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Taking a crack at measuring faultlines. Sherry M.B. Thatcher (University of Arizona) Katerina Bezrukova (Rutgers University) Karen A. Jehn (Leiden University). Agenda. Interactive Exercise Why? Importance of faultlines vs. other composition measures How? What we did Huh?

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taking a crack at measuring faultlines

Taking a crack at measuring faultlines

Sherry M.B. Thatcher (University of Arizona)

Katerina Bezrukova (Rutgers University)

Karen A. Jehn (Leiden University)

Academy of Management, New Orleans, 2004

agenda
Agenda
  • Interactive Exercise
  • Why?
    • Importance of faultlines vs. other composition measures
  • How?
    • What we did
  • Huh?
    • Problems we ran into (and how we fixed them)
  • Oh, that!
    • Issues that journal reviewers are likely to raise

Academy of Management, New Orleans, 2004

interactive exercise
Interactive exercise

5

3

1

4

2

6

Academy of Management, New Orleans, 2004

interactive exercise1
Interactive exercise
  • In breaking the group into subgroups, what characteristics did you look at?
  • How homogeneous are the subgroups?
  • What assumptions did you make when breaking the group into subgroups?

Academy of Management, New Orleans, 2004

slide5
Why?
  • Mixed effects of diversity and demography studies
  • Focus on more than one attribute at a time
  • Takes into account interdependence among attributes

Academy of Management, New Orleans, 2004

how from diversity to faultlines
How?From Diversity to Faultlines

Step 1: Picturing what we need to measure

Group A: Strong Faultline

Group B: Weak Faultlines

HWM

HWM

PBF

PBF

HWM

HBF

PBM

PWF









♂H

♂H

♀P

♀P

♂H

♀H

♂P

♀P

Educ.

Educ.

♂H

♂H

♀P

♀P

♂P

♀H

♂H

♀P

Race

Race

♂H

♂H

♀P

♀P

♀P

♀H

♂P

♂H

Sex

Sex

H = High school, P = PhD, W = White, B = Black, M = Male, F = Female

Academy of Management, New Orleans, 2004

how step 2 understanding diversity formulas

1

Index of heterogeneity (Blau, 1977; Bantel & Jackson, 1989);

Diversity or entropy index (Teachman, 1980; Ancona & Caldwell, 1992).

(1 – SPi2)

Group-level categorical variables.

2

Coefficient of variation (Allison, 1978).

SD

Group-level interval variables.

3

Relational demography /individual dissimilarity score (Tsui & O’Reilly, 1989).

[1/nS(Xi - Xj)2]1/2]

Individual-level categorical and interval variables.

How? Step 2: Understanding diversity formulas

x

Academy of Management, New Orleans, 2004

how step 3 creating a faultline strength formula
How?Step 3:Creating a faultline strength formula

Faultline strength – Clustering Algorithm based on Euclidean distance formula (Thatcher, Jehn, & Zanutto, 2003)

  • xijk = the value of the jth characteristic of the ith member of subgroup k
  • x•j• = the overall group mean of characteristic j
  • x•jk = the mean of characteristic j in subgroup k
  • ngk = the number of members of the kth subgroup (k=1,2) under split g
  • the faultline strength = the maximum value of Faug over all possible splits g=1,2,…S.

Academy of Management, New Orleans, 2004

measuring faultlines

FAULTLINE STRENGTH/ L & M

FAU ALGORITHM based on Euclidean distance formula

None

0

Weak

(1 align; 4 ways)

0.463 (strongest split is AC, BD but AB, CD is also a strong split)

Weak

(1 align; 3 ways)

0.557 (strongest split is AB, CD, but BC, AD is also close)

Strong

(3 align; 2 ways)

0.688 (strongest split is AC, BD)

Very Strong

(4 align; 1 way)

0.996 (strongest split is AB, CD)

Measuring Faultlines

Academy of Management, New Orleans, 2004

how revisiting step 1 faultline distance
How?Revisiting Step 1: Faultline Distance

Faultline distance reflects how far apart the subgroups are from each other

Group A: Farther Apart

Group B: Closer Together









55

30

55

21

Age

Age

Ph.D.

M.S.

Ph.D.

B.A.

Education

Education

22

11

22

3

Tenure

Tenure

Academy of Management, New Orleans, 2004

faultline distance cont d
Faultline Distance (cont’d)

Faultline distance - the Euclidean distance between the two sets of averages

where centroid (vector of means of each variable) for subgroup 1 = ( ), centroid for subgroup 2 = ( ).

Group faultline score

Fau = Strength (Faug) x Distance (Dg)

Academy of Management, New Orleans, 2004

faultlines strength and distance and group faultlines scores
Faultlines Strength and Distance, and Group Faultlines Scores

Group Faultlines Score

Faultline Strength

Faultline Distance

Academy of Management, New Orleans, 2004

rescaling considerations
Rescaling Considerations
  • Theory driven approach
    • to use SME’s judgments to weight characteristics
  • Empirical approach
    • to view participants’ responses as a “true” measure of faultlines
  • Statistical approach
    • to use standard deviations

Academy of Management, New Orleans, 2004

sas faultline calculation version 1 0 july 26 2004
SAS Faultline Calculation (Version 1.0, July 26, 2004)
  • WHAT THIS CODE DOES
    • faultline strength and distance for groups of size 3 to 16 (two sets: incl and excl 1-person subgroups).
  • WHAT WE ASSUME ABOUT THE DATA
    • a comma-separated data text file (save as .csv file).
    • dummy variables for categorical vars.
    • no missing values
    • group ID variable (groups are numbered from 1 to n)
  • WHAT WE ASSUME ABOUT THE RESCALING FACTORS
    • rescaling factors must be specified for each variable
    • rescaling factors must be specified in a comma-separated text file (save as .csv file).

Academy of Management, New Orleans, 2004

sas faultline calculation version 1 0 july 26 2004 cont d
SAS Faultline Calculation (Version 1.0, July 26, 2004): Cont’d

4. HOW TO RUN THE CODE

  • download the SAS code and data files into C:\Faultline\FL_code\FL_Code_parameters.txt
  • go to the C:\Faultline\FL_Code directory and double click on FL_Code_1_0.sas
  • right click the mouse and select “Submit All”

5. HOW TO MODIFY THE INPUT PARAMETERS

  • all user inputs are specified in the file C:\Faultline\FL_Code\FL_Code_parameters.txt.
  • keep exact names of files.

Academy of Management, New Orleans, 2004

huh problems we ran into and how we fixed them
Huh?Problems we ran into (and how we fixed them)
  • Group size
  • Number of possible subgroups
  • Subgroups of size “1”
  • Calculating the overall faultline score
  • Measuring faultline distance for categorical variables
  • Rescaling

Academy of Management, New Orleans, 2004

oh that issues that journal reviewers have raised
Oh That!Issues that journal reviewers have raised
  • Rescaling (influence on results)
    • solution: rerun analyses
  • Importance of distance component
    • solution: explain it better
  • Perceptual faultlines = actual faultlines?
    • solution: explain to the reviewers that we didn’t have this data

Academy of Management, New Orleans, 2004

advantages of fau measure
Advantages of Fau Measure
  • allows continuous and categorical variables
  • unlimited number of variables
  • theoretically unlimited group size
  • flexible enough to allow for different rescaling

Academy of Management, New Orleans, 2004

future research work in progress
Future Research & Work in Progress

Testing the theory in experimental settings

  • Faultlines, coalitions, conflict, group identity and leadership profiles
  • Temporal effects of faultlines

Testing the theory in organizational settings

  • Consistency matters! The Effects of Group and Organizational Culture on the Faultline-Outcomes Link

Testing the theory in international settings

  • Peacekeeping and Ethnopolitical conflict
  • A quasi-experimental field study in ethnic conflict zones (i.e., Crimea, Sri Lanka, Burundi and Bosnia)

Academy of Management, New Orleans, 2004

thank you very much for coming

Thank you very much for coming

Any questions?

Academy of Management, New Orleans, 2004