Psychometric properties of a brief non-verbal test of g factor intelligence

(1) * Luis Anunciação Mail (Pontifical Catholic University of Rio de Janeiro, Brazil)
(2) Louise Marques Mail (Pontifical Catholic University of Rio de Janeiro, Brazil)
(3) Christopher Murray Mail (Center on Human Development, University of Oregon, United States)
(4) Anna C Portugal Mail (Pontifical Catholic University of Rio de Janeiro, Brazil)
(5) Ivan Rabelo Mail (Universidade Paulista, Brazil)
(6) Jesus Landeira-Fernandez Mail (Pontifical Catholic University of Rio de Janeiro, Brazil)
(7) Roberto Cruz Mail (Federal University of Santa Catarina, Brazil)
*corresponding author

Abstract


Intelligence is the most studied construct in psychology and cognitive neuroscience. In Brazil, the administration of intelligence tests is needed for a number of social rights, including driving privileges. Such requirements have led to a large testing industry but the vast majority of intelligence tests require extended administration times and language skills. In this study, we sought to investigate the psychometric properties and normative results of a new non-verbal intelligence test, the General Matrix of Intelligence (GMI). The GMI is comprised of 28 matrix-based items and can be administered in as little as six-minutes. In this initial pilot test, the GMI was administered to 1,326 participants, ages 15-64 years old (M = 25.65 years, SD = 9.6 years), from all regions in Brazil. These data were analyzed using a 2PL Item Response Theory model, regression analyses were conducted to determine the role of sociodemographic factors, and preliminary norms were computed. Results indicated a unidimensional solution that reproduced the g factor theory, invariance across genders, evidence that cognitively demanding items involving movement or three-dimensional shapes were more difficult than items with less cognitive load, a normal distribution for results, and an interaction between education level and age group in predicting performance. Implications of these findings for research and practice are discussed and all data and codes are provided at https://osf.io/kvu42/


Keywords


g factor; general intelligence; invariance; intelligence assessment; psychometrics; Brazil

   

DOI

https://doi.org/10.47679/jopp.617532024
      

Article metrics

10.47679/jopp.617532024 Abstract views : 98 | PDF views : 56

   

Cite

   

Full Text

Download

References


Anderson, J. R. (1990). Cognitive psychology and its implications (3rd ed.). W H Freeman; Times Books; Henry Holt & Co.

Alves, I. C. B., & Oliveira, M. S. (2017). Avaliação psicológica para motoristas profissionais: Fatores psicossociais e o teste psicológico como instrumento de intervenção [Psychological assessment for professional drivers: Psychosocial factors and psychological testing as an intervention tool]. Psicologia: Teoria e Prática, 19(1), 139-151.

Anastasi, A., & Urbina, S. (1997). Psychological testing (7th ed.). Prentice Hall/Pearson Education.

Anstey, K. J., Windsor, T. D., Luszcz, M. A., & Andrews, G. R. (2006). Predicting Driving Cessation over 5 Years in Older Adults: Psychological Well-Being and Cognitive Competence Are Stronger Predictors than Physical Health. Journal of the American Geriatrics Society, 54(1), 121–126. https://doi.org/10.1111/j.1532-5415.2005.00471.x

‌ Anstey, K. J., Wood, J., Caldwell, H., Kerr, G., & Lord, S. R. (2009). Comparison of Self-Reported Crashes, State Crash Records and an On-Road Driving Assessment in a Population-Based Sample of Drivers Aged 69-95 Years. Traffic Injury Prevention, 10(1), 84–90. https://doi.org/10.1080/15389580802486399

Anunciação, L., Portugal, A. C., & Landeira-Fernandez, J. (2021). Neuropsychological assessment: Statistical aspects of the relationship between percentile and classification. Interação Em Psicologia, 25(3). https://doi.org/10.5380/riep.v25i3.71375

Anunciação, L., Portugal, A., Rabelo, I., & Landeira-Fernandez, J. (2021). Non-verbal intelligence outperforms selective attention in a visual short-term memory test. Psicologia: Reflexão e Crítica, 34(1), 35. https://doi.org/10.1186/s41155-021-00200-0

Baker, F. B., & Kim, S. H. (2017). The basics of item response theory using R. Springer.

Bünger, A., Grieder, S., Schweizer, F., & Grob, A. (2021). The comparability of intelligence test results: Group- and individual-level comparisons of seven intelligence tests. Journal of School Psychology, 88, 101–117. https://doi.org/10.1016/j.jsp.2021.09.002

Burns, E. (1988). Normalized standard score spreadsheet and norm table generator. Journal of School Psychology. https://doi.org/10.1016/0022-4405(88)90038-6

Caird, J. K., Johnston, K. A., Willness, C. R., Asbridge, M., & Steel, P. (2014). A meta-analysis of the effects of texting on driving. Accident Analysis & Prevention, 71, 311–318. https://doi.org/10.1016/j.aap.2014.06.005

Carroll, J. B. (2003). The Higher-stratum Structure of Cognitive Abilities. The Scientific Study of General Intelligence pp. 5–21. Elsevier. https://doi.org/10.1016/B978-008043793-4/50036-2

Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22. https://doi.org/10.1037/h0046743

Chalmers, R. P. (2012). mirt : A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6). https://doi.org/10.18637/jss.v048.i06

Chen, F. F. (2007). Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834

Ciotta, S., Cruz, R. M., & Dagostin, C. G.. (2021). Avaliação psicológica de condutores no trânsito no Brasil: marcos históricos e desafios atuais. Revista Plural, 1(3)

Colom, R., Karama, S., Jung, R. E., & Haier, R. J. (2010). Human intelligence and brain networks. Dialogues in Clinical Neuroscience, 12(4), 489–501. https://doi.org/10.31887/DCNS.2010.12.4/rcolom

Condon, D. M., & Revelle, W. (2014). The international cognitive ability resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52–64. https://doi.org/10.1016/j.intell.2014.01.004

Contran. (2022). Resolução nº 927, de 28 de março de 2022

Conway, A. R. A., Kovacs, K., Hao, H., Rosales, K. P., & Snijder, J.-P. (2021). Individual Differences in Attention and Intelligence: A United Cognitive/Psychometric Approach. Journal of Intelligence, 9(3), 34. https://doi.org/10.3390/jintelligence9030034

Cramer, Y. (2021). Distribution across Age of Raw and Scaled Intelligence Scores - the DARSIS model. PsyArXiv (OSF Preprints). https://doi.org/10.31234/osf.io/en82j

Das, J. P., Naglieri, J. A., & Kirby, J. R. (1994). Assessment of cognitive processes: The PASS theory of intelligence. Allyn & Bacon.

Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63(1), 453–482. https://doi.org/10.1146/annurev-psych-120710-100353

Detterman, D. (2014). Editor’s note. Intelligence, 42, 135. https://doi.org/10.1016/j.intell.2013.11.001

Flanagan JE, Landa R, Bhat A, et al. (2012) Head lag in infants at risk for autism: a preliminary study. American Journal of Occupational Therapy, 66(5): 577–585

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689-707. https://doi.org/10.1007/s11023-018-9482-5

Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.

Goleman, D. (1995). Emotional Intelligence: Why it can Matter More that IQ. New York: Bantam.

Gottfredson, L. S. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79–132. https://doi.org/10.1016/S0160-2896(97)90014-3

Gottfredson, L., & Saklofske, D. H. (2009). Intelligence: Foundations and issues in assessment. Canadian Psychology / Psychologie Canadienne, 50(3), 183–195. https://doi.org/10.1037/a0016641

Guilmette, T. J., Sweet, J. J., Hebben, N., Koltai, D., Mahone, E. M., Spiegler, B. J., Stucky, K., & Westerveld, M. (2020). American Academy of Clinical Neuropsychology consensus conference statement on uniform labeling of performance test scores. The Clinical Neuropsychologist, 34(3), 437–453. https://doi.org/10.1080/13854046.2020.1722244

Halpern. D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest 8(1):1-51. https://doi.org/10.1111/j.1529-1006.2007.00032.x

Hambleton, R. K., Merenda, P. F., & Spielberger, C. D. (2005). Adapting educational and psychological tests for cross-cultural assessment. Lawrence Erlbaum Associates.

Heckman, J. J. (2006). Skill formation and the economics of investing in disadvantaged children. Science, 312(5782), 1900–1902. https://doi.org/10.1126/science.1128898

Herrnstein, R. J., & Murray, C. (2010). The bell curve: Intelligence and class structure in American life. Simon and Schuster.

Horn, J. L., & McArdle, J. J. (2007). Understanding human intelligence since Spearman. In R. Cudeck & R. C. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 205–247). Lawrence Erlbaum Associates Publishers.

Hutter, F., Kotthoff, L., & Vanschoren, J. (Eds.). (2015). Automatic machine learning: methods, systems, challenges. Springer.

Immekus, J. C., & Maller, S. J. (2010). Factor structure invariance of the kaufman adolescent and adult intelligence test across male and female samples. Educational and Psychological Measurement, 70(1), 91–104. https://doi.org/10.1177/0013164409344491

Jensen, A. R. (1998). The g factor: The science of mental ability. Praeger Publishers; Greenwood Publishing Group.

Johnson, W., Bouchard, T. J., Krueger, R. F., McGue, M., & Gottesman, I. I. (2004). Just one g: Consistent results from three test batteries. Intelligence, 32(1), 95–107. https://doi.org/10.1016/S0160-2896(03)00062-X

Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman assessment battery for children: Second edition (KABC-II). Circle Pines, MN: American Guidance Service.

Keith, T. Z., Reynolds, M. R., Roberts, L. G., Winter, A. L., & Austin, C. A. (2011). Sex differences in latent cognitive abilities ages 5 to 17: Evidence from the Differential Ability Scales—Second Edition. Intelligence, 39(5), 389–404. https://doi.org/10.1016/j.intell.2011.06.008

Kievit, R. A., Fuhrmann, D., Borgeest, G. S., Simpson-Kent, I. L., & Henson, R. N. A. (2018). The neural determinants of age-related changes in fluid intelligence: A pre-registered, longitudinal analysis in UK Biobank. Wellcome Open Research, 3, 38. https://doi.org/10.12688/wellcomeopenres.14241.2

Koch, M., Becker, N., Spinath, F. M., & Greiff, S. (2021). Assessing intelligence without intelligence tests. Future perspectives. Intelligence, 89, 101596. https://doi.org/10.1016/j.intell.2021.101596

Lacko, D., Čeněk, J., Točík, J., Avsec, A., Đorđević, V., Genc, A., Haka, F., Šakotić-Kurbalija, J., Mohorić, T., Neziri, I., & Subotić, S. (2022). The necessity of testing measurement invariance in cross-cultural research: Potential bias in cross-cultural comparisons with individualism– Collectivism self-report scales. Cross-Cultural Research, 56(2–3), 228–267. https://doi.org/10.1177/10693971211068971

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne M. (2009). Social science.Computational social science. Science. 6;323(5915):721-3. https://doi.org/10.1126/science.1167742

Le Carret, N., Lafont, S., Letenneur, L., Dartigues, J.-F., Mayo, W., & Fabrigoule, C. (2003). The effect of education on cognitive performances and its implication for the constitution of the cognitive reserve. Developmental Neuropsychology, 23(3), 317–337. https://doi.org/10.1207/S15326942DN2303_1

Lúcio, P. S., Cogo-Moreira, H., Puglisi, M., Polanczyk, G. V., & Little, T. D. (2019). Psychometric investigation of the raven’s colored progressive matrices test in a sample of preschool children. Assessment, 26(7), 1399–1408. https://doi.org/10.1177/1073191117740205

Matarazzo, J. D. (1990). Psychological assessment versus psychological testing: Validation from Binet to the school, college, and adult intelligence tests. American Psychologist, 45(9), 999-1017. https://doi.org/10.1037//0003-066x.45.9.999

Mayer, J. D., Roberts, R. D., & Barsade, S. G. (2008). Human Abilities: Emotional Intelligence. Annual Review of Psychology, 59(1), 507–536. https://doi.org/10.1146/annurev.psych.59.103006.093646

McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37(1), 1–10. https://doi.org/10.1016/j.intell.2008.08.004

Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., Halpern, D. F., Loehlin, J. C., Perloff, R., Sternberg, R. J., & Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51(2), 77–101. https://doi.org/10.1037/0003-066X.51.2.77

Nisbett, R. E., Aronson, J., Blair, C., Dickens, W., Flynn, J., Halpern, D. F., & Turkheimer, E. (2012). Intelligence: New findings and theoretical developments. Am Psychol. 67(2):130-59. https://doi.org/10.1037/a0026699

Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Bäckman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292–305. https://doi.org/10.1016/j.tics.2012.04.005

Noble, K. G., Houston, S. M., Brito, N. H., Bartsch, H., Kan, E., Kuperman, J. M., Akshoomoff, N., Amaral, D. G., Bloss, C. S., Libiger, O., Schork, N. J., Murray, S. S., Casey, B. J., Chang, L., Ernst, T. M., Frazier, J. A., Gruen, J. R., Kennedy, D. N., Van Zijl, P., & Mostofsky, S. (2015). Family income, parental education and brain structure in children and adolescents. Nature Neuroscience, 18(5), 773–778. https://doi.org/10.1038/nn.3983

Parsons, T. D., & Rizzo, A. A. (2008). Affective outcomes of virtual reality exposure

therapy for anxiety and specific phobias: A meta-analysis. Journal of Behavior Therapy and Experimental Psychiatry, 39(3), 250–261. https://doi.org/10.1016/j.jbtep.2007.07.007

Palejwala, M. H., & Fine, J. G. (2015). Gender differences in latent cognitive abilities in

children aged 2 to 7. Intelligence, 48, 96–108. https://doi.org/10.1016/j.intell.2014.11.004

Plomin, R., & Deary, I. (2015). Genetics and intelligence differences: Five special findings. Molecular Psychiatry, 20 (1), 98–108. https://doi.org/10.1038/mp.2014.105

Redvaldsen, D. (2020). Do the Mega and Titan tests yield accurate results? An investigation into two experimental intelligence tests. Psych, 2(2), 97–113. https://doi.org/10.3390/psych2020010

Reynolds, C. R., & Kamphaus, R. W. (2003). Handbook of psychological assessment (4th ed.). Wiley.

Reynolds, C. R., & Keith, T. Z. (2013). The future of assessment in psychology: predicting, explaining, and preventing behavior problems. Routledge.

Rindermann, H., Becker, D., & Coyle, T. R. (2020). Survey of expert opinion on intelligence: Intelligence research, experts’ background, controversial issues, and the media. Intelligence, 78, 101406. https://doi.org/10.1016/j.intell.2019.101406

Ritchie, S. J., & Tucker-Drob, E. M. (2018). How much does education improve intelligence? A meta-analysis. Psychological Science, 29(8), 1358–1369. https://doi.org/10.1177/0956797618774253

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

Rosseti, M. O., Rabelo, I. S. A., Leme, I. F. A. de S., Pacanaro, S. V., & Güntert, I. B. (2009). Evidências de validade das Matrizes Progressivas Avançadas de Raven em universitários. Psico-USF, 14(2), 177–184. https://doi.org/10.1590/S1413-82712009000200006

Sattler, J. M. (2008). Assessment of children: Cognitive foundations (5th ed.). Jerome M. Sattler, Publisher, Inc.

Salthouse, T. A. (2009). When does age-related cognitive decline begin? Neurobiol Aging, 30(4):507-14. https://doi.org/10.1016/j.neurobiolaging.2008.09.023

Schaie, K. W. (2013). Developmental influences on adult intelligence: The Seattle Longitudinal Study (2nd ed.). Oxford University Press.

Schmidt, F. L., & Hunter, J. (2004). General mental ability in the world of work: Occupational attainment and job performance. Journal of Personality and Social Psychology, 86(1), 162–173. https://doi.org/10.1037/0022-3514.86.1.162

Silva, F. H. V. C. , & Günther, H. (2009). Psicologia do trânsito no Brasil: de onde veio e para onde caminha?. Temas em Psicologia, 17(1), 163-175.

Spearman, C. (1904). “General Intelligence,” Objectively Determined and Measured. The American Journal of Psychology. https://doi.org/10.2307/1412107

Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.

Sternberg, R. J., & Grigorenko, E. L. (2002). The theory of successful intelligence as a basis for instruction and assessment. ASCD.

Sternberg, R. J. (2017). Wisdom, intelligence, and creativity synthesized. Cambridge University Press.

Sternberg, R. J. (2018a). Theories of intelligence. APA Handbook of Giftedness and Talent, 145–161. American Psychological Association. https://doi.org/10.1037/0000038-010

Sternberg, R. J. (2018b). Applying intelligence to the workplace. Routledge.

Stern, Y. (2012). Cognitive reserve in aging and Alzheimer's disease. Lancet Neurol, 11(11):1006-12. https://doi.org/10.1016/s1474-4422(12)70191-6

Thurstone, L.L. (1938). Primary mental abilities. University of Chicago Press: Chicago.

Tomlinson, C. A. (2017). How to differentiate instruction in academically diverse classrooms. ASCD.

Van Der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842–861. https://doi.org/10.1037/0033-295X.113.4.842

Van de Vijver, F. J., & Leung, K. (1997). Methods and data analysis for cross-cultural research. Sage Publications.

Warne, R. T., & Burningham, C. (2019). Spearman’s g found in 31 non-Western nations: Strong evidence that g is a universal phenomenon. Psychological Bulletin, 145(3), 237–272. https://doi.org/10.1037/bul0000184

Waterhouse, L. (2006). Inadequate Evidence for Multiple Intelligences, Mozart Effect, and Emotional Intelligence Theories. Educational Psychologist, 41(4), 247–255. https://doi.org/10.1207/s15326985ep4104_5

Walter, F., Daseking, M., & Pauls, F. (2021). Sex Differences in Intelligence in Children Aged 2:6–7:7: Analysis of the Factor Structure and Measurement Invariance of the German Wechsler Primary and Preschool Scale of Intelligence–Fourth Edition. Journal of Psychoeducational Assessment, 39(4), 395–421. https://doi.org/10.1177/0734282920981398

Warne, R. T., & Burningham, C. (2019). Spearman’s g found in 31 non-Western nations: Strong evidence that g is a universal phenomenon. Psychological Bulletin, 145(3), 237–272. https://doi.org/10.1037/bul0000184

Warne, R. T., Godwin, L. R., & Smith, K. V. (2013). Are There More Gifted People Than Would Be Expected in a Normal Distribution? An Investigation of the Overabundance Hypothesis. Journal of Advanced Academics, 24(4), 224–241. https://doi.org/10.1177/1932202X13507969

Waschl, N. A., Nettelbeck, T., Jackson, S. A., & Burns, N. R. (2016). Dimensionality of the Raven’s Advanced Progressive Matrices: Sex differences and visuospatial ability. Personality and Individual Differences, 100, 157–166. https://doi.org/10.1016/j.paid.2015.12.008

Waterhouse, L. (2006). Inadequate Evidence for Multiple Intelligences, Mozart Effect, and Emotional Intelligence Theories. Educational Psychologist, 41(4), 247–255. https://doi.org/10.1207/s15326985ep4104_5

Weiss, L. G., Saklofske, D. H., Prifitera, A., & Holdnack, J. A. (Eds.). (2010). WISC-V assessment and interpretation: Scientist-practitioner perspectives. Academic Press.

Weinberg, R. A. (1989). Intelligence and IQ: Landmark issues and great debates. American Psychologist, 44(2), 98–104. https://doi.org/10.1037/0003-066X.44.2.98

Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225–240. https://doi.org/10.1162/105474698565686


Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Luis Anunciação, Anna C Portugal, Ivan Rabelo, Roberto Cruz, Louise Marques, Jesus Landeira-Fernandez

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Journal of Psychological Perspectives
Published by:
Lucky Arya Residence 2 No. 18
Jalan HOS. Cokroaminoto Kab. Pringsewu
Lampung - Indonesia, Postal code 35373

Creative Commons License
Journal of Psychological Perspectives is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.