What is the Data Analysis chapter?
The Data Analysis chapter is where you present the results of your research and explain what they mean. It is the core of your project — this is where raw numbers become findings. For IGNOU MCom projects, clarity and honest interpretation matter more than fancy statistics.
Prepare your data before analysis
- Clean the data: Remove duplicates, correct obvious errors, check missing values.
- Organise in tables: Use Excel to keep raw data, and create summary tables for analysis.
- Label clearly: Give descriptive headings and units (e.g., income in ₹, age in years).
- Code responses: Convert text answers into codes where needed (e.g., 1 = Male, 2 = Female).
Simple, neat data reduces mistakes during analysis and makes your report look professional.
Common analysis tools and techniques
For most MCom projects the following tools are adequate and practical:
- MS Excel: For tables, charts, basic stats (mean, median, percentage).
- SPSS / PSPP: For tests like chi-square, t-test, ANOVA (useful but not mandatory).
- Manual calculations: For ratio analysis, trend percentages, common-size statements in finance projects.
Use the simplest tool that answers your research questions reliably.
How to structure the Data Analysis chapter
Follow a logical flow — present summary first, then detailed analysis, then interpretation.
- Start with a short intro: Restate objectives and data sources briefly.
- Present sample profile: Show respondent demographics in a small table.
- Show key findings with tables/charts: Use one table or chart per result.
- Interpret results: Explain what the numbers mean in simple language.
- Relate to objectives: Link each analysis to specific research objectives.
- Summary of findings: End with 4–6 bullet points of main results.
Example: Presenting sample profile
| Characteristic | Frequency (n=120) | Percentage |
|---|---|---|
| Age (18–25 years) | 45 | 37.5% |
| Age (26–35 years) | 50 | 41.7% |
| Age (36+ years) | 25 | 20.8% |
| Male | 70 | 58.3% |
| Female | 50 | 41.7% |
After the table, write a simple sentence: “Most respondents (41.7%) were aged 26–35; males formed 58.3% of the sample.”
Example: Cross-tabulation and interpretation
Cross-tabs help show relationships between two variables (for example, age group and use of online banking).
If you use chi-square to test association, report the test result plainly: “Chi-square test (χ²=6.2, p=0.013) indicates a significant association between age group and mobile banking usage.”
How to present financial analysis
For finance/accounting projects, present common-size statements, ratio analysis and trend analysis clearly.
- Show a table of ratios for 3–5 years (e.g., Current Ratio, ROA, Profit Margin).
- Plot trends in a simple line chart (can be exported from Excel).
- Interpret trends: e.g., “Profit margin increased from 6% to 9% over three years, indicating improved cost control.”
Tips for charts and tables
- Use simple charts (bar, column, line, pie) — not too many per page.
- Label axes and provide source notes where needed.
- Each table/chart must have a short caption and be referred in text (e.g., “Table 4.2 shows…”).
- Avoid colourful or decorative charts — keep them clean and readable for examiners.
Interpretation — the most important part
Numbers are useless without explanation. For each result:
- State the finding in one line (what the data shows).
- Explain why it might be so (link to theory or practical reasons).
- Compare with previous studies where relevant (“This finding aligns with Sharma (2019)…”).
- State implications (for business, policy or future research).
Write interpretations in simple language — this helps examiners and non-technical readers.
Common mistakes in Data Analysis and how to fix them
| Mistake | Fix |
|---|---|
| Showing tables without explanation | Always add one or two sentences interpreting each table/chart |
| Using complex tests without justification | Choose tests that answer your research question and explain why you used them |
| Poorly labelled charts | Label axes, add captions and units |
| Over-interpreting non-significant results | Be honest — report non-significant findings and possible reasons |
Final summary and how to end the chapter
Conclude the Data Analysis chapter with a short summary of key findings (4–6 bullets). Do not include suggestions here — save that for the Findings & Suggestions chapter. Example summary:
- Majority of respondents are young professionals (26–35 years).
- High adoption of mobile banking among 26–35 age group (χ² significant).
- Customer satisfaction scores are moderate (mean = 3.4/5), with convenience scoring highest.
- Financial ratios show gradual improvement over three years for Company X.
Quick checklist before you finalise Data Analysis
- Are tables and charts clear and properly captioned?
- Did you explain each table/chart in simple words?
- Are statistical tests appropriate and results reported correctly?
- Have you linked analysis to research objectives?
- Are limitations of data acknowledged?