Data Analysis Techniques.ppt presentation

Data Analysis Techniques.ppt presentation

2. Overall: Reinforce your understanding from the
First two Lectures
Specific:
Concepts of data analysis
Some data analysis techniques
Some tips for data analysis

3. 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

4. Data Information Knowledge
Statements about reality
(Acharya, 2001).
Organized, systematized
data (Acharya, 2001).
Human interaction with
reality (Acharya, 2001).
Unsorted bits of fact (Dixon,
2000).
Data that has been
sorted, analyzed, and
displayed (Dixon, 2000).
Meaning full links that
people make in their
minds between
information and its
application in action (Dixon,
2000).
Fact, number, word, image,
picture, or sound
Measurements (Applehans et al.,
1999).
Data that has been
assigned a meaning
(Liebowtiz and Wilcox, 1999).
Insights, experiences, and
procedures, that guide
the thoughts, behavior,
and communication of
people (Liebowtiz and Wilcox, 1999).
Discrete, objective, and
non-contextual raw facts,
images, or sounds
constitute the database
which will be interpreted and
meaning attached (Tobin, 1996;
Beckman, 1997; Liebowitz, 1999).
filtered, formatted,
summarized, meaningful
data. This is the basis for
action and applications
(Tobin, 1996; Beckman, 1997; Liebowitz,
1999).
represented by ideas,
rules, and procedures that
guide actions and
decisions (Tobin, 1996; Beckman,
1997; Liebowitz, 1999).

5. Narrative (e.g. laws, arts)
Descriptive (e.g. social sciences)
Statistical/mathematical(pure/applied
sciences/Technological)
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

6. Simple and Multiple Regression
ANOVA (Analysis of Variance)
Simple , Multiple, and Partial Correlation Analysis
Multivariate Analysis Techniques- Factor Analysis, Multivariate
Analysis of Variance (MANOVA)
Structural Equation Modelling, Path Analysis
Cluster Analysis
Sensitivity Analysis
Set theory
Game Theory
Diffusion Innovation theory
Linear Programming and Non Linear Programming
Data Envelopment Analysis
Quantitative Analysis-Multi Criteria Decision Making Methods,
Advanced Optimization Techniques.

7. Crystalize the research problem
→
operability of it!
Read literature on data analysis techniques.
Evaluate various techniques that can do
similar things w.r.t. to research problem
Know what a technique does and what it
doesn’t
Consult people, esp. supervisor
Pilot-run the data and evaluate results
Don’t do research??

8. 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

9.  Data can’t “talk”
 An analysis contains some aspects of scientific
reasoning/argument:
* Define
* Interpret
* Evaluate
* Illustrate
* Discuss
* Explain
* Clarify
* Compare
* Contrast

10. 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,…)

11. 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

12. To make sure the questions and your data
collection instrument will get the information
you want.
To align your desired “report” with the results
of analysis and interpretation.
To improve reliability--consistent measures over
time.