Identifying Hispanic Gifted Children: A Screening

Identifying Hispanic Gifted Children: A Screening

Brice, Alejandro


Identification of Hispanic children tor gifted programs, particularly those in a rural environment, has been problematic. The question still remains as to what are effective identifying procedures to screen Hispanic students. Specifically, the purpose of this study was to investigate the relationship between standardized test scores and teacher ratings of student behaviors. Thirty-two Mexican-American students and 23 general education teachers from a small, rural school district in South-Central Florida served as the study participants. Fourteen correlations were calculated involving Stanford Reading and Stanford Math scores with the teacher ratings. Nine correlations were significant (9/14=64%). The Academic Checklist scores and Intellectual scores from the teacher ratings correlated the highest with the Stanford Math scores. The Math portion of the Stanford test may be less linguistically biased. Almost one fourth of the teacher rating items seemed to reflect an Anglo-American bias. Those items which seemed to reflect cultural or linguistic bias are discussed.


As the Hispanic population of the United States continues to dramatically increase, education professionals repeatedly face the challenge of how best to provide services for those whose primary language is Spanish. As of 1999, 32 million individuals identified themselves as being Hispanic (U.S. Census Bureau, 2000). Hispanics are one of the fastest growing ethnic groups in the United States (Valdivicso & Davis, 1998). It should be noted that Hispanic children are the fastest growing culturally and linguistically diverse (CLD) student population (U.S. Department of Education, 1995). It is predicted that by the year 2005 Hispanics will become the largest CLD ethnic group in the U.S. (Valdivieso & Davis, 1998). Thus, the need for a Rill understanding of bilingualism and its effect upon the education of children, particularly those students who may speak both Spanish and English, is warranted by the changing U.S. demographics.

Rural schools make up 49% of all U.S. schools (Harmon, 2001) and almost 40% of where all teachers work. Currently, rural communities are experiencing a shortage of teachers for math, science, and particularly special education (Harmon, 2001). In addition, a national shortage of qualified special education teachers with bilingual education backgrounds who are willing to work in rural settings is critical (Sealander, Eigcnbcrger, Peterson, Shcllady & Prater, 2001 ). The National Education Association (1998) states that teachers in rural communities “tend to be less educated, slightly less experienced, younger, and less likely to belong to a minority group” (p. 3). Rural schools are also less likely to provide adequate bilingual or English language learning (ELL) services, although one in five children in rural schools belongs to a minority group (National Education Association, 1998). Rural schools are also likely to use narrow identification procedures for identifying gifted students (Aamidor & Spickler, 1995; Obi & Obiakor, 2000).

There is a tremendous undcr-rcpresentation of Hispanic students in programs for the gifted and talented (Bcrmudez & Marquez, 1998; Castellano, 1998; Cohen, 2001 ; Cohen, 1988; De Leon & ArgusCalvo, 1997; Forsbach & Pierce, 1999; Irby & LaraAlecio, 1996; Kloosterman, 1997; Masten, Plata, Wenglar, & Thedford, 1999; Schwartz, 1997) Donovan and Cross (2002) found that Hispanics are under-represented when compared to White (non-Hispanic) students yielding an odds ratio of 0.48 (the number of Hispanic gifted children divided by the number of White gifted children). This figure indicates that Hispanic children are identified by approximately half as much as White students. In addition, children from rural cultural backgrounds may also experience these same difficulties (Aamidor & Spickler, 1995; Obi & Obiakor, 2000). Research indicates that this may be the result of currently used practices to identify gifted students, that is, there is an overuse and reliance upon use of LQ. tests (Frasier, 1987; Irby & Lara-Alccio, 1996; Ortiz, 1989). The use of these tests represent a biased approach to identifying gifted behaviors in children from CLD backgrounds, e.g., Hispanic students (Frasier, 1987; Irby & Lara-Alecio, 1996; Ortiz, 1989). Lewis (2001 ) states that, “These kind of tests are frequently biased in favor of white, middle to upper class suburban and urban children” (p. 123). Frasier (1987) recommends the use of checklists, rating scales, and teacher nominations to accommodate cultural differences in the identification process. Without a change in the identification procedure, Hispanic children “may not move beyond the beginning screening process” (Irby &Lara-Alecio, 1996; p. 122). This point is reiterated by Lewis (2001) when she stated that children from culturally and linguistically diverse backgrounds typically do not make it past the screening process. Hence, as teachers rely more on use of checklists and scales, it becomes more important to have “accurate denning characteristics upon which to screen diverse populations” (Irby & Lara-Alecio, 1996; p. 123). This includes screening children from rural backgrounds.

The definition of gifted has moved from use of a fixed I.Q. score to one of aptitude including indicators of future achievement (Fcldhausen, 2001; Schwartz, 1997). Stcrnbcrg (1991) and Gardner (1983) proposed a diagnostic model where talents and aptitudes became the focus for intelligence identification. Sternberg (1991) suggested a number of general components making up intelligence: metacomponents (planning, monitoring, and evaluation), performance components (skills and abilities), and knowledge-acquisition components (processing and encoding). Gardner proposed different and multiple intelligences (e.g., linguistic, logical-mathematical, spatial, musical, kinesthetic, interpersonal and intrapcrsonal). Hence, the emphasis has shifted from what a child knows to what he or she can learn. Educators today are more likely to recognize giftcdncss as a talent and ability to learn that can be found in all children (e.g., Hispanic children and children from rural communities). Giftedness occurs across cultures and is not specific to any one particular cultural group. Congress recognized this aspect when they passed the Jacob K. Javits Gifted and Talented Students Education Act of 1988 (PL 100-297). Congress stated that giftedness is found in all cultural groups, i.e., also including rural cultures. Thus, the issue of identifying gifted rural children becomes exacerbated by the above mentioned conditions.


Identification of gifted children has involved grades, standardized test scores, teacher referral, parent referral, and self referral (Forsbach & Pierce, 1999). Some other procedures for gifted identification have included the use of ethnographic assessment, dynamic assessment, and use of behavioral checklists (Castellano, 1998). Masten, Plata, Wenglar and Thcclford (1999) also mentioned the use of behavioral rating scales for identification of Hispanic gifted students. Therefore, the question remains as to what are effective identifying procedures to screen Hispanic students. Do standardized tests reveal useful information regarding gifted characteristics, e.g., are math scores better indicators of giftedness than language-influenced reading scores with Hispanic children? Would teacher ratings of behaviors be useful in screening for giftedness in a group of Hispanic students!” The aim of this investigation is to identify those behaviors teachers recognize in the classroom that may serve as indicators of giftedness. Specifically, the purpose of this study is to investigate the relationship between standardized test scores and teacher ratings of behaviors in a group of Hispanic children in a small, rural school district.



Thirty-two Mexican-American students from a small, rural school district in South-Central Florida were identified and served as the participants in this study. The group contained 19 males and 13 females. This group consisted of elementary aged students: two first graders, 19 second graders, 10 third graders, and one fourth grader. They ranged in age from 7 years eight months to 10 years four months old. This study also utilized the help of 23 general education teachers in grades one through four.


This study was conducted in four elementary schools located in a small, rural school district in South-Central Florida. The community has a population of 35,910 (U.S. Census Bureau, 2002). The community is predominantly White (79.3%) with 18.6% Hispanic. This is similar for Florida demographics (White-78%; Hispanic-16.78%). This community has 34.9% children under 18 years of age compared with 31.3% for the state. However, this community is slightly less affluent with 20% living below poverty compared with 14.4% for the entire state of Florida. The percentage of children living below poverty is also higher, 28.5% (community) when compared to the state figure of 21.8%.

Method of Data Collection

The data for this study were collected through a review of cumulative academic records, and administration of a teacher rating scale or checklist. Specifically, standardized test scores (i.e., Stanford Reading and Stanford Math) were obtained from the cumulative academic records. These standardized test scores were obtained from the students’ records due to convenience and availability. I.Q. testing, was not administered to the participants because of the over-reliance on use of these tests and due to the fact that this was a district wide screening procedure and not a complete evaluation for giftcdness.

The Stanford Reading and Stanford Math achievement test scores were obtained from the students’ academic cumulative files. Scores were from the previous academic school year (2000). The students had not taken the current test when this study was conducted. The Stanford was chosen for this study as other tests used in this school district were less prominent in the students’ files and were less nationally recognized (i.e., possibly being less valid and reliable).

A district-developed teacher rating scale used to screen for giftedness (Teacher Checklist T.A.R.G.E.T. B Gifted Program-Appendix A) was administered. This teacher checklist mandated by this school district was used to evaluate the students’ classroom performance on a Likert type of scale. The checklist contained an overall total score and scores for the six different categories (General Characteristics, Intellectual Characteristics, Motivational Characteristics, Leadership Characteristics, Creativity Characteristics, and Academic Characteristics). Each category, with the exception of Motivational Characteristics, contained ten observable behaviors. The Motivational Characteristics category contained five behaviors. Teachers rated their respective student’s individual behaviors. The checklists were distributed by the school counselor. Each teacher indicated that he or she was familiar with the checklist and behaviors. The T.A.R.G.E.T. B Gifted Program checklist has been implemented in this district since 1991 (i.e., ten years at the time of this study).


The measures of Stanford Reading percentiles, Stanford Mathematics percentiles, and measures from the district Teacher Checklist T.A.R.G.E.T. B Gifted (General Characteristics, Intellectual Characteristics, Motivational Characteristics, Leadership Characteristics, Creativity Characteristics, Academic Characteristics, and Total) were gathered. The teacher checklist Likert scores were converted to percentages of total possible points. The Stanford Reading and Stanford Math perccntilc scores were compared to the gifted teacher checklist ratings General Characteristics, Intellectual Characteristics, Motivational Characteristics, Leadership Characteristics, Creativity Characteristics, Academic Characteristics, and Total using a Pcarson Product Moment Correlation. Thus, the following correlations were obtained: (a) Stanford Reading and General Characteristics, (b) Stanford Reading and Intellectual Characteristics, (c) Stanford Reading and Motivational Characteristics, (d) Stanford Reading and Leadership Characteristics, (c) Stanford Reading and Creativity Characteristics, (f) Stanford Reading and Academic Characteristics, (g) Stanford Reading and Total Checklist, (h) Stanford Math and General Characteristics, (i) Stanford Math and Intellectual Characteristics, (j) Stanford Math and Motivational Characteristics, (k) Stanford Math and Leadership Characteristics, (1) Stanford Math and Creativity Characteristics, (m) Stanford Math and Academic Characteristics, (n) Stanford Math and Total Checklist.

Stength of Correlations

Voelker and Orton (1993) stated that the strength of a correlation depends greatly on the phenomenon being investigated. When correlations are composed of scores obtained by subjective means, such as the T.A.R.G.R.T. R teacher rating checklist, then lower correlations (e.g., .50 to .60) may be indicative of a strong relationship (Kubiszyn & Borich, 1996). Based on the interpretive guide for correlation coefficients from Schiavetti and Metz (1997) the following correlation coefficients for interpretation were based on alpha significance levels of .05 and .01.


There was a moderate correlation with alpha at the 0.05 level for the following: (a) Stanford Reading and Intellectual Characteristics (r=0.443) with r^sup ^^ of 0.196 indicating a shared variance of 20%, (b) Stanford Reading and Total Checklist (r-0.352) with r^sup 2^ of 0.124 indicating a shared variance of 12%, and (c) Stanford Math and General Characteristics (r=0.369) with r^sup 2^ of 0.136 with a shared variance of 14%. There was a strong relationship with alpha at the 0.01 level for the following: (a) Stanford Reading and Academic Characteristics (r=0.535) with r^sup 2^ of 0.286 with a shared variance of 29%, (b) Stanford Math and Intellectual Characteristics (r=0.660) with r^sup 2^ of 0.436 with a shared variance of 44%, (c) Stanford Math and Motivation Characteristics (r=0.465) with r^sup 2^ of 0.216 with a shared variance of 22%, (d) Stanford Math and Leadership Characteristics (r=0.600) with r^sup 2^ of 0.360 with a shared variance of 36%, (e) Stanford Math and Academic Characteristics (r=0.739) with r^sup 2^ of 0.546 with a shared variance of 55%, and (f) Stanford Math and Total Checklist (r=0.615) with r^sup 2^ of 0.378 with a shared variance of 38%. These results are reported in Table One.

Conclusions and Discussion

This study asked the relative question of the effectiveness of standardized tests measuring giftcdness with Hispanic children. It was felt that other indicators of performance, i.e., teacher ratings, could supplement existing procedures in identifying Hispanic students for gifted programs. It was specifically hypothesized that teacher ratings of behaviors would be useful in screening for giftedness ofHispanic students. Thus, the purpose of this study was to investigate the relationship between standardized test scores and teacher ratings of behaviors in a group of Hispanic children in a small, rural school district.

Fourteen Pearson Product Correlations were calculated. Of these 14 comparisons involving Stanford Reading and Stanford Math scores and the T.A.R.G.E.T. checklist nine correlations were significant at the .05 or .01 level of significance (9/14=64%). Of these, three comparisons were found to be moderately significant (3/14=21%), while six were found to be highly significant (6/14=43%).

Moderate relationships were found between the Reading and the Total teacher checklist scores and the Math and the General teacher checklist scores indicating that general characteristics and averaged checklist scores (across the entire checklist) are not strongly sensitive to discriminate behaviors. Peterson (2000) found that teachers are the gatekeepers for selective programs in schools, particularly, when students are culturally different from them. Thus, global or averaged scores may not be sensitive when teachers are rating children from other cultural and linguistic backgrounds.

Strong correlations between Reading and Academic checklist scores were found. This was the only strong correlation found when Reading was compared to the teacher checklist. It is known that English language learning Hispanic students are at a disadvantage in terms of verbal skills when measured with traditional, standardized tests (Brice, 2002; Brice & Perkins, 1997; Cheng, 1996; Hakuta, 1986; Peterson, 2000; Rosebcrry-McKibbin, 2002). However, strong correlations were found for Math teacher checklist scores of: Intellectual, Motivation, Leadership, Academic and Total Checklist. Thus, if educators are to consider the use of standardized test scores, such as the Stanford Achievement test scores, then it makes sense that math may be less linguistically biased against bilingual or culturally and linguistically diverse students. It should be noted that the Academic Checklist scores and Intellectual scores correlated the highest with the Stanford Math scores. Total Checklist and Leadership student behaviors were also highly correlated. Thus, these behaviors are directly observable and should guide teachers in making in classroom assessments of their students’ abilities.

The T.A.R.G.E.T. B Teacher Checklist

The T.A.R.G.E.T. B teacher checklist is a districtdeveloped instrument to assist teachers identifying gifted behaviors in children. However, upon closer examination, with aid from a review of the literature, the authors felt that 13/S5 (24%) of the items were culturally biased. Those items which are possibly biased items are reported in Table 2. Almost one fourth of the items seemed to reflect a NorthAmerican, middle class or Anglo-American bias. These items will be discussed to illustrate the potential biases in examining student behaviors. While these biases may not have been intentional, their perspective is still damaging to culturally and linguistically diverse students. The majority of the critiques deal with the noted difference in cultures between the Mexican-American population sampled in this study and that of the North, Anglo-American orientation that the schools display, particularly that the schools display an “Individualistic” or “I” culture, whereas, Hispanic students are known to display characteristics of a “Collcctivistic” culture or display a group orientation (Gudykunst, 1991; TingToomcy, 1994; Triandis, 199S). For example, the behaviors of “Works well independently”, “Initiates a lot of activities”, “Is very assertive”, and “Does not always go with the crowd” are not always typical of the Hispanic culture (Bricc, 2002). Other behaviors dealt with asking questions and contributing to discussions. Again, Hispanic students may not share in a large classroom environment where this type of behavior is seen as “showing off” and not “being humble” (Roseberry-McKibbin, 2002; Pctcrson, 2000). These and other concerns regarding this teacher checklist are presented in Table Two. Those behaviors which are suspected of being culturally inappropriate for Hispanic students are noted by an asterisk on the T.A.R.G.E.T. B teacher checklist.


It is important for school personnel to be aware of girted behaviors and characteristics in the Hispanic population and rural populations in order to assist all students in their education. Comparison of gifted behaviors and skills across cultural groups should assist school personnel in making proper educational decisions for their students. In turn, students learning English as a second language and who are bilingual will have increased opportunities to acquire the skills they need to function as competent communicators in their schools and in society. Given the limited number of bilingual special educators in rural communities, it is imperative that educators utilize their existing resources appropriately to their maximal benefit (Dc Leon & Cole, 1994). Educators can make more accurate assessment of giftedness with a screening tool such as, the T.A.R.G.E.T. E teacher checklist (with noted modifications to reduce bias), that differentiates students with gifted traits from those who do not show the traits. Awareness of and sensitivity to persons who differ in culture, language, or ability, are critical for success in our culturally diverse exceptional education programs.


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Alejandro Brice, Ph.D.


Roanne Brice, M.A.

University of Central Florian

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