Collecting and Analyzing Diagnostic information:Sampling, The Analysis of Data

<< Collecting and Analyzing Diagnostic information:Methods for Collecting Data, Observations

Designing Interventions:Readiness for Change, Techno-structural Interventions >>

Organization Development MGMT 628

Lesson 21

Collecting and Analyzing Diagnostic information

Table 4: A Comparison of Different Methods of Data Collection

A Comparison of Different Methods of Data Collection

Method

Major Advantages

Major Potential Problems

· Responses can be quantified · non-empathy

Questionnaires

· Predetermined

and easily summarized

· Easy to use with large

questions/missing issues

· Over-interpretation of data

samples

· Relatively inexpensive

· Response bias

· Can obtain large volume of

data

· adaptive-allows

data · Expense

Interviews

collection on a range of · Bias in interviewer responses

possible subjects

· coding and interpretation

Source of "rich" data

difficulties

Empathic

· self-report bias

Process of interviewing

can build rapport

· collects data on behavior, · coding and interpretation

Observations

rather than reports of

difficulties

· Sampling inconsistencies

behavior

· Real time, not retrospective

· Observer

bias

and

· Adaptive

questionable reliability

· Expense

· Non-reactive- no response · Access

Unobtrusive measures

and

retrieval

bias

difficulties

· High face validity

· Validity concerns

· Easily quantified

· Coding and interpretation

difficulties

Sampling:

Before discussing how to analyze data, the issue of sampling needs to he emphasized. Application of the

different data-collection techniques invariably raises the following questions: "How many people should be

interviewed and who should they be?" "What events should be observed and how many?" "How many

records should be inspected and which ones?"

Sampling is not an issue in many OD cases. Because practitioners collect interview or questionnaire data

from all members of the organization or department in question, they do not have to worry about whether

the

information

representative

the

organization

unit.

Sampling becomes an issue in OD, however, when data are collected from selected members, behaviors, or

records. This is often the case when diagnosing organization-level issues or large systems. In these cases, it

may be important to ensure that the sample of people, behaviors, or records adequately represents the

characteristics of the total population. For example, a sample of fifty employees might be used to assess the

perceptions of all three hundred members of a department. A sample of production data might be used to

evaluate the total production of a work group. OD practitioners often find that it is more economical and

quicker to gather a sampling of diagnostic data than to collect all possible information. If done correctly,

the sample can provide useful and valid information about the entire organization or unit.

Sampling design involves considerable technical detail, and consultants may need to become familiar with

basic references in this area or to obtain professional help. The first issue to address is sample size, or how

many people, events, or records are needed to carry out the diagnosis or evaluation. This question has no

simple answer: the necessary sample size is a function of population size, the confidence desired in the

quality of the data, and the resources (money and time) available for data collection.

First, the larger the population (for example, number of organization members or total number of work

outcomes) or the more complex the client system (for example, the number of salary levels that must he

sampled or the number of different functions), the more difficult it is to establish a "right" sample size. As

Organization Development MGMT 628

the population increases in size and complexity, the less meaning one can attach to simple measures, such

as an overall average score on a questionnaire item. Because the population comprises such different types

of people or events, more data are needed to ensure an accurate representation of the potentially different

subgroups. Second, the larger the proportion of the population that is selected, the more confidence one

can have about the quality of the sample. If the diagnosis concerns an issue of great importance to the

organization, then extreme confidence may be needed, indicative of a very large sample size. Third, limited

resources constrain sample size. If resources are limited but the required confidence is high, then

questionnaires will be preferred over interviews because more information can be collected per member per

dollar.

The second issue to address is sample selection. Probably the most common approach to sampling

diagnostic data in OD is a simple random sample, in which each member, behavior, or record has an equal

chance of being selected. For example, assume that an OD practitioner would like to select fifty people

randomly out of the three hundred employees at a manufacturing plant. Using a complete list of all three

hundred employees, the consultant can generate a random sample in one of two ways. The first method is

to use a random number table printed in the back of almost any statistics text; the consultant would pick

out the employees corresponding to the first fifty numbers under three hundred beginning anywhere in the

table. The second method is to pick every sixth name (300/50 = 6) starting anywhere in the list.

If the population is complex or many subgroups need to be represented in the sample, a stratified sample

may be more appropriate than a random one. In a stratified sample, the population of members, events, or

records is segregated into a number of mutually exclusive subpopulations and a random sample is taken

from each subpopulation. For example, members of an organization might be divided into three groups

(managers, white-collar workers, and blue-collar workers), and a random sample of members, behaviors, or

records could be selected from each grouping to reach diagnostic conclusions about each of the groups.

Adequate sampling is critical to gathering valid diagnostic data, and the OD literature has paid little

attention to this issue. OD practitioners should gain rudimentary knowledge in this area and use

professional help if necessary.

The Implementation of Data Collection:

Data collection begins with a decision about who to obtain data from and how many respondents there

should be. The use of interviews may limit the number of respondents, whereas the use of a questionnaire

may increase the number. Data should be collected from several levels and departments in the organization,

but different questions may be needed for each of them. The results of a survey of OD practitioners about

the methods they use to gather data are reported by Burke, Lark, and Koopman. The one-to-one interview

is the most common data-gathering method, used by 87 percent of the respondents. Other methods

include observation (60) percent), group interviews (52 percent), questionnaires (45 percent), and existing

documents (37 percent).The survey also shows that practitioners normally rely on a variety of data-

gathering methods.

Once an appropriate technique has been selected, the actual data-collection program must be

accomplished. This includes the operational aspects of designing, printing, distributing, and collecting the

data-collection instrument. Outside data-collection agents are more effective than internal personnel. The

use of outside data-collection agents is recommended because it apparently makes respondents feel more

secure and trusting that candid answers will not be used against them. There are companies that develop

data-collection instruments, test them and make them available commercially. The disadvantage is that such

instruments may be too generalized and not focused enough for a specific organization to get reliable and

useful data.

Once again, confidentiality of data is a critical issue. A small pilot study or beta test of the data-collection

instrument is also a good idea. This should include a practice analysis before the large-scale data collection

begins to ensure that every possible problem is corrected.

The Analysis of Data:

The techniques for analyzing data vary from relatively straightforward, simple methods to highly

sophisticated statistical techniques. Several important questions must be considered before a data-collecting

method is selected: How are the data to be analyzed? Are they to be analyzed statistically, and if so, what

type of analysis is to be used? Will the data be processed by hand or by computer? Will they be coded, and

if so how? These questions must be taken into account prior to data collection so that the data can be used

to draw inferences and conclusions. This is especially true with large-scale surveys or interviews, because

the large amount of data makes processing a difficult task. The analysis may include comparisons of

different divisions within the organization. Management levels can also be compared. To make

comparisons, however, it is necessary to properly code the surveys or interviews. "If you can't measure it,

you can't control it," says Meg Whitman, CEO of eBay Inc.

Organization Development MGMT 628

Evaluating the Effectiveness of Data Collection:

A systematic data-collection program has to establish some criteria for how well the data meet the

objectives in terms of quantity and quality. Obviously, the sample has to be large enough sample to enable

generalization of results. The accuracy of the data, that is, the degree to which the data deviate from the

truth, is also an important factor.

A number of criteria may be used to compare data-collection techniques. There is necessarily a trade-off

between data quantity and accuracy, on the one hand, and collection cost and time spent collecting, on the

other. Naturally the practitioner wants to obtain the best available data that can be generated within the

given cost and time constraints. The following criteria lay out some guidelines.

The Validity of the Data:

Probably the most important question is: Are we measuring and collecting data on the dimensions that we

intend to measure? OD programs frequently have to deal with difficult subjective parameters such as

attitudes and values.

The Time to Collect Data:

How long will it take to gather the data using any given technique? How much time is available? Experience

suggests that data collection usually takes longer than planned.

The Cost of Data Collection:

How much do the data cost? A large-scale interviewing program costs a great deal of time and money. The

practitioner and the client must determine how much money can be spent in the data-gathering stage. They

should also consider the problem of diminishing returns: What is the minimum number of interviews

needed for a reliable measure?

The Organization Culture and Norms:

The practitioner has to decide what techniques are best suited to a given organization's culture and will

yield the most valid data given these constraints. For example: Are people likely to be open and candid, or

hidden and resistant? Does the climate call for open confrontation and questions or a more indirect form

of data gathering?

The Hawthorne Effect in Data Collecting:

One of the most difficult factors to eliminate is the so-called Hawthorne effect--the effect the observer

has on the subject. The very act of investigating and observing may influence the behavior of those being

investigated.

One characteristic of successful change programs is that they gather data about organizational problems

before initiating a change effort. An effective data-collection process enables the change effort to focus on

specific problems rather than rely upon a generalized program. The data-collection stage provides managers

and organization members with hard data that can he compared with intuitive, subjective problem

awareness.

Techniques for Analyzing Data:

Data analysis techniques fall into two broad classes: qualitative and quantitative. Qualitative techniques

generally are easier to use because they do not rely on numerical data. That fact also makes them easier to

understand and interpret. Quantitative techniques, on the other hand, can provide more accurate readings

of the organizational problem.

Qualitative Tools:

Of the several methods for summarizing diagnostic data in qualitative terms, two of the most important are

content analysis and force-field analysis.

Content Analysis:

A popular technique for assessing qualitative data, especially interview data, is content analysis, which

attempts to summarize comments into meaningful categories. When done well, a content analysis can

reduce hundreds of interview comments into a few themes that effectively summarize the issues or

attitudes of a group of respondents. The process of content analysis can be quite formal, and specialized

references describe this technique in detail. In general, however, the process can be broken down into three

major steps.

Organization Development MGMT 628

First, responses to a particular question are read to gain familiarity with the range of comments made and

to determine whether some answers are occurring over and over again.

Second, based on this sampling of comments, themes are generated that capture recurring comments.

Themes consolidate different responses that say essentially the same thing. For example, in answering the

question "What do you like most about your job?" different respondents might list their co-workers, their

supervisors, the new machinery, and a good supply of tools. The first two answers concern the social

aspects of work, and the second two address the resources available for doing the work.

Third, the respondents' answers to a question are then placed into one of the categories. The categories

with the most responses represent those themes that are most often mentioned.

Force-Field Analysis:

A second method for analyzing qualitative data in OD derives from Kurt Lewin's three-step model of

change. Called force-field analysis, this method organizes information pertaining to organizational change

into two major categories: forces for change and forces for maintaining the status quo or resisting change.

Using data collected through interviews, observation, or unobtrusive measures, the first step in conducting

a force-field analysis is to develop a list of all the forces promoting change and all those resisting it. Then,

based either on the OD practitioner's personal belief or perhaps on input from several members of the

client organizations a determination is made of which of the positive and which of the negative forces are

most powerful. One can either rank the order or rate the strength of the different forces.

Figure 27 illustrates a force-field analysis of the performance of a work group. The arrows represent the

forces, and the length of the arrows corresponds to the strength of the forces. The information could have

been collected in a group interview in which members were asked to list those factors maintaining the

current level of group performance and those factors pushing for a higher level. Members also could have

been asked to judge the strength of each force, with the average judgment shown by the length of the

arrows.

This analysis reveals two strong forces pushing for higher performance: pressures from the supervisor of

the group and competition from other work groups performing similar work. These forces for change are

offset by two strong forces for maintaining the status quo: group norms supporting present levels of

performance and well-learned skills that are resistant to change. According to Lewin, efforts to change to a

higher level of group performance shown by the darker band in Figure 27 should focus on reducing the

forces maintaining the status quo. This might entail changing the group's performance norms and helping

members to learn new skills. The reduction of forces maintaining the status quo is likely to result in

organizational change with little of the tension or conflict typically accompanying change caused by

increasing the forces for change.

Organization Development MGMT 628

Figure 27: Force-Field Analysis of Work Group

Performance:

An example of how force-field analysis can be used may be helpful. The general manager of a hospital

employing 300 workers and her immediate subordinates identified the 6 percent daily absentee rate as an

area of concern. They determined that a 3 percent absentee rate would be much more acceptable. In other

words, they found a "performance gap." After going over the survey results with the OD practitioner, it

was decided to use force-field analysis to gain an improved diagnosis of this problem. In a brainstorming

session, the work team listed all of the forces tending to restrain and increase absenteeism. (figure28)

The managers made the length of the arrows proportionate to the strength of the forces. They had a choice

of several strategies to reduce the performance gap. They could decrease the strength of the restraining

forces; increase the strength of the driving forces, or a combination of both. Generally, if the forces that

put pressure on people (such as fear of losing their job) are increased, the tension within the system will

also increase, possibly bringing about stronger resistance and unpredictable behavior, It is often better to

increase forces that do not put pressure on people (for instance, a promotion policy that is more closely

tied to an employee's absentee rate), to reduce restraining forces, or to add new driving forces.

Organization Development MGMT 628

Fig 28: Example of the Use of Force-Field Analysis

Quantitative Tools:

Methods for analyzing quantitative data range from simple descriptive statistics of items or scales from

standard instruments to more sophisticated, multivariate analysis of the underlying instrument properties

and relationships among measured variables. The most common quantitative tools are means, standard

deviations, frequency distributions, scattergrams, correlation coefficients, and difference tests. These

measures are routinely produced by most statistical computer software packages. Therefore, mathematical

calculations are not discussed here.

Means, Standard Deviations, and Frequency Distributions:

One of the most economical and straightforward ways to summarize quantitative data is to compute a

mean and standard deviation for each item or variable measured. These represent the respondents' average

score and the spread or variability of the responses, respectively. These two numbers easily can be

compared across different measures or subgroups. For example, Table 5 shows the means and standard

deviations for six questions asked of one hundred employees concerning the value of different kinds of

organizational rewards. Based on the five-point scale ranging from one (very low value) to five (very high

value), the data suggest that challenging work and respect from peers are the two most highly valued

rewards. Monetary rewards, such as pay and fringe benefits, are not as highly valued.

Table 5. Descriptive Statistics of Value of Organizational Rewards.

Descriptive Statistics of Value of Organizational Rewards

Organizational Rewards

Mean

Standard Deviation

Challenging work

4.6

0.79

Respect from peers

4.4

0.81

Pay

4.0

0.71

Praise from supervisor

4.0

1.55

Promotion

3.3

0.95

Fringe benefits

2.7

1.14

Number of respondents = 100

1 = very low value, 5 = very high value

But the mean can be a misleading statistic. It only describes the average value and thus provides no

information on the distribution of the responses. Different patterns of responses can produce the same

Organization Development MGMT 628

mean score. Therefore, it is important to use the standard deviation along with the frequency distribution

to gain a clearer understanding of the data. The Frequency distribution is a graphical method for displaying

data that shows the number of times a particular response was given. For example, the data in Table 5

suggest that both pay and praise from the supervisor are equally valued with a mean of 4.0. However, the

standard deviations for these two measures are very different at 0.71 and 1.55, respectively. Table 6 shows

the frequency distributions of the responses to the questions about pay and praise from the supervisor.

Employees' responses to the value of pay are distributed toward the higher end of the scale, with no one

rating it of low or very low value. In contrast, responses about the value of praise from the supervisor fall

into two distinct groupings: twenty-five employees felt that supervisor praise has a low or very low value,

whereas seventy-five people rated it high or very high. Although both rewards have the same mean value,

their standard deviations and frequency distributions suggest different interpretations of the data.

Table 6: Frequency Distribution of Responses to "Pay" and praise from Supervisor" items.

Frequency Distributions of Responses to "Pay" and "praise from Supervisor" Items

Pay (Mean = 4.0)

Response

Number checking each response

Graph

(1) Very low vlue

(2) Low value

(3) Moderate value

Xxxxx

(4) High value

Xxxxxxxxxx

(5) Very high value

xxxxx

Praise from Supervisor (Mean = 4.0)

Response

Number checking each response

Graph

(1) Very low value

Xxx

(2) Low value

(3) Moderate value

(4) High value

(5) Very high value

xxxxxxxxxxxx

In general, when the standard deviation for a set of data is high, there is considerable disagreement over the

issue posed by the question if the standard deviation is small; the data are similar on a particular measure.

In the example described above, there is disagreement over the value of supervisory praise (some people

think it is important but others do not), but there is fairly good agreement that pay is a reward with high

value.

Scatter grams and Correlation Coefficients:

In addition to describing data, quantitative techniques also permit OD consultants to make inferences

about the relationships between variables. Scattergrams and correlation coefficients are measures of the

strength of a relationship between two variables. For example, suppose the problem being faced by an

organization is increased conflict between the manufacturing department and the engineering design

department. During the data-collection phase, information about the number of conflicts and change

orders per month over the past year is collected. The data are shown in Table 7and plotted in a

Scattergrams in Fig 29.

Organization Development MGMT 628

Table 7: Relationship between Change Orders and Conflicts

Relationship Between Change Orders and Conflicts

Month

Number of Change Orders

Number of Conflicts

April

May

June

July

August

September

October

November

December

January

February

March

A Scattergram is a diagram that visually displays the relationship between two variables; it is constructed by

locating each case (person or event) at the intersection of its value for each of the two variables being

compared. For example, in the month of August, there were eight change orders and three conflicts, whose

intersection is shown on Figure 29as an X.

Three basic patterns can emerge from a Scattergram, as shown in Fig 30. The first pattern is called a

positive relationship because as the values of x increase, so do the values of y. The second pattern is called

a negative relationship because as the values of x increase, the values of y decrease. Finally, there is the

"shotgun" pattern wherein no relationship between the two variables is apparent. In the example shown in

Figure 29, 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.

Organization Development MGMT 628

Figure 29: Scattergram of change order versus conflict

Figure 30: Basic Scattergram Patterns

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 perfect, positive relationship

between two variables, whereas a correlation of -1.0 signifies a perfectly negative relationship. A correlation

of 0 implies a "shotgun" scattergram where there is no relationship between two variables.

Organization Development MGMT 628

Difference Tests:

The final technique for analyzing quantitative data is the difference 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." 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 one hundred

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, 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 absenteeism. 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 deviation 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 standard 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 assumptions are discussed in most

standard statistical texts, and OD practitioners should consult them before calculating difference scores for

purposes of diagnosis or evaluation.

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

usually is responsible for organizing and presenting them to the client. Properly analyzed and meaningful

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 31, 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.

We now 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 on efforts warrants a special look.

Organization Development MGMT 628

Figure 31: Possible Effects of Feedback

Determining the Content of the Feedback:

In the course of diagnosing the organization, a large amount of data is collected. In fact, there is often more

information than the client needs or could interpret in a realistic period of time. If too many data are fed

back, the client may decide that changing is impossible. Therefore, OD practitioners need to summarize the

data in ways that enable clients to understand the information and draw action implications from it. The

techniques for data analysis described earlier can inform this task. Additional criteria for determining the

content of diagnostic feedback are described below.

Several characteristics of effective feedback data have been described in the literature. They include the

following nine properties:

Relevant. Organization members are likely to use feedback data for problem solving when they

find the information meaningful. Including managers and employees in the initial data-collection

activities can increase the relevance of the data.

Organization Development MGMT 628

Understandable. Data must be presented to organization members in a form that is

readily

interpreted. Statistical data, for example, can be made understandable through the use of graphs

and charts.

Descriptive. Feedback data need to be linked to real organizational behaviors if they are to arouse

and direct energy. The use of examples and detailed illustrations can help employees gain a better

feel for the data.

Verifiable. Feedback data should be valid and accurate if they are to guide action. Thus,

the

information should allow organization members to verify whether the

findings really describe

the organization. For example, questionnaire data might include information about the sample of

respondents as well as frequency distributions for each item or measure. Such information can help

members verify whether the feedback data accurately represent organizational events or attitudes.

Timely. Data should be fed back to members as quickly as possible after being collected and

analyzed. This will help ensure that the information is still valid and is linked to members'

motivation to examine it.

Limited. Because people can easily become overloaded with too much information, feedback data

should be limited to what employees can realistically process at one time.

Significant. Feedback should be limited to those problems that organization members can do

something about because it will energize them and help direct their efforts toward realistic changes.

Comparative. Feedback data can be ambiguous without some benchmark as a reference.

Whenever possible, data from comparative groups should be provided to give organization

members a better idea of how their group fits into a broader context.

Un-finalized. Feedback is primarily a stimulus for action and thus should spur further diagnosis

and problem solving. Members should be encouraged, for example, to use the data as a starting

point for more in-depth discussion of organizational issues.

Characteristics of the Feedback Process:

In addition to providing effective feedback data, it is equally important to attend to the process by which

that information is fed back to people. Typically, data are provided to organization members in a meeting

or series of meetings. Feedback meetings provide a forum for discussing the data, drawing relevant

conclusions, and devising preliminary action plans. Because the data might include sensitive material and

evaluations about organization members' behaviors, people may come to the meeting with considerable

anxiety and fear about receiving the feedback. This anxiety can result in defensive behaviors aimed at

denying the information or providing rationales. More positively, people can be stimulated by the feedback

and the hope that desired changes will result from the feedback meeting.

Because people are likely to come to feedback meetings with anxiety, fear, and hope, OD practitioners need

to manage the feedback process so that constructive discussion and problem solving occur. The most

important objective of the feedback process is to ensure that organization members own the data.

Ownership is the opposite of resistance to change and refers to people's willingness to take responsibility

for the data, their meaning, and the consequences of using them to devise a change strategy. If the feedback

session results in organization members rejecting the data as invalid or useless, then the motivation to

change is lost and members will have difficulty engaging in a meaningful process of change.

Ownership of the feedback data is facilitated by the following five features of successful feedback

processes:

Motivation to work with the data. People need to feel that working with the feedback data

will have beneficial outcomes. This may require explicit sanction and support from powerful

groups so that people feel free to raise issues and to identify concerns during the feedback

sessions. If people have little motivation to work with the data or feel that there is little chance

to use the data for change, then the information will not be owned by the client system.

Structure for the meeting. Feedback meetings need some structure or they may degenerate

into chaos or aimless discussion. An agenda or outline and a discussion leader can usually

provide the necessary direction. If the meeting is not kept on track, especially when the data

are negative, ownership can be lost in conversations that become too general. When this

happens, the energy gained from dealing directly with the problem is lost.

Appropriate attendance. Generally, people who have common problems and can benefit

from working together should be included in the feedback meeting. This may involve a fully

intact work team or groups comprising members from different functional areas or

hierarchical levels. Without proper representation in the meeting, ownership of the data is lost

because participants cannot address the problem(s) suggested by the feedback.

Appropriate power. It is important to clarify the power possessed by the group. Members

need to know on which issues they can make necessary changes, on which they can only

Organization Development MGMT 628

recommend changes, and over which they have no control. Unless there are clear boundaries,

members are likely to have some hesitation about using the feedback data for generating action

plans. Moreover, if the group has no power to make changes, the feedback meeting will

become an empty exercise rather than a real problem-solving session. Without the power to

address change, there will be little ownership of the data.

Process help. People in feedback meetings require assistance in working together as a group.

When the data are negative, there is a natural tendency to resist the implications, deflect the

conversation onto safer subjects, and the like. An OD practitioner with group process skills

can help members stay focused on the subject and improve feedback discussion, problem

solving, and ownership.

When combined with effective feedback data, these features of successful feedback meetings enhance

member ownership of the data. They help to ensure that organization members fully discuss the

implications of the diagnostic information and that their conclusions are directed toward relevant and

feasible organizational changes.

Table of Contents: