HypothesisH0: r = 0 ("there is no relation when the population correlation coefficient is 0) H1: r > 0 ("there exists a positive relation between IQ and GPA when the population correlation >0) H1: r < 0 (there exists a negative relation between IQ and GPA when the population correlation coefficient is less than 0)

Various studies have indicated holding other factors constant, individuals with a higher cognitive ability achieve higher in academics compared to people with lower cognitive skills. Therefore, it is hypothesized that there is a positive correlation between IQ and GPA.

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MethodsParticipants in the research were reported in data by Howell (1997) that presented the IQ score and the Grade Point Average (GPA) of 30 students. The independent variable is the IQ while GPA is the dependent variable. Meaning that the GPA of a student depends on their level of IQ. Both variables are quantitative and are continuous having an infinite number of possible values. A higher IQ value shows high levels of intelligence while 0 is meaningless. A high GPA value shows high academic performance and 0 indicates the student did not earn any points in their school work. SPSS was used to analyze the level of correlation between IQ and GPA, exploring frequencies, chi-square analysis, and Pearson's analysis.

AnalysisTable SEQ Table \* ARABIC 1: Data frequencies

Statistics

IQ Grade Point Average (GPA)

N Valid 30 30

Missing 0 0

Mean 97.27 2.5830

Std. Error of Mean 2.424 .15488

Median 95.00 2.7100

Mode 85a 2.75

Std. Deviation 13.279 .84833

Variance 176.340 .720

Skewness .623 -.231

Std. Error of Skewness .427 .427

Minimum 79 .67

Maximum 131 4.00

Percentiles 25 85.00 2.0000

50 95.00 2.7100

75 108.25 3.0000

a. Multiple modes exist. The smallest value is shown

GPA had a mean index of 2.58, a median score of 2.7100, and standard deviation of 0.848 and a modal average of 2.75 while IQ had a mean of 97.27, the standard deviation of 13.279, median value 95.00 and a modal score of 85. The skewness of the distribution of the data primarily depends on the mean, median and the mode, the greater, the higher the degree of skewness. Therefore, the distribution of GPA is skewed (-0.231) while IQ has a skew of 0.623.

Table SEQ Table \* ARABIC 2: Chi-square output

Chi-Square Tests

Value df Asymptotic Significance (2-sided)

Pearson Chi-Square 286.833a 280 .377

Likelihood Ratio 125.567 280 1.000

Linear-by-Linear Association 14.280 1 .000

N of Valid Cases 30 a. 315 cells (100.0%) have expected count less than 5. The minimum expected count is .03.

From table 2 above, since the p-value is greater than the alpha, it is therefore concluded that there is inadequate suggestion to relate between IQ and GPA (Jackson, 2015).

Table SEQ Table \* ARABIC 3: Pearson Correlation

Correlations

IQ Grade Point Average (GPA)

IQ Pearson Correlation 1 .702**

Sig. (1-tailed) .000

N 30 30

Grade Point Average (GPA) Pearson Correlation .702** 1

Sig. (1-tailed) .000 N 30 30

**. Correlation is significant at the 0.01 level (1-tailed).

Figure SEQ Figure \* ARABIC 2: Scatterplot

DiscussionAccording to the dataset, IQ and Grade point Average (GPA) had a strong correlation (0.702). A correlation between 0.7 and 1 is considered a strong correlation. This means that it is conforming to the hypothesis that students with a high level of IQ will perform better than those with a lower IQ, performance being measured by the GPA. The results of the experiment are statistically significant since it p<0.01. The scatterplot yields a positive slope with no major outliers. The slope represents the direction of the relationship between GPA and IQ, meaning a higher index in IQ, a higher score in GPA. A consistent pattern depicted by the data indicates that the relationship is strong, hence a high probability that the correlation will continually be positive (Thorndike & Thorndike-Christ, 2010). Thus, students with high-Grade Point Average (GPA) are expected to show high levels of reasoning, thinking and a good comprehension ability equated to other students with a lower GPA. Crooks (2008) highlights the various similarities between correlation and causation. Correlation provides evidence for causation, however, it is solely not adequate to prove causation (Duckworth & Seligman, 2006). Both causation and correlation indicate the existence of a relationship between IQ and GPA and they significantly depend on each other. Limitation and Suggestions for Further StudiesHowever, despite having a strong positive relation between IQ and GPA, it is vital to consider that correlation is not causation (Ruble, 2016). Because the correlation is positive does not directly mean that a student with a low IQ would score a low GPA or vice versa. When a student with a high IQ wishes to do what they are not required in school will eventually have a lower GPA score compared to a low IQ student who chooses to work smart can attain high GPA scores.

Moreover, correlation studies are able to indicate a relationship, however, it does not show or prove the cause of something. Correlation is essential in predicting the GPA of a student, however, there are other factors that might affect the relationship such as socioeconomic status and ethnic background. A student who attends a good school right from the beginning is likely to have a higher GPA than a student from a less formative school, regardless of the IQ levels (Borghans et.al, 2011). Therefore, it requires several tests and a consideration of various factors to predict the GPA of a student. A combination of various tests such as SATs would be an effective predictor of GPA. Therefore, according to the research, higher IG will result in a high GPA, however, correlation only cannot determine it as causation.

ReferencesBorghans, L., Golsteyn, B. H., Heckman, J., & Humphries, J. E. (2011). Identification problems in personality psychology. Personality and individual differences, 51(3), 315-320. https://doi.org/10.1016/j.paid.2011.03.029

Crooks, T. J. (2008). The Impact of Classroom Evaluation Practices on Students. Review of Educational Research, 58(4), 438-481. https://doi.org/10.3102/00346543058004438

Duckworth, A. L., & Seligman, M. E. (2005). Self-discipline outdoes IQ in predicting the academic performance of adolescents. Psychological science, 16(12), 939-944. https://doi.org/10.1111/j.1467-9280.2005.01641.x

Duckworth, A. L., & Seligman, M. E. (2006). Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of educational psychology, 98(1), 198. DOI: 10.1037/0022-0663.98.1.198

Jackson, S. L. (2015). Research methods and statistics: A critical thinking approach. Cengage Learning.

Ruble, R. (2016). Ambiguous Psychological Misconceptions. The teaching of Psychology, 13(1), 34-36. https://doi.org/10.1207/s15328023top130110

Tanner, D. (2011). Statistics for the Behavioral & Social Sciences. San Diego, CA: Bridgepoint Education, Inc. This text is a Constellation course digital materials (CDM) title.

Thorndike, R. M., & Thorndike-Christ, T. M. (2010). Measurement and evaluation in psychology and education. Pearson. One Lake Street, Upper Saddle River, New Jersey 07458.

Appendices

Figure SEQ Figure \* ARABIC 3: GPA frequency graph

Figure SEQ Figure \* ARABIC 4: IQ frequency graph