[Figure 7.4]
wherein no relationship between the two variables is apparent. In the example shown in Figure 7.3, an apparently strong positive relationship exists between the number of change orders and the number of conflicts between the engineering design department and the manufacturing department. This suggests that change orders may contribute to the observed conflict between the two departments.
The correlation coefficient is simply a number that summarizes data in a scattergram. Its value ranges between ⫹1.0 and −1.0. A correlation coefficient of ⫹1.0 means that there is a perfectly positive relationship between two variables, whereas a correlation of − 1.0 signifies a perfectly negative relationship. A correlation of 0 implies a “shotgun” scatter- gram where there is no relationship between two variables.
Difference Tests The final technique for analyzing quantitative data is the differ- ence test. It can be used to compare a sample group against some standard or norm to determine whether the group is above or below that standard. It also can be used to determine whether two samples are significantly different from each other. In the first case, such comparisons provide a broader context for understanding the meaning of diagnostic data. They serve as a “basis for determining ‘how good is good or how bad is bad.’ ”15 Many standardized questionnaires have standardized scores based on the responses of large groups of people. It is critical, however, to choose a comparison group that is similar to the organization being diagnosed. For example, if 100 engineers take a standardized attitude survey, it makes little sense to compare their scores against standard scores representing married males from across the country. On the other hand, if industry-specific data are available, a comparison of sales per employee (as a measure of productivity) against the industry average would be valid and useful.
The second use of difference tests involves assessing whether two or more groups differ from one another on a particular variable, such as job satisfaction or absentee- ism. For example, job satisfaction differences between an accounting department and a sales department can be determined with this tool. Given that each group took the same questionnaire, their means and standard deviations can be used to compute a difference score (t-score or z-score) indicating whether the two groups are statistically different. The larger the difference score relative to the sample size and standard devia- tion for each group, the more likely that one group is more satisfied than the other.
Difference tests also can be used to determine whether a group has changed its score on job satisfaction or some other variable over time. The same questionnaire can be given to the same group at two points in time. Based on the group’s means and stan- dard deviations at each point in time, a difference score can be calculated. The larger the score, the more likely that the group actually changed its job satisfaction level.
The calculation of difference scores can be very helpful for diagnosis but requires the OD practitioner to make certain assumptions about how the data were collected. These assump- tions are discussed in most standard statistical texts, and OD practitioners should consult them before calculating difference scores for purposes of diagnosis or evaluation.16
SUMMARY
This chapter described several different methods for collecting and analyzing diagnostic data. Because diagnosis is an important step that occurs frequently in the planned change process, a working familiarity with these techniques is essential. Methods of data collection include questionnaires, interviews, observation, and unobtrusive mea- sures. Methods of analysis include qualitative techniques, such as content analysis and force-field analysis, and quantitative techniques, such as the determination of mean, standard deviation, and frequency distributions; scattergrams and correlation coeffi- cients; as well as difference tests.
138 PART 2 The Process of Organization Development
1. S. Mohrman, T. Cummings, and E. Lawler III,
“Creating Useful Knowledge with Organizations:
Relationship and Process Issues,” in Producing Useful Knowledge for Organizations, eds. R. Kilmann and K. Thomas (New York: Praeger, 1983): 613–24;
C. Argyris, R. Putnam, and D. Smith, eds., Action Science (San Francisco: Jossey-Bass, 1985); E. Lawler III, A. Mohrman, S. Mohrman, G. Ledford Jr., and T. Cummings, Doing Research That is Useful for Theory and Practice (San Francisco: Jossey-Bass, 1985).
2. D. Nadler, Feedback and Organization Develop ment:
Using Data-Based Methods (Reading, Mass.: Addison- Wesley, 1977): 110–14.
3. W. Nielsen, N. Nykodym, and D. Brown, “Ethics and Organizational Change,” Asia Pacific Journal of Human Resources 29 (1991).
4. Nadler, Feedback, 105–7.
5. W. Wymer and J. Carsten, “Alternative Ways to Gather Opinion,” HR Magazine (April 1992): 71–78.
6. Examples of basic resource books on survey method- ology include W. Saris and I. Gallhofer, Design, Evaluation, and Analysis for Survey Research (New York: Wiley- Interscience, 2007); L. Rea and R. Parker, Designing and Conducting Survey Research: A Comprehensive Guide (San Francisco: Jossey-Bass, 2005); S. Seashore, E. Lawler III, P. Mirvis, and C. Cammann, Assessing Organiza tional Change (New York: Wiley-Interscience, 1983); J. Van Mannen and J. Dabbs, Varieties of Qualitative Research (Beverly Hills, Calif.: Sage Publications, 1983); and E. Lawler III, D. Nadler, and C. Cammann, Organizational Assessment: Perspectives on the Measurement of Organizational Behavior and the Quality of Worklife (New York: Wiley- Interscience, 1980).
7. J. Taylor and D. Bowers, Survey of Organiza tions: A Machine Scored Standardized Question naire Instrument (Ann Arbor: Institute for Social Research, University of Michigan, 1972); C. Cammann, M. Fichman, G. Jenkins, and J. Klesh, “Assessing the Attitudes and Percep tions of Organizational Members,” in Assessing Organizational Change: A Guide to Methods, Measures, and Practices, eds. S. Seashore, E. Lawler III, P. Mirvis, and C. Cammann (New York: Wiley-Interscience, 1983):
71–138.
8. M. Weisbord, “Organizational Diagnosis: Six Places to Look for Trouble with or without a Theory,”
Group and Organization Studies 1 (1976): 430–37;
R. Preziosi, “Organizational Diagnosis Questionnaire,”
in The 1980 Handbook for Group Facilitators, ed.
J. Pfeiffer (San Diego: University Associates, 1980);
W. Dyer, Team Building: Issues and Alternatives (Reading, Mass.: Addison-Wesley, 1977); J. Hackman and G. Oldham, Work Redesign (Reading, Mass.:
Addison-Wesley, 1980); K. Cameron and R. Quinn, Diagnosing and Changing Organizational Culture (Reading, Mass.: Addison-Wesley, 1999).
9. J. Fordyce and R. Weil, Managing WITH People, 2d ed.
(Reading, Mass.: Addison-Wesley, 1979); W. Wells,
“Group Interviewing,” in Hand book of Marketing Research, ed. R. Ferder (New York: McGraw-Hill, 1977);
R. Krueger, Focus Groups: A Practical Guide for Applied Research, 2d ed. (Thousand Oaks, Calif.: Sage Publica- tions, 1994).
10. S. Lohr, Sampling: Design and Analysis (Pacific Grove, CA: Duxbury Press, 1999).
11. W. Deming, Sampling Design (New York: John Wiley & Sons, 1960); L. Kish, Survey Sampling (New York: John Wiley & Sons, 1995).
12. K. Krippendorf, Content Analysis: An Introduction to Its Methodology, 2d ed. (Thousand Oaks, Calif.: Sage Publications, 2003).
13. K. Lewin, Field Theory in Social Science (New York:
Harper & Row, 1951).
14. A simple explanation on quantitative issues in OD can be found in: S. Wagner, N. Martin, and C. Hammond,
“A Brief Primer on Quantitative Measurement for the OD Professional,” OD Practitioner 34 (2002): 53–57.
More sophisticated methods of quantitative analysis are found in the following sources: W. Hays, Statistics (New York: Holt, Rinehart, & Winston, 1963);
J. Nunnally and I. Bernstein, Psychometric Theory, 3d ed.
(New York: McGraw-Hill, 1994); F. Kerlinger, Founda- tions of Behavioral Research, 2d ed. (New York: Holt, Rinehart, & Winston, 1973); J. Cohen and P. Cohen, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2d ed. (Hillsdale, N.J.: Lawrence Erlbaum Associates, 1983); E. Pedhazur, Multiple Regression in Behavioral Research (New York: Harcourt Brace, 1997).
15. A. Armenakis and H. Field, “The Development of Organizational Diagnostic Norms: An Application of Client Involvement,” Consultation 6 (Spring 1987):
20–31.
16. Cohen and Cohen, Applied Multiple Regression.
NOTES
Feeding Back Diagnostic Information
Perhaps the most important step in the diagnostic process is feeding back diagnostic information to the client organization. Although the data may have been collected with the client’s help, the OD practitioner often organizes and presents them to the client. Properly analyzed and mean- ingful data can have an impact on organizational change only if organization members can use the information to devise appropriate action plans.
A key objective of the feedback process is to be sure that the client has ownership of the data.
As shown in Figure 8.1, the success of data feedback depends largely on its ability to arouse organizational action and to direct energy toward organizational problem solving. Whether
feedback helps to energize the organization depends on the content of the feedback data and on the process by which they are fed back to organization members.
In this chapter, we discuss criteria for developing both the content of feedback information and the processes for feeding it back. If these criteria are overlooked, the client is not apt to feel ownership of the problems facing the organization. A flexible and potentially powerful technique for data feedback that has arisen out of the wide use of questionnaires in OD work is known as survey feedback. Its central role in many large-scale OD efforts warrants a special look.