Before we get to crunching numbers, let’s take a moment to answer the question,
Where is all this data coming from? And how do we make sense of it?
Primary vs. Secondary Data
Primary Data: Data collected firsthand by the researcher for a specific purpose.
- Original, direct source
- Time-consuming but specific to the research problem
- Example: Conducting a survey on consumer preferences
Secondary Data: Data already collected and published by someone else.
- Less costly and time-saving
- May not align perfectly with your study’s needs
- Example: Census reports, government publications
Methods of Primary Data Collection
Secondary Data Sources
- Government Publications: Census, Economic Surveys, SEBI Reports
- Academic Sources: Research papers, theses
- Databases: World Bank, IMF, RBI, CMIE
- Industry Reports: Business houses, Consultancy reports
Classification of Data
Quantitative vs. Qualitative
Quantitative: Numerical data – measurable and countable (e.g., sales, height).
Qualitative: Non-numeric – descriptive characteristics (e.g., opinions, gender).
Discrete vs. Continuous
Discrete: Countable, distinct values (e.g., number of workers).
Continuous: Can take any value within a range (e.g., weight, time).
Scales of Measurement
- Nominal: Categories with no inherent order (e.g., gender, blood group)
- Ordinal: Ordered categories (e.g., ranks – 1st, 2nd, 3rd)
- Interval: Numeric scales with equal intervals, no true zero (e.g., temperature in Celsius)
- Ratio: Like interval but with a true zero (e.g., weight, height, age)
Tabulation and Data Summary Basics
Once data is collected and classified, it's time to present it in digestible formats:
- Frequency Distribution Tables: Show how often each value occurs
- Bar Charts and Histograms: Graphical summaries
- Pie Charts: Useful for proportions and categories
Why Does It Matter?
Because all analysis begins with data. And bad data? It means bad decisions.
Knowing the difference between nominal and ordinal could mean understanding whether you're allowed to take an average or not.
It’s not just theory. These distinctions shape every statistical method you'll learn next.
NOTE:
Don't rush past data collection and classification as they’re the invisible roots of sound research. If you build a strong base here, everything else in statistics becomes a whole lot easier.
Reflection: Next time you look at a report or graph, ask yourself where did this data come from, and how was it classified?