1. Analyzing Information??

2. A: Hi, how are you doing. B: What do you mean when you say “how”
A: Hi, how are you doing? B: What do you mean when you say “how”? A: You know. What’s happening with you? B: What do you mean “happening”? A: Happening, you know, what’s going on. B: I’m sorry. Could you explain what you mean by “what”? A: What do you mean, what I mean? Do you want to talk to me or not?

3. What is an analysis?
An analysis is an argument in which you study the parts of something to understand
how it works?
what it means?
Why it might be significant?
It is basically when you use a principle or definition (or any analytical tool) on the basis of which an object, an event, or a behavior can be divided into parts and examined to reach a conclusion.

4. Things are analyzed To identify key elements To identify their causes
To identify their possible results

5. Program/Event Data Collection Administrative Data Collection
Method Choices
Qualitative
Quantitative
Observation
Observing behavior of individuals or groups in a setting
Program/Event Data Collection
Collecting and organizing data about a program or event
and its participants
Surveying
Administering a structured series of questions with discrete choices
Focus Groups
Facilitating a discussion about a particular issue/question among people who share common characteristics
Administrative Data Collection
Collecting data (related to program outcomes) that can be obtained from local and federal government sources
Case Studies
Using a combination of methods (e.g interviewing, surveying, program data) to describe experiences of people or groups in a real-life setting
Interviews
Conducting one-on-one interviews with key people knowledgeable about a subject

6. Triangulation of Data
When a piece of data or finding is able to be verified with several different research methods.

7. Qualitative Analysis Read through all data
Organize comments into similar themes
Label themes
Identify patterns, or associations and causal relationships

8. Quantitative Analysis
Use mathematical/ computer programs to tabulate information
For scale questions, consider computing a mean, or average
For ranking questions consider conveying the range of answers

9. Research Philosophy: Contrasting Positivist and Interpretive approaches
Reality is “real” – exists independent of human consciousness
Human beings are rational creatures governed by social laws
Science is based on strict rules based on universal causal laws
Science is value free
Interpretive
Reality is in the minds of people
Human beings are actors who create social reality by assigning meaning systems to events
Science represents reality symbolically in a descriptive way
Science is not value free, value neutrality is neither necessary or possible
A research philosophy is a belief about the way in which data about a phenomenon
should be gathered, analysed and used. The term epistemology (what is known to
be true) as opposed to doxology (what is believed to be true) encompasses the
various philosophies of research approach. The purpose of science, then, is the
process of transforming things believed into things known: doxa to episteme. Two
major research philosophies have been identified in the Western tradition of science,
namely positivist (sometimes called scientific) and interpretivist (also known as antipositivist)(
Galliers, 1991).
Introduction . Problem . Literature . Data . Quantitative . Qualitative . Presentation . Cases .

10. Contrasting Positivist and Interpretive approaches
Controlled setting for research
Subject is an object in the research
Research design fixed
Researcher ‘outside’
Emphasis on reliability
Interpretive
Complex, real world setting for research
Subject is a participant in the research process
Research design evolving
Researcher ‘inside’
Emphasis on validity
Introduction . Problem . Literature . Data . Quantitative . Qualitative . Presentation . Cases .

11. Data Analysis Descriptive Data Analysis
The results are merely used to provide a summary of what has been gathered (e.g., how many liked or dislike a product) without making a statement of whether the results hold up to statistical evaluation.
For quantitative data collection the most common methods used for this basic level of analysis are visual representations, such as graphs, charts and tables, and measures of central tendency including averages (i.e., mean value).
For qualitative data collection, where analysis may consist of the researcher’s own interpretation of what was learned, the information may be coded or summarized into grouping categories.

12. Inferential Data Analysis
The results of research should allow the researcher to accomplish other goals such as:
Using information obtained from a small group (i.e., sample of customers) to make judgments about a larger group (i.e., all customers)
Comparing groups to see if there is a difference in how they respond to an issue
Forecasting what may happen based on collected information

13. The Crisis of Interpretation
If there are no absolute truths and everything is relative, how can we say anything definitive about anything?

14. Interpretation
“Interpretation is an art; it is not formulaic of mechanical” (Denzin, 2007, p. 317).
However, it is based on systematic approaches to data collection and analysis.
It can only be learned through doing.

15. Strategies for Interpretation
Looking for patterns
Clustering – grouping similar experiences or phenomena
Creating metaphors – mapping abstract ideas on to more concrete ideas
Counting – tallying instances of particular ideas or experiences

16. Strategies for Interpretation
Comparing and contrasting
Factoring – determining a “factor” for a set of related words or facts – i.e. what they do or what they are an example of.

17. Strategies for Interpretation
8. Building a logical chain of evidence – in explaining how and why particular processes occur (cause and effect), provide evidence at every stage of that process. Movement through the stages must be logical and substantiated by the data.

18. Validity Threats
Holistic fallacy – ignoring discrepant data and interpreting events or experiences as more patterned or uniform than they actually are.
Elite bias – over-relying on more prominent, powerful or articulate informants and negating other voices.

19. A good analysis
Should report accomplishment by highlighting major findings.
Should interpret your data by making suggestions as to why the results are the way they are.
Should relate and evaluate the data in the light of previous research.
Should anticipate and deal with potential criticism.

20. Activity
“Young hospitalized children should be asked the methods for relieving their pain.”
Secondary data: Children aged 8-12 years old are completely capable of describing what they are feeling and what exactly they want (Pederson, 2000)
Primary data:
Dr. Ahson says: School-aged children feel independent by giving suggestions to doctors.(interview)
Noman (9 year old, hospitalized boy) says: I like my doctor because whenever I ask the doctor not to give me an injection he doesn’t.(interview)
When your child was 6 years old was he/she able to express himself/herself clearly?
Yes……30 %
No…….70% (survey)

21. Summary Vs. Analysis
While summary just describes things, Analysis gives insight into the significance of a piece's meaning

22. Analyzing Data What pattern do you see? What does this graph tell you?
Who could use this data? How could they use it?
Why is this data shown in a ______?

23. Amount spent on various items by tourists during 2012-2013