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. Author manuscript; available in PMC: 2022 Jun 23.
Published in final edited form as: Prof Sch Couns. 2021 Nov 22;25(1):10.1177/2156759x211053818. doi: 10.1177/2156759x211053818

Validation of the College Planning Behaviors Scale

Katherine D Cook 1, Erin E Hardin 1, Melinda M Gibbons 1, Marlon C Johnson 2, Christina Peterson 1, Anna Lora Taylor 3, Sean Murphy 1
PMCID: PMC9218678  NIHMSID: NIHMS1815184  PMID: 35754850

Abstract

College preparation is an important topic in the educational attainment of high school students. Much of the research on college planning focuses on the importance and timing of preparing for postsecondary education; however, little research has explored the steps students actually take while preparing for college. The current study utilized the Social Cognitive Career Theory (SCCT) framework to create a validated measure to assess choice behavior. The purpose of the current study was to create a validated measure for choice actions that could be used with diverse student groups. The measure was found to demonstrate good reliability and validity in this population, providing strong internal consistency and construct validity. Further, these findings support college-planning behaviors’ linkage to barriers, college-going self-efficacy, and college outcome expectations (COE).

Keywords: social cognitive career theory, college planning, choice actions, Appalachia, college-going self-efficacy

College-Planning Goals versus College-Planning Behaviors

One of the primary tasks of school counselors is to assist with career and college readiness (American School Counselor Association [ASCA], 2019). School counselors must use data to understand and identify gaps in student achievement and to address information or opportunity gaps (ASCA, 2019, p. 6). They are also tasked with understanding how culture and environmental context impact achievement and future expectations (ASCA, 2019, p. 4).

To help accomplish these tasks, ASCA created 35 mindset and behavior standards related to student college and career readiness (ASCA, 2014). Items related to student behaviors include: M4 Understanding that postsecondary education and life-long learning are necessary for long-term career success; B-LS4 Apply self-motivation and self-direct to learning; B-LS7 Identify long- and short-term academic, career and social/emotional goals; B-LS10 Participate in enrichment and extracurricular activities; and B-SMS5 Demonstrate perseverance to achieve long- and short-term goals (ASCA, 2014, p. 2). To understand whether school counselors are successful in promoting college and career readiness, a valid measure of college-planning behaviors is needed. If a student says they plan to attend a two-or 4-year college after graduation, are they completing the tasks needed to accomplish this goal? School counselors, especially those working with high school students, must know the behaviors needed to help with postsecondary transition and have an effective way to identify what actions their students are taking to achieve their postsecondary goals.

A variety of college-planning checklists are available online to students to help them begin the college-planning process (e.g., Johnson, 2015; Millis, 2007a, 2007b, 2007c). However, few studies have investigated what types of college preparation students are actually using during high school. Moreover, these checklists have not been validated. Studying actual student behaviors and assessing which actions students are taking may help school counselors and researchers uncover the needs of individuals engaged in the college-planning process. The results could also support how counselors and researchers understand the discrepancy between students’ postsecondary aspirations and their actual attainment. Finally, given the disparity between those who want to attend college and those who actually go on to attend (BLS, 2019), a well-validated measure of actual college-planning behaviors would allow school counselors and others to better distinguish between aspirations and planning behaviors. A framework for developing such a measure is discussed in Gibbons and colleagues’ (2006) article, which addresses factors important to the college-planning process such as college exploration or research, finances associated with college, and college-going social support. These, combined with general college-planning checklists, may lead the way toward a measure of college-planning behaviors.

College Planning for Underrepresented Students

While the need to analyze college-planning behaviors is important for all students, it is especially relevant for those who have been historically underrepresented in the postsecondary population, including those from low-income families, those who would be the first in their families to attend college (prospective first-generation college students), and students from rural communities. Prior literature has recognized the need for career education within the region of rural Appalachia (Bennett, 2008), which includes students that represent all three of these categories. Students in this region often face significant challenges to college going, including financial barriers and access to resources. The region of Appalachia comprises 420 counties, 13 states, and more than 25 million people. Of these counties, 25% are designated “at-risk” of becoming economically disadvantaged (Appalachian Regional Commission [ARC], 2017). Adults in the Appalachian region from economically distressed counties are less likely to attend any type of postsecondary education, with only around 49% of individuals in this area attending some type of postsecondary education after high school, compared to an average of 65.1% from nondistressed areas (ARC, 2017). Although many students from rural Appalachian communities report a high desire to attend college, the rates of postsecondary education are still very low in these regions, with an average 32.3% of adults holding an associate’s degree or higher compared to the national average of 39.2% (Pollard & Jacobsen, 2019).

Parental or caretaker support and involvement are also significant topics in much of the current research about college planning (Myers & Myers, 2012). Students with parents or guardians with knowledge about the college process are more likely to plan for college (Perna & Titus, 2005), but parents from low-education backgrounds may be unsure how to begin a conversation about college planning (Hallett & Griffen, 2015). Students whose parents lack postsecondary education (i.e., prospective first-generation college students) may be less likely to engage in advanced coursework, such as nonrequired math and science classes, during high school (Horn & Nunez, 2000). Moreover, it appears that lack of adequate academic preparedness may contribute to prospective first-generation students being less likely to attend college and, if they do attend, having higher attrition rates than continuing-generation college students (Hand & Payne, 2008). They also report a lack of professional or career network (Tate et al., 2015) and less overall social capital (Nicholas & Islas, 2016). In this study, we defined prospective first-generation college students using Gibbons and colleagues’ (2006) definition, which describes them as “students whose parents lack education beyond high school.” Those whose parents or caretakers had some formal education or training after high school, even if they did not graduate, were not included in this category because we suspect that even some education or training may be relevant.

Socioeconomic status (SES) also impacts postsecondary education planning and action, with students from less financially secure families being less likely to attend college (Koricich et al., 2018). They tend not to complete advanced math courses and often have lower postsecondary aspirations (Sciarra, 2010). Beyond inequalities in academic preparedness and access to information, opportunity gaps also exist in supplemental activities, such as visiting college campuses or volunteering in the community. Students may also have financial concerns or family obligations that impact planning behaviors (Mitchall & Jaeger, 2018), so they may hold a part-time job or help with childcare rather than actively participate in college planning. All of these underrepresented groups may have barriers to college-planning behaviors.

Social Cognitive Career Theory

Social Cognitive Career Theory (SCCT; Lent et al., 1994) is an important model for career development in various populations. Social Cognitive Career Theory has specifically been used to understand how students and adolescents contemplate their career aspirations and interests (Rasheed Ali & Saunders, 2009). The SCCT model’s main focus is to conceptualize how individuals consider their academic and career aspirations, including factors that promote choice actions for pursuing career and educational opportunities (Lent et al., 2003). Within the framework, an individual’s self-efficacy evaluation (whether or not they feel capable of successfully completing specific activities) and outcome expectations (the extent to which they anticipate positive results from the activities) influence the extent to which they are interested in attempting the activity. Furthermore, self-efficacy and outcome expectations are associated with choosing goals, and with choosing actions to meet those goals. The paths from the core constructs of self-efficacy and outcome expectations have been shown to correlate with choice actions in other domains, such as science, technology, engineering, and math (STEM; Lent et al., 2003) and college going (Gonzalez et al., 2013). What’s more, as SCCT would predict, those variables influenced by self-efficacy and outcome expectations (interests, choice actions, and goals) directly affect performance and attainment.

Although college planning seems to be an intuitive predictor of career outcomes for students, relatively little theory-informed research has been conducted in this area. In the SCCT model, college planning can be considered a choice action because it requires that intentional behaviors be taken by students to prepare for college and careers. Importantly, researchers have posited that intentional, well-measured, and timely goals are more likely to lead to choice actions (Bandura, 1986; Lent et al., 2003). Because of this, measures of intention and persistence have been used as proxies for choice actions in relation to other SCCT variables. These types of measures, however, might not capture that an intentional behavior is different from an interest or aspiration and must be recognized as an independent construct. Moreover, no specific, well-validated measure for college-planning behaviors exists. Our study sought to bridge this gap by creating a single, validated survey that could be used to evaluate the actual behaviors of high school students and add to the SCCT literature.

Purpose of Study

The present study is part of a larger intervention project that provides an SCCT-grounded career education intervention with students in rural Appalachian high schools to improve college-going and science, technology, engineering, math, and medical (STEMM) efficacy and interest in rural Appalachian high school students. In this article, we explore the ways in which students from this sample are (or are not) planning for college by creating a scale to operationalize the educational planning behaviors of high school students: The College Planning Behaviors (CPB) scale. The main purpose of this scale development was to create an assessment that can be used by students, school counselors, and other career educators to foster evidence-based college planning. The main research questions for this study were: (a) What are the psychometric properties of the newly created CPB scale? and (b) To what extent do college-planning behaviors differ based on gender and prospective first-generation college student status?

Method

Phase I: Item Generation

Following scale development recommendations (Colton & Covert, 2007), our first step was identification of constructs and item generation. Our research team consisted of graduate students and professors in both counselor education and counseling psychology, all of whom had experience delivering career education to high school students and discussing college planning. Based on review of several existing nonvalidated checklists (e.g., Johnson, 2015; Millis, 2007a, 2007b, 2007c), prior college-going literature (Gibbons et al., 2006), and a research team brainstorming session, we identified 135 potential items. Of these, 115 came from existing checklists. We developed the remaining 20 new items to fill in what we perceived as gaps in the existing list, such as items related to finances and paying for college, academic preparation (ACT prep courses, dual-enrollment courses), and instrumental support (e.g., identifying potential letter writers).

Next (see Colton & Covert, 2007), two of the research team members (a faculty member and graduate student in counselor education) served as content experts and individually coded each of the items into various categories. Based on previous research (Gibbons et al., 2006) the hypothesized categories were: academic preparation, career knowledge, college knowledge, finances, general planning, and instrumental support. These two researchers then met with a third member of the team who acted as mediator, to compare the items and make final category decisions. The first two research team members then reviewed the list of items for redundancy and narrowed the list to 82 potential items.

Finally, the entire research team separately coded the items into three categories: behaviors specific to career planning only, behaviors specific to college planning only, and behaviors specific to both college and career planning. To ensure that the items on the final scale pertained to college planning specifically, we removed items that, after discussion, we agreed were relevant only to career planning. We then reviewed the remaining items for developmental appropriateness and to identify any potentially redundant items. This process resulted in 49 items that were ultimately retained for the initial CPB survey.

Phase II: Exploratory Factor Analysis and Validation

The purpose of this phase was to test and validate the measure (see Colton & Covert, 2007). After administering the measure, we explored the factor structure and psychometric properties of the 49-item CPB through exploratory factor analysis (EFA), which then allowed us to refine the measure.

Participants.

The students who participated in this study were 10th graders at five rural high schools in economically distressed counties in east Tennessee that were participating in a federal grant-funded, in-school, multi-week career education intervention targeted to rural Appalachian students. This program utilized SCCT (Lent et al., 1994) to help students consider broad college and career pursuits (see Gibbons et al., 2019, for a detailed description of the intervention).

For the EFA, we used data collected from 99 11th graders in fall 2017 and 404 10th graders in fall 2017, fall 2018, and fall 2019. Of these 503 students, most (93.63%) identified as White (n = 471); 3.38% (n = 17) self-identified as Black or African American, 3.38% (n = 17) as Latinx, 4.37% (n = 22) as American Indian, 1.79% (n = 9) as Asian or Pacific Islander, and 6.96% (n = 35) also identified as multiracial; nine did not respond. Students who reported that neither parental figure had any college experience were identified as prospective first-generation college students. Based on this definition, one quarter of participants (n = 127) were in the first-generation category; 66.4% of students reported having at least one parent who attended some form of postsecondary education, and thus were categorized as prospective continuing-generation students. Finally, 3% of students were unsure of their parental education level.

Measures

College Planning Behaviors.

All students completed the 49-item CPB survey. The survey assesses whether or not students have engaged in activities to prepare for 2-year or 4-year college, such as “I have a plan to pay for college” or “I have started exploring careers.” Instructions read: “For each behavior below, answer YES or NO about whether you have done the listed activity. For example, if the activity was “I have taken Algebra I,” you will choose YES if you have already taken this course (or are currently taking it) and you will respond NO if you have not already taken this course. ONLY RESPOND YES IF YOU HAVE ALREADY DONE THIS LISTED ACTIVITY, NOT WHETHER YOU PLAN TO DO THE LISTED ACTIVITY.” Responses were coded 1 for “yes” or 0 for “no.” College Planning Behaviors scores were averaged, yielding scores ranging from 0 to 1 that indicate the percentage of items that were endorsed. Scores closer to one indicate more planning behaviors and scores closer to zero indicate fewer planning behaviors.

Demographic Items.

All students provided their gender, age, race, school, grade, and parental education status. Demographic factors were all self-reported.

College-Going Self-Efficacy.

Tenth graders completed the College-Going Self-Efficacy Scale (CGSES; Gibbons & Borders, 2010), a 30-item scale designed to measure students’ confidence in their ability to engage in tasks necessary to attend and persist in postsecondary education. The scale includes items that ask participants: “How sure are you about being able to do the following,” using a 4-point, Likert-type response scale (1 = not at all sure to 4 = very sure). Sample items are “I can choose a good college” and “I could pay for college each year.” We included two instructed-response items (e.g., Please select “sure”) to check for participants’ attention (Meade & Craig, 2012). The CGSES has been used to measure self-efficacy in diverse middle school students (Gibbons & Borders, 2010), Latino youth (Gonzalez et al., 2013), and rural Appalachian high school students (Rosecrance et al., 2019). The scale demonstrated an excellent internal consistency (α = .95) within our sample.

College Outcome Expectations.

Tenth graders also completed the College Outcome Expectations (COE; Flores et al., 2008) scale, a 19-item scale that was revised from a previous outcome expectations scale for STEM beliefs (Fouad & Smith, 1996). Items assess students’ beliefs about outcomes they might experience if they attend college (e.g., “A college education will allow me to obtain a job I like doing”). The measure uses a Likert-type response scale, ranging from 1 (strongly disagree) to 10 (strongly agree). We included one instructed-response item, asking students to select “9.” Scores were averaged in the present study to yield total scores that may range from 1 to 10, with higher scores indicating greater belief in the value of a college education. The COE scale has been used successfully with diverse youth, including rural Appalachian high school students (Rosecrance et al., 2019). The measure had high internal consistency within this sample (α = .94).

Perceived Educational Barriers.

The Perception of Educational Barriers Scale – Revised (PEB-R; Gibbons, 2005, from McWhirter et al., 2000) was administered to 10th-grade students in our sample to assess perceived barriers to college going. The PEB has been used with college students (Raque-Bogdan & Lucas, 2016), rural youth within a predominantly Latino community (Rasheed Ali & Menke, 2014), and rural Appalachian students (Gibbons et al., 2019), although Gibbons and colleagues (2019) raised concerns about the extent to which the measure might underestimate barriers. The scale contains 45 items that list potential college attendance barriers students may face, such as “Not enough money” and “Teachers don’t support my plans” and uses a 4-point Likert-type response (not at all likely to definitely likely). The scale also includes an instructed-response item, which instructs students to select “a little likely.” Cronbach’s alpha demonstrated good internal consistency (α = .89) in the current sample.

Procedure.

After gaining permission through the university institutional review board, researchers provided parent and guardian consent forms at the beginning of the school year, describing the career education program and giving caregivers the opportunity to opt their students out of research. Students attending these high schools completed a battery of measures each semester through electronic surveys delivered on hand-held tablet devices in intact classrooms. Measures were presented in counterbalanced order, with the demographics questions always presented last. After students completed all the measures for program evaluation purposes, they had the opportunity to provide or decline assent for their responses to be used for research purposes. In this study, we used only data from students who provided assent and whose caregivers did not decline consent.

Results

Exploratory Factor Analysis

After confirming that the responses had no outliers, we conducted an EFA using the statistical software R to determine the factor structure underlying the CPB items, and to identify potentially problematic items. We used a Promax (nonorthogonal) rotation, assuming that the items were correlated with one another, with a weighted least squares estimation method, which has been demonstrated to be most effective with large dichotomous datasets (Glockner-Rist & Hoijtink, 2003). Although few clear guidelines exist for the minimum number of participants needed for an EFA (Osborne & Costello, 2004), with 49 items and 503 participants, we did achieve the common heuristic guideline of at least 10 participants per item.

Determination of the Number of Factors.

To uncover the factor structure of the measure, we specified one-, two-, three-, and four-factor solutions. In addition to examining the scree plot and variance accounted for by each additional factor, we also evaluated the item loadings on each of the factors in each solution, looking for interpretable solutions with items loading highly (≥.30) on only one factor, and at least three items loading >.30 per factor (Gorsuch, 1997). Based on these criteria, we concluded that a three-factor structure is preferred (Table 1). Overall, the three factors accounted for 44% of the total variance.

Table 1.

Factor Loadings and Communalities Based on an Exploratory Factor Analysis with Promax Rotation for 45 items from the College Planning Behaviors Scale (CPB; N = 503).

Item
No		 Item Exploration Concrete
activities		 Supplemental
activities		 Communality
6 I have researched the education or training required for my careers of interest .79 −.08 −.02 .56
7 I have researched colleges that provide training for my career of interest .79 −.07 .00 .58
25 I have researched what programs of study are available in college .78 .04 −.07 .57
31 I have researched information about a career .75 −.30 .20 .67
8 I have reviewed college admissions requirements .70 .20 −.10 .57
42 I have compared the costs of different colleges .70 .16 −.02 .59
35 I have identified the steps I need to get the career I want .68 .04 .03 .51
33 I have figured out my career interests and abilities .66 −.13 .09 .46
24 I have explored colleges on the computer .66 .04 −.03 .44
19 I have identified steps to reach my educational goals .63 −.05 .17 .52
9 I have researched how to apply for college .62 .27 −.06 .55
15 I have found out how much it would cost to attend college .61 .23 −.12 .46
30 I have started exploring careers .61 −.23 .38 .41
20 I have talked with my family about what I want to do after high school .56 −.18 .21 .44
27 I have learned the difference between grants and loans .49 .05 .15 .23
34 I have taken/am currently taking classes related to my career interests .48 −.02 .15 .32
13 I have researched how to apply for financial aid .45 .28 −.05 .36
11 I have talked with my family about how to pay for college .45 .05 .15 .36
46 I have talked with friends about what they want to do after high school .39 −.23 .38 .43
41 I have searched for scholarships .38 .31 .06 .37
29 I have enrolled in a college prep curriculum program at my high school .36 .30 −.12 .26
44 I have talked with a college representative or admissions counselor .35 .27 .00 .27
3 I have a plan to pay for college .32 .21 .09 .26
12 I have completed the FAFSA −.22 .82 .06 .58
37 I have made a list of college application deadlines .19 .73 −.13 .66
26 I have attended a financial aid workshop or scholarship night −.08 .73 .10 .66
2 I have applied to college .11 .73 −.02 .61
38 I have gathered applications for the colleges I am considering .15 .72 −.17 .60
1 I have taken the ACT and/or SAT −.43 .70 .18 .39
5 I have sent my ACT and/or SAT scores to colleges where I plan to apply .15 .68 .08 .60
10 I have requested that my transcript be sent to the colleges where I have been applying −.05 .67 .07 .44
17 I have taken the PSAT −.10 .60 −.02 .32
18 I have taken math beyond Algebra II .09 .46 −.04 .25
21 I have identified someone who can write a recommendation letter for me .25 .35 .18 .35
36 I complete my homework regularly .02 −.18 .62 .40
47 I have (or do now) volunteered or participate in community service −.04 .10 .61 .36
45 I have participated/currently participate in an extracurricular club or sport .05 −.08 .60 .38
16 I study hard to do well in school .22 −.18 .56 .48
49 I have run for a leadership position in a club or sport −.15 .34 .56 .35
23 I have visited a college campus .02 .07 .54 .33
22 I have completed a college tour −.04 .22 .52 .31
48 I have talked with my parents or other adults about their jobs .29 −.20 .34 .29
14 I have found someone who can answer my questions about college .27 .08 .29 .28
40 I/my family have started saving for college .23 .20 .28 .29
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The items on the first factor involved researching or finding information about colleges or careers (e.g., “I have researched what programs of study are available in college”); we thus labeled this factor Exploration. The items on the second factor involved concrete behaviors or actions that the individual is engaging in to prepare for college or a career path (e.g., “I have attended a financial aid workshop or scholarship night”); we thus labeled this factor Concrete Activities. The items on the third and final factor involved behaviors that are helpful, but not necessarily required, for college acceptance (e.g., “I have participated/currently participate in an extracurricular club or sport”); we thus labeled this factor Supplemental Activities. These behaviors may help improve a student’s competitiveness in the college application process given that colleges often value—but do not require—extracurricular engagement and leadership.

Determination of Retained Items.

To determine whether any of the 49 items should be removed from the scale, we considered factor loadings and communalities. In the three-factor solution, we found four items (4, 28, 32, and 43) that did not load above .25 on any of the three factors and removed them from the scale. All remaining items had factor loadings > .25 and communalities ≥ .20, as recommended (Child, 2006).

We chose to retain some items that did not fit within our criteria. Three items cross-loaded between factors, defined as items that either had loadings greater than .32 on more than one factor or items that had a difference of less than .15 between the top two loadings (Worthington & Whittaker, 2006). Those items were #1: “I have taken the Scholastic Assessment Test (SAT) and/or American College Test (ACT)”; #46: “I have talked with friends about what they want to do after high school”; and #49: “I have run for a leadership position in a club or sport”. Item #1 (“I have taken the SAT and/or ACT”) cross-loaded, but we retained the item on factor two due to its higher loading on this factor. In the state of Tennessee, students are required to take the ACT during their 11th-grade year of high school. The standardized test is given to all students for free and administered during the regular school day; thus, for students in Tennessee, taking the ACT is not a college-planning behavior. However, we retained this item among the 45 CPB items because the SAT and ACT are tests that are generally required for college admittance, and this item is included on many of the checklists that we utilized in item generation.

Item #46 (“I have talked with friends about what they want to do after high school”) cross-loaded on Factors 1 (Exploration: .39) and 3 (Supplemental Activities: .38). We retained this item in the first factor after reviewing the literature and deeming it important to talk to others for support and exploration during college planning. Item #49 (“I have run for a leadership position in a club or sport”) cross-loaded on Factors 2 and 3 (Concrete Activities: .34 and Supplemental Activities: .56). Ultimately, we retained this item on the third factor due to its high (>.40) loading and its fit with this specific construct. Supplemental activities involved behaviors that may require more time and are helpful but not required for college planning.

Thus, the final CPB is a 45-item, three-factor scale. The first factor, Exploration, includes 23 items. The second factor, Concrete Activities, includes 12 items. The third and final factor, Supplemental Activities, includes 10 items that are helpful, but not necessarily required, for college acceptance. Inter-item reliabilities using a tetrachoric correlation matrix were good for all three factors (a = .89, .77, and .79, respectively) and the total score (a = .88).

Evidence for Construct Validity

We examined the relations among CPB scores and scores on several other theoretically related measures using data from the 10th-grade students (n = 404; as noted above, 11th-grade students did not receive the other measures). Supporting the construct validity of CPB, simple correlation analyses indicated that CPB scores correlated with these other variables as would be predicted by SCCT (Lent et al., 1994): Greater college-planning behaviors were associated with lower perceived barriers (r = −.24, p < .01), higher COE (r = .33, p < .01), and higher college-going self-efficacy (r = .53, p < .01). Table 2

Table 2.

Correlations Among SCCT Variables.

Variable 1 2 3 4 5 6 7 M SD
1. Barriers — 2.04 0.55
2. CGSES −0.47** — 2.90 0.60
3. COE −0.31** 0.61** — 7.62 1.40
4. CPB −0.24** 0.53** 0.33** — 0.40 0.16
5. Factor 1 −.20** .44** .30** .94** — 0.51 0.23
6. Factor 2 −.02 .09 .01 .37** .21** — 0.06 0.10
7. Factor 3 −.30** .53** −.30** .72** .51** .10* — 0.58 0.23
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*

p < .05.

**

p < .01.

Group Differences Among Participants

We analyzed differences in college-planning behaviors based on generation status, grade, and gender, first using the CPB mean total score as the dependent variable. The results of the one-way ANOVA found that college-planning behaviors varied significantly based on generation status, F (2, 497) = 5.28, p = .005, partial η2 = .02. Post hoc comparisons using the Tukey HSD test indicated that the mean score for continuing-generation college students (M = .44, SD = .16) was significantly higher than that of prospective first-generation college students (M = .38, SD = .15) and students who are unsure about their caretakers’ educational attainment (M = .34, SD = .18); the latter two groups did not differ significantly. Young women (M = .44, SD = .15) reported engaging in significantly more college-planning behaviors than the young men (M = .38, SD = .16). Results showed no significant differences between grade level based on the total mean CPB score.

We next examined differences in the subscale scores using a 2 (gender) x 2 (grade level) x 3 (first-generation status) multivariate analysis of variance (MANOVA). We found a small main effect of gender (p < .01, partial η2 = .03): young women engaged in significantly more Exploration (Factor 1) planning behaviors (M = .58, SD = .22) than young men (M = .49, SD = .24). Factors 2 (Concrete Activities) and 3 (Supplemental Activities) did not show significant gender differences. College-planning behaviors also varied significantly based on generation status (Tables 3 and 4). Prospective continuing-generation college students scored higher on Exploration (Factor 1; M = .57, SD = .23) and Supplemental Activities (Factor 3; M = .08, SD = .12) than their first-generation peers (M = .50, SD = .23 and M = .06, SD = .10, respectively) and students unsure of their parents’ educational attainment (M = .44, SD = .26 and M = .05, SD = .07, respectively). We found no generation differences on Concrete Activities (Factor 2), but did find a small but significant main effect for grade level (p < .05, partial η2 = .02). Students in 11th grade reported more Concrete Planning activities (M =. 12, SD = .12) than 10th graders did (M = .06, SD = .10). The results showed no significant two- or three-way interactions.

Table 3.

Means and Standard Deviations for Measures in Regard to Gender, Prospective First-Generation College Student Status, and Grade Level.

Measure Gender Grade
level		 Non-first-generation
(N = 337)		 First-generation
(N = 127)		 Unsure (N = 39) Total (N = 503)
CPB—total mean score Male 10th grade .40 (.16) .32 (.15) .32 (.20) .37 (.16)
11th grade .47 (.18) .32 (.12) .27 (.22) .42 (.18)
Female 10th grade .43 (.15) .42 (.14) .27 (.22) .42 (.14)
11th grade .57 (.13) .48 (.12) .36 (.25) .53 (.15)
Total (N = 503) .44 (.16) .38 (.15) .34 (.16) .51 (.14)
CPB—exploration Male 10th grade .50 (.22) .41 (.24) .43 (.28) .47 (.23)
11th grade .62 (.25) .40 (.18) .28 (.34) .55 (.26)
Female 10th grade .55 (.22) .55 (.20) .48 (.23) .54 (.21)
11th grade .76 (.17) .66 (.16) .50 (.33) .71 (.19)
Total (N = 503) .56 (.23) .50 (.23) .44 (.24) .54 (.24)
CPB—concrete activities Male 10th grade .07 (.13) .05 (.09) .05 (.06) .06 (.12)
11th grade .10 (.10) .06 (.07) .08 (.12) .07 (.08)
Female 10th grade .05 (.09) .06 (.09) .05 (.08) .05 (.09)
11th grade .16 (.13) .12 (.16) .06 (.08) .14 (.14)
Total (N = 503) .07 (.12) .06 (.10) .05 (.07) .07 (.11)
CPB—supplemental activities Male 10th grade .59 (.23) .46 (.21) .45 (.26) .55 (.24)
11th grade .56 (.23) .43 (.15) .50 (.14) .53 (.21)
Female 10th grade .65 (.21) .58 (.19) .47 (.22) .62 (.21)
11th grade .63 (.19) .54 (.17) .48 (.33) .59 (.20)
Total (N = 503) .62 (.22) .52 (.20) .46 (.24) .58 (.22)
Barriers Male 10th grade 1.92 (.57) 2.07 (.38) 2.29 (.62) 2.00 (.55)
Female 10th grade 2.02 (.57) 2.11 (.54) 2.23 (.45) 2.06 (.56)
Total (N = 404) 1.97 (.57) 2.11 (.49) 2.26 (.53) 2.04 (.55)
CGSES Male 10th grade 2.99 (.55) 2.52 (.62) 2.51 (.65) 2.82 (.61)
Female 10th grade 3.05 (.57) 2.96 (.47) 2.68 (.70) 2.99 (.56)
Total (N = 404) 3.02 (.56) 2.74 (.58) 2.61 (.67) 2.91 (.60)
COE Male 10th grade 7.72 (1.27) 7.24 (1.51) 7.33 (1.46) 7.57 (1.41)
Female 10th grade 7.82 (1.30) 7.57 (1.41) 7.36 (1.25) 7.71 (1.33)
Total (N = 404) 7.76 (1.30) 7.40 (1.45) 7.34 (1.61) 7.63 (1.38)
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Note. CPB = College Planning Behaviors; CGSES = College-Going Self-Efficacy Scale; COE = College Outcome Expectations.

Table 4.

Frequency of Item Endorsement Based on Prospective First-Generation College Student Status.

Item Continuing-generation, % First-generation, % Unsure, %
1 13.8 11.0 11.9
2 2.1 0.0 0.0
3 45.8 27.6 26.2
5 3.0 0.8 2.4
6 71.3 68.5 57.1
7 62.6 57.5 47.6
8 39.8 36.2 23.8
9 37.1 28.3 23.8
10 3.9 3.9 4.8
11 57.8 43.3 40.5
12 1.5 1.6 2.4
13 14.7 9.4 11.9
14 59.6 45.7 50.0
15 50.6 46.5 40.5
16 86.2 84.3 78.6
17 5.1 7.9 2.4
18 12.6 11.0 7.1
19 65.3 55.9 57.1
20 88.9 86.6 73.8
21 30.2 23.6 26.2
22 36.2 38.6 26.2
23 69.8 55.9 50.0
24 56.6 53.5 47.6
25 56.3 48.8 45.2
26 4.2 3.1 2.4
27 54.5 44.9 52.4
29 21.9 20.5 11.9
30 75.4 71.7 64.3
31 85.0 79.5 64.3
33 76.0 66.1 64.3
34 62.0 52.8 52.4
35 59.0 58.3 54.8
36 83.8 86.6 83.3
37 3.9 3.1 0.0
38 4.8 6.3 2.4
39 3.9 1.6 2.4
40 47.3 23.6 21.4
41 24.0 17.3 16.7
42 42.5 35.4 28.6
44 16.8 11.8 14.3
45 70.7 56.7 50.0
46 88.0 85.8 61.9
47 41.6 26.0 28.6
48 84.4 76.4 66.7
49 32.9 20.5 16.7
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Discussion

The results suggest that the CPB scale is beneficial for uncovering which steps students are taking to plan for college. The CPB thus appears to be a reliable and valid measure for choice actions within the SCCT framework with rural Appalachian students, and likely with other diverse student populations. Content validity was supported by the clear grounding of the scale items in the existing literature. Construct validity was reflected through the measure’s relation to other SCCT (Lent et al., 1994) variables. Students who perceived more barriers to college going or lower college-going self-efficacy reported engaging in fewer college-planning behaviors.

We found a clear three-factor structure: Exploration, Concrete Activities, and Supplemental Activities. These categories are consistent with prior literature, which suggests that multiple constructs are involved in the college-planning process, such as college knowledge and finances (Gibbons et al., 2006; Perna, 2005, 2006). The first factor recognizes the importance of participating in exploratory actions to inform postsecondary plans (e.g., “I have explored colleges on the computer”). The Concrete Behaviors factor involves specific preparatory actions (e.g., “I have taken math beyond Algebra II”). Finally, the Supplemental Activities construct includes behaviors that, although not necessary for college admissions, certainly help individuals be more competitive. These items may require time, resources, or other types of privilege for participation (e.g., “I have visited a college campus”). The three subscales cover a range of specific behaviors without redundancy, and the scale provides SCCT researchers with a measure for choice actions.

Between-Groups Differences

Our findings suggest that some statistically significant differences exist based on generation status, gender, and grade level. Previous research identified that parent education level impacted the postsecondary plans of students (Chenoweth & Galliher, 2004). We found that students whose parents have at least some college experience engaged in more college-planning behaviors than prospective first-generation college students and students who are unsure about their caretakers’ educational backgrounds. More specifically, although the two groups did not differ in terms of concrete planning activities, continuing-generation students performed more exploratory and supplemental behaviors. Perhaps continuing-generation students are more likely to engage in exploration because they have more access to information from their caretakers about college, or they may have more conversations about the college-going process within the household than first-generation students or students who are unsure about their caretakers’ educational backgrounds. It is understandable that continuing-generation students may be more involved in supplemental activities because their families may have more access to the time and resources required to engage in some of these behaviors (e.g., “I have completed a college tour”). The lack of differences among generation status within the Concrete Behaviors factor may be due to the fact that some items are requirements for all students in Tennessee (e.g., “I have taken the PSAT”) or may be emphasized school-wide by school counselors as critical to college planning (e.g., “I have completed the FAFSA”).

We also found that young women engaged in more college-planning behaviors than young men. This is consistent with data showing that, in the United States, women enroll in college at higher rates (69.8%) than men (62%; BLS, 2019). More specifically, young women are more likely to engage in exploratory behaviors, with no gender differences in our sample in engagement in concrete or supplemental activities. Perhaps this could be explained by prior research indicating that young women might have better access to social capital than young men (Klevan et al., 2016); greater access to information may partially explain this gender difference in exploratory behaviors. Another finding is that students in 11th grade performed more concrete planning behaviors than 10th graders. This is consistent with research on timing of college planning (Royster et al., 2015) and highlights the need for researchers and career educators to work with students at an earlier stage of their career development.

Limitations and Future Directions

As with all studies, limitations did exist. First, we only surveyed students from rural Appalachia. Although working with this population is important due to the educational disadvantages within the region, and our sample did represent diversity related to parent education level, SES, and gender, students’ responses may not be fully aligned with those of other high school students. The CPB items themselves are likely to be relevant beyond this population given that they were drawn from existing measures that have been used nationwide. Of course, exploring the factor structure of the measure in other populations is still important, and future research is also needed to determine whether other specific findings (e.g., gender differences) generalize to other groups.

Research has demonstrated that students from rural Appalachia may have an unrealistic understanding of college-going finances (Gibbons et al., 2019). This region also is characterized by strong economic inequities and disadvantages, which may have influenced the ways students understood and responded to items about financial-related planning (Items 3 and 40). Second, we decided to maintain a few of the items that cross-loaded on more than one factor. Although we provided a rationale for these decisions, it is possible that the items may not fully fit in the CPB. Third, we found that most students reported low engagement in Factor 2 (Concrete Activities). On Factor 2, students in both 10th and 11th grades had low average levels of completing these activities (e.g., 10% for 11th grade young men and 7% for 10th grade young men; see Table 3). Although we found grade level differences within this factor, students were still endorsing a low number of items. Research clearly indicates the importance of these activities, but the low endorsement levels may have skewed our factor analyses and results. Last, all information is self-reported and from a single response. We embedded validity checks to help ensure that students were reading the questions, but it is still possible that their responses do not fully match their college-planning actions. A further possibility is that students engaged in behaviors after taking the survey, which may impact our results.

Implications for School Counselors.

School counselors have been tasked with helping students navigate the complexities of planning for postsecondary education and careers (ASCA, 2019). This scale can help with data collection to assess where students are in the planning process. Results from this study might also give school counselors more insight into the college-planning behaviors of their students who are Appalachian, prospective first-generation college students, or students from low-income households. They can advocate for more information and resources for their students from these underrepresented backgrounds.

School counselors can also use this scale to determine which behaviors students are already engaging in to plan for college. Then, they can use the results of the survey to design classroom lesson plans or workshops based on the more infrequent behaviors. Isolating the items within each of the three factors in the CPB can help school counselors organize postsecondary preparation activities. School counselors may be interested in more exploratory behaviors or concrete behaviors that students are doing to prepare for college. Or, they might want to use the supplemental activities subscale for high-achieving students, specifically.

The factors and their definitions might help school counselors determine which activities are most necessary to support their students’ college-going development. School counselors may also divide the items into activities that are more appropriate for specific grade levels (e.g., students do not usually start applying for colleges until 12th grade). Utilizing these items appropriately may help students better understand what activities are important to support their journey toward postsecondary education. The CPB does not provide specific identifiers for students with learning disabilities or individual education plans that may impact the college-planning behaviors in which they are able to participate. School counselors and career educators might consider this when utilizing the CPB survey.

The strong link between college-going self-efficacy and college-planning behaviors is crucial for school counselors, who should be aware that students who are not engaging in college-planning behaviors may also lack self-efficacy for their postsecondary pursuits. Thus, school counselors can not only focus on helping students engage in college-planning behaviors, but can also endeavor to do so in ways that foster students’ self-efficacy. For example, they could implement vicarious experiences by bringing back students who previously graduated from their high school to talk to students about their college-going successes. School counselors could also be more intentional about providing verbal persuasion by consistently reminding students of their own belief that every student can engage in planning behaviors and go on to enroll in college. Emotional cues can be addressed in small groups and individual sessions by helping students uncover the causes behind their stress and anxiety to help them become more confident in their ability to succeed. Finally, school counselors can continue to boost self-efficacy by reminding students about their successful past performances (e.g., passing difficult courses). These suggestions may help improve dynamics between school counselors and students and bolster students’ self-efficacies to attend college.

Future Research.

Despite evidence supporting the psychometrics and utility of the final 45-item CPB in this population, future researchers could consider potential revisions. In this study, we focused on 10th and 11th graders; future research might consider utilizing a sample of students who are currently engaging in the application process. In the current study, we focused on the population of high school students from a primarily rural area of Appalachian east Tennessee. Future studies should explore college-planning behaviors with students from diverse backgrounds. For example, students in urban and suburban schools often have better access to advanced coursework (Gagnon & Mattingly, 2015). It may be possible that these advancements in other school systems allow students to engage in more college-planning activities or may change the timing of these activities. Some school systems also may mandate particular college-planning activities that are optional in others. Scores on the CPB and the timing of completion may vary across school districts, which in turn may affect the ways in which CPB scores correlate with other college-going variables.

The CPB scale provides school counselors and researchers flexibility in using some or all subscale scores or the total score. For example, a researcher may want to study specific concrete behaviors, rather than exploratory behaviors. Other researchers may only be interested in the total score, without differentiating among exploratory, concrete, and supplemental planning behaviors. Another approach might assess not only which of these behaviors students are engaging in, but also how important students perceive these behaviors to be for college planning. Assessing perceived importance could help researchers and career educators identify reasons students may fail to engage in important planning behaviors, which can in turn inform education and intervention efforts.

This study explored the development and utility of a new scale to measure college-planning behaviors in high school students and illuminated the college-planning process for students from rural Appalachian communities. School counselors may find the scale and the results of the validation study useful in developing, executing, and reinforcing systematic activities that support college and career readiness within their schools. We highlight the importance of college planning in this study, and our hope is that this scale can promote more college-going research and practice to help remove barriers and increase self-efficacy for high school students who wish to attend college.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by National Institutes of Health, R25 GM129177

Biographies

Katherine D. Cook is a doctoral student in counseling psychology at the University of Tennessee Knoxville, Knoxville.

Erin E. Hardin, PhD, is a professor in the Department of Psychology at the University of Tennessee Knoxville.

Melinda M. Gibbons, PhD, is a professor and the coordinator of the doctoral program in counselor education at the University of Tennessee Knoxville.

Marlon C. Johnson, PhD, is an assistant professor in counselor education at the Seminary of the Southwest in Austin, TX.

Christina Peterson is an internal research consultant in statistics and quantitative methods with the University of Tennessee Knoxville.

Anna Lora Taylor, PhD, is an assistant professor of counseling at Carson–Newman University in Jefferson City, TN.

Sean Murphy is a doctoral student in counseling psychology at the University of Tennessee Knoxville.

Footnotes

Declaration of Conflicting Interests

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

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