This study evaluated the construct validity of the Traits, Aptitudes, and Behaviors Scale (TABS). Data for this study were collected from two separate school districts across three different academic years in the Southeastern United States. Of the total sample (N = 2,330), 64.6% of the children were identified as African American, 29.0% as Caucasian, and 6.4% had no or other racial category specified. A two-step process was used to assess evidence of construct validity in the TABS. The first step was to identify an optimal factor structure using exploratory factor analysis (EFA) techniques. The second step was to confirm the structure using confirmatory factor analysis (CFA) methods. Results point to a one-factor solution as the optimal factor structure for the TABS EFA sample; however, CFA showed mixed results for model fit. Despite these limitations, results of this study suggest that the TABS might help reduce teacher bias during the referral/nomination process of gifted children. Although the TABS factor structure suggests that it measures a single construct of giftedness, additional studies need to be conducted to confirm these findings.
BaldwinA. Y. (2004). I’m Black but look at me, I am also gifted. In BaldwinA. Y.ReisS. M. (Eds.), Culturally diverse and underserved populations of gifted students (pp. 1–9). Thousand Oaks, CA: Corwin Press.
2.
BernalE. (1978). The identification of gifted Chicano children. In BaldwinA.GearG.LucitoL. (Eds.), Educational planning for the gifted (pp. 14–17). Reston, VA: Council for Exceptional Children.
ComreyA.LeeH. (1992). A first course in factor analysis. Hillsdale, NJ: Lawrence Erlbaum.
5.
CostelloA. B.OsborneJ. W. (2005). Exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.
DaliaN.AgneB. (2012). The empirical validation of cognitive domain characteristics in the gifted screening checklist. Gifted Education International, 29, 199–210.
DavisJ. L. (2010). Bright, talented, & Black: A guide for families of African American gifted learners. Tucson, AZ: Great Potential Press.
10.
EngelhardG. (2013). Invariant measurement: Using Rasch models in the social, behavioral, and health sciences. New York, NY: Routledge.
11.
FabrigarL. R.WegenerD. T. (2012). Exploratory factor analysis. New York, NY: Oxford University Press.
12.
FordD. Y. (2007). Diamonds in the rough: Recognizing and meeting the needs of gifted children from low SES backgrounds. In VanTassel-BaskaJ.StambaughT. (Eds.), Overlooked gems: A national perspective on low-income promising learners (pp. 37–41). Washington, DC: National Association for Gifted Children.
13.
FordD. Y. (2010). Underrepresentation of culturally different students in gifted education: Reflections about current problems and recommendations for the future. Gifted Child Today, 33(3), 31–35.
14.
FordD. Y.GranthamT. C.WhitingG. W. (2008). Culturally and linguistically diverse students in gifted education: Recruitment and retention issues. Exceptional Children, 74, 289–306.
15.
FrasierM. M. (1987). The identification of gifted Black students: Developing new perspectives. Journal for the Education of the Gifted, 10, 155–180.
16.
FrasierM. M. (1990). Identifying the gifted: Observation and rating scales. Athens: The University of Georgia, The Torrance Center for Creative Studies.
17.
FrasierM. M.MartinD.GarcíaJ. H.FinleyV. S.FrankE.KriselS.KingL. L. (1995). A new window for looking at gifted children (RM95222). Storrs: University of Connecticut, The National Research Center on the Gifted and Talented.
18.
FrasierM. M.PassowA. H. (1994). Toward a new paradigm for identifying talent potential (RM94112). Storrs: University of Connecticut, The National Research Center on the Gifted and Talented.
19.
GardnerH. (1983). Frames of mind: The theory of multiple intelligences. New York, NY: Basic Books.
20.
GiessmanJ. A.GambrellJ. L.StebbinsM. S. (2013). Minority performance on the Naglieri Nonverbal Ability Test, second edition, versus the Cognitive Abilities Test, Form 6: One gifted program’s experience. Gifted Child Quarterly, 57, 101–109.
21.
GilliamJ. E.CarpenterB. O.ChristensenJ. R. (1996). Gifted and Talented Evaluation Scales. Austin, TX: Pro-Ed.
22.
GranthamT. C. (2011). New directions for gifted Black males suffering from bystander effects: A call for upstanders. Roeper Review, 33, 263–272.
23.
HairJ. F.AndersonR. E.TathamR. L.BlackW. C. (1995). Multivariate data analysis with readings. Englewood Cliffs, NJ: Prentice Hall.
24.
HarrisB.PluckerJ. A.RappK. E.MartínezR. S. (2009). Identifying gifted and talented English language learners: A case study. Journal for the Education of the Gifted, 32, 368–393.
25.
HaydukL. (2014). Seeing perfectly fitting factor models that are causally misspecified: Understanding that close-fitting models can be worse. Psychological Measurement, 74, 905–926.
26.
HornJ. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 38, 179–185.
27.
HunsakerS. L.FinleyV. S.FrankE. L. (1997). An analysis of teacher nominations and student performance in gifted programs. Gifted Child Quarterly, 41, 19–24.
28.
KingoreB. (2001). The Kingore Observation Inventory (2nd ed.). Austin, TX: Professional Associates.
ManningS. (2006). Recognizing gifted students: A practical guide for teachers. Kappa Delta Pi Record, 42, 64–68.
32.
McBeeM. T. (2006). A descriptive analysis of referral sources for gifted identification screening by race and socioeconomic status. Journal of Secondary Gifted Education, 17, 103–111.
33.
McBeeM. T. (2010). Modeling outcomes with floor or ceiling effects: A brief introduction to the Tobit model. Gifted Child Quarterly, 54, 314–320.
McCoachD. B.SiegleD. (2014). Underachievers. In PluckerJ. A.CallahanC. M. (Eds.), Critical issues and practices in gifted education: What the research says (2nd ed., pp. 691–706). Waco, TX: Prufrock Press.
PegnatoC. W.BirchJ. W. (1959). Locating gifted children in junior high schools: A comparison of methods. Exceptional Children, 25, 300–304.
39.
PetersS. J.GentryM. (2010). Multi-group construct validity evidence of the HOPE Scale: Instrumentation to identify low-income elementary students for gifted programs. Gifted Child Quarterly, 54, 298–313.
40.
PetersS. J.GentryM. (2012). Group-specific norm and teacher-rating scales: Implications for underrepresentation. Journal of Advanced Academics, 23, 125–144.
PfeifferS. I.PetscherY.KumtepeA. (2008). The Gifted Rating Scales-School Form: A validation study based on age, gender, and race. Roeper Review, 30, 140–146.
43.
RenzulliJ. S.SiegleD.ReisM. R.GavinM. K.Sytsma ReedR. E. (2009). An investigation of the reliability and factor structure of four new scales for rating the behavioral characteristics of superior students. Journal of Advanced Academics, 21, 84–108.
44.
RenzulliJ. S.SmithL. H.WhiteA. J.CallahanC. M.HartmanR. K. (1976). Scales for Rating the Behavioral Characteristics of Superior Students. Mansfield Center, CT: Creative Learning Press.
45.
RenzulliJ. S.SmithL. H.WhiteA. J.CallahanC. M.HartmanR. K.WestbergK. L. (2002). Scales for Rating the Behavioral Characteristics of Superior Students (Rev. ed.). Mansfield Center, CT: Creative Learning Press.
46.
RenzulliJ. S.WestbergK. (1991). Scale for rating students’ participation in the local gifted education program. Storrs: University of Connecticut.
47.
RomeyE. A. (2006). A quantitative study of the Alabama gifted matrix identification process: Implications for underserved populations (Unpublished doctoral dissertation). University of Connecticut, Storrs.
48.
Speirs NeumeisterK. L.AdamsC. M.PierceR. L.CassadyJ. C.DixonF. A. (2007). Fourth-grade teachers’ perceptions of giftedness: Implications for identifying and serving diverse gifted students. Journal for the Education of the Gifted, 30, 479–499.
49.
TabachnickB. G.FidellL. S. (2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson.
50.
TorranceE. P. (1969). Creative positives of disadvantaged children and youth. Gifted Child Quarterly, 13, 71–81.
51.
U.S. Department of Education. (1993). National excellence: A case for developing America’s talent. Washington, DC: Office of Educational Research and Improvement.
52.
Van ProoijenJ.-W.Van der KlootW. A. (2001). Confirmatory analysis of exploratively obtained factor structures. Educational and Psychological Measurement, 61, 777–792.
53.
VanTassel-BaskaJ. (2007). Introduction. In VanTassel-BaskaJ.StambaughT. (Eds.), Overlooked gems: A national perspective on low-income promising learners (pp. 1–5). Washington, DC: National Association for Gifted Children.
54.
VelicerW. F.JacksonD. N. (1990). Component analysis versus common factor analysis: Some further observations. Multivariate Behavioral Research, 25, 97–114.
