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Development patterns of occupational aspirations in adolescents with high-incidence disabilities.

Theory-building knowledge of the career behavior of adolescents with high-incidence disabilities and the application of this knowledge to a better understanding of transitions to postsecondary education, work, and adult life has received relatively limited empirical attention (Gregg, 2009; Rojewski & Gregg, 2011). This situation is surprising given the large number of secondary students diagnosed with specific learning disabilities or behavioral disorders and the unsatisfactory educational and occupational outcomes they experience (Murray, 2003). Estimates place approximately 9% of the U.S. student population as being diagnosed with high-incidence disabilities, representing over two thirds of all high school students that receive special education services (Newman, Wagner, Cameto, & Knokey, 2009).

Adolescents with high-incidence disabilities are more likely to experience substandard transition outcomes compared to their peers without disabilities as evidenced by high secondary retention and dropout rates (Gregg, 2007; Newman et al., 2009), lower postsecondary enrollment and attainment (Stodden, Jones, & Chang, 2002; Wagner, Newman, Cameto, Garza, & Levine, 2005), restricted labor force participation (Barkley, 2006), and lower earnings (Day & Newburger, 2002). Several factors that contribute to these negative transition outcomes include lower self-esteem and a greater sensitivity to the effect of socioeconomic background on academic achievement (Wagner et al., 2005) and career attainment (Barkley, 2006). Reduced familiarity with the exigencies of the workplace, impaired judgment about attainable career goals, and delayed or impaired career maturity (Rojewski, 1996) paint a picture that suggests the presence of substantial obstacles in the career development of adolescents with high-incidence disabilities.

Despite the role that theory plays in explaining the career development and behavior of people without disabilities, as well as the potential that knowledge of career theories holds in supporting the transition planning process, it is surprising that researchers have paid such limited attention to the career choice and behavior of adolescents with high-incidence disabilities in the career development and transition processes. As a result, we focused our analysis on the longitudinal development of one career construct--occupational aspirations--to better understand how this particular aspect of career choice changes throughout adolescence and into young adulthood for people with high-incidence disabilities.


Occupational aspirations constitute a person's desired work-related goals under ideal circumstances; these goals can reflect information about self-concept, perceived opportunities, and interests and hopes (Rojewski, 2005). Though aspirations are different from the types of occupations that people expect to obtain (realistic expectations), knowledge of aspirations is important to career development and individual job attainment. Aspirations can prompt or hinder educational and career planning, guide learning, help organize life options and choices, and contribute to young people's preparation for adult life. Some researchers have voiced disagreement about the actual role aspirations play in eventual job attainment, that is, whether they play a significant role in determining eventual job obtainment. Are aspirations merely reflections of perceived or real opportunity structures (availability of or access to jobs) that are developed and conditioned as a result of life experiences? Whether the role of occupational aspirations is in shaping and guiding educational and career choices or simply in reflecting past experiences, consistent or coherent aspirations do have value as a predictor of future career-related choices and, to a lesser degree, actual career choices (Mau & Bikos, 2000).

Researchers often treat occupational aspirations as a unidimensional construct that is determined by asking people what type of occupation they would choose at some point in the future if they were free of barriers or limiting factors (Rojewski, 2005). Responses to this general question are usually reported either in terms of prestige/status or type of work/interests. Expressing aspirations in terms of prestige or status usually involve a numerical score for each occupation, based on a scale or index. Indices are typically calculated from information about wages earned, education required, and perceived value to society of each occupation (Stevens & Cho, 1985). These indices result in a hierarchy of occupations where jobs that require higher skills and represent greater value to society are assigned higher numeric values. Type of work or interests is a second way that expresses or describes occupational aspirations. Type of work is usually determined by the tasks, duties, and responsibilities of the occupation. Holland's RIASEC typology is a common method used with this approach (Junk & Armstrong, 2010).

Our database was limited to a prestige measure of occupational aspirations, which we adopted as our measurement scheme. Prestige scores, a widely used coding and reporting scheme, offered several advantages for our analysis. First, prestige scores provide a continuous variable that facilitates data analysis. Second, prestige levels reflect people's perceptions about the relative worth, power, and status of occupations. Third, prestige codes may offer some insight into individual estimates of ability and self-concept, as well as societal expectations and constraints that can be used in considering career aspirations and choice (Hotchkiss & Borow, 1996). Finally, prestige scores are appropriate because they are likely to reflect adolescents' increasing sensitivity to the social evaluation connected to occupations in U.S. society.

Despite the advantages of using prestige scores, we acknowledge certain limitations, most notably how scores are interpreted. For example, because we did not treat aspirations as a reflection of personal values or self-concept, we were limited in our interpretation from venturing into these areas. In fact, a second concern is how to interpret adolescents' aspirations to low- or moderate-prestige occupations. Lower occupational aspirations, by themselves, are not negative. Lowered aspirations may result from realistic assessments of interests, values, abilities, or limited labor market openings. Lower prestige occupational aspirations may be negative, however, if young people prematurely or unnecessarily limit their future opportunities or self-imposed limitations early in adolescence (Rojewski, 1996).

Rosenbaum (1981) explained that young people are gradually eliminated from high-prestige occupations through a series of complex interactions and processes. For example, students who do not complete certain academic prerequisites in middle school are often unable to enroll in advanced mathematics and science courses in high school. This, in turn, reduces the possibility of attaining a college degree, which results in diminished opportunities for attaining high-prestige occupations.

Early adolescents are likely to express high-prestige occupational aspirations, regardless of the likelihood of attaining them. Gradually, aspirations are lowered through compromise as young people realize that they may not .possess necessary skills or abilities, believe that educational or entry-level requirements are beyond their abilities, do not receive support or possess aspirations that are at odds with family and friends, or perceive significant community or societal barriers to job entry or success (Armstrong & Crombie, 2000; Lee & Rojewski, 2009). Occupational aspirations, when measured by prestige levels, are relatively stable during adolescence and provide substantial predictive power for later aspirations (Rojewski & Kim, 2003; Rojewski & Yang, 1997). When changes do occur, however, people are more likely to compromise their aspirations by shifting between occupations within the same level, rather than between levels (Gottfredson, 2005).



The development and expression of occupational aspirations can be explained from several perspectives, including developmental (Super, Savickas, & Super, 1996) and status attainment theories (Hotchkiss & Borow, 1996). Although presenting different theoretical positions, both perspectives explain that aspirations reflect people's assessments of personal capabilities and available opportunities. Aspirations are also influenced by personal and societal characteristics, including gender and socioeconomic status (SES). The theory supporting our analysis, Gottfredson's (2005) theory of compromise and circumscription, shares many of these same assumptions.

Gottfredson (2005) viewed vocational choice as a process of matching one's career interests with individual skills and abilities. To accomplish this process successfully, young people must navigate four progressive developmental stages focusing on cognitive growth, self-creation, circumscription, and compromise.

* A focus on cognitive growth occurs from 3 to 5 years of age and centers on acquiring age. appropriate learning and reasoning abilities. Outcomes of this developmental stage include a positive self-concept (including children's awareness of their emerging talents, skills, and interests)and an understanding about the world of work and the types of occupations available leading to the beginnings of a cognitive map of aspirations.

* The emergence of self-creation occurs from 6 to 8 years of age and requires that children engage in early experiences related to work and potential occupations. Positive results at this stage allow children to begin establishing tolerable boundaries, based on levels of acceptable effort and occupational alternatives.

* Gottffedson's (2005) third stage occurs from 9 to 13 years of age and is known as circumscription, which refers to eliminating occupations that conflict with one's self-concept. The act of eliminating certain career options while retaining others results from childhood experiences defined by an orientation to size and power (3-5 years of age), sex roles (6-8 years of age), social valuation/prestige (9-13 years of age), and unique self/interests (14 years of age and older). Circumscription involves the progressive elimination of occupations deemed unacceptable.

* The final stage, compromise, occurs when young people relinquish their most preferred occupations for less compatible but more accessible ones. Together, circumscription and compromise contribute to a zone or range of acceptable occupational alternatives considered to reflect where people feel they fit in society.

From Gottfredson's perspective, occupations can be arranged to reflect social hierarchies by sex type, prestige, and interests. During the career-compromise process, occupations are eliminated in reverse order of their acquisition or in relation to how close the occupation reflects a person's core self-concept. Interests, which are incorporated into the self-concept last, are compromised first. Orientation to prestige is incorporated into the self-concept before interests but after sex type, and is compromised second. Sex type is closest to the core self-concept since it is incorporated first, and is the last dimension to be compromised. Our analysis focused on aspirations as measured by prestige scores, and the development of aspirations over time.


Gottfredson's (2005) theory of career compromise and circumscription framed our understanding of the development of occupational aspirations. Little research is available, however, to explain how occupational aspirations develop and change over time. Even less is known about occupational aspirations for adolescents and young adults with high-incidence disabilities. Two studies that have examined this issue for people without disabilities are Rojewski and Yang (1997) and Lee and Rojewski (2009).

Rojewski and Yang (1997) used the National Education Longitudinal Study of 1988 (NELS:88) to test the effects of selected variables on the development of occupational aspirations at three points in the career development process--early, mid-, and late adolescence. A structural equation model found aspirations to be relatively stable across the 4-year time period studied. Structural coefficients indicated that SES had a significant effect. In contrast, academic achievement, self-concept, and locus of control had only modest initial effects on aspirations, which then decreased over time.

Lee and Rojewski (2009) used the NELS:88 database to analyze a latent growth curve model that examined the trajectory of occupational aspirations over a 12-year period, from Grade 8 through 8 years after high school completion. The researchers noted two distinct trajectories, separated by high school graduation:

* During high school an increasingly positive trajectory in occupational aspirations was observed from Grade 8 to 12. Almost three fourths (73.1%) of the total change in aspirations prestige scores occurred between Grade 8 and 10, whereas the remaining one quarter (26.9%) of the positive growth happened between Grade 10 and 12.

* After high school graduation, a negative trajectory in occupational aspirations was revealed. As young adults, one third of the total negative change in aspiration prestige scores occurred between high school graduation and 2 years post-high school, and a majority of the total change in aspirations (67.4%) happened during the period of 2 to 8 years post-high school completion.

Interestingly, young women had higher occupational aspirations in high school, but were more likely to lower their aspirations after graduation than their male peers. SES had a significant influence on Grade 8 aspirations, but did not impact the rate of change (Lee & Rojewski, 2009).


We identified several factors to include in our longitudinal latent growth curve model, including gender, SES, academic achievement, and two concepts connected to the idea of self-determination, locus of control and self-concept. Not only have these variables been identified as factors in career development and choice, but they have also been implicated in shaping the experiences of individuals with high-incidence disabilities.

Gender has been a prominent factor in research on occupational aspirations. Male adolescents are more likely to aspire to moderate-prestige occupations, while females are more likely to aspire to either high- or low-prestige occupations Despite higher aspirations, females tend to restrict their range of potential occupations at an early age and are more likely than males to adjust or narrow their educational and occupational expectations downward over time. These differences, coupled with lower occupational attainment for females, have led some to conclude that gender is one of the most powerful and persistent influences on occupational development (Gottfredson, 2005; Mello, 2008; Rojewski, 1996, 1999; Rojewski & Yang, 1997).

Despite contradictory findings regarding the influence of SES on occupational aspirations (e.g., Garg, Kauppi, Lewko, & Urajnik, 2002), it appears that SES has a positive influence, either directly or indirectly, on aspirations (e.g., Lee & Rojewski, 2009; Mau & Bikos, 2000; Rojewski & Yang, 1997). Usually, adolescents from higher SES backgrounds aspire to more prestigious occupations than those from lower class backgrounds. Though the role of SES on the development of occupational aspirations can be quite strong, its influence is likely to occur by early adolescence. Once incorporated into longitudinal models, SES does not appear to present any long-term influence on aspirations formation.

Many researchers have posited that educational aspirations and academic achievement are central to an understanding of career development and choice (Arbona, 2000; Mau & Bikos, 2000; Rojewski, 1999; Rottinghaus, Lindley, & Green, 2002). In fact, Mau and Bikos (2000) declared that academic achievement was perhaps the single best predictor of occupational aspirations. The influence of academic achievement on career behavior is best viewed as a complex set of interactions where strong academic achievement encourages high educational goals, which encourage engagement in opportunities to acquire advanced education. Additional education and doing well over a long period allow for greater occupational possibilities in adulthood. By contrast, lower achievement may dampen educational goals, which may preclude involvement in certain academic activities and limit future occupationally-related opportunities and experiences (Arbona, 2000; Wang & Ma, 2001).

Several studies support the connections between academic achievement and occupational aspirations. Rojewski and Yang (1997) reported that academic achievement had a modest but positive influence on aspirations that was strongest in Grade 8 but decreased over time. Several studies focusing on adolescents deemed at risk of school failure (Rojewski, 1995; Rojewski & Hill, 1998) reported that students at minimal academic risk were more likely to report higher prestige occupational aspirations, whereas those at substantial risk of academic failure were more likely to aspire to lower prestige occupations.

Self-determination, the idea that adolescents with disabilities should advocate and act on their own behalf, is not only an important component in planning for and implementing educational programs while in school, but also when considering educational and occupational futures once school is completed. The database we used for our analysis did not afford us measures of self-determination such as indicators of self-assessment, self-advocacy, or self-reflection. The database did contain a measure of locus of control, which we included in our model. Locus of control is the degree to which people perceive positive or negative events as being a consequence of their own actions and under personal control. People are judged internally controlled if they believe they exert control over their successes and failures, whereas externally controlled people attribute their performance to outside forces beyond their control (Hallahan & Kauffman, 1997). A person with an external locus of control views career decisions in terms of contextual influences and is more likely to be passive to changes in those contexts. Externally controlled people are likely to base career decisions on peer influences, labor market trends, and so forth. External locus of control has been reported for adolescents with high-incidence disabilities (Dowdy, Carter, & Smith, 1990) and may also play a role in shaping occupational aspirations (Gottfredson, 2005; Rojewski & Yang, 1997).

The self-esteem construct is relatively stable. Rojewski and Yang (1997) found that self-esteem had only a minimal effect on occupational aspirations in early adolescence, which diminished throughout high school. In contrast, Patton and Creed (2007) reported that high-achieving students were not only more career mature and had higher self-esteem, but were also more likely to aspire to professional-status occupations than students who held lower skilled aspirations. It is likely that self-esteem may influence occupational aspirations and be especially relevant to people with high-incidence disabilities because poor self-esteem and low self-concept caused by repeated failure, limited social skills, or difficulty serving as a self-advocate are often cited as major problems for this population (Dowdy et al., 1990).


Given the importance of occupational aspirations with respect to postsecondary transition outcomes, an analysis of factors that influence the development of occupational aspirations is essential. The need for analysis is intensified for students with high-incidence disabilities who are often affected by additional risk factors that threaten the achievement of positive transition outcomes. Our analysis sought to examine the influence of selected factors on the formation and trajectory of occupational aspirations from adolescence through early adulthood. The fact that occupational aspirations are likely to be established in early life (Rojewski, 1996, 2005) was reflected in our longitudinal approach that provided a holistic view on the aspirations trajectories of adolescents with high-incidence disabilities. Factors in our analysis included gender, SES, self-esteem and locus of control, and academic achievement. We used a latent growth curve model (LGM) to examine the development and change of occupational aspirations over a 12-year period and to explore the influence of potential predictors of that change. We investigated this issue by integrating prominent factors posited to affect the formation of occupational aspirations from Grade 8 to 8 years post-high school graduation (see Figure 1).



The National Education Longitudinal Study of 1988 (NELS:88). Data from the NELS:88 (National Center for Educational Statistics, 2002), administered by the National Center for Educational Statistics of the U.S. Department of Education, was used for this analysis. NELS:88 contains nationally representative, longitudinal data designed for the study of the educational, vocational, and personal development of adolescents and young adults. The database includes an initial two-stage stratified cluster sample of approximately 25,000 adolescents attending 1,052 schools (815 public and 237 private) nationwide followed at 2-year intervals since 1988 (base year-8th grade, first follow-up-10th grade, second follow-up-12th grade, third follow-up-2 years postsecondary, fourth followup-8 years postsecondary). Data sources include various combinations of school administrators, parents, teachers, and students at each data collection point (Curtin, Ingels, Wu, & Heuer, 2002).


Cautions. Though the NELS:88 database provided an excellent opportunity to examine the questions we posed, one issue related to identification of people with learning disabilities and behavioral disorders posed a challenge, that is, the initial exclusion of some students with physical or mental disabilities. Ingels et al. (1994) reported that 5.4% of eligible participants were excluded in the base year because survey instruments were deemed unsuitable. Almost half (140 of 322), however, were eventually reassessed, deemed eligible, and integrated into the sample. Ingels and Scott (1993) concluded that the overall biasing effect caused by undercoverage for people with disabilities was relatively small. Even so, the possibility exists that some students with learning disabilities or behavioral disorders were excluded from participation. Although the NELS:88 database was not designed to look at adolescents with disabilities, the data offers an important resource for studying the career issues of this population because it is nationally representative, longitudinal, and provides extensive amounts of data (Hodapp & Krasner, 1994-1995; Reschly & Christenson, 2006; Rossi, Herting, & Wolman, 1997).


The NELS:88 database offers information from students, parents, teachers, and school officials that can determine the presence of a disability. Of all these sources, parent-derived responses to two base-year questionnaire items determining the presence of a disability and receipt of disability-related services provide the most stable and reliable sources of information (Hodapp & Krasner, 1994-1995; Ingels & Scott, 1993). This definition was applied to our analysis. Thus, our sample includes all young people in the NELS:88 database whose parents reported that their child had a learning disability or behavioral disorder and had received one or more disability-related services.

To examine the effect of selected covariates on the trajectory of occupational aspirations, adolescents had to meet our established eligibility criteria (i.e., positive parental responses to two disability-related base-year (BYP) questions) and also be included in all five data collection points. The initial sample size totaled 585 and included 341 male (58.29%) and 244 female (41.71%) adolescents. A total of 462 (78.97%) were reported as possessing a learning disability (BYP47G) and receiving one or more disability-related services, and 100 (17.09%) were reported as having a behavioral disability (BYP48G) and receiving at least one disability-related service (BYP48H). (Note: in BYP47G and BYP48H, the numerals and the alphabetic designations are simply the item numbers for parent responses.) Another 23 (3.93%) students had both a learning disability and a behavioral disorder, which we included in the behavioral disorder (BD) group. Inclusion of dually identified students in the BD group was based on the federal definition of learning disabilities (LD), which states that when LD occurs concomitantly with other disabilities it should not be considered the primary disability in terms of diagnosis or treatment (National Joint Committee on Learning Disabilities, 1991). Thus, our BD group contained a total of 123 (21.03%) young people. Table 1 provides further information about participants' demographic characteristics.


Our data were taken from all five waves of the NELS:88 database. This section briefly describes how these variables were measured.

Demographic Variables. Demographic variables included gender (male = 0, female = 1) and high-incidence disability (behavioral disabilities = 0, learning disabilities = 1). Standardized SES scores (M = -.039, SD = .043) using family income, parents' education levels, and parents' occupations (Owings et al., 1994) were used.

Self-Esteem. Self-esteem was measured using three statements similar to items developed by Rosenberg (1965). Participants self-rated the following statements describing themselves, "I feel good about myself," "I am a person of worth, equal of others," and "On the whole, I am satisfied with myself." Response options ranged from 1 (Strongly agree) to 4 (Strongly disagree). An internal consistency reliability coefficient of .687 was obtained.

Locus of Control. Locus of control was measured using three statements similar to items used by Rotter (1966). Participants self-rated the following statements describing themselves, "Every time, I try to get ahead, something or somebody stops me," "My plans hardly ever work out, so planning only makes me unhappy," and "Chance and luck play an important role in my life." Response options ranged from 1 (Strongly agree) to 4 (Strongly disagree). An alpha coefficient for internal consistency was .587.

Academic Achievement. The Educational Testing Service (ETS) was commissioned to develop academic achievement tests in reading, mathematics, science, and history/citizenship/geography throughout the NELS study. We used standardized reading, mathematics, and science achievement scores at Grade 8 to indicate academic achievement in our analysis. Twenty-one multiple-choice items that consisted of five separate reading passages measured Grade 8 reading achievement, with an internal consistency reliability coefficient of .84. The base year mathematics achievement test contained 40 multiple-choice items covering algebra, arithmetic, geometry, data/probability, and advanced topics and possessed an internal consistency reliability coefficient of .90. Grade 8 science achievement tests consisted of 25 multiple-choice items covering earth, life chemistry, and the scientific method and had a reported internal reliability coefficient of .75 (Rock & Pollack, 1991).

Occupational Aspirations. Occupational aspirations were assessed by asking individuals to indicate the job they expected to have at age 30 from a listing of 17 (8th grade), 19 (10th grade), 19 (12th grade), 30 (2 years after high school), and 42 (8 years after high school) major occupational categories. Responses were coded using socioeconomic index (SEI) codes calculated by Stevens and Cho (1985) assigning these categories a continuous score (Hotchkiss & Borow, 1996). For example, occupations such as "homemaker" or "not working" were assigned a SEI value of 15.71 (lowest score), and occupations in the "science, engineering, or professional" professions were assigned SEI values of 68.51. Similar coding decisions were made for all remaining occupations. SEI scores were transformed by dividing them by 10 to facilitate statistical analysis. SEI mean scores across the five time points were 4.322 (SE = .099), 4.667 (SE = .084), 4.734 (SE = .106), 4.455 (SE = .090), and 4.205 (SE = .089), respectively.


Table 2 show the means, univariate skewness and kurtosis, variances, and covariances for all select covariates and the base year, first follow-up, second follow-up, third follow-up, and fourth follow-up measures of occupational aspirations. Because of high missing rates of several variables (e.g., missing value rates ranged from 2 to 38), multiple imputation (MI) was used to replace missing values for selected covariates and occupational aspirations. Ten separate multiply-imputed data sets were created. MI assumes that data are at least missing at random, meaning that the probability of missing data on observed variables is associated with some observed variables in the model but not with being missing itself. Although the missing-at-random assumption cannot be tested statistically, we examined correlations between the indicators of missing values for each variable with every other variable. We found that the finding of being missing for most variables was not associated with each other (r < .25), except for scores on science achievement, which were highly associated with data being missed on items that measured self-esteem and locus of control (r = .678). Therefore, we eliminated the science achievement variable. As a result, we ended up with a construct of academic achievement explained by reading and mathematics achievement scores.



As a preliminary step, we plotted mean occupational aspiration prestige scores of the gender--disability type subgroups at each of the five data collection points (see Figure 2). Overall, occupational aspirations expressed by young people in our sample were in the moderate to moderate-high prestige range. Of particular interest in this analysis, however, was the trajectory of aspirations over time. We noted several interesting descriptive patterns.


Females with learning disabilities reported higher prestige aspirations than did males with learning disabilities through the data collection period. Females with learning disabilities, however, expressed their highest aspirations in 10th grade, whereas males with learning disabilities reported their highest aspirations in the 12th grade. Females with behavioral disorders also reported higher aspirations than their male peers with behavioral disorders at each data point. Though the general trend line for males with behavioral disorders was roughly curvilinear in nature, Figure 2 shows that the trend line for females with behavioral disorders was vastly different.

Males with either learning disabilities or behavioral disorders expressed aspirations that were close in prestige level and trajectory throughout high school. After school completion, the aspirations of males with learning disabilities declined, while aspirations for males with behavioral disorders dipped slightly then stabilized. Trajectories for female adolescents with learning disabilities and behavioral disorders were not similar. Following high school completion, the prestige of aspirations for females with learning disabilities declined, but aspirations for females with behavioral disorders actually spiked upward.


We first examined time-specific occupational aspiration prestige mean scores. Mean values increased until the third time point and then showed a downturn. However, rates of mean change between time points were not equal. Considering this curvilinear trend, as well as the explicit transition represented between the third and fourth time points--the period between high school graduation and 2 years post-high school--a piecewise latent growth model was selected. Piecewise trajectory modeling is used when the trajectory shows a combination of two or more linear trends (Bollen & Curran, 2006). Because our model showed two trends, the rates of mean change between time points were not equal, and data were collected at inconsistent time points during the third and fourth follow-ups, we adopted a piecewise growth model with freely estimated loadings.

Our model showed two trends. Prior to the transition, the slope for the first time point (Grade 8) was fixed at zero; and the third time point (Grade 12) at 1. The slope for the second time point (Grade 10) was freely predicted. After the transition, the slope for the third time point (Grade 12) was fixed at zero, and the fifth point (8 years after high school) at 1. The slope for the fourth time point (2 years after high school) was estimated freely. Because no significant difference between a model with equal variances and one with unequal variances was found [[DELTA][chi square](4) = 6.726, p = .151], we retained the restriction of equal error variances over time in the model. Therefore, the current study reports findings of a piecewise model with equal error variance over time.


Mplus 6.0 with a maximum likelihood (ML) estimation was used to test our model (Muthen & Muthen, 1998-2010). To assess the overall latent growth model fit to the data, we used chi-square statistics, as well as the following indices with recommended cutoff criteria: SRMR [less than or equal to] .08, RMSEA [less than or equal to] .08, CFI [greater than or equal to] .95, and TLI [greater than or equal to] .90 (Hu & Bentler, 1999; Marsh, Hau, & Wen, 2004). The overall fit indices strongly suggested that the hypothesized model fit the data well [[chi square](8, N = 585) = 8.529, p = .384; SRMR = .035; RMSEA = .011 (a 90% confidence interval of .000 and .050); CFI = .998; and TLI = .997].

When total growth in occupational aspirations before high school completion was set at 1, 93.4% of total positive change was achieved between Grades 8 and 10, whereas 6.6% of the total positive change occurred between Grades 10 and 12. When setting total growth in occupational aspiration from the 12th grade to 8 years after graduation at 1, 38.5% of the total negative change was observed between Grade 12 and 2 years after graduation, while 61.5% of the total negative change was obtained during the period between 2 and 8 years after graduation. The intercept of the unconditional model was 4.323, indicating that, on average, occupational aspiration scores were 4.323 units in the eighth grade. Before high school completion, the average rate of change in the slope was .374. This means that on average, students with high-incidence disabilities aspired to occupations with medium-level prestige requiring at least some college education. (These numbers reflect transformed SEI scores [Stevens & Cho, 1985] and can be easily converted to their original state by multiplying them by 10 to counteract our transformation to support analysis. An SEI score of 43.23 reflects a medium level of prestige.) Aspiration scores increased .343 points between Grades 8 and 10, but scores increased only .180 points between Grades 10 and 12. After high school, the average rate of change in slope was -.510, meaning that, on average, occupational aspiration scores decreased .510 points by 8 years after high school. Occupational aspiration scores decreased .204 points between Grade 12 and 2 years after high school completion, whereas scores decreased .305 points between 2 years after high school and 8 years after high school. Both the mean intercept and mean slopes differed significantly from zero (p < .001).

Statistical significance for the variance of intercept (1.852) and slope (1.185 for pretransition) suggested participants varied in their initial occupational aspiration scores and their rates of change. However, the variance of slope for posttransition (.885) did not significantly vary. The statistically significant negative covariance between the intercept and slope for the pretransition period (-.695; r = -.462) implied that young people with higher initial aspirations had relatively slower change than did those with lower initial aspirations. [R.sup.2] values were .444, .408, .417, .380, and .404, respectively, indicating that variability in occupational aspiration scores explained by the underlying latent growth factors ranged from 38% to 44%. Table 3 shows parameter estimates and standard errors.


To test the effect of covariates on latent growth factors, we first fixed the freely estimated slopes for the second time point (Grade 10) and the fourth time point (2 years after high school graduation) at .934 and .385, respectively. Next, gender, disability status, SES, locus of control, self-esteem, and eighth-grade academic achievement were introduced to the unconditional piecewise growth model. Before testing our hypothesized model, we first tested whether the measurement model (i.e., all variables and growth factors were correlated) fit the data well. Overall, model indices indicated the model fit the data well [[chi square](2(79, N = 585) = 97.235, p = .080; SRMR = .039; RMSEA = .020 (a 90% confidence interval of.000 and .032); CFI = .985; and TLI = .977].

The chi-square model fit of the conditional model showed a good fit to the data [[chi square](80, N = 585) = 98.999, p = .074]. Also, overall model fit indices suggested that the model fit the data well [SRMR = .039; RMSEA = .020 (a 90% confidence interval of .000 and .032); CFI =.984; and TLI = .977]. The only covariate significantly associated with the intercept factor was academic achievement (.076), indicating that adolescents with higher academic achievement had higher aspirations in Grade 8. The standardized estimate for academic achievement on the intercept factor (.329) was approximately three times greater than SES (.108) or gender (.108). Although locus of control was not significantly associated with the intercept, the standardized estimate for this path (.263) was almost as large as academic achievement. The prior-slope (growth before high school completion) was significantly associated only with SES (.418), indicating that higher SES was associated with a positive change in the slope of occupational aspiration scores across the three time points before high school completion. Specifically, one-unit increase in SES was associated with a .418-unit increase in the pretransition slope. Moreover, a level of SES one standard deviation above the mean was associated with a prior-slope level about .30 standard deviations above the mean, controlling for other covariates on the prior-slope factor. The magnitude of the standardized estimate for locus of control on the intercept factor was greater than for other covariates, except for academic achievement, on the intercept. The magnitude of the locus of control standardized estimate on the prior-slope factor was two times greater than other covariates.

After high school, only disability status had a significant association with aspiration growth rate. The conditional mean slope was .675 units lower for individuals with learning disabilities than for those with behavioral disorders. The estimated standardized estimate for the direct effect of disability status on the post-slope factor was -.308, controlling for other covariates. After including covariates, [R.sup.2] values at each time point were .439, .403, .412, .377, and .405, respectively, indicating that the proportion of the observed variability in occupational aspirations explained by the underlying latent growth factors ranged from 38% to 44%. The inclusion of covariates explained 31% of the variance of the intercept factor, and 20% and 15% of the variances of the prior- and post-slope factors, respectively. Table 4 shows coefficient estimates and standard errors for the effects of covariates on the intercepts and slopes.


In the unconditional model, overall fit indices indicated that the hypothesized model fit the data well. All parameter estimates were consistent with the hypotheses, and [R.sup.2] values were reasonable. After including covariates with latent variables, overall model fit indices were still reasonable and suggested that the model fit the data well. [R.sup.2] values were similar to the unconditional model and standard errors were not large. We tried to check the modification indices, but our use of multiple imputation data sets did not support this analysis. Although some path coefficients were not statistically significant, these paths were not critical to our model. Overall, the conditional piecewise latent curve model with equal error variances had a reasonable and stable fit.


Several interesting findings emerged from our analysis, including information about the long-term trends and changes in occupational aspirations, and the influence of selected factors on both the development and trajectories of the occupational aspirations of our target population.


Perhaps the most important outcome of this study revealed the trajectory of occupational aspirations for people with high-incidence disabilities. We expected to find aspiration change patterns similar to those reported for adolescents without disabilities (Lee & Rojewski, 2009), albeit lower in prestige scores (Kortering, Braziel, & McClannon, 2010; Rojewski, 1996, 1999). Our analysis revealed this to be the case. In general, we found a curvilinear trajectory of occupational aspirations that started in the eighth grade with a positive trend continuing through high school, at which point a negative trend began that existed until data collection ceased 8 years after high school completion. Closer visual inspection of occupational aspiration patterns, based on gender-disability type interactions, however, revealed more chaotic and disruptive patterns. This was particularly true for young people with behavioral disorders, and especially so for females with behavioral disorders.

Lower prestige aspirations are not necessarily a negative outcome for people with or without disabilities. As a group, however, the occupational aspirations of young people with high-incidence disabilities were consistently lower in prestige than the aspirations of their peers without disabilities, indicating more restricted postsecondary work and education options. These self-imposed restrictions occurred early and were, for the most part, consistent throughout our analysis timeframe. Thus, it appears that people with high-incidence disabilities are more likely to restrict their boundaries of tolerable effort (Gottfredson, 2005). Gottfredson theorized that people rule out occupations if they are considered to be too difficult to attain with reasonable effort or pose too high a risk of failure. The boundary of tolerable effort is based to a large extent on academic ability.

Academic ability, and perhaps academic self-efficacy (Baird, Scott, Dearing, & Hamill, 2009), is key to understanding the results we obtained. Although we did not directly study the link of academic achievement (ability) to occupational aspirations, the connection between the two has been well established (Hotchkiss & Borow, 1996). It is likely that the connection between educational difficulties typically experienced by adolescents with high-incidence disabilities and restricted (lower prestige) occupational aspirations may play an important role in understanding our results, because prestige ratings of occupations are based, in part, on required levels of education (Stevens & Cho, 1985). We speculate that the lowered aspirations observed throughout the time-span of our analysis was the result, in part, of the academic difficulties experienced by adolescents with high-incidence disabilities throughout middle and high school. Thus, these aspirations may be perceived as realistic in terms of acknowledging academic difficulties, but may also reflect feelings of academic frustration or limited academic self-efficacy. The latter may be especially true in light of the fact that most of the high-incidence disability group in our analysis would have had average or near-average intelligence. If this is true, then the lower prestige occupational aspirations reported may have been unnecessarily restrictive, based on individuals' perceptions of their academic limitations or negative academic self-efficacy. Because this is speculative, additional investigation is warranted to address this important issue.

The rate of change in occupational aspirations was somewhat unexpected. In our analysis, over 90% of change in adolescents' expressed aspirations occurred between the eighth and 10th grades. Thus, it appears that adolescents with high-incidence disabilities are more likely to restrict their occupational (and educational) futures at an early age. This premature restriction could explain problems often reported for this population, such as lower postsecondary enrollment and attainment (Stodden et al., 2002; Wagner et al., 2005) and restricted labor force participation (Barkley, 2006). Rojewski (1996) suggested that even though adolescents with disabilities may raise their aspirations as they progress through high school, this may often be too late to attain basic educational prerequisites or occupational experiences needed to prepare for or enter certain advanced career paths.

Significant challenges present themselves to adolescents as they transition from middle school to high school, including more challenging academic coursework, greater autonomy, added social pressures, and physical development (Letrello & Miles, 2003). Future investigations should examine the role of special education services on supporting the career-related needs of adolescents with disabilities during this turbulent time. Our data could not determine whether the lack of positive change in aspirations during high school resulted from a lack of school support services that emphasized self-determination and career/transition planning, or from the effects of academic delay and poor self-esteem or bias. These are critical issues, given changes in educational policy regarding transition planning and implementation.

Of course, other explanations can be advanced for the stagnant or stable trends (depending on perspective) in occupational aspirations that were observed, including our decision to use occupational prestige codes, theoretical explanations, and the influence of disability characteristics. We chose the Socioeconomic Index (SEI) developed by Stevens and Cho (1985) to code the prestige of occupational aspirations. The SEI is an established measure of occupational prestige and, while advantages to using this approach outweigh disadvantages, the scheme often groups together individual occupations into broader categories for coding purposes. For example, various engineering- or science-related occupations all receive the same SEI code, based on prestige, even though each distinct occupation might receive different codes if government or work personality coding schemes were used. Therefore, it is likely that our broader approach to coding obscured some movement in expressed aspirations over time.

Gottfredson's (2005) theory of career compromise and circumscription provided some structure to guide our understanding of the development and growth of occupational aspirations. According to the theory, an orientation to occupational prestige in forming career choice options becomes prominent around 9 to 13 years of age, whereas additional refinement of aspirations based on personal interests and self-appraisal occurs from age 14 on. In our sample, the bulk of change in aspiration prestige scores occurred between the eighth and 10th grades, suggesting that adolescents with high-incidence disabilities prematurely restricted their career options. Our results clearly underscore the significant negative effect that disability can have on successfully addressing the career development tasks faced by all adolescents. As a result of restricted career alternatives, adolescents with high-incidence disabilities may be more likely to experience diminishing opportunities for career growth and increasingly limited access to career exploration and work opportunities (Shahnasarian, 2001). The early occurrence of restrictions to aspirations is especially troubling in that the adolescents we studied experienced this phenomenon before they had even entered high school when career-related programs are more likely to be available.

Possible interpretations of stagnant or stable occupational aspirations from 10th to 12th grades could include identified career choices and plans, or reflections of perceived barriers and bias inherent in school or the work community. The moderate prestige levels of occupational aspirations and limited trajectories we observed may reflect a combination of negative self-concept, past experiences, perceived lack of opportunities or immature affective or cognitive career choice-related skills (Rojewski, 1996; Super et al. 1996). Gottfredson (1986) suggested that cultural or social isolation and differential treatment that often accompanies special group status lead to restricted experiences and opportunities and can contribute to lowered occupational aspirations.

The time immediately after high school completion can be a difficult time of adjustment for all adolescents, but particularly so for individuals with disabilities (Barkley, 2006; Day & Newburger, 2002; Stodden et al., 2002; Wagner et al., 2005). A negative or downward trend, which was not unexpected, represents movement toward less prestigious but more accessible occupations, and is an appropriate adjustment to occupational goals. This activity likely results from crystallizing career interests, assessing strengths and abilities, encountering work-related challenges and competition, and recognizing educational and career opportunities or barriers. Gottfredson (2005) characterized the process of career compromise as the process of letting go of preferred occupations for less compatible, but more accessible ones. However, only about one third of the negative trend in expressed aspirations was observed during the first 2 years after high school, whereas the remainder occurred from 2 to 8 years postsecondary. Our analysis did not determine why this pattern of career compromise occurred. We did not consider activities that might delay career decision-making such as postsecondary education, vocational training, or decisions that might make career compromise a moot point such as full-time employment. Future investigations should model these factors to determine their influence on the career compromise process.


We were somewhat surprised to find what was and was not significant when the influence of selected variables on the development and trajectory of occupational aspirations was examined. For example, academic achievement was the only factor that was significant in explaining occupational aspirations before high school completion. Adolescents with higher academic achievement generally reported higher aspirations. This connection, however, was evident only in the eighth grade. Past studies that looked at the occupational aspirations of adolescents with learning disabilities (Rojewski, 1996, 1999) and of a sample of adolescents without disabilities (Lee & Rojewski, 2009) from longitudinal perspectives did not report a similar influence for achievement. However, connections between academic achievement (Arbona, 2000; Lent, 2005) or educational aspirations (Rojewski & Yang, 1997) and occupational aspirations have been made.

The academic achievement--occupational aspirations connection makes sense when considering the highly academic nature of high-incidence disabilities, particularly learning disabilities. A heightened focus on academics experienced by most middle school students might take on added emphasis for those young people who are involved in developing individualized education programs, receiving support in enhancing their self-determination skills, or discussing plans for curriculum choices or possible career goals. Further study on the influence of academic achievement on career development is particularly relevant for adolescents with high-incidence disabilities. Arbona (2000) noted that learning deficits are cumulative. The effects of early academic problems are progressively more difficult to overcome making the range of available career choices increasingly constrained throughout adolescence.

After high school, disability status was significant in explaining occupational aspirations. This may be attributable to differences in the trends we observed for young adults with behavioral disorders and learning disabilities. Throughout the data-collection period, young people with learning disabilities had a trajectory much more like people without disabilities, only lower. People with behavioral disorders had aspirations patterns that were increasingly divergent from their peers with learning disabilities.

We expected gender to have some influence on occupational aspirations, particularly in early adolescence. However, no statistically significant gender differences were detected. Females expressed higher aspirations at all data points, but also presented greater score variability than their male counterparts. Even so, the literature has consistently shown that females with disabilities tend to experience poorer occupational outcomes than their male counterparts (Wagner et al., 2005).

Initially, we were puzzled to find that SES did not present consistent effects on occupational aspirations but varied depending on whether we considered aspirations at separate data points or the trajectory of aspirations over time, specifically the prior-slope (i.e., Grades 8-12). Interestingly, these findings are directly opposite of those reported from a sample without disabilities taken from the same database (Lee & Rojewski, 2009). Past studies of people with learning disabilities (Rojewski, 1996, 1999) and samples without disabilities (Lee & Rojewski, 2009; Rojewski & Yang, 1997) have shown SES to have an early and persistent influence on occupational aspirations. That early influence was not observed in our results. It is possible that support services offered through special education during early school grades may provide some type of moderating effect on the influence of SES on occupational aspirations for these early adolescents with high-incidence disabilities. In other words, special education might initially reduce or smooth over the potential negative effects of SES on the development or expression of aspirations during middle school. Regardless, SES did negatively affect the long-term development of aspirations. The overall low level of SES in our sample may have contributed to this anomalous finding. Whatever the reason, future study should examine the role of special education in elementary grades to the formation of occupational aspirations and the diminished influence of SES in adolescence, which has not, to date, been anticipated or considered.

Finally, we examined several factors that had some connection to the broader concept of self-determination, that is, the notion that people with disabilities should advocate and act on their own behalf when planning for and implementing their future (Wehmeyer et al., 2007). One of the measures selected, locus of control, exerted a relatively strong effect on occupational aspirations in the eighth grade, even though results were not significant. Given limited attention on the role of self-determination on career issues for adolescents with disabilities and the possible connections indicated by our findings, we believe that further investigation on this topic is warranted.

One strength of this study was the use of a large nationally representative longitudinal database to examine the influence of certain factors on the (a) formation of and (b) developmental changes in occupational aspirations of individuals with high-incidence disabilities at five points over a 12-year period spanning from adolescence through early adulthood. Whereas results contribute to our understanding of this phenomenon, additional questions remain unanswered. For example, the question of whether special education services enhance or dampen the career behavior of this population in middle and high school has not been adequately examined. This would be a fruitful area for future study and seems essential when considering the importance of transition planning. Another area that warrants continued attention is the role of disabilities on career development. It seems clear that the presence of high-incidence disabilities altered occupational aspirations patterns in our study, but reasons for these deviations are not clear. Further work is needed to determine the influence of both psychological and sociological factors on the career behavior and transition preparation of adolescents and young adults with high-incidence disabilities.

Manuscript received January 2011; accepted May 2011.


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Address correspondence concerning this article to In Heok Lee, Department of Workforce Education, Leadership, & Social Foundations, University of Georgia, Athens, GA 30602 (e-mail:

The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R324A100232 to the University of Georgia. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education.




University of Georgia


National Centre for Vocational Education Research, Australia


JAY W. ROJEWSKI (Georgia CEC), Professor, Department of Workforce Education, Leadership, & Social Foundations; IN HEOK LEE, Postdoctoral Fellow, Department of Workforce Education, Leadership, & Social Foundations; and NOEL GREGG (Georgia CEC), Associate Dean for Research, College of Education, University of Georgia, Athens. SINAN GEMICI, Postdoctoral Fellow, National Centre for Vocational Education Research, Adelaide, Australia.

Participants'Demographic Characteristics by Gender and Disability Type

 (N = 585)

 Male Female
 (n = 341) (n = 244)

Characteristic M SE M SE

Socioeconomic status -0.039 0.043 -0.319 0.051
Time 1: Occupational aspirations 4.326 0.134 4.316 0.151
Time 2: Occupational aspirations 4.434 0.124 4.993 0.150
Time 3: Occupational aspirations 4.647 0.129 4.856 0.168
Time 4: Occupational aspirations 4.274 0.197 4.707 0.148
Time 5: Occupational aspirations 3.976 0.108 4.526 0.151
Reading achievement score 43.989 0.453 44.350 0.530
Mathematics achievement score 45.115 0.415 43.617 0.475
Science achievement score 46.618 0.521 44.987 0.489
Item 1: Self-esteem 1.638 0.034 2.031 0.043
Item 2: Self-esteem 1.783 0.039 1.891 0.045
Item 3: Self-esteem 1.828 0.038 2.101 0.048
Item 1: Locus of control 2.668 0.047 2.496 0.054
Item 2: Locus of control 2.899 0.044 2.723 0.054
Item 3: Locus of control 2.587 0.049 2.349 0.060

 (N = 585)

 (n = 123) (n = 462)

Characteristic M SE M SE

Socioeconomic status -0.194 0.072 -0.145 0.037
Time 1: Occupational aspirations 4.467 0.196 4.283 0.113
Time 2: Occupational aspirations 4.734 0.206 4.649 0.098
Time 3: Occupational aspirations 4.861 0.218 4.701 0.119
Time 4: Occupational aspirations 4.586 0.185 4.402 0.100
Time 5: Occupational aspirations 4.775 0.180 4.054 0.100
Reading achievement score 47.876 0.847 43.145 0.361
Mathematics achievement score 46.729 0.785 43.894 0.333
Science achievement score 48.045 0.931 44.902 0.388
Item 1: Self-esteem 2.037 0.067 1.739 0.030
Item 2: Self-esteem 1.916 0.072 1.805 0.032
Item 3: Self-esteem 2.108 0.074 1.898 0.033
Item 1: Locus of control 2.450 0.085 2.635 0.039
Item 2: Locus of control 2.670 0.081 2.867 0.038
Item 3: Locus of control 2.552 0.088 2.471 0.042

 Behavioral Disorders (BD)
 (n = 123)

 Male Female
 (n = 66) (n = 57)

Characteristic M SE M SE

Socioeconomic status -0.022 0.099 -0.393 0.097
Time 1: Occupational aspirations 4.355 0.280 4.597 0.294
Time 2: Occupational aspirations 4.472 0.286 5.037 0.292
Time 3: Occupational aspirations 4.688 0.256 5.060 0.318
Time 4: Occupational aspirations 4.431 0.246 4.766 0.284
Time 5: Occupational aspirations 4.410 0.249 5.198 0.276
Reading achievement score 47.095 1.277 48.780 1.081
Mathematics achievement score 47.326 1.134 46.038 1.081
Science achievement score 50.071 1.396 45.699 1.150
Item 1: Self-esteem 1.879 0.091 2.220 0.095
Item 2: Self-esteem 1.802 0.091 2.048 0.112
Item 3: Self-esteem 2.016 0.091 2.213 0.118
Item 1: Locus of control 2.433 0.117 2.470 0.121
Item 2: Locus of control 2.786 0.106 2.536 0.120
Item 3: Locus of control 2.554 0.128 2.549 0.121

 Learning Disabilities (LD)

 Male Female
 (n = 275) (n = 187)

Characteristic M SE M SE

Socioeconomic status -0.043 0.047 -0.296 0.059
Time 1: Occupational aspirations 4.318 0.148 4.230 0.170
Time 2: Occupational aspirations 4.425 0.127 4.979 0.184
Time 3: Occupational aspirations 4.637 0.147 4.794 0.181
Time 4: Occupational aspirations 4.236 0.118 4.689 0.172
Time 5: Occupational aspirations 3.872 0.119 4.321 0.171
Reading achievement score 43.244 0.468 42.999 0.578
Mathematics achievement score 44.584 0.429 42.879 0.517
Science achievement score 45.790 0.541 43.596 0.532
Item 1: Self-esteem 1.580 0.035 1.974 0.048
Item 2: Self-esteem 1.778 0.044 1.843 0.048
Item 3: Self-esteem 1.782 0.042 2.067 0.051
Item 1: Locus of control 2.725 0.051 2.504 0.060
Item 2: Locus of control 2.926 0.048 2.780 0.059
Item 3: Locus of control 2.596 0.053 2.289 0.068

Means, Univariate Skewness and Kurtosis, Variances, and Covariances
for All Covariates and Measures of Occupational Aspirations

Variable 1 2 3 4 5 6

1. Time 1: OA 4.144
2. Time 2: OA 1.189 3.878
3. Time 3: OA 1.248 1.727 3.762
4. Time 4: OA 1.000 1.296 1.560 3.803
5. Time 5: OA 1.148 1.089 1.316 1.135 3.981
6. Reading 3.335 3.266 3.589 3.143 2.485 67.455
7. Mathematics 3.765 3.498 3.649 3.199 3.318 34.907
8. Item 1: SE -0.091 0.044 0.039 0.019 0.018 0.417
9. Item 2: SE -0.074 -0.017 -0.147 -0.096 -0.111 -0.374
10. Item 3: SE -0.044 0.070 -0.020 -0.018 -0.043 0.172
11. Item 1: LC 0.260 0.170 0.184 0.181 0.097 0.410
12. Item 2: LC 0.270 0.022 0.068 0.126 0.145 0.631
13. Item 3: LC 0.311 0.240 0.326 0.219 0.292 1.663
14. Female -0.002 0.136 0.051 0.105 0.134 0.088
15. $E$ 0.354 0.502 0.468 0.445 0.446 2.172
16. LD -0.031 -0.014 -0.027 -0.028 -0.120 -0.786
Ma 4.322 4.667 4.734 4.455 4.205 44.139
Skewness 0.120 -0.166 -0.346 0.024 0.144 1.020
Kurtosis -1.666 -1.608 -1.460 -1.614 -1.581 0.535

Variable 7 8 9 10 11 12

1. Time 1: OA
2. Time 2: OA
3. Time 3: OA
4. Time 4: OA
5. Time 5: OA
6. Reading
7. Mathematics 56.128
8. Item 1: SE 0.345 0.443
9. Item 2: SE -0.406 0.165 0.493
10. Item 3: SE -0.099 0.256 0.188 0.532
11. Item 1: LC 0.702 -0.082 -0.055 -0.092 0.715
12. Item 2: LC 0.915 -0.103 -0.084 -0.120 0.289 0.660
13. Item 3: LC 1.467 -0.033 -0.067 -0.060 0.179 0.238
14. Female -0.364 0.096 0.026 0.066 -0.042 -0.043
15. $E$ 2.501 -0.005 -0.028 -0.041 0.095 0.116
16. LD -0.471 -0.049 -0.018 -0.035 0.031 0.033
Ma 44.490 1.802 1.828 1.942 2.596 2.825
Skewness 1.090 0.603 0.746 0.628 -0.165 -0.479
Kurtosis 0.849 0.702 0.937 0.530 -0.554 -0.098

Variable 13 14 15 16

1. Time 1: OA
2. Time 2: OA
3. Time 3: OA
4. Time 4: OA
5. Time 5: OA
6. Reading
7. Mathematics
8. Item 1: SE
9. Item 2: SE
10. Item 3: SE
11. Item 1: LC
12. Item 2: LC
13. Item 3: LC 0.831
14. Female -0.058 0.243
15. $E$ 0.158 -0.068 0.643
16. LD -0.013 -0.010 0.008 0.166
Ma 2.488 0.417 -0.156 0.790
Skewness 0.048 0.337 0.238 -1.426
Kurtosis -0.801 1.893 -0.257 0.033

Note. Skewness and kurtosis were calculated using data without missing
values. OA = occupational aspirations; SE = self-esteem; LC = locus of
control; Female = influence of gender; LD = disability status.

(a) Percentage of variables for female and LID.

Parameter Estimates and Standard Errors for an Unconditional Model

Parameter Estimate SE

Factor means
 Intercept ([[mu].sub.a]) 4.323 ** 0.100
 Slope 1 ([[mu].sub.[beta]1]) .374 ** 0.114
 Slope 2 ([[mu].sub.[beta]2]) -.510 ** 0.111
Factor variances
 Intercept ([[psi].sub.[alpha][alpha]]) 1.852 ** 0.386
 Slope 1 ([[psi].sub.[beta]1[beta]1]) 1.185 ** 0.443
 Slope 2 ([[psi].sub.[beta]2[beta]2]) 0.885 0.468
Factor covariances
 Intercept-Slope 1 -.695 * 0.333
 Intercept-Slope 2 -0.061 0.232
 Slope 1-Slope 2 -0.424 0.307

Parameter t Estimate

Factor means
 Intercept ([[mu].sub.a]) 43.292
 Slope 1 ([[mu].sub.[beta]1]) 3.274
 Slope 2 ([[mu].sub.[beta]2]) -4-594
Factor variances
 Intercept ([[psi].sub.[alpha][alpha]]) 4.796 1.000
 Slope 1 ([[psi].sub.[beta]1[beta]1]) 2.677 1.000
 Slope 2 ([[psi].sub.[beta]2[beta]2]) 1.891 1.000
Factor covariances
 Intercept-Slope 1 -2.088 -0.462
 Intercept-Slope 2 -0.262 -0.046
 Slope 1-Slope 2 -1.383 -0.409

* p < .05, ** p < .01.

Parameter Estimates and Standard Errors for a Conditional Model

Parameter Unstandardized SE

Factor means
 Intercept ([[mu].sub.[alpha]]) 4.271 ** 0.226
 Slope 1 ([[mu].sub.[beta]1]) 0.190 0.268
 Slope 2 ([[mu].sub.[beta]2]) -0.089 0.264
Residual variances and covariances
 Intercept 1.269 ** 0.341
 Slope 1 ([[psi].sub.[beta]1][beta]1) .949 * 0.429
 Slope 2 ([[psi].sub.[beta]2[beta]2]) 0.758 0.451
 Intercept-Slope 1 -0.567 0.323
 Intercept-Slope 2 -0.086 0.232
 Slope 1-Slope 2 -0.372 0.298
Measurement model
 Self-esteem by
 Item 1 1.000 0.000
 Item 2 0.631 0.078
 Item 3 0.953 0.099
 Locus of control by
 Item 1 1.000 0.000
 Item 2 1.206 0.147
 Item 3 0.878 0.123
 Academic achievement by
 Reading 1.000 0.000
 Mathematics 1.045 0.094
 Self-esteem [right arrow] Intercept -0.171 0.277
 Locus of control 0.744 0.398
 [right arrow] Intercept
 Academic achievement .076 ** 0.026
 [right arrow] Intercept
 SES [right arrow] Intercept 0.181 0.156
 Female [right arrow] Intercept 0.299 0.236
 LD [right arrow] Intercept -0.060 0.225
 Self-esteem [right arrow] Slope 1 0.127 0.310
 Locus of control [right arrow] Slope 1 -0.583 0.470
 Academic achievement [right arrow] -0.024 0.028
 Slope 1
 SES [right arrow] Slope 1 .418 ** 0.159
 Female [right arrow] Slope 1 0.343 0.279
 LD [right arrow] Slope 1 0.139 0.258
 Self-esteem [right arrow] Slope 2 -0.312 0.281
 Locus of control [right arrow] Slope 2 0.133 0.348
 SES [right arrow] Slope 2 -0.066 0.131
 Female [right arrow] Slope 2 0.240 0.243
 LD [right arrow] Slope 2 -.675 ** 0.254

Parameter t Standardized

Factor means
 Intercept ([[mu].sub.[alpha]]) 18.926
 Slope 1 ([[mu].sub.[beta]1]) 0.710
 Slope 2 ([[mu].sub.[beta]2]) -0.338
Residual variances and covariances
 Intercept 3.718 0.693
 Slope 1 ([[psi].sub.[beta]1][beta]1) 2.211 0.804
 Slope 2 ([[psi].sub.[beta]2[beta]2]) 1.680 0.851
 Intercept-Slope 1 -1.756 -0.504
 Intercept-Slope 2 -0.372 -0.084
 Slope 1-Slope 2 -1.250 -0.436
Measurement model
 Self-esteem by
 Item 1 0.000 0.784
 Item 2 8.043 0.469
 Item 3 9.654 0.682
 Locus of control by
 Item 1 0.000 0.567
 Item 2 8.179 0.712
 Item 3 7.166 0.462
 Academic achievement by
 Reading 0.000 0.704
 Mathematics 11.064 0.806
 Self-esteem [right arrow] Intercept -0.616 -0.067
 Locus of control 1.871 0.263
 [right arrow] Intercept
 Academic achievement 2.996 0.329
 [right arrow] Intercept
 SES [right arrow] Intercept 1.160 0.108
 Female [right arrow] Intercept 1.267 0.108
 LD [right arrow] Intercept -0.265 -0.018
 Self-esteem [right arrow] Slope 1 0.409 0.059
 Locus of control [right arrow] Slope 1 -1.241 -0.268
 Academic achievement [right arrow] -0.873 -0.127
 Slope 1
 SES [right arrow] Slope 1 2.629 0.315
 Female [right arrow] Slope 1 1.232 0.158
 LD [right arrow] Slope 1 0.539 0.053
 Self-esteem [right arrow] Slope 2 -1.110 -0.187
 Locus of control [right arrow] Slope 2 0.381 0.062
 SES [right arrow] Slope 2 -0.503 0.137
 Female [right arrow] Slope 2 0.988 -0.053
 LD [right arrow] Slope 2 -2.659 0.308

Note: SES = socioeconomic status; Female = influence of gender;
LID = disability status.

p<.05,* p<.01.
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Author:Rojewski, Jay W.; Lee, In Heok; Gregg, Noel; Gemici, Sinan
Publication:Exceptional Children
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2012
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