PUBH2203: Biostatistics Assignment University of Western Australia

Questions

1. Use SPSS and the file bsn81.sav to produce the requested output and answer the following questions. 

(a) Produce a table that shows the count and mean FVC for adults with and without ASTHMA by SEX. Comment on the observed difference in mean FVC between adults with and without ASTHMA separately for males and females.

(b) Separately for males and females, perform a t-test that compares mean FVC for adults with and without ASTHMA. Quote an appropriate test result and state your conclusion about any difference. If you conclude there is a difference, write a sentence interpreting the estimate and its 95% CI.

Use Data/Select Cases to restrict analysis to men and women respectively. Use Analyze/Compare Means/Independent Samples T-test with FVC as Test variable and ASTHMA as grouping variable to obtain the following results.

(c) Separately for males and females, first fit the linear regression model that allows a quadratic trend in FVC with HEIGHT. Quote an appropriate test result to determine if the relationship is significantly curved and if not, fit the straight line trend regression model. Use the appropriate fitted trend models to obtain an estimate of FVC for a man with height 1.7 metres and a woman with height 1.7 metres. Use Transform/Compute to create a new variable HeightSq = height*height. Use Data/Select Cases to restrict analysis to men and women respectively.

Assessment Summary

The assessment required students to use SPSS software and the dataset bsn81.sav to perform a series of statistical analyses focused on understanding the relationship between Forced Vital Capacity (FVC), asthma status, and height, across male and female adults.

Key Assessment Requirements

1. Descriptive Analysis:

   • Generate a table showing the count and mean FVC for adults with and without asthma, categorized by sex (male/female).
   • Provide a brief commentary on observed differences in FVC values between the groups.

2. Inferential Analysis (T-Test):

   • Conduct Independent Samples T-tests for males and females separately to compare mean FVC between adults with and without asthma.
   • Quote the appropriate test statistic, p-value, and interpret the results including the 95% confidence interval for any significant difference.

3. Regression Analysis (Height and FVC):

   • For each gender, fit a linear regression model incorporating both height and height squared (HeightSq) to test for a quadratic relationship between height and FVC.
   • Determine whether the relationship is significantly curved; if not, refit the model using a linear trend.
   • Using the fitted model, estimate FVC for a person with height = 1.7 meters for both males and females.

Academic Mentor’s Step-by-Step Guidance

The academic mentor adopted a structured, instructional approach to guide the student through each analytical stage, ensuring conceptual clarity and correct SPSS application.

Step 1: Understanding the Data and Defining Objectives

The mentor began by explaining the purpose of the dataset and clarifying the research focus analyzing how asthma affects FVC across genders and how height correlates with lung capacity. The student was shown how to explore the dataset using Variable View in SPSS to confirm variable names and data types.

Step 2: Performing Descriptive Analysis (Task 1a)

The mentor guided the student to navigate to Analyze → Compare Means → Means, selecting FVC as the dependent variable and ASTHMA and SEX as grouping variables.

• The resulting table displayed count and mean FVC for each subgroup.
• The mentor then demonstrated how to interpret patterns for example, noting whether males generally had higher FVC values than females and identifying how asthma presence influenced those means.

Step 3: Conducting T-Tests (Task 1b)

The student was instructed to use Data → Select Cases to filter the dataset separately for males and females. Then, using Analyze → Compare Means → Independent-Samples T-Test, the mentor explained how to:

• Define FVC as the test variable and ASTHMA as the grouping variable.
• Read and interpret the Levene’s Test for Equality of Variances and t-test significance values (p-values).
  The mentor emphasized how to write results correctly quoting the t-statistic, degrees of freedom, and p-value, followed by a conclusion such as whether asthma status significantly affected FVC.

Step 4: Regression Analysis (Task 1c)

The mentor then introduced the concept of curvilinear relationships, guiding the student to create a new variable using Transform → Compute Variable and defining HeightSq = Height * Height.
Next, the student performed separate regressions for males and females using Analyze → Regression → Linear, with FVC as the dependent variable and both Height and HeightSq as independent variables.

• The mentor explained how to interpret the coefficient of HeightSq to determine if curvature was statistically significant.
• If not significant, a simpler linear model was refitted.
  Finally, the mentor showed how to use the model equation to predict FVC for a person with height = 1.7 m.

Step 5: Interpreting and Presenting Results

The mentor guided the student on summarizing findings in a clear and academic format:

• Reporting mean comparisons and test outcomes.
• Explaining statistical conclusions in plain language (e.g., “Males with asthma had significantly lower FVC compared to those without asthma”).
• Highlighting the practical meaning of regression results (e.g., “FVC increases linearly with height in both sexes”).

Outcome and Learning Achievements

By the end of the assessment, the student successfully:

• Generated and interpreted descriptive statistics, t-tests, and regression outputs in SPSS.
• Understood how to test hypotheses regarding mean differences and continuous predictors.
• Learned to apply data selection, transformation, and model fitting techniques appropriately.
• Developed critical interpretation skills by connecting statistical findings to real-world biomedical implications.

Learning Objectives Covered:

• CLO1: Application of appropriate statistical methods using SPSS.
• CLO2: Interpretation of descriptive, inferential, and regression analyses.
• CLO3: Presentation of analytical results in clear, evidence-based academic reporting.

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