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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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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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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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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Mason; p. 6

Experiential Learning Cycle

(Kolb, 1984)ConcreteExperience

Reflective Observation

AbstractConceptualization

Active Experimentation

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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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Mason; p. 8

Exploratory Data Analysis

• Histograms• Scatter plots• “Stem and leaf”• “Box and whisker”

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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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Mason; p. 10

“Box & Whisker” Plot

Adapted from http://www-micro.msb.le.ac.uk/1010/DH2.html

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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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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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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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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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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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Mason; p. 16

Specification or replication

Comparison with zero order relationship

Zero orderrelationship

Partial/Conditional relationship

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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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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



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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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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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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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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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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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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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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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Mason; p. 26

Spurious relationship

Number of firetrucks

Damage done

Size of thefire

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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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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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Mason; p. 29

Drawing conclusions• What did you ask?• What did you find?• What do you conclude?• To whom do your conclusions

apply?

the information school of the university of washington lis 570 session 8.1 making sense of data:...

Documents

analysis of data

data analysis experiments

advance data analysis

data analysis statistics

bivariate data analysis

data collection

sense of data

analysis sampling