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A procedure for occupational clustering
ORGANIZATIONAL BEHAVIOR AND H U M A N PERFORMANCE 8, 1 0 9 - 1 1 7
(1972)
A Procedure for Occupational Clustering 1 FRANK J. LANDY Pennsylvania State University A logical extension of the individual-differences approach is object clustering or empirical typing. The present paper is a demonstration of this approach in the industrial setting. The subject pool comprised 1346 employees of 46 organizations who were classified by occupation as well as type of organization. Subjects were empirically allocated to one of 15 multivariate "types" on the basis of reported meaning, as measured by semantic differential scales. Occupation was shown to have a greater effect on type appearance than organization. Strategies for personnel program implementation were discussed. In a recent publication dealing with occupational attitudes and characteristics, Robinson, Athanasiou, and Head (1969) state that " A fundamental aim of attitude measures is the meaningful location of those population groups which are maximally different from other groups." While this proposition has been followed in a number of diverse areas, it has received little attention in the realm of occupational similarity as Robinson et al. point out in their review. The present paper attempts to provide a strategy, with both applied and basic concerns, for clustering occupations on the basis of the job-related perceptions of individuals falling into various occupational categories. 0b]ect clustering or empirical typing has done much to encourage the consideration of individual differences in psychological research. Substantive problems investigated using these techniques have generally fallen into the areas of specific abilities, psychopathology or sociopolitical behavior. Only recently have these techniques begun to appear in the industrial literature (e.g., Dunnette, Campbell, & Hakel, 1967; Graen, Dawis, & Weiss, 1968) and mostly in the form of inverse or (?-type factor analyses. The logic of subgrouping on the basis of individual differences has been presented by Owens both in print (1968) and also in his 1970 Presidential Address to Division 14 of the APA. Since the individual-differences approach seems to be proving valuable This study was supported in part by a faculty grant from The Central Fund for Research at the Pennsylvania State University. 109 O 1972 by Academic Press, Inc.
]10
FRANK J. LANDY
in the solution of many of the problems confronting industrial psychologists, a demonstration of the potential of at least one Wping procedure would seem appropriate at this time. The data to be considered in this paper were gathered as part of a larger proiect attempting to relate job meaning to work motivation. 0nly the area of iob meaning will be considered here. METHOD The subjects comprised 1346 employees of 46 different organizations. The subjects could be classified as belonging to one of the following nine occupational groupings: nurses, technicians, supervisors and administrators, clerical personnel, bank tellers, teachers, professionals, executives, and semiskilled workers. They Could be further classified as working for one of the following five types of organizations: banks, hospitals, research organizations, miscellaneous commercial organiza: tions, or universities. This dual classification was carried out. for the purpose of determining the independent effects of organizational and occupational classification on job perceptions. Job perception was measured by 31 bipolar scales arranged in a semantic differential format. The scales appeared under the concept heading "My Job." The first step in the procedure was to determine the dimensions on which the subjects could be empirically typed. For the maximum differentiation among groups it is best to find dimensions which are fairly stable and relatively independent of each other. All analyses were carried out using a computer system of cluster and factor analysis known as the BC TRY system (Tryon & Bailey, ]966). The component known as ¥(ariable)-analysis, a key-clustering procedure, was used to identify the independent dimensions to be used in subsequent subject typing. The variables analyzed were the responses of the subiects to the 31 bipolar scales. Since it would be necessary to compare subiects from different organizations and occupations on common job perception dimensions, separate cluster analyses were run for each of the nine occupational groupings and five organizational groupings. Through the use of another BC TRY component (COMP), t h e factorial structure common to all occupational and organizational groupings was identified. While such a procedure is tedious and costly, it insures that subiects will be clustered in a score space common to all. RESULTS The V-analyses and COMP indicated that three distinct dimensions could be used for subiect typing. The three dimensions and scales which comprised them were as follows:
P R O C E D U R E FOR O C C U P A T I O N A L C L U S T E R I N G
111
(1) Stimulus value: challenging-monotonous; deadening-stimulating ; meaningful-meaningless; exciting-dull; boring-interesting. (2) Structure imposed by the work: inexact-exact; detailed-general ; precise-vague. (3) Degree of autonomy: independent-dependent; guided-free; closely supervised-not closely supervised; unrestricted-governed. These three clusters proved to be relatively independent, the highest intereorrelation of cluster scores being 0.24. The component of the BC TRY system which accomplishes subject typing is known as OTYPE, a condensation method of object clustering. It is performed on the cluster scores derived from the V-analysis. The purpose is to allocate N objects, with scores on/c dimensions (three in this case), to m object clusters; in each cluster, objects have similar score profiles. The allocations are made on the basis of Euclidean distances (D values) between the objects on k dimensions. There are three basic steps in the condensation method: (1) Breaking down the score range on each of the dimensions into several discrete categories. High, moderate, and low sectors were used in the present study. High = T scores above 60; low = T scores below 40; and moderate = T scores falling between 40 and 60. (2) Objects are assigned to these score sectors yielding initial OTYPES. (3) Objects are continuously reassigned to OTYPES in an iterative process until none of the OTYPES changes the locus of its center of gravity in the Euclidean score space. Using the three dimensions listed above, the 1346 subjects were allocated to either one of 14 types or a group of isolates termed "unique." The types and their mean Z-score patterns appear in Table 1. The question which could now be asked is whether the same frequency pattern of types appears in each of the organizational and occupational classifications. The frequency patterns, expressed in percentages, for each of the various organizational and occupational groups appear in Table 2. The first logical question which presents itself is whether there are greater distinctions between type occurrences in occupational groups or in organizational groups. To answer this question, it is necessary to compute some index of similarity of frequency patterns. The index suggested by Tryon (1968) and others is P, the general index of proportionality. If two patterns are exactly similar, the value of P = 1.00; if two patterns are completely different, the value of P = .00. A matrix of the P values for the organizational groupings appears in
112
FRANK J.
LANDY
TABLE
1
SCORE CONFIGURATIONS AND FREQUENCIES OF JoB ~/[EANING TYPES a
Z-Scores Type
Frequency
Stimulus value
Autonomy
Job demands
1 2 3 4 5 6 7 8 9 10 11 12 13 14 Ub
40 69 56 72 56 110 117 70 143 138 82 122 142 104 37
35 32 35 53 46 48 47 55 54 50 57 57 57 58
36 45 52 37 35 51 46 61 62 57 35 50 48 63
54 49 52 47 59 38 52 29 45 56 61 47 61 58
R a w scores were t r a n s f o r m e d to )~ = 50, S D = 10. b T h a t is, s u b i e c t s n o t placed in one of t h e 14 types.
TABLE
2
SIMILARITY OF FREQUENCY PATTERNS OF MINE OCCUPATIONAL G R o u P s AND FIVE ORGANIZATIONAL GROUPS a
Type 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Unique
Organizational group 1 2 3 4 5 4 6 5 4 5 7 8 7 12 15 3 7 8 6 2
7 4 6 7 12 4 12 1 4 8 13 2 15 2 4
1 4 1 6 3 5 5 2 10 5 12 14 20 11 1
4 7 6 4 3 13 13 5 10 13 4 5 5 5 5
1 4 3 6 1 11 8 7 13 9 4 12 7 11 3
1
2
2 6 0 4 9 2 11 0 0 6 34 8 17 2 0
0 2 11 6 11 6 6 0 4 15 4 11 6 17 0
a All frequencies are expressed in p e r c e n t a g e s .
Occupational group 3 4 5 6 7 1 3 3 6 2 10 7 5 14 11 5 11 11 9 2
8 9 8 7 5 6 11 1 3 10 6 7 12 2 5
4 2 4 4 26 0 11 2 0 7 26 0 16 0 0
0 1 1 5 1 6 6 8 15 8 3 14 10 16 1
3 6 4 2 2 8 11 8 18 16 6 5 6 7 3
8
9
0 5 0 0 0 13 8 26 24 5 0 16 3 0 0
8 19 6 19 3 6 3 0 0 8 6 3 3 0 17
113
PROCEDURE FOR O C C U P A T I O N A L C L U S T E R I N G
TABLE 3 INTERORGANIZATIONAL SIMILARITIESa
1 2 3 4 5
Research Banks Hospitals Miscellaneous commercial Universities
1
2
3
4
5
.94 b .75 .76 .94 .92
.75 .76 .76 .75 .63
.76 .76 .83 .67 .83
.94 .75 .67 .94 .90
.92 .63 .83 .90 .92
Cell entries = P values. b Diagonal value = highest column entry. Table
3. I t is o b v i o u s t h a t
t h e r e is a h i g h d e g r e e of s i m i l a r i t y
among
these patterns. In other words, the types appear with relatively the same f r e q u e n c y r e g a r d l e s s of w h i c h kind of o r g a n i z a t i o n t h e i n d i v i d u a l w o r k e d
for. In contrast to Table 3, there are marked differences in the relative frequency of occurrence of types when considered by occupational groupings. Table 4 presents these P values. Both a linkage analysis of the matrix of similarities of occupational groups and a graphic cluster analysis of these similarity patterns yielded three clusters comprising seven groups and two isolate groups. Figure I shows the cluster comprising Supervisors and Administrators, Teachers and Professionals. Figure 2 describes the cluster comprising Tellers and Nurses and the cluster Technicians and Clerical. Figure 3 describes the patterns of the two isolate groups, Executives and Semiskilled workers. TABLE 4 INTEROCCUPATIONAL SIMILARITIESa
1
1 Nurses 2 Teclmicians 3 Superintendents&Administrators 4 Clerical 5 Tellers 6 Teachers 7 Professionals 8 Executives 9 Semiskilled
2
3
4
5
6
7
8
9
.87 b .48 .48 .80 .54 .80
.54 .80 .96
.67 .73 .79
.87 .52 .44
.42 .77 .96
.48 .71 .92
.19 .36 .73
.40 .43 .43
.67 .87 .42 .48 .19 .40
.79 .44 .96 .92 .73 .43
.79 .64 .63 .76 .42 .74
.64 .87 .30 .43 .13 .35
.63 .30 .96 .84 .75 .32
.76 .43 .84 .92 .75 .46
.42 .13 .75 .75 .75 .19
.74 .35 .32 .46 .19 .46
.73 .52 .77 .71 .36 .43
Cell entries equal P values. b Diagonal values = highest column entry.
114.
FRANK J. LANDY
90
80 P Va I ue
70 60 50 40 30 20 10
2
3
4
5
Occupational
6
7
Group
8
9
•
Teachers
(6)
•
Sup
•
Professionals
& Admin
(3) (7)
Fie. 1. Occupational Cluster 1.
DISCUSSION The value of this technique lies in its flexibility. For example, as T r y o n (1968) points out, more interesting than the similarity of frequency patterns are the specific type differences between the various groups. Using the formula for the computation of the standard error of
90
80 P Value
70 60 50
4o! 3o 20 10 1
2
3
Occupational
4
5 Group
6
7 oTellers D Nurses
8
9
(5) (1)
e Technician(2)
.Clerical FIG. 2. Occupational Clusters 2 and 3.
(4)
115
PROCEDURE FOR OCCUPATIONAL CLUSTERING
90 80 P Value
70 60 50 40 30 20 10 1
2
3
Occupational
4
5
6
8
9
• Executives
7
(8)
Group • Semi-skilled
(9)
Fro. 3. Isolate Groups.
the difference between two true proportions, attention can then be directed to specific differences in type occurrence, e.g., in comparing Teachers with Professionals; it could be shown in the present data that a significantly greater proportion of Teachers are of type 14 than are Professionals; conversely, it could be shown that there is a greater proportion of Professionals of type 3 than there are Teachers. In this way differences within the previously mentioned occupation clusters could be examined. Another strategy would be to collapse across groups within the previously derived clusters (e.g., collapse the occurrence frequencies for Supervisors and Administrators, Teachers and Professionals), determine new percentages and consider the differences between these newly formed and broader groupings. Obviously, one strategy does not preclude the other, and, for adequate understanding of the subtle as well as gross pattern differences, both strategies should be employed. Raw data such as those described above are often gathered as aids in the construction of an employee motivation program in industry. The relevance of the analytic procedure for the policy decisions which are a necessary by-product of the conception of such a motivational program relates to the logic of subgrouping. While personnel administrators might very much desire to treat each employee as an individual, we know that this is a practical impossibility. On the other hand, it is inefficient to treat everyone in the same manner. The data point out that people in different occupational categories have quite different perceptions of their job. The data further point out
116
fRANK
J.
LANDY
however, that c e r t a i n occupational groups are similar in their job perceptions. What seems to be needed is a guide for the compromise situation--a template for subgrouping. Such a template is provided, in the present study, by a clustering of type profiles. Thus, the personnel administrator might decide to construct one program geared to the Technicians and Clerks, and another program geared to the Supervisors and AdministratorsY An additional point might be made about the multivariate nature of the procedure. The strategy described here has the advantage of dealing with subgroups of real people. Many personnel development programs are directed to groups who do not exist, to mathematical averages, etc. For example, many subgrouping attempts are simply a noninteractive combination of univariate scores. This occasionally leads to types which consist of mutually exclusive properties (i.e., are not represented by real people). In addition, it seems quite popular in certain Circles to anthropomorphize factors in an " R " type factor analysis (e.g., "This factor represents the type of individual who . . ."). The strategy suggested in the present paper contributes to the minimization of such errors. If there is one thing which is becoming more and more obvious about the application of psychological principles to personnel research, it is that we must address ourselves to real situations and real people. There is, of course, some optimal number of types in a given situation. This number will probably be decided by the individual manager and should, I feel, be arrived at by combining practicality (in the form of the number of subgroups a manager can feasibly work with) and predictive efficiency (in the form of the strength of the relationship between subgroup membership and some other variable of interest). By systematically collapsing types into each other on the basis of distances between their centroids (or condensing them heirarchically) and looking at the changing relationship between subgroup membership and ~he variable of interest, one can determine the most efficient number of types in a statistical sense. By combining this information with the practical constraints, one can arrive at a decision regarding a feasible number of subgroups to work with. To end on a note consistent with the introduction, the present paper suggests a slightly different approach to answering the questions dealing with occupational similarity. In certain instances, it is considerably more salient to cluster occupations on the basis of perceptions of lob incumbents than on the basis of role demands or job descriptions. This The use of the term "program" is not intended to imply a single treatment.. It is meant to imply one or more treatments depending on the characteristics of the types falling into the cluster.
PROCEDURE FOR OCCUPATIONAL CLUSTERING
117
m i g h t be considered as the first, albeit tentative, step in examining the area of job m e a n i n g as related to job satisfaction and w o r k motivation. REFERENCES DUNNETTE, M., CAMPBELL,J., & ItAKEL, M. Factors contributing to job satisfaction and job dissatisfaction in six occupational groups. Organizational Behavior and Human Performance, 1967, 2, 143-174. GRIN, G., DAwIs, R. V., & WEISS, D. J. Need type and job satisfaction among industrial scientists. Journal of Applied Psychology, 1968, 52, 286-289. OWENS, W. Toward one discipline of scientific psychology. American Psychologist, 1968, 23, 782-785. ROBINSON, J. P., ATttANASIOU, R., (~ t:IEAD, K. B. Measures o] occupational attitudes and occupational characteristics. Ann Arbor: Survey Research Center, Institute for Social Research, 1969. TRYON, R. C. Comparative cluster analysis of variables and individuals: Ho~zinger abilities and the MMPI. Multivariate Behavioral Research, 1968, 3, 115-144. TRYON, R. C., (~ BAILEY, D. The BC TRY computer system of cluster and factor analysis. Multivariate Behavioral Research, 1966, 1, 95-111. RECEIVED: August 15, 1971
(1972)
A Procedure for Occupational Clustering 1 FRANK J. LANDY Pennsylvania State University A logical extension of the individual-differences approach is object clustering or empirical typing. The present paper is a demonstration of this approach in the industrial setting. The subject pool comprised 1346 employees of 46 organizations who were classified by occupation as well as type of organization. Subjects were empirically allocated to one of 15 multivariate "types" on the basis of reported meaning, as measured by semantic differential scales. Occupation was shown to have a greater effect on type appearance than organization. Strategies for personnel program implementation were discussed. In a recent publication dealing with occupational attitudes and characteristics, Robinson, Athanasiou, and Head (1969) state that " A fundamental aim of attitude measures is the meaningful location of those population groups which are maximally different from other groups." While this proposition has been followed in a number of diverse areas, it has received little attention in the realm of occupational similarity as Robinson et al. point out in their review. The present paper attempts to provide a strategy, with both applied and basic concerns, for clustering occupations on the basis of the job-related perceptions of individuals falling into various occupational categories. 0b]ect clustering or empirical typing has done much to encourage the consideration of individual differences in psychological research. Substantive problems investigated using these techniques have generally fallen into the areas of specific abilities, psychopathology or sociopolitical behavior. Only recently have these techniques begun to appear in the industrial literature (e.g., Dunnette, Campbell, & Hakel, 1967; Graen, Dawis, & Weiss, 1968) and mostly in the form of inverse or (?-type factor analyses. The logic of subgrouping on the basis of individual differences has been presented by Owens both in print (1968) and also in his 1970 Presidential Address to Division 14 of the APA. Since the individual-differences approach seems to be proving valuable This study was supported in part by a faculty grant from The Central Fund for Research at the Pennsylvania State University. 109 O 1972 by Academic Press, Inc.
]10
FRANK J. LANDY
in the solution of many of the problems confronting industrial psychologists, a demonstration of the potential of at least one Wping procedure would seem appropriate at this time. The data to be considered in this paper were gathered as part of a larger proiect attempting to relate job meaning to work motivation. 0nly the area of iob meaning will be considered here. METHOD The subjects comprised 1346 employees of 46 different organizations. The subjects could be classified as belonging to one of the following nine occupational groupings: nurses, technicians, supervisors and administrators, clerical personnel, bank tellers, teachers, professionals, executives, and semiskilled workers. They Could be further classified as working for one of the following five types of organizations: banks, hospitals, research organizations, miscellaneous commercial organiza: tions, or universities. This dual classification was carried out. for the purpose of determining the independent effects of organizational and occupational classification on job perceptions. Job perception was measured by 31 bipolar scales arranged in a semantic differential format. The scales appeared under the concept heading "My Job." The first step in the procedure was to determine the dimensions on which the subjects could be empirically typed. For the maximum differentiation among groups it is best to find dimensions which are fairly stable and relatively independent of each other. All analyses were carried out using a computer system of cluster and factor analysis known as the BC TRY system (Tryon & Bailey, ]966). The component known as ¥(ariable)-analysis, a key-clustering procedure, was used to identify the independent dimensions to be used in subsequent subject typing. The variables analyzed were the responses of the subiects to the 31 bipolar scales. Since it would be necessary to compare subiects from different organizations and occupations on common job perception dimensions, separate cluster analyses were run for each of the nine occupational groupings and five organizational groupings. Through the use of another BC TRY component (COMP), t h e factorial structure common to all occupational and organizational groupings was identified. While such a procedure is tedious and costly, it insures that subiects will be clustered in a score space common to all. RESULTS The V-analyses and COMP indicated that three distinct dimensions could be used for subiect typing. The three dimensions and scales which comprised them were as follows:
P R O C E D U R E FOR O C C U P A T I O N A L C L U S T E R I N G
111
(1) Stimulus value: challenging-monotonous; deadening-stimulating ; meaningful-meaningless; exciting-dull; boring-interesting. (2) Structure imposed by the work: inexact-exact; detailed-general ; precise-vague. (3) Degree of autonomy: independent-dependent; guided-free; closely supervised-not closely supervised; unrestricted-governed. These three clusters proved to be relatively independent, the highest intereorrelation of cluster scores being 0.24. The component of the BC TRY system which accomplishes subject typing is known as OTYPE, a condensation method of object clustering. It is performed on the cluster scores derived from the V-analysis. The purpose is to allocate N objects, with scores on/c dimensions (three in this case), to m object clusters; in each cluster, objects have similar score profiles. The allocations are made on the basis of Euclidean distances (D values) between the objects on k dimensions. There are three basic steps in the condensation method: (1) Breaking down the score range on each of the dimensions into several discrete categories. High, moderate, and low sectors were used in the present study. High = T scores above 60; low = T scores below 40; and moderate = T scores falling between 40 and 60. (2) Objects are assigned to these score sectors yielding initial OTYPES. (3) Objects are continuously reassigned to OTYPES in an iterative process until none of the OTYPES changes the locus of its center of gravity in the Euclidean score space. Using the three dimensions listed above, the 1346 subjects were allocated to either one of 14 types or a group of isolates termed "unique." The types and their mean Z-score patterns appear in Table 1. The question which could now be asked is whether the same frequency pattern of types appears in each of the organizational and occupational classifications. The frequency patterns, expressed in percentages, for each of the various organizational and occupational groups appear in Table 2. The first logical question which presents itself is whether there are greater distinctions between type occurrences in occupational groups or in organizational groups. To answer this question, it is necessary to compute some index of similarity of frequency patterns. The index suggested by Tryon (1968) and others is P, the general index of proportionality. If two patterns are exactly similar, the value of P = 1.00; if two patterns are completely different, the value of P = .00. A matrix of the P values for the organizational groupings appears in
112
FRANK J.
LANDY
TABLE
1
SCORE CONFIGURATIONS AND FREQUENCIES OF JoB ~/[EANING TYPES a
Z-Scores Type
Frequency
Stimulus value
Autonomy
Job demands
1 2 3 4 5 6 7 8 9 10 11 12 13 14 Ub
40 69 56 72 56 110 117 70 143 138 82 122 142 104 37
35 32 35 53 46 48 47 55 54 50 57 57 57 58
36 45 52 37 35 51 46 61 62 57 35 50 48 63
54 49 52 47 59 38 52 29 45 56 61 47 61 58
R a w scores were t r a n s f o r m e d to )~ = 50, S D = 10. b T h a t is, s u b i e c t s n o t placed in one of t h e 14 types.
TABLE
2
SIMILARITY OF FREQUENCY PATTERNS OF MINE OCCUPATIONAL G R o u P s AND FIVE ORGANIZATIONAL GROUPS a
Type 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Unique
Organizational group 1 2 3 4 5 4 6 5 4 5 7 8 7 12 15 3 7 8 6 2
7 4 6 7 12 4 12 1 4 8 13 2 15 2 4
1 4 1 6 3 5 5 2 10 5 12 14 20 11 1
4 7 6 4 3 13 13 5 10 13 4 5 5 5 5
1 4 3 6 1 11 8 7 13 9 4 12 7 11 3
1
2
2 6 0 4 9 2 11 0 0 6 34 8 17 2 0
0 2 11 6 11 6 6 0 4 15 4 11 6 17 0
a All frequencies are expressed in p e r c e n t a g e s .
Occupational group 3 4 5 6 7 1 3 3 6 2 10 7 5 14 11 5 11 11 9 2
8 9 8 7 5 6 11 1 3 10 6 7 12 2 5
4 2 4 4 26 0 11 2 0 7 26 0 16 0 0
0 1 1 5 1 6 6 8 15 8 3 14 10 16 1
3 6 4 2 2 8 11 8 18 16 6 5 6 7 3
8
9
0 5 0 0 0 13 8 26 24 5 0 16 3 0 0
8 19 6 19 3 6 3 0 0 8 6 3 3 0 17
113
PROCEDURE FOR O C C U P A T I O N A L C L U S T E R I N G
TABLE 3 INTERORGANIZATIONAL SIMILARITIESa
1 2 3 4 5
Research Banks Hospitals Miscellaneous commercial Universities
1
2
3
4
5
.94 b .75 .76 .94 .92
.75 .76 .76 .75 .63
.76 .76 .83 .67 .83
.94 .75 .67 .94 .90
.92 .63 .83 .90 .92
Cell entries = P values. b Diagonal value = highest column entry. Table
3. I t is o b v i o u s t h a t
t h e r e is a h i g h d e g r e e of s i m i l a r i t y
among
these patterns. In other words, the types appear with relatively the same f r e q u e n c y r e g a r d l e s s of w h i c h kind of o r g a n i z a t i o n t h e i n d i v i d u a l w o r k e d
for. In contrast to Table 3, there are marked differences in the relative frequency of occurrence of types when considered by occupational groupings. Table 4 presents these P values. Both a linkage analysis of the matrix of similarities of occupational groups and a graphic cluster analysis of these similarity patterns yielded three clusters comprising seven groups and two isolate groups. Figure I shows the cluster comprising Supervisors and Administrators, Teachers and Professionals. Figure 2 describes the cluster comprising Tellers and Nurses and the cluster Technicians and Clerical. Figure 3 describes the patterns of the two isolate groups, Executives and Semiskilled workers. TABLE 4 INTEROCCUPATIONAL SIMILARITIESa
1
1 Nurses 2 Teclmicians 3 Superintendents&Administrators 4 Clerical 5 Tellers 6 Teachers 7 Professionals 8 Executives 9 Semiskilled
2
3
4
5
6
7
8
9
.87 b .48 .48 .80 .54 .80
.54 .80 .96
.67 .73 .79
.87 .52 .44
.42 .77 .96
.48 .71 .92
.19 .36 .73
.40 .43 .43
.67 .87 .42 .48 .19 .40
.79 .44 .96 .92 .73 .43
.79 .64 .63 .76 .42 .74
.64 .87 .30 .43 .13 .35
.63 .30 .96 .84 .75 .32
.76 .43 .84 .92 .75 .46
.42 .13 .75 .75 .75 .19
.74 .35 .32 .46 .19 .46
.73 .52 .77 .71 .36 .43
Cell entries equal P values. b Diagonal values = highest column entry.
114.
FRANK J. LANDY
90
80 P Va I ue
70 60 50 40 30 20 10
2
3
4
5
Occupational
6
7
Group
8
9
•
Teachers
(6)
•
Sup
•
Professionals
& Admin
(3) (7)
Fie. 1. Occupational Cluster 1.
DISCUSSION The value of this technique lies in its flexibility. For example, as T r y o n (1968) points out, more interesting than the similarity of frequency patterns are the specific type differences between the various groups. Using the formula for the computation of the standard error of
90
80 P Value
70 60 50
4o! 3o 20 10 1
2
3
Occupational
4
5 Group
6
7 oTellers D Nurses
8
9
(5) (1)
e Technician(2)
.Clerical FIG. 2. Occupational Clusters 2 and 3.
(4)
115
PROCEDURE FOR OCCUPATIONAL CLUSTERING
90 80 P Value
70 60 50 40 30 20 10 1
2
3
Occupational
4
5
6
8
9
• Executives
7
(8)
Group • Semi-skilled
(9)
Fro. 3. Isolate Groups.
the difference between two true proportions, attention can then be directed to specific differences in type occurrence, e.g., in comparing Teachers with Professionals; it could be shown in the present data that a significantly greater proportion of Teachers are of type 14 than are Professionals; conversely, it could be shown that there is a greater proportion of Professionals of type 3 than there are Teachers. In this way differences within the previously mentioned occupation clusters could be examined. Another strategy would be to collapse across groups within the previously derived clusters (e.g., collapse the occurrence frequencies for Supervisors and Administrators, Teachers and Professionals), determine new percentages and consider the differences between these newly formed and broader groupings. Obviously, one strategy does not preclude the other, and, for adequate understanding of the subtle as well as gross pattern differences, both strategies should be employed. Raw data such as those described above are often gathered as aids in the construction of an employee motivation program in industry. The relevance of the analytic procedure for the policy decisions which are a necessary by-product of the conception of such a motivational program relates to the logic of subgrouping. While personnel administrators might very much desire to treat each employee as an individual, we know that this is a practical impossibility. On the other hand, it is inefficient to treat everyone in the same manner. The data point out that people in different occupational categories have quite different perceptions of their job. The data further point out
116
fRANK
J.
LANDY
however, that c e r t a i n occupational groups are similar in their job perceptions. What seems to be needed is a guide for the compromise situation--a template for subgrouping. Such a template is provided, in the present study, by a clustering of type profiles. Thus, the personnel administrator might decide to construct one program geared to the Technicians and Clerks, and another program geared to the Supervisors and AdministratorsY An additional point might be made about the multivariate nature of the procedure. The strategy described here has the advantage of dealing with subgroups of real people. Many personnel development programs are directed to groups who do not exist, to mathematical averages, etc. For example, many subgrouping attempts are simply a noninteractive combination of univariate scores. This occasionally leads to types which consist of mutually exclusive properties (i.e., are not represented by real people). In addition, it seems quite popular in certain Circles to anthropomorphize factors in an " R " type factor analysis (e.g., "This factor represents the type of individual who . . ."). The strategy suggested in the present paper contributes to the minimization of such errors. If there is one thing which is becoming more and more obvious about the application of psychological principles to personnel research, it is that we must address ourselves to real situations and real people. There is, of course, some optimal number of types in a given situation. This number will probably be decided by the individual manager and should, I feel, be arrived at by combining practicality (in the form of the number of subgroups a manager can feasibly work with) and predictive efficiency (in the form of the strength of the relationship between subgroup membership and some other variable of interest). By systematically collapsing types into each other on the basis of distances between their centroids (or condensing them heirarchically) and looking at the changing relationship between subgroup membership and ~he variable of interest, one can determine the most efficient number of types in a statistical sense. By combining this information with the practical constraints, one can arrive at a decision regarding a feasible number of subgroups to work with. To end on a note consistent with the introduction, the present paper suggests a slightly different approach to answering the questions dealing with occupational similarity. In certain instances, it is considerably more salient to cluster occupations on the basis of perceptions of lob incumbents than on the basis of role demands or job descriptions. This The use of the term "program" is not intended to imply a single treatment.. It is meant to imply one or more treatments depending on the characteristics of the types falling into the cluster.
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m i g h t be considered as the first, albeit tentative, step in examining the area of job m e a n i n g as related to job satisfaction and w o r k motivation. REFERENCES DUNNETTE, M., CAMPBELL,J., & ItAKEL, M. Factors contributing to job satisfaction and job dissatisfaction in six occupational groups. Organizational Behavior and Human Performance, 1967, 2, 143-174. GRIN, G., DAwIs, R. V., & WEISS, D. J. Need type and job satisfaction among industrial scientists. Journal of Applied Psychology, 1968, 52, 286-289. OWENS, W. Toward one discipline of scientific psychology. American Psychologist, 1968, 23, 782-785. ROBINSON, J. P., ATttANASIOU, R., (~ t:IEAD, K. B. Measures o] occupational attitudes and occupational characteristics. Ann Arbor: Survey Research Center, Institute for Social Research, 1969. TRYON, R. C. Comparative cluster analysis of variables and individuals: Ho~zinger abilities and the MMPI. Multivariate Behavioral Research, 1968, 3, 115-144. TRYON, R. C., (~ BAILEY, D. The BC TRY computer system of cluster and factor analysis. Multivariate Behavioral Research, 1966, 1, 95-111. RECEIVED: August 15, 1971