PSYC FPX4600 Assessment 3 Data Analysis and Interpretation

Data collected for research is interpreted and made sense of in the data analysis section. This research collects numeric data from a survey conducted in a school. Therefore, the ANOVA single-factor analysis will aptly test its hypothesis. Single-factor ANOVA is commonly used to analyze data with one independent and one dependent variable (Kyonka et al., 2019). The data will be interpreted using the statistical APA style.

PSYC FPX4600 Assessment 3 Data Analysis and Interpretation

Interpretation of Statistical Findings

The research was carried out in a secondary school. The survey was conducted using Google Forms. 30 people participated in the research. The teachers were assigned profession 1 while students were assigned Profession 2. The grades were divided into five categories with 5 being the highest grade and 1 being the lowest. The data was recorded and tabulated in Microsoft Excel and it was observed that 14 teachers and 16 students participated in the research. There were a total of 3 ethnicities in the school and their grades in six subjects were recorded. 

Single-factor ANOVA was conducted in Excel to determine if there was a plausible correlation between the independent variable (ethnicity of the students) and the dependent variable (grades obtained by them). 

This analysis explains that the relationship between the two variables, that is, the ethnic background of the student and their grades, is near zero. This is because the value of p is 5.3. When the value of p is greater than 0.05, the hypothesis that predicts a significant relationship between the independent and dependent variables is rejected (Knekta et al., 2019). The statistical significance value between groups, SS, has a value of 208.43. In single-factor ANOVA, when SS value is higher than 5, it means that there is no correlation between dependent and independent variables (Kyonka et al., 2019).

PSYC FPX4600 Assessment 3 Data Analysis and Interpretation

 The effect size is measured by comparing the value of eta with F critical. If the value of eta is smaller than F critical, the hypothesis is rejected (Kyonka et al., 2019). The value of eta is calculated by dividing the value of MSbg by sums of MSbg and MSwg, (where ‘bg’ means between groups and ‘wg’ means within groups). In this study, the value of eta is 0.9018, and the value of F critical is 1.664263, which means that eta is smaller than F critical, and therefore the hypothesis is not supported. 

Demographic Statistics

The survey was conducted in a secondary school. There were a total of 30 responses, recorded through Google Forms. 3 ethnicities were recorded through the surveys in the school. The grades of the students were divided into five levels, with 1 being the lowest grade and 5 being the highest. The gender of the participants and their role in the school (student or teacher) were also recorded. Data is then organized in a table form in Excel. 

A few factors related to demographics should be considered in this research like the age group of the participants, their sex, race, and language (Hayes, 2021). All research involving demographic data can give a different result if done again. The primary reason for this is a change in control factors such as social status, literacy rate, and socioeconomic situation of participants in the sample size (Mishra et al., 2019). If these control variables are kept constant, the ANOVA single-factor analysis will give repeatable results (Hoijtink et al., 2019). 

Conclusion

The research began by hypothesizing that the ethnicity of the students has a significant impact on their grades of the students. The research was quantitative in nature, it employed a survey method to collect numeric data for statistical analysis. Single-factor ANOVA was conducted to test the correlation between the two variables. Results were interpreted and no significant correlation was found between the independent and the dependent variable. Thus, it can safely be included that there is no significant relation between the grades of the student and the student’s ethnicity.

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 

PSYC FPX4600 Assessment 3 Data Analysis and Interpretation

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.

 https://doi.org/10.1002/jeab.509 

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

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