8210MOD1WK1 DISCUSSION

Response 1

Tricia Anderson's Original DQ

COLLAPSE

Ryan (2020) studied how race impacts students’ perception and success in a community college composition course. While a correlation was not found between students’ ethnicity and their self-efficacy, this quantitative research study is useful because it demonstrates the importance of considering students’ race when teaching writing. Educators should consider the effect race has on apprehension and how this apprehension affects self-efficacy and its relationship to writing, so educators can best support all students by improving pedagogical practices.

Using the Y = f(X) + E notation, the independent variable is students’ race (the cause of the dependent variable), and the dependent variable (the variable to be explained) is the effect race has on students’ belief of their self-efficacy when it comes to writing.

The research model may be wrong because race might not be the only factor influencing students’ belief in their self-efficacy in terms of writing. Errors may include external factors including past experiences, school culture, lack of background or ability, emotional factors, etc. Other errors that should be considered include measurement due to the small sample size and the lack of diversity in the population studied.

References

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2019). Social Statistics for a Diverse Society (9th Edition). SAGE Publications, Inc. (US). https://mbsdirect.vitalsource.com/books/9781544358666

Ryan, P. (2020). How race can impact our writing self-efficacy in college composition. Educational Research: Theory and Practice, 31(1), 63-66. https://www.nrmera.org/wp-content/uploads/2020/03/11-Ryan-2020.pdf

Response 2

Nadia Campbell

RE: Discussion - Week 1

COLLAPSE

Initial Post:

Overview of Article

The quantitative study, Risk Factors for School Absenteeism and Dropout: A Meta-Analytic Review, synthesized the identified risk factors for problematic school absenteeism and permanent school dropout status from 75 studies (Gubbles, van der Put, & Assink, 2019). The study aimed to estimate the mean effect of various risk domains for school absenteeism and various risk domains for school dropout, whether and how risk factors for school absenteeism differ from risk factors for permanent school dropout, and whether the percentage of boys in samples moderates the overall strength of individual risk domains for school absenteeism or dropout (Gubbles et al., 2019). The results of the three-level meta-analyses study were: of the 781 potential risk factors for school absenteeism and 635 potential risk factors for dropout, after being categorized into 44 risk domains for school absenteeism and 42 risk domains for dropout, there was a significant mean effect for 28 school absenteeism risk domains and 23 dropout risk domains. Also, gender did not moderate most of the risk domains for school absenteeism and dropout status as they seemed similar for boys and girls (Gubbles et al., 2019).

Usefulness, Variables, & Error

The research findings in the article are useful because, according to the researchers, it adds to the knowledge of the root causes (risk factors) that contribute to school absenteeism and dropout statuses (Gubbels et al., 2019). With knowledge of the 28 school absenteeism risk domains and 23 dropout risk domains, school professionals and developers of absenteeism and dropout prevention and intervention strategies can be more informed when developing solutions and risk-needs assessments for students facing the identified risk factors (Gubbels et al., 2019). The research contributes to solving the problems of problematic school absenteeism and permanent school dropout.

Within the Y=f(X) +E equation for quantitatively modeling this research, the independent variable (X) was risk factors, and the dependent variable (Y) was absentee or dropout status (Dietz & Kalof, 2009). Dietz and Kalof (2009) explained that the error (E) is the difference between Y’s actual value and what was predicted using X. In this research, E is other risk domains that were not focused on or not identified that may have contributed to problematic absenteeism and permanent school dropout (Dietz & Kalof, 2009). The researchers’ model within this study could have been wrong. However, as recognized, these results may still be useful.

References

Dietz, T., & Kalof, L. (2009). Introduction to social statistics: The logic of statistical reasoning. Wiley-Blackwell.

Gubbels, J., van der Put, C., & Assink, M. (2019). Risk factors for school absenteeism and dropout: A meta-analytic review. Journal of Youth & Adolescence, 48(9), 1637–1667. https://doi.org/10.1007/s10964-019-01072-5