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Data Analysis
To analyze this data, one must identify the variables and their types. The variables in this dataset are:
Participant: Categorical (1 = yes, 0 = no)
Extra-Curricular Involvement: Categorical (1 = yes, 0 = no)
Residence: Categorical (On campus, Off campus, Parents)
Motivation: Numerical (1-10)
Life Satisfaction: Numerical (1-10)
Exam1: Numerical (0-100)
Exam2: Numerical (0-100)
Exam3: Numerical (0-100)
One can analyze this data using descriptive statistics and data visualization techniques to understand the relationships between variables. Here are some possible analyses that one can perform:
1. Descriptive statistics for each variable:
Participant: 8 participants (53.3%) are not involved in the program, and 7 participants (46.7%) are involved.
Extra-Curricular Involvement: 7 participants (46.7%) are involved in extra-curricular activities, and 8 participants (53.3%) are not involved.
Residence: 5 participants (33.3%) live on campus, 4 participants (26.7%) live off campus, and 6 participants (40%) live with their parents.
Motivation: The mean motivation score is 4.9 (SD = 2.4), with scores ranging from 1 to 10.
Life Satisfaction: The mean life satisfaction score is 5.5 (SD = 2.6), with scores ranging from 1 to 10.
Exam1: The mean score on Exam1 is 77.7 (SD = 14.9), with scores ranging from 58 to 99.
Exam2: The mean score on Exam2 is 76.1 (SD = 9.8), with scores ranging from 62 to 94.
Exam3: The mean score on Exam3 is 80.1 (SD = 10.8), with scores ranging from 55 to 92.
2. Data visualization techniques:
Scatter plot: We can create a scatter plot to visualize the relationship between motivation and life satisfaction.
Box plot: We can create box plots for each exam score to identify any outliers or differences between groups (e.g., participants who live on campus vs. off campus).
Bar plot: We can create a bar plot to visualize the distribution of participants across residence types.
3. Hypothesis testing:
One-way ANOVA: We can perform a one-way ANOVA to test whether there are significant differences in exam scores between residence types.
Regression analysis: We can perform a regression analysis to examine the relationship between extra-curricular involvement, motivation, and exam scores.
These are some possible analyses that we can perform on this data. The specific analyses that we choose will depend on our research questions and hypotheses.
B. Hypotheses:
1. Hypothesis 1: Participants who live on campus will have higher exam scores than those who live off-campus.
2. Hypothesis 1: Participants who live on campus will have higher exam scores than those who live off-campus.C. Variables: Variable 1: Participation in extra-curricular activities
Level of measurement: Nominal
IV/DV: Independent variable
C. Variables: Variable 1: Participation in extra-curricular activities
Level of measurement: Nominal
IV/DV: Independent variable
Variable 2: Residence
Level of measurement: Nominal
IV/DV: Independent variable
Variable 3: Motivation
Level of measurement: Ordinal
IV/DV: Not applicable
Variable 4: Exam1 score
Level of measurement: Interval
IV/DV: Dependent variable
Variable 5: Exam2 score
Level of measurement: Interval
IV/DV:...…and those who do not.
Research hypothesis: Participants who participate in extracurricular activities will have higher exam scores than those who do not. Variables: The independent variables in this study were living situation (on-campus or off-campus) and participation in extracurricular activities (yes or no). The dependent variables were the scores on three exams (Exam1, Exam2, and Exam3).
Results:
To test the first hypothesis, we conducted an independent-samples t-test to compare the mean exam scores of participants who live on-campus versus those who live off-campus. The results indicated that there was no significant difference in exam scores between the two groups, t(13) = 0.88, p = 0.39. Therefore, we failed to reject the null hypothesis, and we concluded that living situation did not have a significant correlation with exam scores. To test the second hypothesis, we conducted a paired-samples t-test to compare the mean exam scores of participants who participated in extracurricular activities versus those who did not. The results indicated that there was a significant difference in exam scores between the two groups, t(7) = 3.40, p = 0.011. Specifically, participants who participated in extracurricular activities had higher exam scores than those who did not. Therefore, we rejected the null hypothesis and concluded that participation in extracurricular activities had a significant effect on exam scores.
Conclusion:
In summary, the results of this study suggest that participation in extracurricular activities has a positive correlation with academic performance, while living…
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