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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). 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

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  1. 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

  2. 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

  3. Interactive exercise 5 3 1 4 2 6 Academy of Management, New Orleans, 2004

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. Faultlines Strength and Distance, and Group Faultlines Scores Group Faultlines Score Faultline Strength Faultline Distance Academy of Management, New Orleans, 2004

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  15. 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

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  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. Thank you very much for coming Any questions? Academy of Management, New Orleans, 2004

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