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Write the objectives (atleast 10) and their explanation for the chapter “Collecting and Analyzing Diagnostic Information “.

THE DIAGNOSTIC RELATIONSHIP

In most cases of planned change, OD practitioners play an active role in gathering data from organization members for diagnostic purposes. For example, they might interview members of a work team about causes of conflict among members; they might survey employees at a large industrial plant about factors contributing to poor product quality. Before collecting diagnostic information, practitioners need to establish a relationship with those who will provide and subsequently use it. Because the nature of that relationship affects the quality and usefulness of the data collected, it is vital that OD practitioners clarify for organization members who they are, why the data are being collected, what the data gathering will involve, and how the data will be used.1 That information can help allay people’s natural fears that the data might be used against them and gain members’ participation and support, which are essential to developing successful interventions. Establishing the diagnostic relationship between the consultant and relevant organization members is similar to forming a contract. It is meant to clarify expectations and to specify the conditions of the relationship. In those cases where members have been directly involved in the entering and contracting process described in the diagnostic contract will typically be part of the initial contracting step. In situations where data will be collected from members who have not been directly involved in entering and contracting, however, OD practitioners will need to establish a diagnostic.

Who am I?

The answer to this question introduces the OD practitioner to the organization, particularly to those members who do not know the consultant and yet will be asked to provide diagnostic data. Why am I here, and what am I doing? These answers are aimed at defining the goals of the diagnosis and data-gathering activities. The consultant needs to present the objectives of the action research process and to describe how the diagnostic activities fit into the overall developmental strategy.

Who do I work for?

This answer clarifies who has hired the consultant, whether it be a manager, a group of managers, or a group of employees and managers. One way to build trust and support for the diagnosis is to have those people directly involved in establishing the diagnostic contract. Thus, for example, if the consultant works for a joint labor–management committee, representatives from both sides of that group could help the consultant build the proper relationship with those from whom data will be gathered.

What do I want from you, and why?

Here, the consultant needs to specify how much time and effort people will need to give to provide valid data and subsequently to work with these data in solving problems. Because some people may not want to participate in the diagnosis, it is important to specify that such involvement is voluntary.

How will I protect your confidentiality?

This answer addresses member concerns about who will see their responses and in what form. This is especially critical when employees are asked to provide information about their attitudes or perceptions. OD practitioners can either ensure confidentiality or state that full participation in the change process requires open information sharing. In the first case, employees are frequently concerned about privacy and the possibility of being punished for their responses. To alleviate concern and to increase the likelihood of obtaining honest responses, the consultant may need to assure employees of the confidentiality of their information, perhaps through explicit guarantees of response anonymity. In the second case, full involvement of the participants in their own diagnosis may be a vital ingredient of the change process. If sensitive issues arise, assurances of confidentiality can co-opt the OD practitioner and thwart meaningful diagnosis. The consultant is bound to keep confidential the issues that are most critical for the group or organization to understand.

Who will have access to the data? Respondents typically want to know whether

they will have access to their data and who else in the organization will have similar

access. The OD practitioner needs to clarify access issues and, in most cases,

should agree to provide respondents with their own results. Indeed, the collaborative

nature of diagnosis means that organization members will work with their

own data to discover causes of problems and to devise relevant interventions.

What’s in it for you? This answer is aimed at providing organization members

with a clear delineation of the benefits they can expect from the diagnosis. This

usually entails describing the feedback process and how they can use the data to

improve the organization.

Can I be trusted? The diagnostic relationship ultimately rests on the trust established

between the consultant and those providing the data. An open and honest

exchange of information depends on such trust, and the practitioner should provide

ample time and face-to-face contact during the contracting process to build

this trust. This requires the consultant to listen actively and discuss openly all questions

raised by participants.

Careful attention to establishing the diagnostic relationship helps to promote the three

goals of data collection.4 The first and most immediate objective is to obtain valid information

about organizational functioning. Building a data collection contract can ensure that

organization members provide honest, reliable, and complete information.

Data collection also can rally energy for constructive organizational change. A good

diagnostic relationship helps organization members start thinking about issues that

concern them, and it creates expectations that change is possible. When members trust

the consultant, they are likely to participate in the diagnostic process and to generate

energy and commitment for organizational change.

Finally, data collection helps to develop the collaborative relationship necessary for

effecting organizational change. The diagnostic stage of action research is probably

the first time that most organization members meet the OD practitioner, and it can

be the basis for building a longer-term relationship. The data collection contract and

subsequent data-gathering and feedback activities provide members with opportunities

for seeing the consultant in action and for knowing her or him personally. If the

consultant can show employees that he or she is trustworthy, is willing to work with

them, and is able to help improve the organization, then the data collection process

will contribute to the longer-term collaborative relationship so necessary for carrying

out organizational changes.

METHODS FOR COLLECTING DATA

The four major techniques for gathering diagnostic data are questionnaires, interviews,

observations, and unobtrusive measures. Table 7.1 briefly compares the methods and

lists their major advantages and problems. No single method can fully measure the kinds

of variables important to OD because each has certain strengths and weaknesses.5 For

example, perceptual measures, such as questionnaires and surveys, are open to selfreport

biases, such as respondents’ tendency to give socially desirable answers rather

than honest opinions. Observations, on the other hand, are susceptible to observer

biases, such as seeing what one wants to see rather than what is really there. Because

of the biases inherent in any data collection method, more than one method should be

used when collecting diagnostic data. If data from the different methods are compared

and found to be consistent, it is likely that the variables are being measured validly. For

example, questionnaire measures of job discretion could be supplemented with observations

Questionnaires

One of the most efficient ways to collect data is through questionnaires. Because

they typically contain fixed-response queries about various features of an organization,

these paper-and-pencil measures can be administered to large numbers of

people simultaneously. Also, they can be analyzed quickly, especially with the use

of computers, thus permitting quantitative comparison and evaluation. As a result,

data can easily be fed back to employees. Numerous basic resource books on survey

methodology and questionnaire development are available.6

Questionnaires can vary in scope, some measuring selected aspects of organizations

and others assessing more comprehensive organizational characteristics. They also

can vary in the extent to which they are either standardized or tailored to a specific

organization. Standardized instruments generally are based on an explicit model of

organization, group, or individual effectiveness and contain a predetermined set of

questions that have been developed and refined over time.

Interviews

A second important measurement technique is the individual or group interview.

Interviews are probably the most widely used technique for collecting data in OD.

They permit the interviewer to ask the respondent direct questions. Further probing

and clarification is, therefore, possible as the interview proceeds. This flexibility

is invaluable for gaining private views and feelings about the organization and for

exploring new issues that emerge during the interview.

Interviews may be highly structured—resembling questionnaires—or highly unstructured—

starting with general questions that allow the respondent to lead the way.

Structured interviews typically derive from a conceptual model of organization functioning;

the model guides the types of questions that are asked. For example, a structured

interview based on the organization-level design components identified

would ask managers specific questions about technology, strategy, organization structure,

measurement systems, human resources systems, and organization culture.

Unstructured interviews are more general and include the following broad questions

about organizational functioning:

What are the major goals or objectives of the organization or department?

How does the organization currently perform with respect to these purposes?

What are the strengths and weaknesses of the organization or department?

What barriers stand in the way of good performance?

Although interviewing typically involves one-to-one interaction between an OD practitioner

and an employee, it can be carried out in a group context. Group interviews

save time and allow people to build on others’ responses. A major drawback, however,

is that group settings may inhibit some people from responding freely.

A popular type of group interview is the focus group or sensing meeting.These are

unstructured meetings conducted by a manager or a consultant. A small group of 10 to

15 employees is selected to represent a cross section of functional areas and hierarchical

levels or a homogeneous grouping, such as minorities or engineers. Group discussion is

frequently started by asking general questions about organizational features and functioning,

an intervention’s progress, or current performance. Group members are then

encouraged to discuss their answers more fully. Consequently, focus groups and sensing

meetings are an economical way to obtain interview data and are especially effective

in understanding particular issues in greater depth. The richness and validity of the

information gathered will depend on the extent to which the manager or the consultant

develops a trust relationship with the group and listens to member opinions.

Another popular unstructured group interview involves assessing the current state

of an intact work group. The manager or the consultant generally directs a question to

the group, calling its attention to some part of group functioning. For example, group

members may be asked how they feel the group is progressing on its stated task. The

group might respond and then come up with its own series of questions about barriers

to task performance. This unstructured interview is a fast, simple way to collect data

about group behavior. It allows members to discuss issues of immediate concern and

to engage actively in the questioning and answering process. This technique is limited,

however, to relatively small groups and to settings where there is trust among employees

and managers and a commitment to assessing group processes.

Interviews are an effective method for collecting data in OD. They are adaptive,

allowing the interviewer to modify questions and to probe emergent issues during the

interview process. They also permit the interviewer to develop an empathetic relationship

with employees, frequently resulting in frank disclosure of pertinent information.

A major drawback of interviews is the amount of time required to conduct and analyze

them. Interviews can consume a great deal of time, especially if interviewers take

full advantage of the opportunity to hear respondents out and change their questions

accordingly. Personal biases also can distort the data. Like questionnaires, interviews

are subject to the self-report biases of respondents and, perhaps more important, to

the biases of the interviewer. For example, the nature of the questions and the interactions

between the interviewer and the respondent may discourage or encourage certain

kinds of responses. These problems suggest that interviewing takes considerable skill to

gather valid data. Interviewers must be able to understand their own biases, to listen

and establish empathy with respondents, and to change questions to pursue issues that

develop during the course of the interview.

Observations

One of the more direct ways of collecting data is simply to observe organizational behaviors

in their functional settings. The OD practitioner may do this by walking casually

through a work area and looking around or by simply counting the occurrences of specific

kinds of behaviors (for example, the number of times a phone call is answered after

three rings in a service department). Observation can range from complete participant

observation, in which the OD practitioner becomes a member of the group under study,

to more detached observation, in which the observer is clearly not part of the group or

situation itself and may use film, videotape, and other methods to record behaviors.

Observations have a number of advantages. They are free of the biases inherent

in self-report data. The Process of Organization Development

real-time data, describing behavior occurring in the present rather than the past. This

avoids the distortions that invariably arise when people are asked to recollect their

behaviors. Finally, observations are adaptive in that the consultant can modify what

he or she chooses to observe, depending on the circumstances.

Among the problems with observations are difficulties interpreting the meaning

underlying the observations. Practitioners may need to devise a coding scheme to

make sense out of observations, and this can be expensive, take time, and introduce

biases into the data. Because the observer is the data collection instrument, personal

bias and subjectivity can distort the data unless the observer is trained and skilled in

knowing what to look for; how, where, and when to observe; and how to record data

systematically. Another problem concerns sampling: Observers not only must decide

which people to observe, they also must choose the time periods, territory, and events

in which to make those observations. Failure to attend to these sampling issues can

result in highly biased samples of observational data.

When used correctly, observations provide insightful data about organization and

group functioning, intervention success, and performance. For example, observations

are particularly helpful in diagnosing the interpersonal relations of members of work

groups. As discussed in Chapter 6, interpersonal relationships are a key component

of work groups; observing member interactions in a group setting can provide direct

information about the nature of those relationships.

Unobtrusive Measures

Unobtrusive data are not collected directly from respondents but from secondary

sources, such as company records and archives. These data are generally available in

organizations and include records of absenteeism or tardiness; grievances; quantity and

quality of production or service; financial performance; meeting minutes; and correspondence

with key customers, suppliers, or governmental agencies.

Unobtrusive measures are especially helpful in diagnosing the organization, group,

and individual outputs presented in Chapters 5 and 6. At the organization level, for

example, market share and return on investment usually can be obtained from company

reports. Similarly, organizations typically measure the quantity and quality of the

outputs of work groups and individual employees. Unobtrusive measures also can help

to diagnose organization-level design components—structure, work systems, control

systems, and human resources systems. A company’s organization chart, for example,

can provide useful information about organization structure. Information about control

systems usually can be obtained by examining the firm’s management information

system, operating procedures, and accounting practices. Data about human resources

systems often are included in a company’s personnel manual.

Unobtrusive measures provide a relatively objective view of organizational functioning.

They are free from respondent and consultant biases and are perceived as being

“real” by many organization members. Moreover, unobtrusive measures tend to be

quantified and reported at periodic intervals, permitting statistical analysis of behaviors

occurring over time. Examining monthly absenteeism rates, for example, might reveal

trends in employee withdrawal behavior.

The major problems with unobtrusive measures occur in collecting such information

and drawing valid conclusions from it. Company records may not include data

in a form that is usable by the consultant. If, for example, individual performance

data are needed, the consultant may find that many firms only record production

information at the group or departmental level. Unobtrusive data also may have

their own built-in biases. Changes in accounting procedures and in methods of

recording data are common in organizations, and such changes can affect company

records independently of what is actually happening in the organization. For example,

observed changes in productivity over time might be caused by modifications

in methods of recording production rather than by actual changes in organizational

functioning.

Despite these drawbacks, unobtrusive data serve as a valuable adjunct to other diagnostic

measures, such as interviews and questionnaires. Archival data can be used in

preliminary diagnosis, identifying those organizational units with absenteeism, grievance,

or production problems. Then, interviews might be conducted or observations

made in those units to discover the underlying causes of the problems. Conversely,

unobtrusive data can be used to cross-check other forms of information. For example,

if questionnaires reveal that employees in a department are dissatisfied with their jobs,

company records might show whether that discontent is manifested in heightened withdrawal

behaviors, in lowered quality work, or in similar counterproductive behaviors.

SAMPLING

Before discussing how to analyze data, the issue of sampling needs to be 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 OD 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 is representative

of the organization or 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 50 employees might be used to assess

the perceptions of all 300 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.11 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, the 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 be sampled or the number of different

functions), the more difficult it is to establish a “right” sample size. As 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.

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 50 people randomly out of the 300 employees at

a manufacturing plant. Using a complete list of all 300 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 50 numbers under 300 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 sub population.

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.

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 more open to subjective biases but also 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.12 In general, however, the process can be

broken down into three major steps. 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 coworkers, 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.13 Using data collected

through interviews, observations, 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 organization, 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. 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 performanceand 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 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.

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