IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012
-
Upload
ignatius-mcleod -
Category
Documents
-
view
34 -
download
3
description
Transcript of IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012
![Page 1: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/1.jpg)
IT 499: Seminar CourseWeek 3
Faculty: Dr. Afshan Jafri
28 February 2012
![Page 2: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/2.jpg)
2
Outline
1. What is research?
2. How to prepare yourself for IT research?
3. How to identify and define a good IT research problem?
- Research Area
- Research Question / Topic
4. How to solve it?
- Research methods
- Research phases
5. How to write and publish an IT paper?
6. Research Ethics
![Page 3: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/3.jpg)
How to solve it?
• Understanding the problem
– Distinguishing the unknown, the data and the condition
• Devising a plan
– Connecting the data to the unknown, finding related problems, relying on previous findings
• Carrying out the plan
– Validating each step, (if possible) proving correctness
• Looking back
– Checking the results, contemplating alternative solutions, exploring further potential of result/method
![Page 4: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/4.jpg)
Research Methods
![Page 5: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/5.jpg)
A Research Method Classification
• Scientific: understanding nature
• Engineering: providing solutions
• Empirical: data centric models
• Analytical: theoretical formalism
• Computing: hybrid of methods
From W.R.Adrion, Research Methodology in Software Engineering, ACM SE Notes, Jan. 1993
![Page 6: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/6.jpg)
Scientist vs. Engineer
• A scientist sees a phenomenon and asks “why?” and proceeds to research the answer to the question.
• An engineer sees a practical problem and wants to know “how” to solve it and “how” to implement that solution, or “how” to do it better if a solution exists.
• A scientist builds in order to learn, but an engineer learns in order to build
![Page 7: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/7.jpg)
The Scientific Method
• Observe
• Propose a model
• Measure and analyze
• Validate model/theory
• Repeat
Observe real world
Propose a model or theoryof some real world phenomena
Measure and analyzeabove
Validate hypotheses of the model or theory
If possible, repeat
![Page 8: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/8.jpg)
The Engineering Method
• Observe (solutions)
• Propose improvement (build/develop)
• Measure and analyze
• Repeat, until no further improvement
Observe existing solutions
Propose better solutions
Build or develop bettersolution
Measure, analyze, andevaluate
Repeat until no further improvements are possible
![Page 9: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/9.jpg)
The Empirical Method
• Propose a model
– Develop statistical or other methods
• Apply to case studies
• Measure and analyze
• Validate
• Repeat
Propose a model
Develop statistical or otherbasis for the model
Apply to case studies
Measure and analyze
Validate and then repeat
![Page 10: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/10.jpg)
The Analytical Method
• Propose a formal theory or set of axioms
• Develop a theory
• Derive results
• Compare with empirical observations (if possible)
• Refine theory
Propose a formal theory or set of axioms
Develop a theory
Derive results
If possible, compare withempirical observations
Refine theory if necessary
![Page 11: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/11.jpg)
Computing
![Page 12: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/12.jpg)
Research Phases
![Page 13: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/13.jpg)
Research Phases
• Informational: gathering information through reflection, literature, people survey
• Propositional: Proposing/formulating a hypothesis, method, algorithm, theory or solution
• Analytical: analyzing and exploring proposition, leading to formulation, principle or theory
• Evaluative: evaluating the proposal
R.L. Glass, A structure-based critique of contemporary computing research, Journal of Systems and Software Jan (1995)
![Page 14: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/14.jpg)
Method-Phase Matrix
Methods/Phases Informational Propositional Analytical Evaluative
Scientific Observe the world
Propose a model or theory or behavior
Measure and analyze
Validate hypothesis of the model or theory; if possible repeat
Engineering Observe existing solutions
Propose better solutions; build or develop
Measure and analyze
Measure and analyze; repeat until no further improvements possible
Empirical Propose a model; develop statistical or other methods
Apply to case studies; measure and analyze
Measure and analyze; validate model; repeat
Analytical Propose a formal theory or set of axioms
Develop a theory; derive results
Derive results; compare with empirical observations if possible
![Page 15: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/15.jpg)
Example - Software Engineering• Informational phase - Gather or aggregate information via
– reflection
– literature survey
– people/organization survey
– case studies
• Propositional phase - Propose and build hypothesis, method or algorithm, model, theory or solution
• Analytical phase - Analyze and explore proposal leading to demonstration and/or formulation of principle or theory
• Evaluation phase - Evaluate proposal or analytic findings by means of experimentation (controlled) or observation (uncontrolled, such as case study or protocol analysis) leading to a substantiated model, principle, or theory.
![Page 16: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/16.jpg)
Computing Schism …
• “ … computing research … is characterized largely by research that uses the analytical method and few of its alternatives … the evaluative phase is seldom included.” [Glass, 95]
• “Relative to other sciences, the data shows that computer scientists validate a smaller percentage of their claims [Tichys 98]
![Page 17: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/17.jpg)
SCIENTIFIC METHOD
![Page 18: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/18.jpg)
Formulate Research Hypotheses• Typical hypotheses:
– Hypothesis about user characteristics (tested with user studies or user-log analysis, e.g., clickthrough bias)
– Hypothesis about data characteristics (tested with fitting actual data)
– Hypothesis about methods (tested with experiments):
• Method A works (or doesn’t work) for task B under condition C by measure D (feasibility)
• Method A performs better than method A’ for task B under condition C by measure D (comparative)
• Introduce baselines naturally lead to hypotheses
• Carefully study existing literature to figure our where exactly you can make a new contribution (what do you want others to cite your work as?)
• The more specialized a hypothesis is, the more likely it’s new, but a narrow hypothesis has lower impact than a general one, so try to generalize as much as you can to increase impact
• But avoid over-generalize (must be supported by your experiments)
• Tuning hypotheses
![Page 19: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/19.jpg)
Hypothesis Procedure
• Clearly define the hypothesis to be tested (include any necessary conditions)
• Design the right experiments to test it (experiments must match the hypothesis in all aspects)
• Carefully analyze results (seek for understanding and explanation rather than just description)
• Unless you’ve got a complete understanding of everything, always attempts to formulate a further hypothesis to achieve better understanding
![Page 20: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/20.jpg)
Clearly Define a Hypothesis
• A clearly defined hypothesis helps you choose the right data and right measures
• Make sure to include any necessary conditions so that you don’t over claim
• Be clear about any justification for your hypothesis (testing a random hypothesis requires more data than testing a well-justified hypothesis)
![Page 21: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/21.jpg)
Design the Right Experiments
• Flawed experiment design is a common cause of rejection of a paper (e.g., a poorly chosen baseline)
• The data should match the hypothesis
– A general claim like “method A is better than B” would need a variety of representative data sets to prove
• The measure should match the hypothesis
– Multiple measures are often needed (e.g., both precision and recall)
• The experiment procedure shouldn’t be biased
– Comparing A with B requires using identical procedure for both
– Common mistake: baseline method not tuned or not tuned seriously
• Test multiple hypotheses simultaneously if possible (for the sake of efficiency)
![Page 22: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/22.jpg)
Carefully Analyze the Results
• Do the significance test if possible/meaningful
• Go beyond just getting a yes/no answer
– If positive: seek for evidence to support your original justification of the hypothesis.
– If negative: look into reasons to understand how your hypothesis should be modified
– In general, seek for explanations of everything!
• Get as much as possible out of the results of one experiment before jumping to run another
– Don’t throw away negative data
– Try to think of alternative ways of looking at data
![Page 23: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/23.jpg)
Modify a Hypothesis
• Don’t stop at the current hypothesis; try to generate a modified hypothesis to further discover new knowledge
• If your hypothesis is supported, think about the possibility of further generalizing the hypothesis and test the new hypothesis
• If your hypothesis isn’t supported, think about how to narrow it down to some special cases to see if it can be supported in a weaker form
![Page 24: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/24.jpg)
Derive New Hypotheses
• After you finish testing some hypotheses and reaching conclusions, try to see if you can derive interesting new hypotheses
– Your data may suggest an additional (sometimes unrelated) hypothesis; you get a by-product
– A new hypothesis can also logically follow a current hypothesis or help further support a current hypothesis
• New hypotheses may help find causes:
– If the cause is X, then H1 must be true, so we test H1
![Page 25: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/25.jpg)
ENGINEERING METHOD
![Page 26: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/26.jpg)
Research Cycle
![Page 27: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/27.jpg)
Validation
![Page 28: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/28.jpg)
EMPIRICAL METHODS
![Page 29: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/29.jpg)
Components of Empirical Research
• Problem statement, research questions, purposes, benefits
• Theory, assumptions, background literature
• Variables and hypotheses
• Operational definitions and measurement
• Research design and methodology
• Instrumentation, Experiment
• Data analysis
• Conclusions, interpretations, recommendations
![Page 30: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/30.jpg)
What is an Experiment?
• Research method in which
– conditions are controlled
– so that 1 or more independent variables
– can be manipulated to test a hypothesis
– about a dependent variable
• Allows
– evaluation of causal relationships among variables
– while all other variables are eliminated or controlled.
![Page 31: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/31.jpg)
Variables
• Dependent Variable
– Criterion by which the results of the experiment are judged.
– Variable that is expected to be dependent on the manipulation of the independent variable
• Independent Variable
– Any variable that can be manipulated, or altered, independently of any other variable
– Hypothesized to be the causal influence
![Page 32: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/32.jpg)
Setup
• Experimental Treatments
– Alternative manipulations of the independent variable being investigated
• Experimental Group
– Group of subjects exposed to the experimental treatment
• Control Group
– Group of subjects exposed to the control condition
– Not exposed to the experimental treatment
– Serve as the standard for comparison
![Page 33: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/33.jpg)
Testing
• Test Unit
– Entity whose responses to experimental treatments are being observed or measured
• Randomization
– Assignment of subjects and treatments to groups is based on chance
– Provides “control by chance”
– Random assignment allows the assumption that the groups are identical with respect to all variables except the experimental treatment
![Page 34: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/34.jpg)
Steps in Empirical Research
Build Apparatus(integrate prototype and
test conditions into experimental apparatus
& software)
Experiment Design(tweak software, establish
experimental variables, procedure, design, run
pilot subjects)
User Study (collect data,
conduct interviews)
Analyse Data(build models, check for significant differences,
etc.)
Publish Results
Next iteration
![Page 35: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/35.jpg)
RECAP
![Page 36: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/36.jpg)
Recap
• What are the claims?
• What are the factors and considerations?
• What is the evaluation approach?
• What are metrics?
• How’s the data collected?
• What are the results compared to?
• How did you validate your results?
• What are your findings?
![Page 37: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/37.jpg)
The Claims
• At the outset, there’s an indicated purpose to the work
• Consequently, the purpose needs to be clearly defined
• Also, the evaluation needs to be aligned to fit the
purpose
• So, what are your claims?
![Page 38: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/38.jpg)
Types of Claims…
• Reduced requirements
• Better performance
• Ease of use
• Higher utility
• Cost reduction
• Best practice
• Insights
• Etc …
![Page 39: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/39.jpg)
The Considerations
• What are the inputs?
• What factors affect your results?
• Are they all variable?
• Are they all controllable?
• Is it possible to isolate their effect
• Individually vs. collectively
• Completely vs. partially?
![Page 40: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/40.jpg)
Approach
• Carry the thrust from clear purpose and clear understanding factors
• Requires care as it will be subject to scrutiny
• Must be structured and logical
• Must exhibit an appropriate level of sophistication
• Must be clear
![Page 41: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/41.jpg)
Metrics
• Define them
• How were they measured before (if applicable)?
• Are previous measures good enough?
![Page 42: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/42.jpg)
Data Collection
• Could be made through …
– Measurements
– Simulation
– Survey
• Who/what collected the data?
• To what extent was the data collection process trustworthy?
• How is this “extent” verified?
![Page 43: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/43.jpg)
Baselines
• Is there a need for baseline?
• Can you establish a reasonable baseline?
– Previous proposal(s)
– Random behavior
– Optimal behavior
– Current behavior
• Are you creating the baseline? (i.e., benchmarking?)
![Page 44: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/44.jpg)
Validating Your Results
• In part, stems from the validity of what’s been leading up to the results section
• There’s also the breadth and depth of evaluation
• Breadth: The span of cases and considerations under which the evaluation was performed
• Depth: How far you go with each case or factor?
• Scope of evaluation also raises credibility
![Page 45: IT 499: Seminar Course Week 3 Faculty: Dr. Afshan Jafri 28 February 2012](https://reader035.fdocuments.us/reader035/viewer/2022062422/56812bb4550346895d8ff486/html5/thumbnails/45.jpg)
Credits
• http://www.cs.usyd.edu.au/~info5993/
• http://www.cs.uiuc.edu/homes/czhai
• Abd Elhammid Taha, Research Methods, slides
• From W.R.Adrion, Research Methodology in Software Engineering, ACM SE Notes, Jan. 1993
• Matti Tedre; “Teaching the Philosophy of Computer Science”, Journal of Information Technology Education; Know Your Discipline, 2007.