An Exploration of the Psychosocial Characteristics of High Achieving Students and Identified Gifted Students

Abstract

High achieving students or “bright children” are often denied access to gifted services because they do not meet “gifted” criteria. Although psychosocial factors play an integral role in academic success, and can be useful in providing a clearer picture of student need, they are seldom considered in the decision to identify a student for gifted services. This study compared identified gifted students and non-identified high achieving students on their self-perceptions of several psychosocial factors to provide additional evidence that gifted services, depending on the framework and content of these services, may be appropriate for non-identified high achieving students as well. Results indicated that non-identified high achieving students’ psychosocial self-perceptions, as measured by the School Attitude Assessment Survey–Revised (SAAS-R) Subscales, were comparable with the identified gifted students, suggesting that the high achieving students could have benefited from the gifted services their school offered.

Keywords

gifted high achievers psychosocial School Attitude Assessment Survey–Revised

More than 25 years ago, Szabos (1989) created the very popular “Bright Child” and “Gifted Learner” comparison chart. Although it continues to be used by well-intentioned educators of the gifted in an effort to justify the special programming needs of gifted students, researchers challenge the “Bright Child” and “Gifted Leaner” dichotomy. For example, Peters (2014) contended, “What about the distinction of ‘bright vs. gifted’ is educationally helpful if the students are otherwise the same in what they need from their school or teacher?” (para. 3). Rather, educators should place more emphasis on identifying student need as opposed to just identifying students for gifted services that may or may not meet their needs. Another issue that arises from this dichotomy is the false assumption that non-identified students who achieve at high levels cannot benefit from gifted services. Based on a large-scale study of gifted programs in the United States, Callahan, Moon, and Oh (2013a, 2013b) found the vast majority of elementary and middle school gifted services are delivered using either no framework, differentiation, or enrichment and are most fully developed in reading and mathematics.

Given this information, one cannot help but ask the following question: Is it defensible, simply because they are not labeled “gifted,” to deny differentiated learning activities and/or enrichment in reading and/or math to motivated non-identified students whose actual achievement is well above average in one or both areas? Although it is beyond the scope of the current study to answer this question, a comparison of identified gifted students and non-identified high achieving students on several psychosocial factors (e.g., self-efficacy, self-regulation, motivation) that are typically not measured or heavily weighted in the identification process could move this discussion forward. If educators are apprehensive about offering gifted services to students based on measures of current achievement alone, they might consider also using valid measures of psychosocial factors to help determine if these students could potentially benefit from services.

Ability and Achievement

The idea that a high IQ score or a high composite score on an ability test (e.g., the Cognitive Abilities Test [CogAT]) are the only measures of giftedness is considered outdated (Kaufman & Sternberg, 2008), as many prominent thinkers in the field of gifted education have continued to challenge this notion and actually classify it as one of the common “myths” of gifted education (e.g., Borland, 2009; Renzulli, 1982). Although, according to Subotnik, it is unlikely that a student with a high ability score represents a false positive for giftedness, false negatives can also occur for students who do not meet a pre-determined cutoff score on a measure of ability depending on the identification process used (Carpenter, 2001; McBee, Peters, & Waterman, 2014). Therefore, denying students access to gifted services solely on the basis of not meeting a cutoff score on a test of ability is not a defensible practice (Callahan et al., 2013a, 2013b).

Interestingly enough, educators and even researchers tend to treat ability and achievement as two distinct constructs with their own unique forms of measurement that do not overlap one another. This makes sense, given that certain ability tests like the CogAT place emphasis on reasoning abilities and achievement tests assess more general knowledge typically acquired through school-based activities (Lohman, 2006). Ability and achievement, however, may be viewed as a unitary construct. Tests of each will invariably overlap because the measurement of ability “ . . . is not a fixed capacity but something very much the product of education and experience” (Lohman, 2006, p. 33). In other words, our ability to reason is heavily influenced by what we already know. Consequently, as Worrell, Olszewski-Kubilius, and Subotnik (2012) contended, “ability and achievement are so inextricably intertwined that a schism between them is at best improbable, and maybe impossible” (p. 226).

Identification and Ability and/or Achievement

Although the identification process for gifted services often varies from state to state, district to district, and even from school to school within a district, a common scenario occurs. Although some students may automatically qualify for gifted services because they meet a pre-determined cutoff on an ability test (IQ ≥ 130), a combination of pre-determined cutoff scores on ability and achievement measures is often used. A very simplified but common example of the latter would be a student needing to have a CogAT composite score at the 90th percentile or higher and an Iowa Test of Basic Skills (ITBS) score at the 95th percentile or higher to qualify for gifted services. Although other criteria may be required (e.g., rating scales, nominations, products) and even assigned arbitrary point values, meeting pre-determined cutoffs on both aforementioned test scores ultimately determines if a student is identified or not (Callahan et al., 2013a, 2013b).

Requiring a combination of cutoff scores, also referred to as the “Conjunctive Model” of identification, is problematic because it can inflate false negatives (McBee et al., 2014). In other words, students who are likely to benefit from gifted services are not always identified using this approach. Callahan et al. (2013a) cautioned against the use of a Conjunctive Model because it “creates ‘multiple hurdles’—each [criteria] represent[s] a place where a student could stumble and be eliminated from the opportunity to receive services” (p. 15). Although requiring a particular cutoff score on a measure of ability or a measure of achievement (i.e., the “Or Rule”) may over-identify students for gifted services, this will most likely not present an issue for programs that primarily utilize enrichment or differentiation frameworks (McBee et al., 2014), two of the more popular frameworks for providing services (Callahan et al., 2013a, 2013b). These types of services are considered low risk because they have the potential to benefit any student, identified gifted or not (Borland, 2008).

Still, a qualifying score on a single measure is not considered “best practice” in the field of gifted education. Although students who qualify for gifted programming via a single measure may benefit from the gifted services offered at their schools, others factors in addition to achievement and/or ability should be taken into consideration when appropriate. Researchers have known for years that the correlation between ability scores and grades is probably somewhere between .5 and.6 (Reis & McCoach, 2000), which means IQ scores only explain 25% to 36% of the variance in grade point average (GPA). This leaves well above half of the variance that must be accounted for by other factors. Reis and McCoach (2000) posited that this additional variance may be explained by factors that are psychosocial in nature. Assessing these additional factors can help educators see the “whole child” and make a more informed decision about whether the available gifted program can potentially meet the needs of the student.

Psychosocial Factors

Neurological as well as educational research support the premise that psychosocial factors play an integral role in achievement (Olenchak, 2009) and are essential components in “manifestations of giftedness at every developmental stage” (Subotnik, Olszewski-Kubilius, & Worrell, 2011, p. 7). Rinn (2012), in fact, goes so far as to question whether psychosocial factors should be assessed before students ever participate in gifted programs or honors courses. While Merriam-Webster broadly defines psychosocial as “involving both psychological and social aspects,” there is really no consensus on exactly which of these factors is the most influential in the field of gifted education.

Throughout the literature, various psychosocial factors have been deemed important to the achievement of outstanding academic performance and/or the development of talent. These factors encompass everything from broad constructs such as motivation (Rinn, 2012; Subotnik et al., 2011), self-concept (Huang, 2011), and interest (Lohman, 2005a; Worrell et al., 2012) to more specific learning characteristics like persistence (Lohman, 2005a; Worrell et al., 2012), passion, risk taking, self-regulation, and resilience (Subotnik et al., 2011). Despite lack of complete certainty regarding the most influential of these factors, however, it does seem clear that they interact with ability and aptitude in the manifestation of high achievement and/or gifted behavior (Olenchak, 2009; Subotnik et al., 2011).

While there is ongoing deliberation regarding which psychosocial factors should be included in discussion and research around gifted education, motivation repeatedly emerges as a key consideration (Rinn, 2012; Subotnik et al., 2011; Worrell, 2009; Worrell et al, 2012). For example, Worrell (2009) pointed out that literature in the field of gifted education “suggests that time commitment, motivation, and actual performance are ancillary to outstanding performance and crowns IQ as the defining characteristic of giftedness” (p. 243). Similarly, Lohman (2006) stated that it is impossible to separate ability from either motivation or feeling.

Motivation is a multi-dimensional construct that consists of varying components such as interest, value, or autonomy, depending on whose model/theory one chooses to reference. Researchers have found that motivation is a strong predictor of academic success (Gottfried, Gottfried, Cook, & Morris, 2005). For the sake of simplicity, motivation in the field of gifted education has recently been proposed through the Achievement-Orientation Model (Siegle & McCoach, 2005) to include the combination of self-efficacy, task meaningfulness, and environment that, when present in a positive manner, result in self-regulation that allows students to engage and achieve at levels commensurate with their ability. The School Attitude Assessment Survey–Revised (SAAS-R) can be used to measure students’ self-perceptions of these factors as they relate to learning in school (Rubenstein, Siegle, Reis, McCoach, & Burton, 2012). Although these factors have not traditionally been referred to as “psychosocial factors” in studies utilizing the SAAS-R, variations of these factors have been described as psychosocial in the fields of gifted education and psychology (e.g., Assouline, Foley Nicpon, & Whiteman, 2010; Huang, 2011; Richardson, Abraham, & Bond, 2012; Subotnik et al., 2011).

Despite this general acceptance of psychosocial factors, in the role of outstanding accomplishment, gifted education continues to focus primarily on ability and/or achievement test scores in the identification of gifted students (Callahan et al., 2013a, 2013b; Worrell, 2009). Incorporation of measures of individual psychosocial factors may provide valuable information necessary for meeting the academic needs of students who would otherwise be overlooked for gifted programming.

Non-Identified High Achievers and Identified Gifted Students

Card and Giuliano (2014) investigated the impact of an intensive, full-day gifted program on the achievement of fourth grade program participants. Students who attained a state-mandated IQ cutoff score were automatically placed in a separate classroom for gifted students. Open seats were then offered to non-identified students who achieved the highest scores on the last year’s standardized state assessment. Findings revealed academic gains (based on statewide achievement data) in the areas of reading and math for the non-identified high achievers. These gains in math and reading continued into the fifth grade and were also evidenced in the students’ science scores that year. The high-IQ gifted students, for whom the gifted program was designed, showed minimal to no academic gains based on achievement data.

Lohman (2005b) contended that “a biased selection procedure is one that, in any group, does not select the students most likely to profit from the treatment offered” (p. 134). The high achieving students in the aforementioned study clearly profited from the treatment offered (i.e., the intensive gifted program). Had these students been denied access to this program because they did not meet an intelligence score cutoff, they may not have made similar academic gains. This raises the question of how effective (or ethical) it is to preclude students from participating in potentially beneficial gifted programming because they do not meet a certain threshold on a measure of ability, a practice that is likely to occur when school districts use a Conjunctive Model to identify students (McBee et al., 2014).

Interestingly, in the Card and Giuliano (2014) study, one group of identified gifted students had initial achievement scores similar to the non-identified high achievers, but it was only the high achievers who showed academic gains as a result of the gifted program, suggesting that other factors contributed to their success. Based on interviews with teachers, Card and Giuliano concluded that positive, individual psychosocial traits, such as task commitment, in combination with past high achievement seemed to contribute to the success of the non-identified students in the gifted program.

Bui, Craig, and Imberman (2014) similarly contended that the academic success of high achieving students who participate in gifted programming may be due in large part to individual motivation. According to Bui et al., motivation not only affects a student’s initial placement in an ability group for “gifted” students, but also her or his future academic success. Similarly, Gottfried et al. (2005) found that motivation was a better predictor of cumulative high school GPA than IQ and went so far as to recommend that motivation, by itself or in conjunction with other measures, be used as a criterion in the gifted identification process. It does seem clear that in addition to past or actual achievement, “psychosocial variables are determining factors in the successful development of talent” (Subotnik et al., 2011, p. 4). As many would argue against the sole use of a measure of high achievement to determine gifted placement, the combination of such a measure coupled with high levels of certain psychosocial factors believed to promote academic success (e.g., motivation), could serve as an alternative pathway for high achieving students who do not meet ability score cutoffs but who could potentially benefit from a particular program being offered.

Purpose

Given that current achievement is a strong predictor of academic success (Card & Giuliano, 2014; Lohman, 2006) and certain psychosocial factors are believed to play an important role in the development of talent (Subotnik et al., 2011), the purpose of this study was to compare identified gifted students and non-identified high achieving students on their self-perceptions of several of these factors to provide additional evidence that gifted services, depending on the framework and content of these services, may be appropriate for non-identified high achieving students as well.

Method

Participants and Setting

The setting was a dual-language immersion charter school which served 360 students in Grades K-7 in a southeastern U.S. state. All students in the fourth through seventh grades were conveniently sampled for this study (n = 203). More than one third of these students were identified as gifted (36%). Gifted and non-identified students were predominately female (52% and 56%, respectively) and in Grades 5 and 6 (see Table 1) Ethnicity data were not collected for this study. However, the school reported that the majority of its students (84%) were White. Of the original 203 students sampled, 73 were identified as “gifted” and 68 were classified as non-identified “high achievers” based on overall GPA of 3.5 or higher.

Table 1.

Grade and Gender Descriptives for Non-Identified High Achieving and Identified Gifted Students.

	Grade	%	Gender	%
High achieving (n = 68)	4	23.5	M	42.6
	5	22.1
	6	36.8	F	57.4
	7	17.6
Identified Gifted (n = 73)	4	17.8	M	47.9
	5	34.2
	6	26	F	52.1
	7	21.9

Gifted Identification Process

Students were identified as eligible for gifted services at the end of second grade in one of two ways, both of which used the composite age percentile score on the CogAT (Lohman & Hagen, 2005). Students scoring at or above the 97th percentile were automatically eligible for placement in gifted programming. Students whose percentile score fell between the 90th and 96th percentile qualified for placement, if they also obtained a score of 95th percentile or higher on the Reading Total, Math Total, or Total Battery of the Iowa Tests of Basic Skills.

Gifted Programming

Differentiated learning opportunities in reading and math were provided by a gifted facilitator using pull-out and push-in delivery methods. Students in Grades 4 and 5 were pulled out for direct instruction and/or enrichment reading and math with the gifted facilitator twice a week. Identified gifted students in Grades 6 and 7 were grouped by ability for reading and math. The gifted facilitator “pushed in” for these classes on a weekly basis and co-taught lessons with the teacher using instructional practices like curriculum compacting and independent investigations related to unit objectives.

Instrumentation

SAAS-R

The SAAS-R (McCoach & Siegle, 2003b) measures students’ self-perceptions of five psychosocial factors associated with academic achievement: (a) Academic Self-Perceptions (i.e., self-efficacy), (b) Attitude Toward Teachers (i.e., environmental perceptions), (c) Attitudes Toward School (i.e., environmental perceptions), (d) Goal Valuation (i.e., task meaningfulness), and (e) Motivation/Self-Regulation. Table 2 breaks down each subscale of the SAAS-R and provides selected sample items from this instrument.

Table 2.

SAAS-R Subscales and Sample Items.

Subscales	Number of total items	Sample item
Academic Self-Perceptions	7	I am smart in school.
Attitudes Toward Teachers	7	My teachers care about me.
Attitudes Toward School	5	This school is a good match for me.
Goal Valuation	6	Doing well in school is one of my goals.
Motivation/Self-Regulation	10	I am self-motivated to do my schoolwork.

Note. SAAS-R = School Attitude Assessment Survey–Revised.

Respondents rate agreement with each of the 35 SAAS-R items using a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). The instrument takes approximately 15 min to administer.

Each subscale of the instrument has an internal consistency reliability coefficient of at least .80 (McCoach & Siegle, 2003b). Furthermore, scores on the SAAS-R demonstrate evidence of adequate content and criterion-related validity. Content validity was assessed through a panel of 18 experts. The panel provided two ratings for each item. First, items were categorized into their respective constructs. Next, experts rated confidence in their classification on a 5-point Likert-type scale. At least 80% agreement between the two ratings among the panel was needed for an item to be retained (McCoach, 2002).

Criterion-related validity was examined through two studies with high school students. First, McCoach and Siegle (2003a) investigated whether the SAAS-R distinguished a national sample of 178 high school gifted underachievers from gifted achievers. Findings indicated that the underachieving group reported more negative attitudes toward school on all SAAS-R scales except Academic Self-Perceptions. The Motivation/Self-Regulation and Goal Valuation factor scores were the strongest predictors of group membership. A second study examined whether the SAAS-R could distinguish 244 high achieving students from low-achieving students in one high school (McCoach & Siegle, 2001); achievement classification was determined based on self-reported GPA. Results demonstrated high achieving students reported more positive attitudes within each of the five constructs than low-achieving students.

Construct validity of the SAAS-R was demonstrated in the form of significant correlations between SAAS-R scales and other indicators theoretically related to each scale. Furthermore, construct validity was established between all SAAS-R scales and students’ school satisfaction. Discriminant validity was supported by “findings consistent with the notion that students’ beliefs about their academic abilities should be constant across school environments as well as behavior in out of school” (Suldo, Shaffer, & Shaunessy, 2007, p. 12).

Self-reported GPA

Self-reported GPA is an item on SAAS-R. It is measured on a 10-point scale where 1 = all A’s (GPA of 4.0 or above), 2 = mostly A’s (GPA of 3.75-3.99), 3 = more A’s than B’s (3.5-3.74), to 10 = mostly D’s and F’s (GPA < 1.0). To ensure a more representative group of high achieving students (e.g., twice-exceptional students, culturally linguistically diverse students), students who selected a 1, 2, and 3 were classified as high achievers.

Although standardized achievement tests can be used to measure achievement, the merit of using GPA as an achievement measure should not be discounted. A distinction is often made between expected achievement and actual achievement (e.g., Reis & McCoach, 2000; Rubenstein et al., 2012) and can help provide a more accurate picture of student need. Expected achievement, commonly measured by CogATs and standardized achievement test scores, is comparable with aptitude or potential. Measures of actual achievement, however, are observed evidence of student accomplishment. Grades and teacher evaluation are commonly used measures of actual achievement (Reis & McCoach, 2000). Although the usage of grades to measure actual achievement is not without flaws, especially self-reported GPA, the extant literature on gifted underachievement dating as far back as Gowan (1957) and as recently as Ritchotte, Matthews, and Flowers (2014) demonstrated the applicability of this measure to achievement classification. It is also important to note that standardized achievement tests like the ITBS (a preferred measured of achievement) and ability tests like the CogAT are just as unlikely to identify the same students for gifted services as grades and CogAT scores (Lohman, 2005a). Given this rationale, self-reported GPA was used for this study.

Data Gathering Procedures

Surveys were administered by the gifted facilitator to all students in Grades 4 to 7 who returned parental consent and assented to participate in the study. This was done over the course of several days after morning announcements so as not to interfere with instruction. The gifted facilitator provided directions to all the students on how to complete the survey. For students in Grades 4 and 5, the survey was administered in the class where they spent the majority of their school day. Although the students completed the survey on their own, the gifted facilitator and classroom teacher were available to answer questions regarding the survey. For students in Grades 6 and 7, the surveys were administered in their homeroom teacher’s class. Students completed the survey on their own and the gifted facilitator and classroom teacher were available to answer survey-related questions.

To de-identify the data, students were assigned a numerical code by the gifted facilitator. Each number corresponded to a student’s name and only the gifted facilitator was able to identify students using these assigned numbers. The gifted facilitator pre-labeled student surveys with these numerical codes. On completion of data collection, the gifted facilitator provided the primary researcher with a list of numerical codes for the identified gifted students in the school. This information was used to match survey responses with identification status.

Data Analysis

A one-way MANOVA was conducted to determine mean differences between non-identified high achieving students (n = 68) and identified gifted students (n = 73) on the five factors of the SAAS-R. The dependent variables were the five SAAS-R factors (i.e., Academic Self-Perceptions, Attitudes Toward School, Attitudes Toward Teachers, Goal Valuation, and Motivation/Self-Regulation). Relationships between the factors of the SAAS-R are best approximated with MANOVA, while testing for significant differences between groups on the related factors and simultaneously accounting for correlations among the factors.

Assumptions of random sampling, observations independent of one another, and within group covariance matrices equal across groups for each dependent measure were met. Covariance matrices for non-identified high achieving and identified gifted students are presented in Table 3. Pairwise correlations between dependent variables across groups were significant and equal except for Goal Valuation and the other four SAAS-R factors (see Table 4). Pairwise correlations were significant for the non-identified high achieving group as well as unequal to non-significant pairwise correlations for the identified gifted group.

Table 3.

Summary of Covariances for High Achieving and Identified Gifted Students.

	ASP	ATT	ATS	GV	M/SR
High achieving
ASP	0.431	0.194	0.233	0.093	0.322
ATT	—	0.476	0.450	0.091	0.232
ATS	—	—	1.112	0.158	0.177
GV	—	—	—	0.123	0.093
M/SR	—	—	—	—	0.438
Identified gifted
ASP	0.424	—	—	—	—
ATT	0.176	0.310	—	—	—
ATS	0.262	0.257	1.595	—	—
GV	0.041	0.034	0.056	0.078	—
M/SR	0.213	0.162	0.215	0.057	0.314

Note. Covariance for high achieving students (n = 68) are shown above the diagonal and for identified gifted (n = 73) shown below the diagonal. Dimensions of School Attitude Assessment Survey–Revised (SAAS-R) are ASP = Academic Self-Perceptions; ATT = Attitudes Toward Teachers; ATS = Attitudes Toward School; GV = Goal Valuation; M/SR = Motivation/Self-Regulation.

Table 4.

Summary of Correlations, Means, and Standard Deviations for High Achieving and Identified Gifted Students.

	ASP	ATT	ATS	GV	M/SR	High Achieving M(SD)	Identified Gifted M(SD)	Effect size (Cohen’s d)
ASP	—	0.428**	0.335*	0.404**	0.740***	5.79(0.65)	6.05(0.65)	.40
ATT	0.485***	—	0.617***	0.374*	0.508***	6.15(0.56)	6.34(0.56)	.18
ATS	0.319*	0.366*	—	0.426**	0.253	6.13(1.16)	6.15(1.16)	.00
GV	0.224	0.217	0.158	—	0.403**	6.85(0.28)	6.82(0.28)	.09
M/SR	0.583***	0.583***	0.304	0.363*	—	6.19(0.56)	6.06(0.56)	.21
M	6.05	6.15	6.34	6.82	6.06
SD	0.66	0.69	1.06	0.35	0.66

Note. Correlations for high achieving students (n = 68) are shown above the diagonal and for identified gifted (n = 73) are shown below the diagonal. Dimensions of School Attitude Assessment Survey–Revised (SAAS-R) are ASP = Academic Self-Perceptions; ATT = Attitudes Toward Teachers; ATS = Attitudes Toward School; GV = Goal Valuation; M/SR = Motivation/Self-Regulation.

p < .01. **p < .001. ***p < .0001.

Results

Means, standard deviations, and correlations for the five factors of the SAAS-R are presented in Table 4. A MANOVA was conducted on the five SAAS-R factors for the non-identified high achieving and identified gifted groups. Statistically significant group differences were found for the five SAAS-R factors, Wilks’s λ = 0.878, F(5, 135) = 3.74, p = .0034 (see Table 5).

Table 5.

MANOVA Results.

Effects	Wilks’s λ	F	df	p
HA vs. IG	0.878	3.74	(5, 135)	.0034*

Note. Effects are HA = high achieving; IG = identified gifted.

p < .01

Univariate ANOVAs for each factor were conducted as follow-up tests to the MANOVA. Table 6 presents the ANOVA results. The ANOVA for Academic Self-Perceptions was statistically significant, F(1, 139) = 5.39, p = .0218. Non-identified high achieving students had an average Academic Self-Perception score of 5.79 whereas identified gifted students’ average score was 6.05. Cohen’s d was used to calculate effect size which found a small to moderate effect of 0.4 for Academic Self-Perceptions. ANOVA results were not statistically significant for the other four SAAS-R factors.

Table 6.

ANOVA Results.

Dependent variable	F	df	p
ASP	5.39	139	.022*
ATS	1.20	139	.276
ATT	0.00	139	.958
GV	0.30	139	.585
M/SR	1.25	139	.265

Note. Dimensions of SAAS-R are ASP = Academic Self-Perceptions; ATT = Attitudes Toward Teachers; ATS = Attitudes Toward School; GV = Goal Valuation; M/SR = Motivation/Self-Regulation.

p < .05

Discussion

The purpose of this study was to compare identified gifted students and non-identified high achieving students on their self-perceptions of several psychosocial factors to provide additional evidence that gifted services, depending on the framework and content of these services, may be appropriate for non-identified high achieving students as well.

A statistically significant difference and small to moderate effect were found for the Academic Self-Perceptions Subscale. This finding, however, does not imply that the non-identified high achievers sampled in this study have low Academic Self-Perceptions. A mean of 5.79 on this particular subscale of the SAAS-R is actually considered “Average/Normal” (McCoach, 2002). This finding only demonstrates that within this study, identified gifted students, on average, had higher values on the Academic Self-Perceptions Subscale than students in the non-identified high achieving group.

This finding, however, is not surprising given that students with the “gifted” label tend to have higher academic self-perceptions than students without the “gifted” label (McCoach & Siegle, 2002). McCoach and Siegle (2003a) even found that underachieving gifted students had higher than expected academic self-perceptions, suggesting that the “gifted” label may serve to protect students’ perception of their self-worth as it relates to academic achievement. In other words, whether or not identified gifted students perform at levels commensurate with their ability, they know that they are capable of high performance and do not doubt this, given a label of “gifted” has been bestowed on them. In light of this finding, one might ask, if non-identified high achieving students were given access to services commonly reserved only for identified gifted students, would their Academic Self-Perceptions scores still be lower?

On the remainder of the subscales, the two groups of students were quite similar, and no statistically significant differences were found. At this particular school, the only criteria that prevented these high achieving students from receiving gifted services were arbitrary cutoff scores on a combination of cognitive ability and achievement tests. Otherwise, these students’ GPAs and psychosocial characteristics were comparable with the identified gifted students at this school, suggesting that they may have also benefited from the type of programming available to identified gifted students.

Findings further suggest that depending on the nature of a gifted program, educators should consider using measures of actual, observed achievement and psychosocial factors in the identification process, when appropriate, to ensure students’ academic needs are met. Relying on any single data source to make decisions about who may or may not benefit from gifted programming does not give educators a picture of “the whole child.” Although there are students who qualify for gifted programming via a single measure that do benefit from these services, this is greatly dependent on the types of services offered. A high IQ, for example, in and of itself does not imply need (Peters, 2014). Identification using a single measure may be justified in certain cases, but it is by no means a recommended practice.

Just as no single measure should be used to identify students for gifted programming, no single measure should be used to preclude students from receiving potentially beneficial services. This directly applies to the practice of “ungifting” identified gifted students when they fail to achieve at levels commensurate with their ability (Ritchotte, 2015). A label “bright,” “gifted,” “underachiever,” should not dictate gifted services, rather the focus should be on ensuring that appropriate gifted services are available to those who need them (Peters, Matthews, McBee, & McCoach, 2013).

Limitations

There are several limitations that need to be discussed. As McCoach and Siegle (2002) have noted, the use of self-reported GPA is not as accurate and reliable as students’ actual GPA. Furthermore, an arbitrary cutoff GPA of 3.5 or higher was selected for this study when classifying the non-identified students as high achievers. There may have been a more objective way of determining which students should be classified as high achievers.

Furthermore, the sample size for this study was relatively small. The two comparison groups consisted of less than 100 students each. Increasing the sample size by including other schools in the sample, especially schools with more diverse student populations, would have increased the reliability and representativeness of the sample. Finally, sampling more traditional types of schools would have also benefited this study as the site used was a dual-language immersion charter school, limiting the generalizability of these findings.

Suggestions for Future Research

Future research should dig deeper into the effects labeling has on students’ academic self-perceptions. More specifically, future research might qualitatively investigate if being labeled “gifted” or “not gifted” affects how a student perceives his or her self-concept in terms of academic competence.

Future research might also explore the learning experience of non-identified high achieving students. For example, do these students feel appropriately challenged? Do they think they could benefit from the type of gifted services offered at their school? Also, in schools that do group non-identified high achieving and identified students together, similar to the Card and Giuliano (2014) study, future research might systematically replicate elements of this study to further examine the effects of gifted program participation on both the psychosocial characteristics and achievement of the non-identified and identified gifted students. Last, given the benefits of gifted programming have been called into question (Adelson, McCoach, & Gavin, 2012), more studies are needed to demonstrate the efficacy of specific frameworks for providing services to students who require academic challenge.

Implications for Practice

Although this is just one study, it seems clear that school districts need to focus more on student need and less on determining who is “gifted” and who is not based on flawed identification procedures. Relying heavily on a combination of test scores to identify students is certainly easier, more time efficient, and appears to comply with the “best practice” of using multiple-criteria; however, how many students encounter “multiple hurdles” during this process and are denied access to appropriately differentiated services (Callahan et al., 2013a, 2013b; McBee et al., 2014)? Not only are high achieving students denied services on this basis, but also underachieving students, culturally and/or linguistically diverse students, economically disadvantaged students, twice-exceptional students, and students who may represent unique combinations of all these different special populations. It goes without saying that students who want to be challenged, who demonstrate that they are ready to be challenged, should be provided with appropriate challenge. However, the challenge for educators then becomes determining who these students are. Ability and/or achievement tests may help identify need for some students, whereas measures of actual achievement and psychosocial factors may provide a clearer picture of need for other students. Perhaps, as many researchers have espoused over the years and continue to espouse, meeting the needs of students in an appropriate manner needs to take precedence over the imperfect practice of labeling.

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.

About the Authors

Jennifer A. Ritchotte is an assistant professor of special education with an emphasis in gifted and talented education at the University of Northern Colorado. Her research interests include gifted underachievement and secondary school outcomes for high potential students.

Diana Suhr holds an MA in Special Education with an emphasis in Gifted and Talented Education, an MS in Research and Statistics, and a PhD in Educational Psychology. Her research interests include early childhood gifted education and the development of assessments for gifted education.

Naif F. Alfurayh is a doctoral student in special education with an emphasis in gifted and talented education at the University of Northern Colorado. His primary research interest is gifted underachievement.

Amy K. Graefe is a high school gifted education specialist in Greeley-Evans School District 6, a doctoral student in educational psychology with an emphasis in gifted education at the University of Northern Colorado (UNC), and also helps coordinate the MA in gifted education at UNC. Her research interests center on gifted education at the secondary level.

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