Chapter Four
Results
Introduction
This chapter presents the statistical results of the correlational study of the relationship between students and college persistence who were enrolled from partnering high schools (Clay County, Corbin, McCreary County, North Laurel, South Laurel, and Whitley County) and the dual credit program. The study theorized that the provision of dual credit programs to participating high schools would affect the students’ choice to attend an institution of higher education or not based upon participation levels in dual credit classes. Furthermore, the study assumed that a relationship will exist in the grade point average earned and dual credit participation. The study was designed to test the assumptions
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Table 2 represents the results which portrays no difference in the groups, (F [1,154] = 136.6, p > .01) where the p value is 5.43. This means that the groups are not statistically different and there is no relationship between the number of dual credit hours earned and college persistence.
Table 2
Source of Variation SS df MS F P-value F crit
Between Groups 4268.308 1 4268.308 136.592 5.43E-23 3.902553
Within Groups 4812.282 154 31.24858 Total 9080.59 155
Research Question #2: “What relationship exists between the grade point average earned in dual credit courses and college persistence?” An Anova test was used to compare the GPA earned and college persistence. Table 3 represents the results which portrays no difference in the groups, (F [1,154] = 643.02, p > .01) where the p value is 7.57. This means that the groups are not statistically different and there is no relationship between the GPA earned and college persistence.
Table 3
Source of Variation SS df MS F P-value F crit
Between Groups 195.2387 1 195.2387 643.0247 7.57E-57 3.902553
Within Groups 46.75832 154 0.303625 Total 241.997 155
Research Question #3: “Does a relationship exist between dual credit enrollment and English courses?” An Anova test was used to compare the GPA earned and college persistence. Table 4 represents the results which portrays no difference in the groups, (F
The Null Hypothesis for this test was Ho: u1- u2 = 0. Dr. Williams Found that the t-value = 0.98603, the p-value = 0.328213, and that p < 0.05. This means his results were not significant at a 0.05 level. Therefore, we fail to reject the null. Dr. Williams can conclude there is no difference between the scores of his two Intro Psych. classes.
The null hypothesis that there is no relationship between the amount of coffee consumption and GPA (p = .62).
In this news article, Jessica Bock investigates the value of AP courses and dual credit classes in the high schools throughout Missouri. She describes the merits of both AP courses and dual credit in planning for college, and she explores the personal motivations of some high school students for taking AP or dual credit
Using the MM207 Student Data Set: a) What is the correlation between student cumulative GPA and the number of hours spent on school work each week? Be sure to include the computations or StatCrunch output to support your answer. My answer :
The relevant independent variable in his study is hours worked by a student; the relevant dependent variables in his study are credits completed per semester and student GPA. Darolia’s study also considers disparate effects on GPA and credit completion based on student characteristics (e.g., gender, full or part-time status).
While in high school, students have the opportunity to earn college credit to work towards a degree, while earning high school credit at the same time. This is referred to as dual credit, and more and more students are beginning to take advantage of its many purposes. Two types of these dual credit programs are dual enrollment and advanced placement. While both programs have their advantages, there are several reasons that dual enrollment is rightfully preferred by students than its counterpart. Dual enrollment courses benefit students more overall than advanced placement courses, as dual enrollment programs give students the same benefits, if not more, without the intense rigor and risks associated with advanced placement.
While most academic pathways are being implemented initially at the secondary level, partnerships involving high schools and colleges need to move from good intentions to being firmly embedded in organizational and curricular structures that span all levels of the educational system. Ongoing dialogue between the secondary and postsecondary levels is necessary to facilitate successful student transition from high school to college and success in college. This reform movement is driven by the current pathways that exist across the states; dual credit/dual enrollment was recognized as the top priority by 17 states, while officials in 19 states cited an emphasis on a particular pathway. Many others believe that multiple pathways must exist to meet the achievement needs of diverse student populations.
In these tables we are looking at the relationship between the race and how much education one have completed. Observing at the chi-square, the P-value of .000, which lies beneath the cut off .05. Since the P-value is less than .05, there is a statistical significant relationship between the notion of race and education.
Dual enrollment is a huge perk for many high school students, as it allows you to earn college credit without attending a college. There are many obvious perks to this, such as earning your associate’s degree faster, it’s cheaper than taking the course at a campus, and it gets you ready for the college experience better than much of anything else. Many more examples of why dual enrollment is great blind students from doing their own research and many students fail to recognize that they might not be able to take on college classes. While dual enrollment is certainly a great way to start college early, there are some instances where it’s not the best idea. To evaluate if taking college credit is ideal, we need some statistics on the dual enrollment program.
In conclusion, my hypothesis was incorrect. People who procrastinate the most does not have a higher GPA than the people who procrastinate the least. The data shows that the average GPA of people who procrastinate the least is 3.98 while people who the most is 3.23. It is clear that procrastinating have lowers GPA. Procrastination does not have a positive effect on GPA. An extraneous variable could be that student failed their classes that resulted in lower GPA. They had sports to play in school resulting in procrastination. the survey looked bad and they did not get it seriously. They put down random numbers since they might have been bored with the surveys. The class is AP so they would worker than the people who are not in AP classes. Also,
Analysis of Variance is utilized when we want to “Compare multiple groups without increasing the probability of error. The Analysis of Variance (ANOVA) is such a test” (Mirabella, 2011, p. 5-6). When computing this test it is always non-directional. As we are only testing to see if there is a difference or if there is not a difference (Mirabella, 2011). Furthermore, ANOVA allows you to compare an infinite amount of groups. In our case, we are conducting a hypothesis test using Analysis of Variance to determine if there is a difference in the mean GPA for those who are unemployed versus, those who work part-time, and versus those who work full-time.
Once the data was processed and displayed in excel, observations of data and pattern were obtained. By just looking at the display of data the relationship among variables are perceived; the type of class can be the independent variable and the dependent variables are the number of participants that might be influenced by another variable that can be unknown for us. However, We tend to assume and expect results based on what is observed in the data which can take us to wrong conclusions. According to Sharpe, N.D., De Veaux, R., & Vellerman, P. (2014), “ you are not allowed to look at the data
Sex and Cumulative GPA, my third pair is another one that could grant us useful information. This is a fairly easy pair to understand. We might be able to say that one gender might have a higher cumulative GPA over the other. If we can say this then using this information will be able to help choose what genders to pick from the applicants in order to ensure that they will be successful in college.
For the second research question, the pre-test and the immediate-post-test essays are compared within each group by using the paired t-test. In addition, the one-way analysis of variance (ANOVA) was conducted to investigate the difference in the number of each cohesive device between the three treatment groups. The same statistical analysis was conducted for the fourth research question; the pre-test and the post-test essays are compared within groups as well as the number of cohesive devices in the post-test essays was compared between groups.
The utilization of t-test, ANOVA, and comparing group means are essential in social research. When the researcher uses a test, the goal is to compare the averages of the two groups in order to determine if the sample population has shown any differences in the variables studied. Comparing group means through t-test and ANOVA is needed in social research as its goal is to produce results from the sample that can be generalized to the the population. In this paper will discuss what the author of paper completed to develop a t-test and ANOVA analysis in SPSS, the author’s findings of the research, and what the author learned from utilizing the data set provided.