**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.

- correlation
- anova
- manova
- regression
- 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

* Visualise the “expected” answers

* 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.