Abstract
This study aimed to investigate the relationships between tasks of the Jouve Cerebrals Crystallized Educational Scale (JCCES) and General Ability Measure for Adults (GAMA) using multidimensional scaling (MDS) analysis. The JCCES measures Verbal Analogies, Mathematical Problems, and General Knowledge, while the GAMA assesses nonverbal cognitive abilities through Matching, Analogies, Sequences, and Construction tasks. A total of 63 participants completed both assessments. MDS analysis revealed a 2-dimensional solution, illustrating a diagonal separation between nonverbal and verbal abilities, with Mathematical Problems slightly closer to the verbal side. Seven groups were identified, corresponding to distinct cognitive processes. The findings suggest that JCCES and GAMA tasks are not independent and share common underlying dimensions. This study contributes to a more nuanced understanding of cognitive abilities, with potential implications for educational, clinical, and research settings. Future research should address the study’s limitations, including the small sample size and potential methodological constraints.
Keywords: cognitive abilities, JCCES, GAMA, multidimensional scaling, verbal abilities, nonverbal abilities, fluid intelligence, crystallized intelligence
Introduction
The study of cognitive abilities has been an area of significant interest in psychometrics, which aims to develop and refine methods for assessing individual differences in mental capabilities (Embretson & Reise, 2000). Among the diverse cognitive abilities, crystallized and fluid intelligence have been particularly influential constructs in the understanding of human cognition (Cattell, 1963). Crystallized intelligence refers to the acquired knowledge and skills, while fluid intelligence reflects the capacity for abstract reasoning and problem-solving, independent of prior knowledge or experience (Cattell, 1963; Horn & Cattell, 1966). Various instruments have been developed to assess these cognitive abilities, including the Jouve Cerebrals Crystallized Educational Scale (JCCES; Jouve, 2010a) and the General Ability Measure for Adults (GAMA; Naglieri & Bardos, 1997).
Although JCCES and GAMA are used as independent measures of crystallized and nonverbal cognitive abilities, respectively, the relationships between the tasks within these instruments remain less explored. Previous research has identified separate factors for JCCES and GAMA subtests (Jouve, 2010b), but a more detailed investigation into the underlying cognitive processes is warranted. Multidimensional scaling (MDS) is a statistical technique that can provide insight into the relationships between tasks by representing them as points in a multidimensional space (Cox & Cox, 2001; Borg & Groenen, 2005). The present study aims to apply MDS to analyze the relationships between the tasks of JCCES and GAMA, in order to identify common underlying dimensions and provide a more nuanced understanding of the cognitive abilities assessed by these instruments.
The literature on cognitive abilities suggests that tasks within JCCES and GAMA may not be entirely independent and could share some common underlying dimensions (Carroll, 1993; Spearman, 1927). For instance, the verbal analogies (VA) and general knowledge (GK) tasks in JCCES tap into language development, a crucial aspect of crystallized intelligence (Horn & Cattell, 1966). Similarly, the matching (MAT), analogies (ANA), sequences (SEQ), and construction (CON) tasks in GAMA are related to fluid intelligence, involving abstract reasoning and problem-solving skills (Naglieri & Bardos, 1997). However, the specific relationships between these tasks and their underlying cognitive processes remain to be further elucidated.
Method
Research Design
The current study employed a correlational research design to investigate the relationships between the tasks from the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the General Ability Measure for Adults (GAMA). This design was chosen because it allowed the researchers to examine the associations between the variables of interest without manipulating any variables or assigning participants to experimental conditions (Creswell, 2014).
Participants
A total of 63 participants were recruited for the study. Demographic information regarding age, gender, and ethnicity was collected but not used in this study. The participants were selected based on their willingness to participate and their ability to complete the JCCES and GAMA assessments. No exclusion criteria were set.
Materials
The JCCES is a measure of crystallized cognitive abilities (Jouve, 2010a), which reflect an individual’s acquired knowledge and skills (Cattell, 1971). It consists of three subtests: Verbal Analogies (VA), Mathematical Problems (MP), and General Knowledge (GK).
The GAMA is a standardized measure of nonverbal and figurative general cognitive abilities (Naglieri & Bardos, 1997). It consists of four subtests: Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON).
Procedures
Data collection was conducted in a quiet and well-lit testing environment. Participants first completed the JCCES, followed by the GAMA. Standardized instructions were provided to ensure that participants understood the requirements of each subtest. The JCCES and GAMA were administered according to their respective guidelines.
Statistical Analyses
The data were analyzed using Multidimensional Scaling (MDS) with XLSTAT. The choice of MDS was informed by its ability to represent the structure of complex datasets by reducing their dimensionality while preserving the relationships between data points (Borg & Groenen, 2005). Kruskal’s stress (1) was used to measure the goodness of fit for the MDS solution (Kruskal, 1964). Analyses were conducted for dimensions ranging from 1 to 7, with random initial configuration, 10 repetitions, and stopping conditions set at convergence = 0.00001 and iterations = 500.
Results
The data included scores from the Jouve Cerebrals Crystallized Educational Scale (JCCES), which measures Verbal Analogies (VA), Mathematical Problems (MP), and General Knowledge (GK), and the General Ability Measure for Adults (GAMA), a nonverbal assessment comprising Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON) tasks.
Results of the Statistical Analyses
Based on Kruskal’s stress values, the best solution was obtained for a 2-dimensional representation space (stress = 0.100), as higher dimensions did not result in significant improvements. The results for the 2-dimensional space (RSQ = .9418) are illustrated in Figure 1.
Interpretation of Results
The results from the MDS analysis indicate that the 2-dimensional solution provides a nuanced representation of the relationships between the JCCES and GAMA tasks. The findings suggest that the tasks from the two measures are not independent, and there are common underlying dimensions that account for their relationships. The MDS configuration reveals a diagonal separation between nonverbal abilities (MAT, ANA, SEQ, and CON) on one side and verbal abilities (GK and VA) on the other, with MP being unrelated to either side but slightly closer to the verbal side.
Based on their proximities, seven groups can be identified (in alphabetical order):
- Abstract Reasoning and Pattern Recognition: This group, comprising SEQ and CON tasks, reflects abilities related to identifying and extrapolating patterns, as well as spatial visualization and manipulation. Both tasks share the common cognitive processes of abstract reasoning and pattern recognition, making them closely related to fluid intelligence.
- Nonverbal Analogical Reasoning: Represented solely by the ANA task, this group reflects the ability to identify relationships and draw analogies between seemingly unrelated objects. The unique focus on analogical reasoning in a figurative context sets this task apart from the other tasks within the fluid intelligence group.
- Crystallized Intelligence: Consisting of MP, GK, and VA, this group represents abilities related to accumulated knowledge and experience, as well as the application of learned information in problem-solving situations.
- Fluid Intelligence: Comprised of ANA, SEQ, and CON, this group represents cognitive processes involving reasoning, problem-solving, and abstract thinking, which are not dependent on prior knowledge or experience.
- Language Development: Represented by GK and VA, this group reflects abilities related to language comprehension, vocabulary, and the use of language in various contexts.
- Quantitative Reasoning and Knowledge: Represented uniquely by MP, this group reflects abilities related to understanding, interpreting, and applying numerical information and mathematical concepts.
- Visual-Spatial Representation: Represented solely by MAT, this group reflects abilities related to visualizing and manipulating spatial information.
Particularities
The MDS analysis suggests that the CON task is more closely related to the fluid intelligence subgroup (ANA, SEQ, and CON) rather than the visual-spatial representation subgroup (MAT). This finding could be attributed to the nature of the CON task, which involves problem-solving and reasoning abilities that extend beyond visual-spatial skills. Although visual-spatial abilities may be necessary for the task, the CON task requires individuals to analyze and identify patterns, think abstractly, and apply their reasoning skills, which are more closely aligned with fluid intelligence processes. As a result, the CON task seems to tap into a broader range of cognitive processes than just visual-spatial representation, making it a better fit for the fluid intelligence subgroup.
In the fluid intelligence group, the MDS analysis reveals an intriguing difference in the proximity between the tasks. SEQ and CON are closely related, while ANA is positioned farther apart. This distinction can be better understood by examining the underlying processes involved in each task.
The SEQ task requires individuals to identify patterns and complete a sequence by deducing the logical progression. This involves abstract reasoning, pattern recognition, and the ability to extrapolate from given information. Similarly, the CON task involves assembling objects or shapes to create a specific configuration. This also requires abstract reasoning and pattern recognition, as well as spatial visualization and manipulation skills. Due to these shared cognitive processes, SEQ and CON tasks form a closely related subgroup within fluid intelligence.
On the other hand, the ANA task involves identifying relationships between pairs of objects or concepts and applying that understanding to a new set of objects or concepts. Although this task also requires abstract reasoning and problem-solving skills, it differs from SEQ and CON tasks in the sense that it demands a higher level of analogical reasoning, which involves identifying similarities and relationships between seemingly unrelated entities. This unique cognitive demand in the ANA task sets it apart from the other tasks in the fluid intelligence group.
Limitations
There are some limitations in the current study that may have affected the results. Firstly, the sample size of 63 participants may not be sufficient to provide robust and generalizable results. A larger sample would improve the reliability of the MDS analysis and potentially lead to more conclusive findings. Secondly, a lack of inclusion criteria could have been introduced in the recruitment process, affecting the representativeness of the sample and the generalizability of the results. Finally, there may be methodological limitations associated with the use of MDS, such as the assumption that the data are interval-scaled and that the relationships between tasks can be represented in a Euclidean space. These assumptions may not be entirely accurate for the current dataset, potentially affecting the interpretation of the results.
Discussion
Interpretation of Results and Comparison with Previous Research
The current study aimed to investigate the relationships between the tasks of the JCCES and GAMA, with the intent of identifying underlying dimensions that account for these relationships. In line with our hypotheses, we found a clear distinction between verbal and nonverbal abilities. The MDS analysis provided a nuanced representation of the relationships between the tasks, revealing a diagonal separation between nonverbal abilities (MAT, ANA, SEQ, and CON) and verbal abilities (GK and VA), with MP being closer to the verbal side. This finding is consistent with previous research (Jouve, 2010b), which identified separate factors for JCCES and GAMA subtests, emphasizing the distinctiveness of the two instruments in assessing crystallized and nonverbal cognitive abilities.
Detailed Analysis of the Groups
The seven groups identified in our analysis not only align with the distinction between crystallized and fluid intelligence (Cattell, 1963) but also offer a more granular understanding of the cognitive processes involved in each task. The following sections provide a more in-depth discussion of these groups, linking them with relevant literature.
Abstract Reasoning and Pattern Recognition
This group, consisting of SEQ and CON tasks, reflects abilities related to identifying and extrapolating patterns and spatial visualization and manipulation. Both tasks share the common cognitive processes of abstract reasoning and pattern recognition, making them closely related to fluid intelligence (Carroll, 1993). Research has demonstrated that these abilities play a significant role in various cognitive domains, such as problem-solving and decision-making (Sternberg, 1985).
Nonverbal Analogical Reasoning
The ANA task represents a unique group that reflects the ability to identify relationships and draw analogies between seemingly unrelated figurative objects. This ability is closely related to fluid intelligence (Spearman, 1927) and has been associated with higher-order cognitive processes, such as problem-solving, creativity, and critical thinking (Gentner, 1983; Holyoak & Thagard, 1995).
Crystallized Intelligence
The group consisting of MP, GK, and VA represents abilities related to accumulated knowledge and experience, as well as the application of learned information in problem-solving situations (Cattell, 1963). Crystallized intelligence is considered to be a product of both genetic factors and environmental influences, such as education and cultural exposure (Horn & Cattell, 1966).
Fluid Intelligence
Comprising ANA, SEQ, and CON, this group represents cognitive processes involving reasoning, problem-solving, and abstract thinking, which are not dependent on prior knowledge or experience (Cattell, 1963). Fluid intelligence is thought to be primarily determined by genetic factors and is believed to decline with age (Horn & Cattell, 1967).
Language Development
Represented by GK and VA, this group reflects abilities related to language comprehension, vocabulary, and the use of language in various contexts. Language development has been linked to both crystallized intelligence (Horn & Cattell, 1966) and general cognitive ability (Carroll, 1993).
Quantitative Reasoning and Knowledge
The MP task represents a unique group that reflects abilities related to understanding, interpreting, and applying numerical information and mathematical concepts. Quantitative reasoning and knowledge have been associated with both crystallized and fluid intelligence (Horn & Cattell, 1966; McGrew, 2009) and are considered essential components of general cognitive ability (Carroll, 1993).
Visual-Spatial Representation
The MAT task represents a distinct group that reflects abilities related to visualizing and manipulating spatial information. Visual-spatial representation is closely linked to fluid intelligence (Carroll, 1993) and has been shown to play a crucial role in various cognitive domains, such as navigation, mental rotation, and object recognition (Kosslyn, 1994).
Unexpected and Significant Findings
One intriguing finding was the positioning of the CON task within the fluid intelligence group rather than the visual-spatial representation subgroup. The CON task appeared to tap into a broader range of cognitive processes, such as abstract reasoning and pattern recognition, which are more closely aligned with fluid intelligence processes. Another interesting observation was the distinction between the ANA task and the other tasks within the fluid intelligence group. The unique focus on analogical reasoning sets the ANA task apart from the other tasks, emphasizing its distinct cognitive demands.
Implications for Theory, Practice, and Future Research
The present study adds to the growing body of literature on the relationships between cognitive abilities and contributes to our understanding of the cognitive processes involved in various tasks. The findings suggest that employing JCCES and GAMA as complementary tools can provide a more comprehensive assessment of an individual’s cognitive profile. This approach has practical implications for educational, clinical, and research settings, where a thorough understanding of cognitive abilities is crucial for making informed decisions.
Future research should address the limitations of the current study by employing larger and more diverse samples, as well as investigating the potential utility of combining JCCES and GAMA to predict cognitive and academic outcomes. Additionally, exploring the relationships between these cognitive abilities and other relevant factors, such as socioeconomic background or educational attainment, would provide valuable insights into the broader context of cognitive functioning.
Limitations
There are several limitations in the current study that may have affected the results or the interpretation of the findings. First, the sample size of 63 participants may limit the generalizability and robustness of the results. Second, the lack of inclusion criteria in the recruitment process could affect the representativeness of the sample. Third, there may be methodological limitations associated with the use of MDS, such as the assumptions regarding interval-scaled data and Euclidean space representation.
Conclusion
This study provides a nuanced understanding of the relationships between the JCCES and GAMA tasks, revealing a clear distinction between verbal and nonverbal abilities, and further dividing these abilities into seven groups. These findings contribute to the existing literature on cognitive abilities and suggest that using JCCES and GAMA as complementary tools can offer a comprehensive assessment of an individual’s cognitive profile. The implications for theory and practice include the potential to develop targeted interventions and assessments in educational, clinical, and research settings.
However, the study is not without limitations, such as a small sample size, lack of inclusion criteria in the recruitment process, and methodological constraints associated with the use of MDS. Future research should address these limitations and explore the potential utility of combining JCCES and GAMA to predict cognitive and academic outcomes, as well as investigate the relationships between cognitive abilities and other relevant factors.
References
Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling: Theory and applications (2nd ed.). New York: Springer.
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York: Cambridge University Press. https://doi.org/10.1017/CBO9780511571312
Cattell, R. B. (1971). Abilities: Their structure, growth, and action. Boston, MA: Houghton Mifflin.
Cox, T. F., & Cox, M. A. A. (2001). Multidimensional scaling (2nd ed.). New York: Chapman & Hall/CRC. https://doi.org/10.1201/9780367801700
Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: Sage.
Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates. https://doi.org/10.4324/9781410605269
Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170. https://doi.org/10.1016/S0364-0213(83)80009-3
Holyoak, K. J., & Thagard, P. (1995). Mental leaps: Analogy in creative thought. Cambridge, MA: MIT Press.
Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallized general intelligences. Journal of Educational Psychology, 57(5), 253–270. https://doi.org/10.1037/h0023816
Jouve, X. (2010a). Jouve Cerebrals Crystallized Educational Scale. Retrieved from https://www.cogn-iq.org/jouve-cerebrals-educational-scale.php
Jouve, X. (2010b). Differentiating Cognitive Abilities: A Factor Analysis of JCCES and GAMA Subtests. PsychoLogic https://pubscience.org/ps-1mVDO-492a2c-WPkX
Kosslyn, S. M. (1994). Image and brain: The resolution of the imagery debate. Cambridge, MA: MIT Press.
Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27. https://doi.org/10.1007/BF02289565
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
Naglieri, J. A., & Bardos, A. N. (1997). General Ability Measure for Adults (GAMA). Minneapolis, MN: National Computer Systems.
Spearman, C. (1927). The abilities of man: Their nature and measurement. New York: Macmillan.
Sternberg, R. J. (1985). Implicit theories of intelligence, creativity, and wisdom. Journal of Personality and Social Psychology, 49(3), 607–627. https://doi.org/10.1037/0022-3514.49.3.607
