Analysing quantitative data from a questionnaire involves systematically processing numerical responses to uncover insights and trends. This typically includes comparing results, segmenting audiences, and using statistical measures and visualisations.
Key Methods for Analysing Questionnaire Data
Based on common practices and the provided references, here are effective ways to analyse quantitative data:
1. Make Simple Comparisons
Simple comparisons are fundamental for understanding basic preferences and trends revealed by your data, especially from questions like multiple-choice surveys designed to identify user preferences.
- How to do it: Calculate frequencies or percentages for each response option in a question.
- Example: If 60% of respondents chose 'Product A' and 40% chose 'Product B' in a preference question, you can simply compare these percentages to see which product is preferred overall.
- Insight: This quick method helps identify dominant choices or areas of strongest agreement/disagreement within your survey group.
2. Use Cross-Tabulation Charts and Graphs
Cross-tabulation (or crosstab) is a powerful technique to compare results from different audience segments. It allows you to see how responses to one question vary depending on responses to another question or based on demographic data (like age, gender, location).
- How to do it: Create tables that display the relationship between two or more variables. For instance, you could cross-tabulate product preference by age group. Visualise this using clustered bar charts or stacked bar charts.
- Example: A crosstab table might show that while 'Product A' is preferred overall, 'Product B' is significantly more popular among the 18-24 age group compared to older demographics.
- Insight: This reveals nuanced patterns and helps tailor strategies for specific segments. It moves beyond overall averages to show how different groups behave or feel.
3. Analyze Scale Data Using Mode, Mean, and Bar Charts
Scale data, such as responses from Likert scales (e.g., 1-5, Strongly Disagree to Strongly Agree) or rating scales, requires specific analytical tools.
- Mode: The most frequent response.
- Use: Quickly identify the most common opinion or rating.
- Example: If most people chose '4' on a 1-5 satisfaction scale, the mode is 4.
- Mean: The average response.
- Use: Get an overall average score for a question. This is useful for comparing the average sentiment across different items or groups.
- Example: The mean score for satisfaction might be 3.8, indicating a generally positive sentiment.
- Bar Charts: Visualise the distribution of responses across the scale.
- Use: Easily see the spread of opinions, identify whether responses cluster towards one end of the scale, or if there's a bimodal distribution (two common responses).
- Example: A bar chart showing the percentage of respondents selecting each point on a 5-point scale gives a clear picture of how opinions are distributed.
Practical Steps for Analysis:
- Clean your data: Remove incomplete responses or errors.
- Organise data: Structure your quantitative data, often in a spreadsheet format.
- Choose your tools: Use statistical software (like SPSS, R, Python) or spreadsheet programs (like Excel, Google Sheets) with analytical features.
- Apply methods: Conduct the analyses described above (simple comparisons, cross-tabs, descriptive statistics for scale data).
- Visualise results: Create charts and graphs (bar charts, pie charts, line graphs) to make findings easy to understand.
- Interpret findings: Look for patterns, significant differences, and correlations in your results.
- Report insights: Summarise your findings clearly, highlighting key takeaways relevant to your research objectives.
By applying these techniques, you can transform raw questionnaire data into actionable insights about your audience's preferences, attitudes, and behaviours.
