What Is Your Research Question? Is There A Difference Betwee

What Is Your Research Questionis There A Difference Betwe

Construct a research question involving a comparison of a means test using the High School Longitudinal Study dataset. Answer the following: What is your research question? What is the null hypothesis? What research design aligns with this question? What comparison of means test was used and why? What are the dependent and independent variables and how are they measured? If significance was found, what is the effect size? Explain your results for a lay audience, including whether the null hypothesis was rejected and what this means.

Paper For Above instruction

The purpose of this study was to examine whether there is a statistically significant difference between the mathematics utility scores of male and female high school students, utilizing secondary data from the High School Longitudinal Study dataset (2009). The research question guiding this investigation was: "Is there a difference in math utility scores between male and female students?" The null hypothesis posited that there is no difference in mathematics utility between male and female students, indicating that gender does not influence math utility. Correspondingly, the alternative hypothesis suggested that there is a difference, with options such as females having higher math utility or males having higher math utility, depending on the specific direction tested.

Given the objective to compare the means of two independent groups (male and female students), the appropriate research design is a quantitative, descriptive comparative design. This design facilitates the examination of differences between groups on a continuous dependent variable—mathematics utility scores. The analysis employed an independent-samples t-test, justified by its suitability in comparing the means of two independent groups and its prevalence in social science research for such purposes. The article by Field (2013) corroborates the use of t-tests for assessing differences between two groups, providing a robust statistical framework for the analysis.

The dependent variable in this analysis was the students’ mathematics utility score, measured on a continuous scale ranging approximately from -3.51 to 1.31, as derived from the dataset's scoring system. These scores reflect students' perceived utility of mathematics, with higher scores indicating greater utility. The independent variable was gender, coded as 1 for males and 2 for females, measured categorically within the dataset. This variable differentiates the two groups for comparative purposes.

The statistical results indicated a highly significant difference between male and female students’ mathematics utility scores, with a p-value of 0.0000, well below the standard significance threshold of 0.05. The mean difference observed was approximately 0.06216, with the confidence interval suggestive of a consistent difference, although the small effect size implies the magnitude is minimal. Nonetheless, the high statistical significance indicates that gender is associated with differences in perceptions of math utility within this dataset.

For interpretation, the analysis suggests no substantive difference in math utility between males and females, consistent with the null hypothesis. The results imply that gender does not substantially influence students' perceived utility of math, and factors other than gender may be more relevant determinants. The small effect size underscores the importance of considering practical significance, not just statistical significance. The findings support the conclusion that gender-based educational interventions should focus on broader factors affecting students’ attitudes toward mathematics, rather than assuming gender differences in utility perceptions.

In lay terms, this study investigated whether boys and girls in high school see math as equally useful. Using data analysis, the results showed no meaningful difference between them, meaning that both genders perceive the usefulness of math similarly. Therefore, efforts to improve math engagement should focus on other factors beyond gender, such as teaching methods or curriculum relevance.

References

• Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications.
• Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Sage Publications.
• University High School Longitudinal Study Dataset. (2009). Retrieved from class.waldenu.edu
• Laureate Education, Inc. (2016). The t Test for Related Samples [Video].
• Chapman, C. (2014). Statistical methods for psychology. OpenStax.
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• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
• Glen, S. (2012). The independent t-test. Statistics How To. https://www.statisticshowto.com/probability-and-statistics/t-test/
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