PSYC FPX4600 Assessment 4 Research Report

Abstract

Ethnicity plays an important role in shaping the psychology of children and adults. As such, ethnic backgrounds play a very important role in everyone’s life. This research attempts to find out the role of ethnicity on the academic performance of students. The research is based on assumptions by the psychological theory concept. It will use survey methods and surveys to collect data. The sample size includes 30 respondents in a secondary school where teachers and students participated voluntarily in this research. Google forms were used to collect data while Microsoft Excel was used to record and analyze data. Single-factor ANOVA was used to conduct statistical analysis. The findings indicate that the ethnicity of the students does not have a statistically significant effect on their academic performance.

PSYC FPX4600 Assessment 4 Research Report

Research Report

Literature Review

Although there are many theories associated with psychological concepts, they can be grouped into five main categories. The distinct categories offered different perspectives on comprehending human behavior and psychology, covering areas such as biological processes, unconscious drives, learned behaviors, cognitive frameworks, personal experiences, and personal growth. Each concept has its own strengths and weaknesses, but when combined, they provide a more comprehensive understanding of human behavior (Mcleod, 2023).

The behavioral concept takes a distinctive perspective on individuals, emphasizing how environmental factors impact observable behavior and responses (Li et al., 2021). In contrast, the psychodynamic concept places emphasis on the role of unconscious thoughts and childhood experiences in shaping adult behavior, with past experiences often serving as a strong predictor of how a person responds to familiar stimuli (Immordino-Yang et al., 2019).

The assumption that a student’s ethnicity impacts their academic performance is often based on underlying factors related to their ethnic background. According to Phojanakong et al. (2019), students from minority ethnic groups may experience challenges related to their daily lives, such as limited access to healthcare facilities and food insecurity, which can negatively impact their mental well-being and academic performance. Phojanakong’s research suggests that there is a plausible relationship between a student’s ethnicity and their academic performance.

The incidence of ethnic and racial discrimination in the United States is on the rise, and as a result, students from ethnic minority groups may experience significant mental strain. Students who have encountered discrimination are prone to experiencing heightened levels of stress (Weeks and Sullivan’s, 2019). The authors analyzed data from a sample of over 60,000 students in the United States and observed that students belonging to ethnic minority groups had greater chances of experiencing anxiety, behavior disorders, and depression. The research supports a correlation between a student’s ethnic background and their mental health.

According to Verkuyten et al. (2019), the existence or potential for racial discrimination can lead to the development of coping strategies that distract students from their academic pursuits. The study revealed that student disengagement can be influenced by factors such as ethnic diversity in the classroom, teacher-student relationships, and the existence of multicultural educational environments. The study also concluded that students from minority ethnic backgrounds may have to balance various aspects of their lives, which can affect their academic performance.

Methods

Participants

The survey was conducted at a secondary school and administered through Google Forms, resulting in a total of 30 responses. The study categorized the grades of the participants into five levels, with 1 indicating the lowest grade and 5 indicating the highest grade. The research also collected information on the participants’ ethnicity, gender, and role in the school (i.e., student or teacher), and organized the data in an Excel table. However, it’s important to consider demographic factors such as age group, sex, race, and language when conducting research that involves demographics (Hayes, 2021). Mishra et al. (2019) have suggested that failure to control for variables such as social status, literacy rate, and socioeconomic status in the sample size can lead to variations in demographic data, potentially affecting study results. To obtain consistent results, it is essential to perform single-factor ANOVA analysis while controlling for these factors, according to Hoijtink et al. (2019).

Measures

To measure the correlation between the ethnicity of students and its impact on their grades, I employed correlational research. Correlational research is a method of examining and interpreting relationships between different variables, identifying trends and patterns in collected data and analyzing them to determine if there is a correlation between the variables (van Schalkwyk, 2023). This type of research is useful for identifying associations between variables, and it enables researchers to identify potential relationships between variables of interest.

PSYC FPX4600 Assessment 4 Research Report

Correlational research was instrumental in gathering and organizing quantitative data for my study. To examine the correlation between the dependent and independent variables, I used single-factor ANOVA (Analysis of Variance). This statistical analysis method is typically used when there is only one independent variable and is used to confirm or reject hypotheses that are based on small sample populations (Gomila, 2020). The single-factor ANOVA is an effective way to compare means across groups and is a useful tool for determining whether there are significant differences between groups.

Procedure

To gather quantitative data, I employed the survey method using Google Forms that included questionnaires with multiple choices. This approach facilitated the quantification and tabulation of data for subsequent statistical analysis. Only students from minority ethnicities and teachers who volunteered participated in this research. During the study, efforts were made to control variables such as social status and education quality in order to ensure accuracy and validity of the results (Atinc & Simmering, 2021). To collect data for this study, online platforms such as Google Forms were used to distribute surveys to the participants. The surveys consisted of multiple-choice questions that were simple and relevant. Initially, the students were asked to indicate their ethnicity and then to provide their grades in various subjects. Additionally, teachers completed a separate form in which they were asked to report the typical grades that students from ethnic minority groups earned in their respective subject and the typical grades received by other students. The data collected was then analyzed.

Efficient methods for collecting and analyzing quantitative data were crucial for this study, given that it involved quantitative research. Quantitative data refers to numerical values that can be measured and analyzed to establish direct relationships between variables (Wiss et al., 2021).

The data obtained from the questionnaires was compiled and structured in Microsoft Excel. To analyze the data, single-factor ANOVA was performed. Microsoft Excel was selected as the software for data analysis because it offers a wide range of statistical functions for tabulated quantitative data, such as single-factor ANOVA. Heuer (2020) performed an analysis of the results to determine whether there was a statistically significant correlation between the independent variable and the dependent variable.

Results

A Google Forms survey was administered to a total of 30 participants in a secondary school, consisting of 14 teachers and 16 students. The survey collected data on their professions and grades, which were categorized on a scale of 1 to 5, with 5 representing the highest grade. The data was subsequently organized and analyzed using Microsoft Excel to identify any potential correlations between the variables (Heuer, 2020). The data included the ethnicity of the participants, which was categorized into 3 groups, and their grades in six subjects. There was a potential correlation between the independent variable (ethnicity of the students) and the dependent variable (grades obtained by students), and the data was analyzed using single-factor ANOVA in Excel to investigate this correlation.

ANOVA gives insight into the variation in the data. Based on the analysis, the study found that there was no significant correlation between the ethnic background of the students and their grades. The p-value of 0.05 suggests that the hypothesis proposing a significant relationship between the independent variable (ethnicity of students) and the dependent variable (grades obtained by students) is not statistically significant at the chosen level of significance. Additionally, the SS value was 208.43, which is higher than the threshold value of 5, suggesting no correlation between the variables.

The effect size was measured using eta and F critical. The calculated value of eta was 0.9018, and the value of F critical was 1.664263. Since eta was smaller than F critical, the hypothesis was not supported. These findings suggest that the ethnic background of the students did not have a significant impact on their grades in the six subjects examined in this study.

PSYC FPX4600 Assessment 4 Research Report

Discussion

The result of this research is that there is no significant relationship between the ethnicity of the students and their educational performance. The results compare drastically to the initial expectations because existing literature proves the correlation between ethnicity and mental health (Weeks & Sullivan 2019), ethnicity and educational disengagement (Verkuyten et al., 2019), and ethnicity and access to healthcare (Phojanakong et al., 2019).

Although the study is carried out in a secondary school, the results can be generalized to include any educational institution. It follows that similar research in a similar setting will probably yield similar results (Wiss et al., 2021). For example, the results of this research can be assumed for high schools and universities as well. Furthermore, the research is applicable in environments similar to schools such as daycare centers, nursing schools, and so on.

However, the study is limited because of its small sample size, the unique dynamics of the secondary school taken in the study, and other socio-economic factors. These factors might be the financial situation of the students, their social status, parents’ literacy, and the quality of education provided at school (Atinc & Simmering, 2021). 

The results do not support the behavioral concept because the past experiences of the students showed no significance in their educational performance. It follows that the psychological theory builds on the assumption that the ethnicity of students might play a decisive role in their life, including their educational performance (Knekta et al., 2019). However, it was not observed to be the case in this research.

Summary and Conclusion

The research aimed to explore the relationship between the ethnicity of the students and their academic performance measured in grades. The ethnicity of the students served as the independent variable in this study, while their grades in school were the dependent variable. The initial hypothesis proposed that there was a significant relationship between a student’s ethnicity and their academic performance. Survey methods were used to collect data. Single-factor ANOVA was used to conduct statistical analysis. The research was limited because of the sample size, sampling location, and limited scope. Results declared that the hypothesis was false. In conclusion, there is no plausible or significant impact of ethnicity on the grades of the student. Both variables are not related.

References

Atinc, G., & Simmering, M. J. (2021). Control variables in management research. Oxford Research Encyclopedia of Business and Management. https://doi.org/10.1093/acrefore/9780190224851.013.221

Gomila, R. (2020). Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. Journal of Experimental Psychology: General. https://doi.org/10.1037/xge0000920 

Hayes, A. (2021, July 28). Demographics. Investopedia. https://www.investopedia.com/terms/d/demographics.asp 

Heuer, A. (2020). Research data management. It – Information Technology, 62(1), 1–5. https://doi.org/10.1515/itit-2020-0002

Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods, 24(5), 539–556. https://doi.org/10.1037/met0000201 

Immordino-Yang, M. H., Darling-Hammond, L., & Krone, C. R. (2019). Nurturing nature: How brain development is inherently social and emotional, and what this means for education. Educational Psychologist, 54(3), 185–204. https://doi.org/10.1080/00461520.2019.1633924 

Li, X., Zhou, Y., Wong, Y. D., Wang, X., & Yuen, K. F. (2021). What influences panic buying behaviour? A model based on dual-system theory and stimulus-organism-response framework. International Journal of Disaster Risk Reduction, 64. https://doi.org/10.1016/j.ijdrr.2021.102484 

Mcleod, S. (2023, February 20). Psychology perspectives | Simply Psychology.
https://simplypsychology.org/perspective.html 

Phojanakong, P., Brown Weida, E., Grimaldi, G., Lê-Scherban, F., & Chilton, M. (2019). Experiences of racial and ethnic discrimination are associated with food insecurity and poor health. International Journal of Environmental Research and Public Health, 16(22).
https://doi.org/10.3390/ijerph16224369 

van Schalkwyk, G. I. (2023). Editorial: Best practices for correlational research in CAPMH. Child and Adolescent Psychiatry and Mental Health, 17(1). https://doi.org/10.1186/s13034-023-00562-6 

Verkuyten, M., Thijs, J., & Gharaei, N. (2019). Discrimination and academic (dis)engagement of ethnic-racial minority students: a social identity threat perspective. Social Psychology of Education, 22.
https://doi.org/10.1007/s11218-018-09476-0 

Wiss, D. A., Brewerton, T. D., & Tomiyama, A. J. (2021). Limitations of the protective measure theory in explaining the role of childhood sexual abuse in eating disorders, addictions, and obesity: an updated model with emphasis on biological embedding. Eating and Weight Disorders – Studies on Anorexia, Bulimia and Obesity. https://doi.org/10.1007/s40519-021-01293-3 

Weeks, M. R., & Sullivan, A. L. (2019). Discrimination matters: Relations of perceived discrimination to student mental health. School Mental Health, 11(3), 425–437.
https://doi.org/10.1007/s12310-019-09309-1