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01 Introduction to Quantitative Methods

Course by Gabriel Pictet, Graduate Institute | Geneva. Illustrations by Dave Lobevich (http://kevinbobs.com/). Video by Hans Rosling (http://gapminder.org/). Prezi design by Raimondo Pictet.
by

Gabriel Pictet

on 4 November 2015

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Transcript of 01 Introduction to Quantitative Methods

communicate
results
ASK
a question
CONSTRUCT
a hypothesis
background
research
TEST
your hypothesis
experiment
DRAW
conclusions
Scientific Method
analyze
data
measure
predict
evaluate
convince
decide
generalize
describe
Quantitative
Methods
explain
Demographic
methods
direct observations of populations
censuses, registrars
Statistical
methods
subset of population,
surveys
based on probabilities
Models and predictions
population projections
econometrics
(statistics / economics)
epidemiology
(statistics / health)
Big
Biases
Critical
Thinking

Clarity
Consistency
Accuracy
Ethics
measure
predict
evaluate
convince
decide
generalize
describe
explain
Design
Sampling
Data collection
Analysis
validity issues
sampling design limitations
regression towards the mean
unpublicized caveats
small numbers
non-random sampling
omission of minorities
refusals
absences
self-selection
sample attrition
measurement errors
leading questions
missing values
clear, unambiguous,
quantified hypothesis
confirmation bias:
fitting the data to your opinion
Quantitative methods test...
explore
explore
conclusion based on
presented data only
reliable data
correct measured values
measurements in appropriate units
sound method of analysis
complete conceptual model
indicators relevant to the concepts
method relevant to the context
few missing data
internally consistent data
conclusion fits data
conclusion answers question
do no harm
concise model
concise explanation
Clear, unambiguous, quantified hypothesis?
Concise model?
Concise explanation?
Reliable data?
Correct measured values?
Measurements in appropriate units?
Sound method of analysis?
Complete conceptual model?
Indicators relevant to the concepts?
Method relevant to the context?
Few missing data?
Internally consistent data?
Conclusion fits data?
Conclusion answers question?
Conclusion based on presented data only?
No harm done?
Students will like numbers more after taking this course.
Students who sleep in class dream of statistics.
Did you hate or love the course?
0. hate 1. love
Lecturer interviews his students on course:
1. does not like much 2. sort of likes but not really
3. likes 4. loves
Lecturer need not change course since all students like it.*
*the others were expelled.
Example: QM course evaluation
Absent students and dropouts excluded from survey.
Percentage of students who like the course.
data
Full transcript