The Information School of the University of Washington LIS 570 Session 8.1 Making Sense of Data: Exploratory data analysis; Elaboration Model

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 4 Perspective on data analysis Experiments and Other Studies Experiments Planned Controlled (to differing degrees) Other studies Planned or opportunistic Offer opportunities to extend understanding

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 5 Experiments Theory usually precedes design Often: objective is to test hypothesis Designed in advance: data collection, coding, analysis Sampling designed for statistical significance Statistical tests specified in advance Outcome may be “generalizable” and often provides either “support” or “non-support” for hypothesis and theory

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 6 Experiential Learning Cycle (Kolb, 1984) Concrete Experience Reflective Observation Abstract Conceptualization Active Experimentation

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 8 Exploratory Data Analysis Histograms Scatter plots “Stem and leaf” “Box and whisker”

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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 Adapted from

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 10 “Box & Whisker” Plot Adapted from

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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?

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 13 Elaboration Paradigm Objective to provide a logical/ statistical technique that would allow researchers to elaborate on the nature of observed relationships

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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)

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 16 Specification or replication Comparison with zero order relationship Zero order relationship Partial/Conditional relationship

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 17 Replication “Do you approve or disapprove of the proposition that men and women should be treated equally in all regards” Epsilon = 12 percentage points

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 18 Replication Epsilon = 12 percentage points Epsilon = 12 percentage points

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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.

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 20 Specification (Glock) Social Class and Church Involvement Church involvement provides an alternative form of gratification for people denied gratification in secular society People of lower social class have fewer opportunities to gain self esteem from secular society

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 21 Specification Social Class and Holding Office in Organisations Social class is strongly related to the likelihood that a woman has every held an office in a secular organization

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 22 Specification Church Involvement by Social Class and Holding Secular Office Mean church involvement for Rough indicator of gratification in secular society

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 23 Interpretation - Intervening Variable (Stoufler) Education and Acceptance of Being Drafted Education Friends Deferred Attitudes

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 24 Intervening Variable Relating education to acceptance of being drafted through the factor of having friends deferred

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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 Likelihood of war Likelihood of war International tensions

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 26 Spurious relationship Number of fire trucks Damage done Size of the fire

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & 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

The Information School of the University of Washington LIS 570: Data Analysis Exploratory & Elaboration Models Mason; p. 29 Drawing conclusions What did you ask? What did you find? What do you conclude? To whom do your conclusions apply?