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)
• Others

Most research analyses, arguably, adopt the first three.

The second and third are, arguably, most popular  in pure, applied, and social sciences

Statistical Methods

• 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.
1.  correlation
2. anova
3. manova
4. regression
5. econometric modelling

Two main categories:

• Descriptive statistics
• Inferential statistics

Descriptive statistics

Use sample information to explain/make abstraction of population “phenomena”.

Common “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)

Inferential statistics

Using sample statistics to infer some “phenomena” of population parameters

Common “phenomena”: cause-and-effect   * One-way r/ship

* Multi-directional r/ship

* Recursive

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.

Outputs/results expected

* 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:

* Define

* Interpret

* Evaluate

* Illustrate

* Discuss

* Explain

* Clarify

* Compare

* Contrast

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

* Validate the answers with data

* Don’t tell something not supported by data

• When analysing:

* Be objective

* Accurate

* True

• Separate facts and opinion
• Avoid “wrong” reasoning/argument. E.g. mistakes in interpretation.