Quantitative Investment Strategies: The Unintended Consequences
Principles and Strategies of Quantitative Data Analysis
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Principles and Strategies of Quantitative Data Analysis
SLC515
Research Methods for Socio-Legal Studies and Criminology
2007/2008
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Outline
Foundation of QR: PositivismValidity and reliability in QRCore issues of concern in QRCritique of QR
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Outline
Descriptive and inferential statisticsInferential Statistics
Crosstabs CorrelationRegression
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Positivism - Historical Facts
16/17th century: blooming of European thought Beginning of modern science Auguste Comte (1798-1857):
Sociology as the “queen” of social sciences “Social physics”; idea of progress, social science works like
natural science Precise and certain methods, basing theoretical laws on
sound empirical observation Knowledge is derived from empirical evidence
19th century: natural sciences gain influence impacts thinking in social science
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Positivism - Emile Durkheim (1858-1917)
Human and material phenomena are equally real but human phenomena cannot be reduced to pure material facts.
Social facts - society as a moral reality, expressed in institutions such as law, religion etc. which are external to us and constrain us.
Sociologists should describe characteristics of facts and explain how they came into being. Explanation of social facts by causes: single cause effect, law-like relationship Same general methods of scientific inquiry can be used.
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Positivism - Key Elements
Social research as ‘science’ Universal laws - testing theories Cause and effect relationships between
variables Solid methods Value neutral Objectivity
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Science is …
Positivism
Interpret. Soc.
Science
Critical perspectiv
eBased on strict rules procedures
Just common sense (no science)
Between the positions of positivism and interpretivism
Deductive Inductive Emancipating, empowering
Nomothetic (based on laws)
Relies on interpretations
Brain-washed, misled, conditioned
Value free Not value free Not value free
Adapted from Sarantakos (1993) Table 2.2, page 38.
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Purpose of research …
Positivism Interpr. Soc.
Science
Critical perspective
To explain facts/causes/
effects
To interpret the world
To get below the surface; to expose real relations
To predict To understand social life
To disclose myths and illusions
Emphasises removing false beliefs/ideas, emancipation and empowerment
Adapted from Sarantakos (1993) Table 2.2, page 38 & 39.
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Theories, Hypotheses and Research Design
Deductive approach Theory testing Derive hypotheses from theory (if… then…
sentences) and test them Research design:
Cross-sectional survey Longitudinal design Case study design Comparative design
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Operationalisation
Translation of a theoretical concept into something that can be measured Example (natural sciences): temperature - degrees
Celsius, velocity - km/h
How do you measure the frustration caused by unemployment or the level of alienation in a society or a society’s satisfaction with its government?
Operational definition through indicators
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Indicators
Difference between a measure and an indicator (quantities versus complex concepts)
An indicator is employed as though it were a measure of a concept.
Example: job satisfaction
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Research Sites and Subjects
Depending on research design, methods and sources of data
Establish an appropriate setting:Decisions are involved: where? and who?
Sampling strategiesProbability and non-probability samplingRepresentative sample - generalizability
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Collecting and Processing Data
Depends on the chosen research design: Experiments: pre- and post-testing Survey interviews: questionnaire and interviews Etc.
Gathered information is then transformed into ‘data’ Information will be quantified - coding - to be
processed by a computer
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Analysing Data and Research Findings
Statistical techniques/analysis, special software Results/findings have to be interpreted based
on theoretical reflections in the beginning (verification/falsification of hypotheses)
Objectives of QR: Support or reject theoretical concepts or findings of
other studies Detecting trends, patterns Uncover common sense knowledge Building typologies
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Writing up Findings and Conclusion
Results enter the public domainConference paper, article, report, thesis,
bookSignificance and validity of findingsImplications? (policy advice etc.)Presentation of quantitative data is
different than in qualitative research
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Validity and Reliability
Validity, reliability and generalizability are measures of the quality, rigour and wider potential of research Validity = are you observing what you want to
observe (construct validity) Is your set of indicators really measuring what you want to
measure? Reliability = are the measures, devised for the
concept, concise (stability of measure) Stability over time, consistency of indicators (internal
reliability) and observers (inter-observer consistency)
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Core Issues of Concern in QR
Measurement Causality - Explanation (dependent and
independent variable)Generalisation (representative sample)Replication Testing theory
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Critique of QR
Positivism vs. interpretive social sciences
Objectivity?
Generalization - but too simplistic?
Causality
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Contrasting Qualitative and Quantitative Research
Quantitative Numbers Researcher’s view Researcher distant Theory testing Static Structured Generalization Hard, reliable data Macro Behaviour Artificial setting
Qualitative Words/Text Participant’s view Researcher close Theory emergent Process Unstructured Contextual Rich, deep data Micro Meaning Natural setting
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Descriptive and Inferential Statistics
Univariate Descriptive analysis of one variable (column in data
set)
Bivariate Relationship between two variables
Dependent and independent variables Relation between dependent and independent variables Differences between dependent and independent
variables
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Bivariate Analysis
Questions we can ask: Is the relationship significant? If so, how strong is the relationship? In which direction does the relationship go?
Positive relationships Negative relationships
Some statistical tests: Crosstabulation Correlation Regression analysis
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Cross Tabs
All levels of measurement are allowedCross tabs express common frequencies
of the categories of two different variables
Significance test: CHI SquareHow strong?: lambda, gamma, r2Which direction?: gamma, tau
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Example
? High-school
Coll Uni Rowtotal
Working class
74850.4%
8713.9%
1010.2%
215.1%
86633.1%
Middle class
69446.8%
47475.6%
6768.4%
26664.9%
150157.3%
Upper class
422.8%
6610.5%
2121.4%
12330.0%
2529.6%
Column total
148456.7%
62723.9%
983.7%
41015.7%
2619100%
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Example
Grouped literacy rates * Grouped GDP Crosstabulation
5 1 0 0 0 6
83.3% 16.7% .0% .0% .0% 100.0%
17.9% 3.6% .0% .0% .0% 5.6%
15 4 0 0 0 19
78.9% 21.1% .0% .0% .0% 100.0%
53.6% 14.3% .0% .0% .0% 17.8%
5 7 5 1 2 20
25.0% 35.0% 25.0% 5.0% 10.0% 100.0%
17.9% 25.0% 22.7% 16.7% 8.7% 18.7%
3 16 17 5 21 62
4.8% 25.8% 27.4% 8.1% 33.9% 100.0%
10.7% 57.1% 77.3% 83.3% 91.3% 57.9%
28 28 22 6 23 107
26.2% 26.2% 20.6% 5.6% 21.5% 100.0%
100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
Count
% within Groupedliteracy rates
% within Grouped GDP
Count
% within Groupedliteracy rates
% within Grouped GDP
Count
% within Groupedliteracy rates
% within Grouped GDP
Count
% within Groupedliteracy rates
% within Grouped GDP
Count
% within Groupedliteracy rates
% within Grouped GDP
Very low literacy
Low literacy
Medium literacy
High literacy
Groupedliteracyrates
Total
Very low GDP Low GDP Medium GDP High GDPVery high
GDP
Grouped GDP
Total
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Correlation
Correlations measure statistical associations, but do not allow any inferences about causal patterns
Requirement: Ordinal and interval data Normally distributed and linear relation
Coefficient: Pearson’s r (interval data) Spearman’s correlation coefficient (ordinal data)
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Example
Correlations
1 .552
.000
107 107
.552 1
.000
107 109
Pearson Correlation(r)
Sig. (2-tailed)
N
Pearson Correlation(r)
Sig. (2-tailed)
N
People who read (%)
Gross domesticproduct / capita
People whoread (%)
Grossdomesticproduct /
capita
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Regression Analysis
You can visualize correlation in a scatter diagram
Regression line Regression coefficients Formula: y=a+b(x) Requirements:
Interval data Normally distributed Linear relationship
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Example
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Controlling for Variables
Purpose of controlling for variables: e.g. exploration of variables that intervene in the relationship between other variables
Example: examination of the relationship between GDP and literacy rates in different regions of the world Independent variable: GDP Dependent variable: literacy Control variable: regions
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Example
Pearson’s r was used to measure the correlation between GDP and literacy rates in three regions of the world OECD countries r=0.616 Latin America r=0.608 Africa r=0.421
Conclusion: The strength of the association between GDP and literacy rates varies between different regions. In some, GDP is a better predictor of literacy rates than in others.