A Potential Avenue for Academic Success: Hope Predicts an Achievement-Oriented Psychosocial Profile in African American Adolescents

Abstract

In a sample of 117 African American students, we examined how well hope predicts five psychosocial variables—school belonging, academic self-concept, goal valuation, attitude toward teachers, and academic motivation/self-regulation—that altogether make up an achievement-oriented psychosocial profile. Results indicated that, after controlling for demographics and previous achievement, the subscales of hope accounted for a meaningful portion of all five psychosocial variables, ranging from 17.2% to 29.9%. The agency subscale of hope was a significant predictor of all five psychosocial variables, while pathways was only a significant predictor of goal valuation and academic self-concept. Given that several quick and effective universal hope interventions have been developed, these results suggest that hope may be a promising avenue to improve the achievement-related outcomes of African American students as well as aid in mitigating the achievement gap.

Keywords

hope African Americans achievement psychosocial factors adolescents

One of the biggest issues facing African Americans students today is the achievement gap, that is, the phenomenon of European American and Asian American students outperforming African American students across almost every major measure of academic success, including dropout rates, standardized test scores (e.g., SAT, ACT), college graduation rates, and school achievement (e.g., grade point average; Aud, Fox, & KewalRamani, 2010; Ginder, Kelly-Reid, & Mann, 2017; Snyder, de Brey, & Dillow, 2016). For example, Musu-Gillette et al. (2017) found that 63% of European American students and 71% of Asian American students graduated from college within 6 years compared with just 41% of African American students within the same time frame. This achievement gap has persisted for over five decades despite the best efforts of researchers, school administrators, and politicians (American Civil Liberties Union, 2000; Coleman et al., 1966; KIPP Foundation, 2012).

A relatively new, but promising, area of research geared toward mitigating the gap is research that centers on hope. Recent research has revealed that not only is hope closely related to achievement in school (r = .69; Feldman & Kubota, 2015), but it is also both easy to change (i.e., hope may be changed in a single 90-minute session; Feldman & Dreher, 2012) and an avenue of change for disadvantaged populations (Dixson, Keltner, Worrell, & Mello, 2017). However, despite all the evidence of hope’s potential to help mitigate the achievement gap, little research has been conducted on hope in African American samples to better understand how it can be leveraged to both increase their achievement and potentially contribute toward mitigating the achievement gap.

In this study, we examine how well hope predicts five achievement-oriented psychosocial variables—school belonging, academic self-concept, goal valuation, attitude toward teachers, and academic motivation/self-regulation—to better understand how hope relates to an achievement-oriented psychosocial profile within African American adolescents. We begin with a discussion of hope. Next, we discuss an achievement-oriented psychosocial profile. Finally, we discuss how hope can theoretically relate to an achievement-oriented psychosocial profile for African American students.

Hope

Hope is one’s perceived ability to envision paths to a better future, irrespective of one’s current circumstances, as well as one’s belief that she can achieve a better future via envisioned paths. Hope is broken down into two subcomponents: pathways and agency. Each component is seen as both necessary for one’s hope and complementary to one another (Snyder, 2002). The term pathways refers to one’s perceived ability to envision paths, as well as alternative pathways in case of setbacks or impediments, to desirable future goals. Agency, on the other hand, is one’s belief in oneself, as well as her corresponding motivation, to bring about her future goals via the envisioned paths. The synergy of pathways and agency can be seen in the following example. If a student wants to become a lawyer, the student’s perceived ability to envision the steps, and alternative steps, to becoming a lawyer (i.e., doing well in school, taking the Law School Admission Test [LSAT], getting into law school, and graduating from law school) will make up the student’s pathways. The student’s belief in her ability and motivation to succeed in school, attain a high score on the LSAT, graduate from law school, and pass the bar exam will make up the student’s agency. According to previous research, both the will (agency) and the ways (pathways) are imperative for goal accomplishment (Snyder, 2002).

Hope is typically measured using the Adult Hope Scale (AHS; Snyder et al., 1991) in adult populations and the Children’s Hope Scale (CHS; Snyder et al., 1997) in child and adolescent populations. Research on hope has found it to be both theoretically and empirically different from similar constructs like optimism and self-efficacy (Dixson, Worrell, Olszewski-Kubilius, & Subotnik, 2016; Feldman & Kubota, 2015; Magaletta & Oliver, 1999; Snyder, 2002). For example, in a sample of 204 college students, Magaletta and Oliver (1999) conducted an exploratory factor analysis (EFA) using the items of a commonly used hope scale (AHS), self-efficacy scale (Self-Efficacy Scale; Sherer et al., 1982), and optimism scale (The Life Orientation Test; Scheier & Carver, 1985). They found that the items of the different scales loaded on their respective factor and not the other factors (with few exceptions), indicating that each scale and the corresponding construct were unique from each other. Furthermore, several additional researchers have found that a modest to moderate amount of variance is shared between hope and optimism (10%-25%; Feldman & Kubota, 2015; Rand, Martin, & Shea, 2011) and hope and self-efficacy (20%-45%; Dixson et al., 2016; Feldman & Kubota, 2015).

Hope has also been found to be an important construct within the school context. Researchers reported that it has meaningfully predicted global self-worth (r = .56, p < .001; Marques, Gallagher, & Lopez, 2017), perseverance (r = .48, p < .003; Dixson et al., 2016), social acceptance (r = .43, p < .001; Snyder et al., 1997), and depression (r = −.47, p < .05; Marques et al., 2017). Moreover, hope has been found to be a predictor of several school-related outcomes, such as behavioral conduct (r = .41, p < .001; Snyder et al., 1997) and school adjustment (r = .35, p < .001; Gilman, Dooley, & Florell, 2006).

A Psychosocial Profile of Academic Success

In 2011, Yeager and Walton contended that interventions that target students’ psychology may have magical-appearing effects several years later by starting a positive feedback loop between students’ psychology and their environment. Dixson, Worrell, and Mello (2017) followed up on this theoretical framework by asserting that a student’s psychosocial perspectives that are relevant for school (e.g., school belonging, work ethic, curiosity, feelings toward the peers) make success-oriented behaviors in school either more or less likely, which in turn makes favorable responses from peers and teachers more or less likely (e.g., teacher and student attention). This process ultimately affects a student’s achievement. Finally, Dixson et al. indicated that the student’s achievement then shapes the student’s psychosocial perspectives relevant for school (e.g., higher achieving students have higher academic self-concepts, r = .57; Ghazvini, 2011), closing the feedback cycle.

Many different psychosocial perspectives are important for success in school (e.g., academic self-concept, curiosity, and educational expectations; Dixson, Worrell, et al., 2017; Marsh & Yeung, 1997). Five broad domains of psychosocial perceptions that have been highlighted to encompass the most influential perceptions for school, and altogether make up a psychosocial profile of achievement in adolescents, are students’ perceptions about their goals, self, teachers, school, and motivation (McCoach & Siegle, 2003a, 2003b; Suldo, Shaffer, & Shaunessy, 2008). In this examination, goal valuation, academic self-concept, feelings toward teachers, school belonging, and motivation/self-regulation were chosen to represent these five broad aspects of an achievement-oriented psychosocial profile.

Goal valuation, one’s perceived value of school-oriented goals (McCoach & Siegle, 2003b), was chosen to represent students’ perceptions about their goals. Academic goals play an important part in school achievement as they provide students with both a target to ascend to and additional motivation to accomplish the goal (Valle et al., 2010). Academic goals have been found to relate to academic engagement (r = .42, p < .01), intrinsic motivation (r = .71, p < .01), and academic achievement (r = .20, p < .01; Church, Elliot, & Gable, 2001; Neff, Hsieh, & Dejitterat, 2005). Academic self-concept, one’s perceived academic competence (Ghazvini, 2011), was chosen to represent students’ self-perceptions within the academic domain. Academic self-concept has been found to be an integral aspect of school achievement. Scholars have found that academic self-concept is not only meaningfully related to academic self-regulation (r = .44, p < .01), academic motivation (r = .32, p < .01), and creativity (r = .47, p < .01; Ordaz-Villegas, Acle-Tomasini, & Reyes-Lagunes, 2013), but also longitudinal studies have revealed evidence that academic self-concept might cause higher academic achievement. For instance, Marsh and Yeung (1997) found in a sample of 603 adolescents that academic self-concept scores at Time 1 were meaningfully related to students’ achievement a year later (β = .37, p < .05).

Feelings toward teachers, or students’ feelings about how competent and supportive their teachers are, were chosen to represent students’ feelings toward the people instructing them and responsible for assigning them a grade. Teachers play an enormous role in the academic lives of students and have the potential to influence student achievement via expectations (r = .55, p < .05; Jussim & Eccles, 1992) and knowledge (r = .71, p < .01; Darling-Hammond, 2000). Students’ feelings toward their teachers are likely to be pivotal for their achievement and other school-related behaviors (e.g., Cohen, Steele, & Ross, 1999). School belonging, or one’s sense of acceptance in and affinity toward one’s school and the people in it, was chosen to capture students’ feelings toward their school more broadly. School belonging has been found to be an influential psychosocial variable within the school context, especially with regard to student achievement. For example, in a meta-analysis that included over 25 studies and 11,300 students, Moallem (2013) found that school belonging was meaningfully related to both standardized test performance (r = .35, p < .01) and academic achievement (r = .25, p < .01).

Finally, academic motivation and self-regulation, or students’ motivation to achieve in school as well as their perceived ability to regulate themselves to academically achieve (McCoach & Siegle, 2003b), were used to capture students’ levels of motivation and commitment within school. Both student motivation and self-regulation have been found to be some of the most important variables for school achievement with several researchers even arguing for a causal link between them and a host of positive academic outcomes, including academic achievement (e.g., Green et al., 2012; Wang & Eccles, 2013). For example, Green et al. (2012) found in a sample of 1,866 high school students that academic motivation at Time 1 was meaningfully related to academic self-concept (r = .37, p < .05), class participation (r = .37, p < .05), and homework completion (r = .36, p < .05) a year later. Altogether, goal valuation, academic self-concept, feelings toward teachers, school belonging, and motivation/self-regulation provide extensive coverage of a psychosocial mind-set that is related to student success in school.

Hope and a Psychosocial Profile of Academic Success in African American Students

Although little research has been conducted on hope in African American samples, a few studies shed light on how it relates to an achievement-oriented psychosocial profile in African Americans. For example, in a sample of 661 African American adolescents, Adelabu (2008) found that agency was significantly related to academic achievement (r = .20, p < .01), future orientation (r = .39, p < .01), and ethnic identity (r = .42, p < .01), while pathways was significantly related to future orientation (r = .27, p < .01) and ethnic identity (r = .35, p < .01). In a different study, Valle, Huebner, and Suldo (2006) found in a mostly African American sample that hope scores were significantly related to internalizing behavioral problems (cross-sectionally, r = −.31, p < .01; longitudinally, r = −.28, p < .01), externalizing behavioral problems (cross-sectionally, r = −.35, p < .01; longitudinally, r = −.26, p < .01), and life satisfaction (cross-sectionally, r = .50, p < .01; longitudinally, r = .41, p < .01), both cross-sectionally (i.e., Time 1 to Time 1) and longitudinally (Time 1 to Time 2, a year later). Finally, Cedeno, Elias, Kelly, and Chu (2010) found in a sample of 132 African American fifth-grade students that hope scores were significantly related to self-concept (r = .42, p < .01) and internalizing behaviors (r = –.31, p < .01). Collectively, these studies indicate that hope is related to a productive mind-set in school-aged African American students.

The Current Study

The current study is an examination of how hope relates to an achievement-oriented psychosocial profile in African American high school students. More specifically, this study is an examination of how hope relates to five different psychosocial variables mentioned earlier that altogether cover five major areas of psychosocial perception that are closely related to achievement in school (McCoach & Siegle, 2003a, 2003b). In addition, age, gender, parental education, and previous achievement are controlled for throughout this study for two primary reasons. First, these constructs are either fixed or difficult to change (e.g., Haier, 2014; Hart & Risley, 2003), making any variance that these variables account for in the psychosocial outcomes unlikely to be changed as a result of a hope intervention. Thus, controlling for demographics and previous achievement is more likely to provide a better estimate of hope’s relationship to the aspects of the psychosocial outcomes that can be changed via intervention. Second, several researchers have found that demographics and previous achievement are related to future academic achievement (e.g., Lubinski, Webb, Morelock, & Benbow, 2001; Pomerantz, Altermatt, & Saxon, 2002; Sirin, 2005). Given that all five psychosocial outcome variables have also been found to relate to future academic achievement as well (McCoach & Siegle, 2003b), controlling for demographics and previous achievement is likely to provide a more stringent test of hope’s relationship to the psychosocial outcomes by controlling for these possible confounding factors.

Consistent with the findings of previous research that indicate that hope is a meaningful predictor of positive academic outcomes in African American students (e.g., Adelabu, 2008; Valle et al., 2006), we hypothesized that hope would be meaningfully and positively related to school belonging, academic self-concept, goal valuation, attitude toward teachers, and academic motivation/self-regulation. In addition, we hypothesized that hope would account for at least a medium effect size amount of variance (i.e., 9%; Newton & Rudestam, 1999) in all five psychosocial outcomes variables beyond demographics and previous achievement.

Method

Participants and Procedure

This study’s sample consisted of 117 (53.8% female, as indicated by parents during school registration) African American adolescents, aged 14 to 19 years (M_age = 16.2 years, SD = 1.53) years, attending an urban high school in a Western state. The mean cumulative grade point average of the sample was 2.64 (SD = 0.70; range = 0.5-4.0). The parent-reported parental education of the sample was 6.2% not a high school graduate, 12.4% a high school graduate, 34.5% some college, 33.6% college graduate, and 13.3% graduate school graduate.

Data were collected as a part of a school administered survey to better understand the psychosocial perceptions of students to both improve school climate and better serve the psychosocial needs of students. All students completed the survey in one session during the school day. The survey began with demographic questions, and then, students were presented with psychosocial questions in a randomized order. The survey took them about 20 to 30 minutes to complete. The current sample includes all the African Americans students from that data collection who had completed standardized testing.

Measures

Hope

Hope was measured via the CHS, a six-item scale that measures one’s perceived ability to execute envisioned routes to future goals (Snyder et al., 1997). The scale consisted of three items that measured pathways (e.g., “I can think of many ways to get the things in life that are most important to me”) and three items that measured agency (e.g., “I am doing just as well as other kids my age”). Response options were on a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Pathways and agency scores were combined to form a total hope score, with higher scores indicating higher levels of hope.

The results of previous studies have provided evidence that CHS scores were reliable with alpha estimates ranging from .70 to .85 and omega estimates ranging from .78 to .89 (Dixson, 2017; McBride, 2012; Snyder et al., 1997; Valle, Huebner, & Suldo, 2004). Previous research has also provided evidence that CHS scores were structurally sound. For example, in three different groups of adolescent students, Dixson (2017) found that CHS scores exhibited good fit in a series of confirmatory factor analyses, and that the two-factor model of hope (i.e., agency and pathways) fitted significantly better than the one-factor model of hope. Finally, researchers have found evidence that CHS scores exhibited acceptable convergent and discriminant validity. Evidence of convergent validity was found via CHS scores exhibiting meaningful relationships with similar constructs like optimism (rs = .48 and .49, ps < .001; Magaletta & Oliver, 1999) and future orientation (rs = .27 and .39, ps < .001; Adelabu, 2008), while evidence of discriminant validity was found via CHS scores exhibiting nonsignificant relationships with nonrelated constructs like intelligence (r = .03, p > .05; Snyder et al., 1997) and health care utilization (r = −.04, p > .05; Lewis & Kliewer, 1996).

Academic self-concept

Academic self-concept was measured using the Academic Self-Perceptions (ASP) scale from the School Attitude Assessment Survey-Revised (SAAS-R; McCoach & Siegle, 2003b). This seven-item scale measured students’ perceptions about their academic ability (e.g., “I am good at learning new things in school”). Response options were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores were indicative of more positive academic self-perceptions.

Previous research has indicated that ASP scores were reliable with alpha estimates ranging from .86 to .88 (Dedrick, Shaunessy-Dedrick, Suldo, & Ferron, 2015; McCoach & Siegle, 2003b; Suldo, Shaffer, & Shaunessy, 2008). In addition, several researchers have provided evidence that ASP scores were structurally valid. For example, Suldo, Shaffer, and Shaunessy (2008) found via EFA with ASP items (using principal components extraction) that the expected factor structure was supported in their sample (factor coefficients ranged from .44 to .75). In a related study, McCoach and Siegle (2003b) conducted a confirmatory factor analysis that revealed good fit (i.e., comparative fit index [CFI] and Tucker–Lewis index [TLI] > .90, a root mean square error of approximation [RMSEA] < .06, and a standardized root mean square residual [SRMR] < .05; Marsh, Hau, & Wen, 2004) and factor loadings that ranged from .58 to .80 (McCoach & Siegle, 2003b).

Finally, several scholars have provided evidence that APS scores have exhibited convergent, discriminant, and predicative validity. For instance, Suldo, Shaffer, and Shaunessy (2008) found that APS scores were meaningfully related to similar constructs like academic self-efficacy (r = .64, p < .001) and school satisfaction (r = .50, p < .001), while exhibiting nonsignificant relationships with dissimilar constructs like conduct problems in school (r = −.04, p > .05) and missing classes (r = −.25, p < .001). Similarly, Dedrick et al. (2015) found that APS scores predicted both academic achievement (r = .41, p < .05) and academic motivation (r = .43, p < .05) in a sample of 711 high school students.

School belonging

School belonging was measured using the Attitude Toward School (ATS) scale from the SAAS-R (McCoach & Siegle, 2003b). This five-item scale measured students’ feelings toward and sense of inclusion in their school (e.g., “This school is a good match for me”). Response options were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores were indicative of a higher sense of belonging in one’s school.

Previous research utilizing the ATS scale has provided evidence that ATS scores were internally consistent (reported alpha estimates have ranged from .87 to .93; McCoach & Siegle, 2003a, 2003b; Suldo, Shaffer, & Shaunessy, 2008). Additionally, researchers have reported evidence that ATS scores were structurally sound (McCoach & Siegle, 2003b; Suldo, Shaffer, & Shaunessy, 2008). For instance, Suldo, Shaffer, and Shaunessy (2008) conducted an EFA on the ATS items and concluded that the items had the expected factor structure (factor coefficients ranged from 67 to .85). Finally, several scholars have reported findings supporting the convergent, discriminant, and predictive validity of the ATS scores. For example, Suldo, Shaffer, and Shaunessy (2008) found that ATS scores were meaningfully related to similar constructs like positive student-teacher relationships (r = .58, p < .001) and positive peer relations (r = .52, p < .001). They also found that ATS scores exhibited a nonsignificant relationship with the conduct problems in school (r = –.15, p > .05), a dissimilar construct. Erkman, Caner, Hande Sart, Börkan, and Şahan (2010) found that ATS scores were also not significantly related to self-concept (r = .18, p > .05), another unrelated construct. Similarly, Suldo, Shaffer, and Riley (2008) reported that ATS scores predicted school satisfaction (r = .53, p < .05), global life satisfaction (r = .37, p < .05), and academic achievement (r = .35, p < .05).

Goal valuation

Goal valuation was measured via a shortened version of the Goal Valuation (GV) scale from the SAAS-R (McCoach & Siegle, 2003b). This five-item subscale measured students’ perceived value of academic-oriented goals (“It’s important to get good grades in school”). It should be noted that the full GV scale has six items. One item (i.e., “I want to do my best in school”) was left out of the survey by school administrators due to survey time constraints. Response options for the GV scale were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores were indicative of students placing a higher value on academic-related goals.

Evidence of the reliability of GV scores has been reported in several studies (alpha estimates ranging from .88 to .95; McCoach & Siegle, 2003a, 2003b; Suldo, Shaffer, & Riley, 2008). In addition, researchers from multiple studies have reported results suggesting that GV scores were structurally valid (McCoach & Siegle, 2003b; Suldo, Shaffer, & Riley, 2008). For example, Dedrick et al. (2015) reported that a confirmatory factor analysis that consisted of GV items revealed both good fit and acceptable factor loadings (.69 to .88) in a sample of 560 adolescents.

Finally, several researchers have provided evidence that GV scores have exhibited convergent, discriminant, and predictive validity. For instance, Suldo, Shaffer, and Riley (2008) found that GV scores were meaningfully related to similar constructs like hours spent on homework per week (r = .30, p < .001) and academic self-efficacy (r = .45, p < .001). They also found that GV scores predicted important outcomes like academic achievement (r = .36, p < .001) and school satisfaction (r = .40, p < .001). Similarly, researchers have found that GV scores were not significantly related to dissimilar constructs like teacher acceptance (r = .12, p > .05; Erkman et al., 2010) and student perceptions of safety and discipline at school (r = .12, p > .05; Suldo, Shaffer, & Riley, 2008).

Motivation and self-regulation

Motivation and self-regulation were measured using a shortened version of the Motivation and Self-Regulation (MSR) scale from the SAAS-R (McCoach & Siegle, 2003b). This eight-item scale measured both students’ motivation to achieve in school as well as their self-regulation within the academic domain (e.g., “I am motivated to do my schoolwork”). Two items (i.e., “I concentrate on my schoolwork” and “I use a variety of strategies to learn new material”) were left out of the survey due to time constraints. Response options for the MSR scale were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores were indicative of higher self-regulation and motivation within the academic domain.

The results of several studies have provided evidence that MSR scores were reliable with alpha estimates ranging from .88 to .94 (McCoach & Siegle, 2003a, 2003b; Suldo, Shaffer, & Riley, 2008). Additionally, several researchers reported that the structural validity of MSR scores in their sample were sound (Dedrick et al., 2015; McCoach & Siegle, 2003b; Suldo, Shaffer, & Riley, 2008). For example, McCoach and Siegle (2003b) conducted a confirmatory factor analysis with all the MSR items in a sample of 537 high school students and found that the model had good fit. MSR factor loadings ranged from .41 to .89.

Evidence of convergent validity has been found via MSR scores meaningfully predicting similar constructs like hours spent on homework per week (r = .36, p < .001) and school attendance (r = .29, p < .001; Suldo, Shaffer, & Riley, 2008). Evidence of discriminant validity has been found via MRS scores not significantly predicting dissimilar constructs like students’ perceptions of access to school resources (r = .07, p > .05) and students’ perceptions of their school building appearance (r = .08, p > .05; Suldo, Shaffer, & Riley, 2008). Relatedly, several researchers found that MRS scores meaningfully predicted important outcomes like academic achievement (r = .41, p < .001; Dedrick et al., 2015) and teacher acceptance (r = .40, p < .001; Suldo, Shaffer, & Riley, 2008).

Attitude toward teachers

Attitude toward teachers was measured with the Attitude Toward Teachers (ATT) scale from the SAAS-R (McCoach & Siegle, 2003b). This seven-item scale measured students’ perceptions of their teachers’ competence, relatability, and supportiveness (e.g., “My teachers make learning interesting”). Response options were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores were indicative of more positive perceptions toward teachers.

ATT scores have been found to be valid and reliable in previous research with adolescents. For example, the results of several studies indicated that ATT scores were reliable in adolescent samples with reported alpha estimates ranging from .88 to .95 (McCoach & Siegle, 2003a, 2003b; Suldo, Shaffer, & Riley, 2008). In addition, both an EFA (principal components extraction, factor loadings ranging from .57 to .83; Suldo, Shaffer, & Riley, 2008) and a confirmatory factor analysis (acceptable fit, standardized factor loadings ranged from .69 to .84; McCoach & Siegle, 2003b) have provided empirical support for the internal structure of ATT scores in adolescent samples.

Suldo, Shaffer, and Riley (2008) have found that ATT scores were not meaningfully related to dissimilar constructs like conduct problems in school (r = –.07, p > .05) and average time spent on homework per week (r = .09, p > .05). In contrast, they did find that ATT scores were meaningfully related to similar constructs like positive student-teacher relations (r = .73, p < .001). Erkman et al. (2010) provided additional support for the latter, finding that ATT scores were meaningfully related to student perceptions of teacher acceptance (r = .55, p < .01). Relatedly, both teams of researchers noted in their respective studies that ATT scores were related to academic self-efficacy (r = .49, p < .001; Suldo, Shaffer, & Riley, 2008) and self-concept (r = .53, p < .01; Erkman et al., 2010).

Previous achievement

Previous achievement was measured via student performance on their standardized state assessments in math and English/language arts (i.e., the Smarter Balanced Assessments [SBA] of the California Assessment of Student Performance and Progress). Both assessments were administered online via computer in a quiet room of students and were both hand scored (according to a rubric) and computer scored depending on the test item.

The math assessment (SBA-math) consisted of 35 to 42 items that covered mathematical concepts and procedures (e.g., explaining and applying mathematical concepts), problem solving, modeling/data analysis (e.g., real-world data modeling and problem solving), and communicating reasoning (e.g., forming mathematical arguments to support reasoning and critique the reasoning of others; Smarter Balanced Assessment Consortium, 2016b). The estimated testing time for the exam was 3 hours and 30 minutes. The SBA-math test scores have been concluded to be structurally valid and reliable. A confirmatory factor analysis of a higher order model of math (with mathematical concepts and procedures, problem solving, modeling/data analysis, and communicating reasoning as subfactors) revealed a good fit (CFI = .965, TLI = .964, and RMSEA = .024), and alpha estimates in previous research have ranged from .84 to .89 (American Institutes for Research & Ohio Department of Education, 2017).

The English/language arts (SBA-ELA) assessment consisted of 44 to 47 items that covered reading (literacy and informational), writing (organization, purpose, evidence, elaboration, and conventions), speaking/listening, and research (Smarter Balanced Assessment Consortium, 2016a). The SBA-ELA assessment took students about 4 hours to complete. SBA-ELA scores have been concluded to be both reliable and structurally valid based on the evidence of previous alpha estimates ranging from .74 to .81 and a confirmatory factor analysis revealing a model of good fit (CFI = .992, TLI = .992, and RMSEA = .015; American Institutes for Research & Ohio Department of Education, 2017).

Evidence of convergent validity for both assessments was displayed via meaningful correlations with other standardized tests such as i-Ready (English, r range = .81 to .83; i-Ready math, r range = .82 to .85; Curriculum Associates, 2016) and the Standardized Test for the Assessment of Reading (STAR test, r range = .73 to .77; Stern, 2015). Evidence of discriminant validity was displayed via a nonsignificant relationship with persistence in college (r range = .14 to .17; Kurlaender, Kramer, & Jackson, 2018). Finally, both assessments have been found to predict important outcomes such as high school achievement (r range = .28 to .34; Kurlaender et al., 2018), college achievement (r range = .55 to .64; Stern, 2015), and total units completed at the end of the first year of college (r range = .46 to .52; Kurlaender et al., 2018).

Parent education level

Parental education was collected from school records via a single item that parents completed during student registration for the 2017-2018 school year. Parents were asked what the “highest parent education level” for the student was. Response options were 1 (not a high school graduate), 2 (high school graduate), 3 (some college), 4 (college graduate), and 5 (graduate school). Parental education has been used effectively as a proxy for socioeconomic status in previous research (e.g., see Sirin, 2005, for meta-analysis). In addition, parental education has been found to correlate with academic achievement (r = .30, p < .01; Sirin, 2005), intelligence (r = .35, p < .01; Dubow, Boxer, & Huesmann, 2009), educational aspirations (r = .40, p < .01; Dubow et al., 2009), and parental aspirations for their child (r = .38, p < .001; Spera, Wentzel, & Matto, 2009).

Gender

Gender was collected from school records. Parents were asked during student registration “What is the student’s gender?” Response options were 0 (male) and 1 (female).

Validity item

The final item of the survey was a validity item that asked students whether “they have read each of the items on this survey and answered them seriously.” Response options were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Higher scores were indicative of students having thoroughly read and considered each survey question.

Results

Descriptive Statistics

The means, standard deviations, intercorrelations, and reliability estimates of study variables are presented in Table 1. About 1.5% of data in this study were missing at the time of analysis. The missing data were found to be missing completely at random (Little’s MCAR [missing completely at random] test p = .412), and all missing psychosocial values were imputed using the expectation maximization algorithm (25 iterations; Gold & Bentler, 2000). The percentage of imputed values ranged from 0.5% to 1.9% per item, within the acceptable range of 5% (Schafer, 1999). The study variable scores were neither extremely skewed nor kurtotic (i.e., −2 < x < 2; George & Mallery, 2010).

Table 1.

Descriptive Statistics and Correlations for Study Variables.

Variable	1	2	3	4	5	6	7	M	SD	Min-Max
1. Hope	—							3.61	0.76	1.00-5.00
2. Agency	.94	—						3.60	0.89	1.00-5.00
3. Pathways	.92	.73	—					3.62	0.74	1.00-5.00
4. Attitude Toward Teachers	.44	.40	.42	—				3.23	0.70	1.00-4.86
5. Motivation/Self-Regulation	.55	.54	.48	.53	—			3.55	0.67	1.00-4.88
6. Academic Self-concept	.59	.53	.57	.50	.63	—		3.46	0.67	1.00-5.00
7. School Belonging	.47	.48	.39	.52	.47	.25	—	3.42	0.98	1.00-5.00
8. Goal Valuation	.55	.50	.51	.33	.66	.49	.42	4.21	0.76	1.00-5.00

Note. N = 117; skewness ranged from −1.02 to 0.04; kurtosis ranged from 0.03 to 1.36. All ps <.0013.

In keeping with best practice, the reliability and structural validity of the study’s scale scores were examined (American Educational Research Association, American Psychological Association, & the National Council on Measurement in Education, 2014). An EFA was used to assess the structural validity of the study’s scale scores. The sample sizes and scale data included in this study were found to be suitable for an EFA based on the following: (a) all their Kaiser-Meyer-Olkin measures of sampling adequacy were >.70 (see Table 2; Watkins, 2018), (b) all Bartlett’s Tests of Sphericity were statistically significant (all ps < .001; Watkins, 2018), (c) all communalities were >.30 (see Table 2; Costello & Osborne, 2005; Tabachnick & Fidell, 2001), and (d) there were at least 10 study participants per item for all EFAs conducted (Costello & Osborne, 2005). In keeping with recommendations from both Watkins (2018) and Costello and Osborne (2005), the principal axis factoring extraction method was employed for all study variables with no rotation for the one-factor models (i.e., attitude toward teachers, motivation/self-regulation, academic self-concept, school belonging, and goal valuation) and with oblimin rotation for the two-factor model (i.e., hope; converged after 35 iterations). Finally, parallel analysis was conducted using the procedure outlined in Hayton, Allen, and Scarpello (2004).

Table 2.

Psychometric Properties of Measures.

Variable (no. of items)	α	KMO	Communalities	Factor coefficients	No. of factors indicated by PA
Hope (6)	.87	.87	.47-.79	.50-.93*	2
Agency (3)	.80
Pathways (3)	.75
Attitude Toward Teachers (7)	.89	.89	.43-.63	.66-.79	1
Motivation/Self-Regulation (8)	.87	.87	.26-.66	.51-.81	1
Academic Self-concept (7)	.80	.75	.31-.52	.56-.72	1
School Belonging (4)	.90	.83	.68-.75	.82-.87	1
Goal Valuation (5)	.89	.87	.57-.71	.76-.84	1

Note. N = 117; KMO = Kaiser–Meyer–Olkin measures of sampling adequacy; PA = parallel analysis. The factor coefficients for the Children Hope Scale are from the pattern matrix.

As can be seen in Table 2, all factor coefficients were >.4 (Costello & Osborne, 2005), and parallel analysis indicated that the number of factors that should be retained for each scale was theoretically consistent. As a consequence, it was concluded that the psychometric evidence was indicative of the scores of all included scales being structurally valid for the current sample. In addition, it was also concluded that the scores of all included variables were internally consistent based on scales having an alpha estimate >.7 in the current sample (Newton & Rudestam, 1999). Finally, the sample’s average for the validity item on the survey was 4.30 (SD = 0.96; range 1-5), indicating that the study’s participants, on average, read and took the survey seriously.

To assess for differences across study variables based on gender and parental education, 16 one-way analyses of variance were conducted. While all 16 one-way analyses of variance had the eight psychosocial variables as dependents, eight were conducted with gender as the factor and eight were conducted with parental education as the factor. Results indicated that there were almost no significant differences among study variables across gender or parental educational level (all ps > .05). The only difference found was for goal valuation across gender, F(1, 115) = 8.24, p = .005. Female students (M = 4.39, SD = 0.67) reported valuing academic-related goals more than male students (M = 4.00, SD = 0.80) with a medium effect size (Hedges’s g = .53).

Hope Predicting an Achievement-Oriented Psychosocial Profile

Two sets of analyses were conducted to examine the nature of relations between the subscales of hope and the five psychosocial variables included in this study (i.e., school belonging, academic self-concept, goal valuation, attitude toward teachers, and academic motivation/self-regulation). One set of analyses involved correlations between the three hope subscales and the five psychosocial variables. The other analyses were a series of hierarchical regression using demographics and previous achievement as covariates, the two hope subscales as the predictors, and the five psychosocial variables as the outcome variables.

Correlations

As can be seen in Table 1, all 15 correlations between the three indicators of hope and the psychosocial outcomes were statistically significant (ps < .0013) and meaningful (i.e., r > .30; Newton & Rudestam, 1999). Correlations ranged from .39 to .59 (Mdn = .50). Higher levels of hope, agency, and pathways were related to higher levels of school belonging, academic self-concept, goal valuation, attitude toward teachers, and academic motivation/self-regulation. All three indicators of hope exhibited a similar relationship with each respective psychosocial outcome.

Hierarchical regressions

The results of all five hierarchical regressions are presented in Tables 3 through 7.

Table 3.

Hierarchical Regression Predicting Motivation and Self-Regulation.

Model	B	β	p	sr ²	Adjusted R²	R² change
Block 1					.056
Gender	.119	.091	.343	.008
Age	−.018	−.043	.643	.002
Parent educational level	−.009	−.014	.888	.000
Standardized math score	.002	.253	.052	.033
Standardized English score	.000	.031	.817	.000
Block 2					.343	.287
Gender	.270	.205*	.013	.037
Age	.012	.027	.730	.001
Parent educational level	−.016	−.026	.758	.001
Standardized math score	.001	.093	.397	.004
Standardized English score	.001	.092	.418	.004
Agency	.324	.437**	.001	.083
Pathways	.140	.158	.168	.011

Note. N = 117; sr² = squared semipartial coefficient.

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

Motivation and self-regulation

In the first regression (see Table 3), demographics and previous achievement accounted for 5.6% of MSR’s variance in Block 1, with none of the variables making a statistically significant contribution in this model. After adding agency and pathways in Block 2, the model explained an additional 28.7% of MSR’s variance, bringing the total amount accounted for by the final model to 34.3%. Agency and gender were significant contributors in the final model. Thus, women reported higher motivation and self-regulation than men, and adolescents with higher levels of agency generally reported higher levels of motivation and self-regulation.

Attitude toward teachers

In the second regression (see Table 4), demographics and previous achievement explained just 3.6% of ATT’s variance in Block 1, with none of the control variables reaching the level of statistical significance. After adding the agency and pathways subscales in Block 2, the subscales of hope explained an additional 17.2% beyond Block 1. The total amount of ATT’s variance accounted for by the final model was 20.8%, with age and agency being significant predictors. Older adolescents reported a more positive attitude toward teachers than the younger adolescents and that those reporting a higher level of agency also had a more positive attitude toward teachers.

Table 4.

Hierarchical Regression Predicting Attitude Toward Teachers.

Model	B	β	p	sr ²	Adjusted R²	R² change
Block 1					.036
Gender	−.013	−.010	.920	.000
Age	.062	.136	.147	.018
Parent educational level	.112	.170	.092	.025
Standardized math score	.002	.235	.073	.028
Standardized English score	−.001	−.091	.505	.004
Block 2					.208	.172
Gender	.103	.074	.407	.005
Age	.081	.178*	.042	.030
Parent educational level	.107	.162	.077	.023
Standardized math score	.001	.103	.394	.005
Standardized English score	.000	−.038	.762	.001
Agency	.198	.253*	.050	.028
Pathways	.209	.223	.076	.023

Note. N = 117; sr² = squared semipartial coefficient.

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

Academic self-concept

In the third regression (see Table 5), demographics and previous achievement explained 7.6% of academic self-concept’s variance in Block 1, with standardized math scores as a statistically significant contributor. After Block 2, with agency and pathways included in the model, the model accounted for an additional 29.9% of academic self-concept’s variance. With the addition of Block 2, the total amount of academic self-concept’s variance accounted for by the final model increased to 37.5%, with pathways and agency as the only significant contributors. Hence, students reporting higher agency and pathways scores generally reported a higher academic self-concept.

Table 5.

Hierarchical Regression Predicting Academic Self-Concept.

Model	B	β	p	sr ²	Adjusted R²	R² change
Block 1					.076
Gender	−.085	−.063	.506	.004
Age	.037	.085	.353	.007
Parent educational level	.030	.048	.629	.002
Standardized math score	.002	.345**	.008	.061
Standardized English score	.000	−.014	.915	.000
Block 2					.375	.299
Gender	.055	.041	.609	.001
Age	.057	.131	.093	.016
Parent educational level	.024	.037	.646	.001
Standardized math score	.001	.173	.110	.014
Standardized English score	.000	.057	.603	.002
Agency	.202	.267*	.020	.031
Pathways	.312	.345**	.002	.054

Note. N = 117; sr² = squared semipartial coefficient.

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

School belonging

In the fourth regression (see Table 6), demographics and previous achievement explained 2.6% of school belonging’s variance in Block 1, with standardized math scores as a statistically significant contributor. With the addition of pathways and agency in Block 2, an additional 17.7% of school belonging’s variance was explained beyond Block 1. Altogether, the final model accounted for 20.3% of school belonging’s variance. Agency was the only significant contributor in the final model. Students who reported higher agency scores also reported a higher sense of belonging in their schools.

Table 6.

Hierarchical Regression Predicting School Belonging.

Model	B	β	p	sr ²	Adjusted R²	R² change
Block 1					.026
Gender	−.168	−.085	.381	.007
Age	−.079	−.122	.195	.015
Parent educational level	.088	.094	.350	.008
Standardized math score	.003	.278*	.035	.040
Standardized English score	−.002	−.172	.212	.014
Block 2					.203	.177
Gender	.016	.008	.928	.000
Age	−.039	−.061	.488	.003
Parent educational level	.079	.085	.352	.006
Standardized math score	.001	.155	.203	.012
Standardized English score	−.001	−.126	.311	.007
Agency	.432	.388**	.003	.066
Pathways	.106	.080	.525	.003

Note. N = 117; sr² = squared semipartial coefficient.

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

Goal valuation

In the last regression (see Table 7), demographics and previous achievement explained 14.2% of goal valuation’s variance in Block 1, with statistically significant contributions from gender, age, and standardized math scores in the model. Agency and pathways were added in Block 2, explaining an additional 27.7% beyond Block 1. The final model explained 41.9% of goal valuation’s variance, with gender, age, agency, and pathways being statistically significant contributors in the final model. Women and older students generally reported valuing academic-related goals more than their male and younger counterparts. In addition, students who reported higher agency and pathways scores also generally reported valuing academic-related goals more than those who reported lower agency and pathways scores.

Table 7.

Hierarchical Regression Predicting Goal Valuation.

Model	B	β	p	sr ²	Adjusted R²	R² change
Block 1					.142
Gender	.320	.212*	.022	.042
Age	−.110	−.222*	.013	.049
Parent educational level	−.037	−.052	.585	.002
Standardized math score	.002	.285*	.022	.042
Standardized English score	−.001	−.104	.420	.005
Block 2					.419	.277
Gender	.473	.313**	.001	.087
Age	−.086	−.176*	.020	.029
Parent educational level	−.044	−.062	.428	.003
Standardized math score	.001	.121	.246	.007
Standardized English score	.000	−.036	.735	.001
Agency	.236	.278*	.012	.034
Pathways	.317	.312*	.004	.044

Note. N = 117; sr² = squared semipartial coefficient.

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

Discussion

This study’s primary goal was to examine how well hope predicted five different psychosocial variables—school belonging, academic self-concept, goal valuation, attitude toward teachers, and academic motivation/self-regulation—that altogether make up an achievement-oriented psychosocial profile that is likely to facilitate academic success. Consistent with what was hypothesized, results indicated that (a) some aspect of hope is meaningfully related to all five achievement-oriented psychosocial variables included in this study and (b) after controlling for demographics and previous achievement, the subscales of hope explained 17% to 30% of each psychosocial variable, with agency as the most consistent predictor across all five psychosocial variables and pathways as a significant predictor for goal valuation and academic self-concept. These results are important for several reasons. First, they indicate that hope may be a promising avenue to facilitate the academic success of African American students. Given that hope meaningfully predicted five psychosocial variables that are related to academic success in school, implementing a universal hope intervention is now a particularly attractive course of action as improving the hope of students could take as little as 90 minutes (g = .57 for agency and g = .60 for pathways; Feldman & Dreher, 2012). That is about as little time as an in-class movie.

It is important to highlight that these findings do not suggest that increasing the hope of an African American student will guarantee that the student will succeed academically. Instead, the results merely suggest that increasing the hope of African American students is likely to increase the extent to which they will have an achievement-oriented psychosocial profile, which, according to previous research (e.g., Marsh & Yeung, 1997; Moallem, 2013), is likely to increase their probability of academic success. This directionality of influence is supported by the multitude of psychological experiments that indicate targeting and increasing hope via intervention leads to higher psychological assets (e.g., Duggleby et al., 2007; Marques, Lopez, & Pais-Ribeiro, 2011). For instance, a hope intervention carried out by Marques et al. in 2011 found that their hope intervention not only resulted in students reporting higher levels of hope (g = .92, p < .01) but also higher life satisfaction (g = .64, p < .05), self-worth (g = .92, p < .01), and academic achievement (g = .52, p < .05).

Second, this study’s findings underscore the importance of African American students continuing to envision themselves accomplishing their goals and striving to make them a reality despite their current circumstances and various setbacks they encounter along the way. The unfortunate reality for many African American students is that they face hindrances like racism (e.g., Krieger, Smith, Naishadham, Hartman, & Barbeau, 2005), poverty (e.g., Macartney, 2013), and low teacher expectations (e.g., Elhoweris, Mutua, Alsheikh, & Holloway, 2005) at much higher rates than other ethnic groups. The results of this study indicate that these students’ hope, that is, their ability to see past these enduring and pervasive daily deterrences to their future goals as well as their belief that they can accomplish those goals, can facilitate the students’ achievement-oriented mind-set. Hope’s connection to these variables in turn may increase the likelihood of their academic success (e.g., Moallem, 2013).

Additional support for the benefits of continuing to maintain hope in unfavorable circumstances is evident in a study conducted by Worrell and Hale (2001). In a sample of 97 adolescents attending a continuation high school, they found that hope was the only variable included in the study (the other variables were scholastic competence, perceived school climate, expectation of a good job, and importance of college) that significantly predicted dropout versus graduate status 2 years after data collection (correctly predicting 78% of the participants). Worrell and Hale’s findings, like the current study, indicate that despite various hindrances (like those associated with being in a continuation high school [i.e., low school belonging and low school achievement]), having higher hope is associated with higher rates of academic success.

Third, this study’s findings indicate that hope might be more important for improving students’ perceptions about themselves within the academic domain than their perceptions toward the school community. Although the subscales of hope accounted for a meaningful proportion of all five psychosocial outcomes, they explained about 7% to 10% more variance for the self-perception variables (academic self-concept, goal valuation, and motivation/self-regulation) than the feelings toward the school variables (i.e., school belonging and perceptions toward teachers). This finding is noteworthy because many targeted hope interventions are delivered indiscriminately in schools based on the underlying assumption that hope is similarly related to the majority of success-oriented academic perceptions (e.g., Marques et al., 2011; Weis & Speridakos, 2011). The finding of hope being more closely related to students’ academic self-perceptions than their perceptions of the school community indicates a more efficient way to employ targeted hope interventions. Hope interventions might be slightly more effective for those students experiencing academic self-perception issues as opposed to those suffering from school belonging and low teacher expectation issues. Nonetheless, more research should be conducted on hope in schools to better understand the nuances of how hope relates to the different aspects of school success and under what circumstances is hope most beneficial.

Fourth, multiple demographic findings were reported. In this study, age was a significant predictor of attitude toward teachers, gender was a significant predictor of motivation and self-regulation, and both were significant predictors of goal valuation. However, the first two findings had small effect sizes (i.e., <9%; Newton & Rudestam, 1999), and the third finding is likely, at least partially, a reflection of the gender difference across goal valuation reported in this study. Nevertheless, all these findings are consistent with previous research where researchers found that age and gender were meaningfully related to psychosocial perceptions of achievement (e.g., Markstrom-Adams & Adams, 1995; McCoach & Siegle, 2003b). For instance, McCoach and Siegle (2003b) found in a sample of 176 adolescents that females were much more likely than males to have psychosocial perceptions conducive to academic success. Given that few studies focus on gender differences across achievement-oriented psychosocial constructs in African American students, more research is needed in this area to better clarify the relations.

Finally, this study suggests that agency might be more closely aligned with a psychosocial mind-set of academic success than pathways. Agency was a significant predictor for all five psychosocial outcomes, while pathways was a significant predictor for only goal valuation and academic self-concept. This finding is consistent with other recent research. For example, Dixson et al. (2017) found that high agency thinkers (i.e., students with high agency and average to low pathways) had a more adaptive psychosocial profile than high pathway thinkers (i.e., students with high pathways and average to low agency) across several school related variables (e.g., academic investment, educational expectations). These studies together demonstrate that at least within the context of academic success, the pathways component of hope may be slightly less important than the agency component. Nonetheless, it is important to note that despite the agency component appearing to be more important when directly compared with the pathways component, the best overall psychosocial profile is created when they are added together. This synergy is seen in both the current study and other research (e.g., Dixson, 2018; Dixson, Worrell, et al., 2017).

Limitations, Implications, and Future Research

This study, like all research, has limitations. First, this study uses a cross-sectional design, meaning the directionality of influence as well as causality cannot be inferred. Although several studies indicate that hope leads to adaptive psychological perceptions (e.g., Duggleby et al., 2007; Marques et al, 2011), only a psychological experiment with the proper design and manipulations employing the same variables used in this study would be conclusive evidence that hope leads an achievement-oriented psychosocial profile in African American adolescents. Thus, follow-up research should employ longitudinal and experimental designs to determine both directionality and causality.

Second, although sufficient to carry out this study’s analyses, the sample consisted of only 117 African American students from one high school in a Western state. Future studies should seek larger samples with students from multiple schools in multiple states to replicate and generalize this study’s findings to the African American community as a whole. Finally, this study’s sample had a relatively high average socioeconomic status (i.e., over 70% of the sample reported that their parents had at least some college experience). Future studies should seek a more socioeconomically diverse sample to better establish the generalizability of this study’s finding.

Despite these limitations, this study adds to the psychological literature on improving the achievement of African American students via improving their psychosocial mind-set. More specifically, this study provides evidence that hope is meaningfully related to achievement-oriented psychosocial variables that appear to constitute a specific profile for African American adolescents. However, for these variables to be considered as legitimate profile for hope, future research is needed with a larger sample size to examine the relationship of hope to this composite of variables as well as the pattern between hope, this psychosocial profile, and various indicators of academic success. At this time, these findings do cement the theoretical foundation for hope as a promising construct for increasing the academic success of African American students and potentially aiding in the mitigation of the achievement gap. If universal hope interventions raise the hope of African American students and, as a consequence of the increased hope, students develop a more achievement-oriented profile, they would be more likely to succeed in school (Marsh & Yeung, 1997; Moallem, 2013).

Quick and effective hope interventions have already been developed, tested, and implemented in schools (e.g., Feldman & Dreher, 2012). However, it is currently unknown whether those same interventions increase the likelihood of African American students developing a more achievement-oriented psychosocial profile despite the current study indicating that hope plays a role. Future studies should examine whether hope interventions increase the likelihood or cause African American students to develop a more achievement-oriented psychosocial profile. Despite all the research that still needs to be conducted, a promising takeaway from the current study is that the thought of a better tomorrow mixed with the belief that one can make it a reality is generally a powerful perspective for African American students to have within the academic domain. The best part of this promising takeaway is that all African American students have the potential to be hopeful.

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.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Dante D. Dixson

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