Dual Credit Thesis

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).

The null hypothesis that there is no relationship between the amount of coffee consumption and GPA (p = .62).

The study extends over serval semesters. Also, all research was complete in the testing center. The research lasted for four days that was completed over time. However, students had the option of when to take a test, and the test had no time limits. The test was multiple choice. Nevertheless, the research included the mean=.437, which is the GPA test scores, exam day -.312, exam number -.292, the statistically significance=0.000. Due to the significance value at 0.000 the null hypothesis will be rejected. The statistical correlation defined the relationship between the students. Furthermore, the dependent variable is the mean test scores, and the categorical independent variable used was the four-day test and two different courses. The researcher used to control variables. Moreover, the pairwise difference showed data of unequal numbers and unequal variances (homogenous variances) on days 1, and 3. In addition, the ANOVA analysis reported the mean GPA scores significantly different on days 1, 2, and 3. The results of the ANOVA showed a decrease in scores of students that tested later in the semester. Also, in descriptive statistics, Brown Forsythe test was completed. The scores were measured at 95% confidence interval and showed that students scored increased on the second day of

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.

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.

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, the results were in line with the hypothesis. Whether the stressor was work, childcare or sports each had some sort of an impact on schoolwork performance. The best way for this hypothesis to be fully analyzed would be by comparing this to a controlled group, which did not fall into any of these categories.

We conducted a statistical test to analyze the relationship between college overall GPA and computer science GPA. We examined the overall GPA and computer science GPA of 105 students who graduated with a B.S. in computer science. In this data set, it shows that computer science GPA has a high positive correlation to the overall GPA. The coefficient of determination equals to 88.12%, which means 88.12% of the variation in overall college GPA is explained by the independent variable computer science GPA. There is a correlation between the two variables.

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.

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,

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.

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

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.

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.