1. Data Analysis & Processing Fundamentals
Meaning of Data Analysis: Data analysis is the process of gathering, arranging, classifying, modeling, and analyzing data with the purpose of generating useful information, making suggestions, supporting conclusions, and aiding decision-making.
💡 In Simple Words:
Data analysis transforms raw facts into actionable insights through
systematically grouping, modeling, and evaluating data.
5 Data Processing Procedures
1. Editing
2. Coding
3. Classification
4. Tabulation
5. Summarizing
Data processing techniques make raw data valid, simple, reliable, and organized by nature, quality, and trends.
Tabulation: Advantages & Essential Structure
5 Advantages of Tabulation
- Makes data easily understandable
- Facilitates quick comparison
- Saves time and energy
- Avoids unnecessary repetition
- Aids easy retention/memory
8 Main Parts of a Table
- Table Number
- Table Title
- Column Caption
- Row Title (Stub)
- Body of Table
- Head Note
- Footnote
- Sources
2. Diagrams & Graphical Presentations
8 Rules for Constructing Diagrams
🧠 Mnemonic: T-P-S-N-F-S-S-I
(Title, Proportion, Scale, Neatness, Footnote, Selection,
Simplicity, Index)
- Title: Clear and self-explanatory header.
- Proper Proportion: Balanced ratio between width and height.
- Selection of Scale: Appropriate units for accurate presentation.
- Neatness & Cleanliness: Legible layout without clutter.
- Footnote: Clarifying details appended at the bottom.
- Selection of Diagram: Right chart type for data nature.
- Simplicity: Easy to comprehend visually.
- Index: Guide key to symbols or color codes used.
Classification of Diagrams and Graphs
| Category | Types & Descriptions |
|---|---|
| Bar Diagrams |
• Simple:
Presents single dimension/characteristic. • Sub-divided: Displays total values divided into constituent parts. • Percentage: Highlights component variables in percentage format. • Multiple: Displays two or more data sets side-by-side. |
| Pie Chart | Diagrammatic presentation of data converting percentages into proportions of a 360° circle. |
| Types of Graphs |
• Time Series Graph:
Data distribution over time units. • Scatter Diagram: Maps bivariate distribution between two variables. • Functional Graphs: Shows Linear (straight line) vs. Non-linear (curved line) relationships. |
3. Quantitative Analysis: Descriptive & Inferential Statistics
Descriptive Statistics
Tools used to explain activities or fundamental characteristics of data.
- Frequency Distribution
- Central Tendency: Mean (Simple, Weighted, Geometric), Median
- Dispersion: Range, Quartile Deviation, Mean Deviation, Standard Deviation, CV
Inferential Statistics
Estimates population parameters based on sample data analysis.
- Estimation Statistics: Confidence intervals & Parameter estimation
- Hypothesis Testing: Evaluating population assumptions
5-Step Procedure for Hypothesis Testing
1. State H₀ & H₁
2. Set Significance Level (α)
3. Select Test Statistic
4. Obtain Critical Value
5. Make Conclusion
(Hypothesis testing relies on evaluating calculated values against critical distribution thresholds).
Parametric vs. Non-Parametric Hypothesis Tests
| Category | Key Features | Common Statistical Tests Included |
|---|---|---|
| Parametric Tests | Assumes samples are drawn from a normally distributed population. |
• z-test • t-test • Two independent sample test • Two related sample test • K-independent sample test • ANOVA (F-test for >2 sample means) |
| Non-Parametric Tests | Distribution-free tests; makes no explicit distributional assumptions. |
• One-sample Chi-Square (χ²) test • Chi-Square test for 2 independent samples • Two related sample test • K-independent & K-related sample tests |
4. Measures of Association & Advanced Techniques
4 Key Statistical Measures of Association
- Correlation: Evaluates the directional relationship between dependent and independent variables.
- Regression Analysis: Measures the precise degree of dependence/impact of independent variables on dependent variables.
- Time Series Analysis: Examines variations across time intervals to identify trends.
- Multivariate Analysis: Examines datasets involving simultaneous measurements across multiple variables.
💡 ANOVA Execution Steps:
Construct Hypothesis → Conduct Statistical Test →
Set Significance Level → Calculate F-Value →
Check Critical Value → Formulate Interpretation.
5. Qualitative Data Analysis Methods
Qualitative Data Analysis: Data expressed subjectively or in language format rather than numbers (gathered via observation, interviews, or focus groups).
3 Core Steps in Qualitative Processing
1. Data Reduction
2. Data Presentation
3. Drawing Conclusions
(These foundational steps convert complex qualitative transcripts into systematic conclusions).
Comparison of 3 Major Qualitative Analysis Methods
| Method | Core Definition & Features | Key Steps / Elements |
|---|---|---|
| 1. Content Analysis |
Systematic, objective, and quantitative description
of verbal or written data. Features: Systematic, Objectivity, Generalizability. |
1. Identify essential data 2. Develop tabulation bases 3. Classify variables & design categories 4. Establish material procedures 5. Prepare analysis outline |
| 2. Narrative Analysis |
Recording and analyzing info based on stories told
by respondents related to specific events. Elements: Data collection, analysis, understanding key actors/events. |
1. Obtain data 2. Focus on autobiography/interview data 3. Codify data using signs/symbols 4. Identify relationships among classes |
| 3. Thematic Analysis | Identifies, searches, and records recurring themes/patterns of data relevant to phenomena. |
1. Review literature 2. Generate initial codes 3. Search for themes 4. Review & refine themes 5. Define/name themes 6. Prepare final report |
