost twice the average rate for all occupations across the economy (U.S. BLS, 2009).

Although the U.S. BLS projects overall employment in the manufacturing sector will decline by 9% (U.S. BLS, 2009), U.S. manufacturers reported a severe shortage in the skilled manual trades (Shankel, 2010), particularly for electricians, carpenters/joiners, and welders (Manpower, 2009). More than half of companies dependent on skilled production work of machinists, craft workers, and technicians report shortages that they project will increase (Deloitte Development LLC, The Manufacturing Institute, & Oracle [Inc.], 2009).

need for an Educated Workforce

The United States is faced with a shortfall of workers with a post secondary education. According to the U.S. BLS (2010), between 2008 and 2018, occupations in categories of jobs requiring some postsecondary education should grow at a higher rate than occupations in categories of jobs with on-the-job training (see Figure 1). Drivers for an increasing demand for postsecondary credentials and associated skills include emerging technologies, technology services, globalization, and changes in the nature of work (Osterman, 2008).

Workers without a postsecondary degree often lack the skills employers seek. Workers with a postsecondary degree may still lack the desired skill set. According to employers, more than 64% of persons with a high school diploma and 40% of persons with a college degree lack the applied skills necessary for success in the workforce (ASTD, 2008). Despite the skills gap, many new jobs will necessitate a college degree.

Using U.S. BLS data, Soares (2009) estimated that between 2006 and 2016 the U.S. economy will add 15.6 million new jobs, almost half of which will require a postsecondary degree. Soares predicted a 17% increase in jobs requiring postsecondary credentials, almost double the 8.8% growth rate for jobs without a secondary degree requirement.

United States BLS data indicate that between 2006 and 2016, almost half of newly created jobs require employees with postsecondary credentials (Soares & Mazzeo, 2008). The proportion of jobs requiring a postsecondary degree is estimated to increase from 59% to 63% in the next decade (Carnevale, Smith, & Strohl, 2010). Between 75% and 90% of positions in industries comprising 40% of all jobs in 2018 will require postsecondary education or training: information services, private education services, government and public education services, financial services, professional and business services, and healthcare services (Carnevale et al., 2010). About 90% of positions in four of the five fasting growing occupational clusters—healthcare professional and technical occupations; science, technology, engineering, and math (STEM) occupations; community services and arts occupations; and education occupations—will require a postsecondary degree (Carnevale et al., 2010).

Postsecondary education and skills development are essential for workers to be successful in the current and emerging knowledge and innovation economy (Soares & Mazzeo, 2008). Postsecondary education is positively correlated with economic growth, productivity, income, civic participation, and quality of life (Osterman, 2008). As displayed in Figure 2, median earnings rise and average rates of unemployment fall with increased education (U.S. BLS, 2010).

In the coming decade, the United States will face a shortfall of workers with postsecondary degrees at the associate degree level or higher, according to a recent study by the Georgetown University Center on Education and the Workforce (Carnevale et al., 2010). A projected gap of at least 3 million postsecondary degrees is estimated, compared with the estimated need for 22 million new college degrees by 2018, equivalent to an annual deficit of 300,000 college graduates. Alarmingly, 75 million Americans ages 18 to 64 lack postsecondary credentials and are not currently enrolled in postsecondary education (Soares, 2009).


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diverse Workforce with diverse needs

The traditional postsecondary education system is inadequate to meet the needs of many American adults who require additional education to remain competitive in today’s job market and the emerging economy (Soares, 2009). Many American adults cannot pursue a traditional postsecondary because they must earn an income and support families; therefore, many adults are seeking alternative avenues to obtain postsecondary degrees by enrolling in non-traditional degree programs, both online and ground, and becoming working learners. Soares (2009) described working learners as

individuals already in the workforce who currently lack a postsecondary credential and are needed wage earners for themselves or their families . . . if these working Americans are to obtain a postsecondary credential they will have to combine learning, employment and family responsibilities over their working lives.(p. 13)

Working learners have diverse prior educational experiences and varied English language literacy (Soares, 2009). Working learners are typically older than traditional college students, financially self-supporting, more likely to drop out of college, and less likely to complete their degree within 6 years due to juggling multiple roles (Soares, 2009). To earn a postsecondary degree, they either work and learn simultaneously or shift frequently between work and school. Working learners are motivated to build skills and earn credentials that will increase their employability or income potential (Soares, 2009).

The estimated potential pool of working learners age 18 to 64 currently in the workforce comprises 75 million, representing 60% of the workforce (Soares, 2009; Soares & Mazzeo, 2008). The 75 million do not include working learners who already hold a college degree, i.e., workers studying to earn an additional college degree at any level.

According to the 2010-2011 Occupational Outlook (U.S. BLS, 2009), the civilian workforce will increase by 8.2% to almost 167 million persons by 2018, and the workforce will continue to become more diverse. The Hispanic workforce is projected to grow 33.1%, from 14.3% to 17.6% of the total workforce (U.S. BLS, 2009). Growth in the female workforce will slightly outpace growth in male workers. As shown in Figure 3, the age distribution of the workforce will shift as the proportion of workers age 55 and older grows (U.S. BLS, 2009).

changing Jobs and changing careers

The current economic crisis means that most baby boomers, those individuals born between 1957 and 1964, will remain in the workforce in the coming decade. According to Capelli (2003), some studies conducted before the economic downturn revealed that up to 80% of baby boomers planned to work after they reached typical retirement age, although they might change occupations. Increases in the Social Security eligibility age will also influence how long baby boomers continue to work.

The American workforce is dynamic in terms of changing jobs and careers. According to Terkanian (2006), no statistical data are available on how many times the average worker changes careers; the U.S. Department of Labor does not collect these data. The lack of a clear definition of career change impedes the ability to count accurately how many times people change careers (Terkanian, 2006). For example, career change might involve a change of field, a change in occupation, or

Figure 1. Growth rate for jobs in occupational categories by educational attainment (based on data from U.S. BLS, 2010b).

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10%8%

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Growth rate

Figure 2. Unemployment rates and earnings for full-time wage and salary workers aged 25 and over, by educational attainment, 2009 (Source: U.S. BLS, 2010a).

Median weekly earnings(hundreds of $)

Unemployment rate (%)

0%

6%

12%

18%16%14%

10%8%

4%2%

Docto

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Prof

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a promotion. A career change might involve changing employers or changing from one occupation to another in the same organization (Terkanian, 2006).

Based on 2008-2009 data from the National Longitudinal Survey of Youth (NLSY) 1979 (U.S. BLS, 2010b), the average American baby boomer held 11 jobs from age 18 to 44; boomers held more than 60% of the jobs from age 18 to 27. These data are based on annual (1979-1994) and biannual (1994-present) follow-up with respondents from the original survey administered in 1979 and are representative of latter-year American baby boomers who were U.S. residents at the time of the initial survey (U.S. BLS, 2010b).

Results revealed gender differences in education and numbers of jobs held (U.S. BLS, 2010b). On average, the number of jobs held decreased with education for men, but increased with education for women (U.S. BLS, 2010b). Young (age 18-22) White workers changed jobs more frequently than young Black or Hispanic workers; no significant differences were found by race/ethnicity for workers age 23 to 44 (U.S. BLS, 2010b). Workers who were older when they began a job stayed on that job longer than workers who were younger when hired. Seventy-two percent of workers who began their job at age 18 to 22 remained on the job for less than 1 year, and 94% remained for less than 5 years, compared to 33% and 68% respectively for workers who were age 39 to 44 when hired (U.S. BLS, 2010b). Becoming employed is the first challenge new graduates face.

Skills, Education, and Employability

Higher education increases worker employability. Increasing employability is a goal of obtaining a postsecondary degree. Employability pertains to a graduate’s ability to obtain a position and advance in their career (Mason, Williams, & Cranmer, 2009; Mason, Williams, Cranmer, & Guile, 2003). Employability also pertains to the extent to which graduates develop abilities and experience, including critical thinking skills (Harvey, 2005). According to Cranmer (2006), measuring employability outcomes is challenging. Factors such as unequal access to employment opportunities and labor market preferences (Knight, 2001), and social structures, such as class, race, gender, and disability (Morley, 2001), influence employability and employment.

Leaders in higher education institutions recognize that skills needed to enhance graduate employability include problem solving, literacy, and teamwork (Mason et al., 2009). In contrast, employers typically conceive of employability in terms of work readiness--the knowledge, attributes, skills, understanding, and attitudes that new graduates need to contribute value to the organization following their hire (Mason et al., 2009). The focus on skills can be attributed to technological advancements and new models of efficiency in organizational operations that necessitate an increased emphasis on teamwork, problem solving, communication, and self-management (Ashton et al., 2000).

Employers recognize the value of work experience in preparing students to be effective quickly in the workplace (Harvey, Moon, & Geall, 1997; Lambert et al., 2001; Purcell, Pitcher, & Simm, 1999; Rover Group, 1998; Sewell, 2001). Two factors have clear positive effects on immediate employability of graduates in their fields: structured work experience and employer involvement in degree course design and delivery (Mason et al., 2009). Building upon prior research on the strong impact of student work experience on their performance in the labor market, Mason et al. (2009) found a positive association between sandwich participation and employer involvement in course design and delivery and the quality of graduates’ first employment after controlling for age, gender, degree class and subject, and other influences. Sandwich programs are programs in which students participate in a year-long work placement. Mason et al.’s finding supports the importance of acquiring skills in the workplace prior to graduation and the need to involve employers when developing education initiatives to address needed workplace skills.

Figure 3. Projected changes in workforce share by age (based on data from U.S. BLS, 2009).

2008

20180%

10%

20%

30%

40%

50%

60%

70%

80%

Youth (16-24) Primary WorkingAge (25-54)

Older Workers (55+)


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Summary

In summary, the body of research supports the need to examine employers’ and workers’ perspectives on the growing gap between the education and training of the American workforce and the demand for qualified persons to fill the types of positions to be available in the emerging economy. A partnership with Zoomerang provided an opportunity to access a network of employers and workers to examine the extent to which disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands.

PuRPoSE

The purpose of the current quantitative descriptive study was to examine the extent to which disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands. Two research questions guided the research. The first research question was to what extent do disparities exist between working learners and other workers in terms of demographic characteristics, skills, and perceptions of current and future workforce demands? The null and alternative hypotheses are below. A disparity between working learners and other workers in terms of demographic characteristics, skills, and perceptions of current and future workforce demands was expected.

H1o: There is no disparity between working learners and other workers in terms of demographic characteristics, skills,

and perceptions of current and future labor force demands.

H11: There is a disparity between working learners and other workers in terms of demographic characteristics, skills,

and perceptions of current and future labor force demands.

The second research question was to what extent do disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands? The null and alternative hypotheses are below. A disparity between employers and workers in terms of workers’ skills and current and future workforce demands was expected.

H2 o: There is no disparity between employers and workers in terms of workers’ skills and current and future labor

force demands.

H21: There is a disparity between employers and workers in terms of workers’ skills and current and future labor

force demands.

METhod

Population

The current study included two populations. The first population, workers, consisted of (a) unemployed persons who were actively seeking employment opportunities, (b) employees planning to work for at least 6 more years, and (c) working learners. Working learners met one of four criteria: (a) currently enrolled in a postsecondary degree program while working full-time, (b) recent graduate (2009 or later) of a postsecondary degree program attended while working full-time, (c) considering returning to school in the next 2 years as a full-time student and seeking a job, or (d) considering returning to school in the next 2 years while working full-time. The second population, employers, consisted of persons employed at the manager level or higher who self-identified as regularly involved in employee recruitment efforts and hiring decisions.

Sampling Frame and Sample

The sampling frame included panelists within MarketTools ZoomPanel. MarketTools ZoomPanel is a sample provider to some of the largest companies that conduct consumer research, including Microsoft® and General Mills® (MarketTools, Inc., 2010a). ZoomPanel is powered by TrueSample, a comprehensive sample quality technology to ensure survey respondents are “real, unique, and engaged™” (MarketTools, Inc., 2010b, para. 1).

Participants included a random sample of panelists within the MarketTools ZoomPanel. To access the worker population, surveys were sent to a random sample of 4,105 panelists. To ensure sufficient data were collected from working learners, a second survey was sent to a random sample of 22,160 panelists. These two samples were mutually exclusive. To access a sample from the employer population, surveys were sent to a random sample of 4,686 panelists who self-reported having a manager or higher functional title.


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To narrow the sampled panelists to only those who met the population criteria, screening questions were included at the beginning of each worker, working learner, and employer survey. Quotas were set to ensure responses were received from population members across specific industries. Target industries included education, healthcare, manufacturing, corporate (including financial services, banking, technology, professional services, and marketing), and government/public/nonprofit.

data collection

Workers and employers were asked to complete individual surveys (see Appendix A). While working learner data were captured in the worker survey, a third survey, the working learner boost survey, was launched to target more data from working learners. The surveys included statements about job seekers’ intention to use current or anticipated higher education degrees for career change and/or career advancement and to which job categories and in which industry sectors they might advance, rated on a Likert-type scale. The worker survey (WS) and working learner boost survey (WLS) were used to gather data on 20 variables. The variables and survey question number for each survey are below.

• Current situation (WS/WLS Q2)

• Current industry sector (WS Q3, WLS Q4)

• Current job status (WS Q4, WLS Q5)

• Job-seeking sector (WS Q5, WLS Q6)

• Demand in field (WS Q6, WLS Q7)

• Current career cluster (WS Q7, WLS Q8)

• Demand in cluster (WS Q8, WLS Q9)

• Demand by education a-d (four items) (WS Q9, WLS Q10)

• Skill level a-g (seven items) (WS Q10, WLS Q11)

• Educational attainment (WS Q11, WLS Q12)

• Educational status (WS Q12, WLS Q13)

• Educational intent a-b (two items) (WS Q13, WLS Q14)

• Working learner value (WS Q14, WLS Q15)

• Industry/occupational shift (WS Q15, WLS Q16)

• Years of experience (WS Q16, WLS Q17)

• Age (WS Q17, WLS Q18)

• Race/ethnicity (WS Q18, WLS Q19)

• Gender (WS Q19, WLS Q20)

• Military status (WS Q20, WLS Q21)

• Language proficiency a-e (5 items) (WS Q21, WLS Q22)

• Language intent a-e (5 items) (WS Q22, WLS Q23)

The reliability of the worker and working learner surveys was assessed using Cronbach’s alpha, which was calculated for groups of scaled questions for ordinal variables. The analysis was conducted on the combined datasets, as the questions were identical. The results appear in Table 1. Results revealed good internal consistency, with reliability coefficients ranging from .73 to .88.


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Table 1Cronbach’s Alpha – Worker Survey

Scale Questions number of items Reliability coefficient

Demand WS Q6, Q9 a-e 6 .73

Skills WS Q10 a-g 8 .88

Likelihood WS Q15, Q22 a-e 6 .78

The employer survey (ES) was used to gather data on 10 variables. The variables and survey question number for each survey are below.

• Industry sector (ES Q3)

• Company size (ES Q4)

• Demand in field (ES Q5)

• Demand in cluster (ES Q7 a, b, c)

• Demand by education a-d (four items) (ES Q8)

• Finding skilled workers now a-g (seven items) (ES Q9)

• Finding skilled workers later a-g (seven items) (ES Q10)

• Working learner value (ES Q11)

• Language demand now a-e (5 items) (ES Q12)

• Language demand later a-e (5 items) (ES Q13)

The reliability of the employer survey was assessed using Cronbach’s alpha, which was calculated for groups of scaled questions for ordinal variables. The results appear in Table 2. Results revealed good to excellent internal consistency, with reliability coefficients ranging from .86 to .94.

Table 2Cronbach’s Alpha – Employer Survey

Scale Questions number of items Reliability coefficient

Demand ES Q7 a-c, Q8 a-d, Q12 a-e, Q13 a-e

17 .86

Difficulty ES Q9 a-g, Q10 a-g 14 .94


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Table 3 displays the common variables across the worker/working learner and employer surveys, the values which will be compared to examine disparities in perceptions between these groups of survey respondents.

Table 3Crosswalk of Variables in Worker, Working Learner, and Employer Surveys

Worker/Working learner survey variables Employer survey variables

Current industry sector (WS Q3, WLS Q4) Industry sector (ES Q3)

Demand in field (WS Q6, WLS Q7) Demand in field (ES Q5)

Demand in cluster (WS Q8, WLS Q9) Demand in cluster (ES Q7 a, b, c)

Demand by education a-d (four items) (WS Q9, WLS Q10) Demand by education a-d (four items) (ES Q8)

Skill level a-g (seven items) (WS Q10, WLS Q11) Finding skilled workers now a-g (seven items) (ES Q9)

Working learner value (WS Q14, WLS Q15) Working learner value (ES Q11)

Language proficiency a-e (5 items) (WS Q21, WLS Q22) Language demand now a-e (5 items) (ES Q12)

Language intent a-e (5 items) (WS Q22, WLS Q23) Language demand later a-e (5 items) (ES Q13)

Data were collected over a 10-day period using three separate surveys administered using Zoomerang’s online survey research software. For each survey, MarketTools panelists received an email that included a hyperlink to the survey. Prior to accessing the survey questions, participants were asked to read an informed consent form. Only those panelists who provided informed consent received access to the survey.

Upon providing consent to participate, panelists were asked one or more screening questions to ensure they met the population criteria. Panelists who did not meet population criteria were screened out and thanked for their time. Panelists who did meet the criteria, were provided access to the remaining survey questions.

Analyses conducted on the survey data include descriptive statistics (frequency distributions, cross-tabulations, measures of central tendency, and inferential statistics (e.g., chi square and other pertinent non-parametric tests appropriate to categorical and ordinal data). The units of analysis for worker data are individual and group (workers who may or may not be currently employed and workers who are current or recent working learners), whereas the units of analysis for employer data are aggregated for occupational cluster and industry sector.

RESulTS

Completed surveys were received from 930 survey respondents. The final worker sample included 511 completed surveys, of which 100 were working learners. The final employer sample included 419 completed surveys. A response rate cannot be calculated because it is unknown how many individuals invited to participate in the surveys met the criteria for each target population.

Figure 4. ZoomPanel study population by gender.

Figure 5. ZoomPanel study population by race/ethnicity.

Worker survey

Employer survey

Working learner boost

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Africa

n-Amer

ican

Asian/

Pac I

slander

Hispan

ic

White

Other

0%

20%

40%

60%

80%

100%

Worker survey

Employersurvey

Working learner boostMale Female


14

The ZoomPanel available populations for the current sudy were skewed in terms of the proportion of men and women. More than three-quarters of the worker survey population and working learner subpopulation, and almost 64% of the employer population were female (see Figure 4). More than three-quarters of the employer and workers populations was White, compared to just over 38% of the working learner subpopulation, who were much more diverse in representation of African-Americans, Asian/Pacific Islanders, Hispanic origin, and Other (see Figure 5).

Almost one-fifth (19.96%) of worker survey respondents met the criteria for working learners (n = 100)—currently enrolled, a recent graduate (2009 or later), or considering returning to school as a working learner in the next 2 years (see Figure 6).

The worker survey did not include a question about intent to return to school as a working learner in the next 2 years; such intent was used to screen in respondents to the working learner boost survey. Thus, the actual proportion of persons who intend to become working learners in the next 2 years is likely to be higher than these data reflect.

The sample sizes from the worker and employer surveys are sufficient to be representative of the available worker and employer populations accessible via ZoomPanel. According to Mugo (n.d.), factors that influence the sample size selection include funding, the nature of the population, the nature of the variables, the nature of the analysis, the precision, the variance, and the number of operations to be performed. When the available population is plus or minus 500, a common approach is to sample 50%. When the available population is plus or minus 1,500, sampling 20% is appropriate, and when N is greater than 5,000, 400 is an appropriate sample size (Mugo, n.d.). Given the MarketTools ZoomPanel population size of 4,105 workers (including working learners), 22,160 for the working learner boost survey, and 4,686 employers, data from approximately 300 individuals would yield the data necessary to generalize the results from the sample to the MarketTools ZoomPanel population at a 95% level of confidence and a confidence interval of 5%.

Workers, including the subpopulation of working learners, were asked to self-report gender, age, intent to continue working, race/ethnicity, military status, language proficiency, current work situation, industry sector and career cluster of current occupation, years of experience, level of education, and (if a working learner) current working learner status. Employers were asked to self-report industry sector and company size.

Figure 8. Age of worker survey participants.

50-64

40-49

30-39

23-29

Other

9.98%

3.71%

36.99%

28.77%

20.55%

Figure 6. ZoomPanel study population by worker/working learner status.

Worker (not a working learner)

Working learner80.43%

19.57%

Figure 7. Gender of worker survey participants.

Female

Male

No answer

68.10%

31.70%

0.20%


15

Results are summarized below; frequency distributions are reported in Appendix B (workers) and Appendix C (employers). Differences in the distribution of demographic variables for key variables are also reported below.

Worker demographics

Gender, age, and race/ethnicity. More than two-thirds of workers self-identified as female (see Figure 7). More than two-thirds (67.72%) were age 40 or older; almost 40% were age 50 or older (see Figure 8). Although the age distribution is skewed toward older workers, almost 45% of workers indicated they are planning to continue working 10 or more years (see Figure 9). Workers were predominantly White, with small percentages of respondents self-identifying as African-American, Hispanic, Asian, biracial, or other categories (see Figure 10).

A chi square test of independence was used to examine differences in the distribution of demographic variables based on current job situation/intent to continue working. The results were significant for age (X2(14, N = 511) = 59.04, p < .001) and approached significance for race (X2(16, N = 511) = 24.07, p < .10), indicating a relationship between intent to continue working and age and intent to continue working and race. Older workers were more likely to continue working fewer years than younger workers, as to be expected. Persons of color were more likely to continue working than White workers.

A chi square test of independence was used to examine differences in the distribution of worker survey variables based on race. The results were significant for perceived skill level in working independently, X2(32, N = 511) = 68.27, p < .001 and producing work, X2(32, N = 511) = 68.26, p < .001; current educational status, X2(24, N = 511) = 66.45, p < .001; change occupation or industry, X2(32, N = 511) = 54.75, p < .01; current language proficiency, X2(32, N = 511) = 182.65, p < .001 (p = .000); and intent to become proficient in the next 10 years to conduct business in Arabic, X2(40, N = 511) = 69.15, p < .01; Chinese, X2(40, N = 511) = 83.21, p < .001; English, X2(40, N = 511) = 72.63, p = .001; Russian, X2(40, N = 511) = 57.30, p < .05; and Spanish, X2(40, N = 511) = 110.22, p < .001. The results indicate a relationship between these variables and race. The results approached significance for current job situation, X2(16, N = 511) = 24.07, p < .10; job-seeking sector, X2(48, N = 511) = 62.59, p < .10; and skill level in teaming, X2(32, N = 511) = 45.56, p < .10).

Asian/Pacific Islanders and Hispanics were more likely to rate their skills lower in working independently and producing high quality work. Asian/Pacific Islanders were more likely to rate their skills lower in teaming. African-Americans and Asian/Pacific Islanders were over-represented in the corporate job-seeking sector; African-Americans were

Figure 11. Military status of worker survey participants.

ActiveDuty0.2%

Non-military86.68%

RetiredMilitary1.76%

Other or no answer

4.31%

Honorablydischarged from active

duty orreserves7.05%

Figure 9. Intent of worker survey participants to remain in the workforce.

Plan to work 6-10 yrs

Plan to work 10+ yrs

Not employed, but seeking employment

44.42%

44.81%

10.76%

Figure 10. Race/ethnicity of worker survey participants.

Other4%

Asian/PacIslander3%

Hispanic6%

White81%

African-American6%


16

over-represented in the corporate and education job-seeking sectors. African-Americans were also most likely to be unsure about whether or not they would shift occupations. Hispanics were mostly likely to be proficient to conduct business in Spanish. African-Americans were most likely to be current enrollees in or recent graduates of a full-time postsecondary degree program attended while working full-time. African-Americans and Asian/Pacific Islanders were most likely to become proficient in Arabic, Chinese, or Russian in the future.

While most workers (86.69%) were never associated with the military, a small proportion (7.25%) was on active duty or honorably discharged from active duty in the military or the reserves (see Figure 11).

A chi square test of independence was used to examine differences in the distribution of worker survey variables based on military status. The results were significant for intent to become proficient to conduct business in Arabic, X2(30, N = 511) = 49.85, p < .05; Chinese, X2(30, N = 511) = 66.48, p < .001; English, X2(30, N = 511) = 51.36, p < .01; Russian, X2(30, N = 511) = 81.34, p < .001; and Spanish, X2(30, N = 511) = 46.02, p < .05 (p = .031), indicating a relationship between these variables and military status. The results approached significance for demand for a doctoral degree, X2(24, N = 511) = 33.85, p = .10. Participants with current or past military affiliation were less likely to become proficient in Arabic, Chinese, English, Russian, or Spanish. Participants with current or past military affiliation were more likely to rate the demand for a doctoral degree as moderate or high.

language proficiency. Most workers self-identified as proficient in English (91.59%). Small numbers of participants were proficient in other languages currently in high demand or likely to be in high demand in the future (see Figure 12).

Figure 12. Language proficiency of worker survey participants.

English92%

Russianless than

1%Chinese andArabic

less than1%

Spanish3%

Other5%

Figure 13. Industry sector of current jobs held.

20.35%

18%

16.83%

16.83%

11.55%8.02%

8.61%

Corporate

Education

Government

Healthcare

Manufacturing

Not Employed

Other

Figure 14. Career clusters of workers’ current occupations.

Career Cluster0%

5%

10%

15%

20%

Agricu

lture

Archite

ctArts

Busines

s

Educa

tion

Finan

ce

Gover

nment

Health

Scien

ces

Hospita

lity

Human Se

rvice

s

Info

rmati

on Law

Man

ufactu

ring

Mar

ketin

g

Not Em

ployed

Other

Scien

ce

Transp

ort


17

industry sector. Worker survey participants identified their current jobs as fitting into a number of industry sectors (see Figure 13). A chi square test of independence was used to examine the observed differences in the distribution of the demographic variables and industry sector of respondents’ current job. The results were significant for gender, X2(12, N = 511) = 26.5507, p < 0.01) and age, X2(42, N = 511) = 73.14, p < .01, indicating a relationship between current industry sector and gender and current industry sector and age.

Career cluster. Participants also identified the career clusters of their current occupations. More than half (50.69%) of workers worked in careers in government, education, health services, and business. More than three-quarters (77.31%) worked in occupations in those sectors or in information, manufacturing, finance, human services, or science (see Figure 14). A chi square test of independence was used to examine the observed differences in the distribution of demographic variables and the career cluster of workers’ current occupations. The results were significant for gender, X2(34, N = 511) = 69.2588, p < .001), indicating a relationship between career cluster and gender.

years of experience. Workers were highly experienced in their fields. Almost 57% reported having at least 11 years of experience, and more than 36% reported having 20 or more years of experience (see Figure 15). A chi square test of independence was used to examine the observed differences in the distribution of demographic variables and years of experience. The results were significant for age, X2(21, N = 511) = 241.81, p < .001, indicating that years of experience is related to age, as to be expected.

level of education. Workers in the sample had high levels of educational attainment (see Figure 16). More than two-thirds (67.15%) reported having earned a postsecondary degree. More than half (52.84%) had earned a bachelor’s degree or higher, and almost one-fifth (19.77%) had earned a master’s or doctoral degree. A chi square test of independence was used to examine the observed differences in the distribution of demographic variables and current educational attainment. The results approached significance for age (X2 (42, N = 511) = 55.24, p < .10).

While the worker sample includes 100 self-identified working learners, those 100 include persons who intend to enroll in a postsecondary degree program in the next 2 years while working full-time. Almost 13% of workers were currently enrolled in or had graduated from a postsecondary degree program in 2009 or later while working full-time (see Figure 17).

Figure 15. Workers’ years of experience in current field.

Years of experience

0%

10%

20%

30%

40%

5+ 6-10 11-19 20+

Figure 16. Workers’ educational attainment.

0%

5%

10%

15%

20%

25%

30%

35%

Som

e H

S

HS

Dip

lom

a

Som

e Co

llege

Ass

ocia

te D

egre

e

Bach

elor

’s D

egre

e

Mas

ter’

s D

egre

e

Doc

tora

l Deg

ree

Educationalattainment

Figure 17. Workers’ current/recent working learner status.

Currently enrolled

Graduated 2009 or later as a working learner

Graduated 2009 or later and re-enrolled

Not enrolled; did not graduate as a working learner since 2009

87.28%

7.44%4.11%

1.17%


18

Figure 20. Current job status.

Employer demographics

industry sector. Despite efforts to obtain equal representation across all five sectors, manufacturing is over-represented and corporate is under-represented (see Figure 18). Approximately 28% of employers were from the manufacturing industry sector. Between 16 and 19% of employers were from each of the other industry sectors: healthcare, education, corporate, and government/public nonprofit industry sectors.

Company size. Almost half (44%) of responding employers were from large companies with 500 or more employees (see Figure 19). Fewer responding employers were from medium and small companies. With more responding employers from large companies, the results may be skewed toward the attitudes and opinions of hiring managers at large companies.

Worker and Employer Survey Question Responses overview

The data sets for the worker survey and the working learner boost survey were combined to form a single data set for workers. Basic frequency distributions for responses to all questions contained in the combined worker/working learner boost data appear in Appendix D. The numbering of questions for the combined worker/working learner frequencies tables corresponds to the question numbering on the working learner survey. Basic frequency distributions for responses to all questions on the employer survey also appear in Appendix D, along with measures of central tendency for questions with ordinal response scales.

Worker Survey Question Responses

utilization of current skills or education. More than two-fifths of workers reported not utilizing or under-utilizing their current skills or education. Thirty-eight percent reported either actively seeking new employment opportunities and/or seeking to move to a higher-level position (see Figure 20).

Demand for skilled and educated employees in

the field. Approximately 58% of workers indicated a moderate to high demand in the next 10 years for skilled and educated employees in their field (Median = 4); more than one-third was unsure (see Figure 21).

Figure 19. Size of company.

Figure 18. Industry sector of hiring managers’ companies.

27.68%

18.38%

18.14%

18.85%

16.95%

Corporate

Education

Government/public/non-profit

Healthcare

Manufacturing

Educationalattainment

0%

10%

20%

30%

40%

50%

Small (fewerthan 50

employees)

Medium (50to 499

employees)

Large (500 or more

employees)

Unemployed

Actively seeking new employment opportunities

Seeking to move to a higher-level position in current career

No response

Employed but not utilizing or under-utilizing skills and/or edcucation

24%

14%

43%

7%11%


19

Demand for skilled and educated employees in

career cluster. Approximately 58% of workers indicated a moderate to high demand in the next 10 years for skilled and educated employees in their career cluster (median = 4); more than one-third was unsure (see Figure 22).

Demand for employees by degree level. Workers were asked to project demand in the next 10 years in the career cluster for employees with associate, bachelor’s, master’s, and doctoral degrees. The results are displayed in Figure 23.

While almost 40% of workers projected a moderate to high demand for an associate degree in their career cluster, almost a third projected very low to low demand; more than 29% were unsure (median = 3). More than 75% of workers projected a moderate or high 10-year demand for workers in their career cluster with a bachelor’s degree (median = 4). More than 63% of workers projected a moderate or high 10-year demand for workers in their career cluster with a master’s degree (median = 4). More than 63% of workers projected a moderate or high 10-year demand for workers in their career cluster with a doctoral degree (median = 3); almost one-third was unsure.

skill levels. Workers were asked to share perceptions of their skill levels in seven different skills areas. The results are displayed in Figure 24. More than 65% of workers rated themselves as highly or very highly skilled in thinking critically; more than one-third rated themselves as somewhat or moderately skilled (median = 4). More than 61% of workers rated themselves as highly or very highly skilled in solving complex problems; 37.38% rated themselves as somewhat or moderately skilled (median = 4). Almost 70% of workers rated themselves as highly or very highly skilled in communicating clearly both in written and oral formats; almost 29% rated themselves as somewhat or moderately skilled (median = 4). More than 86% of workers rated themselves as highly or very highly skilled in working independently; 11.94% rated themselves as somewhat or moderately skilled (median = 5).

Almost 73% of workers rated themselves as highly or very highly skilled in teaming; more than 26% rated themselves as somewhat or moderately skilled (median = 4). More than 68% of workers rated themselves as highly or very highly skilled in working in a multicultural environment; more than 29% rated themselves as somewhat or moderately skilled (median = 4). More than 87% of workers rated themselves as highly or very highly skilled in producing work that is consistently of high quality; 12.13% rated themselves as somewhat or moderately skilled (median = 4).

Figure 21. 10-year demand for skilled and educated employees in field.

0%

10%

20%

30%

40%

Very l

ow (1

)

Low (2

)

Unsure

(3)

Mod

erate

(4)

High (5

)

Demand in field

Figure 22. 10-year demand for skilled and educated employees in occupational cluster.

0%

10%

20%

30%

40%

Very l

ow (1

)

Low (2

)

Unsure

(3)

Mod

erate

(4)

High (5

)

Demand in career cluster

Figure 23. 10-year demand for employees by degree level.

0%

10%

20%

30%

40%

50%

Very l

ow (1

)

Low (2

)

Unsure

(3)

Mod

erate

(4)

High (5

)

Associate

Bachelor’s

Master’s

Doctoral


20

likelihood of enrolling in continuing education. Workers were asked to indicate the likelihood of enrolling in a postsecondary degree program or participating in for-credit and not-for-credit (professional development or continuing education) offerings in the next 5 years in order to increase their employability. The results are displayed in Figure 25.

Almost 56% of workers were somewhat or highly unlikely to enroll in a postsecondary degree program in the next 5 years; slightly more than 24% were somewhat or highly likely to enroll, and slightly more than 20% were unsure (median = 2). About 38% of workers were somewhat or highly unlikely to participate in professional development or continuing education in the next 5 years; almost 39% were somewhat or highly likely to participate, and slightly more than 23% were unsure (median = 3).

Working learners increased value by higher

education activities. Workers were asked to rate the extent to which working learners increase their value as employees as a result of their higher education activities. The results are displayed in Figure 26. Slightly less than half of the workers identified a slight or some increase, whereas 45.99% identified a moderate to high increase (median = 3). Only 4.11% identified no increase in value.

likelihood of moving into a different occupation

or industry. Workers were asked to rate the likelihood they would move into a different occupation or industry in the next 5 years. Slightly more than half reported it was somewhat or highly unlikely that they would change their occupation or industry in the next 5 years; 21.33% thought it was somewhat to highly likely (median = 2, see Figure 27).

likelihood of becoming proficient. Workers were asked the likelihood of becoming proficient in the next 10 years to conduct business in Arabic, Chinese, English, Russian, and Spanish. The results are presented in Figure 28.

More than 82% of workers reported they were somewhat or highly unlikely to become proficient to conduct business in Arabic; 9.39% indicated they were already fluent (median = 1). More than 80% of workers reported they were somewhat or highly unlikely to become proficient to conduct business in Chinese; 10.18% indicated they were already fluent in Chinese (median = 1).

Slightly more than 70% of workers indicated they were already fluent in English. Slightly more than 16% indicated they were likely to become proficient to conduct business in English in the next 10 years (median = 5); however, 91.59% reported current proficiency to conduct business in English.

Figure 24. Workers’ perceived skill levels.

Slightly skilled (1)

Somewhat skilled (2)

Moderately skilled (3)

Highly skilled(4)

0%

10%

20%

30%

40%

50%

60%

Thin

king

Solv

ing

Com

mun

icat

ing

Wor

k in

dust

ry

Team

ing

Mul

ticu

ltura

l

Hig

h qu

alit

y

Figure 25. 5-year likelihood of enrolling in educational offerings.

Post-secondary degree program

Professional development or continuing education courses

0%

10%

20%

30%

40%

50%

Hig

hly

unlik

ely (1)

Som

ewha

t unl

ikel

y(2

)

Uns

ure

(3)

Som

ewha

t lik

ely

(4)

Hig

hly

likel

y(5

)

Figure 26. Workers’ perceived increase in value of working learners.

Increase in value

0%

10%

20%

25

30%

35

40%

No

incr

ease

(1)

Ligh

t inc

reas

e (2

)

Som

e in

crea

se (3

)

Mod

erat

ein

crea

se (4

)

Hig

h in

crea

se (5

)


21

More than 82% of workers reported they were somewhat or highly unlikely to become proficient to conduct business in Russian; 9.39% indicated they were already fluent in Russian (median = 1). More than 59% of workers reported they were somewhat or highly unlikely to become proficient to conduct business in Spanish, 21.14% was somewhat or highly likely to become proficient, and 7.44% was already fluent in Spanish (median = 2).

Employer Survey Question Responses

Projected demand for skilled and educated

employees. Employers were asked to project demand for skilled and educated employees in their field (see Figure 29). More than three-quarters (76.14%) of employers reported a moderate or high demand in their field (median = 4).

Highest demand. Employers were asked to report the three highest demand clusters within their company. More than 30% of employers identified business, management, and administration and information technology. More than 20% of employers identified education and training; health science; marketing, sales, and service; and science, technology, engineering, and mathematics.

Projected demand. Employers were asked to project demand in the next 10 years for skilled and educated employees in the three highest demand clusters in their fields (see Figure 31). The demand was moderate across the clusters (median = 4).

Employers were asked to project the 10-year demand in the top three career clusters for employees with associate (median = 3), bachelor’s (median = 4), master’s (median = 4), and doctoral (median = 3) degrees. The projected 10-year demand is highest for employees with bachelor’s and master’s degrees (see Figure 32).

A comparison of 10-year demand by industry sector revealed differences in the median demand for both associate and doctoral degrees (see Table 4). A chi square comparison of the distribution of the medians was significant for doctoral degrees, X2(4, N = 419) = 14.64, p < .01; the difference in the medians was not significant for associate degrees, X2(4, N = 419) = 6.40, p > .10

Figure 27. 5-year likelihood of moving to a different occupation or industry sector.

Shift occupationor industry in the next five years

0%

10%

20%

30%

40%

50%

60%

Unlikely

Unsure

Likely

Figure 28. 10-year likelihood of becoming proficient to conduct business in languages.

Arabic

Chinese

Russian

Spanish

Unlikely

Unsure

Likely

Alread

y Fluen

t0%

20%

40%

60%

80%

100%

Figure 29. Demand for skilled and educated employees in the field.

Demandin field0%

10%

20%

30%

40%

50%

Very l

ow (1

)

Low (2

)

Unsure

(3)

Mod

erate

(4)

High (5

)


22

Table 4Employers’ Median 10-year Demand for Employees with Degrees by Sector

associate Bachelor’s Master’s doctoral

Corporate 3 4 4 3

Education 3 4 4 4

Government/nonprofit 3 4 4 3

Healthcare 4 4 4 4

Manufacturing 3 4 4 3

Difficulty finding employees with needed skills. Employers were asked the perceived difficulty of finding employees with the needed skills in seven skill areas. They were also asked the perceived difficulty in the next 10 years of finding employees with the needed skills in the same seven skill areas. The distributions are displayed in Figure 33 for skills a-d and in Figure 34 for skills e-g.

Table 5 displays the proportion of responses to employer questions 9 a-g, current difficulty in finding workers with needed skills in seven areas, and 10 a-g, projected 10-year difficulty in the same skill areas, grouped by current and projected demand and difficulty level: slightly/somewhat difficult and very/extremely difficult.

Table 6 depicts the distribution of medians for employer questions 9 a-g and 10 a-g. A Wilcoxon Sign-ranked test was used to compare the medians for producing work of consistently high quality, as that was the only skill for which the medians differed. The results were not significant, Z = 0.17, p > .10.

Table 5Current/10-year Difficulty in Finding Skilled Workers

current Projected 10-year

SkillsSlightly/

somewhatVery/

extremelySlightly/

somewhatVery/

extremely

Thinking critically 47.02% 50.83% 44.87% 52.75%

Solving complex problems 42.48% 55.84% 41.77% 55.84%

Communicating clearly 39.38% 57.51% 41.05 55.84%

Working independently 52.75% 41.05% 51.79% 41.05%

Teaming 61.10% 31.02% 55.84% 36.54%

Working in multicultural environment 58.71% 27.21% 55.37% 26.49%

Consistently producing high quality work 45.10% 52.27% 47.50% 49.88%

Figure 30. Career clusters with current highest demand in company.

Business, management &administration

Information technology

Education & training

Science, technology, engineering& mathematics

Marketing, sales & service

0% 10% 20% 30% 40%


23

Table 6Median Difficulty in Finding Skilled Workers Now / Next 10 Years

Variable now next 10 years

Thinking critically 4 4

Solving complex problems 4 4

Communicating clearly 4 4

Working independently 3 3

Teaming 3 3

Working in multicultural environment 3 3

Consistently producing high quality work 4 3

Employers reported it is very difficult currently and will be very difficult in the future to find workers who can think critically, solve complex problems, and communicate clearly in written and oral formats. They reported it is somewhat difficult currently and projected it will be difficult in the future to find employees who can work independently, work as a team, and work in a multicultural environment. Employers projected that the current high difficulty in finding workers who can produce consistently high quality work will decrease in the future.

Working learners increasing value. Employers were asked the extent to which working learners increase their value as employees because of their higher education activities. While 48.21% perceived a slight or some increase in value, 45.82% indicated a moderate or high increase in value (see Figure 35). Few employers (5.97%) indicated no increase in value from working learners’ higher education activities.

Current and future demand for business language proficiency. Employers were asked to indicate the current and future demand for employees who have the proficiency to conduct business in Arabic, Chinese, English, Russian, or Spanish (see Figure 36).

Table 7 displays the proportion of responses to questions about current and future demand for employees with proficiency in five different languages grouped by current and projected demand and demand level: very low/low and moderate/high demand.

Employers reported somewhat low current and future demand for Arabic and Russian. Many employers were unsure about future demand for Arabic (24.11%), Chinese (24.34%), and Russian (29.12%). Current demand for Chinese is low to moderate. Although more than half of employees rated it as low or very low, more than one quarter rated it as moderate to high. Future demand for Chinese is also moderate. Current and future demand for English is high, although is projected to be lower in the future. Current and future demand for Spanish is moderate. Arabic and Chinese led In terms of the increase in the percentage of employers who rated current and 10-year demand as moderate to high, followed by Russian and Spanish.

Figure 31. Demand for skilled and educated employees in three highest current demand career clusters in employers’ organization.

Highest demandcareer cluster

Second highest demand career cluster

Third highest demand career cluster

0%

10%

20%

30%

40%

50%

Very l

ow Low

Unsure

Mod

erate High


24

Table 8 depicts the distribution of medians for questions 12 and 13. Question 12 pertains to current demand for business language proficiency in five languages, Arabic, Chinese, English, Russian, and Spanish. Question 13 pertains to future demand for business language proficiency in the same five languages. A Wilcoxon Sign-ranked test was used to compare the medians for Chinese, as that was the only language for which the medians differed. The results were significant, Z = 9.81, p > .001.

Table 7Current and Projected 10-Year Demand for Business Language Proficiency

current demand 10-year demand current/10-year

language Very low/low Moderate/high Very low/low Moderate/high Difference moderate/high

Arabic 63.73% 16.94% 50.83% 25.06% 8.12%

Chinese 51.08% 34.13% 33.65% 42.00% 7.87%

English 3.34% 88.55% 3.35% 86.16% -2.39%

Russian 64.44% 11.93% 53.70% 17.19% 5.26%

Spanish 21.24% 64.91% 14.80% 69.69% 4.58%

Table 8Median Difficulty in Language Demand Now / Next 10 Years

Variable Now Next 10 Years

Language demand

Arabic 2 2

Chinese 2 3

English 5 5

Russian 2 2

Spanish 4 4

Figure 32. 10-year demand for employees by education.

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Figure 33. Current / 10-year difficulty in finding employees (a-d).

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25

Research Question 1

The first research question guiding the current study was To what extent do disparities exist between working learners and other workers in terms of demographic characteristics, skills, and perceptions of current and future workforce demands? A chi square test of independence was used to examine the observed differences in the distribution of responses to the worker survey questions based on whether respondents were working learners (N = 100) or non-working learners (N = 411). Cross-tabulations for significant comparisons appear in Appendix F. The chi square results were significant for the variables in Table 9, indicating that the distribution of these variables is related to survey respondents’ working learner status.

Table 9Demographic Variables by Working Learner Status – Significant

Question Variable chi Square

WS Q3 Current job industry sector X2(6, N = 511) = 8.23, p < .01

WS Q5a Job seeking sector X2(6, N = 511) = 32.97, p < .001

WS Q12 Current educational status X2(3, N = 511) = 306.08, p < .001

WS Q13a Likelihood of enrolling in a postsecondary program in the next 5 years to increase employability

X2(4, N = 511) = 97.57, p < .001

WS Q13b Likelihood of enrolling in professional development or continuing education in the next 5 years to increase employability

X2(4, N = 511) = 27.36, p < .001

WS Q14 Perceived working learner value X2 (4, N = 511) = 12.20, p < .05

WS Q15 Likelihood of shifting occupation X2 (4, N = 511) = 30.20, p < .001

WS Q16 Years of experience X2 (8, N = 511) = 31.68, p < .001

WS Q17 Age X2 (7, N = 511) = 87.09, p < .001

WS Q18 Race X2(8, N = 511) = 15.20, p < .10*

WS Q19 Gender X2(2, N = 511) = 10.31, p < .01

WS Q22a Become proficient in Arabic X2(5, N = 511) = 28.89, p < .001

WS Q22b Become proficient in Chinese X2(5, N = 511) = 27.15, p < .001

WS Q22c Become proficient in English X2(5, N = 511) = 17.72, p < .01

WS Q22d Become proficient in Russian X2(5, N = 511) = 26.95, p < .001

WS Q22e Become proficient in Spanish X2(5, N = 511) = 31.09, p < .001

The results support rejecting the null hypothesis for the first research question that no disparity exists between working learners and other workers in terms of demographic characteristics. Working learners were more likely than other workers to be in the Education and Other current sector categories. Working learners were more likely to job seek in the categories of education, healthcare, manufacturing, and other.

Working learners, by definition, comprised all of the survey respondents who were currently enrolled in or recent graduates of a postsecondary degree program attended while working full-time. Working learners were more likely to enroll in a postsecondary degree program and to participate in professional development or continuing education in the next 5 years.

Figure 34. Current / 10-year difficulty in finding employees (e-g).

Not difficult (1)

Slightly difficult (2)

Somewhat difficult (3)

Very difficult (4)

Team

ing

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26

Working learners were more likely to have fewer years of experience compared to other workers. Working learners were also more likely to be between the ages of 18-39 compared to other workers, were more likely to be male, and were much more likely to be persons of color.

Table 10 includes the medians for scaled questions pertaining to worker survey question 14, working learner value, and worker survey question 15, intent to shift sector occupation in the next 5 years.

Table 10Working Learner Median Comparisons

Variable non-working learner Working learner

WS Q14: Working learner value 3 4

WS Q15: Likelihood of shift into different occupation or industry 2 3

A Wilcoxon Signed-ranks test was used to examine the significance of the difference between the medians for working learner value and likelihood to shift into a different occupation or industry in the two subsamples of workers (non-working learners and working learners). The results were significant for working learner value (Z = -3.24, p < .01) and for likelihood of shift (Z = -4.99, p < .001), indicating that working learners perceived a higher increased value of working learner postsecondary pursuits and were more likely than other workers to shift their industry sector or occupation in the next 5 years. The results support rejecting the null hypothesis of no disparity between working learners and other workers in terms of value of postsecondary pursuits and likelihood of shifting sector or occupation in the next 5 years.

Chi square tests for independence were not significant for working learner status and the variables in Table 11. The results indicate that the variables are independent of working learner status. Thus, the results do not support rejecting the null hypothesis of no disparities between working learners and other workers on these variables.

Table 11Demographic Variables by Working Learner – Not Significant

Question demographic variables chi square

WS Q2 Current situation/intent to continue working

X2(6, N = 511) = 2.22, p > .10

WS Q4 Current job status X2(4, N = 511) = 1.15, p > .10

WS Q7 Current career cluster X2(4, N = 511) = 20.66, p > .10

WS Q11 Educational attainment X2(6, N = 511) = 9.73, p > .10

WS Q20 Military status X2(4, N = 511) = 5.40, p > .10

WS Q21 Current language proficiency X2(4, N = 511) = 6.11, p > .10

The results of chi square tests for independence were significant for the perceived demand variable (see Table 12), indicating the distribution of perceived demand for employees with a bachelor’s degree is related to respondents’ working learner status. Working learners were more likely than other workers to rate the demand as low or high, whereas workers were more likely to be unsure or rate the demand as very low. The results indicate support for rejecting the null hypothesis of no disparity in perceived demand for employees with bachelor’s degrees.

Figure 35. Employers’ perceived increase in value of working learners.

Increase in value0%

5%

10%

15%

20%

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35%


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Table 12Perceived Demand Variable by Working Learner – Significant

Question Variable chi square

WS Q9c Perceived demand for bachelor’s degree X2(4, N = 511) = 14.38, p < .01

Chi square tests for independence were not significant for working learner status and the perceived demand variables in Table 13, indicating that perceived demand variables are independent of working learner status. The results do not support rejecting the null hypothesis of no disparities between working learners and workers on the demand variables.

Table 13Perceived Demand Variables by Working Learner – Not Significant

Question Perceived demand variables chi square

WQ Q6 Perceived 10-year demand in field X2(4, N = 511) = 5.29, p > .10

WS Q8 Perceived 10-year demand in cluster X2(4, N = 511) = 2.79, p > .10

WS Q9a Perceived demand for employees with Associate degree X2(3, N = 511) = 1.31, p > .10

WS Q9c Perceived demand for employees with Master’s degree X2(4, N = 511) = 5.58, p > .10

WS Q9d Perceived demand for employees with doctoral degree X2(3, N = 511) = 7.29, p > .10

Table 14 depicts the median response for the questions associated with the demand variables. A Wilcoxon Signed-ranks test was used to examine the significance of the difference between the medians for Q9d, demand for doctoral degree, for workers (non-working learners) and working learners. Demand for a doctoral degree was the only demand question for which the median differed between the workers (non-working learners) and the working learners. The results were significant (Z = -2.57, p < .05), indicating that working learners perceived a higher demand for the doctoral degree, compared to other workers. The results support rejecting the null hypothesis that no disparities exist between working learners and other workers in demand for employees with a doctoral degree.

Figure 36. Current/10-year demand for business language proficiency.

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Table 14Working Learner Medians for Demand Variables

Question non-working learner Working learner

WS Q6 Demand in field 4 4

WS Q8 Demand in cluster 4 4

WS Q9 Demand by education

Associate degree 3 3

Bachelor’s degree 4 4

Master’s degree 4 4

Doctoral degree 3 4

Chi square tests for independence were not significant for working learner status for any of the perceived skill variables, indicating that the variables are independent of working learner status (see Table 15). The results do not support rejecting the null hypothesis that no disparity exists between working learners and other workers on the skills variables.

Table 15Perceived Skill Variables by Working Learner – Not Significant

Question Perceived skill variables chi square

WS Q10a Perceived skill level in thinking critically X2(4, N = 511) = 4.89, p > .10

WS Q10b Perceived skill level in solving complex problems X2(4, N = 511) = 4.51, p > .10

WS Q10c Perceived skill level in communicating clearly X2(4, N = 511) = 74.32, p > .10

WS Q10d Perceived skill level in working independently X2(4, N = 511) = 6.23, p > .10

WS Q10e Perceived skill level in teaming X2(4, N = 511) = 2.05, p > .10

WS Q10f Perceived skill level in working in a multicultural environment X2(4, N = 511) = 2.73, p > .10

WS Q10g Perceived skill level in producing consistently high quality work

X2(4, N = 511) = 2.50, p > .10

WS Q21 Current language proficiency X2(4, N = 511) = 6.11, p > .10

Table 16 depicts the median response for the worker survey questions associated with the skill variables. A Wilcoxon Signed-rank test was used to examine the significance of the difference between the medians for Q22e, likelihood to become proficient in Spanish, for workers (non-working learners) and working learners. Likelihood to become proficient in Spanish was the only question for which the median differed between workers and working learners. The results were significant (Z = -3.45, p < .001), indicating that working learners were more likely than other workers to become proficient in Spanish. The results support rejecting the null hypothesis that no disparity exists between working learners and other workers in their likelihood to become proficient in Spanish.

Figure 37. Industry sector distribution comparison – employers and workers.

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Table 16Working Learner Medians for Skill Variables

Variable non-working learner Working learner

WS Q10: Skill level





Teaming 4 4



WS Q22: Intent to become proficient

Arabic 1 1

Chinese 1 1

English 6 6

Russian 1 1

Spanish 1 3

Taken together, the results support rejecting the null hypothesis of no disparity between working learners and other workers in terms of demographic characteristics, skills, and perceptions of current and future workforce demands for the following demographic characteristics and demand variables, as presented in Table 17. Working learners perceived a higher demand for workers with bachelor and doctoral degrees, compared to other workers. The results do not support rejecting the null hypothesis for disparities between working learners and other workers in terms of the skills variables.

Table 17Summary of Significant Disparities between Working Learners and Workers

category Variables

Demographic characteristics Current job sectorJob-seeking sectorCurrent education statusWorking learner valueLikelihood to shift sectorYears of experienceAge RaceGenderLikelihood to enroll in a postsecondary degree program Likelihood to participate in professional development or continuing education Likelihood to become proficient in Spanish

Demand variables Bachelor’s degreeDoctoral degree


30

Research Question 2

The second research question was To what extent do disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands? Comparisons of other variables were made in addition to the comparisons expressly stated in the second research question. Tables for the employer survey/worker survey comparisons appear in Appendix G. The distribution of industry sectors is not equivalent in the two samples; manufacturing is over-represented in the employer survey and under-represented in the worker survey (see Figure 37).

A chi square test of independence was used to examine the observed differences in the distribution of responses to the worker and employer survey questions. The results were significant for industry sector X2(4, N = 930) = 39.53, p < .001 (p < .001).

Workers and employers were compared on their perceptions of whether working learners increase their value as employees as a result of their postsecondary activities. The median values did not differ for workers (median = 3) and employers (median = 3) for WS Q14/ES Q11, perceived working learner value, indicating no difference in how workers and employers perceive the increased value of postsecondary pursuits of working learners. Almost equal percentages of workers (45.82%) and employers (45.99%) reported a moderate or high increase in value.

The results of chi square tests for independence were significant for the perceived demand variables in Table 18, indicating the distribution of the variables is related to the worker or employer status. The results were not significant for demand for bachelor’s degree (X2(4, N = 930) = 4.55, p >.10).

Table 18Perceived Demand Variables by Worker/Employer–Significant

Question Variable chi square

WS Q6/ES Q5 Demand in field X2(3, N = 930) = 54.63, p < .001

WS Q9a/ESQ8a Demand for associate degree X2(4, N = 930) = 17.72, p = .001

WS Q9c/ES Q8c Demand for master’s degree X2(4, N = 930) = 21.61, p <.001

WS Q9d/ES Q8d Demand for doctoral degree X2(4, N = 930) = 21.21, p < .001

Employers perceived a lower demand for employees with associate degrees and doctoral degrees, and a higher demand for master’s and bachelor’s degrees, as compared to workers. However, for every education level, a larger proportion of workers was unsure about the 10-year demand for employees with postsecondary degrees, as compared to the employers in the sample (see Table 19). Although slightly more employers than workers perceived the 10-year demand for employees with an associate degree as moderate or high, employers were more likely than workers to rate the demand for the associate degree as very low or low. Compared to employers, more workers were unsure about the demand for associate degrees.

Employers were more likely than workers to rate the demand for bachelor’s degrees as moderate or high, although the difference in the distribution was not significant at p = .05. Employers were more likely than workers to rate the demand for master’s degrees as moderate or high. Employers were less likely than workers to rate the demand for doctoral degrees as moderate or high. Employers were also more likely than workers to rate the demand for doctoral degrees as very low or low.

The results support rejecting the null hypothesis for the second research question that no disparity exists between employers and workers in terms of future workforce demand in field and for demand for employees with associate, master’s, and doctoral degrees.


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Table 19Employer/Worker Distribution of Education 10-year Demand Responses

Employers Workers

degree Very low/low Unsure Moderate/high Very low/low Unsure Moderate/high

% % % % % %

Associate 40.57 20.05 39.38 32.10 29.35 38.55

Bachelor’s 13.13 17.18 69.69 12.33 22.11 65.56

Master’s 19.33 12.89 67.78 13.11 23.68 63.21

Doctoral 33.17 19.57 42.25 24.07 29.35 46.57

Table 20 depicts the median responses for the worker and employer questions associated with the demand variables. The medians are the same for the worker and the employer samples for all of the demand variables. Therefore, the medians for the overall responses were not compared statistically using the Wilcoxon Signed-ranks test. Instead, medians were examined by industry sector.

Table 20Worker/Employer Median Demand Variables

Worker Employer

WS Q6/ES Q5 Demand in field 4 4

WS Q8/ES Q7 Demand in cluster 4 4

WS Q9/ES Q8 Demand by education

a. Associate degree 4 4

b. Bachelor’s degree 4 4

c. Master’s degree 4 4

d. Doctoral degree 3 3

For 10-year demand in field (WS Q6/ES Q5), the medians differed for healthcare (WS median = 4, ES median = 5) and manufacturing (WS median = 3, ES median = 4). The results of Wilcoxon Signed-ranks tests were not significant for healthcare (Z = 0.99, p > .10); the results were significant for manufacturing (Z = 2.51, p < .05). For demand in cluster (WS Q8, ES Q7), the medians differed for manufacturing. The results of a Wilcoxon Signed-ranks test were not significant (Z = 1.445, p > .10). The results support rejecting the null hypothesis pertaining to no disparity between employers and workers in terms of future workforce demand in the manufacturing field.

For demand for associate degree (WS Q9/ES Q8), the medians differed within education (WS median = 2, ES median = 3) and healthcare (WS median =3, ES median =4). The results of Wilcoxon Signed-ranks tests were significant for education (Z = 1.93, p < .10), but were not significant for healthcare (Z = 0.20, p > .10). For demand for master’s degree, the medians differed within manufacturing (WS median = 3, ES median = 4). The results of a Wilcoxon Signed-ranks test were significant for master’s in manufacturing (Z = 1.799, p < .10). The results support rejecting the null hypothesis pertaining to no disparity between employers and workers in terms of future workforce demand for associate degrees in education and master’s degrees in the manufacturing field. The medians were the same for all sectors for demand for bachelor’s and doctoral degrees.


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The medians for the skill variables are presented in Table 21. Because the questions and scales pertaining to skills differed in the two surveys, chi square tests for independence were not appropriate. Instead, the Wilcoxon Signed-ranks test was used to examine whether or not the distributions and the medians were the same between the matched worker and employer samples for working independently (WS Q10d/ES Q9d), teaming (WS Q10e/ES Q9e), and working in a multicultural environment (WS Q10f/ES Q9f). These variables were selected because their median values differed between the two groups.

Table 21Worker/Employer Median Skill Level/Difficulty Finding Skilled Workers

Variable Workers Employers

WS Q10/ES Q9: Skill level/difficulty finding a

a. Thinking critically 4 4

b. Solving complex problems 4 4

c. Communicating clearly 4 4

d. Working independently 5 3

e. Teaming 4 3

f. Working in multicultural environment 4 3

g. Consistently producing high quality work 4 4

aWS skill level 1-5: slightly to very highly skilled; ES difficulty level 1-5: not difficult to extremely difficult

The results of the Wilcoxon Signed-rank tests were significant for working independently (Z = -16.11, p < .001), teaming (Z = -13.67, p < .001), and working in a multicultural environment (Z = -14.16, p < .001). The perceived skill level of the workers in working independently, teaming, and working in a multicultural environment was significantly higher than the employers’ ease of finding workers with these skills. The results support rejecting the null hypothesis that no disparity exists between employers and workers in terms of current and future workforce skills for working independently, teaming, and working in a multicultural environment.

The percentage of workers who indicated proficiency to conduct business in Arabic, Chinese, English, Russian, or Spanish (WS Q21) was compared to the percentage of employers who indicated a moderate or high current demand in these languages (ES Q12 a-e). The results indicate a disparity between workers’ skills and the percentage of employers who indicated moderate to high demand for employees who are proficient to conduct business in Arabic, Chinese, Russian, or Spanish, with the largest disparity in Spanish (see Figure 38).

As indicated in Table 22, some employers are unsure about the current demand for Arabic, Chinese, Russian, or Spanish, particularly for Russian and Arabic.

Figure 38. Workers’ language proficiency versus current demand for proficient employees

0%

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100%

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Arabic

Chinese

Engli

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Table 22Workers’ Current Language Proficiency by Current Employer Demand

language

Proficiency ofworkers

current demand

Very low/low Unsure Moderate/high

% % % %

Arabic 0.00 63.73 19.33 16.94

Chinese 0.39 51.08 14.80 34.14

English 91.59 3.34 8.11 88.55

Russian 0.39 64.44 23.63 11.93

Spanish 2.74 21.24 13.84 64.91

The medians for workers and employers for intent to become proficient/future demand for employees who are proficient in Arabic, Chinese, Russian, and Spanish (WS Q22/ES Q13) are presented in Table 23.

The Wilcoxon Signed-ranks test was used to examine whether the distributions and the medians were the same between the matched worker intent to become proficient in and the employers’ perceived future demand for the four languages for which the medians differed: Arabic, Chinese, Russian, and Spanish. The results were significant for these languages; the intent of workers to become proficient in these languages was significantly lower than the employers’ anticipated demand for the languages in the next 10 years, as follows: Arabic (Z = 12.14, p < .001), Chinese (Z = 13.97, p < .001), Russian (Z = 11.28, p < .001), and Spanish (Z = 13.22, p < .001). The results support rejecting the null hypothesis for no disparity between employers and workers in terms of future workforce skills in Arabic, Chinese, Russian, and Spanish. Taken together, the results support rejecting the hypothesis that no disparity exists between employers and workers in terms of workers’ skills and current and future workforce demands for the demand and skills variables presented in Table 24.

Table 23Worker/Employer Median Skill Level/Future Demand for Workers Proficient in Language


WS Q22/ES Q13: Intent to become proficient/future demanda

a. Arabic 1 2

b. Chinese 1 3

c. English 5 5

d. Russian 1 2

e. Spanish 2 4

aWS likelihood 1-5 from highly unlikely to highly likely; ES future demand level 1-5: very low to high demand


34

Table 24Summary of Significant Disparities between Employers and Workers

category Variables

Demand variables 10-year demand in field overall10-year demand in field in manufacturing sector10-year demand for education:Associate degree overallAssociate degree in education sectorMaster’s degree overallMaster’s degree in manufacturing sectorDoctoral degree overall

Skill variables Working independentlyTeamingWorking in a multicultural environmentFuture demand/proficiency in:ArabicChineseRussianSpanish

dIScuSSIon

Worker Survey Findings

More than two-thirds of the workers in the current study were age 40 or older; almost 40% was age 50 or older. Although the age distribution is skewed toward older workers, almost 45% of worker survey respondents indicated they are planning to continue working 10 or more years. The demographics of workers in the current study are consistent with U.S. BLS (2009) projections that growth in the female workforce will slightly outpace growth in the male workforce, and the proportion of workers age 55 and older will increase. According to Capelli (2003), up to 80% of baby boomers intend to work after typical retirement age.

The workers in the current study were predominantly White (82.97%), with small percentages self-identifying as African-American, Hispanic, Asian, biracial, or other categories. The results indicate a relationship between intent to continue working and age, with older workers more likely to work fewer years than younger workers, as to be expected. The workers of color were more likely to be job-seeking than White workers, suggesting that persons of color may be disproportionately affected by the current economic downturn. African-Americans were also most likely to be unsure about whether they would shift occupations. African-Americans and Asian/Pacific Islanders were over-represented in the corporate job-seeking sector; African-Americans were over-represented in the corporate and education job-seeking sectors.

The results revealed Asian/Pacific Islanders and Hispanics were more likely than respondents of other races to rate their skill level lower in working independently and producing high-quality work. Asian/Pacific Islanders were more likely to rate their skills lower in teaming. Hispanics were most likely to be proficient to conduct business in Spanish. African-Americans were most likely to be current enrollees in or recent graduates of a full-time postsecondary degree program attended while working full-time. African-Americans and Asian/Pacific Islanders were most likely to become proficient in the future in Arabic, Chinese, or Russian.

While most workers in the current study were never associated with the military, a small proportion was on active duty or honorably discharged from active duty in the military or the reserves. A chi square test of independence revealed those with current and past military affiliation were more likely to become proficient to conduct business in Arabic, Chinese, English, Russian, or Spanish. The results are not surprising, given the U.S. Department of Defense investment in training its personnel to be proficient in other languages. The Defense Language Institute trains more than 3,000 students annually; in 2005, 70% were studying Modern Standard Arabic, Korean and Chinese, and just under 1,000 were enrolled in the Institute’s three schools in Arabic Studies (Miles, 2005). The results indicated persons with a military background were more likely to perceive a moderate to high demand for a doctoral degree.


35

More than half of the workers had careers in government, education, health services, and business. More than three-quarters worked in occupations in the above sectors or in information, manufacturing, finance, human services, or science. Between 75% and 90% of positions in industries comprising 40% of all jobs in 2018 will require postsecondary education or training; these industries include information services, private education services, government and public education services, financial services, professional and business services, and healthcare services (Carnevale et al., 2010). About 90% of positions in four of the five fastest growing occupational clusters—healthcare professional and technical occupations, STEM occupations, community services and arts occupations, and education occupations—will require a postsecondary degree (Carnevale et al., 2010).

Workers reported high levels of educational attainment, with more than two-thirds having earned a postsecondary degree. More than half had earned a bachelor’s degree or higher, and almost one-fifth had earned a master’s or doctoral degree. The findings suggest that workers already possess the experience and postsecondary degrees that will be in demand in the career clusters in the next decade (Carnevale et al., 2010; Soares & Mazzeo, 2008).

While almost 40% of workers projected a moderate to high demand for an associate degree in their career cluster, almost a third projected very low or low demand. More than 75% projected a moderate or high 10-year demand for workers in their career cluster with a bachelor’s degree. More than 63% of workers projected a moderate or high 10-year demand for workers in their career cluster with a master’s degree, and more than 63% projected a moderate or high 10-year demand for workers with a doctoral degree. The results are consistent with Carnevale et al. (2010) and Soares and Mazzeo (2008).

The workers in the current study were highly experienced in their fields, with almost 57% reporting having at least 11 years of experience, and more than 36% reporting 20 years of experience in their current fields. However, more than two-fifths of workers reported not utilizing or under-utilizing their current skills or education, and 38% were either actively seeking new employment opportunities and/or seeking to move to a higher-level position. The findings may reflect the current economic climate in which fewer job opportunities exist at levels appropriate to job seekers. Almost 58% of workers indicated a moderate to high demand in the next 10 years for skilled and educated employees in their field, and 58.12% indicated a moderate to high demand in the next 10 years for skilled and educated employees in their career cluster, indicating optimism about future job opportunities.

The Society for Human Resource Management (2008) indicated that, according to employers, more than 64% of persons with a high school diploma and 40% of persons with a college degree lack the applied skills that are necessary for success in the workforce. However, more than 60% of workers rated themselves as highly or very highly skilled in thinking critically, solving complex problems, communicating clearly both in written and oral formats, and working in a multicultural environment. More than 70% rated themselves as highly or very highly skilled in teaming. More than 85% rated themselves as highly or very highly skilled in working independently and producing work that is consistently of high quality. The discrepancy between the skills self-ratings and the Society for Human Resource Management findings may be attributable to the years of experience of current Worker Survey respondents. Alternatively, the discrepancy may be due to Worker misjudgment of individual skill level.

Fewer than half of the workers perceived that working learners increase their value as employees to a moderate or high extent as a result of their higher education activities, although only 4.11% identified no increase in value. The results reflect the high educational attainment of the sample, as most survey respondents already earned a postsecondary degree. However, the findings may also indicate a need to publicize the availability and benefits of postsecondary degree programs for persons who want to earn a degree, but must remain in the workforce full-time. The traditional postsecondary education system is inadequate to meet the needs of workers who need a postsecondary credential to remain competitive in today’s job market and the emerging economy (Soares, 2009). The potential working learner pool includes 75 million Americans ages 18 to 64 who lack postsecondary credentials and who are not currently enrolled in postsecondary education (Soares, 2009). Increased educational attainment is associated with higher economic growth, productivity, income, civic participation, and quality of life (Osterman, 2008). Increased educational attainment is also associated with higher median earnings and lower rates of unemployment (U.S. BLS, 2010a).

Workers had low levels of current proficiency to conduct business in Arabic, Chinese, Russian, or Spanish. More than four-fifths of workers reported they were somewhat or highly unlikely to become proficient to conduct business in Arabic, Chinese, or Russian. Almost three-fifths were somewhat or highly unlikely to become proficient to conduct business in Spanish. The findings indicate a lack of awareness of current and emerging demand for these languages, as reflected in the Employer Survey findings discussed in the next section.


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Employer Survey Findings

More than three-quarters of Employers reported a moderate or high demand in their field. When asked to identify the three clusters with the highest demand within the Employer’s company, more than 30% identified business, management, and administration and information technology. More than 20% identified education and training; health science; marketing, sales, and service; and science, technology, engineering, and mathematics. The highest-demand clusters appear to reflect opportunities for workers with more advanced education, such as associate, bachelor’s, and master’s degrees. The results align with Carnevale et al. (2010), who found opportunities at the lower end of the educational attainment spectrum are diminishing and opportunities in occupations such as management and professional, education, healthcare professional and technical, STEM, and sales and office support are projected to increase through the year 2018.

Employers projected the highest 10-year demand in the top three career clusters for employees with bachelor’s and master’s degrees. The findings are consistent with Carnevale et al. (2010) and Soares and Mazzeo (2008). A significant relationship was found between industry sector and demand for doctoral degrees (X2(4, N = 419) = 14.64, p < .01), with the highest demand in the education and healthcare sectors. The results are not surprising given the doctoral-level credentials required for many occupations in both sectors.

Employers perceived both current and future difficulty in finding employees with the needed skills in seven skill areas. Employers typically conceive of employability in terms of work readiness—the knowledge, attributes, skills, understanding, and attitudes that new graduates need to contribute value to the organization following their hire (Mason et al., 2009). Employers reported that it is very difficult currently and will be very difficult in the future to find workers who can think critically, solve complex problems, and communicate clearly in written and oral formats. Employers reported having somewhat difficulty currently and projected future difficulty in finding employees who can work independently, work in a team, and work in a multicultural environment. Employers projected the current high difficulty in finding workers who can produce consistently high-quality work will decrease in the future. The findings are consistent with research by Alssid (2010), Choitz et al. (2010), Galagan (2010), Holzer and Lerman (2009), and the Society for Human Resource Management (2008).

Almost half of employers perceived a slight or some increase in the extent to which working learners increase their value as employees because of their higher education activities, and 45.82% indicated a moderate or high increase in value. Few employers (5.97%) indicated no increase in value from working learners’ higher education activities. Other research has revealed that employers recognize the value of work experience in preparing students to be effective quickly in the workplace (Harvey, Moon, & Geall, 1997; Lambert et al., 2001; Purcell, Pitcher, & Simm, 1999; Rover Group, 1998; Sewell, 2001). Given research that demonstrated clear positive effects of structured work experience and employer involvement in degree course design and delivery on employability (Mason et al., 2009), employers who are more involved in the design and delivery of postsecondary degree programs targeting working learners may place greater value on working learners’ higher education activities.

As expected, current and future demand was high for employees who are proficient in English and moderate for employees proficient in Spanish. Current and future demand for Spanish is moderate, reflecting the growth in the Hispanic population in the United States in the past 2 decades and projected growth through 2050 (U.S. Census Bureau, 2006). The levels of demand also reflect domestic business needs and the dominance of English as the language of global trade. However, to remain competitive and not fall behind in the Asian Pacific and Central and South American markets, U.S. companies will need employees who are proficient to conduct business in Spanish and in Asian languages (Zakaria, 2008).

Employers identified a current demand for Chinese at about twice the current demand for Arabic and three times the current demand for Russian. The finding reflects the growing importance of China in the global economy. Arabic and Chinese led in terms of the increase between the percentages of Employers who rated current and 10-year demand as moderate to high, followed by Russian and Spanish. According to the U.S. Department of Defense (Garamone, 2010), world languages currently in most demand include Arabic, Chinese, and Russian.

research Question 1

The first research question pertained to disparities between working learners and other workers in terms of demographic characteristics, skills, and perceptions of current and future workforce demands. The results provide partial support for an existing disparity between working learners and other workers in terms of multiple demographic characteristics, perceived 10-year workforce demands for employees with bachelor’s and doctoral degrees, and intent to become proficient in Spanish. The results do not support existing disparities in skills.


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Working learners were more likely than were other workers to be in the Education and Other current sector categories, perhaps reflecting the need for credentials in educational settings. Working learners were more likely to job-seek in the categories of Education, Healthcare, Manufacturing, and Other. Working learners were more likely to enroll in a postsecondary degree program and to participate in professional development or continuing education in the next 5 years. The findings reflect that working learners are motivated to build skills and earn credentials that will be valuable in the workplace in terms of hiring and remuneration (Soares, 2009). The findings also reflect the diversity of working learners in terms of prior educational experiences (Soares, 2009).

Working learners were more likely than were other workers to have fewer years of experience and be between the ages of 18 and 39. According to Soares (2009), working learners are typically older than traditional college students. The working learners in the sample were much more diverse than were other workers in terms of race or ethnicity. The findings may reflect fewer opportunities for persons of color to access traditional higher education programs. To earn a postsecondary degree, working learners need to either work and learn simultaneously or shift frequently between work and school (Soares, 2009).

The results revealed an existing disparity between working learners and other workers in terms of value of postsecondary pursuits and likelihood of shifting sector or occupation in the next 5 years. Not surprisingly, working learners perceived a higher increased value of working learner postsecondary pursuits and were more likely to shift their industry sector or occupation in the next 5 years than were other workers. The finding reflects motivation to build skills and earn credentials that will benefit working learners’ future employability and remuneration (Soares, 2009).

The findings support an existing disparity in perceived demand for employees with a bachelor’s or a doctoral degree. Working learners were more likely than other workers to rate the 10-year demand for employees with bachelor’s degrees as low or high, whereas workers were more likely to be unsure or rate the demand as very low. Projected future demand for employees with a bachelor’s degree is high (Carnevale et al., 2010; Soares, 2009; Soares & Mazzeo, 2008). Working learners also perceived a higher demand for the doctoral degree, compared to other workers.

Results revealed perceived skill level in thinking critically, solving complex problems, communicating clearly, working independently, teaming, working in a multicultural environment, and producing work of consistently high quality is independent of working learner status. Working learners did not rate their skill level as different from that of other workers, despite the different routes they took toward earning a postsecondary degree. Whether working learners increased their perceived level of skill as a result of their higher education activities is not known.

Working learners did not differ from other workers in terms of language proficiency, in contrast to Soares’ (2009) finding that working learners are more diverse than other workers in terms of English language literacy. Working learners were more likely to become proficient in Spanish than were other workers, perhaps reflecting needs within their industry sector or their emphasis on developing skills to improve their employability. Regardless of working learner status, workers’ skills and perceptions differed from those of employers in multiple domains.

research Question 2

The second research question pertained to disparities between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands. Taken together, the results support an existing disparity between employers and workers in demand variables: 10-year demand in field overall and in manufacturing, 10-year demand for employees with associate degrees overall and in education, 10-year demand for employees with master’s degrees overall and master’s degrees in manufacturing, and 10-year demand for doctoral degrees overall. The results also support an existing disparity between employers and workers in specific skill variables: working independently, teaming, working in a multicultural environment, and future demand versus proficiency in Arabic, Chinese, Russian, and Spanish.

The distribution of industry sectors is not equivalent in the two samples; manufacturing was under-represented in the worker survey and over-represented in the employer survey. Despite overall declines in employment in the manufacturing sector (U.S. BLS, 2009), U.S. manufacturers reported a severe shortage in the skilled manual trades (Shankel, 2010). Some significant differences between workers and employers may be an artifact of the differences in representation in manufacturing between the two samples.

Workers and employers did not differ in their perceptions of whether working learners increase their value as employees as a result of their postsecondary activities. Almost equal percentages of workers (45.82%) and employers (45.99%) reported a moderate or high increase in value. Given the projected need for employees with postsecondary degrees (Carnevale et al., 2010; Soares, 2009; Soares & Mazzeo, 2008), the percentages are likely to increase as more employers and workers recognize that pursuing a postsecondary degree while working full-time is a viable and necessary alternative for many of the 75 million Americans ages 18 to 64 who lack postsecondary credentials and are not currently enrolled in postsecondary education (Soares,2009).


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Workers were less likely than were employers to rate the demand in their field as moderate or high. The difference in 10-year demand in field was significant for manufacturing (Z = 2.51, p < .05). Employers perceived a lower demand for employees with associate degrees and doctoral degrees, and a higher demand for master’s and bachelor’s degrees as compared to workers, although the difference was not significant for the bachelor’s degree. Workers were much more likely to respond that they were unsure of the demand for each of these degrees. The difference in perceived 10-year demand for employees with an associate degree was significant for education (Z = 1.93, p < .10), and the perceived demand for the master’s degree was significant for manufacturing (Z = 1.799, p < .10).

Based on the current study findings, employers may be much more aware of future labor market demand by education than are workers. Almost half of new jobs created between 2006 and 2016 will require employees with postsecondary credentials (Soares & Mazzeo, 2008). The proportion of jobs requiring a postsecondary degree will increase from 59% to 63% in the next decade (Carnevale et al., 2010). In 2018, more than 75% of positions in industries comprising 40% of all jobs will require postsecondary education or training; these industries include information services, private education services, government and public education services, financial services, professional and business services, and healthcare services (Carnevale et al., 2010). In four of the five fastest growing occupational clusters (healthcare professional and technical occupations, STEM occupations, community services and arts occupations, and education occupations) about 90% of positions will require a postsecondary degree (Carnevale et al., 2010).

The findings indicate a disparity between workers’ current language proficiency and the percentage of employers who indicated moderate to high demand for employees who are proficient to conduct business in Arabic, Chinese, Russian, and Spanish; the largest disparity is in Spanish. The intent of workers to become proficient in Arabic, Chinese, Russian, or Spanish was significantly lower than the employers’ anticipated demand for the languages in the next 10 years. The results suggest that employers will continue to have difficulty meeting domestic needs for bilingual personnel to serve a rapidly growing Hispanic population (U.S. Census Bureau, 2006). A lack of proficiency in Chinese and Spanish will negatively affect the competitiveness of U.S. companies in Asia and in Central and South America (Zakaria, 2008).

Low levels of intent to become proficient in Arabic, Chinese, and Russian may be problematic for the U.S. Department of Defense, given current and projected demands for persons fluent in Chinese and Spanish languages (Garamone, 2010; Miles, 2005) and the increasing reliance on defense contractors (Schwartz, 2010). For example, contractors make up 54% of the U.S. Department of Defense workforce in Iraq and Afghanistan (Schwartz, 2010).

The perceived skill level of the workers in working independently, teaming, and working in a multicultural environment was significantly higher than the employers’ ease of finding workers with these skills. Although workers rated their skill level in working independently, teaming, and working in a multicultural environment as high or very high, employers still had some difficulty finding employees with the skills. The findings suggest the need for ongoing professional development and continuing education opportunities and increased motivation and incentives for employees to participate in the opportunities.

The disparity also reflects that a postsecondary degree is not sufficient for prospective workers to have the desired skill set. The finding is consistent with the Society for Human Resource Management (2008) finding that 40% of persons with a college degree lack the applied skills that are necessary for success in the workforce. The finding raises the question of employability for new graduates of postsecondary degree programs, whether traditional college students or working learners.

One way of defining employability is to consider graduates’ ability to obtain a position in and advance in their career (Mason et al., 2003, 2009). Another is the extent to which graduates develop abilities and experience, including critical thinking (Harvey, 2005). Increasing employability is a goal of obtaining a postsecondary degree. Leaders in higher education understand that skills needed to enhance graduate employability must include problem solving, literacy, and teamwork (Mason et al., 2009). When employers consider employability, they consider work readiness—the knowledge, attributes, skills, understanding, and attitudes that new graduates need in order to contribute value to the organization following their hire (Mason et al., 2009).

As the U.S. economy recovers from the recession of 2008-2009, employers will be searching for new hires to fill projected vacancies and new jobs. The new hires, whether new graduates of traditional postsecondary programs, current or recent working learners, or other workers, must have the skills and credentials employers will need to be competitive in the domestic and global marketplace. The disparities between workers and employers in the current study are consistent with other research supporting a current and growing skills and education gap in the workforce (Allsid, 2010; Carnevale et al., 2010; Choitz et al., 2010; Galagan, 2010; Holzer & Lerman, 2009; Osterman, 2008; Robert Half International, 2007; Shankel, 2010; Soares, 2009; Soares & Mazzeo, 2008).


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lIMITaTIonS

The internal validity or confidence in the results of the current study may be affected by several variables. One limitation may be the demographics of the workers and working learners sampled from the ZoomPanel populations. The sampling frame included panelists within MarketTools ZoomPanel. The ZoomPanel populations were fairly homogenous, with a low proportion of working learners and low representation of persons of color compared to the overall U.S. workforce. In 2005, persons of color comprised more than 30% of the U.S. workforce; the proportion is projected to rise to more than 48% by 2050 (Toossi, 2006). The sample was also skewed in terms of over-representation of women, who comprised 46.5% of the U.S. workforce in 2008 (U.S. Department of Labor, n.d.). The manufacturing sector was under-represented in the workers sample and over-represented for employers.

Another limitation of the currrent study may be self-selection bias, both in terms of who chooses to participate in ZoomPanel and who chose to participate in the current study. ZoomPanel participants may be motivated to participate in consumer research based on incentives offered. Employers, workers, and working learners who chose to participate in the current study may differ in unknown ways from other ZoomPanelists and from other employers and labor market participants in the general population. The differences may limit generalizability from the current study samples to the ZoomPanel and general employer and worker populations.

A third limitation is using different surveys and different scales to measure similar variables. Workers and employers were surveyed using two separate surveys. Comparisons were made between worker and employer responses. In some cases, there was difficulty comparing responses for certain variables due to the use of different scales to measure variables appropriate to the population for the two surveys and due to the use of separate surveys. The limitation is particularly important in terms of being able to compare the two samples by industry sector and occupational cluster.

A final limitation is the level of measurement in the surveys, as multiple categorical variables and ordinal response scales necessitated use of non-parametric tests. Non-parametric statistical tests are less robust than parametric tests. Thus, significant differences between workers and working learners and between workers and employers may have gone undetected.

IMPlIcaTIonS FoR FuRThER RESEaRch

The results did not support existing differences in perceived skill level between working learners and workers in any of the seven skills variables. Further research is needed to examine whether working learners perceive an increase in their levels of applied skills through their higher education and professional development activities, or if the similarities between the two groups is due to other factors. Working learners perceived a higher increased value of working learner postsecondary pursuits and were more likely to shift their industry sector or occupation in the next 5 years than were other workers. The findings suggest a need for additional research to explore the perceived benefits of postsecondary education for working learners in terms of their current and future employability.

Additional research is needed to understand the gap between workers’ perceptions of their applied skills and employers’ difficulty in finding workers with the needed skill sets. Future research could focus on how to bridge the gap between the skills workers acquire from their higher education pursuits, whether as traditional college students or working learners, and the skills they need to perform well on the job. Future research could focus on the ways employers can contribute to make higher education curricula and professional development offerings more responsive to their needs for highly skilled and educated employees. Further research is also needed on how employers can contribute to infusing development of high-level applied skills into postsecondary degree programs for working learners.


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SuMMaRy

A disparity between the education and skill set of current job seekers and employer needs is growing (Galagan, 2010). The United States is experiencing a current skills recession (Alssid, 2010) that is projected to worsen. Approximately two-thirds of job openings between 2008 and 2018 will involve replacing workers in existing positions (U.S. BLS, 2009), while the numbers of occupations requiring postsecondary education will increase (Soares, 2009; U.S. BLS, 2010). A shortage of employees with the needed skills, abilities, education, and knowledge will impede business growth and competition (Galagan, 2010).

The current quantitative descriptive study involved examining the extent to which disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands. Two research questions guided the current study: (a) To what extent do disparities exist between working learners and other workers in terms of demographic characteristics, skills, and perceptions of current and future workforce demands? (b) To what extent do disparities exist between employers and workers (including working learners) in terms of workers’ skills and current and future workforce demands?

Surveys were sent to a random sample of more than 30,000 employers and workers, including working learners, within the MarketTools ZoomPanel. To narrow the sampled panelists to only those who met the population criteria, screening questions were included at the beginning of each survey. Quotas were set to promote receiving responses from population members across specific industries: education, healthcare, manufacturing, corporate (including financial services, banking, technology, professional services, and marketing), and government/public/nonprofit. Completed surveys were received from 930 survey respondents meeting the population criteria: 511 workers, of which X were working learners, and 419 employers.

The results provide partial support for an existing disparity between working learners and other workers in terms of multiple demographic characteristics, perceived 10-year workforce demands for employees with bachelor’s and doctoral degrees, and intent to become proficient in Spanish. The results do not support existing disparities in skills. The results support an existing disparity between employers and workers in (a) demand variables, (b) specific skill variables, and (c) future demand versus proficiency in Arabic, Chinese, Russian, and Spanish.

While limitations exist that affected the extent of data analysis, current study findings indicate employers may be much more aware than workers of future labor market demand by education. Employers may continue to have difficulty meeting domestic needs for bilingual personnel to serve a rapidly growing Hispanic population (U.S. Census Bureau, 2006). Although workers rated their skill level in working independently, teaming, and working in a multicultural environment as high or very high, employers still had some difficulty finding employees with these skills. The findings suggest the need for ongoing professional development and continuing education opportunities for workers, and increased motivation and incentives for employees to participate in these offerings. A postsecondary degree may not be sufficient for prospective workers to have the skill set employers seek.

As the U.S. economy recovers from the recession, employers will seek to fill projected vacancies and new jobs with workers who have the necessary skills and credentials to do the job well. A well-prepared workforce is essential to support and maintain industry competitiveness, domestically and in the global economy. The disparities between workers and employers in the current study are consistent with other research supporting a current and growing skills and education gap in the workforce (Allsid, 2010; Carnevale et al., 2010; Choitz et al., 2010; Galagan, 2010; Holzer & Lerman, 2009; Osterman, 2008; Robert Half International, 2007; Shankel, 2010; Soares, 2009; Soares & Mazzeo, 2008). More research is needed to understand and identify solutions to this education and skills gap.


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Choitz, V., Soares, L., & Pleasants, R. (2010, March). A new national approach to career navigation for working learners. Washington, DC: Center for American Progress.

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aPPEndIx a: SuRVEy InSTRuMEnTS

Worker Survey

Question 1: [Consent to Participate Question]

Question 2: Which one of the following best describes your current situation?

m Employed and planning to work for at least the next 6-10 years

m Employed and planning to work for more than 10 years

m Employed but planning to retire in the next 3-5 years [Screen Out]

m Not employed but actively seeking employment opportunities

m Not employed and not actively seeking employment opportunities [Screen Out]

Question 3: an industry sector refers to a particular sector of the economy into which an organization falls. organizations can be categorized into an industry sector based on the nature of their products or services. Please select the industry sector in which you are currently working from among the following choices. (select one)

m Education

m Healthcare

m Manufacturing

m Corporate (for example: financial services/banking, technology, professional services, marketing)

m Government/public/non-profit (for example: law enforcement, military, security services)

m Not currently working

m Other

Question 4: Please select your current job status. (select all that apply)

m Unemployed

m Employed but not utilizing or under-utilizing your skills and/or education

m Actively seeking new employment opportunities

m Seeking to move to a higher-level position in current career


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Question 5: Please select the industry sector in which you are job-seeking from among the following choices. (select one)

m Education

m Healthcare

m Manufacturing

m Corporate (e.g., financial services, insurance, banking)

m Government/public/non-profit (e.g., law enforcement, military, security services)

m Not currently seeking a job

m Other

Question 6: How much demand do you believe there will be in the next 10 years for skilled and educated employees in your field? (select one)

m Very low demand

m Low demand

m Unsure

m Moderate demand

m High demand

Question 7: Career Clusters contain occupations in the same field of work that require similar skills. in which career

cluster does your occupation fit? (select one)

m Agriculture, Food, and Natural Resources

m Architecture and Construction

m Arts, Audio-Video Technology and Communications

m Business, Management and Administration

m Education and Training

m Finance

m Government and Public Administration

m Health Science

m Hospitality and Tourism

m Human Services

m Information Technology

m Law, Public Safety, Corrections, and Security

m Manufacturing

m Marketing, Sales and Service

m Science, Technology, Engineering, and Mathematics

m Transportation, Distribution and Logistics

m Not currently employed

m Other


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Question 8: How much demand do you believe there will be in the next 10 years for skilled and educated employees in your career cluster? (select one)

m Very low demand

m Low demand

m Unsure

m Moderate demand

m High demand

Question 9: Please indicate how much demand you believe there will be in the next 10 years for employees with the following levels of education in your career cluster. (select one)

Very low demand low demand unsure Moderate demand high demand

Associate Degree ? ? ? ? ?

Bachelor’s Degree ? ? ? ? ?

Master’s Degree ? ? ? ? ?

Doctoral Degree ? ? ? ? ?

Question 10: Please indicate your skill level in each of the following areas.

Slightly skilled Somewhat skilled Moderately skilled highly skilled Very highly skilled

Thinking critically ? ? ? ? ?

Solving complex problems ? ? ? ? ?

Communicating clearly in both written and oral formats ? ? ? ? ?

Working independently ? ? ? ? ?

Working in a multicultural climate ? ? ? ? ?

Producing work that is consistently of high quality ? ? ? ? ?

Question 11: Please identify your highest level of educational attainment. (select one)

m No High School

m Some High School

m High School Diploma or GED

m Some College

m Associate Degree

m Bachelor’s Degree

m Master’s Degree

m Doctoral degree


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Questions 12: What is your educational status? (select one)

m Currently enrolled in a postsecondary degree program while working full-time

m A recent graduate (2009 or later) of a postsecondary degree program you attended while working full-time

m Both of the above

m Neither of the above

Question 13: Please indicate how likely you are to do the following.

highly unlikely Somewhat unlikely unsure Somewhat likely highly likely

Enroll in a postsecondary college degree program in the next 5 years to increase your employability

? ? ? ? ?

Participate in non-credit professional development or continuing education courses in the next 5 years to increase your employability

? ? ? ? ?

Question 14: a working learner is a full-time employee who is enrolled in a non-traditional degree program to obtain a postsecondary college degree in a range of degree levels from associate to doctoral. To what extent do working learners increase their value as employees as a result of their higher education activities? (select one)

m No increase in value

m Slight increase in value

m Some increase in value

m Moderate increase in value

m High increase in value

Question 15: How likely are you to move into a different occupation or industry in the next 5 years? (select one)

m Highly unlikely

m Somewhat unlikely

m Unsure

m Somewhat likely

m Highly likely

Question 16: Please identify how many years of experience you have in your current field. (select one)

m <5

m 6-10

m 11-19

m 20 or more


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Question 17: Please identify your age group. (select one)

m 18-22

m 23-29

m 30-39

m 40-49

m 50-64

m 65-69

m 70 or older

m I prefer not to answer

Question 18: Please describe your race/ethnicity. (Check all that apply)

m African-American, non-Hispanic

m African-Caribbean

m Asian/Pacific Islander

m Bi- or multi-racial

m Hispanic

m Native American

m White, non-Hispanic


m Other

Question 19: Please identify your gender:

m Male

m Female


Question 20: Which one of the following best describes your current status? (select one)

m Active duty military

m Honorably discharged from active duty

m Retired military

m Reservist

m Honorably discharged reservist

m I have never been associated with the military


m Other, please specify


48

Question 21: Please identify the languages in which you are proficient to conduct business. (Check all that apply)

m Arabic

m Chinese

m English

m Russian

m Spanish

m Other

Question 22: Please indicate how likely you are to become proficient in the next 10 years in the following languages in order to increase your employability. (Please select na if you are already proficient in the language

highly unlikely

Somewhat unlikely unsure

Somewhat likely highly likely na

Arabic ? ? ? ? ? ?

Chinese ? ? ? ? ? ?

English ? ? ? ? ? ?

Russian ? ? ? ? ? ?

Spanish ? ? ? ? ? ?


49

WoRkIng lEaRnER BooST SuRVEy


Question 1: Which one of the following best describes your current situation? (select one)

m Employed and planning to work for at least the next 6-10 years

m Employed and planning to work for more than 10 years

m Employed but planning to retire in the next 3-5 years. [Screen Out]

m Not employed but actively seeking employment opportunities

m Not employed and not actively seeking employment opportunities [Screen Out]

Question 2: Do any of the following apply to you?

- Currently enrolled in a postsecondary degree program while working full-time

-A recent graduate (2009 or later) of a postsecondary degree program you attended while working full-time

-Considering returning to school in the next 2 years as a full-time student and seeking a job

-Considering returning to school in the next 2 years while working full-time

m Yes

m No [Screen Out]

Question 4: an industry sector refers to a particular sector of the economy into which an organization falls. organizations can be categorized into an industry sector based on the nature of their products or services. Please select the industry sector in which you are currently working from among the following choices. (select one)

m Education

m Healthcare

m Manufacturing

m Corporate (for example: financial services/banking, technology, professional services, marketing)

m Government/public/non-profit (for example: law enforcement, military, security services)

m Not currently working

m Other


50

Question 5: Please select your current job status. (select all that apply)

m Unemployed

m Employed but not utilizing or under-utilizing your skills and/or education

m Actively seeking new employment opportunities

m Seeking to move to a higher-level position in current career

Question 6: Please select the industry sector in which you are job-seeking from among the following choices. (select one)

m Education

m Healthcare

m Manufacturing

m Corporate (e.g. financial services, insurance, banking)


m Not currently seeking a job

m Other


m Very low demand

m Low demand

m Unsure

m Moderate demand

m High demand


51

Question 8: Career Clusters contain occupations in the same field of work that require similar skills. in which career cluster does your occupation fit? (select one)






m Finance


m Health Science


m Human Services



m Manufacturing




m Not currently employed

m Other

Question 9: How much demand do you believe there will be in the next 10 years for skilled and educated employees in your career cluster? (select one)

m Very low demand

m Low demand

m Unsure

m Moderate demand

m High demand


52

Question 10: Please indicate how much demand you believe there will be in the next 10 years for employees with the following levels of education in your career cluster. (select one)



Bachelor’s Degree

? ? ? ? ?


Doctoral Degree ? ? ? ? ?

Question 11: Please indicate your skill level in each of the following areas.

Slightly skilled Somewhat skilled Moderately skilled highly skilled Very highly skilled


Solving complex problems

? ? ? ? ?

Communicating clearly in both written and oral formats

? ? ? ? ?

Working independently ? ? ? ? ?

Working in a multicultural climate

? ? ? ? ?

Producing work that is consistently of high quality

? ? ? ? ?

Question 12: Please identify your highest level of educational attainment. (select one)

m No High School

m Some High School

m High School Diploma or GED

m Some College

m Associate Degree

m Bachelor’s Degree

m Master’s Degree

m Doctoral degree


53

Question 13: What is your educational status? (select one)

m Currently enrolled in a postsecondary degree program while working full-time

m A recent graduate (2009 or later) of a postsecondary degree program you attended while working full-time

m Both of the above

m Neither of the above

Question 14: Please indicate how likely you are to do the following.

highly unlikely

Somewhat unlikely

unsure Somewhat likely highly likely

Enroll in a postsecondary college degree program in the next 5 years to increase your employability

? ? ? ? ?

Participate in non-credit professional development or continuing education courses in the next 5 years to increase your employability

? ? ? ? ?

Question 15: a working learner is a full-time employee who is enrolled in a non-traditional degree program to obtain a post-secondary college degree in a range of degree levels from associate to doctoral. To what extent do working learners increase their value as employees as a result of their higher education activities? (select one)






Question 16: How likely are you to move into a different occupation or industry in the next 5 years? (select one)

m Highly unlikely

m Somewhat unlikely

m Unsure

m Somewhat likely

m Highly likely


54

Question 17: Please identify how many years of experience you have in your current field. (select one)

m <5

m 6-10

m 11-19

m 20 or more

Question 18: Please identify your age group. (select one)

m 18-22

m 23-29

m 30-39

m 40-49

m 50-64

m 65-69

m 70 or older


Question 19: Please describe your race/ethnicity. (Check all that apply)

m African-American, non-Hispanic

m African-Caribbean

m Asian/Pacific Islander

m Bi- or multi-racial

m Hispanic

m Native American

m White, non-Hispanic


m Other

Question 20: Please identify your gender:

m Male

m Female



55

Question 21: Which one of the following best describes your current status? (select one)

m Active duty military

m Honorably discharged from active duty

m Retired military

m Reservist

m Honorably discharged reservist

m I have never been associated with the military


m Other, please specify

Question 22: Please identify the languages in which you are proficient to conduct business. (Check all that apply)

m Arabic

m Chinese

m English

m Russian

m Spanish

m Other

Question 23: Please indicate how likely you are to become proficient in the next 10 years in the following languages in order to increase your employability. (Please select na if you are already proficient in the language).

highly unlikely

Somewhat unlikely unsure

Somewhat likely

highly likely na

Arabic ? ? ? ? ? ?

Chinese ? ? ? ? ? ?

English ? ? ? ? ? ?

Russian ? ? ? ? ? ?

Spanish ? ? ? ? ? ?


56

EMPloyER SuRVEy


Question 2: are you currently employed at the manager or higher level and regularly involved in employee recruitment

efforts and hiring decisions?

m Yes

m No [Screen Out]

Question 3: Please select your industry sector from among the following choices. (select one)

m Education

m Healthcare

m Manufacturing

m Corporate (for example, financial services/banking, technology, professional services, marketing)


m None of the above [Screen Out]

Question 4: Please identify your company size by number of employees. (select one)

m Small (fewer than 50 employees)

m Medium (50 to 499 employees)

m Large (500 or more employees)


m Very low demand

m Low demand

m Unsure

m Moderate demand

m High demand


57

Question 6: Career clusters contain occupations in the same field of work that require similar skills. Please select the three career clusters in which there is currently the highest demand in your company for employees.






m Finance


m Health Science


m Human Services



m Manufacturing




Question 7: Please indicate how much demand you believe there will be in the next 10 years for each of the following:

Very low demand

low demand unsure Moderate demand high demand

Skilled and educated employees in the current highest demand career cluster in your organization

? ? ? ? ?

Skilled and educated employees in the current second highest demand career cluster in your organization

? ? ? ? ?

Skilled and educated employees in the current third highest demand career cluster in your organization

? ? ? ? ?

Question 8: Please indicate how much demand you believe there will be in the next 10 years for employees with the following levels of education in the three selected career clusters.



Bachelor’s Degree ? ? ? ? ?


Doctoral Degree? ? ? ? ?


58

Question 9: Please indicate how difficult it is to find workers with needed skills in the following areas.

not difficult Slightly difficult Somewhat difficult Very difficult Extremely difficult



? ? ? ? ?


? ? ? ? ?

Working independently

? ? ? ? ?


? ? ? ? ?

Producing work that is consistently of high quality ? ? ? ? ?

Question 10: Please indicate how difficult you think it will be in the next 10 years to find workers with needed skills in the following areas.

not difficult Slightly difficult Somewhat difficult Very difficult Extremely difficult



? ? ? ? ?


? ? ? ? ?

Working independently

? ? ? ? ?


? ? ? ? ?

Producing work that is consistently of high quality

? ? ? ? ?


59

Question 11: a working learner is a full-time employee who is enrolled in a non-traditional degree program to obtain a post-secondary college degree in a range of degree levels from associate to doctoral. To what extent do working learners increase their value as employees as a result of their higher education activities? (select one)






Question 12: Please indicate how much demand currently exists for employees who have the proficiency to conduct business in the following languages.


Arabic ? ? ? ? ?

Chinese ? ? ? ? ?

English ? ? ? ? ?

Russian ? ? ? ? ?

Spanish ? ? ? ? ?

Question 13: Please indicate how much demand you think there will be in the next 10 years for employees who have the proficiency to conduct business in the following languages.


Arabic ? ? ? ? ?

Chinese ? ? ? ? ?

English ? ? ? ? ?

Russian ? ? ? ? ?

Spanish ? ? ? ? ?


60

aPPEndIx B: WoRkER dEMogRaPhIcS

Workers’ Gender (n = 511)

gender n %

Female 348 68.1

Male 162 31.7

No Answer 1 0.20

Workers’ age (n = 511)

age n %

18-22 8 1.57

23-29 51 9.98

30-39 105 20.55

40-49 147 28.77

50-64 189 36.99

65-69 7 1.37

70+ 3 0.59

No Answer 1 0.20

Workers’ race/ethnicity (n = 511)

Race n %

African-American, non-Hispanic 34 6.65

African-Caribbean 3 0.59

Asian/Pacific Islander 19 3.72

Bi- or multi-racial 6 1.17

Hispanic 11 2.15

Native American 4 0.78

White, non-Hispanic 424 82.97

I prefer not to answer 6 1.17

Other 4 0.78

Workers’ military status (n = 511)

Military Status n %

Active duty military 1 0.2

Honorably discharged from active duty 31 6.07

Honorably discharged reservist 5 0.98

Never associated with the military 443 86.69


Other, please specify 8 1.57

Retired military 9 1.76


61

Worker’s language Proficiency (n = 511)

language n %

Arabic 0 0.00

Chinese 2 0.39

English 468 91.59

Russian 2 0.39

Spanish 14 2.74

Other 25 4.89

Current Work situation (n = 511)

Situation n (%)

Plan to work 6-10 yrs 227 (44.42)

Plan to work 10+ yrs 229 (44.81)

Not employed, but seeking 55 (10.76)

industry sector of Current Job Held (n = 511)

Sector n (%)

Corporate 86 (16.83)

Education 85 (16.83)

Government 92 (18.00)

Healthcare 104 (20.35)

Manufacturing 44 (8.61)

Not Employed 41 (8.02)

Other 59 (11.55)


62

Career Cluster of Current occupation (n = 511)

career cluster n %

Agriculture 6 1.17

Architect 2 0.39

Arts 10 1.96

Business 52 10.18

Education 72 14.09

Finance 25 4.89

Government 73 14.29

Health Sciences 62 12.13

Hospitality 15 2.94

Human Services 22 4.31

Information 33 6.46

Law 9 1.76

Manufacturing 34 6.65

Marketing 16 3.13

Science 22 4.31

Transport 10 1.96

Not Employed 16 3.13

Other 32 6.26

years of experience (n = 511)

years n %

5+ 87 17.03

6-10 134 26.22

11-19 106 20.74

20 + 184 36.01

Highest level of educational attainment

Educational attainment n %

No HS 0 0.0

Some HS 1 0.20

HS diploma or GED 56 10.96

Some college 109 21.33

Associate degree 75 14.68

Bachelor’s degree 169 33.07

Master’s degree 82 16.05

Doctoral degree 19 3.72


63

Working learners (n = 511)

Status n %

Working learner 100 19.96

Not a working learner 411 80.43

Current Working learner status (n = 511)

Status n %

Currently enrolled working learner 38 7.44

Graduated 2009 or later as a working learner 21 4.11

Both of the above 6 1.17

Neither of the above 446 87.28

aPPEndIx c: EMPloyER dEMogRaPhIcS

employer industry sector (n = 419)

Industry Sector n %

Corporate 71 16.95

Education 79 18.85

Government/public/non-profit 76 18.14

Healthcare 77 18.38


employer Company size (n = 419)

company Size n %

Small (fewer than 50 employees) 127 30.31

Medium (50 to 499 employees) 109 26.01

Large (500 or more employees) 183 43.68


64

aPPEndIx d: BaSIc FREQuEncy dISTRIBuTIonS and MEaSuRES oF cEnTRal TEndEncy

(WhERE aPPRoPRIaTE)

coMBInEd WoRkER/WoRkIng lEaRnER SuRVEyS: FREQuEncIES FoR all QuESTIonS

Ws Question 2: Current situation

n %

Employed, work 6-10 years 227 44.42

Employed, work 10+ years 229 44.81

Not employed, actively seeking employment 55 10.76

Total 511 100.00

Ws Question 3: industry sector of Current Job

n %

Corporate 86 16.83

Education 85 16.83

Government/public/nonprofit 92 18.00

Healthcare 104 20.35


Not currently working 41 8.02

Other 59 11.55

Total 511 100.00

Ws Question 4: status of Current Job (check all that apply)

n %

Unemployed 36 7.05

Employed but not utilizing or under-utilizing skills and/or education 222 43.44

Actively seeking new employment opportunities 72 14.10

Seeking to move to a higher-level position in current career 125 24.46

No response 56 10.96

Total 511 100.00


65

Ws QuesTion 5: Job seekinG seCTor

n %

Corporate 73 14.29

Education 58 11.35

Government/public/nonprofit 72 14.09

Healthcare 76 14.87


Not currently employed 157 30.72

Other 49 9.59

Total 511 100.00

Ws Question 6: Demand for skilled Workers in field in the next 10 years

n % Median Mode Range

Very low (1) 12 2.35 4 3 1.5

Low (2) 31 6.07

Unsure (3) 173 33.86

Moderate (4) 164 32.09

High (5) 131 25.64

Total 511 100.00

Ws Q7a: Career Cluster of Current occupation

n %

Agriculture, food, and natural resources 6 1.17

Architecture and construction 2 0.39

Arts, audio-video technology and communications 10 1.96

Business, management and administration 52 10.18

Education and training 72 14.09

Finance 25 4.89

Government and public administration 73 14.29

Health science 62 12.13

Hospitality and tourism 15 2.94

Human services 22 4.31

Information technology 33 6.46

Law, public safety, corrections, and security 9 1.76


Marketing, sales and service 16 3.13

Science, technology, engineering, and mathematics 22 4.31

Transportation, distribution and logistics 10 1.96

Not currently employed 16 3.13

Other 32 6.26

Total 511 100.00


66

Ws Question 8: Demand in next 10 years for skilled and educated employees in your Career Cluster


Very low (1) 6 1.17 4 3 1-5

Low (2) 37 7.24

Unsure (3) 171 33.46

Moderate (4) 165 32.29

High (5) 132 25.83

Total 511 100.00

Ws Question 9a: Demand by educational level – associate Degree


Very low (1) 66 12.92 3 4 1-5

Low (2) 98 19.18

Unsure (3) 150 29.35

Moderate (4) 158 30.92

High (5) 39 7.63

Total 511 100.00

Ws Question 9b: Demand by educational level – bachelor’s Degree


Very low (1) 18 3.52 4 4 1-5

Low (2) 45 8.81

Unsure (3) 113 22.11

Moderate (4) 214 41.88

High (5) 121 23.68

Total 511 100.00

Ws Question 9c: Demand by educational level – master’s Degree


Very low (1) 32 6.26 4 4 1-5

Low (2) 35 6.85

Unsure (3) 121 23.68

Moderate (4) 167 32.68

High (5) 156 30.53

Total 511 100.00


67

Ws Question 9d: Demand by educational level – Doctoral Degree


Very low (1) 55 10.76 3 3 1-5

Low (2) 68 13.31

Unsure (3) 150 29.35

Moderate (4) 120 23.48

High (5) 118 23.09

Total 511 100.00

Ws Question 10a: skill level – Thinking Critically


Slightly skilled (1) 6 1.17 4 4 1-5

Somewhat skilled (2) 21 4.11

Moderately skilled (3) 150 29.35

Highly skilled (4) 199 38.94

Very highly skilled (5) 135 26.42

Total 511 100.00

Ws Question 10b: skill level – solving Complex Problems







Total 511 100.00

Ws Question 10c: skill level – Communicating Clearly in Writing and orally







Total 511 100.00


68

Ws Question 10d: skill level – Working independently







Total 511 100.00

Ws Question 10e: skill level – Teaming







Total 511 100.00

Ws Question 10f: skill level – Working in a multicultural Climate







Total 511 100.00

Ws Question 10g: skill level – Consistently Producing High Quality Work







Total 511 100.00


69

Ws Question 11: Highest level of education attained

n %

No high school 0 0.00

Some high school 1 0.20

High school diploma or GED 56 10.96

Some college 109 21.33

Associate degree 75 14.68

Bachelor’s degree 169 33.07

Master’s degree 82 16.05

Doctoral degree 19 3.72

Total 511 100.00

Ws Question 12: Current educational status

n %

Currently enrolled in a post-secondary degree program while working F/T

38 7.44

A recent graduate (2009 or later) of a PS degree program attended while working F/T

21 4.11

Both of the above 6 1.17

Neither of the above 446 87.28

Total 511 100.00

Ws Question 13a: likelihood of enrolling in a Postsecondary Degree Program in the next 5 years to increase employability


Highly unlikely (1) 226 44.23 2 1 1-5

Somewhat unlikely (2) 60 11.74

Unsure (3) 102 19.96

Somewhat likely (4) 74 14.48

Highly likely (5) 49 9.59

Total 511 100.00

Ws Question 13b: likelihood of Participating in a non-Credit Professional Development or Continuing education Courses in the next 5 years to increase employability


Highly unlikely (1) 124 24.27 3 1 1-5


Unsure (3) 120 23.48



Total 511 100.00


70

Ws Question 14: Working learners’ increased value as a result of Higher ed


No increase in value (1) 21 4.11 3 3 1-5

Slight increase in value (2) 76 14.87

Some increase in value (3) 179 35.03

Moderate increase in value (4) 147 28.77

High increase in value (5) 88 17.22

Total 511 100.00

Ws Question 15: move into a Different occupation or industry in the next 5 years


Highly unlikely (1) 159 31.12 2 1 1-5


Unsure (3) 143 21.98



Total 511 100.00

Ws Question 16: years of experience in Current field

n %

< 5 87 17.03

6 – 10 134 26.22

11 – 19 106 20.74

20 or more 184 36.01

Total 511 100.00

Ws Question 17: age

age n %

18-22 8 1.57

23-29 51 9.98

30-39 105 20.55

40-49 147 28.77

50-64 189 36.99

65-69 7 1.37

70+ 3 0.59

No Answer 1 0.20

Total 511 100.00


71

Ws Question 18: race/ethnicity

Race n %

African-American, non-Hispanic 34 6.65

African-Caribbean 3 0.59

Asian/Pacific Islander 19 3.72

Bi- or multi-racial 6 1.17

Hispanic 11 2.15

Native American 4 0.78

White, non-Hispanic 424 82.97


Other 4 0.78

Total 511 100.00

Ws Question 19: Gender

gender n %

Female 348 68.1

Male 162 31.7

No answer 1 0.20

Total 511 100.00

Ws Question 20: military status

Military Status n %

Active duty military 1 0.2

Honorably discharged from active duty 31 6.07

Honorably discharged reservist 5 0.98

Never associated with the military 443 86.69


Other, please specify 8 1.57

Retired military 9 1.76

Total 511 100.00

Ws Question 21: language Proficiency to Conduct business

language n %

Arabic 0 0.00

Chinese 2 0.39

English 468 91.59

Russian 2 0.39

Spanish 14 2.74

Other 25 4.89

Total 511 100.00


72

Ws Question 22a: likelihood of becoming Proficient in arabic in the next 10 years


Highly unlikely (1) 396 77.50 1 1 1-6


Unsure (3) 20 3.91



N/A (already fluent)(6) 48 9.39

Total 511 100.00

Ws Question 22b: likelihood of becoming Proficient in Chinese in the next 10 years


Highly unlikely (1) 383 74.95 1 1 1-6


Unsure (3) 25 4.89




Total 511 100.00

Ws Question 22c: likelihood of becoming Proficient in english in the next 10 years


Highly unlikely (1) 47 9.20 5 5 1-6


Unsure (3) 15 2.94




Total 511 100.00

Ws Question 22d: likelihood of becoming Proficient in russian in the next 10 years


Highly unlikely (1) 394 77.10 1 1 1-6


Unsure (3) 22 4.31



N/A (already fluent) 48 9.39

Total 511 100.00


73

Ws Question 22e: likelihood of becoming Proficient in spanish in the next 10 years


Highly unlikely (1) 247 48.34 2 1 1-6


Unsure (3) 63 12.33




Total 511 100.00

EMPloyER SuRVEy: FREQuEncIES FoR all QuESTIonS

es Question 3: industry sector

Industry sector n %

Corporate 71 16.95

Education 79 18.85

Government/public/non-profit 76 18.14

Healthcare 77 18.38


Total 419 100.00

es Question 4: Company size

company size n %

Small (fewer than 50 employees) 127 30.31

Medium (50 to 499 employees) 109 26.01

Large (500 or more employees) 183 43.68

Total 419 100.00

es Question 5: Demand for skilled Workers in field in the next 10 years


Very low (1) 15 3.58 4 4 1-5

Low (2) 30 7.16

Unsure (3) 55 13.13

Moderate (4) 189 45.11

High (5) 130 31.03

Total 419 100.00


74

es Q6: Top Three Highest Demand Career Clusters in Company

n %

Agriculture, food, and natural resources 6 1.43

Architecture and construction 9 2.15

Arts, audio-video technology and communications 22 5.25

Business, management and administration 146 34.84

Education and training 106 25.30

Finance 69 16.47

Government and public administration 64 15.27

Health science 93 22.20

Hospitality and tourism 8 1.91

Human services 68 16.23

Information technology 130 31.03

Law, public safety, corrections, and security 32 7.64


Marketing, sales and service 93 22.20

Science, technology, engineering, and mathematics 97 23.15

Transportation, distribution and logistics 25 5.97

es Question 7: Demand by Career Cluster

Very low demand

n (%)

low demand

n (%)

unsuren (%)

Moderate demand

n (%)

highdemand

n (%)Median

a. Skilled and educated employees in the current highest demand career cluster in your organization

7 (1.67) 34 (8.10) 80 (19.10) 183 (43.7) 115 (27.45) 4

b. Skilled and educated employees in the current second highest demand career cluster in your organization

6 (1.43) 33 (7.88) 114 (27.2) 187 (44.63) 79 (18.85) 4

c. Skilled and educated employees in the current third highest demand career cluster in your organization

8 (1.90) 41 (9.79) 140 (33.41) 160 (38.19) 70 (16.71) 4

es Question 8a: 10-year Demand by educational level – associate Degree


Very low (1) 50 11.93 3 4 1-5

Low (2) 120 28.64

Unsure (3) 84 20.05

Moderate (4) 126 30.07

High (5) 39 9.31

Total 419 100.00


75

es Question 8b: 10-year Demand by educational level – bachelor’s Degree


Very low (1) 16 3.82 4 4 1-5

Low (2) 39 9.31

Unsure (3) 72 17.18

Moderate (4) 175 41.77

High (5) 117 27.92

Total 419 100.00

es Question 8c: 10-year Demand by educational level – master’s Degree


Very low (1) 32 7.64 4 4 1-5

Low (2) 49 11.69

Unsure (3) 54 12.89

Moderate (4) 148 35.32

High (5) 136 32.46

Total 419 100.00

es Question 8d: 10-year Demand by educational level – Doctoral Degree


Very low (1) 74 17.66 3 4 1-5

Low (2) 65 15.51

Unsure (3) 82 19.57

Moderate (4) 118 28.16

High (5) 80 19.09

Total 419 100.00

es Question 9a: Difficulty in finding skilled Workers – Thinking Critically


Not difficult (1) 9 2.15 4 4 1-5

Slightly difficult (2) 47 11.22

Somewhat difficult (3) 150 35.80

Very difficult (4) 154 36.75

Extremely difficult (5) 59 14.08

Total 419 100.00


76

Ws Question 9b: Difficulty in finding skilled Workers – solving Complex Problems


Not difficult (1) 7 1.67 4 4 1-5





Total 419 100.00

es Question 9c: Difficulty in finding skilled Workers – Communicating Clearly in Writing and orally


Not difficult (1) 13 3.10 4 4 1-5





Total 419 100.00

es Question 9d: Difficulty in finding skilled Workers – Working independently


Not difficult (1) 26 6.21 3 3 1-5





Total 419 100.00

es Question 9e: Difficulty in finding skilled Workers – Teaming


Not difficult (1) 33 7.88 3 3 1-5





Total 419 100.00


77

es Question 9f: Difficulty in finding skilled Workers – Working in a multicultural Climate


Not difficult (1) 59 14.08 3 2 1-5





Total 419 100.00

es Question 9g: Difficulty in finding skilled Workers – Consistently Producing High Quality Work


Not difficult (1) 11 2.63 4 4 1-5





Total 419 100.00

es Question 10a: Difficulty in finding skilled Workers in the next 10 years – Thinking Critically


Not difficult (1) 10 2.39 4 4 1-5





Total 419 100.00

es Question 10b: Difficulty in finding skilled Workers in the next 10 years – solving Complex Problems


Not difficult (1) 10 2.39 4 4 1-5





Total 419 100.00


78

es Question 10c: Difficulty in finding skilled Workers in the next 10 years – Communicating Clearly in Writing and orally


Not difficult (1) 13 3.10 4 4 1-5





Total 419 100.00

es Question 10d: Difficulty in finding skilled Workers in the next 10 years – Working independently


Not difficult (1) 30 7.16 3 3 1-5





Total 419 100.00

es Question 10e: Difficulty in finding skilled Workers in the next 10 years – Teaming


Not difficult (1) 32 7.64 3 3 1-5





Total 419 100.00

es Question 10f: Difficulty in finding skilled Workers in the next 10 years – Working in a multicultural Climate


Not difficult (1) 76 18.14 3 3 1-5





Total 419 100.00


79

es Question 10g: Difficulty in finding skilled Workers in the next 10 years – Consistently Producing High Quality Work


Not difficult (1) 11 2.63 3 3 1-5





Total 419 100.00

es Question 11: Working learner value


No increase in value (1) 25 5.97 3 3 1-5

Slight increase in value (2) 68 16.23

Some increase in value (3) 134 31.98

Moderate increase in value (4) 110 26.25

High increase in value (5) 82 19.57

Total 419 100.00%

es Question 12a: Current Demand for language - arabic


Very low (1) 198 47.26 2 1 1-5

Low (2) 69 16.47

Unsure (3) 81 19.33

Moderate (4) 44 10.50

High (5) 27 6.44

Total 419 100.00

es Question 12b: Current Demand for language - Chinese


Very low (1) 138 32.94 2 1 1-5

Low (2) 76 18.14

Unsure (3) 62 14.80

Moderate (4) 98 23.39

High (5) 45 10.74

Total 419 100.00


80

es Question 12c: Current Demand for language - english


Very low (1) 6 1.43 5 5 1-5

Low (2) 8 1.91

Unsure (3) 34 8.11

Moderate (4) 83 19.81

High (5) 288 68.74

Total 419 100.00

es Question 12d: Current Demand for language - russian


Very low (1) 175 41.77 2 1 1-5

Low (2) 95 22.67

Unsure (3) 99 23.63

Moderate (4) 38 9.07

High (5) 12 2.86

Total 419 100.00

es Question 12e: Current Demand for language - spanish


Very low (1) 47 11.22 4 4 1-5

Low (2) 42 10.02

Unsure (3) 58 13.84

Moderate (4) 155 36.99

High (5) 117 27.92

Total 419 100.00

es Question 13a: 10-year Demand for language - arabic


Very low (1) 126 30.07 2 1 1-5

Low (2) 87 20.76

Unsure (3) 101 24.11

Moderate (4) 68 16.23

High (5) 37 8.83

Total 419 100.00


81

es Question 13b: 10-year Demand for language - Chinese


Very low (1) 89 21.24 3 2, 3 1-5

Low (2) 52 12.41

Unsure (3) 102 24.34

Moderate (4) 102 24.34

High (5) 74 17.66

Total 419 100.00

es Question 13c: 10-year Demand for language - english


Very low (1) 3 0.72 5 5 1-5

Low (2) 11 2.63

Unsure (3) 44 10.50

Moderate (4) 84 20.05

High (5) 277 66.11

Total 419 100.00

es Question 13d: 10-year Demand for language - russian


Very low (1) 135 32.22 2 1 1-5

Low (2) 90 21.48

Unsure (3) 122 29.12

Moderate (4) 56 13.37

High (5) 16 3.82

Total 419 100.00

es Question 13e: 10-year Demand for language - spanish


Very low (1) 32 7.64 4 5 1-5

Low (2) 30 7.16

Unsure (3) 65 15.51

Moderate (4) 133 31.74

High (5) 159 37.95

Total 419 100.00


82

aPPEndIx E: WoRkER WIThIn gRouP cRoSS-TaBulaTIon TaBlES

chI SQuaRE dEMogRaPhIc RESulTS SIgnIFIcanT aT P < .05 and < P = .10*

Worker age by Questions:

age by Question 2: Current Work status (intent to Continue Working)

agePlan to work 6-10 years

Plan to work 10+ years

not employed but seeking

n (%) 10+ yrs but seeking x2 p value

18-22 1 (0.44) 5 (2.18) 2 (3.64) < .001

23-29 20 (8.81) 27 (11.79) 4 (7.27)

30-39 35 (15.42) 65 (28.38) 5 (9.09)

40-49 57 (25.11) 75 (32.75) 15 (27.27)

50-64 109 (48.02) 55 (24.02) 25 (45.45)

65-69 3 (1.32) 0 (0) 4 (7.27)

70 + 2 (0.88) 1 (0.44) 0 (0)

I prefer not to answer (PNTA) 0 (0) 1 (0.44) 0 (0)

Age by Q2, X2(14, N = 511) = 59.04, p < .001

age by Question 3: Job sector

corp Edgov/

nonprofhc Manuf

not employed

other

age n (%) n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

18-22 0 (0) 3 (3.53) 2 (2.17) 2 (1.92) 0 (0) 1 (2.44) 0 (0) .002

23-29 12 (15.12) 12 (14.12) 6 (6.52) 11 (10.58) 2 (4.55) 2 (4.88) 5 (8.47)

30-39 23 (26.74) 18 (21.18) 13 14.13) 24 (23.08) 6 (13.64) 4 (9.76) 17 (28.81)

40-49 24 (27.91) 16 (18.82) 31 (33.7) 29 (27.88) 14 31.82) 13 (31.71) 20 (33.9)

50-64 24 (27.91) 34 (40) 38 (41.3) 38 (36.54) 22 (50) 17 (41.46) 16 (27.12)

65-69 1 (1.16) 0 (0) 2 (2.17) 0 (0) 0 (0) 4 (9.76) 0 (0)

70+ 1 (1.16) 2 (2.35) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

PNTA 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.69)

Age By Q3, X2(42, N = 511) = 73.14, p < .01


83

age by Question 5a: Job seeking sector

corp Edgov/

nonprofhc Manuf not employed other


18-22 0 (0) 3 (3.53) 2 (2.17) 2 (1.92) 0 (0) 1 (2.44) 0 (0) .047

23-29 13 (15.12) 12 (14.12) 6 (6.52) 11 (10.58) 2 (4.55) 2 (4.88) 5 (8.47)

30-39 23 (26.74) 18 (21.18) 13 (14.13) 24 (23.08) 6 (13.64) 4 (9.76) 17 28.81)

40-49 24 (27.91) 16 (18.82) 31 (33.7) 29 (27.88) 14 (31.82) 13 (31.71) 20 (33.9)

50-64 24 (27.91) 34 (40.00) 38 (41.3) 38 (36.54) 22 (50.00) 17 (41.46) 16 27.12)

65-69 1 (1.16) 0 (0) 2 (2.17) 0 (0) 0 (0) 4 (9.76) 0 (0)

70 + 1 (1.16) 2 (2.35) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

PNTA 0 (0) 0(0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.69)

Age by Q5a, X2(56, N = 511) = 74.80, p < .05

age by Question 10d: Work independently

age Slightly

skilled (1) Somewhat skilled (2)


Very skilled (4)

Extremely skilled (5)

n (%) n (%) n (%) n (%) n (%) x2 p value

18-22 0 (0) 0 (0) 0 (0) 4 (2.35) 4 (1.45) .10*

23-29 1 (20.00) 2 (25.00) 10 (18.87) 15 (8.82) 23 (8.36)

30-39 2 (40.00) 3 (37.50) 14 (26.42) 37 (21.76) 49 (17.82)

40-49 1 (20.00) 2 (25.00) 17 (32.08) 36 (21.18) 91 (33.09 )

50-64 1 (20.00) 1 (12.50) 11 (20.75) 74 (43.53) 102 (27.09)

65-69 0 (0) 0 (0) 0 (0) 2 (1.18) 5 (1.82)

70 + 0 (0) 0 (0) 0 (0) 2 (1.18) 1 (0.36)

PNTA 0 (0) 0 (0) 1 (1.89) 0 (0) 0 (0)

Age by Q10d, X2(28, N = 511) = 37.85, p = .10

age by Question 10e: Teaming

Slightly



Very skilled (4)


age n (%) n (%) n (%) n (%) n (%) x2 p value

18-22 1 (25.00) 0 (0) 1 (0.88) 2 (0.91) 4 (2.61) .037

23-29 0 (0) 4 (19.05) 9 (7.89) 18 (8.22) 20 (13.07)

30-39 2 (50.00) 7 (33.33) 32 (28.07) 39 (17.81) 25 (16.34)

40-49 1 (25.00) 5 (23.81) 26 (22.81) 69 (31.51) 46 (30.07)

50-64 0 (0) 5 (23.81) 43 (37.72) 87 (39.73) 54 (35.29)

65-69 0 (0) 0 (0) 1 (0.88) 2 (0.91) 4 (2.61)

70 + 0 (0) 0 (0) 1 (0.88) 2 (0.91) 0 (0)

PNTA 0 (0) 0 (0) 1 (0.88) 0 (0) 0 (0)

Age by Q10e, X2(28, N = 511) = 42.77 p < .05


84

age by Question 10g: Producing Work

Slightly



Very skilled (4)



18-22 0 (0) 0 (0) 0 (0) 3 (1.38) 5 (2.18) .097*

23-29 0 (0) 2 (33.33) 8 (14.29) 18 (8.29) 23 (10.04)

30-39 2 (66.67) 1 (16.67) 16 (28.57) 48 (22.12) 38 (16.59)

40-49 1 (33.33) 2 (33.33) 15 (26.79) 49(22.58) 80 (34.93)

50-64 0 (0) 1 (16.67) 16 (28.57) 92 (42.4) 80 (34.93)

65-69 0 (0) 0 (0) 0 (0) 4 (1.84) 3 (1.31)

70 + 0 (0) 0 (0) 0 (0) 3 (1.38) 0 (0)

PNTA 0 (0) 0 (0) 1 (1.79) 0 (0) 0 (0)

Age by Q10g, X2(35, N = 511) = 38.09, p = .10

age by Question 11: educational attainment

associate Bachelor’s doctoral high school Master’s Some college Some hS


18-22 2 (2.67) 2 (1.18) 0 (0) 0 (0) 0 (0) 4 (3.67) 0 (0) 0.083

23-29 6 (8.00) 22 (13.02) 0 (0) 5 (8.93) 11 (13.41) 7 (6.42) 0 (0)

30-39 18 (24.00) 37 (21.89) 3 (15.79) 11 (19.64) 17 (20.73) 19 (17.43) 0 (0)

40-49 22 (29.33) 46 (27.22) 3 (15.79) 15 (26.79) 18 (21.95) 42 (38.53) 1 (100.00)

50-64 27 (36.00) 59 (34.91) 11 (57.89) 24 (42.86) 33 (40.24) 35 (32.11) 0 (0)

65-69 0 (0) 3 (1.78) 2 (10.53) 0 (0) 1 (1.22) 1 (0.92) 0 (0)

70 or older 0 (0) 0 (0) 0 (0) 0 (0) 2(2.44) 1 (0.92) 0 (0)

PNTA 0 (0) 0 (0) 0 (0) 1 (1.79) 0 (0) 0 (0) 0 (0)

Age by Q11, X2(42, N = 511) = 55.24, p < .10

age by Question 12: Current educational status

currently enrolled Recent grad Both neither

age n (%) n (%) n (%) n (%) x2 p value

18-22 0 (0) 0 (0) 4 (10.53) 4 (0.90) < .001

23-29 7 (33.33) 3 (50.00) 11 (28.95) 30 (6.73)

30-39 7 (33.33) 1 (16.67) 15 (39.47) 82 (18.39)

40-49 5 (23.81) 2 (33.33) 4 (10.53) 136 (30.49)

50-64 2 (9.52) 0 (0) 4 (10.53) 183 (41.03)

65-69 0 (0) 0 (0 ) 0 (0) 7 (1.57)

70 + 0 (0) 0 (0) 0 (0) 3 (0.67)

PNTA 0 (0) 0 (0) 0 (0) 1 (0.22)

Age by Q12, X2(21, N = 511) = 92.42, p < .001


85

age by Question 13a: enroll in Postsecondary Program

highly

unlikely (1) Somewhat unlikely (2)

unsure (3) Somewhat likely (4)

highly likely (5)


18-22 1 (0.44) 0 (0) 2 (1.96) 1 (1.35) 4(8.16) < .001

23-29 11 (4.87) 6 (10.00) 14 (13.73) 10 (13.51) 10 (20.41)

30-39 31 (13.72) 11 (18.33) 30 (29.41) 21 (28.38) 12 (24.49)

40-49 65 (28.76) 15 (25.00) 29 (28.43) 24 (32.43) 14 (28.67)

50-64 109 (48.23) 27 (45.00) 26 (25.49) 18 (24.32) 9 (18.37)

65-69 7 (3.1) 0 (0) 0 (0) 0 (0) 0 (0)

70+ 2 (0.88) 1 (1.67) 0 (0) 0 (0) 0 (0)

PNTA 0 (0) 0 (0) 1 (0.98) 0 (0) 0 (0)

Age by Q13a, X2(28, N = 511) = 78.429, p < .001

age by Question 15: Change industry sector

highly unlikely (1)

Somewhat unlikely (2)

unsure (3)Somewhat likely (4)

highlylikely (5)


18-22 4 (11.43) 2 (1.26) 0 (0) 1 (1) 1 (0.70) < .001

23-29 8 (22.86) 9 (5.66) 12 (16.22) 10 (10) 12 (8.39)

30-39 10 (28.57) 22 (13.84) 16 (21.62) 16 (16) 41 (28.67)

40-49 7 (20.00) 42 (26.42) 25 (33.78) 27 (27) 46 (32.17)

50-64 6 (17.14) 81 (50.94) 18 (24.23) 46 (46) 38 (26.57)

65-69 0 (0) 1 (0.63) 3 (4.05) 0 (0) 3 (2.10)

70+ 0 (0) 2 (1.26) 0 (0) 0 (0) 1 (0.70)

PNTA 0 (0) 0 (0) 0 (0) 0 (0) 1 (0.70)

Age by Q15, X2(28, N = 511) =82.40, p < .001


86

age by Question 16: years of experience

<5 5-10 11-19 20+

age n (%) n (%) n (%) n (%) x2 p value

18-22 0 (0) 0 (0) 0 (0) 8 (9.2) < .001

23-29 19 (14.18) 3 (2.83) 0 (0) 29 (33.33)

30-39 46 (34.33) 30 (28.3) 6 (3.26) 23 (26.44)

40-49 37 (27.61) 44 (41.51) 52 (28.26) 14(16.09)

50-64 30 (22.39) 28 (26.42) 120 (65.22) 11 (12.64)

65-69 1 (0.75) 0 (0) 5 (2.72) 1 (1.15)

70 + 1 (0.75) 1 (0.94) 1 (0.54) 0 (0)

PNTA 0 (0) 0 (0) 0(0) 1 (1.15)

Age by Q16, X2(21, N = 511) = 241.81, p < .001

age by Question 22a: become Proficient in arabic in the next 10 years

highly



highly likely (5)

not applicable

age n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

18-22 5 (1.26) 1 (3.70) 1 (5.00) 0 (0) 0 (0) 1 (2.08) < .001

23-29 34 (8.59) 3 (11.11) 5 (25.00) 3 (25) 1 (12.5) 5 (10.42)

30-39 72 (18.18) 10 (27.04) 7 (35.00) 3 (25) 3 (37.5) 10 (20.83)

40-49 117 (29.55) 6 (22.22) 5 (25.00) 3 (25) 0 (0) 16 (33.33)

50-64 161 (40.66) 6 (22.22) 2 (10.00) 1 (8.33) 4 (50.00) 15 (31.25)

65-69 4 (1.01) 1 (3.70) 0 (0) 1 (8.33) 0 (0) 1 (2.08)

70 + 3 (0.76) 0 (0) 0 (0) 0 (0) 0 (0) 0(0)

PNTA 0 (0) 0 (0) 0 (0) 1 (8.33) 0 (0) 0 (0)

Age by Q22a, X2(35, N = 511) = 81.38, p < .001

age by Question 22b: become Proficient in Chinese in the next 10 years

highly



highly likely (5)

not applicable


18-22 6 (1.57) 0 (0) 1 (4.00) 0 (0) 0 (0) 1 (1.92) < .001

23-29 32 (8.36 ) 4 (13.79) 4 (16.00) 4 (28.57) 2 (25.00) 5 (9.62)

30-39 70 (18.28) 8 (27.59) 10 (40.00) 3 (21.43) 1 (12.50) 13 (25.00)

40-49 112 (29.24) 9 (31.03) 6 (24.00) 3 (21.43) 0 (0) 17 (32.69)

50-64 157 (40.99) 7 (24.14) 4 (16.00) 2 (14.29) 5 (62.50) 14 (26.92)

65-69 3 (0.78) 1 (3.45) 0 (0) 1 (7.14) 0 (0) 2 (3.85)

70+ 3 (0.78) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

PNTA 0 (0) 0 (0) 0 (0) 1(7.14) 0 (0) 0 (0)

Age by Q22b, X2(35, N = 511) = 76.22, p < .001


87

age by Question 22c: become Proficient in english in the next 10 years

highly



highly likely (5)

not applicable


18-22 1 (2.13) 0 (0) 0 (0) 0 (0) 2 (2.50) 5 (1.39) < .001

23-29 3 (6.38 ) 1 (50.00) 3 (20.00) 1 (12.50) 12 (15.00) 31 (8.64)

30-39 9 (19.15) 0 (0) 6 (40.00) 0 (0) 18 (22.50) 72 (20.06)

40-49 12 (25.53) 1 (50.00) 4 (26.67) 4 (50.00) 27 (33.75) 99 (27.58)

50-64 21 (44.68) 0 (0) 2 (13.33) 2 (25.00) 19 (23.75) 145 (40.39)

65-69 1 (2.13) 0 (0) 0 (0) 0 (0) 1 (1.25) 5 (1.39)

70+ 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.25) 2 (0.56)

PNTA 0 (0) 0 (0) 0 (0) 1 (12.50) 0 (0) 0 (0)

Age by Q22c, X2(35, N = 511) = 90.17, p < .001

age by Question 10d: become Proficient in russian in the next 10 years

highly



highly likely (5)

not applicable


18-22 7 (1.78) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.08) < .001

23-29 32 (8.12) 2 (7.41) 4 (18.18) 4 (36.36) 4 (44.44) 5 (10.42)

30-39 72 (18.27) 9 (33.33) 5 (22.73) 4 (36.36) 2 (22.22) 13 (27.08)

40-49 114 (28.93) 8 (29.63) 9 (40.91) 1 (9.09) 0 (0) 15 (31.25)

50-64 161 (40.86) 7 (25.93) 4 (18.18) 1 (9.09) 3 (33.33) 13 (27.08)

65-69 5 (1.27) 1 (3.70) 0 (0) 0 (0) 0 (0) 1 (2.08)

70+ 3 (0.76) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

PNTA 0 (0) 0 (0) 0 (0) 1 (9.09) 0 (0) 0 (0)

Age by Q22d, X2(35, N = 511) = 89.96, p < .001

age by Question 10e: become Proficient in spanish in the next 10 years

highly



highly likely (5)

not applicable


18-22 1 (0.40) 1 (1.82) 1 (1.59) 2 (2.78) 1 (2.78) 2 (5.26) .01

23-29 14 (5.67) 8 (14.55) 4 (6.35) 9 (12.5) 11 (30.56) 5 (13.16)

30-39 48 (19.43) 13 (23.64) 15 (23.81) 13 (18.06) 10 (27.78) 6 (15.79)

40-49 73 (29.55) 11 (20.00) 22 (34.92) 23 (21.94) 4 (11.11) 14 (26.84)

50-64 107 (43.32) 22 (40.00) 19 (30.16) 22 (30.56) 9 (25.00) 10 (26.32)

65-69 2 (0.81) 0 (0) 1 (1.59) 2 (2.78) 1 (2.78) 1 (2.63)

70+ 2 (0.81) 0 (0) 1 (1.59) 0 (0) 0 (0) 0 (0)

PNTA 0 (0) 0 (0) 0 (0) 1 (1.39) 0 (0) 0 (0)

Age by Q22e, X2(35, N = 511) = 57.72, p < .01


88

WoRkER gEndER By QuESTIonS:

By Q3 – Job sector X2(12, N = 511) = 26.55, p <.01 (p = .009)

By Q5a – Seeking sector X2(12, N = 511) = 35.76, p <.001 (p = .001)

By Q6 – 10-year demand X2(8, N = 511) = 17.55, p <.05 (p = .025)

By Q7a – Career Cluster X2(35, N = 511) = 69.25, p =.001 (p = .001)

By Q8 - 10-year demand cluster X2 (8, N = 511) = 14.03, p < .10 (p = .081)

By Q9d – Demand master’s X2(8, N = 511) = 16.9005, p <.05 (p = .031)

By Q10e – Teaming X2(8, N = 511) = 14.14, p < .10 (p = .078)

By Q13b – Take non-credit PD or CED X2 (8, N = 511) = 14.72, p < .10 (p = .065)

By Q15 - Shift occupation X2(8, N = 511) = 14.42, p < .10 (p = .071)

By Q22a – Arabic X2(10, N = 511) =20.89, p < .05 (p = .013)

By Q22b – Chinese X2(10, N = 511) = 31.00, p =.001 (p = .001)

By Q22c – English X2(10, N = 511) = 31.66, p < .001 (p = .0001)

By Q22d – Russian X2(10, N = 511) = 27.80, p < .001 (p = .0001)

By Q22e – Spanish X2(10, N = 511) = 22.525, p < .05 (p = .013)

Worker Gender by Question 3: Job sector

gender corp Edgov/non

Profithc Manuf

not employed

other

n (%) n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 50 (58.14) 64 (75.29) 57 (61.96) 85 (81.73) 23 (52.27) 30 (73.17) 39 (66.1) .009

Male 35 (40.70) 21 (24.71) 35 (38.04) 19 (18.27) 21 (47.73) 11 (26.83) 20 (33.90)

No Answer 1 (1.16) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Gender by Q3, X2(12, N = 511) = 26.55, p <.01

Gender by Question 5a: seeking sector

gender corp Edgov/non

Profithc Manuf

not employed

other

n (%) n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 50 (68.49) 34 (58.62) 39 (54.17) 62 (81.58) 13 (50 ) 121 (77.07) 29 (59.18) < .001

Male 23 (31.51) 24 (41.38) 33 (45.83) 14 (18.42) 13 (50) 36 (22.93) 19 (38.78)

No Answer 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.04)

Gender by Q5a, X2(12, N = 511) = 35.76, p <.001


89

Gender by Question 6a: 10-year Demand

genderVery low demand

low demand unsureModerate demand

high demand

n (%) n (%) n (%) n (%) n (%) x2 p value

Female 7 (58.33) 21 (67.74) 119 (68.79) 107 (65.24) 94 (71.76) .025

Male 5 (41.67) 9 (29.03) 54 (31.21) 57 ( 34.76) 37 (28.24)

No answer 0 (0) 1 (3.23) 0 (0) 0 (0) 0 (0)

Gender by Q6, X2(8, N = 511) = 17.55, p <.05

Gender by Question 7a: Career Cluster

Male Female no answer

n (%) n (%) n (%) x2 p value

Agriculture 2 (33.33) 4 (66.67) 0 (0) < .001

Architect 2 (100.00) 0 (0) 0 (0)

Arts 5 (50.00) 5 (50.00) 0 (0)

Business 40 (76.92) 12 (23.08) 0 (0)

Education 55 (76.39) 17 (23.61) 0 (0)

Finance 15 (60.00) 10(40.00) 0 (0)

Government 50 (68.49) 23 (31.51) 0 (0)

Health Sciences 53 (85.48) 9 (14.52) 0 (0)

Hospitality 13 (86.67) 2 (13.33) 0 (0)

Human Services 15 (68.18) 7 (31.82) 0 (0)

Information 14 (42.42) 18 (54.55) 1 (3.03)

Law 4 (44.44) 5 (55.56) 0 (0)

Manufacturing 20 (58.82) 14 (41.18) 0 (0)

Marketing 9 (56.25) 7 (43.75) 0 (0)

Not Employed 13 (3.00) 3 (18.75) 0 (0)

Other 26 (6.00) 6 (18.75) 0 (0)

Science 10 (45.45) 12 (54.55) 0 (0)

Transport 2 (20.00) 8 (80.00) 0 (0)

Gender by Q7a, X2(35, N = 511) = 69.25, p =.001


90

Gender by Question 8: 10-year demand in field



high demand

n (%) n (%) n (%) n (%) n (%) x2 p value

Female 4 (66.67) 22 (59.46) 115 (67.25) 115 (69.70) 92 (69.70) .081*

Male 2 (33.33) 14 (37.84) 56 (32.75 ) 50 (30.30) 40 (30.30)

No Answer 0 (0) 1 (2.7) 0 (0) 0 (0) 0 (0)

Significant at p < .10

Gender by Q8, X2 (8, N = 511) = 14.03, p < .10

Gender by Question 9c: Demand master’s Degree



high demand

n (%) n (%) n (%) n (%) n (%) x2 p value

Female 107 (68.59) 24 (68.57) 119 (71.26) 77 (63.64) 21 (65.63) .031

Male 49 (31.41) 11 (31.43) 48 (28.74) 44 (36.36) 10 (31.25)

No answer 0 (0) 0 (0) 0 (0) 0 (0) 1

Gender by Q9d, X2(8, N = 511) = 16.9005, p <.05

Gender by Question 10e: Teaming

genderSlightly

skilled (1)Somewhat skilled (2)


Very skilled (4)


n (%) n (%) n (%) n (%) n (%) x2 p value

Female 4 (100.00) 9 (42.86) 71 (62.28) 150 (68.49) 114 (74.51) .078*

Male 0 (0) 12 (57.14) 43 (37.72) 68 (31.05) 39 (25.49)

No Answer 0 (0) 0 (0) 0 (0) 1 (0.46) 0 (0)

Gender by Q10e, X2(8, N = 511) = 14.14, p < .10

Gender by Question 13b: Participate in non-credit PD/CeD in the next 5 years

genderSlightly

skilled (1)Somewhat skilled (2)


Very skilled (4)


n (%) n (%) n (%) n (%) n (%) x2 p value

Female 83 (66.94) 38 (55.07) 84 (70.00) 74 (67.89) 69 (77.53) .065*

Male 41 (33.06) 31 (44.93) 36 (30.00) 35 (32.11) 19 (21.35)

No Answer 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.12)

Gender by Q13b, X2 (8, N = 511) = 14.72, p < .10


91

Gender by Question 15: Change industry

genderhighly

unlikely Somewhat

unlikely unsure

Somewhat likely

highly likely

n (%) n (%) n (%) n (%) n (%) x2 p value

Female 21 (60.00) 109 (68.55) 52 (70.27) 68 (68) 98 (68.53) .071*

Male 13 (37.14) 50 (31.45) 22 (29.73) 32 (32) 45 (31.47)

No Answer 1 (2.86) 0 (0) 0 (0) 0 (0) 0 (0)

Gender by Q15, X2(8, N = 511) = 14.42, p < .10

Gender by Question 22a: become Proficient in arabic in the next 10 years

gender highly



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 278 (70.20) 13 (48.15) 12 (60.00) 5 (41.67) 5 (62.70) 35 (72.92) .013

Male 118 (29.80) 14 (51.85) 8 (40.00) 7 (58.33) 3 (37.50) 12 (25.00)

No Answer 0 (0) 0 (0) 0 (0) 0 ( 0) 1 (0.20) 1 (0.02)

Gender by Q22a, X2(10, N = 511) =20.89, p < .05

Gender by Question 22b: become Proficient in Chinese in the next 10 years

genderhighly



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 272 (71.02) 14 (48.28) 16 (64.00) 4 (28.57) 3 (37.50) 39 (75.00) < .001

Male 111 (28.98) 15 (51.72) 9 (36.00) 10 (71.43) 5 (62.50) 12 (23.08)

No Answer 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.92)

Gender by Q22b, X2(10, N = 511) = 31.00, p =.001

Gender by Question 22c: become Proficient in english in the next 10 years

genderhighly



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 36 (76.6) 1 (50) 12 (80) 0 (0) 42 (52.5) 257 (71.59) .02

Male 11 (23.4) 1 (50) 3 (20) 8 (100) 38 (47.5) 101 (28.13)

No Answer 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (0.28)

Gender by Q22c, X2(10, N = 511) = 31.66, p < .001


92

Gender by Question 22d: become Proficient in russian in the next 10 years

genderhighly



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 275 (69.8) 15 (55.56) 11 (50) 7 (63.64) 2 (22.22) 38 (79.17) .002

Male 119 (30.2) 12 (44.44) 11 (50) 4 (36.36) 7 (77.78) 9 (18.75)

No Answer 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.08)

Gender by Q22d, X2(10, N = 511) = 27.80, p < .01

Gender by Question 22e: become Proficient in spanish in the next 10 years

genderhighly



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

Female 169 (68.42) 45 (81.82) 42 (66.67) 44 (61.11) 20 (55.56) 28 (73.68) .013

Male 78 (31.58) 10 (18.18) 21 (33.33) 28 (38.89) 16 (44.44) 9 (23.68)

No Answer 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.63)

Gender by Q22e, X2(10, N = 511) = 22.525, p < .05 (p = .013)

WoRkER RacE By:

By Q2 - Current job situation X2(16, N = 511) = 24.07, p < .10 (p = .088)

By Q5a - Seeking sector X2(48, N = 511) = 62.59, p < .10 (p = .07)

By Q10d – Working independently X2(32, N = 511) = 68.27, p < .001 (p = .000)

By Q10e – Teaming X2(32, N = 511) = 45.56, p < .10 (p = .057)

By Q10g – Producing work X2(32, N = 511) =68.26, p < .001 (p = .000)

By Q12 – Ed status X2(24, N = 511) = 66.45, p < .001 (p = .000)

By Q15 – Shift occupation X2(32, N = 511) = 54.75, p < .01 (p = .007)

By Q21 - Language X2(32, N = 511) = 182.65, p < .001 (p = .000)

By Q22a – Arabic X2(40, N = 511) = 69.15, p < .01 (p = .003)

By Q22b – Chinese X2(40, N = 511) = 83.21, p < .001 (p = .000)

By Q22c – English X2(40, N = 511) = 72.63, p = .001 (p = .001)




93

race by Question 2: Current Job situation

RacePlan to work

6-10 yrsPlan to work

10+ yrsnot employed

but seeking

n (%) n (%) n (%) x2 p value

African-American, non-Hispanic 16 (7.05) 12 (5.24) 6 (10.91) 088*

African-Caribbean 2 (0.88) 0 (0) 1 (1.82)

Asian/Pacific Islander 7 (3.08) 9 (3.93) 3 (5.45)

Bi- or multi-racial 2 (0.88) 1 (0.44) 3 (5.45)

Hispanic 6 (2.64) 3 (1.31) 2 (3.64)

Native American 3 (1.32) 0 (0) 1 (1.82)

White, non-Hispanic 187 (82.38) 198 (86.46) 39 (70.91)

PNTA* 2 (0.88) 4 (1.75) 0 (0)

Other 2 (0.88) 2 (0.87) 0 (0)

*Prefer not to answer

Race by Q2, X2(16, N = 511) = 24.07, p < .10

race by Question 5a: seeking sector

Race corp Edgov/non

profithc Manu

not employed

other

n (%) n (%) n (%) n (%) n (%) n (%) n (%)

African-American, non-Hispanic 7 ( 9.59) 8 (13.79) 4 (5.56) 3 (3.95) 0 (0) 9 (5.73) 3 (6.12)

African-Caribbean 1 (1.37) 0 (0) 0 (0) 1 (1.32) 0 (0) 1 (0.64) 0 (0)

Asian/Pacific Islander 6 (8.22) 3 (5.17) 2 (2.78) 3 (3.95) 1 (3.85) 2 (1.27) 2 (4.08)

Bi- or multi-racial 1 (1.37) 0 (0) 0 (0) 1 (0) 0 (0) 1 (0.64) 3 (6.12)

Hispanic 2(2.74) 2 (3.45) 1 (1.39) 1 (1.32) 1 (3.85) 3 (1.91) 1 (2.04)

Native American 0 (0) 2 (3.45) 1 (1.39) 0 (0) 1 (3.85) 0 (0) 0 (0)

White, non-Hispanic 55 (75.34) 43 (74.14) 60 (83.33) 66 (86.84) 22 (84.62) 140 (89.17) 38 (77.55)

PNTA 1 (1.37) 0 (0) 2 (2.78) 0 (0) 0 (0) 1 (0.64) 2 (4.08)

Other 0 (0) 0 (0) 2 (2.78) 1 (1.32) 1 (3.85) 0 (0) 0 (0)

Race by Q5a, X2(48, N = 511) = 62.59, p < .10


94

race by Question 10d: Working independently

RaceSlightly



Very Skilled (4)


n (%) n (%) n (%) n (%) n (%) x2 p value

African-American, non-Hispanic 0 (0) 1 (12.50) 6 (11.32) 4 (2.35) 23 (3.26)

African-Caribbean 0 (0) 0 (0) 1 (1.89) 2 (1.18) 0 (0)

Asian/Pacific Islander 0 (0) 3 (37.50) 2 (3.77) 6 (3.53) 8 (2.91)

Bi- or multi-racial 0 (0) 0 (0) 1 (1.89) 2 (1.18) 3 (1.09)

Hispanic 1 (20.00) 0 (0) 1 (1.89) 2 (1.18) 7 (2.25)

Native American 0 (0) 1 (12.50) 0 (0) 1 (0.59) 2 (0.73)

White, non-Hispanic 4 (80.00) 3 (37.50) 40 (75.47) 150 (88.24) 227 (82.55)

PNTA 0 (0) 0 (0) 1 (1.89) 3 (1.76) 2 (0.73)

Other 0 (0) 0 (0) 1 (1.89) 0 (0) 3 (1.09)

Race by Q10d, X2(32, N = 511) = 68.27, p < .001

race by Question 10e: Teaming

RaceSlightly



Very skilled (4)


n (%) n (%) n (%) n (%) n (%) x2 p value

African-American, non-Hispanic 0 (0) 1 (4.76) 5 (4.39) 11 (5.02) 17 (11.11) .057*

African-Caribbean 0 (0) 0 (0) 1 (0.88) 1 (0.46) 1 (0.65)

Asian/Pacific Islander 1 (25.00) 3 (14.29) 5 (4.39) 8 (3.65) 2 (1.31)


Hispanic 1 (25.00) 0 (0) 1 (0.88) 4 (1.83) 5 (3.27)

Native American 0 (0) 1 (4.76) 2 (1.75) 1 (0.46) 0 (0)


PNTA 0 (0) 0 (0) 2 (1.75) 3 (1.37) 1 (0.65)

Other 0 (0) 0 (0) 1 (0.88) 1 (0.46) 2 (1.31)

Race by Q10e, X2(32, N = 511) = 45.56, p < .10


95

race by Question 10g: Producing Work

RaceSlightly



Very Skilled (4)


n (%) n (%) n (%) n (%) n (%) x2 p value

African-American, non-Hispanic 0 (0) 0 (0) 2 (3.57) 10 (4.61) 22 (9.61) < .001

African-Caribbean 0 (0) 0 (0) 1 (1.79) 1 (0.46) 1 (0.44)



Hispanic 1 (33.33) 0 (0) 3 (5.36) 1 (0.46) 6 (2.62)

Native American 0 (0) 0 (0) 0 (0) 3 (1.38) 1 (0.44)


PNTA 0 (0) 0 (0) 1 (1.79) 3 (1.38) 2 (0.87)

Other 0 (0) 0 (0) 0 (0) 2 (0) 2 (0.87)

Race by Q10g, X2(32, N = 511) =68.26, p < .001

race by Question 12: Current ed status

Race currently enrolled Recent grad Both neither

n (%) n (%) n (%) n (%) x2 p value

African-American, non-Hispanic 1 (4.76) 2 (33.33) 7 (18.42) 24 (5.38) < .001

African-Caribbean 0 (0) 0 (0) 0 (0) 3 (0.67)

Asian/Pacific Islander 3 (14.29) 0 (0) 2 (5.26) 14 (3.14)

Bi- or multi-racial 0 (0) 0 (0) 2 (5.26) 4 (0.90)

Hispanic 0 (0) 2 (33.33) 0 (0) 9 (2.02)

Native American 1 (4.76) 0 (0) 0 (0) 3 (0.67)

White, non-Hispanic 16 (76.19) 2 (33.33) 27 (71.05) 379 (84.98)

PNTA 0 (0) 0 (0) 0 (0) 6 (1.35)

Other 0 (0) 0 (0) 0 (0) 4 (0.90)

Race by Q12, X2(24, N = 511) = 66.45, p < .001


96

race by Question 15: shift occupation

Racehighly



highly likely (5)

n (%) n (%) n (%) n (%) n (%) x2 p value

African-American, non-Hispanic 4 (11.43) 3 (1.89) 11 (14.86) 6 (6.00) 10 (6.99) 007

African-Caribbean 1 (2.86) 1(0.63) 0 (0) 0 (0) 1 (0.70)


Bi- or multi-racial 1 (2.86) 0 (0) 2 (2.70) 0 (0) 3 (2.10)

Hispanic 2 (5.71) 5 (3.14) 2 (2.70) 0 (0) 2 (1.40)

Native American 0 (0) 2 (1.26) 1 (1.35) 0 (0) 1 (0.70)


PNTA 2 (5.71) 2 (1.26) 0 (0) 2 (2.00) 0 (0)

Other 0 (0) 0 (0) 1 (1.35) 0 (0) 3 (2.10)

Race by Q15, X2(32, N = 511) = 54.75, p < .01 (p = .007)

race by Question 21: Current language Proficiency

arabic chinese English Russian Spanish other

Race n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

< .001

African-American, non-Hispanic 0 (0) 0 (0) 32 (6.84) 0 (0) 1 (7.14) 1 (4.00)

African-Caribbean 0 (0) 0 (0) 3 (0.64) 0 (0) 0 (0) 0 (0)

Asian/Pacific Islander 0 (0) 2 (100.00) 15 (3.21) 0 (0) 0 (0) 2 (8.00)

Bi- or multi-racial 0 (0) 0 (0) 5 (1.07) 0 (0) 1 (7.14) 0 (0)

Hispanic 0 (0) 0 (0) 5 (1.07) 0 (0) 6 (42.86) 0 (0)

Native American 0 (0) 0 (0) 3 (0.64) 0 (0) 0 (0) 1 (4.00)

White, non-Hispanic 0 (0) 0 (0) 396 (84.62) 2 (100.00) 5 (35.71) 21 (84.00)

PNTA 0 (0) 0 (0) 5 (1.07) 0 (0) 1 (7.14) 0 (0)

Other 0 (0) 0 (0) 4 (0.85) 0 (0) 0 (0) 0 (0)

Race by Q21, X2(32, N = 511) = 182.65, p < .001


97

race by Question 22a: become Proficient in arabic in the next 10 years

highly



highly likely (5)

not applicable


0.003

African-American, non-Hispanic 17 (4.29) 5 (18.52) 4 (20.00) 1 (8.33) 1 (12.50) 6 (12.50)

African-Caribbean 1 (0.25) 0 (0) 1 (5.00) 0 (0) 0 (0) 1 (2.08)

Asian/Pacific Islander 9 (2.27) 4 (14.81) 1 (5.00) 2 (16.67) 0 (0) 3 (6.25)

Bi- or multi-racial 4 (1.01) 0 (0) 0 (0) 1 (8.33) 0 (0) 1 (2.08)

Hispanic 7 (1.77) 1 (3.70) 0 (0) 0 (0) 0 (0) 3 (6.25)

Native American 3 (0.76) 0 (0) 1 (5.00) 0 (0) 0 (0) 0 (0)

White, non-Hispanic 347 (87.63) 17 (62.96) 13 (65.00) 8 (66.67) 7 (87.5) 32 (66.67)

PNTA 5 (1.26) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.08)

Other 3 (0.76) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.08)

Race by Q22a, X2(40, N = 511) = 69.15, p < .01

race by race by Question 22b: become Proficient in Chinese in the next 10 years

highly



highly likely (5)

not applicable


< .001

African-American, non-Hispanic 15 (3.92) 5 (17.24) 3 (12) 4 (28.57) 1 (12.50) 6 (11.54)

African-Caribbean 1 (0.26) 0 (0) 1 (4) 0 (0) 0 (0) 1 (1.92)

Asian/Pacific Islander 8 (2.09) 1 (3.45) 1 (4) 4 (28.57) 1 (12.50) 4 (7.69)

Bi- or multi-racial 5 (1.31) 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.92)

Hispanic 6 (1.57) 2 (6.90) 0 (0) 0 (0) 0 (0) 3 (5.77)

Native American 3 (0.78) 0 (0) 1 (4.00) 0 (0) 0 (0) 0 (0)


PNTA 4 (1.04) 1 (3.45) 0 (0) 0 (0) 0 (0) 1 (1.92)

Other 3 (0.78) 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.92)

Race by Q22b, X2(40, N = 511) = 83.21, p < .001


98

race by Question 22c: become Proficient in english in the next 10 years

highly



highly likely (5)

not applicable


.001

African-American, non-Hispanic 3 (6.38) 1 (50) 1 (6.67) 0 (0) 10 (12.50) 19 (5.29)

African-Caribbean 0 (0) 0 (0) 1 (6.67) 0 (0) 2 (2.50) 0 (0)

Asian/Pacific Islander 1 (2.13) 0 (0) 1 (6.67) 2 (25.00) 7 (8.75) 8 (2.23)

Bi- or multi-racial 1 (2.13) 0 (0) 0 (0) 0 (0) 1 (1.25) 4 (1.11)

Hispanic 2 (4.26) 0 (0) 0 (0) 1 (12.50) 4 (5.00) 4 (1.11)

Native American 1 (2.13) 0 (0) 0 (0) 0 (0) 1 (1.25) 2 (0.56)


PNTA 1 (2.13) 0 (0) 1 (6.67) 0 (0) 2 (2.50) 2 (0.56)

Other 1 (2.13) 0 (0) 0 (0) 0 (0) 1 (1.25) 2 (0.56)

Race by Q22c, X2(40, N = 511) = 72.63, p = .001 (p = .001)

race by Question 22d: become Proficient in russian in the next 10 years

highly



highly likely (5)

na


0.032


African-Caribbean 1 (0.25) 0 (0) 1 (4.55) 0 (0) 0 (0) 1 (2.08)

Asian/Pacific Islander 10 (2.54) 3 (11.11) 2 (9.09) 1 (9.09) 1 (11.11) 2 (4.17)

Bi- or multi-racial 5 (1.27) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.08)

Hispanic 7 (1.78) 0 (0) 1 (4.55) 0 (0) 1 (11.11) 2 (4.17)

Native American 3 (0.76) 0 (0) 1 (4.56) 0 (0) 0 (0) 0 (0)


PNTA 4 (1.02) 0 (0) 0 (0) 1 (9.09) 0 (0) 1 (2.08)

Other 3 (0.76) 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.08)

Race by Q22d, X2(40, N = 511) = 57.30, p < .05


99

race by Question 22e: become Proficient in spanish in the next 10 years

highly



highly likely (5)

na


< .001


African-Caribbean 0 (0) 1 (1.82) 1 (1.59) 0 (0) 1 (2.78) 0 (0)

Asian/Pacific Islander 7 (2.83) 3 (5.45) 1 (1.59) 5 (6.94) 1(2.78) 2 (5.26)

Bi- or multi-racial 1 (0.4) 0 (0) 1 (1.59) 1 (1.39) 2 (5.56) 1 (2.63)

Hispanic 3 (1.21) 0 (0) 0 (0) 1 (1.39) 7 (19.44) 0 (0)

Native American 2 (0.81) 1 (1.82) 0 (0) 1 (1.39) 0 (0) 0 (0)

White, non-Hispanic 222 (89.88) 48 (87.27) 52 (82.54) 54 (75) 20 (55.56) 28 (73.68)

PNTA 3 (1.21) 0 (0) 0 (0) 0 (0) 1 (2.78) 2 (5.26)

Other 2 (0.81) 0 (0) 1 (1.59) 0 (0) 0 (0) 1 (2.63)

Race by Q22e, X2(40, N = 511) = 110.22, p < .001

WoRkER MIlITaRy STaTuS By:

By Q9e – Doctoral degree X2(24, N = 511) = 33.85, p < .10 (p = .087)

By Q22a – Arabic X2(30, N = 511) = 49.85, p < .05 (p = .013)

By Q22b – Chinese X2(30, N = 511) = 66.48, p < .001 (p = .000)

By Q22c – English X2(30, N = 511) = 51.36, p < .01 (p = .009)



military status by Demand Doctoral Degree

Very low demand

low demand

unsureModerate demand

high demand

Military Status n (%) n (%) n (%) n (%) n (%) x2 p value

.087*

Active duty military 0 (0) 0 (0) 0 (0) 1(0.83) 0 (0)

Honorably discharged from active duty 1 (1.82) 4 (5.88) 8 (5.33) 10 (8.33) 8 (6.78)

Honorably discharged reservist 1 (1.82) 0 (0) 1 (0.67) 0 (0) 3 (2.54)

Never associated with the military 50 ( 90.91) 59 (86.76) 131 (87.33) 100 (83.33) 103 (87.29)

PNTA 2 (3.64) 0 (0) 2 (1.33) 8 (6.67) 2 (1.69 )

Other 0 (0) 3 (4.41) 3 (2.00) 0 (0) 2 (1.69)

Retired military 1 (1.82) 2 (2.94) 5 (3.33) 1 (0.83) 0 (0)

By Q9e, X2(24, N = 511) = 33.85, p < .10


100

military status by become Proficient – arabic

highly

unlikelySomewhat

unlikely unsure

Somewhat likely

highly likely

n/a

Military Status n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

.013

Active duty military 0 (0) 0 ( 0) 1 (5.00) 0 (0) 0 (0) 0 (0)

Honorably discharged from active duty 26 (6.57) 2 (7.41) 1 (5.00) 1 (8.33) 1 (12.50) 0 (0)

Honorably discharged reservist 5 (1.26) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)

Never associated with the military 345 (87.12) 23 (85.19) 16 (80.00) 9 (75.00) 5 (62.50) 45 (93.75)

PNTA 8 (2.02) 1 (3.7) 1 (5.00) 1 (8.33) 1 (12.50) 2 (4.17)

Other 6 (1.52) 0 (0) 1 (5.00) 0 (0) 1 (12.50) 0 (0)

Retired military 6 (1.52) 1 (3.7) 0 (0) 1 (8.33) 0 (0) 1 (2.08)

By Q22a, X2(30, N = 511) = 49.85, p < .05

military status by become Proficient in Chinese

highly

unlikelySomewhat

unlikely unsure

Somewhat likely

highly likely

n/a


< .001

Active duty military 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0)




PNTA 8 (2.02) 1 (3.70) 1 (5.00) 1 (8.33) 1 (12.50) 2 (4.17)

Other, please specify 6 (1.52) 0 (0) 1 (5.00) 0 (0) 1 (12.50) 0 (0)

Retired military 6 (1.52) 1 (3.70) 0 (0) 1 (8.33) 0 (0) 1 (2.08)

By Q22b, X2(30, N = 511) = 66.48, p < .001

military status by become Proficient in english

highly

unlikelySomewhat

unlikely unsure

Somewhat likely

highly likely

n/a


.009

Active duty military 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.25) 0 (0)

Honorably discharged from active duty 3 (6.38) 1 (50.00) 0 (0) 1 (12.50) 4 (5.00) 22 (6.13)

Honorably discharged reservist 0 (0) 0 (0) 0 (0) 0 (0) 1 (1.25) 4 (1.11)


PNTA 2 (4.26) 0 (0) 0 (0) 1 (12.50) 4 (5.00) 7 (1.95)

Other, please specify 0 (0) 0 (0) 1 (6.67) 0 (0) 1 (1.25) 6 (1.67)

Retired military 1 (2.13) 0 (0) 0 (0) 2 (25.00) 2 (2.50) 4 (1.11)

By Q22c, X2(30, N = 511) = 51.36, p < .01 (p = .009)


101

military status by become Proficient in russian

highly

unlikelySomewhat

unlikely unsure

Somewhat likely

highly likely

n/a


< .001

Active duty military 0 (0) 0 (0) 0 (0) 1 0 (0) 0 (0)




PNTA 8 (2.03) 1 (3.70) 0 (0) 1 (9.09) 2 (22.22) 2 (4.17)

Other 6 (1.52) 0 (0) 1 (4.55) 1 (9.09) 0 (0) 0 (0)

Retired military 6 (1.52) 1 (3.70) 0 (0) 0 (0) 1 (11.11) 1 (2.08)

By Q22d, X2(30, N = 511) = 81.34, p < .001

military status by become Proficient in spanish

highly

unlikelySomewhat

unlikely unsure

Somewhat likely

highly likely

n/a


0.031

Active duty military 0 (0) 0 (0) 0 (0) 0 (0) 1 (2.78) 0 (0)

Honorably discharged from active duty 14 (5.67) 3 (5.45) 7 (11.11) 4 (5.56) 3(8.33) 0 (0)

Honorably discharged reservist 3 (1.21) 0 (0) 2 (3.17) 0 (0) 0 (0) 0 (0)


PNTA 3 (1.21) 3 (5.45) 0 (0) 4 (5.56) 2 (5.56) 2 (5.26)

Other 3 (1.21) 1 (1.82) 2 (3.17) 0 (0) 2 (5.56) 0 (0)

Retired military 4 (1.62) 0 (0 ) 0 (0) 2 (2.78) 1 (2.78) 2 (5.26 )

By Q22e, X2(30, N = 511) = 46.02, p < .05 (p = .031)


102

aPPEndIx F: chI SQuaRE WoRkER/WoRkIng lEaRnER RESulTS SIgnIFIcanT aT P < .05 and < P = .10* (nWl n = 411, Wl n = 100)

By Q3 – Job sector, X2 (6, N = 511) = 8.23, p < .01 (p = .0006)

By Q5a, X2(6, N = 511) = 32.97, 4p < .001 (p < .001)

By Q9b – Demand Bachelor’s, X2(4), N = 511) = 14.38, p < .01 (p < .006)

By Q12 - Current Ed Status, X2 (3, N = 511) = 306.08, p < .001 (p < .0001)

By Q13a – Enroll in postsecondary, X2 (4, N = 511) = 97.57, p < .001 (p < .0001)

By Q13b – Enroll in PD/CED, X2(4, N = 511) = 27.36, p < .001 (p < .0001)

By Q14 - Current Status, X2 (4, N = 511) = 12.20, p < .05 (p = .016)

By Q15 - Shift occupation, X2 (4, N = 511) = 30.20, p < .001 (p < .0001)

By Q16 – Years of experience, X2 (8, N = 511) = 31.68, p < .001 (p < .0001)

By Q17 – Age, X2 (7, N = 511) = 87.09, p < .001 (p < .0001)

By Q18 – Race, X2(8, N = 511) = 15.20, p < .10 (p = .055)

By Q19 – Gender, X2(2, N = 511) = 10.31, p < .01 (p = .006)

By Q22a – Arabic, X2(5, N = 511) = 28.89, p < .001 (p < .001)

By 22b – Chinese, X2(5, N = 511) = 27.15, p < .001 (p < .001)

By Q22c – English, X2(5, N = 511) = 17.72, p < .01 (p = .003)

By Q22d – Russian, X2(5, N = 511) = 26.95, p < .001 (p < .001)

By Q22e – Spanish, X2(5, N = 511) = 31.09, p < .001 (p < .001)

Working learner by Question 3 – Job sector

corp Edgov/

nonprofhealthcare Mfg

not currently working

other

n (%) n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 71 (82.56%) 61 (71.76) 81 (88.04) 84 (80.77) 41 (93.18) 33 (80.49) 40 (67.80) .006

WL 15 (17.44) 24 (28.24) 11 (11.96) 20 (19.23) 3 (6.82) 8 (19.51) 19 (32.20)

WL by Q3, X2 (6, N = 511) = 8.23, p < .01

Working learner by Question 5a: seeking sector

corp Edgov/

nonprofhealthcare Mfg

not currently working

other

n (%) n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 58 (79.45) 36 (62.07) 61 (84.72) 55 (72.73) 18 (69.23) 145 (92.36) 38 (77.55) < .001

WL 15 (20.55) 22 (37.93) 11 (15.28) 21 (27.63) 8 (30.77) 12 (7.64) 11 (22.45)

WL by Q5a – Seeking sector (6, N = 511) = 32.97, p < .001


103

Working learner by Question 9b – Demand bachelor’s Degree

Very low demand

low demand

unsureModerate demand

high demand

n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 18 (100.00) 29 (64.44) 96 (84.96) 175 (81.78) 93 (76.86) < .01

WL 0 (0) 16 (35.56) 17 (15.04) 39 (18.22) 28.1 (23.14)

WL by Q9b, X2(4), N = 511) = 14.38, p < .01

Working learner by Q12 Current ed status

currently enrolled Recent grad Both neither

n (%) n (%) n (%) n (%) x2 p value

NWL 0 (0) 0 (0) 0 (0) 411 (92.15) < .001

WL 38 (100.00) 21 (100.00) 6 (100%) 35 (7.85)

WL by Q12, X2 (3, N = 511) = 306.08, p < .001

Working learner by Question 13a: Working learner by enroll in Postsecondary Program

highly



highly likely (5)

n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 211 (93.36) 55 (91.67) 82 (80.39) 41 (55.41) 22 (44.99) < .001

WL 15 (6.64) 5 (8.33) 20 (19.61) 33 (44.59) 27 (55.10)

WL by Q13a, X2 (4, N = 511) = 97.57, p < .001

Working learner by Question 13b: Participate in non-credit PD/CeD in the next 5 years

highly



highly likely (5)

n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 113 (91.13) 63 (91.30) 90 (75.00) 74 (67.89) 71 (79.78) < .001

WL 11 (8.87) 6 (8.70) 30 (25.00) 35 (32.11) 18 (20.22)

WL by Q13b, X2(4, N = 511) = 27.36, p < .001

Working learner by Question 14: Working learner value

no increase

in value Slight increase

in valueSome increase

in value Moderate

increase in valuehigh increase

in value

n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 18 (85.71) 65 (85.53) 153 (85.47) 114 (77.55) 61 (69.32) .016

WL 3 (14.29) 11 (14.47) 26 (14.53) 33 (22.45) 27 (30.66)

By Q14, Current Status, X2 (4, N = 511) = 12.20, p < .05


104

Working learner by Question 15: Change industry sector

highly

unlikely (1)Somewhat unlikely (2)

unsure (3)Somewhat likely (4)

highlylikely (5)

n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 142 (89.31) 84 (84.00) 115 (80.42) 51 (68.98) 19 (54.29) < .001

WL 17 (10.69) 16 (16.00) 28 (19.58) 23 (31.08) 16 (45.71)

By Q15, Change industry sector, X2 (4, N = 511) = 30.20, p < .001

WL by Q15, X2 (4, N = 511) = 30.20, p < .001 (p < .0001)

Working learner by Question 16 – years of experience

<5 5-10 11-19 20+

n (%) n (%) n (%) n (%) x2 p value

NWL 56 (64.37) 101 (75.37 85 (80.19) 169 (91.85) < .001

WL 31 (35.63) 33 (24.63) 21 (19.81) 15 (8.15)

W: by Q16, X2 (8, N = 511) = 31.68, p < .001

Working learner by Question 17 – age

18-22 23-29 30-39 40-49 50-64 65-69 70+

n (%) n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 3 (37.50) 23 (45.10) 73 (69.52) 121 (82.31) 180 (95.25) 7 (100.00) 3 (100.00) < .001

WL 5 (62.50) 28 (54.90) 32 (30.48) 26 (17.69) 9 (4.76) 0 (0) 0 (0)

Working Learner by Q17 – Age, X2 (7, N = 511) = 87.09, p < .001 (p < .0001)

Working learner by Question 18: race

non-Working learner Working learner

Race n (%) n (%) x2 p value

.055*

African-American, non-Hispanic 22 (64.71) 12 (35.29)

African-Caribbean 3 (100.00) 0 (0)

Asian/Pacific Islander 13 (68.42) 6 (31.58)

Bi- or multi-racial 4 (66.67) 2 (33.33)

Hispanic 6 (54.55) 5 (45.45)

Native American 3 (75.00) 1 (25.00)

White, non-Hispanic 352 (83.02) 72 (16.98)

PNTA 5 (83.33) 1 (16.67)

Other 3 (75.00) 1 (25.00)

WL by Q18, X2(8, N = 511) = 15.20, p < .10


105

Working learner by Question 19: Gender

Male Female PnTa

n (%) n (%) n (%) x2 p value

NWL 117 (72.22) 293 (84.20) 1 (100.00 < .006

WL 45 (27.78) 55 (15.80) 0 (0)

WL by Q19, X2(2, N = 511) = 10.31, p < .01

Working learner by become Proficient in arabic in the next 10 years

highly unlikely (1)



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 333 (84.09) 16 (59.26) 9 (45.00) 9 (75.00) 5 (62.50) 39 (81.25) < .001

WL 63 (15.91) 11 (40.74) 11 (55.00) 3 (25.00) 3 (37.50) 9 (18.75)

WL by Q22a, Arabic, X2(5, N = 511) = 28.89, p < .001

Working learner by become Proficient in Chinese in the next 10 years

highly unlikely (1)



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 324 (84.60) 17 (58.62) 16 (64.00) 7 (50.00) 5 (62.50) 42 (80.77) < .001

WL 59 (15.40) 12 (41.38) 9 (36.00) 7 (50.00) 3 (37.50) 10 (19.23)

WL by 22b, X2(5, N = 511) = 27.15, p < .001

Working learner by become Proficient in english in the next 10 years

highly unlikely (1)



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 41 (87.23) 0 (0) 12 (80.00) 7 (87.50) 55 (68.75) 296 (82.45) .003

WL 6 (12.77) 2 (100.00) 3 (20.00) 1 (12.50) 25 (31.25) 63 (17.55)

WL by Q22c, X2(5, N = 511) = 17.72, p < .01

Working learner by become Proficient in russian in the next 10 years

highly unlikely (1)



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 329 (83.50) 17 (62.96) 14 (63.64) 7 (63.64) 3 (33.33) 41 (85.42) < .001

WL 65 (16.50) 10 (37.04) 8 (36.36) 4 (36.36) 6 (66.67) 7 (14.58)

WL by Q22d, X2(5, N = 511) = 26.95, p < .001


106

Working learner by become Proficient in spanish in the next 10 years

highly unlikely (1)



highly likely (5)

not applicable

n (%) n (%) n (%) n (%) n (%) n (%) x2 p value

NWL 211 (85.43) 48 (87.27) 49 (77.78) 52 (72.22) 18 (50.00) 33 (86.84) < .001

WL 36 (14.57) 7 (12.73) 14 (22.22) 20 (27.78) 18 (50.00) 5 (13.16)

WL by Q22e, X2(5, N = 511) = 31.09, p < .001

aPPEndIx g: oThER EMPloyER/WoRkER coMPaRISonS

Significant ES/WS chi square tests for independence:

Industry Sector X2(4, N = 930) = 39.53, p < .001 (p < .001)

Demand in Field, X2 (3, N = 930) = 54.63, p < .001 (p < .001)

Demand for Associate Degree X2(4, N = 930) = 17.72, p = .001 (p = .001)

Demand for Master’s Degree X2(4, N = 930) = 21.61, p <.001 (p < .001)

Demand for Doctoral Degree X2(4, N = 930) = 21.21, p < .001 (p < .001)

not significant:

Demand for Bachelor’s Degree, X2(4, N = 930) = 4.55, p >.10 (p = .336)

Working learner value, X2(4, N = 930) = 3.73, p > .10 (p = .443)

industry sector

Employers Workers

n (%) n (%) x 2 p value

Corporate 71 (16.95) 86 (20.92) < .001

Education 79 (18.85) 85 (20.68)

Government/nonprofit 76 (18.14) 92 (22.38)

Healthcare 77 (18.38) 104 (25.30)

Manufacturing 116 (27.68) 44 (10.71)

By Industry Sector X2(4, N = 930) = 39.53, p < .001 (p < .001)


107

Demand in field

Employers Workers

n (%) n (%) x 2 p value

Low 30 (7.16) 31 (6.07) < .001

Unsure 55 (13.13) 173 (33.86)

Moderate 189 (45.11) 164 (32.09)

High 130 (31.03) 131 (25.64)

By Demand in Field, X2 (3, N = 930) = 54.63, p < .001 (p < .001)

Demand for associate Degree

Employers Workers

n (%) n (%) x2 p value

. 001

Very low 50 (11.93) 66 (12.92)

Low 120 (28.64) 98 (19.18)

Unsure 84 (20.05) 150 (29.35)

Moderate 126 (30.07) 158 (30.92)

High 39 (9.31) 39 (7.63)

By Demand for Associate Degree, X2(4, N = 930) = 17.72, p = .001 (p = .001)

Demand for bachelor’s Degree

Employers Workers x2 p value

n (%) n (%)

.336*

Very low 16 (3.82) 18 (3.52)

Low 39 (9.31) 45 (8.81)

Unsure 72 (17.18) 113 (22.1)

Moderate 175 (41.77) 214 (41.88)

High 117 (27.82) 121 (23.88)

By Demand for Bachelor’s Degree, X2(4, N = 930) = 4.55, p >.10 (p = .336)

Demand for master’s Degree


n (%) n (%)

<.001

Very low 32 (7.64) 32 (6.26)

Low 49 (11.69) 35 (6.85)

Unsure 54 (12.89) 121 (23.68)

Moderate 148 (35.32) 167 (32.68)

High 136 (32.46) 156 (30.53)

By Demand for Master’s Degree, X2(4, N = 930) = 21.61, p <.001 (p < .001)


108

Demand for Doctoral Degree


n (%) n (%)

< .001

Very low 74 (17.66) 55 (10.76)

Low 65 (15.51) 68 (13.31)

Unsure 82 (19.57) 150 (29.35)

Moderate 118 (23.16) 120 (23.48)

High 80 (19.09) 118 (23.09)

By Demand for Doctoral Degree, X2(4, N = 930) = 21.21, p < .001 (p < .001)

Working learner value


n (%) n (%)

.443

No increase in value 25 (5.97) 21 (4.11)

Slight increase in value 68 (16.23) 76 (14.87)

Some increase in value 134 (31.98) 179 (35.03)

Moderate increase in value 110 (26.25) 147 (28.77)

High increase in value 82 (19.57) 88 (17.22)

By Working learner value –X2(4, N = 930) = 3.73, p > .10 (p = .443)

Worker/employer median Demand variables

Workers Employers

WS Q6/ES Q5 Demand in field 4 4

WS Q8/ES Q7 Demand in cluster 4 4

WS Q9/ES Q8 Demand by education

a. Associate degree 4 4

b. Bachelor’s degree 4 4

c. Master’s degree 4 4

d. Doctoral degree 3 3


109

Worker/employer median skill variables


WS Q10/ES Q9: Skill level/difficulty finding *





Teaming 4 3



WS Q22/ES Q13: Intent to become proficient/future demand

Arabic 1 2

Chinese 1 3

English 5 5

Russian 1 2

Spanish 2 4

*WS skill level 1-5: slightly to very highly skilled, ES difficulty level 1-5: not difficult to extremely difficult

**WS likelihood 1-5 from highly unlikely to highly likely, ES demand level 1-5: very low to high demand

Worker/employer median Working learner value

Variable Worker Employer

WS Q14/ES Q11: Working learner value 3 3

median Demand in field (Ws Q6, es Q5) by Job sector

Sector Workers Employers

Corporate 4 4

Education 4 4

Gov/nonprofit 4 4

Healthcare 4 5

Manufacturing 3 4

Not currently working 3 -

Other 3 -

Overall for Q 4 4


110

median Demand in Cluster (Ws Q8, es Q7) by Job sector


Corporate 4 4

Education 4 4

Gov/nonprofit 4 4

Healthcare 4 4

Manufacturing 3 4


Other 3 -

Overall for Q 4 -

median Demand by associate Degree (Ws Q9a, es Q8a)


Corporate 3 3

Education 2 3

Gov/nonprofit 3 3

Healthcare 3 4

Manufacturing 3 3


Other 3 -

Overall for Q 3 3

median Demand by bachelor’s Degree (Ws Q9b, es Q8b)


Corporate 4 4

Education 4 4

Gov/nonprofit 4 4

Healthcare 4 4

Manufacturing 4 4


Other 4 -

Overall for Q 4 4


111

median Demand by master’s Degree (Ws Q9c, es Q8d)


Corporate 4 4

Education 4 4

Gov/nonprofit 4 4

Healthcare 4 4

Manufacturing 3 4


Other 3 -

Overall for Q 4 4

median Demand by Doctoral Degree (Ws Q9d, es Q8d)


Corporate 3 3

Education 4 4

Gov/nonprofit 3 3

Healthcare 4 4

Manufacturing 3 3


Other 3 -

Overall for Q 3 3


112

acknoWlEdgMEnTS

The University of Phoenix Research Institute would like to thank Colin Parker and Stephanie Melkonian for their assistance with the research. Ms. Melkonian, a doctoral candidate in the Department of Health Studies at The University of Chicago, supported all quantitative data analysis efforts. Colin Parker, a sales account manager at MarketTools, was proactive and instrumental in helping us target our desired populations and gathering necessary data.

aBouT ThE auThoRS

keri l. heitner, Ph.d. is an academician and researcher with more than 24 years of experience conducting service delivery research and development, evaluation, needs assessment, and labor market research in the education, health care, business, government, and nonprofit sectors. She earned a Ph.D. and an M.Phil. in environmental psychology from the City University of New York and a master’s in general/experimental psychology from the Graduate Faculty of the New School for Social Research.

leslie a. Miller, Ph.d., PhR, Executive Director of the University of Phoenix Research Institute, is an academician and consultant with more than 20 years of experience researching, teaching, and working directly with organizations and institutions of higher education. Dr. Miller earned a Ph.D. in educational psychology from the University of Maryland and a master’s in experimental/cognitive psychology from Washington State University.

© 2010 university of Phoenix Research Institute

www.phoenix.edu/institute

[email protected]

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