8210 WK 8 DISCUSSION
Candyy31
WK8DISCUSSION.docx
RESPONSE1
Cherie McElroy-Burch
RE: Discussion - Week 8
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Wagner (2020) explains that regression analysis is the process that allows researchers to predict one variable based on information contained about other variables. Bivariate regression is defined as the examination of how changes in one independent variable influence the value of a dependent variable (Frankfort-Nachmias et al., 2020). Correlation defined by Frankfort-Nachmias et al. (2020) is the association used to identify the presence and strength of the relationship amongst interval-ratio variables. The purpose of this post is to critique the article, Relationship between Personality and Academic Motivation in Education Degrees Students, written by Fuertes et al. (2020).
Summary of the Article
In the article Relationship between Personality and Academic Motivation in Education Degrees Students, Fuertes et al. (2020) examined the link between five big factors of personality and academic motivation. Fuertes et al. (2020), argued that one’s motivation is dependent and influenced by their personality. The researchers used the Big Five Theory of Personality model as the foundation for their study. The Big Five Theory of Personality model is based on the existence of five basic facets of personality, which could be derived from what the person is like (Fuertes et al., 2020).
Research Design
The research design used by the authors was a quantitative design. Fuertes et al. (2020) described the design as a cross-sectional and non-experimental study, empirical, analytical, and correlational. The authors used the correlation method to examine the link between personality and motivation. In my opinion the most appropriate choice for this study is correlation because the researchers are looking to identify the presence and strength of the relationship amongst personality and motivation. According to Wagner (2020), correlation determines the extent to which variables are related.. The sample population was comprised of a total of 514 students. The students were from the four courses of the three Degrees (Early Childhood, Primary, and Social Education) of the Faculty of Education of the University of León in Leon, Spain. (Fuertes et al., 2020). The participants ages ranged from 18 to 48 years old, with a mean age of 21.48 years old (Fuertes et al., 2020). The results of the study indicated a strong correlation between elements of the students’ personality and motivation variables (Fuertes et al., 2020).
Displaying the Data and Results
Fuertes et al. (2020) displayed the data using Spearman’s Correlation to authenticate the link between motivation and personality traits. Spearman’s Correlation is used to assess the strength of the relationship between two variables using a monotonic function (Spearman’s Rank-Order Correlation, n.d.). The results of the study indicate there was a strong correlation between “the value that students assign to tasks with the extroversion, kindness, and responsibility, but also of intrinsic motivation with openness to experience, kindness, and responsibility” (Fuertes et al., 2020). The conclusion can be made that the results of this study stand alone because there is high level of significance. The null hypothesis was rejected because p was less than 0.05 in ten of the eleven variables (Fuertes et al., 2020).
Effect Size and Meaningfulness
Fuertes et al. (2020) used r of Rosenthal to calculate the effect strengths of the significant differences between men and women in relation to personality and motivation. The analysis of r of Rosenthal concluded that effect sizes were small because they weren’t higher than 0.25, educational studies. Therefore, the researchers concluded the effect sizes were small because the effect only described 1% of the total variance (Fuertes et al., 2020). Fuertes et al. (2020) concluded the findings of the study are very important in relation to enhancing students’ attributes and helping them to choose the best courses that fit to their personality traits and ensuring their success due to their motivation. While this study was conducted in Leon, Spain, it could be applicable to students everywhere. Previous investigations were conducted on the association between personality traits and motivation and the impact on academics. Therefore it is evident that this research is meaningful because the findings contribute to further understanding the correlation between personality traits and motivation, and the influence on academic success.
Conclusion
In conclusion, correlation is defined as the extent to which variables are connected (Wagner, 2020). The results of the study showed a strong correlation between elements of a students’ personality and motivation variables (Fuertes et al., 2020). The study further corroborates previous investigations on the association between student personality and motivation. Therefore, the results of the study are meaningful and can be used to further the research.
References
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Sage Publications
Fuertes, A. M. de C., Blanco Fernández, J., García Mata, M. de los Á., Rebaque Gómez, A., & Pascual, R. G. (2020). Relationship between Personality and Academic Motivation in Education Degrees Students. Education Sciences, 10.
Spearman’s Rank-Order Correlation - A guide to when to use it, what it does and what the assumptions are. (n.d.). Statistics.Laerd.Com. Retrieved July 19, 2022, from https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Sage Publications.
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RESPONSE 2
Kristin Domville
RE: Discussion - Week 8
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Summary and Research Design
In the article, Academic advising and first-generation college students: A quantitative study on student retention, the authors used a multiple logistic regression technique to analyze if there is a relationship between the number of academic advising meetings and the retention of first-generation students. This type of design is used when there is more than one predictor being measured at a point in time. The authors analyzed race, major, gender, and the number of advising meetings to retention of first-generation college students (Swecker, et al., 2013).
Correlation or Bivariate Regression
The authors used a bivariate regression (linear regression analysis) to determine the relationship between two variables. Regression describes the strength of the relationship between dependent and independent variables (Frankfort-Nachmias et al., 2020; Walden University, 2022). In this study, they found the tolerance values were greater than .1 indicating the is a collinear relationship (Swecker, et al., 2013). Next, the Swecker et al. (2013) used a binary logistic regression to analyze multiple demographic factors (gender, race, and major) with the outcome of the student being retained or not (only 2 outcomes). They found that gender, race, and major were not correlated to retention. The number of advising meetings was correlated using the Wald test. The authors found that the number of advising meetings was 13.557 with a 95% confidence interval. This indicates that the odds of retaining first-generation students increase by 13.6% for every advising meeting (Swecker, et al., 2013).
Using a bivariate regression was the appropriate choice. The use of bivariate regression is used to test simple hypotheses and to analyze an association of causality. In this case, the authors analyzed if the relationship between a number of advising appointments was linearly related to retention. A bivariate regression test is used to determine the relationship between two variables. Then binary logistic regression analysis is used when you want to learn about multiple factors (gender, race, and major) influencing a factor with only two outcomes (retention or non-retention. The statistical analysis was displayed appropriately in tables, however, the authors did not use bar graphs or pie charts (Swecker, et al., 2013).
Effect Size and Meaningfulness
The results do not stand alone. The authors identified patterns in retention for first-generation students. The methodological approach answered the specific research question which was if there was a relationship between the number of academic advising meetings and retention. The authors found (N=363) that there was a linear relationship (tolerance >.1 and VIF <10) and a binary regression (Wald=13.281; p=.000) indicating a 13.3% increase in retention per academic advising meeting (Swecker, et al., 2013).
This work is substantive but does not stand alone. The findings leave the reader wondering if it is the academic advising meeting that increases retention or if there is a personal characteristic that makes certain students attend the sessions and then persist at the institution. The students who went to the advising sessions and then subsequently stayed in the institution could have been more proactive. The student retention could have been due to a personal characteristic versus the session with the advisor.
Swecker et al., (2013) report on effect size in table 2 within the article. For the number of advising meetings in the year the tolerance was .984. A tolerance level greater than .10 is recommended as the minimum level. A level of .984 indicates a strong linear relationship between the independent and dependent variable. It is meaningful since it indicates a strong relationship between the 2 variables, and that there is a practical significance between the number of advising meetings and retention of first-generation students.
References
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
Swecker, H. K., Fifolt, M., & Searby, L. (2013). Academic advising and first-generation college students: A quantitative study on student retention. NACADA Journal, 33(1). 46-53. https://doi.org/10.12930/NACADA-13-192
Walden University. (2022). Skill builders. Interpreting Correlation and Regression Coefficients. https://content.waldenu.edu/d1c00f22444bfb7cf79c9487accceada.html
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References
National Center on Educational Outcomes. (2019). Graduation Requirements for Students with Disabilities: Ensuring Meaningful Diplomas for All Students. https://files.eric.ed.gov/fulltext/ED554561.pdf
Polloway, E., Patton, J., Serna, L., & Bailey, J. (2017).Strategies for Teaching Learners with Special Needs (Eleventh Edition).Pearson Publishers
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