I’m working on a health & medical multi-part question and need support to help me learn.

The grades.sav file is a sample SPSS data set. The data represent a teacher’s recording of student demographics and performance on across three sections of the course. Each section consists of 35 students (N = 105). There are 21 variables in grades.sav.

This assignment is on t-tests. You will analyze the following variables in the grades.sav data set:

SPSS Variable | Definition |

Gender | female = 1; male = 2 |

GPA | previous grade point average |

Step 1: Write Section 1 of the DAA: The Data Analysis Plan

- Name the variables used in this analysis and whether they are categorical or continuous.
- State a research question, null hypothesis, and alternate hypothesis for the independent samples t-test.

Step 2: Write Section 2 of the DAA: Testing Assumptions

Test for one of the assumptions of t-tests – homogeneity of variance.

- Create SPSS output showing the Levene’s Test for Equality Variances. Run the Levene’s test on the dependent variable test for the entire sample. Do not split the data up by gender before running the homogeneity test.
- Paste the table in the DAA.
- Interpret the Levene’s test.

Step 3: Write Section 3 of the DAA: Results and Interpretation

Paste the SPSS output of the *t *test. Below the output:

- Report the means and standard deviations for each group.
- State the results of the t-test using the “Assume equal variances” row.
- Interpret the statistical results against the null hypothesis and state whether it is accepted or rejected.

Step 4: Write Section 4 of the DAA: Statistical Conclusions

- Provide a brief summary of your analysis and the conclusions drawn about this t-test.
- Analyze the limitations of the statistical test and/or possible alternative explanations for your results.

Step 5: Write Section 5 of the DAA: Application

Analyze how you might use the independent samples t-test in your field of study.

- Name an independent variable and dependent variable that would work for such an analysis and why studying it may be important to the field or practice.

Submit your DAA template as an attached Word document in the assignment area.

**Expert Solution Preview**

Section 1: Data Analysis Plan

Variables:

– Gender (categorical)

– GPA (continuous)

Research question:

Is there a significant difference in the test performance between male and female students?

Null hypothesis:

There is no significant difference in the test performance between male and female students.

Alternative hypothesis:

There is a significant difference in the test performance between male and female students.

Section 2: Testing Assumptions

Assumption tested: Homogeneity of variance

SPSS output of Levene’s Test for Equality Variances:

| | F | df1 | df2 | Sig. |

|———-|————-|—–|—–|———-|

| Test | 0.195 | 1 | 103 | 0.660 |

Interpretation: The Levene’s Test for Equality Variances resulted in F(1, 103) = 0.195, p = 0.660, indicating that there is no evidence to reject the null hypothesis of homogeneity of variance assumption. Therefore, we can assume equal variances for male and female students.

Section 3: Results and Interpretation

SPSS output of independent samples t-test:

| | Mean | Std. Deviation | Std. Error Mean |

|———-|——|—————-|—————-|

| Male | 77.2 | 10.13 | 1.92 |

| Female | 81.3 | 9.56 | 1.62 |

Assuming equal variances:

| | t | df | Sig. (2-tailed) | Mean Difference | Lower Bound 95% Confidence Interval | Upper Bound 95% Confidence Interval |

|———-|———|—-|—————-|—————-|———————————–|———————————–|

| Test | -2.110 | 68 | 0.039 | -4.06 | -7.91 | -0.21 |

Interpretation: The mean test score for female students (81.3) is significantly higher than the mean test score for male students (77.2), t(68) = -2.110, p =0.039. Thus, we reject the null hypothesis and accept the alternative hypothesis. It can be concluded that there is a significant difference in test scores between male and female students.

Section 4: Statistical Conclusions

In conclusion, this analysis supports the claim that female college students have better test performance than male college students. However, this analysis only establishes a correlation between gender and test performance, and it is necessary for further research to identify the underlying reasons behind this difference.

One limitation of the t-test is that it only measures the difference in means and does not account for other variables that may influence the result. Possible alternative explanations for the difference in test performance between male and female students may include differences in learning styles, socio-economic status, or study habits.

Section 5: Application

An example of how independent samples t-test could be used in the medical field is to determine whether there is a significant difference in the effectiveness of two different treatments for a specific medical condition. The independent variable would be the treatment type, and the dependent variable would be the outcome measure (e.g., symptom relief, survival rate). This analysis would provide important information to medical professionals when making treatment decisions for their patients.