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The findings of this research were statistically significant. The study's level of significance was established at 0.05. The results are statistically significant since the calculated value is 0.0008, which shows that.
The alternative hypothesis would be used in the investigation in place of the null hypothesis. The assessed probability is 0.0008, which indicates that there is a possibility that the null hypothesis will hold true. Since the 0.0008 is less than the significance level of 5% (0.05) chance, the result we obtain from the null hypothesis would happen too frequently for us to be confident that it was the two vaccination methods are equally valid.
There exists enough evidence to support the alternative hypothesis. For example, after treating five hundred people with the shot vaccine, four hundred did not get the flu. On the contrary, after five hundred people received the nasal spray, only three hundred and eighty failed to obtain the flu. These results show that the shot vaccine was more effective than the nasal spray vaccine. Therefore, the results support the alternative hypothesis.
The sample used for this study is appropriate. The sample used here leads to the identification and use of study participants who can best provide the information according to the conceptual requirements of this study.
There are some limitations associated with the data collected and may result in biased results. Inexperienced persons may have received the data leading to biases due to high levels of dishonesty. The performance may have been average values and hence not an accurate reflection of the nature of the group.
A follow-up study would help to determine the effectiveness of the vaccines. For the case of this study, the follow-up would help to determine if the members who never contracted the disease got the complications again after some time (Proofetto-McGrath, Polit, & Beck, 2010, p. 223). This would include conducting a close examination of every participant.
Statistical significance predicts a probability of a relationship between two variables. On the other hand, the practical significance implies the existence of a relationship between variables (Rebecca, 2012, p. 103). For instance, the functional significance will seek to determine if there exists a difference between large samples that are large enough to have real meaning.
The correlation between IQ and grade point average (GPA) expresses the degree of association between them. The value 0.75 is a positive correlation and shows that the two variables have a strong relationship since it is close to 1. The relationship means that the level of IQ increases as the GPA increases. This correlation implies that individuals with high intelligence Quotient have high GPA.
This correlation does not provide evidence that high IQ causes GPA to go higher. Some factors cause the GPA to either increase or decrease. For example, the socio-economic and demographic factors have an impact on the cumulative grade of the student (Erdem, Arslan, & Senturk, 2007, p. 360). Therefore, the current relationship does not reflect the actual relationship between the two variables.
Causation and correlation have a close relationship since causation shows that there exists a causal connection between two variables. Therefore, causation leads to a relationship between two variables.
The relationship between two variables is affected by the amount of variability, lack of linearity, the presence of outliers and measurement of errors. From a personal point of view, different factors affect the GPA. Therefore, regression would be the best method to determine the relationship between the various variables. This fact leads to the conclusion that correlation is not the best measure to determine the GPA.
Lower reaction time
Higher response time
For the case of the lower reaction time group:
For the case of the higher reaction time group:
An outlier is a value that is far much smaller or larger than the other data values. For the case of the lower reaction time group, there are not outliers in the group. However, the higher response time unit has one outlier. The value 15.2 is far much larger when compared to other values. The scatter plot below shows the outlier in this group.
The outlier affects the accuracy of the regression model of the sample. Increased outliers result in a less accurate regression model. The mean of the data is affected the most by the outliers. A value that is far much larger than the mean increases the standard deviation and skews the results. A larger sample size would help to reduce the impacts of the outliers.
Research and Study Critique (Celkan & Linda, 2011)
The study focuses on the performance of the students in higher education. The school system expects that the students will demonstrate high levels of success. However, in many cases, the student performance in classes falls short of meeting the expectancies. The prior education and demographic characteristics can provide details as to why the students tend to perform poorly in their studies. The population of the survey was made up of students attending the Department of Learning Support at Macon State College. The primary aim was to determine the reasons why the learning support students record poor performances in English placement exam that they take during the initial registration. In most cases, these students continue to perform poorly in their remedial writing classes. The study also aims to determine the link between remedial education outcomes in two colleges English Classes. Some high school students were also a part of this study. This study lies in the belief that COMPASS will give the students the chance to be exposed to an intensive remedial program where they have the opportunity to correct their deficiencies.
Statement of the Problem
Even in the presence of SAT, Exit or COMPASS placement exam, students are required to select the best answers. Some students perform poorly while other record excellent performances through luck. Does the COMPASS placement test accurately place students? Does the exit test results predict success? How does student demographics affect student success?
The study was primarily based on correlational research. The researcher looked for and described the relationships without altering them in any way. The study also used a demographic inventory consisting of sixteen questions and administered it in both groups to elicit information on the personal and educational background of the students. The COMPASS Placement and Exit test scores, Nelson-Denny Test Scores, SAT scores, High School GPA's and end term grades were also used. The data collected helped to establish the correlation with the demographic instrument. Individual interviews were conducted with students who volunteered to participate in the study.
The results analyzed showed that the quality of the high school experience as measured by SAT scores and high school averages makes the most significant contribution to the subsequent success in college. The Compass exit scores aligned better than the COMPASS placement scores. International students appeared at the top. 50% of the students who received an IP grade for not having succeeded in the COMPASS exit test ranked three points below the passing score. The correlation between English placement test score, HSGPA and subsequent success in college courses had a weak correlation.
These results fail to show the personal impact of the different factors studied on the performance of the students. Failure to develop a regression model makes the study fail to answer the following research questions: Does the exit test results predict success? How does student demographics affect student success?
The results presented in the study are effective in explaining the nature of the relationship between the demographics of the students and their academic achievements. However, it does not explain the extent to which they affect the performance. The use of qualitative and quantitative approaches in the study makes it high. However, the future research should develop a regression model and create a clear relationship between the factors that affect the performance of the students.
Celkan, G., & Linda, G. (2011). Student Demographic Characteristics and how they relate to the Student Achievement. Journal of Social and Behavioral Science, 341-345.
Erdem, C., Arslan, K. C., & Senturk, I. (2007). Factors Affecting Grade Point Average of University Students. The Empirical Economics Letters, 359-368.
Proofetto-McGrath, J., Polit, F. D., & Beck, T. C. (2010). Canadian Essentials of Nursing Research. Frankfurt: Lippincott Williams & Wilkins.
Rebecca, M. W. (2012). Applied Statics: From Bivariate Through Multivariate Techniques. New York: SAGE.
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