HSH946: Biostatistics Calculations and Stata Analysis Assignment 1

Assignment 1

Questions

1. Previous studies have indicated that sports supporters may experience elevated levels of anxiety. To explore this further, a group of students collected data on the anxiety levels of adults attending a rugby match, along with several lifestyle factors. The data were obtained by approaching adult rugby fans at a recent match and inviting them to take part in a brief health check-up. Use the AT1_anxiety data file for this question.

• What sampling technique did the students employ to collect this data? (0.5 marks)
• Suppose you want the sample to be representative of supporters of both teams who played when the data was collected, what sampling method would be most appropriate and why? (1 mark)
• What is the target population of interest? (0.5 marks)
• What sampling frame can you use? (0.5 marks)
• Could these study findings be generalised to the general public? Provide a reason why. (1 mark)
• What is the variable type for anxiety score? (0.5 marks)
• Conduct an exploratory analysis of the data. Check all individual variables and associated variables for any invalid and/or inconsistent values and take appropriate action. Clearly explain each step. (3 marks)
• Comment on how you think these invalid and/or inconsistent values identified in question (g) could have occurred. (0.5 marks)
• A recent study found that having more than 3 drinks during a game is considered excessive. What percentage of fans reported excessive drinking? (report to 1 decimal)? (1 mark)
• Report the number of females with excessive drinking. (1 mark)
• Suppose you are interested in categorizing the supporters into different anxiety groups using the following criteria:
  • Low anxiety score (0 less than 50)
  • Moderate anxiety score (50 -less than 80)
  • High anxiety score (80+)

• Generate a new variable (anxiety_group) based on the criteria above. (1 mark)

• Add value labels, tabulate anxiety_group and interpret the table results. (2 marks)
• Some studies found that male rugby fans had much higher representation in the high anxiety classification category than female fans. Is this true for this sample? (1 mark)
• In Stata, using the drop-down menu, create a histogram of supporters’ total cholesterol with frequency on the y-axis and be sure to include height labels. Give the graph an appropriate title, axes titles and footnote. (2 marks)
• Comment on the data distribution of total cholesterol. (0.5 marks)
• What is the estimated probability of having 3 to 7 drinks per game? Give your answer as a percentage to 1 decimal place. (2 marks)
• Which group shows the largest spread in anxiety score: smokers or non-smokers? (2 marks)

Summary of Assessment Requirements

The assessment focuses on analysing a survey dataset (AT1_anxiety) related to anxiety levels and lifestyle factors among adult rugby supporters. Students are required to demonstrate their understanding of sampling techniques, variable types, data validation, descriptive analysis, and basic statistical interpretation. Key tasks include:

1. Sampling & Population Concepts

• Identify the sampling technique used in data collection.
• Recommend a suitable method to ensure representation of supporters from both teams.
• Define the target population.
• Identify an appropriate sampling frame.
• Assess whether study findings can be generalised to the general public.

2. Variable Understanding

• Specify the variable type for the anxiety score.

3. Data Cleaning & Exploratory Analysis

• Check all variables for invalid or inconsistent values.
• Explain steps taken to clean or correct these issues.
• Comment on how inconsistencies may have occurred.

4. Descriptive Statistics & Categorisation

• Calculate the percentage of supporters reporting excessive drinking (>3 drinks).
• Identify the number of females with excessive drinking.
• Create a new categorical variable (anxiety_group) based on defined thresholds.
• Apply value labels, generate a frequency table, and interpret the distribution.

5. Comparative Findings

• Determine whether male fans show greater representation in the high anxiety category compared to female fans.

6. Graphical Analysis

• Create a histogram of total cholesterol using Stata’s drop-down menus with proper titles, axes labels, and footnotes.
• Comment on the distribution pattern.

7. Probability & Spread

• Estimate the probability (in percentage) of consuming 37 drinks per game.
• Identify whether smokers or non-smokers exhibit a larger spread of anxiety scores.

How the Academic Mentor Guided the Student (Step-by-Step Approach)

Step 1: Understanding the Purpose of the Task

The mentor began by helping the student understand that the assessment tests both statistical reasoning and practical data management skills. Before working on the dataset, the mentor clarified every question and grouped them into conceptual clusters for easier navigation.

Step 2: Breaking Down Sampling & Population Questions

The mentor first explained types of sampling, target population definitions, sampling frames, and generalisability.
The student was encouraged to reason logically based on how the data were collected at the rugby match.
This step built foundational understanding for the later answers.

Step 3: Identifying Variable Types

To correctly classify the anxiety score variable, the mentor reviewed variable measurement scales (categorical, ordinal, continuous).
The student learned how statistical decisions rely on correct variable classification.

Step 4: Conducting Exploratory Data Analysis (EDA)

The mentor guided the student through EDA in a structured workflow:

1. Scan all variables for missing, impossible, or inconsistent values.
2. Check ranges and codes using commands like summarize, codebook, or browsing the dataset.
3. Correct or recode invalid values based on documentation.
4. Document each cleaning step clearly to demonstrate transparency and reproducibility.

The mentor explained how errors could arise from:
manual data entry misunderstanding survey questions respondent bias or incorrect reporting.

Step 5: Computing Percentages & Counts

The mentor showed the student how to:

• Calculate percentages (e.g., excessive drinkers)
• Filter data to identify females with excessive drinking
  This introduced the student to conditional counting and summary functions.

Step 6: Creating a Categorical Variable

The mentor taught the student how to transform continuous variables into categories (low, moderate, high anxiety).
Together they applied range conditions, created the new variable, and added value labels.
The mentor then guided the student through interpreting frequency tables meaningfully.

Step 7: Comparing Male vs Female Anxiety Groups

The student was coached to:

• Tabulate anxiety_group by sex
• Identify which category has higher representation for each gender
• Interpret patterns based on counts and percentages.

This reinforced skills in cross-tabulation and comparative interpretation.

Step 8: Producing a Histogram in Stata

The mentor walked the student through the drop-down menus in Stata:

• Choosing histogram
• Selecting total cholesterol variable
• Setting frequency on the y-axis
• Adding graph title, axis labels, and footnotes.

The student then learned how to comment on shape, spread, and skewness.

Step 9: Calculating Probability & Spread

The mentor demonstrated:

• How to calculate probability of drinking 37 drinks
• How to compute and compare variability (range or standard deviation) between smokers and non-smokers

This helped the student understand probability estimation and spread analysis.

Final Outcome of the Guided Process

With the mentor’s structured guidance, the student successfully:

• Addressed every assessment question thoroughly
• Conducted complete exploratory data analysis
• Cleaned and validated data logically
• Created and labelled new categorical variables
• Interpreted frequency tables, distributions, and comparisons
• Generated accurate summary statistics and graphs
• Strengthened understanding of sampling, population definitions, and generalisability

The final solution reflected strong statistical reasoning, clear explanations, and correct Stata-based analysis.

Learning Objectives Achieved

By the end of the assessment, the student gained solid competency in:

1. Understanding sampling methods, target populations, and representativeness
2. Classifying variables correctly
3. Performing comprehensive exploratory data analysis
4. Identifying and fixing inconsistent or invalid data
5. Calculating percentages, proportions, and probabilities
6. Constructing and interpreting categorical variables
7. Using Stata to generate graphs and summaries
8. Analysing distribution and variability among groups
9. Drawing logical conclusions from data patterns

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