Data analysis – “The Concept
Approach to de-synthesizing data, informational, and/or factual elements to answer research questions
Method of putting together facts and figures to solve research problem
Systematic process of utilizing data to address research questions
Breaking down research issues through utilizing controlled data and factual information
- Categories of data analysis
- Narrative (e.g. laws, arts)
- Descriptive (e.g. social sciences)
- Statistical/mathematical (pure/applied sciences)
- Audio-Optical (e.g. telecommunication)
Most research analyses, arguably, adopt the first three.
The second and third are, arguably, most popular in pure, applied, and social sciences
Something to do with “statistics”
Statistics: “meaningful” quantities about a sample of objects, things, persons, events, phenomena, etc.
Widely used in social sciences.
Simple to complex issues. E.g.
- econometric modelling
Two main categories:
- Descriptive statistics
- Inferential statistics
Use sample information to explain/make abstraction of population “phenomena”.
* Association (e.g. σ1,2.3 = 0.75)
* Tendency (left-skew, right-skew)
* Causal relationship (e.g. if X, then, Y)
* Trend, pattern, dispersion, range
Used in non-parametric analysis (e.g. chi-square, t-test, 2-way Anova)
Using sample statistics to infer some “phenomena” of population parameters
Common “phenomena”: cause-and-effect
* One-way r/ship
* Multi-directional r/ship
Use parametric analysis
Which one to use?
Nature of research
* Descriptive in nature?
* Attempts to “infer”, “predict”, find “cause-and-effect”, “influence”, “relationship”?
* Is it both?
Research design (incl. variables involved). E.g.
* research issue
* research questions
* research hypotheses
Principles of analysis
Goal of an analysis:
* To explain cause-and-effect phenomena
* To relate research with real-world event
* To predict/forecast the real-world phenomena based on research
* Finding answers to a particular problem
* Making conclusions about real-world event based on the problem
* Learning a lesson from the problem
Data can’t “talk”
An analysis contains some aspects of scientific reasoning/argument:
An analysis must have four elements:
* Data/information (what)
* Scientific reasoning/argument (what? who? where? how? what happens?)
* Finding (what results?)
* Lesson/conclusion (so what? so how? therefore…)
Basic guide to data analysis:
* “Analyse” NOT “narrate”
* Go back to research flowchart
* Break down into research objectives and research questions
* Identify phenomena to be investigated
* Visualise the “expected” answers
* Validate the answers with data
* Don’t tell something not supported by data
* Be objective
Separate facts and opinion
Avoid “wrong” reasoning/argument. E.g. mistakes in interpretation.