The document discusses the concepts of research, the scientific method, and big data. It defines research as the systematic pursuit of knowledge to describe facts and observations, explain relationships between variables, and predict outcomes based on theories and models. The scientific method is described as cumulative, iterative, and objective but also influenced by values and beliefs. The document cautions that research studies often only apply conditionally and their results may not extrapolate if the conditions change. It notes that predicting human behaviors is more difficult than chemical reactions since people can alter their actions based on research findings. Finally, the document discusses how big data and data-driven decision making are influencing many fields but that larger data also increases potential for bias.

1. KRG
Musings from the Chair

2. What is research?
• Accumulation of information?
• Developing theories?
• Creating new knowledge?
• Proving something is true or false?
• Stuff your grandmother already knows?

3. Research: The Scientific Method
• The systematic pursuit of knowledge with a view:
• To describe
• Facts, observations, data
• Validity, reliability, objectivity
• To explain
• Laws
• Stable dependency between variables
• To predict
• Theories and models
• Constructs, hypotheses, postulates

4. Scientific method
• Process versus product
• Cumulative & iterative (new ideas build on the old)
• Public (ideas are open to discussion and inspection)
• Parsimonious (describe, explain, predict)
• Objective (‘unbiased’)
• Misconceptions about science
• Science is always correct (informed opinion)
• Science is always conducted via an idealized method (try and try again)
• Science is always objective (confirmation bias; beliefs; preconceptions)
• Power/abuse of numbers (“Misunderstanding of probability may be the greatest
impediment to scientific literacy.” Stephen Jay Gould)
• Limits to a shared understanding because of race, culture, gender,
social history
• Values and beliefs inextricably linked to what is viewed as legitimate science
• Informed consumers of knowledge
• Given a thimble of facts we make a bathtub of generalizations

5. Misunderstandings arising from
ignoring the condition
• Many research studies involving people study a fairly
restricted group. Thus they result in conditional
probabilities with a fairly restricted condition.
Unfortunately, all too often this restriction is not
emphasized enough. For example, a study of a
cholesterol-lowering medication might be restricted to
men between the ages of 45 and 65 who have previously
had a heart attack. If a physician decides, on the basis of
this study, to prescribe the medication to a woman who is
70 years old and has no previous record of heart attacks,
the physician is extrapolating; the applicability of the study
to this quite different group of people is questionable.

6. The ‘Hard’ Sciences
• In at least one sense the social sciences are truly the “hard”
sciences. A problem unique to these sciences is that the
subjects can read. Because of this, developing a theory to
understand and predict an election outcome or a stock market
crash is fundamentally more difficult than predicting a chemical
reaction or an earthquake. In the latter cases, the publication
of the theory will not effect the prediction. However, for an
election or stock market crash, if the prediction is made public,
and the theory is convincing, individuals have incentives to take
advantage of this information and alter their behaviour. They
made decide to sell off their stocks if they believe a crash is
imminent which may precipitate the crash earlier than predicted
thus making the theory wrong. Any good theory must work
even when the participants know the theory, that is, the theory
must survive its own publication.

7. Big Data

8. Big Data

9. Big Data

10. The age of big data
• D3M or DDDM (data driven decision making)
• DIDM (data informed decision making)
• Data includes numbers, images, text, video, data streams
• Predict will need 1.5 million data literate managers and
200,000 individuals with ‘deep analytic skills’
• Needs from economics to politics to sport
• No area will be untouched
• Moneyball
• Use of data in sports like tennis and soccer
• Public health policy
• Mapping sporting events onto crime hot spots
• Double edged sword
• Bigger the data the bigger the potential bias errors

11. Big Data

12. Managing Big Data
• Understand the underlying question
• Formulate hypotheses
• Collect data
• Gather insights
• Make recommendations
• Question driven research process

13. Writing your abstract
Question
Literature
Purpose
Hypotheses
Method
Analysis
Discussion
Deductive (Quantitative)
Start with a theory that has specific
hypotheses that are then tested
Inductive (Qualitative)
Start with observations and develop
hypotheses and a theory

14. Process of Analysis