The data analysis section interprets and makes sense of the data that was collected for the research. This exploration gathers numeric information from an overview directed in a school. Subsequently, the ANOVA single-factor investigation will appropriately test its speculation. When analyzing data with only one independent and one dependent variable, single-factor ANOVA is frequently used (Kyonka et al., 2019). The statistical APA style will be used to interpret the data.
Translation of Measurable Discoveries
The exploration was completed in an optional school. Google Forms was used to conduct the survey. The research involved 30 people. Students were given profession 2, while teachers were given profession 1. There were five grades, with 5 representing the highest and 1 representing the lowest. Microsoft Excel was used to record and analyze the data, and it was found that 14 teachers and 16 students participated in the study. The school had students of three different ethnicities, and their grades in six different subjects were recorded.
Excel was used to conduct a single-factor ANOVA to see if there was a plausible correlation between the independent variable (the students’ ethnicity) and the dependent variable (their grades).
| Source of Variation | SS | df | MS | F | P-value | F crit |
| Between Groups | 208.4314 | 16 | 13.02696 | 9.186952 | 5.3 | 1.664052 |
| Within Groups | 699.0667 | 493 | 1.417985 | |||
| Total | 907.498 | 509 |
According to the findings of this analysis, there is almost no correlation between the student’s grades and their ethnic background. Because p has a value of 5.3, this is the case. At the point when the worth of p is more noteworthy than 0.05, the speculation that predicts a critical connection between the free and subordinate factors is dismissed (Knekta et al., 2019). The factual importance esteem between gatherings, SS, has a worth of 208.43. When the SS value in a single-factor ANOVA is greater than 5, there is no correlation between the dependent and independent variables (Kyonka et al.,, 2019).
PSYC FPX4600 Assessment 3 Data Analysis and Interpretation
The value of eta and the F critical are compared to determine the effect size. The hypothesis is rejected if the value of eta is less than the F critical (Kyonka et al., 2019). The worth of estimated time of arrival is determined by partitioning the worth of MSbg by amounts of MSbg and MSwg, (where ‘bg’ signifies among gatherings and ‘wg’ signifies inside gatherings). Since eta’s value in this study is 0.9018 and F critical’s value is 1.664263, the hypothesis cannot be supported because eta is lower than F critical.
Statistics on the Demographics
PSYC FPX4600 Assessment 3 Data Analysis and Interpretation
The survey was carried out in a secondary school. Through Google Forms, a total of thirty responses were recorded. The school’s surveys revealed the identities of three ethnic groups. There were five levels for the students’ grades, with 1 being the lowest and 5 being the highest. In addition, the gender of the participants and their position in the school—whether as a student or a teacher—were recorded. In Excel, the data is then arranged in a table.
The participants’ age, sex, race, and language are just a few demographics that should be taken into account in this study (Hayes, 2021). If conducted again, any research involving demographic data may yield a different outcome. This is primarily due to a shift in control factors like the sample size’s participants’ social status, literacy rate, and socioeconomic situation (Mishra et al., 2019). The ANOVA single-factor analysis will yield repeatable results if these control variables are kept constant (Hoijtink et al., 2019).
Conclusion
PSYC FPX4600 Assessment 3 Data Analysis and Interpretation
The hypothesis that the students’ ethnicity has a significant impact on their grades was the starting point of the research. A survey was used to collect numerical data for statistical analysis in this quantitative study. To determine whether the two variables were correlated, a single-factor ANOVA was carried out. The interpretation of the findings revealed that there was no significant correlation between the independent and the dependent variables. As a result, it is safe to include the fact that the student’s ethnicity has no significant impact on their grades.
References
Hayes, A. (2021, July 28). Demographics. Investopedia. https://www.investopedia.com/terms/d/demographics.asp
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
Knekta, E., Runyon, C., & Eddy, S. (2019). One size doesn’t fit all: Using factor analysis to gather validity evidence when using surveys in your research. CBE—Life Sciences Education, 18(1). https://doi.org/10.1187/cbe.18-04-0064
Kyonka, E. G. E., Mitchell, S. H., & Bizo, L. A. (2019). Beyond inference by eye: Statistical and graphing practices in JEAB, 1992-2017. Journal of the Experimental Analysis of Behavior, 111(2), 155–165.
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
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
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
