Lecture -- 1-- Start

Lecture -- 1-- Start

Outline 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Science, Method & Measurement On Building An Index Correlation & Causality Probability & Statistics Samples & Surveys Experimental & Quasi-experimental Designs Conceptual Models Quantitative Models Complexity & Chaos Recapitulation - Envoi

Outline 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Science, Method & Measurement On Building An Index Correlation & Causality Probability & Statistics Samples & Surveys Experimental & Quasi-experimental Designs Conceptual Models Quantitative Models Complexity & Chaos Recapitulation - Envoi

Quantitative Techniques for Social Science Research

Lecture # 1: Science, Method, and Measurement

Ismail Serageldin Alexandria 2012

On Science

On Science

Science Is Driven by Curiosity About the Natural World

WHAT IF?

WHAT IF?

WHAT IF?

??

Defining Science “The organization of our knowledge in such a way that it commands more of the hidden potential in nature ..”

J. Bronowski

Intellectual Activities Sciences Objective

Natural Sciences Physics Chemistry Astronomy Geology Biology Etc.

Human Sciences Psychology Economics Political Science Sociology History Etc.

Subjective Curiosity Influence others

Applied Fields Technology Education Medicine Law Etc.

Humanities Esthetics Ethics Religion Philosophy Etc.

Natural Sciences (classical definitions)

• Physical Sciences: Physics, Chemistry • Life Sciences: Biology (zoology, botany) • Earth Sciences: Geology, Astronomy, Meteorology

Overlapping Domains in Science Biochemistry, Paleontology, Molecular Genetics…

Changed Outlook: Process and System Views

Example: Photosynthesis • Light: the Energy source (physics) • Photosynthesis: The food production process (chemistry) • For plants (biology) Energy…biochemical pathways…cell Biology… plant physiology…

The Nature of Scientific Knowledge • • • •

Falsifiable (Popper) Approximative Empirical Replicable

• And so much more…

Karl Popper (1902-1994)

Before scientific thinking can proceed, certain philosophical presuppositions must be made about the nature of the universe:

Philosophical Presuppositions • Objective reality exists – there really are things out there, everything is not simply a figment of the imagination. • The universe is knowable – no aspects of the universe are beyond human understanding. • The universe’s operation is regular and predictable – if events occur at random, without any warning or pattern, no amount of analysis will uncover any regularity to them.




On The Scientific Method

The Scientific Method

The Method of Science OBSERVATION: Sense specific physical realities or events.

HYPOTHESIS:

PREDICTION:

nature of the phenomenon observed.

REVISED HYPOTHESIS

Forecast a future occurrence

PREDICTION

Create a statement about the general

consistent with the hypotheses.

EXPERIMENT:

Carry out a test to see if predicted

EXPERIMENT

event occurs.

If results DO match prediction, hypothesis is supported.

If results DO NOT match prediction

RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE


HYPOTHESIS:

PREDICTION:


REVISED HYPOTHESIS


PREDICTION



EXPERIMENT:


EXPERIMENT

event occurs.



RECYCLE

No amount of experimentation can ever prove me right; A single experiment can prove me wrong. Albert Einstein

The Scientific Method • • • • • • • •

Conjecture Hypothesis Testing Replicability Falsifiability Cumulative evidence Explanatory power Predictive power

Math • Logical, consistent, proof is absolute within its own axiomatic rules • Math is added to, science is replaced • Math is the science of patterns • It is elegant, beautiful and concise • It demands enormous precision in thinking clearly about abstract objects

Math and Science • Enormous power for manipulating quantitative results • Hence questions of measurement are important • Quantification and qualitative analyses remain important issues

Back To The Social Sciences













Back To The Scientific Method

The Method of Science Observation Hypothesis Prediction Experiment

The Method of Science Observation Hypothesis Prediction Experiment Interpretation

But can we apply that empirical method in the social sciences?

Understanding Society To Design Social Policies

Policy Counts

Economic Growth and Poverty Reduction

Growth and Poverty Reduction • Growth is a necessary but not sufficient condition for poverty reduction • The quality of growth and the nature of the policies matters enormously

Growth and Poverty Reduction

% annual decline in 10.0 poverty (Headcount index)

8.0

6.0

4.0

2.0

0.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

% annual growth in GDP/person



8.0

6.0

4.0

2.0

0.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0




Costa Rica

8.0

Malaysia Taiwan

Indonesia

6.0 Singapore

Thailand Pakistan

4.0

Brazil Sri Lanka

2.0

Mexico

India Bangladesh

0.0 Jamaica

0.0

2.0

4.0

6.0

8.0

10.0

12.0




Costa Rica

8.0

Malaysia Taiwan

Indonesia

6.0 Singapore

Thailand Pakistan

4.0

Brazil Sri Lanka

2.0

Mexico

India Bangladesh

0.0 Jamaica

0.0

2.0

4.0

6.0

8.0

10.0

12.0




Costa Rica

8.0

Malaysia Taiwan

Indonesia

6.0 Singapore

Thailand Pakistan

4.0

Brazil Sri Lanka

2.0

Mexico

India Bangladesh

0.0 Jamaica

0.0

2.0

4.0

6.0

8.0

10.0

12.0




Costa Rica

8.0

Malaysia Taiwan

Indonesia

6.0 Singapore

Thailand Pakistan

4.0

Brazil Sri Lanka

2.0

Mexico

India Bangladesh

0.0 Jamaica

0.0

2.0

4.0

6.0

8.0

10.0

12.0


Policies, Inequality and Welfare

Is Inequality Built Into Economic Structure? • Is movement into knowledge based economy necessarily accompanied by inequality? • Is US economy intrinsically generating more inequality?

Poverty Observed: US and Selected European Countries, 1991 USA UK Sweden Netherlands Italy Ireland France Denmark Canada Belgium

0

5

10

Source: Robert Solow, “Welfare: The Cheapest Country”’in NYRB, 23 March 2000, p. 20-23

15 % POOR

20

25

30

Poverty before Government policy effects US and Selected European Countries, 1991 USA UK Sweden Netherlands Italy Ireland France Denmark Canada Belgium

0

5

10


15 % POOR

20

25

30

Policy Effects on Poverty: Pre and Post tax and transfers, 1991 USA UK Sweden Netherlands Italy Ireland France Denmark Canada Belgium

0

5

10


15 % POOR

20

25

30


0

5

10


15 % POOR

20

25

30


0

5

10


15 % POOR

20

25

30


0

5

10


15 % POOR

20

25

30

Why Quantitative Analysis?

The Importance of Social Research

Much Economic Analysis Erases the Human Factor

The Need for Social Inputs Into Development Decisions • Social policy is more than the social consequences of economic policies • Social goals and policies complement economic ones • Economic Analysis by itself is insufficient: Social, cultural, political and ethical dimensions must be introduced

Elements Of A Social Policy - I • To maintain social cohesion • To foster equity • To reach the ultra poor and other marginalized groups • To uphold cultural identity (shared universal values and solidarity, not divisive micro-identities)

Elements Of A Social Policy - II • To promote participation (voice, choice and empowerment through access to knowledge and resources) • To facilitate social mobility (intergenerational, geographic and occupational) • To support institutional development • To enable participatory social research

Participatory Social Research • Promotes more effective understanding • Leads to sounder policy and program designs • Empowers the people participating in the research

Social Research and Decision Making • Coherent Framework • Predictive • Prescriptive



Crisis in the Non-economic Social Sciences • Absence of theoretical framework for the dynamics of social change • The negative impact of the postmodern currents • Confusion about quantitative and qualitative aspects pf research • The misunderstood role of models

Crisis in the Non-economic Social Sciences • Absence of theoretical framework for the dynamics of social change • The negative impact of the postmodern currents • Confusion about quantitative and qualitative aspects pf research • The misunderstood role of models

Crisis in the Non-economic Social Sciences • Absence of theoretical framework for the dynamics of social change • The negative impact of the postmodern currents • Confusion about quantitative and qualitative aspects of research • The misunderstood role of models

Crisis in the Non-economic Social Sciences • Absence of theoretical framework for the dynamics of social change • The negative impact of the postmodern currents • Confusion about quantitative and qualitative aspects of research • The misunderstood role of models

Crisis in the Non-economic Social Sciences • Absence of a theoretical framework for the dynamics of social change • The negative impact of the postmodern currents • Confusion about quantitative and qualitative aspects of research • The misunderstood role of models

Quantitative Social Analyses: Laplace “Let us apply to the political and moral sciences, the method founded on observation and mathematics that has served so well in the natural sciences.” -- Pierre Simon de Laplace (1749-1827)

Quantitative Social Analyses: Quetelet “The more advanced the sciences have become, the more they have tended to enter the domain of mathematics, which is a sort of center toward which they converge. We can judge of the perfection toward which a science has come by the facility, more or less great, with which it may be approached by calculation.” -- Quetelet (1796-1874)

Quantitative Social Analyses: Quetelet

Quetelet (1796-1874), by the way, invented the notion of the “average man.”

Quantitative Social Analyses: Boorstin “Today, the Cassandras of social science speak the language of numbers”. -- D.J. Boorstin (1914-2004) Source: Daniel J. Boorstin, Cleopatra , (op.cit., p142)

Vehement Reactions • Dehumanizing the humanities • Denies individualism • Treats people like products or machines • Economics is not the whole story • Etc. etc.

Serageldin On Reductionist Views

Serageldin on Reductionist Views • Three buckets of water and a handful of minerals held together by chemical reactions… • A society is more than the sum of its economic and financial transactions…

+

≠

Serageldin on Reductionist Views • Three buckets of water and a handful of minerals held together by chemical reactions… • A society is more than the sum of its economic and financial transactions…

≠

Conclusions • We need more, not less, sophisticated approaches… • Clever word games are not helpful to either explain social realities or to help formulate polices and interventions that improve the wellbeing of people

But we need quantitative analysis to understand, and to measure and to devise appropriate Social policies

Quantitative Analysis complements Qualitative analysis and frequently undergirds it.

On Measurement

Measurement counts

Measurement Is Important • We treasure what we measure • Prescription and dosage depend upon accurate estimation of magnitudes • Establishing trends is as – or more -important than snapshots of magnitudes • Monitoring of progress over time





On Measurement • Accuracy & Precision • Resolution & Randomness • Types of Scales

143/xxx


144/xxx

Accuracy in Measurement • Using the right tool • The quality of the tool is important • How carefully we measure with it is also important • Let’s use a ruler to measure the length of a piece of wood…

Accuracy in Measurement • Using the right tool • The quality of the tool is important

Accuracy in Measurement • Using the right tool • The quality of the tool is important • How carefully we measure with it is also important

Accuracy in Measurement • Using the right tool • The quality of the tool is important • How carefully we measure with it is also important • Let’s use a ruler to measure the length of a piece of wood…

Accuracy & Precision • Accuracy: how close the measured value is to reality (i.e. what it ought to be) -- So if the ruler is defective and two rulers yield different results that is an error of accuracy • Precision: is a measure of the reproducibility of the measurement,

Accuracy & Precision • Precision: is a measure of the reproducibility of the measurement, our confidence that uncertainty of measurement has been reduced to a minimum. • Sometimes the problem is instrumental precision (level of resolution) or the randomness of the event being measured.


Resolution vs. Randomness • We do not re-measure the piece of wood 100 times and take the average. • Assuming the wood was measured carefully, the error here is due to the resolution of the ruler, not the randomness of the event being measured.

Resolution of the tool • So, instead of a ruler use higher resolution instrument s like precision Vernier calipers:

But that is very different from dealing with random events

Random events

Random events/outcomes require a probabilistic treatment

Social Science studies of events/outcomes usually require a statistical probabilistic treatment

Here multiple measurements and probabilistic techniques are used

We will get back to Probabilities Later


What kind of scale do we use in Measurement?

Four Kinds (Types) of Scales • Nominal • Ordinal • Interval • Ratio

Types of Scales: I. Nominal Scales Numbers are used to name, identify or classify.

Level

The only permissible arithmetical procedures are counting and statistical techniques based on counting. Limitations

Example 163

Social Science Examples of Nominal Scales • Marital Status: Married, Unmarried • Nationality: Chinese, American, European, Egyptian • Religion: Muslim, Christian, Jewish, Buddhist, … • Ethnic or tribal group • Race: Black, white • P/F Evaluation: Pass/Fail

Types of Scales: II. Ordinal Scales

Numbers indicate rank or order.

Level

Ranking methods and other statistical techniques based on interpretations of “greater than” or “less than” are permissible. Limitations

Example

165

Social Science Examples of Ordinal Scales • Grading of interpersonal skills • Evaluating managerial skills • You can say greater than, but you cannot really quantify the amount or degree objectively.

Types of Scales: III. Interval Scales The intervals or Addition and subtraction distances between and statistical each number and techniques based on the next are equal, these two operations are but it is not known permissible. how far any of Multiplication and dithem is from zero. vision are not permissible. Level

Limitations

Example 167

Natural Science Examples of Interval Scales • Temperature: – Two days: 20 and 40 degrees Celsius – Difference between them is 20

• Cannot say twice as hot because zero could be: – Celsius scale – Fahrenheit scale – Kelvin scale

Social Science Examples of Interval Scales • Grading school exams: • Say two students took a test: results score was 20 and 40 points (difference is 20 points) but should not say twice as much. • BUT…teacher could have added a few easy questions that would have obtained each student 10 more points • Results would have been 30 and 50.

Social Science Examples of Interval Scales • Height or weight of people: – Say two persons 1.6 m and 1.80 m – Or two persons weigh 60kg and 80 kg

• Note: – No one is really 0.0 height or weight

Types of Scales: IV. Ratio Scales

Each number can be thought of as a distance measured from zero

Level

There are no limitations. All arithmetical operations and all statistical techniques are permissible Limitations

Example 171

Social Science Examples of Ratio Scales • Income and expenditure • Years of schooling • Number of respondents selecting something • Number of respondents who have a particular cardinal quality (e.g. married, unmarried) • Etc.

Social Science Examples of Ratio Scales • Income and expenditure • Years of schooling • Number of respondents selecting something • Number of respondents who have a particular cardinal quality (e.g. married, unmarried) • Etc.

It is important that not all relationships or all mathematical operations can be applied to all scales.

Now let’s move on to some descriptors of groups

What Is An Average?

We say: I examined 20 students and the average score was x

What is the meaning of the word “average”?

Average? • Mean: usually add up the values for all the observations and divide them by the number of observations • Median: the number at which half the observations are smaller and the other half are bigger • Mode: the number that appears most frequently in the distribution of observations.


Lets take 20 observations • 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 7, 8, 10, 10, 20 • What is the Mean ? • The Median ? • The Mode?

Lets Find the Mean • 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 7, 8, 10, 10, 20 • Mean = Total / number of observations • Total = 1+2+3+…. +10+10+20 = 120 • Mean = 120 / 20 = 6

Formula for the Mean

n

=

⋯

183

Lets Find the Median • 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 7, 8, 10, 10, 20 • Median = 5

Lets Find the Mode • 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 7, 8, 10, 10, 20 • Mode = 4

So for these Observations • 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 7, 8, 10, 10, 20 • Mean = 6 • Median = 5 • Mode = 4

Lets Change one Observation • 1, 2, 3, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 7, 7, 8, 10, 10, 1000 • Mean = 1100 / 20 = 55 • Median = 5 • Mode = 4

Introducing: The Normal Curve (The Bell Curve, The Gaussian Distribution)

Mean, Median, Mode


Mixing between these is one of the most common fallacies in reporting social statistics

We will come back to the Normal Curve (The Bell Curve, The Gaussian Distribution) many times in this course

But that will be for later…

SO…

Let’s make sure that we keep our heads above water!

I want you all to be swimming, not drowning!

Then we will all learn to fly…

Thank You

Lecture -- 1-- Start

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