A career readiness typology and typal membership in middle school.

This study explored a career readiness typology of 629 8th-grade students using the Career Factors Inventory. Data revealed developmentally appropriate elevated needs for information, specifically self-knowledge. In addition, cluster analysis demonstrated the heterogeneity of career development needs of 8th-grade students. Typal membership revealed little variance on sociodemographic factors. Career development implications are presented.

**********

Since the inception of the school counseling profession, career development has been the fundamental purpose and a primary focus for the services that school counselors offer (Niles & Harris-Bowlsbey, 2002). Today, the American School Counselor Association National Standards for career development focus on helping students acquire developmentally appropriate career awareness, employment readiness, and the knowledge and skills needed to achieve their career goals (Campbell & Dahir, 1997).

Although counselors seek to advance students' career development across all grade levels, more career development activities are typically offered to high school students than to middle school or elementary school students (Cook et al., 1996). Researchers pay less attention to career development in these early years, even though research results suggest that tentative college plans and career preferences begin emerging in elementary school (Trice & King, 1991). Parents report a desire for their children to receive career guidance in middle school more than at any other educational level (Arrington, 2000). In addition, career theorists suggest that factors such as curiosity, fantasy, identification with workers, gender stereotyping, race, class, and social valuation influence career development processes during the elementary and middle school years (Ginzberg, 1952; Gottfredson, 1981; Super, 1990; Trice, Hughes, Odom, & Woods, 1995). These early school experiences contribute to an emerging identity that develops during early adolescence.

Within-group developmental differences are typical for middle school students. Developmentally, middle school students begin cognitive and moral processing in which concepts such as ambiguity, values, ideals, and fairness play a role in shaping their identity development. They engage in developmental tasks related to identity achievement and role confusion (Erickson, 1968; Marcia, 1980). For example, ability becomes an important determinant for membership in certain groups, and career aspirations are tested in novel experiences (e.g., new extracurricular activities, part-time work; Herr, Cramer, & Niles, 2004; Miller, 1988). These identity formation processes in middle school ideally include increased exploration in relation to career development (Marcia, 1980).

In middle school, students are typically not ready to commit to career choices. They are, however, expected to make curriculum choices that may narrow future career opportunities. This initial choice point has considerable implications for students' future educational and career plans. A question can be raised about how developmentally ready (in terms of level of exploration, awareness of implications, and maturity) students are for making these initial career decisions.

Career maturity, defined as one's readiness for career decision making (Super, 1984), has received a great deal of attention in the literature and is one of the most prevalent variables in research on adolescent career development (Powell & Luzzo, 1998). Conceptually, career maturity has been associated with factors such as aptitude scores, interests, school subject preferences, part-time employment experiences, and engagement in the educational planning process (Vondracek & Schulenberg, 1986; Westbrook, Sanford, & Donnelly, 1990). For example, curricular tracks often serve as a proxy for ability levels, so the relationship between career maturity and curriculum could be mediated by ability levels (Khan & Alvi, 1985). In fact, the correlations between career maturity and academic achievement suggest that career maturity measures may relate more to scholastic aptitude, resulting in a cognitive career choice process (e.g., career aspirations based primarily on achievement). Our understanding of processes related to career maturity is limited because few researchers have investigated middle school career development, and even fewer have investigated career development processes related to middle school students from diverse backgrounds (Trusty & Niles, 2004; Westbrook, Sanford, Merwin, & Fleenor, 1988).

Although it is developmentally appropriate to be undecided about career aspirations, middle school students should be engaged in the career exploration process by developing a beginning understanding of how their crystallizing identity relates to future career options (Super, 1984). Marcia (1980) identified this as a moratorium stage in identity development, in which students are not necessarily committed to key roles and values but are actively engaged in the exploration process.

Consequently, middle school students who are undecided but engaged in the career development process by learning more about themselves in relation to career options are moving in a direction that is developmentally appropriate. Middle school students who are undecided and not engaged systematically in the career development process (termed diffuse in identity development literature) are at risk for encountering career development tasks for which they are not prepared (e.g., making appropriate curriculum choices, identifying appropriate postsecondary plans, developing employability skills). Additionally, students who have committed prematurely, without exploration, may be foreclosed in terms of career identity. It seems that distinct career identities, based on information and decision-making needs, may emerge in middle school.

Assessing, and understanding, the precise nature of career readiness in middle school may be especially relevant to identifying students who may encounter subsequent difficulties (e.g., career indecision) in their career development. One promising way to conceptualize student career decision making is the identification of career readiness types, which are homogeneous subgroups of students who display similar patterns of needs. By identifying career readiness types, counselors might be able to tailor interventions to meet individual needs. Cluster analysis has been used throughout numerous disciplines to understand the nature of individual differences related to dyslexia (Satz, Morris, & Fletcher, 1985) and learning disabilities (Konold, Glutting, & McDermott, 1997; McKinney & Speece, 1986). Through cluster analysis it is also possible to determine if demographic (e.g., gender, race) or school-related factors (e.g., achievement, ability), identified as salient in previous career development research (Gottfredson, 1981; Khan & Alvi, 1985; Miller, 1988; Phinney & Tarver, 1988), are also significant to career readiness types (e.g., decision-making needs).

The purpose of this study was to investigate the levels of career readiness and the career development needs of eighth-grade students. Specifically, we asked, (a) What are current levels of career readiness for eighth-grade students? (b) Can a career readiness typology be developed for eighth-grade students? and (c) Do sociodemographic factors influence typal membership?

Method

Participants

Six hundred and twenty-nine eighth-grade students, from four middle schools in the southeastern United States, participated in the study. These purposefully selected middle schools represented a divergent racial (i.e., from 1% to 76% minority), economic (i.e., from 18% to 37% lunch assistance), and ecological (i.e., two rural schools and two urban schools) sampling. The overall sample included representation of girls (n = 310; 49%) and minority students (30% African American, 4% Latino, and 3% Asian). As an indication of socioeconomic status (SES), the sample included 144 students (23% of the sample) who received free or reduced-price lunches. Fourteen percent of the students received special education services, and 3% received English as a second language services.

Instruments

The Career Factors Inventory (CFI; Chartrand, Robbins, Morrill, & Boggs, 1990) is a tool that measures antecedents to career indecision and may, therefore, be particularly useful when working with students in middle and high school. Lewis and Savickas (1995) noted that the CFI relates more to the crystallization and specification tasks than to implementation and stabilization in career development. The CFI can also be used to differentiate a client's career development needs, thus providing a guide for appropriate intervention (Chartrand et al., 1990).

CFI (Chartrand et al., 1990) data are grouped into two categories labeled information needs and decision needs. The category of information needs contains both the need for career information (e.g., factual data and experience) and the need for self-knowledge (e.g., self-definition and discovery). The category of decision needs examines the decision-making process itself, including career choice anxiety and generalized indecisiveness. The CFI contains 21 items that follow a Likert-type format. Kelly and Lee (2002) found that the CFI (as compared with similar instruments) was the most efficient and provided the broadest coverage to the domain of career decision problems.

A variety of analytic techniques have been used to ensure construct validity, and norms have been established with a diverse group of students (Chartrand & Robbins, 1997). Previous investigations (Lewis & Savickas, 1995) revealed adequate test-retest reliability (.76-.84), internal consistency (.83-.92), and construct and concurrent validity (e.g., gender and ethnic groups, pre- and postintervention, factor structure, convergent validity). Notably, the CFI is not related to intelligence or aptitude measures (Chartrand & Robbins, 1997), which have been shown to influence scores for career maturity (Career Maturity Inventory; Westboook et al., 1990). The coefficient alpha for internal consistency from previous investigations ranged from .73 to .84 (Chartrand et al., 1990), demonstrating adequate reliability.

Cluster Analysis

Raw scores on the CFI were calculated to provide norms for this sample similar to previous research with the CFI (Chartrand & Robbins, 1997). Cluster analysis was used to see if a viable typology emerged. For typology and typal membership, variable selection in cluster analysis was important to maximize subtype differences (Morris, Blashfield, & Satz, 1981). Therefore, the solution was balanced in order to allow equal weighting of students' information and decision needs. The four scales of the CFI served as the basis for cluster formation. Because the scaling of variables in a clustering algorithm can influence their relative contributions in the final solution, for the second and third research questions, all measures were standardized to the same T score metric (M = 50, SD = 10).

Demographic information and school performance data were collected from the participating middle schools through school databases. Categorical demographic variables of student race, gender, SES, special education, English as a second language (ESL), school membership (i.e., School 1, 2, 3, or 4), and school performance (i.e., grade point average [GPA], behavioral referrals, and absences) were formally evaluated. Informed consent documents were distributed, and CFI instruments were administered in various eighth-grade advisory-type classrooms by school counselors or advisory teachers in January. The data were sent to researchers without names or other identifying information beyond student identification numbers.

Results

The first research question, regarding current levels of career readiness for eighth-grade students, was addressed by consideration of means and standard deviations for the entire sample. Mean results for the total sample revealed that these variables helped define a profile that included a need for career information (M = 23.34; SD = 4.45), a need for self-knowledge (M = 15.49; SD = 3.49), career choice anxiety (M = 13.92; SD = 4.15), and generalized indecisiveness (M = 13.92; SD = 4.15).

In order to answer the final two research questions regarding a career readiness typology and the influence of sociodemographics on typal membership, we used a cluster analysis to provide an empirically based description of career readiness profiles in eighth grade. The two information needs and two decision needs were used to identify profiles through cluster analysis. The clustering strategy we adopted, as described in McDermott (1998), was similar to the one used elsewhere for identifying normative profiles (Glutting, McDermott, & Konold, 1997; Konold et al., 1997). Mean profile configurations for the resulting seven-cluster solution are presented in Table 1 and illustrated in Figure 1. Table 1 also provides other psychometric properties for each profile. The average H coefficient (measuring the internal cohesion of clusters; Tryon & Bailey, 1970) across profiles satisfied a priori expectations for internal cluster cohesion (H [greater than or equal to] .60), hereby supporting a homogeneous within-cluster representation. In addition, the average Euclidean distance (Rp; Cattell, 1949) across profiles also satisfied a priori expectations in support of external isolation (Rp < .40).

An overview of the seven cluster profiles is provided later in this section. It is important to note that the profile names were derived from the patterns of dips and rises that reflect the variation in needs of this sample of students as found through empirical methods. Profile names represent classification of students' combined information and combined decision-making needs (i.e., information needs/decision-making needs) based on magnitude differences of .5 standard deviation. For example, those needs within .5 standard deviation were termed average, whereas those above or below .5 standard deviation were termed high or low, respectively, and those above or below one standard deviation were called very high or very low, respectively.

[FIGURE 1 OMITTED]

The summaries below also consider the sociodemographic representation of student race, gender, SES (indicated by free or reduced-price lunches), special education classification (indicated by special needs, advanced or gifted [AG], or none), ESL, school membership, midyear calculations of GPA, number of behavior referrals, and number of absences. Cluster comparisons of the last three variables were conducted with separate one-way analyses of variance (ANOVAs). Results failed to reveal any statistically significant differences between clusters for student GPA, F(6, 604) = 1.77, p = .103, [R.sup.2] = .02. However, statistically significant between-cluster differences were observed on behavior, F(6, 604) = 4.00, p = .001, [R.sup.2] = .04, and absences, F(6, 606) = 3.40, p = .003, [R.sup.2] = .03. Follow-up pairwise cluster comparisons were conducted with Tukey tests. Statistically significant differences (p < .05) are described in the profile descriptions.

Observed demographic proportions within each cluster were compared with what would be expected if these characteristics were proportionately distributed across the seven profiles according to their representation in the total sample (N = 614). For example, the total sample following the trim procedure used in the clustering algorithm (N = 614) comprised 50.6% boys and 49.3% girls. Profile 1 contained 21.3% of the total sample (n = 131). Thus, we would expect 50.6% of this number to be boys and 49.3% of this sample to be girls. This procedure tested for violations of this hypothesis within each profile. Race comparisons were limited to comparisons between White, Black, and other because of the low prevalence rates for some groups. Type I error rates were controlled through the use of Bonferroni adjustments. Results failed to demonstrate any statistically significant differences from population expectance on the demographic variables of race, gender, SES, or ESL within any of the seven clusters. These results suggest that the seven core profiles identified can be expected to generalize across groups differing on these demographic features. In other words, the aforementioned demographic features were proportionately represented in each of the clusters. Special education classification and school membership were the only variables to show departures from expectancy. In both instances, differences were observed only within Profile 7.

Profile Types

1. Average Information/High Decision (prevalence = 21.3%). This profile yielded the highest prevalence rate. The two types of information needs and decision needs were not particularly differentiated. This profile demonstrated general information needs that were slightly below average and decision needs that were somewhat elevated. Students in this profile had higher reported behavior problem scores on average than students in Profile 2.

2. High Information/Average Decision (prevalence = 16.3%). Students with this profile were well above the mean on both measures of information needs. They were somewhat above the mean on career choice anxiety and were centered at the mean on generalized indecisiveness. These students had fewer behavior problems than those with Profiles 1 or 7 and were reported as having fewer absences than students in Profile 7.

3. Average Information/Average Decision (prevalence = 12.7%). Means in this profile were all within .5 standard deviation. Means on self-knowledge and generalized indecisiveness were somewhat elevated, whereas career information or career decision-making needs were below the mean. Students in this profile were reported as having fewer behavior problems than those in Profile 7.

4. High Information/Low Decision (prevalence = 11.1%). Sharp contrasts are evident in this profile with both measures of information needs approximately 1 standard deviation above the mean and both measures of decision needs approximately 1 standard deviation below the mean. This profile was characterized by fewer behavior problems and absences than in Profile 7.

5. Average Information/Low Decision (prevalence = 16.1%). Both information needs were slightly lower than the mean (with the need for self-knowledge somewhat lower than the need for career information). However, the most defining aspects of this profile were the low scores obtained on both measures of decision needs. This profile was further characterized by fewer absences than in Profile 7. No differences were observed on measures of behavior.

6. High Information/Very High Decision (prevalence = 9.9%). This profile demonstrated the lowest rate of prevalence. Scores on information needs and decision needs approached or exceeded 1 standard deviation above the mean. Although the two measures of information needs were over .5 standard deviation above the mean, career choice anxiety was over 1 standard deviation above the mean and generalized indecisiveness was over 1.5 standard deviations above the mean. No statistically significant behavior problems or absences were noted between students in this profile and those of any other profile.

7. Very Low Information/Average Decision (prevalence = 12.5%). This profile yielded scores on both measures of information needs that were approximately 1.5 standard deviation below the mean and well below those observed for any other profile type. Students in this profile were found to have more behavior problems than those in Profiles 2, 3, or 4. Moreover, they were reported as having more absences on average than students in Profiles 2, 4, or 5. This was the only profile type in which special education classifications and school memberships were statistically different from expectation. There were fewer AG students and more students identified with special needs than expected in this profile. With respect to school membership, there were more students from School 1 and fewer students from School 2 than expected. Schools 3 and 4 were represented as expected.

Discussion

Career Decision-Making Readiness of Middle School Students

Considering the means of the profile for the entire sample, the highest priority was the need for self-knowledge. This is developmentally appropriate in middle school, because identity formation begins with awareness of self. This also coincides with Super's (1984) contention that students at this career stage encounter the crystallizing task. It is also developmentally appropriate that the need for career information was the second priority among students participating in this study. For middle school students to cope successfully with the task of specifying tentative vocational preferences (Super's second task in the exploration stage of career development), they need to develop a reservoir of career information to begin the process of translating crystallized self-information into potential career directions. According to these data, informational needs are more pressing than career decision-making assistance. This is logical because eighth-grade students are confronted with educational or curriculum choices rather than explicit career choices. Although these choices serve as antecedents for later career opportunities, students generally appear to demonstrate low anxiety about these choices. Perhaps students do not understand the connection between choices they make in middle school and subsequent educational and career options. If so, then career counselors can help students make these cognitive connections.

Savickas (1989) noted that career indecision requires specific types of interventions based on the different needs that exist. In this investigation, information needs were most prominent. Self- and career-information needs represent areas that school counselors can target with all students. Value card sorts, interest inventories, assessments facilitating the identification of strengths, and narrative or constructivist-interventions can help reveal self-information for students. Given student-counselor ratios in most middle schools, large-scale intervention strategies such as classroom guidance and psychoeducation groups may also be the most appropriate and efficient. These large-scale interventions could offer a point of entry for school counselors that could be followed up with smaller group interventions that allow students the opportunity to process the information they acquire from the initial assessment activity. These modalities can also be used to provide career information, as can complementary interventions such as job shadowing, career days, and career resource libraries.

It is important to note, however, that although mean score data are useful, they do not capture the variance of career readiness within a sample and are less useful when conceptualizing interventions appropriate for individual differences.

Career Decision-Making Readiness Typology of Middle School Students

In contrast to examining means of the entire population, profile types illustrate the importance of accounting for the multivariate aspects of student career indecision. Santos and Coimbra (2000) suggested that undecided individuals do not represent homogenous characteristics and that career indecision is multidimensional. The resulting profiles illustrate that students may show strengths and deficiencies, not only between information needs and decision needs but also between the two scales designed to measure each of the larger constructs (i.e., self- and career information needs).

The cluster profile revealed that each profile represented a homogeneous grouping of participants and that the profiles were clinically distinct. Taken together, these psychometric measures show that the study participants within profiles are similar to one another on the measures used to form the profiles, and students with different profile configurations are distinct in a multivariate sense. The emergence of seven profile types demonstrates significant heterogeneity of career readiness in middle school students.

Table 1 and Figure 1 display the particular profiles for each type. Following Chartrand and Robbins's (1997) suggestion that intense need or high distress can be problematic in scores 1 standard deviation above the mean, our discussion will focus on profile types that approach or exceed 1 standard deviation above or below the mean. Low in prevalence (9.9%), the High Information/Very High Decision profile demonstrates elevated needs for both information and decision-making assistance. Although students beginning career exploration would be expected to have elevated needs for career information, these students demand particular attention because traditional career interventions would need to be augmented by more intense individual counseling or related intervention for decision making. Also, students in the Average Information/Average Decision profile demonstrate a slightly elevated need for acquiring self-information and learning decision-making skills. Chartrand and Robbins termed this an identity concerns pattern, with primary concern for future identity development. These students do not demonstrate any particular struggle with career development but instead may benefit most from self-assessment tools such as interest assessment or values clarification activities.

Students in the High Information/Average Decision and High Information/Low Decision profiles demonstrate elevated needs for information. This pattern may be interpreted as consistent with the moratorium status in identity development, although this research did not assess levels of commitment. In particular, the High Information/Low Decision profile demonstrates the need for precise career development interventions. These students have elevated information needs but a depressed need for help with decision making. Thus, students in this profile may be comfortable working independently and, therefore, require more guidance and direction from counselors rather than intensive counseling assistance because of their comfort and confidence in decision making. For example, these students might benefit from focused interventions that stimulate self-discovery (e.g., values clarification, narrative career activities, or career journaling) and exposure to the world-of-work (perhaps through computer-assisted career guidance systems or occupational information interviews).

In contrast, students in the Very Low Information/Average Decision profile demonstrate little need for self- or career information. Chartrand and Robbins (1997) termed this a validation pattern, in which career readiness concerns are low. Even so, careful interpretation is needed with a middle school population, because students in this profile either are extremely self-aware and career informed or perhaps have premature identity foreclosure. Marcia (1980), in articulating his ego identity status model, suggested that a high percentage of secondary students have a foreclosed identity (an identity determined by others rather than self). Although levels of commitment were not assessed, these students may have pseudocrystallized choices (career decisions dictated by parental figures rather than by an autonomous process of career exploration). In these instances, career counselors should help students explore options beyond those identified by parental figures (Santos & Coimbra, 2000). In terms of a developing career identity, the quality of commitment to choice is important. Santos and Coimbra and others (e.g., Meijers, 1998) have suggested that career investment (commitment) derived from career exploration is an important component of an achieved career identity. The heterogeneity of career readiness and distinctive needs among students suggest the need for diverse and targeted interventions to facilitate career development in middle school students.

Sociodemographics and Typal Membership

Consideration of the influence of sociodemographic factors in career development is essential. Although most investigations approach differences from a variable-centered perspective (e.g., race, gender), Mandara (2003) recommended typological research as an alternative. Typology and typal membership seek to empirically discover the kinds of groupings that are socially meaningful in particular contexts or in relation to specific phenomena (Orellana & Bowman, 2003). Although recent research (Valadez, 1998) demonstrates that socioeconomic background influences career and educational aspirations and expectations (mediating effect sizes of gender and race), data from the current study indicate that SES may have less influence on career decision-making readiness. Perhaps antecedents to career decision making are more generalizable across sociodemographic groups than some might suspect. Although profile types in the current study demonstrate varied levels of need, these career development profiles did not correspond to a particular gender, race, SES, or level of academic achievement.

At the same time, school behavior, attendance, special education, and school membership did suggest that career readiness is related to particular ecological factors. For example, students in the Very Low Information/Average Decision profile, with depressed needs for self- and career knowledge, had more behavior referrals and absences, more special education and less advanced student representation, and more representation from School 1 (less from School 4). As introduced in the discussion of profile types, a clinical view of the data may suggest a foreclosed career identity.

Students who are frequently absent from school and with increased behavior problems may neglect career aspirations because they have less connection to school. Students may be dismissive of further career development and have foreclosed a career identity, thereby narrowing their options for a future career. For example, students with special needs may be relegated to remedial work and not exposed as often to postsecondary educational options (Milsom, Akos, & Thompson, in press). Particularly in middle school, where students tend to draw conclusions about themselves based on ability grouping (Miller, 1988), special needs students may lose interest and have low self-efficacy related to engaging in career exploration. The results of this study may suggest less interest specifically in further self- and career information. It may be possible that students in this profile, because of limited career exploration and aspirations, disconnect from school (in terms of attendance and behavior).

In the same way, school membership also proved to be significant in the Very Low Information/Average Decision profile (more students from School 1 and fewer than expected from School 2). Watson and Van Aarde (1986) and others (e.g., Vondracek & Schulenberg, 1986) have suggested that the influence of the educational milieu is a primary agent in the development of career behavior and grade-related career decisions students are required to make. School 1 (as compared with School 2) had a higher behavior referral mean (.53 versus .16, respectively), a higher absences mean (5.76 versus 2.77, respectively), 14 fewer students identified as AG, and 19 more students identified with special needs. It is interesting that, School 1 represented a rural, low socioeconomic, and low-performing district compared with School 2, which was suburban, high socioeconomic, and extremely high performing. These sociodemographic differences across school settings may indicate the existence of problematic ecological issues that restrict career development for students in School 1. Meijers (1998) and Waterman (1988) suggested the degree to which the individual's environment expects (or even forces) exploration is crucial for facilitating career development. Beyond parental influence, these ecological factors (e.g., being identified with special needs, rural setting) may also contribute to premature foreclosure of career identities. Additional research to identify whether and how such environmental factors (e.g., Vondracek & Schulenberg, 1986) influence the career development process in schools is needed to more fully understand this finding.

School counselors should pay particular attention to students who appear less connected to school (e.g., high absences or referrals) or those identified with special needs in preparing students for career-related decisions such as curriculum choices. Although all middle school students demonstrate career development needs, students with elevated absences, behavioral referrals, and special needs, in particular, may have preconceived notions of ability and a limited view of future opportunity that diminishes an interest in career exploration. Potentially, these students may make educational or curriculum choices that limit their future based on a prematurely foreclosed career identity.

Conclusion

Osipow (1999) highlighted that "career indecision is a state which comes and goes over time as a decision is made, is implemented, grows obsolete, and eventually leads to the need to make a new decision" (p. 147). Krumboltz (1992) and Savickas (1994) indicated that indecision is part of the process and can be viewed positively. Although Chartrand and Robbins (1997) suggested that indecision is not problematic at the middle school level, they also indicated that CFI scores help identify whether students are ready to engage in the career decision-making process. Results from the current study clarify, however, that middle school students exhibit distinct patterns of needs regarding developing their readiness for career decision making. Some of the profiles reflect developmentally appropriate career indecision or moratorium status (e.g., High Information/Low Decision), some indicate the need for more focused and intense personal and career counseling interventions (e.g., Average Information/High Decision and High Information/Very High Decision), whereas others may highlight premature career identity foreclosure (e.g., Very Low Information/Average Decision). Understanding a student's career decision-making readiness profile helps counselors identify specific interventions and intervention strategies that may be most useful in fostering a student's career development.

Moreover, because educational and scheduling decisions made in eighth grade relate to high school curriculum tracks, career readiness becomes a significant construct for middle school students. As early as age 12 or 13, students must understand the long-term implications of course selection decisions for high school (Mahoney & Merritt, 1993). In fact, "data have largely supported a developmental pattern of career maturity.... And it has been suggested that school transition points might account for this" (Patton & Creed, 2001, p. 348). Prideaux and Creed (2001) also suggested that decision points in educational systems disturb career maturity because they can force students into a decision-making time frame that does not coincide with their readiness level.

Thus, middle school represents a point at which students need to be actively engaged in preparing for the significant educational choices they will make as they enter high school. Positive career indecision is an appropriate developmental characteristic. Career readiness needs and demographic variables examined in this study offer useful information to counselors seeking to understand the career development experience of middle school students.

TABLE 1 Career Factor Mean Score Patterns and Psychometric Properties of
Core Profiles

                                             Profile Type (a)
Variable                               1        2       3        4

Career factors
  Need for career information         48       59      46       60
  Need for self-knowledge             46       59      55       59
  Career choice anxiety               57       54      46       40
  Generalized indecisiveness          55       50      52       40
Psychometric properties
  % prevalence                        21.3     16.3    12.7     11.1
  % independent replication across
    three blocks                     100      100      66      100
  Internal profile cohesion (H)         .85      .86     .86      .86
  External isolation (Rp)               .23      .34     .32      .05

                                        Profile Type (a)
Variable                               5        6       7

Career factors
  Need for career information         49       57      35
  Need for self-knowledge             45       58      36
  Career choice anxiety               42       63      46
  Generalized indecisiveness          40       65      49
Psychometric properties
  % prevalence                        16.1      9.9    12.5
  % independent replication across
    three blocks                     100      100     100
  Internal profile cohesion
    (H)                                 .79      .82     .71
  External isolation (Rp)               .16     -.06    -.05

Note. Profile 1 = Average Information/High Decision; 2 = High
Information/Average Decision; 3 = Average Information/Average Decision;
4 = High Information/Low Decision; 5 = Average Information/Low Decision;
6 = High Information/Very High Decision; 7 = Very Low Information/
Average Decision.
(a) Means rounded to the nearest whole number.

References

Arrington, K. (2000). Middle grades career planning programs. Journal of Career Development, 27, 103-109.

Campbell, C., & Dahir, C. (1997). Sharing the vision: The national standards for school counseling programs. Alexandria, VA: American School Counselor Association.

Cattell, R. B. (1949). Rp and other coefficients of pattern similarity. Psychometrika, 14, 279-298.

Chartrand, J., & Robbins, S. (1997). Career Factors Inventory applications and technical guide. Palo Alto, CA: Consulting Psychologists Press.

Chartrand, J., Robbins, S., Morrill, W., & Boggs, K. (1990). Development and validation of the Career Factors Inventory. Journal of Counseling Psychology, 37, 491-501.

Cook, T., Church, M., Ajanaku, S., Shadish, W., Kim, J., & Cohen, R. (1996). The development of occupational aspirations and expectations among inner-city boys. Child Development, 67, 3368-3385.

Erickson, E. (1968). Identity: Youth and crisis. New York: Norton.

Ginzberg, E. (1952). Toward a theory of occupational choice. Occupations, 30, 491-494.

Glutting, J. J., McDermott, P. A., & Konold, T. R. (1997). Ontology, structure and diagnostic benefits of a normative subtest taxonomy from the WISC-III standardization sample. In D. P. Flanagan, J. L. Genshaft, & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests, and issues (pp. 349-372). New York: Guilford.

Gottfredson, L. (1981). Circumspection and compromise: A developmental theory of occupational aspirations. Journal of Counseling Psychology, 28, 545-580.

Herr, E., Cramer, S., & Niles, S. (2004). Career guidance and counseling through the lifespan: Systematic approaches. Boston: Little, Brown.

Kelly, K., & Lee, W. (2002). Mapping the domain of career decision problems. Journal of Vocational Behavior, 61, 302-326.

Khan, S., & Alvi, S. (1985). Career maturity in relation to differences in school curriculum. Canadian Counsellor, 19, 144-151.

Konold, T. R., Glutting, J. J., & McDermott, P. A. (1997). The development and applied utility of a normative aptitude: Achievement taxonomy for the Woodcock-Johnson Psycho-Educational Battery--Revised. Journal of Special Education, 31, 212-232.

Krumboltz, J. (1992). The wisdom of indecision. Journal of Vocational Behavior, 41, 239-244.

Lewis, D., & Savickas, M. (1995). Validity of the Career Factors Inventory. Journal of Career Assessment, 3, 44-56.

Mahoney, J., & Merritt, S. (1993). Educational hopes of Black and White high school seniors in Virginia. Journal of Educational Research, 87, 31-38.

Mandara, J. (2003). The typological approach in child and family psychology: A review of theory, methods, and research. Clinical Child and Family Psychology Review, 6, 129-146.

Marcia, J. (1980). Identity in adolescence. In J. Adelson (Ed.), Handbook of adolescent psychology (pp. 158-187). New York: Wiley.

McDermott, P. A. (1998). MEG: Megacluster analytic strategy for multistage hierarchical grouping with relocations and replications. Educational and Psychological Measurement, 58, 677-686.

McKinney, J., & Speece, D. (1986). Academic consequences and longitudinal stability of behavioral subtypes of learning disabled children. Journal of Educational Psychology, 78, 365-372.

Meijers, F. (1998). The development of a career identity. International Journal for the Advancement of Counseling, 20, 191-207.

Miller, M. (1988). Career counseling for the middle school youngster: Grades 6-9. Journal of Employment Counseling, 25, 172-179.

Milsom, A., Akos, P., & Thompson, M. (in press). A psychoeducational group approach to postsecondary transition planning for students with learning disabilities. Journal for Specialists in Group Work.

Morris, R., Blashfield, R., & Satz, P. (1981). Neuropsychology and cluster analysis: Potentials and problems. Journal of Clinical Neuropsychology, 3, 79-99.

Niles, S. G., & Harris-Bowlsbey, J. (2002) Career development interventions in the 21st century. Columbus, OH: Merrill Prentice Hall.

Orellana, M., & Bowman, P. (2003). Cultural diversity research on learning and development: Conceptual, methodological, and strategic considerations. Educational Researcher, 32, 26-32.

Osipow, S. (1999). Assessing career indecision. Journal of Vocational Behavior, 55, 147-154.

Patton, W., & Creed, P. (2001). Developmental issues in career maturity and career decision status. The Career Development Quarterly, 49, 336-351.

Phinney, J., & Tarver, S. (1988). Ethnic identity search and commitment in Black and White eighth graders. Journal of Early Adolescence, 8, 265-277.

Powell, D., & Luzzo, D. (1998). Evaluating factors associated with the career maturity of high school students. The Career Development Quarterly, 47, 145-158.

Prideaux, L., & Creed, P. (2001). Career maturity, career decision-making self-efficacy, and career indecision: A review of the accrued evidence. Australian Journal of Career Development, 10(3), 7-12.

Santos, P., & Coimbra, J. (2000). Psychological separation and dimensions of career indecision in secondary school students. Journal of Vocational Behavior, 56, 346-362.

Satz, P., Morris, R., & Fletcher, J. M. (1985). Hypotheses, subtypes and individual differences in dyslexia: Some reflections. In D. Gray & J. Kavanogh (Eds.), Biobehavioral measures of dyslexia. Parkton, MD: York Press.

Savickas, M. (1989). Annual review: Practice and research in career counseling and development, 1988. The Career Development Quarterly, 38, 100-134.

Savickas, M. (1994). Measuring career development: Current status and future directions. The Career Development Quarterly, 43, 54-62.

Super, D. (1984). A life-span, life-space approach to career development. In D. Brown, L. Brooks, & Associates (Eds.), Career choice and development (2nd ed., pp. 197-261). San Francisco: Jossey-Bass.

Super, D. (1990). A life-span, life-space approach to career development. In D. Brown, L. Brooks, & Associates (Eds.), Career choice and development (8th ed., pp. 197-261). San Francisco: Jossey-Bass.

Trice, A., Hughes, M., Odom, C., & Woods, K. (1995). The origins of children's career aspirations: IV. Testing hypotheses from four theories. The Career Development Quarterly, 43, 307-322.

Trice, A., & King, R. (1991). Stability of kindergarten children's career aspirations. Psychological Reports, 68, 1378.

Trusty, J., & Niles, S. G. (2004). Realized potential of lost talent: High school variables and bachelor's degree completion. The Career Development Quarterly, 53, 2-15.

Tryon, R. C., & Bailey, D. E. (1970). Cluster analysis. New York: McGraw-Hill.

Valadez, J. (1998). Applying to college: Race, class, and gender differences. Professional School Counseling, 1, 14-20.

Vondracek, F., & Schulenberg, J. (1986). Career development in adolescence: Some conceptual and intervention issues. Vocational Guidance Quarterly, 34, 247-254.

Waterman, A. (1988). Identity status theory and Erikson's theory: Commonalities and differences. Developmental Review, 8, 185-208.

Watson, M., & Van Aarde, J. (1986). Attitudinal career maturity of South African colored high school pupils. Journal of Vocational Behavior, 29, 7-16.

Westbrook, B., Sanford, E., & Donnelly, M. (1990). The relationship between career maturity test scores and appropriateness of career choices: A replication. Journal of Vocational Behavior, 36, 20-32.

Westbrook, B., Sanford, E., Merwin, G., & Fleenor, J. (1988). Career maturity in Grade 9: Can students who make appropriate career choices for others also make appropriate career choices for themselves? Measurement and Evaluation in Counseling and Development, 21, 64-71.

Patrick Akos, School of Education, University of North Carolina at Chapel Hill; Tim Konold, Curriculum Leadership, Foundations, and Policy Studies, University of Virginia; Spencer G. Niles, Department of Counselor Education, Counseling Psychology and Rehabilitation Services, The Pennsylvania State University. Correspondence concerning this article should be addressed to Patrick Akos, University of North Carolina at Chapel Hill, Box 3500-SOE, Chapel Hill, NC 27599 (e-mail: [e-mail]).