Transcript Researching Social Life Autumn Term

Principles and Strategies of
Quantitative Data Analysis
SLC515
Research Methods for Socio-Legal Studies and
Criminology
2007/2008
Outline
Foundation of QR: Positivism
 Validity and reliability in QR
 Core issues of concern in QR
 Critique of QR
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Outline
Descriptive and inferential statistics
 Inferential Statistics
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Crosstabs
 Correlation
 Regression
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Positivism - Historical Facts
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16/17th century: blooming of European thought
Beginning of modern science
Auguste Comte (1798-1857):
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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
Positivism - Emile Durkheim (18581917)
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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.
Positivism - Key Elements
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Social research as ‘science’
Universal laws - testing theories
Cause and effect relationships between
variables
Solid methods
Value neutral
Objectivity
Science is …
Positivism
Interpret.
Soc. Science
Critical
perspective
Based on strict
rules procedures
Just common sense Between the
(no science)
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.
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.
Theories, Hypotheses and Research
Design
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Deductive approach
 Theory testing
 Derive hypotheses from theory (if… then…
sentences) and test them
 Research design:
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Cross-sectional survey
Longitudinal design
Case study design
Comparative design
Operationalisation
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Translation of a theoretical concept into
something that can be measured
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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
Indicators
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Difference between a measure and an
indicator (quantities versus complex
concepts)
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An indicator is employed as though it were a
measure of a concept.
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Example: job satisfaction
Research Sites and Subjects
Depending on research design,
methods and sources of data
 Establish an appropriate setting:
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Decisions are involved: where? and who?
Sampling strategies
Probability and non-probability sampling
 Representative sample - generalizability
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Collecting and Processing Data
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Depends on the chosen research design:
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Experiments: pre- and post-testing
Survey interviews: questionnaire and interviews
Etc.
Gathered information is then transformed into
‘data’
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Information will be quantified - coding - to be
processed by a computer
Analysing Data and Research Findings
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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:
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Support or reject theoretical concepts or findings
of other studies
Detecting trends, patterns
Uncover common sense knowledge
Building typologies
Writing up Findings and Conclusion
Results enter the public domain
 Conference paper, article, report, thesis,
book
 Significance and validity of findings
 Implications? (policy advice etc.)
 Presentation of quantitative data is
different than in qualitative research
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Validity and Reliability
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Validity, reliability and generalizability are
measures of the quality, rigour and wider
potential of research
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Validity = are you observing what you want to
observe (construct validity)
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Is your set of indicators really measuring what you want to
measure?
Reliability = are the measures, devised for the
concept, concise (stability of measure)
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Stability over time, consistency of indicators (internal
reliability) and observers (inter-observer consistency)
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
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Positivism vs. interpretive social
sciences
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Objectivity?
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Generalization - but too simplistic?
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Causality
Contrasting Qualitative and
Quantitative Research
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Quantitative
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Numbers
Researcher’s view
Researcher distant
Theory testing
Static
Structured
Generalization
Hard, reliable data
Macro
Behaviour
Artificial setting
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Qualitative
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Words/Text
Participant’s view
Researcher close
Theory emergent
Process
Unstructured
Contextual
Rich, deep data
Micro
Meaning
Natural setting
Descriptive and Inferential
Statistics
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Univariate
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Descriptive analysis of one variable (column in data
set)
Bivariate
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Relationship between two variables
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Dependent and independent variables
Relation between dependent and independent variables
Differences between dependent and independent
variables
Bivariate Analysis
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Questions we can ask:
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Is the relationship significant?
If so, how strong is the relationship?
In which direction does the relationship go?
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Positive relationships
Negative relationships
Some statistical tests:
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Crosstabulation
Correlation
Regression analysis
Cross Tabs
All levels of measurement are allowed
 Cross tabs express common frequencies
of the categories of two different
variables
 Significance test: CHI Square
 How strong?: lambda, gamma, r2
 Which direction?: gamma, tau
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Example
?
Highschool
Coll
Uni
Row
total
Working
class
748
50.4%
87
13.9%
10
10.2%
21
5.1%
866
33.1%
Middle
class
694
46.8%
474
75.6%
67
68.4%
266
64.9%
1501
57.3%
Upper
class
42
2.8%
66
10.5%
21
21.4%
123
30.0%
252
9.6%
Column
total
1484
56.7%
627
23.9%
98
3.7%
410
15.7%
2619
100%
Example
Grouped literacy rates * Grouped GDP Crosstabulation
Grouped GDP
Grouped
literacy
rates
Very low literacy
Low literacy
Medium literacy
High literacy
Total
Count
% within Grouped
literacy rates
% within Grouped GDP
Count
% within Grouped
literacy rates
% within Grouped GDP
Count
% within Grouped
literacy rates
% within Grouped GDP
Count
% within Grouped
literacy rates
% within Grouped GDP
Count
% within Grouped
literacy rates
% within Grouped GDP
Very low GDP
5
Low GDP
1
Medium GDP
0
High GDP
0
Very high
GDP
0
83.3%
16.7%
.0%
.0%
.0%
100.0%
17.9%
15
3.6%
4
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0
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0
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0
5.6%
19
78.9%
21.1%
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100.0%
53.6%
5
14.3%
7
.0%
5
.0%
1
.0%
2
17.8%
20
25.0%
35.0%
25.0%
5.0%
10.0%
100.0%
17.9%
3
25.0%
16
22.7%
17
16.7%
5
8.7%
21
18.7%
62
4.8%
25.8%
27.4%
8.1%
33.9%
100.0%
10.7%
28
57.1%
28
77.3%
22
83.3%
6
91.3%
23
57.9%
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%
Total
6
Correlation
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Correlations measure statistical associations,
but do not allow any inferences about causal
patterns
 Requirement:
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Ordinal and interval data
Normally distributed and linear relation
Coefficient:
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Pearson’s r (interval data)
Spearman’s correlation coefficient (ordinal data)
Example
Correlations
People who read (%)
Gross domestic
product / capita
Pears on Correlation
(r)
Sig. (2-tailed)
N
Pears on Correlation
(r)
Sig. (2-tailed)
N
People who
read (%)
Gross
domestic
product /
capita
1
.552
107
.000
107
.552
1
.000
107
109
Regression Analysis
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You can visualize correlation in a scatter
diagram
Regression line
Regression coefficients
Formula: y=a+b(x)
Requirements:
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Interval data
Normally distributed
Linear relationship
Example
Controlling for Variables
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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
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Independent variable: GDP
Dependent variable: literacy
Control variable: regions
Example
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Pearson’s r was used to measure the
correlation between GDP and literacy rates in
three regions of the world
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OECD countries
Latin America
Africa
r=0.616
r=0.608
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.