Recieved: 09/07/2019
Accepted: 09/15/2019
Published: 10/20/2019
p.: 32-45
DOI: 10.11621/npj.2019.0304
Keywords: cognitive ability; intelligence; Cattell-Horn-Carroll theory; International Cognitive Ability Resource
Available online: 20.10.2019
Kornilova, T.V., Kornilov Sergei A.. Psychometric properties of the modified International Cognitive Ability Resource (ICAR) test battery. // National Psychological Journal 2019. 3. p.32-45. doi: 10.11621/npj.2019.0304
Copied to Clipboard
CopyBackground. The emergence of the psychometric tradition in Russian psychology necessitates a thorough exploration and adoption of the international experience in the development of appropriate measurement instruments and methodologies. The goal of the present study was thus to perform a comprehensive evaluation of the International Cognitive Ability Resource (ICAR) instrument in a Russian sample. The modified ICAR instrument consisted of visuo-spatial subtests Three-dimensional Rotation, Mental Reasoning (original ICAR subtests), as well as the previously developed verbal subtests Mill-Hill verbal scale and Analogies (from the ROADS test battery).
Design. The ICAR battery was administered to n=681 individuals (377 females) in the age from 17 to 59 years (Med=23, M=25.83, SD=7.58 лет) who were either college students or adults with a college degree from the city of Moscow who volunteered for the study. The test battery was administered without (n=284) as well as with (n=397) a time limit.
Results. The study demonstrated adequate psychometric properties of the modified ICAR battery, and revealed a fundamentally bifactor structure of the battery both at the level of individual items as well as at the level of subtests. Thus, individual’s performance on each item or subtest can be conceptualized as being driven by specific (e.g., fluid or verbal) as well as general (g) intelligence factors. We also show that introducing the time limit distorts the psychometric structure of the battery, lowers internal consistency, and reduces the g-saturation of the resulting scores, a finding that has important implications for the theory and practice of testing.
Fig 1. Alternative Confirmation Factor Models (CFA) structures of the extended ICAR test battery. The numbering of the models is given in accordance with Table 3.
Fig 2. Psychometric properties of tasks and the distribution of scores in the ICAR visually-spatial scales and the extended test battery. Difficulty is pecentage of correct answers. MD - missing data.
Table 1. Psychometric properties of ICAR visual spatial scales and ROADS verbal scales.
|
Temporary |
Temporary |
||||
Subtest |
α |
wh |
ECV |
a |
wh |
ECV |
Rotation |
0.94 |
0.80 |
74% |
0.90 |
0.67 |
58% |
Matrices |
0.68 |
0.44 |
37% |
0.55 |
0.44 |
34% |
МХ Scale |
0.79 |
0.51 |
45% |
0.79 |
0.55 |
49% |
Analogies |
0.65 |
0.32 |
29% |
0.56 |
0.20 |
16% |
Rotation + Matrices (gf) |
0.92 |
0.74 |
66% |
0.86 |
0.54 |
47% |
Scale МХ + Analogies (gc) |
0.84 |
0.48 |
37% |
0.80 |
0.53 |
39% |
All subtests (g) |
0.91 |
0.45 |
26% |
0.80 |
0.41 |
18% |
NB: α - Cronbach alpha coefficient of internal consistency. wh is the reliability coefficient of MacDonald omega. ECV is the percentage of the total variance due to the general factor.
Table 2. Psychometric properties of individual tasks of visual and ICAR spatial subtests.
|
|
Temporary |
Temporary |
Temporary |
Temporary |
||||||
Subtest |
Task |
M |
pBis |
M |
pBis |
Gf |
GS1 |
GS2 |
Gf |
GS1 |
GS2 |
Rotation |
R3D.01 |
0.25 |
0.57 |
0.16 |
0.40 |
0.75 |
0.25 |
- |
0.57 |
-0.39 |
- |
|
R3D.02 |
0.11 |
-0.25 |
0.15 |
-0.24 |
-0.45 |
-0.24 |
- |
-0.46 |
0.24 |
- |
|
R3D.03 |
0.06 |
-0.24 |
0.07 |
-0.24 |
-0.54 |
-0.32 |
- |
-0.66 |
0.40 |
- |
|
R3D.04 |
0.46 |
0.71 |
0.45 |
0.45 |
0.84 |
0.23 |
- |
0.63 |
-0.36 |
- |
|
R3D.05 |
0.48 |
0.65 |
0.41 |
0.53 |
0.68 |
0.49 |
- |
0.52 |
-0.55 |
- |
|
R3D.06 |
0.56 |
0.65 |
0.54 |
0.53 |
0.65 |
0.56 |
- |
0.48 |
-0.58 |
- |
|
R3D.07 |
0.44 |
0.66 |
0.37 |
0.53 |
0.93 |
-0.08 |
- |
0.78 |
-0.40 |
- |
|
R3D.08 |
0.50 |
0.73 |
0.38 |
0.51 |
0.81 |
0.40 |
- |
0.56 |
-0.48 |
- |
|
R3D.09 |
0.56 |
0.65 |
0.50 |
0.56 |
0.72 |
0.47 |
- |
0.59 |
-0.56 |
- |
|
R3D.10 |
0.41 |
0.68 |
0.31 |
0.57 |
0.73 |
0.43 |
- |
0.50 |
-0.59 |
- |
|
R3D.11 |
0.35 |
0.67 |
0.24 |
0.54 |
0.74 |
0.48 |
- |
0.61 |
-0.52 |
- |
|
R3D.12 |
0.41 |
0.69 |
0.35 |
0.60 |
0.81 |
0.29 |
- |
0.50 |
-0.63 |
- |
|
R3D.13 |
0.33 |
0.68 |
0.23 |
0.54 |
0.82 |
0.25 |
- |
0.58 |
-0.53 |
- |
|
R3D.14 |
0.48 |
0.67 |
0.40 |
0.52 |
0.93 |
-0.06 |
- |
0.52 |
-0.53 |
- |
|
R3D.15 |
0.49 |
0.70 |
0.40 |
0.61 |
0.85 |
0.20 |
- |
0.40 |
-0.71 |
- |
|
R3D.16 |
0.57 |
0.60 |
0.48 |
0.56 |
0.65 |
0.45 |
- |
0.23 |
-0.76 |
- |
|
R3D.17 |
0.48 |
0.74 |
0.39 |
0.59 |
0.83 |
0.31 |
- |
0.33 |
-0.74 |
- |
|
R3D.18 |
0.06 |
-0.29 |
0.07 |
-0.22 |
-0.87 |
0.07 |
- |
-0.44 |
0.39 |
- |
|
R3D.19 |
0.52 |
0.64 |
0.39 |
0.57 |
0.89 |
-0.05 |
- |
0.26 |
-0.76 |
- |
|
R3D.20 |
0.62 |
0.63 |
0.47 |
0.49 |
0.68 |
0.53 |
- |
-0.10 |
-0.87 |
- |
|
R3D.21 |
0.32 |
0.66 |
0.18 |
0.47 |
0.81 |
0.30 |
- |
0.14 |
-0.72 |
- |
|
R3D.22 |
0.60 |
0.62 |
0.37 |
0.46 |
0.64 |
0.57 |
- |
-0.19 |
-0.85 |
- |
|
R3D.23 |
0.43 |
0.47 |
0.29 |
0.40 |
0.46 |
0.50 |
- |
-0.18 |
-0.75 |
- |
|
R3D.24 |
0.45 |
0.64 |
0.31 |
0.60 |
0.67 |
0.42 |
- |
0.01 |
-0.90 |
- |
Matrices |
MR.43 |
0.86 |
0.37 |
0.85 |
0.16 |
0.28 |
- |
0.60 |
0.05 |
- |
0.29 |
|
MR.44 |
0.73 |
0.29 |
0.75 |
0.18 |
0.08 |
- |
0.50 |
0.36 |
- |
0.21 |
|
MR.45 |
0.74 |
0.32 |
0.70 |
0.21 |
0.25 |
- |
0.47 |
0.36 |
- |
0.20 |
|
MR.46 |
0.70 |
0.37 |
0.66 |
0.22 |
0.24 |
- |
0.56 |
0.24 |
- |
0.26 |
|
MR.47 |
0.76 |
0.28 |
0.73 |
0.22 |
0.40 |
- |
0.35 |
0.32 |
- |
0.34 |
|
MR.48 |
0.69 |
0.37 |
0.72 |
0.18 |
0.08 |
- |
0.61 |
0.06 |
- |
0.32 |
|
MR.50 |
0.43 |
0.23 |
0.39 |
0.27 |
0.31 |
- |
0.26 |
0.07 |
- |
0.40 |
|
MR.53 |
0.75 |
0.36 |
0.66 |
0.35 |
0.34 |
- |
0.46 |
0.10 |
- |
0.62 |
|
MR.54 |
0.43 |
0.16 |
0.42 |
0.21 |
-0.02 |
- |
0.29 |
-0.08 |
- |
0.42 |
|
MR.55 |
0.54 |
0.42 |
0.35 |
0.31 |
0.31 |
- |
0.59 |
-0.06 |
- |
0.69 |
|
MR.56 |
0.55 |
0.38 |
0.40 |
0.24 |
0.13 |
- |
0.58 |
-0.23 |
- |
0.62 |
NB: Task IDs are in accordance with the original ICAR nomenclature. M is task complexity (% of correct answers). pBis is the point-biserial correlation coefficient between the task and the total scale score. Gf is standardized factor load of the task (common factor). GS1, GS2 are standardized factor load indicators (subtest-specific factors).
Тable 3. Indicators of suitability models of abilities
|
Visual spatial subtests ICAR (MIRT) |
All subtests, expanded battery (CFA) |
||||||||
Model |
М2 |
df |
p |
CFI |
RMSEA |
Y-B |
df |
p |
CFI |
RMSEA |
М1 – 1 factoe |
1141.05 |
560 |
<0.001 |
0.960 |
0.061 |
368.69 |
20 |
<0.001 |
0.627 |
0.245 |
М2 – 2 orthogonal factor |
915.16 |
560 |
<0.001 |
0.975 |
0.047 |
165.94 |
20 |
<0.001 |
0.838 |
0.158 |
М3 – 2 oblique factor |
888.33 |
559 |
<0.001 |
0.977 |
0.046 |
127.87 |
19 |
<0.001 |
0.879 |
0.140 |
М4 – bi-factor* |
679.80 |
525 |
<0.001 |
0.989 |
0.032 |
22.63 |
12 |
0.031 |
0.988 |
0.053 |
М5 – factor of the 2nd order |
- |
- |
- |
- |
- |
127.87 |
19 |
<0.001 |
0.879 |
0.140 |
М6 – bi-factor model with Speed**factor |
949.25 |
539 |
<0.001 |
0.958 |
0.044 |
- |
- |
- |
- |
|
NB: “Temporary Limitation -” M2 - test function of the model; df is the number of degrees of freedom. p is the significance level when testing the hypothesis of the absolute suitability of the model. CFI is a comparative fitness index. Y-B - chi-square corrected for data abnormality. RMSEA is the root of the mean square of the approximation error. * - the model that showed the best suitability indices.
** The indicators for the condition "Temporary Limit +" are given.
Beaujean, A.A. (2015). John Carroll’s view of intelligence: Bi-factor vs higher-order models. Journal of Intelligence, 3(4), 121–136. doi: 10.3390/jintelligence3040121
Becker, N., Koch, M., Schult, J., & Spinath, F.M. (2017). Setting Doesn’t Matter Much. A Meta-Analytic Comparison of the Results of Intelligence Tests Obtained in Group and Individual Settings. European Journal of Psychological Assessment, 33, 1–8.
Canivez, G.L., & Youngstrom, E.A. (2019). Challenges to the Cattell-Horn-Carroll Theory: Empirical, Clinical, and Policy Implications. Applied Measurement in Education, (32)3, 232–248. doi:10.1080/08957347.2019.1619562
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York: Cambridge University Press. doi: 10.1017/CBO9780511571312
Cattell, R.B. (1987). Intelligence: Its structure, growth and action. New York: Elsevier.
Chalmers, R.P. (2012). Mirt: a Multidimensional Item Response Theory Package for the R. Environment. Journal of Statistical Software, 48(6), 1–19. doi: 10.18637/jss.v048.i06
Condon, D.M., & Revelle, W. (2014). The International Cognitive Ability Resource: Development and initial validation of a public-domain measure. Intelligence, 43, 52–64. doi: 10.1016/j.intell.2014.01.004
Cucina, J., & Byle, K. (2017). The bifactor model fits better than the higher-order model in more than 90% of comparisons for mental abilities test batteries. Journal of Intelligence, 5(27), 1–21. doi: 10.3390/jintelligence5030027
Davydov D.G., & Chmykhova E.V. (2016). Application of the test Standard Progressive Raven matrices in the time limit mode. [Voprosy psikhologii], 4, 129–139. doi: 10.1098/rstb.2017.0284
Dubois, J., Galdi, P., Paul, L.K., & Adolphs, R. (2018). A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Philosophical Transaction of the Royal Society B: Biological Sciences, 373 (1756).
Gardner G. (2007). The structure of the mind. Theory of multiple intelligence. Moscow, OOO I.D. Williams.
Gottfredson, L.S. (2016). A g theorist on why Kovacs and Conway’s Process Overlap Theory Amplifies, Not Opposes, g theory. Psychological Inquiry, 27(3), 210–217. doi: 10.1080/1047840X.2016.1203232
Hamel, R., & Schmittmann, V.D. (2006). The 20-Minute Version as a Predictor of the Raven Advanced Progressive Matrices Test. Educational and Psychological Measurement, 66(6), 1039–1046. doi: 10.1177/0013164406288169
Horn, J.L., & Blankson, N. (2005). Foundations for better understanding of cognitive abilities. In: D.P. Flanagan & P.L. Harrison (Eds.), Contemporary intellectual assessment. Theories, tests, and issues (2nd ed.). New York: Guilford Press, 41–68.
Horn, J.L., & Cattell, R.B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of Educational Psychology, 57(5б) 253–270. doi: 10.1037/h0023816
Horn, J.L., & Noll, J. (1997). Human cognitive capabilities: Gf-Gc theory. In: Flanagan, D.P., Genshaft, J.L., & Harrison, P.L., Eds. Contemporary Intellectual Assessment: Theories, Tests and Issues. New York: Guilford Press, 53–91.
Jensen, A.R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.
Kirkegaard, E.O.W., & Nordbjerg, O. (2015). Validating a Danish translation of the International Cognitive Ability Resource sample test and Cognitive Reflection Test in a student sample Group, 4(5). 10.26775/ODP.2015.07.31
Koller, M., & Stahel, W.A. (2011). Sharpening wald-type inference in robust regression for small samples. Computational Statistics & Data Analysis, 55(8), 2504-2515. doi: 10.1016/j.csda.2011.02.014
Kornilov S.A., & Grigorenko E.L. (2010). Methodological complex for the diagnosis of academic, creative and practical abilities. [Psikhologicheskiy zhurnal], 31(2), 90–103.
Kornilova T.V. (2016). Intellectual and personal potential of a person in conditions of uncertainty and risk. Moscow, Nestor – Istoriya.
Kovacs, K., & Conway, A.R.A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. doi: 10.1080/1047840X.2016.1153946
Krasavtseva Yu.V., & Kornilova T.V. (2018). Emotional and academic intelligence as predictors of strategies in the Iowa Game Problem (IGT). [Psikhologicheskiy zhurnal], 39(3), 29-43.
Kyllonen, P., Hartman, R., Sprenger, A., Weeks, J., Bertling, M., McGrew, K., Kriz, S., Bertling, J., Fife, J., & Stankov, L. (2019). General fluid/inductive reasoning battery for a high-ability population. Behavior Research Methods, 51(2), 507–522. doi: 10.3758/s13428-018-1098-4
Mayer, J.D. (2014). Personal Intelligence: The power of personality and how it shapes our lives. N. Y.: Farrar, Straus and Giroux.
Mayer, J.D., Caruso, D.R., & Salovey, P. (2016). The ability model of emotional intelligence: principles and updates. Emotion review, 8(4), 290–300. doi: 10.1177/1754073916639667
McDonald, R.P. (1999). Test theory: A unified treatment. L. Erlbaum Associates, Mahwah.
Nizbett R. (2013). What is intelligence and how to develop it. Moscow, Alpina nonfikshn.
Raven J., Raven J.K., & Cort J.H. (2012). A Guide to Raven's Progressive Matrices and Vocabulary Scales. Moscow, “Kogito-Tsentr”. doi: 10.1037/t10910-000
Raven, J. (1989). The Raven Progressive Matrices: A Review of National Norming Studies and Ethnic and Socioeconomic Variation Within the United States. Journal of Educational Measurement, 26(1), 1–16. doi: 10.1111/j.1745-3984.1989.tb00314.x
Raven, J.C., Court J.H., &Raven J. (1992). Manual for Raven’s Progressive Matrices and Mill Hill Vocabulary Scales. Oxford: Oxford Psychologists Press. doi: 10.1080/00273171.2012.715555
Reise, S.P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696.
Ren, X., Wang, T., Sun, S., Deng, M., & Schweizer, K. (2018). Speeded testing in the assessment of intelligence gives rise to a speed factor. Intelligence, 64–71. doi: 10.1016/j.intell.2017.11.004
Rosseel, Y. (2012). Lavaan: An r package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36. doi: 10.18637/jss.v048.i02
Salovey, P., & Mayer, J.D. (1990). Emotional intelligence. Imagin. Cogn. Personal, 9, 185–211. doi: 10.2190/DUGG-P24E-52WK-6CDG
Schneider, W.J., & McGrew, K.S. (2019). Process Overlap Theory is a milestone achievement among intelligence theories. Journal of Applied Research in Memory and Cognition. 32(3). doi: 10.1016/j.jarmac.2019.06.006
Schneider, W.J., Mayer, J.D., & Newman, D.A. (2016). Integrating Hot and Cool Intelligences: Thinking Broadly about Broad Abilities. Journal of Intelligence, 4(1), Art.1. doi: 10.3390/jintelligence4010001
Spearman, C.E. (1927). The abilities of man: Their nature and measurement. London: MacMillan.
Sternberg, R.J. (1999). The theory of successful intelligence. Review of General Psychology, 3(4), 292–316. doi: 10.1037/1089-2680.3.4.292
Sternberg R.J., & The Rainbow Project Collaborators (2006). The Rainbow Project: Enhancing the SAT through assessments of analytical, practical and creative skills. Intelligence. 34(4), 321–350. doi: 10.1016/j.intell.2006.01.002
Takagi, Y., Hirayama, J., & Tanaka, S.C. (2018) State-Unspecific Modes of Whole-Brain Functional Connectivity Predict intelligence and life outcomes. bioRxiv, preprint first posted. doi: 10.1101/283846
Kornilova, T.V., Kornilov Sergei A.. Psychometric properties of the modified International Cognitive Ability Resource (ICAR) test battery. // National Psychological Journal 2019. 3. p.32-45. doi: 10.11621/npj.2019.0304
Copied to Clipboard
Copy