Research Methodology

3.0 Introduction

Research embraces four interdependent elements: i. the phenomena with which the problem under consideration is concerned; ii. the theories that relate to the phenomena; iii. the methods of data collection; and iv. the statistical procedures for data analysis. Having identified these components of research, Magnusson (1992) asserts that the theory, the methods and the statistical procedures must all be based on systematic descriptions of the phenomena per se. Our first chapter described the phenomenon; the second chapter discussed the theories related to the phenomenon. Now we turn to addressing the third element, the methods. In this chapter, we shall first give a brief description of the framework adopted for the study and then go on to discuss the research design, instruments, variables & measures, the sample and methods of data summarisation and analysis.

3.1 Conceptual Framework

The purpose of the present study, as may be recalled from Chapter I, is to explore and examine the fundamental interpersonal behaviour orientations of the target population. As discussed in our review of literature, the theory of Fundamental Interpersonal Relations Orientation (FIRO) deals exclusively with the domain of our interest. The revised version of this theory identifies the dimensions of Inclusion, Control and Openness as the three exhaustive constituents of interpersonal relations, at the behavioural level. It also elaborates on the expressed, received, perceived and the wanted aspects of each of these dimensions. Given the objectives of our study, the new-FIRO theoretical framework fills the bill and has, therefore, been adopted for conduct of the study.

3.2.0 Research Design

Issues related to interpersonal behaviour could be studied in a variety of ways. They can, for example, be studied by experimentation or by survey methods. Within the survey method, one could adopt a cross-sectional or longitudinal design. In-depth case studies and case clustering methods would be useful, too. Ethnographic techniques could be employed for eliciting interpersonal behaviour dynamics in groups. The final choice of a particular research design, however, is dependent on the purpose one wants to pursue. Given the objectives of the present study, ethnographic and experimental approaches are unnecessary and even inappropriate. As for longitudinal studies of human behaviour, they would indeed be valuable in themselves, but the associated costs of time and other resources do often tend to offset or outweigh the benefits that can emanate or accrue from these studies. In our pursuit to map the existing interpersonal needs of managers in India's cooperative dairy sector, we would need to cover a large sample of subjects and compare/classify the profiles of sub-groups within the sample. Use of complexity-reduction statistical techniques could then provide us with a snapshot picture of what is out there at present. Hence, for the purposes of the present study, it was decided to adopt a cross-sectional, exploratory-integrative research design.

3.2.1 Research Instrument and Data Collection

While discussing the various sources of data, Leary (1957) proposed that data on interpersonal dimensions could be obtained at three levels: public, conscious, and private. According to his definitions, objective ratings of behaviour would be at the public level; subjective ratings or self-reports would be at the conscious level; dream contents and responses to projective tests would be at the private level. Leary also suggested an I-would-like-to-be, or the ideal, level at which meaningful data could be obtained, but, according to Birtchnell's review (1990), "this has been largely neglected by subsequent theorists and researchers".

The major source of data for the present study was at the conscious level: self-reports done in an atmosphere of trust and confidentiality (see section 3.2.2 The Procedure, for details). Data were also elicited at the "I-would-like-to" level.

The revised FIRO instrument, called Element-B, was used in this study. It is an inventory, composed of six nine-item scales developed by means of the Guttman scaling technique, which makes it possible to derive a scale that measures only one dimension. Unidimensionality of a scale means that all the items in the scale measure the same dimension and that the items are in the order of increasing intensity or of decreasing "popularity". The primary measure of unidimensionality for a Guttman scale is reproducibility, i.e., the prospect or possibility of deriving the actual responses to the items of a scale, solely on the basis of the respondent's total score for the scale. A mean reproducibility coefficient of 0.92 has been reported for the instrument (Schutz, 1982). Reproducibility was tested in a crude way in this study and was found to be 89.17 per cent: ten subjects (whose scores were >0 and <9) were randomly selected and their response markings were "predicted" on the basis of the scale scores. Of the 120 predictions, 107 turned out right. Of the 13 predictions that went wrong, 11 were one-item skips and two were two-item skips. The test-retest reliability of the instrument has been reported to be 0.77 (Hutcherson, 1963). The content validity of the items has been ascertained by the method of dichotomous decisions of judges and by including in the final scales only those items whose content was judged at least by 90 per cent of the judges to be consistent with the corresponding scale's definition (Schutz, 1982). Smith (1963) and Vraa (1971) found that the content validity "measure was adequately representative". A significant mean predictive coefficient of 0.436 has also been found, by comparing FIRO measures with observed behaviour of respondents (Gard & Bendig, 1964).

In addition to the local test of reproducibility, discussed above, a test of validation was also undertaken in this study with self-ratings. Pearson's coefficients of correlation were computed for a randomly selected sub-sample of 105 respondents between their scores on the six Perceived-level FIRO scales and the respondents' independent self-ratings (obtained before the inventory was scored) on each of the same six areas of interpersonal behaviour. The coefficients were 0.83, 0.57, 0.61, 0.84, 0.60 and 0.72 for PEI, PEC, PEO, PRI, PRC and PRO, respectively. The magnitude of these values (all at p<.01) suggests a high degree of concurrent validity of the measures. Further, peer ratings on sociability were taken for 56 managerial respondents from 9 to 15 of their peers and/or acquaintances and the mean sociability ratings were correlated with the respondents' Inclusion scores of PEI, WEI, PRI and WRI. The coefficients were 0.74, 0.41, 0.81 and 0.32, respectively; all these coefficients were at p<.01, providing further support to the concurrent validity of the instrument.

The Element-B instrument elicits data on the interpersonal behaviour needs of Inclusion, Control and Openness as Expressed and Received, each at the Perceived as well as at the Wanted or Would-like-to levels. The instrument was, thus, to provide data in terms of how the respondents behaved toward others, how others behaved towards them, how the respondents wanted their behaviour toward others to be, and how they wanted other people to behave towards them, on each of the three interpersonal behaviour areas of Inclusion, Control, and Openness. Three of the six scales in the instrument measured how one behaved (expressed) toward others with regard to Inclusion, Control and Openness, while the remaining three measured how others behaved towards the focal person or what one received from others. Each scale was responded to twice: once for the perceived (as happening) level of behaviour and the second time for the wanted or would-like-to level. Thus data were collected on twelve variables, representing different aspects of the three basic interpersonal behaviour needs. The variables were labeled by using the prefixes of Perceived-Expressed, Wanted-Expressed, Perceived-Received and Wanted-Received to Inclusion, Control and Openness.

3.2.2 The Procedure

The administration of the instrument was invariably done in a behavioural workshop or laboratory setting. A non-threatening and self-diagnostic atmosphere was created in the setting by first discussing the Johari Window concept and the role of feedback in one's journey toward self-development. Participants were then given a copy of Element-B. They were told that the instrument was a very potent feedback giver and that its potency was dependent on their honesty in responding to the items as well as their meticulousness in following the announced procedural details. They were also told that they would themselves score the responses and interpret the scores with the necessary technical and theoretical help from the facilitator, who was the investigator himself.

The participants were asked to respond to the 54 items at the perceived level first. Then the response markings were folded out of sight (the lay-out of the inventory had been designed to make this possible) and the participants were asked to respond to the same items at the I-would-like-to level. This "wanted" perspective was explained with the help of illustrative examples, by picking up some items from the inventory. The investigator was always present and available for any clarifications sought by the participants during the administration, scoring and interpretation phases of the exercise. The responses were scored with the help of the standard key. At the end of the exercise, the individual response sheets, along with their scores, were collected by the investigator and were returned the next day with the group mean scores and, sometimes, also the standard deviations, which provided the participants with an additional reference point in the interpretation of their personal scores. It was ensured that the entire procedure was the same for every group, without exception.

While the FIRO measures were the main data base for the study, some additional data, relating to group performance, satisfaction and success, were generated and used in a limited way. Measures of group performance and student success were quantitative, objective or, what Leary (1957) referred to as `data on the public level'. Satisfaction was measured qualitatively by subjective ratings or self-reports. Managerial success was measured through classification by peers and superiors. For managerial success, "hard" measures, such as the new markets created, new products introduced, new structural changes implemented or number of promotions achieved, could be used. While such quantitative measures may be useful for dichotomous tracing of managerial profiles, use of such data has been criticised on the grounds of reliability, validity and generalisability across a population (Van Maanen, 1979).

3.2.3 Scales of Measurement Used

The scales of Element-B, as already discussed, were all of the Guttman type. They were scored from 0 to 9. The scale score indicates the degree to which the respondent agrees with the scale name (see the declarative sentences in Table 3.1, last column). Zero means least agreement and nine means most agreement. To illustrate the meaning: a score of 0, 1 or 2 on the PEO (Perceived-Expressed Openness) scale, for example, would mean that the respondent was not very open with people.

For measuring satisfaction of group members, a Likert-type interval scale was used. The interval scale was used for its obvious simplicity and easy representability. The scale quality of the compatibility measure was the same as that of the FIRO measures, for it was derived merely by taking the difference between the Expressed and Received FIRO scores. The measure of managerial success was on a nominal scale.

3.2.4 Variables and Measures

The variables in the study were those based on the revised FIRO theory as well as a few contextually defined ones such as group performance, compatibility, success and satisfaction. The basic FIRO variables have been well standardised; the reliability and validity of their measures, as mentioned when discussing the instrument, have already been well established. The twelve FIRO variables are listed in Table 3.1.

Of the contextual variables that were included in the study, group performance was measured by the grades received on group assignments. Success for students was indexed on the basis of their overall grade-point average, whereas for managers it was a dichotomous category variable, reflecting the judgement of "successful" and "not-so successful" by peers and superiors. Satisfaction was measured on a single-item eliciting perceived satisfaction of the respondent. The face validity of the variable was judged to be high. The variable of group compatibility was measured in terms of the absolute difference between the mean expressed and mean wanted-received scores. The smaller the difference score, the greater the compatibility.

3.3 The Sample

Given the purpose of the study, namely, to explore and map the interpersonal need profiles of managers and managers-to-be in the Indian cooperative dairy industry, simple random samples of the two groups were considered adequate.

The sample for the managerial group was randomly drawn from 45 cooperative dairy unions from 13 states of the country. With four to six managers at random from each union, the managerial sample size was 253, composed of deputy managers and departmental heads. It was inevitably an all-male sample, for the number of women managers available in all the unions together was less than ten.

The subjects were contacted either in the MDPs (management development programmes) which they attended or by offering a special two-day training programme, free of charge, in the premises of the organisations where they worked.

The second half of the universe for the study was the student population of the Institute of Rural Management, Anand, because, as was already mentioned in Chapter I, the managerial manpower needs of the sector are to be specially catered to by this Institute: "One of the first purposes (of this Institute) will be to act as an inductor of young graduates--many coming straight from college--into the young executive cadres of India's rapidly growing, specialised cooperatives which already handle milk, oilseeds and cotton -- and which are likely to spread into other fields of farmer-owned enterprise" (Halse 1979). Thus, the Institute of Rural Management is very much part of the Indian cooperative dairy sector, although its activities are not confined to just that one sector.

The students of the two-year post-graduate programme in rural management at the above Institute come from all parts of India and represent a variety of academic disciplines. These students, both male and female, before their completion of the two-year programme, constituted the sample of the would-be managers for the purposes of the study. The size of the student sample was 322, with 276 males and 46 females.

Thus, the total sample for the study was 575 (253 present managers plus 322 would-be managers). The distribution of the sample on certain characteristics is shown in Tables 3.2 - 3.5.

Characteristic           Managers                  Students                 

Sample size (N)          253                       322                      
Males                    253                       276                      
Females                  None                      46                       
North Indians            137                       184                      
South Indians            81                        116                      
Unclassified by region   35                        22                       

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Characteristic           Managers                  Students                 

Below 21 years           None                      84                       
Between 21 & 23 years    None                      150                      
Between 23 & 25 years    None                      64                       
Between 25 & 31 years    74                        24                       
Between 31 & 36 years    96                        None                     
Between 36 & 41 years    35                        None                     
Above 41 years           48                        None                     

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Characteristic           Managers                  Students                 

Arts & Humanities        9                         57                       
Science                  39                        72                       
Agri. & Vet. Sciences    117                       65                       
Agr. Engg. & Tech.       40                        34                       
Engineering              18                        57                       
Commerce                 24                        37                       
No info. on education    6                         None                     

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Characteristic           Managers                  Students                 

Production               70                        Not Applicable           
Procurement & Input      56                        N.A.                     
Marketing                17                        N.A.                     
Quality Control          15                        N.A.                     
Other departments        95                        N.A.                     

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3.4 The Hypotheses

The major purpose of the present study, as has already been mentioned earlier, is to explore the FIRO needs of managers and management students as they obtain in the chosen sector, rather than to test particular relationships. Even a purely descriptive study could achieve that purpose and provide us with results that would then be the empirical base, from which to generate testable hypotheses, later on. All the same, a set of tentative hypotheses were formulated to be tested in this preliminary study itself. Thus, in addition to descriptive presentations of results, the study will test the following hypotheses:

H1: Managers and management students do not differ in any of their interpersonal needs.

H2: Different age groups of managers do not differ in any of their interpersonal needs.

H3: Managers trained in different academic disciplines do not manifest differences in any of their interpersonal needs.

H4: There is no difference in the interpersonal needs of managers of different functional departments.

H5: Managers of North and South Indian origin do not differ in any of their interpersonal needs.

H6: There is no difference in the interpersonal needs of successful and not-so-successful managers.

H7: Students of different age groups are the same in all their interpersonal needs.

H8: Students trained in different academic disciplines are the same in all their interpersonal needs.

H9: The interpersonal needs of male and female students are the same.

H10: Students of North and South Indian origin manifest the same interpersonal need profiles.

H11: Poor performers and better performers (in studies) among the students manifest the same interpersonal need profiles.

3.5 Methods of Analysis

For purposes of constructing overall profiles of managers and management students on the basis of their interpersonal needs, descriptive statistics were used. A frequency analysis of Low and High scorers was done for the two groups so as to look for any possible contrasts between the groups. Since the number of cases in

the two groups was unequal, the relative frequency (percentage) method was adopted, rather than the absolute frequency method. Means and standard deviations were computed on all the twelve variables for the two groups, and the means were tested for significance of difference between the two groups by use of the t-test.

As for the relationship amongst the FIRO variables themselves, we were more concerned with the associations rather than causal links. Pearson's correlation coefficients were adopted for the purpose, because product moment correlations are good enough indicators of relationships among the variables in a first-approximation exploratory study (Thorndike, 1978). ANOVA was used to elicit the intergroup variations among sub-groups of managers and students. Additionally, Multiple Discriminant Analysis was performed to validate the results.

Although cluster analysis is widely used for classification purposes, it has a few disadvantages: the number of groups or clusters extracted are arbitrary; reproducibility of groups is low; and the clusters yield inconsistent membership. In this study, it was decided to use the standardised multiple discriminant analysis. Klecka (1982) recommends this method for exploratory studies because of the obvious advantages the method offers in grouping and estimation of variable influence. To test the validity of the results obtained by the multiple discriminant analysis, both hold-out and predictive validities (as suggested by Johnson and Wichern, 1982) were attempted. For reasons of greater functionality, more will be said of this method in section 4.8 (next chapter), where it is applied.

This chapter described the conceptual framework adopted for the study and discussed the methodology that was employed, along with details of the research design, the sample and methods of analysis. The next chapter presents the results.