How to Analyze Cardiovascular Fitness Using Repeated Measures ANOVA on SPSS Assignment

How to Format the Dataset

In this assignment, each of the 10 runners participated in three separate runs—one on each course. The runner’s heart rate at the 5-mile mark was recorded. Thus, the structure requires three variables (Course1, Course2, and Course3) representing the heart rate for each terrain.

Each row corresponds to one runner, and each column captures heart rate under different conditions. For instance:

Runner Course1 Course2 Course3 1 132 135 138 2 143 148 148 ... ... ... ...

This format is crucial. Each measurement appears in its own column, which reflects the repeated observations per subject.

How to Prepare the Variable Definitions

After entering the data, the next task is to define the repeated measures structure in SPSS. This is done through:

• Analyze > General Linear Model > Repeated Measures
• Define the number of levels (3 for the three courses).
• Assign a label to the factor (e.g., “Course”) and a name for the dependent measure (e.g., “heartrate”).

Click Add and then Define to map the variables correctly in the next screen.

How to Conduct the Repeated Measures ANOVA in SPSS

How to Set Up the Analysis

In the Repeated Measures dialog:

• Move each course variable (Course1, Course2, Course3) into the Within-Subjects Variables list.
• Leave default settings for options unless post-hoc or custom contrasts are required.
• Click OK to run the analysis.

SPSS will output a series of tables including the “Tests of Within-Subjects Effects,” which is central to interpretation.

How to Understand the Hypotheses

The hypotheses tested in a repeated measures ANOVA are:

• Null Hypothesis (H₀): The mean heart rate is the same across all three courses.
• Alternative Hypothesis (H₁): At least one course leads to a different mean heart rate.

Symbolically:

• H₀: μ₁ = μ₂ = μ₃
• H₁: At least one μ differs

Rejecting the null suggests that terrain influences heart rate.

How to Interpret SPSS Output for Repeated Measures ANOVA

How to Analyze the Tests of Within-Subjects Effects

This table provides the main F-test used to determine if course conditions lead to statistically significant differences.

From the output:

Source SS df MS F Sig. Sphericity Assumed 476.467 2 238.233 15.601 .000

Here:

• SS = Sum of Squares
• df = degrees of freedom
• MS = Mean Square
• F = F-statistic
• Sig. = p-value

The p-value < .05 indicates that we reject the null hypothesis. Therefore, terrain significantly affects heart rate.

How to Decide Which Line to Use

SPSS provides different correction lines (Sphericity Assumed, Greenhouse-Geisser, Huynh-Feldt, etc.) due to possible violation of sphericity. For simplicity, and in the absence of extreme violations, you may follow the “Sphericity Assumed” row unless Mauchly’s test suggests otherwise.

In this assignment, the sphericity assumption is acceptable, so the p-value from that line is valid.

How to Report and Interpret the Findings

How to Write the Conclusion for the Assignment

Given the F-value of 15.601 and a p-value of .000, we conclude that there is a statistically significant difference in runners’ heart rates depending on the terrain. In particular, the uphill terrains (graded and steep inclines) likely contribute to higher heart rates due to increased physical demand.

This kind of insight shows both statistical and real-world understanding—something instructors often look for in SPSS assignments.

How to Make Recommendations or Next Steps

If required, students may also suggest:

• Pairwise comparisons to identify which courses differ significantly.
• A graphical summary (line plot of means with standard errors).
• Considerations for individual variability or fitness adaptation.

These additional analyses enrich the assignment and display deeper engagement with the topic.

How to Avoid Common Errors in Repeated Measures SPSS Assignments

How to Prevent Incorrect Data Structuring

A common mistake is entering repeated measures data in a long format (one column for heart rate and another for course) instead of wide format. SPSS Repeated Measures ANOVA requires the wide format: one row per subject and one column per condition.

Always double-check variable arrangement before analysis.

How to Verify Assumptions and Interpret Correctly

Another issue arises from misinterpreting the output:

• Using the wrong correction line (e.g., choosing Huynh-Feldt without justification).
• Ignoring the assumption of sphericity.
• Failing to relate statistical significance back to the context of the research.

Students should aim to link their findings clearly to the original problem—in this case, understanding how terrain affects physical strain on elite runners.

Conclusion

SPSS Repeated Measures ANOVA is a powerful method for analyzing within-subject data, especially when comparing the same group across multiple conditions. In this assignment, analyzing elite runners’ heart rates on three terrains helped reveal meaningful differences due to course difficulty. From correctly formatting data to interpreting SPSS output, every step contributes to a successful statistical analysis.

By following the structured approach outlined above, students can confidently handle similar SPSS assignments involving repeated measures. Always ensure your data is structured properly, hypotheses are clearly stated, outputs are interpreted with attention to assumptions, and conclusions relate directly to the real-world problem. With these steps, your assignment will not only meet academic requirements but also demonstrate solid statistical reasoning and SPSS proficiency.

Completing your SPSS assignments accurately can significantly improve academic performance, especially in data-driven disciplines. For students who find repeated measures or other SPSS tasks challenging, seeking expert guidance is a smart way to ensure that the concepts are applied correctly and that results are meaningful. Whether it's understanding F-values, addressing sphericity, or structuring data for analysis, consistent practice with SPSS enhances your skills in both statistical interpretation and reporting clarity.