Techniques of Data Analysis
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
- When analysing:
* Be objective
- Separate facts and opinion
- Avoid “wrong” reasoning/argument. E.g. mistakes in interpretation.