the information school of the university of washington lis 570 session 8.1 making sense of data:...
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LIS 570
Session 8.1Making Sense of Data:
Exploratory data analysis; Elaboration Model
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 2
Objectives
• Reinforce distinction between experimental design statistics and data analysis statistics
• Review exploratory data analysis methods
• Understand the principles underlying the elaboration model for bivariate data analysis
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 3
Agenda• Review concepts: compare
“experimental design” with “data analysis”
• Review some earlier concepts as ways to do exploratory data analysis
• Discuss the elaboration model for bivariate analysis
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 4
Perspective on data analysis
Experiments and Other StudiesExperiments• Planned• Controlled (to differing degrees)Other studies• Planned or opportunistic• Offer opportunities to extend
understanding
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 5
ExperimentsTheory usually precedes designOften: objective is to test hypothesisDesigned in advance: data collection,
coding, analysisSampling designed for statistical
significanceStatistical tests specified in advanceOutcome may be “generalizable” and
often provides either “support” or “non-support” for hypothesis and theory
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 6
Experiential Learning Cycle
(Kolb, 1984)ConcreteExperience
Reflective Observation
AbstractConceptualization
Active Experimentation
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 7
Analysis of Data from Other Studies
Theory may be sketchy or non-existent Sampling may be non random, but data
collection and coding can be specified in advance
Data analysis intended to detect patterns and uncover associations
Outcome may be “postulates,” “propositions,” or even “hypotheses” that can be further studied or tested
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 8
Exploratory Data Analysis
• Histograms• Scatter plots• “Stem and leaf”• “Box and whisker”
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 9
“Stem & Leaf”Dataset: 39, 42, 44, 47, 48, 48, 51, 52, 53, 53, 54, 55,
55, 55, 55, 56, 56, 57, 57, 58, 58, 59, 59, 59, 59, 61, 61, 62, 63, 63, 64, 65, 65, 65, 66, 66, 66, 67, 69, 69, 71, 71, 76, 81, 84, 92
Plot: 3 | 9
4 | 2 4 7 8 85 | 1 2 3 3 3 4 5 5 5 55 | 6 6 7 7 8 8 8 9 9 9 96 | 1 1 2 3 3 4 5 5 56 | 6 6 6 7 9 97 | 1 1 68 | 1 49 | 2
Adapted from http://www-micro.msb.le.ac.uk/1010/DH2.html
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 10
“Box & Whisker” Plot
Adapted from http://www-micro.msb.le.ac.uk/1010/DH2.html
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 11
Elaboration
Elaborate: “…give more detail about”
Preliminary data analysis shows (or suggests) a relationship
Can anything else be said about this relationship?
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 12
Elaborating Relationships
• Why does the relationship exist?• What is the nature of the
relationship?• How general is the relationship?• Elaboration model
– interpretation method– the Columbia School – Lazarsfeld method
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 13
Elaboration ParadigmObjective
to provide a logical/ statistical technique that would allow researchers to elaborate on the nature of observed relationships
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 14
Elaboration Model• Replication—the relationship is
replicated or repeated under different conditions
• Specification—relationship appears only under certain conditions and not others
• Intervening variable • Spurious relationships—an
“artefact” of the data• Partial correlations
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 15
Specification or Replication
• The original bivariate relationship is called a zero order relationship
• Partial table (trivariate table)– Third variable (control or test variable)
introduced– Within each subgroup of the test
variable, construct a table to examine the original relationship.
– Measurement of bivariate relationships in each of the partial tables (partial relationships)
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 16
Specification or replication
Comparison with zero order relationship
Zero orderrelationship
Partial/Conditional relationship
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 17
Replication
Men Women
Approve 63% 75%
Disapprove 37% 25%
400 400
“Do you approve or disapprove of the proposition that men and women should be treated equally in all regards”
Epsilon = 12 percentage points
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 18
Replication
Under 30 30 and over
Women Men Women Men
Approve 90% 78% 60% 48%
Disapprove 10% 22% 40% 52%
200 200 200 200
Epsilon = 12 percentage points
Epsilon = 12 percentage points
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 19
Specification• the relationship between the original
two variables differs for various types of people
• the specific types for whom it does or does not hold– the relationship is not general but subgroup
specific
• statistical interaction (De Vaus)
– The effect of X on Y is partly dependent on additional characteristics of the person.
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 20
Specification (Glock)
Social class level
Low High
0 1 2 3 4
Mean involvement
.63 .58 .49 .48 .45
Social Class and Church Involvement
Church involvement provides an alternative form of gratification for people deniedgratification in secular societyPeople of lower social class have fewer opportunities to gain self esteem from secular society
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 21
Specification
Social class level
Low High
0 1 2 3 4
% women who have held office in secular organisation
46 47 54 60 83
Social Class and Holding Office in Organisations
Social class is strongly related to the likelihoodthat a woman has every held an office in asecular organization
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 22
Specification
Social class level
Low High
0 1 2 3 4
Have held office
.46 .53 .46 .46 .46
Have not held office
.62 .55 .47 .46 .40
Church Involvement by Social Class and Holding Secular Office
Mean churchinvolvement for
Rough indicator ofgratificationin secular society
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 23
Interpretation - Intervening Variable
(Stoufler)
High Education Low Education
Should not have been deferred
88% 70%
Should have been deferred
12% 30%
(1761) (1896)
Education and Acceptance of Being Drafted
Education FriendsDeferred
Attitudes
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 24
Intervening Variable
Friends deferred No friends deferred
High Edn Low Edn High Edn Low Edn
Should not have been deferred
63% 63% 94% 95%
Should have been deferred
37% 37% 6% 5%
(335) (1484) (1426) (392)
Relating education to acceptance of being drafted through the factor of having friends deferred
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 25
Explanation—Spurious Relationships
• Spurious - not a genuine relationship• Test variable must be antecedent
Strength of peace movement
Strength of peace movement
Likelihoodof war
Likelihoodof war
Internationaltensions
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 26
Spurious relationship
Number of firetrucks
Damage done
Size of thefire
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 27
Test for Spurious Relationship
• Compare the initial bivariate relationship with the conditional relationship—if no relationship in the conditional table, we have explained the original relationship
• Can have completely and partly spurious relationships
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LIS 570: Data AnalysisExploratory & Elaboration Models
Mason; p. 28
Reporting your research
• The presentation of the results• A discussion and interpretation of
the results, i.e., what they mean to you, and any limitations or concerns, for example ethical, validity, reliability.
• Conclusions
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