Abstract
This study aimed to examine the relationship between the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the American College Test (ACT) by conducting a factor analysis. The dataset consisted of 60 observations, with Pearson’s correlation revealing significant associations between all variables. The factor analysis identified three factors, with the first factor accounting for 53.697% of the total variance and demonstrating the highest loadings for all variables. The results suggest that the JCCES and ACT assessments may be measuring a common cognitive construct, which could be interpreted as general cognitive ability or intelligence. However, several limitations should be considered, including the sample size, the scope of the analysis, and the use of factor analysis as the sole statistical method. Future research should employ larger samples, consider additional assessments, and explore alternative statistical techniques to validate these findings.
Keywords: Jouve Cerebrals Crystallized Educational Scale, American College Test, factor analysis, general cognitive ability, intelligence, college admission assessments.
Introduction
Psychometrics has long been a central topic of interest for researchers aiming to understand the underlying structure of cognitive abilities and the validity of various assessment tools. One of the most widely recognized theories in this field is the theory of general intelligence, or g-factor, which posits that an individual’s cognitive abilities can be captured by a single underlying factor (Spearman, 1904). Over the years, numerous instruments have been developed to measure this general cognitive ability, with intelligence tests and college admission assessments being among the most prevalent. However, the extent to which these instruments measure the same cognitive construct remains a subject of debate.
The present study aims to investigate the factor structure of two assessments, the Jouve Cerebrals Crystallized Educational Scale (JCCES; Jouve, 2010) and the American College Test (ACT), to test the hypothesis that a single underlying factor accounts for the majority of variance in these measures. This hypothesis is grounded in the g-factor theory and is further supported by previous research demonstrating the strong correlation between intelligence test scores and academic performance (Deary, et al., 2007; Koenig, et al., 2008).
In recent years, the application of factor analysis has become a popular method for exploring the structure of cognitive assessments and identifying the dimensions that contribute to an individual’s performance on these tests (Carroll, 1993; Jensen, 1998). Factor analysis allows researchers to quantify the extent to which various test items or subtests share a common underlying construct, thus providing insights into the validity and reliability of the instruments in question (Fabrigar, et al., 1999).
The selection of the JCCES and ACT assessments for this study is based on their use in academic and professional settings and their potential relevance to general cognitive ability. The JCCES is a psychometric test that measures crystallized intelligence, which is thought to reflect accumulated knowledge and skills acquired through education and experience (Cattell, 1971). The ACT, on the other hand, is a college admission assessment that evaluates students’ academic readiness in various subject areas, such as English, mathematics, reading, and science (ACT, 2014). By examining the factor structure of these two assessments, the present study aims to shed light on the relationship between intelligence and college admission measures and the extent to which they tap into a common cognitive construct.
In sum, this study seeks to contribute to the ongoing discussion regarding the measurement of cognitive abilities and the relevance of psychometric theories in understanding the structure of intelligence and college admission assessments. By employing factor analysis and focusing on the JCCES and ACT, the study aims to provide a clearer understanding of the relationship between these measures and the g-factor theory. Ultimately, the results of this investigation may help inform the development and validation of future cognitive assessment tools and enhance our understanding of the complex nature of human intelligence.
Method
Research Design
The present study employed a correlational research design to examine the relationship between intelligence and college admission assessments. This design was chosen to analyze the associations between variables without manipulating any independent variables or assigning participants to experimental conditions (Creswell, 2014). The correlational design allows for the exploration of naturally occurring relationships among variables, which is particularly useful in understanding the structure and relationships of cognitive measures.
Participants
A total of 60 participants were recruited for this study, with their demographic characteristics collected, but not reported in this study. Participants were high school seniors or college students who had completed both the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the American College Test (ACT). There were no exclusion criteria for this study.
Materials
The study utilized two separate assessments to collect data: the JCCES and the ACT.
Jouve Cerebrals Crystallized Educational Scale (JCCES)
The JCCES is a measure of crystallized intelligence and assesses cognitive abilities through three subtests (Jouve, 2010). The subtests include Verbal Analogies (VA), Mathematical Problems (MP), and General Knowledge (GK). The JCCES was chosen for its relevance in evaluating cognitive abilities.
American College Test (ACT)
The ACT is a standardized college admission assessment measuring cognitive domains relevant to college readiness (ACT, 2014). The test is composed of four primary sections: English, Mathematics, Reading, and Science Reasoning. The ACT was selected for its widespread use in educational settings and its ability to evaluate cognitive abilities pertinent to academic success.
Procedure
Data collection involved obtaining participants’ scores on both the JCCES and ACT assessments. Participants were instructed to provide their most recent test scores from ACT upon completion of the JCCES online. Then, they were then entered into a secure database for analysis. Prior to data collection, informed consent was obtained from all participants, and they were assured of the confidentiality and anonymity of their responses.
To analyze the data, a factor analysis was conducted to test the research hypotheses (Tabachnick, & Fidell, 2007). Pearson’s correlation was used to measure the associations between variables, with principal factor analysis conducted for data extraction. Varimax rotation was employed to simplify the factor structure, with the number of factors determined automatically and initial communalities calculated using squared multiple correlations. The study employed a convergence criterion of 0.0001 and a maximum of 50 iterations.
The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Cronbach’s alpha were calculated to assess the sample size adequacy and internal consistency, respectively. Factor loadings were computed for each variable, and the proportion of variance explained by the extracted factors was determined.
The present study employed factor analysis to test the research hypotheses. Pearson’s correlation was used to measure the associations between variables, and the principal factor analysis was conducted for data extraction. Varimax rotation was used to simplify the factor structure. The number of factors was determined automatically, with initial communalities calculated using squared multiple correlations. The study employed a convergence criterion of 0.0001 and a maximum of 50 iterations.
Results of the Statistical Analyses
The Pearson correlation matrix revealed significant correlations (α = 0.05) between all variables. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy indicated a KMO value of 0.809, suggesting that the sample size was adequate for conducting a factor analysis. Cronbach’s alpha was calculated at 0.887, indicating satisfactory internal consistency for the variables.
The factor analysis revealed three factors with eigenvalues greater than one, accounting for 63.526% of the total variance. The first factor (F1) had an eigenvalue of 3.759, accounting for 53.697% of the variance. The second factor (F2) had an eigenvalue of 0.437, accounting for 6.242% of the variance, and the third factor (F3) had an eigenvalue of 0.251, accounting for 3.587% of the variance.
Factor loadings were calculated for each variable, with the first factor (F1) showing the highest loadings for all variables. Specifically, F1 had factor loadings of 0.631 for Verbal Analogies (VA), 0.734 for Mathematical Problems (MP), 0.651 for General Knowledge (GK), 0.802 for English (ENG), 0.881 for Mathematics (MATH), 0.744 for Reading (READ), and 0.905 for Science (SCIE). Final communalities ranged from 0.361 for VA to 0.742 for SCIE, indicating the proportion of variance in each variable explained by the extracted factors.
Interpretation of the Results
The results of the factor analysis support the research hypothesis that a single underlying factor (F1) accounts for the majority of the variance in the intelligence and college admission assessments. Specifically, F1 explained 53.697% of the total variance, with all variables loading highly on this factor. This finding suggests that the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the American College Test (ACT) are measuring a common cognitive construct, which may be interpreted as general cognitive ability or intelligence.
Limitations
There are several limitations to consider when interpreting the results of this study. First, the sample size of 60 observations, although adequate for factor analysis based on the KMO measure, may not be large enough to ensure the generalizability of the results. Future studies should employ larger and more diverse samples to validate these findings.
Second, this study only considered the JCCES and ACT assessments, limiting the scope of the analysis. Further research should investigate the factor structure of other intelligence and college admission assessments to provide a more comprehensive understanding of the relationship between these measures and general cognitive ability.
Lastly, the use of factor analysis as the sole statistical method may not account for potential non-linear relationships between the variables. Future studies could employ additional statistical techniques, such as structural equation modeling or item response theory, to better capture the complexity of the relationships between these cognitive measures.
Discussion
Interpretation of the Results and Previous Research
The findings of the present study support the research hypothesis that a single underlying factor (F1) accounts for the majority of the variance in the intelligence and college admission assessments. Specifically, F1 explained 53.697% of the total variance, with all variables loading highly on this factor. This result is consistent with previous research, which has also demonstrated a strong relationship between general cognitive ability, or intelligence, and performance on college admission assessments (Deary et al., 2007; Koenig et al., 2008). The high factor loadings for all variables on F1 suggest that the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the American College Test (ACT) are measuring a common cognitive construct, which may be interpreted as general cognitive ability or intelligence.
Implications for Theory, Practice, and Future Research
The results of this study have important implications for both theory and practice. From a theoretical perspective, the findings support the idea that general cognitive ability is a key underlying factor that contributes to performance on both intelligence and college admission assessments. This suggests that efforts to improve general cognitive ability may be effective in enhancing performance on a wide range of cognitive measures, including college admission assessments.
In terms of practice, the results indicate that the JCCES and ACT assessments are likely measuring similar cognitive constructs, which may have implications for college admission processes. For instance, it may be useful for colleges and universities to consider using a single assessment to evaluate both intelligence and college readiness in applicants, potentially streamlining the admission process and reducing the burden on students.
Moreover, these findings highlight the importance of considering general cognitive ability in educational and career planning. Students, educators, and career counselors can use these insights to develop strategies and interventions aimed at improving general cognitive ability, ultimately enhancing academic and career outcomes.
Limitations and Alternative Explanations
The present study has several limitations that should be considered when interpreting the findings. First, the sample size of 60 observations, although adequate for factor analysis based on the KMO measure, may not be large enough to ensure the generalizability of the results. Future studies should employ larger and more diverse samples to validate these findings.
Second, this study only considered the JCCES and ACT assessments, limiting the scope of the analysis. Further research should investigate the factor structure of other intelligence and college admission assessments, such as the Wechsler Adult Intelligence Scale (WAIS) and the Scholastic Assessment Test (SAT), to provide a more comprehensive understanding of the relationship between these measures and general cognitive ability.
Lastly, the use of factor analysis as the sole statistical method may not account for potential non-linear relationships between the variables. Future studies could employ additional statistical techniques, such as structural equation modeling or item response theory, to better capture the complexity of the relationships between these cognitive measures.
Conclusion
In conclusion, this study’s results indicate that a single underlying factor (F1) accounts for the majority of the variance in the intelligence and college admission assessments, specifically, the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the American College Test (ACT). This finding suggests that both assessments measure a common cognitive construct, which may be interpreted as general cognitive ability or intelligence. The implications of these findings for theory and practice are significant, as they provide insight into the relationship between intelligence assessments and college admission tests, potentially guiding the development of more effective testing methods in the future.
However, some limitations should be considered. The sample size of 60 observations may not be large enough for generalizability, and the study only analyzed JCCES and ACT assessments. Future research should include larger, more diverse samples and investigate other intelligence and college admission assessments. Additionally, employing other statistical methods, such as structural equation modeling or item response theory, may better capture the complexity of the relationships between these cognitive measures.
Despite these limitations, the study highlights the importance of understanding the underlying factors that contribute to performance on intelligence and college admission assessments and opens avenues for future research to improve the assessment of general cognitive ability.
References
ACT. (2014). About the ACT. Retrieved from https://www.act.org/content/act/en/products-and-services/the-act/about-the-act.html
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.
Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: Sage.
Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement. Intelligence, 35(1), 13-21. https://doi.org/10.1016/j.intell.2006.02.001
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger.
Jouve, X. (2010). Jouve Cerebrals Crystallized Educational Scale. Retrieved from https://www.cogn-iq.org/jouve-cerebrals-educational-scale.php
Koenig, K. A., Frey, M. C., & Detterman, D. K. (2008). ACT and general cognitive ability. Intelligence, 36(2), 153–160. https://doi.org/10.1016/j.intell.2007.03.005
Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201-292. https://doi.org/10.2307/1412107
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson Education, Inc.
