Differentiating Cognitive Abilities: A Factor Analysis of JCCES and GAMA Subtests

Abstract

This study aimed to investigate the differentiation between cognitive abilities assessed by the Jouve Cerebrals Crystallized Educational Scale (JCCES) and General Ability Measure for Adults (GAMA). A sample of 63 participants completed both JCCES and GAMA subtests. Pearson correlation and factor analysis were used to analyze the data. The results revealed significant positive correlations between most of the JCCES subtests, while correlations between GAMA and JCCES subtests were generally lower. Factor analysis extracted two distinct factors, with JCCES subtests loading on one factor and GAMA subtests loading on the other. The findings supported the hypothesis that JCCES and GAMA measure distinct cognitive abilities, with JCCES assessing crystallized abilities and GAMA evaluating nonverbal and figurative aspects of general cognitive abilities. This differentiation has important implications for the interpretation of JCCES and GAMA scores and their application in educational, clinical, and research settings.

Keywords: cognitive abilities, JCCES, GAMA, factor analysis, crystallized intelligence, nonverbal cognitive abilities

Introduction

The field of psychometrics has advanced significantly over the years, with numerous theories and instruments developed to assess various aspects of human cognitive abilities (Embretson & Reise, 2000). Among these, both crystallized and fluid intelligence, have been widely acknowledged as two essential dimensions of cognitive functioning (Cattell, 1987; Horn & Cattell, 1966). Crystallized intelligence refers to the acquired knowledge and skills gained through education and experience, while fluid intelligence involves the capacity for abstract reasoning, problem-solving, and adapting to novel situations (Cattell, 1987).

Instruments designed to measure these cognitive abilities often target specific domains, such as the Jouve Cerebrals Crystallized Educational Scale (JCCES) for crystallized intelligence (Jouve, 2010) and the General Ability Measure for Adults (GAMA) for nonverbal, figurative aspects of general cognitive abilities (Naglieri & Bardos, 1997). However, the relationship between these instruments and the cognitive domains they assess remains an area of ongoing research.

The present study aims to investigate the relationship between the JCCES and GAMA subtest scores to determine whether these instruments measure distinct cognitive abilities. In particular, the research hypothesis posits that the JCCES and GAMA subtests will load on separate factors in factor analysis, indicating that they assess different aspects of cognitive functioning. This hypothesis is grounded in previous literature on the differentiation of crystallized and fluid intelligence (Cattell, 1987; Horn & Cattell, 1966) and the design of the JCCES and GAMA instruments (Jouve, 2010a, 2010b, 2010c; Naglieri & Bardos, 1997).

To test the research hypothesis, the study employs Pearson correlation and principal factor analysis with Varimax rotation. These methods are widely used in psychometrics to explore the underlying structure of datasets and identify latent factors that explain shared variance among variables (Fabrigar, et al., 1999; Stevens, 2009). Additionally, the Kaiser-Meyer-Olkin (KMO) measure and Cronbach's alpha are computed to assess the sampling adequacy and internal consistency of the factors, respectively (Field, 2009).

The investigation of the relationship between the JCCES and GAMA subtest scores has practical implications for the assessment of cognitive abilities in various settings, including educational, clinical, and research contexts. By understanding the distinct cognitive domains assessed by these instruments, practitioners can make better-informed decisions about their use and interpretation, leading to more accurate and comprehensive evaluations of an individual's cognitive profile.

Method

Research Design

The study employed a correlational research design to investigate the relationship between cognitive abilities as assessed by the Jouve Cerebrals Crystallized Educational Scale (JCCES) and the General Ability Measure for Adults (GAMA). The correlational design was chosen to identify patterns of association between the two sets of cognitive measures without manipulating any variables (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, 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

Data were analyzed using Excel. Descriptive statistics were computed for the JCCES and GAMA subtest scores. Pearson correlations were calculated to examine the relationships between the JCCES and GAMA subtests. Principal factor analysis with Varimax rotation was conducted to explore the underlying structure of the dataset and identify latent factors that could explain the shared variance among the subtests. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Cronbach's alpha were calculated to assess the quality of the factor analysis results (Cronbach, 1951).

Results

The research hypotheses were tested using Pearson correlation and principal factor analysis with Varimax rotation. The initial communalities were computed using squared multiple correlations, and the analysis was stopped based on convergence criteria (0.0001) and a maximum of 50 iterations. The Kaiser-Meyer-Olkin (KMO) measure was used to assess the sampling adequacy, and Cronbach's alpha was computed to determine the internal consistency of the factors.

Descriptive Statistics and Correlations

The sample consisted of 63 participants, with no missing data for the Jouve Cerebrals Crystallized Educational Scale (JCCES) and General Ability Measure for Adults (GAMA) subtest scores. The Pearson correlation matrix revealed significant positive correlations between most of the subtests.

In this study, the strongest correlations were observed between the JCCES subtests: Verbal Analogies (VA) and General Knowledge (GK) had a correlation of 0.712, indicating a strong positive relationship between these measures of crystallized abilities. Similarly, the VA and Mathematical Problems (MP) subtests were positively correlated (r = 0.542), suggesting a moderate relationship between these variables (Stevens, 2009). The MP and GK subtests also had a moderate positive correlation of 0.590.

Correlations between GAMA subtests and JCCES subtests were generally lower, with the highest correlation observed between MP and the GAMA Matching (MAT) subtest (r = 0.427). This suggests a moderate positive relationship between the nonverbal cognitive abilities assessed by GAMA and the crystallized mathematical abilities assessed by the JCCES MP subtest. The correlations between GAMA Analogies (ANA) and JCCES subtests were weak to moderate, ranging from 0.141 (ANA-GK) to 0.298 (ANA-VA). The GAMA Sequences (SEQ) subtest had weak correlations with JCCES subtests, ranging from 0.076 (SEQ-VA) to 0.391 (SEQ-MP). Lastly, the GAMA Construction (CON) subtest had weak to moderate correlations with JCCES subtests, ranging from 0.169 (CON-GK) to 0.452 (CON-MP).

Factor Analysis

The factor analysis aimed to explore the underlying structure of the dataset and to identify the latent factors that could explain the shared variance among the JCCES and GAMA subtests. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was computed to ensure that the sample size was suitable for conducting factor analysis. A KMO value of 0.695 was obtained, which is considered adequate for factor analysis, as it is above the commonly accepted threshold of 0.6.

Two factors were extracted from the data based on their eigenvalues, which represent the total variance explained by each factor. Factor 1 (F1) had an eigenvalue of 2.904 and accounted for 41.482% of the variance, while Factor 2 (F2) had an eigenvalue of 1.331 and accounted for 19.016% of the variance. The cumulative explained variance by both factors was 60.498%, indicating that a substantial proportion of the total variance in the dataset was explained by these two factors.

To better interpret the factors, Varimax rotation was applied to achieve a simpler factor structure by maximizing the variance of factor loadings within each factor. The rotation resulted in two factors, denoted as D1 and D2, which accounted for 32.256% and 28.242% of the variance, respectively.

The rotated factor pattern demonstrated the relationships between the original subtests and the rotated factors. The GAMA subtests, including Analogies (ANA), Sequences (SEQ), and Construction (CON), had high factor loadings on D1 (0.685, 0.911, and 0.841, respectively). This indicates that these subtests share a common underlying factor, which is represented by D1.

In contrast, the JCCES subtests, including Verbal Analogies (VA), Mathematical Problems (MP), and General Knowledge (GK), had high factor loadings on D2 (0.796, 0.687, and 0.845, respectively). This suggests that these subtests also share a common underlying factor, which is represented by D2.

Internal Consistency

The internal consistency of the factors was assessed using Cronbach's alpha. The results showed that both factors had good internal consistency, with D1 having a Cronbach's alpha of 0.862 and D2 having a Cronbach's alpha of 0.762.

Interpretation and Significance

The factor analysis results provided strong evidence for the research hypothesis that the JCCES and GAMA measure distinct cognitive abilities. The separate cognitive domains represented by the two factors were clearly differentiated by the respective loadings of the JCCES and GAMA subtests.

Factor D1 was primarily associated with the GAMA subtests, which include Matching (MAT), Analogies (ANA), Sequences (SEQ), and Construction (CON). These subtests focus on nonverbal and figurative aspects of general cognitive abilities, capturing skills such as pattern recognition, abstract reasoning, and visual-spatial problem-solving. The high loadings of the GAMA subtests on factor D1 (ANA = 0.685, SEQ = 0.911, CON = 0.841) indicate that this factor reflects the underlying construct of nonverbal and figurative general cognitive abilities, as assessed by the GAMA.

Factor D2, on the other hand, was predominantly associated with the JCCES subtests, which include Verbal Analogies (VA), Mathematical Problems (MP), and General Knowledge (GK). These subtests are designed to measure crystallized abilities, reflecting the accumulated knowledge and skills acquired through education and experience. The high loadings of the JCCES subtests on factor D2 (VA = 0.796, MP = 0.687, GK = 0.845) suggest that this factor represents the underlying construct of crystallized cognitive abilities, as measured by the JCCES.

The distinct loadings of the JCCES and GAMA subtests on separate factors highlight the differences in the cognitive abilities assessed by these instruments. The JCCES primarily focuses on crystallized abilities, capturing an individual's acquired knowledge and skills, whereas the GAMA assesses nonverbal and figurative aspects of general cognitive abilities, tapping into more abstract and fluid cognitive processes. This differentiation between the two instruments supports the research hypothesis and emphasizes the unique contributions of each instrument in evaluating cognitive functioning.

The significant differences between the cognitive domains represented by the two factors have important implications for the interpretation of the JCCES and GAMA scores. These findings suggest that the JCCES and GAMA should be considered complementary tools in assessing an individual's cognitive abilities, as they provide unique insights into different aspects of cognitive functioning. Using both instruments together can offer a more comprehensive understanding of an individual's cognitive profile, facilitating better-informed decisions in educational, clinical, and research settings.

Limitations

There are some limitations to the study that should be considered. First, the sample size was relatively small (N = 63), which may limit the generalizability of the findings. Second, no demographic data were available for the participants, making it difficult to assess whether the sample was representative of the larger population.

Discussion

Interpretation of the Results and Comparison with Previous Research

The results of this study provide strong support for the research hypothesis that the Jouve Cerebrals Crystallized Educational Scale (JCCES) and General Ability Measure for Adults (GAMA) assess distinct cognitive abilities. The factor analysis revealed two separate factors, with JCCES subtests loading on one factor (D2) and GAMA subtests loading on another factor (D1). This finding is consistent with the theoretical distinction between crystallized and fluid cognitive abilities, as proposed by Cattell (1971) and supported by subsequent research (e.g., Carroll, 1993; Horn & Cattell, 1967).

The observed differentiation between the JCCES and GAMA is consistent with previous research demonstrating that crystallized abilities are more closely related to acquired knowledge and skills, while fluid abilities are more associated with abstract reasoning, pattern recognition, and visual-spatial problem-solving (Cattell, 1971; Horn & Cattell, 1967). This distinction is important, as it highlights the unique contributions of each instrument in evaluating cognitive functioning.

Implications for Theory, Practice, and Future Research

The findings of this study have important implications for both theory and practice. The clear differentiation between the JCCES and GAMA supports the notion that crystallized and fluid cognitive abilities are distinct constructs, which can be measured separately using appropriate assessment tools. This distinction has practical implications for educational, clinical, and research settings, where a comprehensive understanding of an individual's cognitive profile is essential for informed decision-making.

For example, in educational settings, the use of both the JCCES and GAMA can provide valuable information about a student's cognitive strengths and weaknesses, facilitating targeted interventions to support learning and development. In clinical settings, the combined use of these instruments can help clinicians identify cognitive impairments associated with various neurological and psychiatric conditions and inform treatment planning.

Future research could extend the current study by examining the relationship between the JCCES and GAMA and other cognitive measures, further exploring the distinctiveness and convergent validity of these instruments. Additionally, the research could investigate the potential impact of demographic factors, such as age, education, and cultural background, on the performance in the JCCES and GAMA subtests, enhancing our understanding of the factors that may influence the assessment of cognitive abilities.

Limitations

Despite the significant findings of this study, several limitations should be acknowledged. First, the relatively small sample size (N = 63) may limit the generalizability of the findings. Future research with larger, more diverse samples is needed to confirm the observed differentiation between the JCCES and GAMA.

Second, the lack of demographic data for the participants precludes an analysis of potential demographic factors that may influence the observed relationships between the JCCES and GAMA subtests. Future research should collect demographic information to explore potential differences in cognitive abilities based on factors such as age, education, and cultural background.

Directions for Future Research

Future research could build on the findings of this study by exploring the relationships between the JCCES, GAMA, and other cognitive measures to further investigate the distinctiveness and convergent validity of these instruments. Moreover, researchers could examine the potential impact of demographic factors, such as age, education level, and cultural background, on performance in the JCCES and GAMA subtests. This would provide valuable insights into the factors that may influence the assessment of cognitive abilities and contribute to a more comprehensive understanding of the constructs measured by these instruments.

Additionally, future research could investigate the predictive validity of the JCCES and GAMA in various applied settings, such as academic performance, vocational success, or clinical outcomes. This would help determine the practical utility of these instruments in making informed decisions across a range of contexts.

It would also be beneficial to examine the potential moderating role of factors such as motivation, test-taking strategies, or test anxiety on the relationship between the JCCES and GAMA subtests. This could provide valuable information regarding the potential influence of non-cognitive factors on cognitive assessment outcomes.

Longitudinal studies could be conducted to explore the developmental trajectories of crystallized and fluid cognitive abilities as assessed by the JCCES and GAMA, as well as the potential factors that may influence these trajectories, such as educational experiences or cognitive interventions. Such studies would contribute to a deeper understanding of the development and change of cognitive abilities over time.

Finally, future research could explore the potential benefits of integrating the JCCES and GAMA into comprehensive cognitive assessment batteries, alongside other cognitive measures assessing additional domains (e.g., working memory, processing speed, or executive functioning). This would help determine the optimal combination of measures for assessing an individual's cognitive profile in a comprehensive and efficient manner.

Conclusion

The present study demonstrated that the JCCES and GAMA assess distinct cognitive abilities, with the JCCES primarily measuring crystallized abilities and the GAMA focusing on nonverbal and figurative aspects of general cognitive abilities. The strong positive correlations observed between JCCES subtests and the moderate positive correlations between GAMA and JCCES subtests support these findings. Factor analysis further substantiated the differentiation between the two instruments, revealing two distinct factors, each associated with either the JCCES or GAMA subtests.

These findings have important implications for the broader field of cognitive assessment, suggesting that the JCCES and GAMA should be employed as complementary tools to obtain a comprehensive understanding of an individual's cognitive profile. This comprehensive approach can better inform decisions in educational, clinical, and research settings. However, the study's small sample size and lack of demographic information limit the generalizability of the results.

Future research should focus on replicating these findings in larger, more diverse samples and exploring the potential utility of combining the JCCES and GAMA to predict various cognitive and academic outcomes. Additionally, the research could investigate the relationship between these cognitive abilities and other relevant factors, such as socioeconomic background or educational attainment. Overall, this study highlights the importance of considering both crystallized and nonverbal cognitive abilities in cognitive assessment and emphasizes the unique contributions of the JCCES and GAMA in evaluating cognitive functioning.

References

Cattell, R. B. (1971). Abilities: Their structure, growth, and action. Boston, MA: Houghton Mifflin.

Cattell, R. B. (1987). Intelligence: Its Structure, Growth and Action. New York: North-Holland.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi.org/10.1007/BF02310555

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

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, 272-299. https://doi.org/10.1037/1082-989X.4.3.272

Field, A. (2009). Discovering statistics using SPSS (3rd ed.). London, UK: Sage Publications.

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 http://www.cogn-iq.org/tests/jouve-cerebrals-crystallized-educational-scale-jcces

Jouve, X. (2010b). Investigating the Relationship Between JCCES and RIAS Verbal Scale: A Principal Component Analysis Approach. Retrieved from https://cogniqblog.blogspot.com/2010/02/on-relationship-between-jcces-and.html

Jouve, X. (2010c). Relationship between Jouve Cerebrals Crystallized Educational Scale (JCCES) Crystallized Educational Index (CEI) and Cognitive and Academic Measures. Retrieved from https://cogniqblog.blogspot.com/2010/02/correlations-between-jcces-and-other.html

Naglieri, J. A., & Bardos, A. N. (1997). General Ability Measure for Adults (GAMA). Minneapolis, MN: National Computer Systems.

Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5th ed.). New York, NY: Routledge.

Exploring the Relationship between JCCES and ACT Assessments: A Factor Analysis Approach

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. 

Statistical Methods

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.

Results

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 http://www.cogn-iq.org/tests/jouve-cerebrals-crystallized-educational-scale-jcces

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.

[Article Review] Unlocking Potential: A New Approach to Alzheimer's Treatment

Reference

Bredesen, D. E. (2014). Reversal of cognitive decline: a novel therapeutic program. Aging (Albany NY), 6(9), 707-17.

Review

This report by Bredesen (2014) offers a refreshing insight into a comprehensive and personalized therapeutic program aimed at counteracting the underlying pathogenesis of Alzheimer's disease. The multi-modal approach, termed Metabolic Enhancement for Neurodegeneration (MEND), was applied to ten patients with varying cognitive conditions, ranging from Alzheimer's disease to subjective cognitive impairment. Encouragingly, nine out of these ten patients exhibited improvements in their cognitive abilities within a span of 3-6 months. The most promising aspect is that six of these individuals, who were either not working or struggling with their professions, managed to resume or continue their work post-therapy.

Nevertheless, it's imperative to note the limited sample size of the study. While the results are undoubtedly promising, drawing substantial conclusions from a cohort of merely ten participants is premature. Additionally, one patient with late-stage Alzheimer's did not exhibit any noticeable improvement, indicating potential limitations or the need for timely intervention. It's also commendable that Bredesen highlights the potential inadequacy of monotherapeutics in Alzheimer's treatment and raises the proposition of a more integrated system where drugs might function more effectively as components rather than standalones.

To summarize, Bredesen's (2014) study sheds light on an innovative therapeutic approach that holds potential in reversing cognitive decline, especially in its early stages. However, the results, though promising, warrant a more extensive trial to validate these findings. With the increasing global prevalence of Alzheimer's and cognitive impairments, such research initiatives underscore the importance of holistic and multi-faceted interventions.