document resume ce 003 330 - eric · the 95-page appendix contains the tables of data (15 pages)...

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DeNisi, Angelo S.; McCormick, Ernest J.The Cluster Analysis of Jobs Based on Data from thePosition Analysis Questionnaire (PAQ). Report No.7.Purdue Univ., Lafayette, Ind. Occupational ResearchCenter.Office of Naval Research, Washington, D.C. Personneland Training Research Programs Office.T R -7

Sep 74119p.; For other PAQ documents, see CE 003 331-2;Much of the appendix is marginally legible; Best CopyAvailable

BF-$0.76 HC-$5.70 PLUS POSTAGE*Cluster Analysis; Graphs; *Job Analysis;*Occupational Clusters; Questionnaires; *ResearchMethodology; *Tables (Data)BC TRY; CODAP; Coordinated Occupational Data AnalysisProgram; PAQ; *Position Analysis Questionnaire

ABSTRACTThe Position Analysis Questionnaire (PAQ) is a

structured job analysis procedure that provides for the analysis ofjobs in terms of each of 187 job elements, these job elements beinggrouped into six divisions: information input, mental processes, workoutput, relationships with other persons, job context, and other jobcharacteristics. Two cluster analysis procedures were used in theclustering of jobs on the basis of data from the PAQ. The BC-TRYprogram was carried out with the 14 overall or general job dimensionsas applied to a reasonable varied sample of 3,700 jobs and resultedin the identification of 33 job clusters. The CODAP (CoordinatedOccupational Data Analysis Program) was based on the scores on 21 ofthe divisional job dimensions for a sample of 800 jobs, and resulted .

in the identification of 45 clusters. The differences in the resultsway be due to the differences in the nature of the job dimensionsused in the two instances, rather than being associated with theclustering procedures as such. The 95-page appendix contains thetables of data (15 pages) and line graphs for the job clustersresulting from the two programs. (Author/AG)

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THE CLUSTER ANALYSIS OF JOBS

BASED ON DATA FROM TILE

POSITION ANALYSIS QUESTIONNAIRE (PAQ)

Angelo_S. De Nisi

and

Ernest J. McCormick

Occupational Research CenterDepartment of Psychological Sciences

Pursue UniversityWest Lafayette, Indiana 47907

Pre ared for:

Personnel and Training Research ProgramsPsychological Sciences DivisionOffice of Naval Research

Contractor:

Purdue Research FoundationErnest J. McCormickPrincipal Investigator

Contract No. N00014-67-A-0226-0016Contract Authority Identification Number, NR 151-331

Report No. 7September 1974

Approved for public release; distribution unlimited.Reproduction in whole or in part is permittedfor any purpose of the United States Government

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444. TITLE (and Subtitle)

The cluster analysis of Jobs based on data

frum the Position Analysis Questionnaire (PAQ)

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Technical Report., i:,,

S. PERFORMING OPIO REPORT NUMEEN..%

7. AU THOR(' . .

Angelo S. De'Nisi and Ernest J. McCormick

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N000014-67-A-0026-0016

S. PeneonhowoOR NAME AND ADDRESS

Ocupational Research CenterDepartment of Psychological SciencesPurdue University, West Lafayette, IN 47907

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NR 151-331

P1. CONTROLLING OFFICE NAME AND ADDRESSPersonnel and Training Research ProgramsOffice of Naval ResearchArlington, Virginia 22217

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It KEY WORDS (Continuo an mom std. it nseatmo and Mat b by block number)

Job analysid, Job clusters, Job families, Job dimensions, Position AnalysisQuestionnaire (PAQ), Cluster analysis, Heirarchical grouping, BC-TRY, CODAP

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SO, ABSTRACT (Cant nue an ovens olds II naeasent and MOWN by NH number).

Two cluster analysis procedures were used in the clustering of jobs on thebasis of data from the Position Analysis Questionnaire (PAQ). The PAQ isa structured job analysis procedure that provides for the analysis of jobsin terms of each of 187 job elements, these job elements being grouped intosix divisions', as follows: (1) Information input; .(2) Mental processes; (3)Work output; (4) Relationships with other persons; (5) Job context; (6) Other

job'characteristios, .,

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.4:On the basis of previous research, a series of principal componentsanalyses of the PAQ data had been carried out.' One series *slatedof independent analyses of the job elements withir each of these sixdivisions, the resultri of this consisting of the iientification'ef30 principle components (called "divisional" job dimensions). 'Ili,turn, an overall or general principlecomponents analysis was based ondata from 168 of the 187 job lements (called "general" - G jabdimensions).

,One of the clustering procedures used was the BC-TRY program (Tryon,R.C., and Bailey, D.E., Cluster Analysis, McGraw-Hill, 1970). This!':'clustering was carried out with the 14 overall or general (G) jobdimensions as applied to a reasonably

varied sample of 3700 jobs.This program resulted in the identification of 33 job clusters..

The other clustering procedure was based on the scores on 21 of the"divisional" job dimensions for a sample of 800 jobs (a, sub- sampleof the 3700 jobs mentioned above). The clustering consisted of theuse of a hierarchical grouping technique as applied to the data forthese jobs. In particular, the clustering was carried out with an .adaptation of the CODAP (Coordinated Occupational. Data AnalysisProgram) as developed by the United States Air Force. This clustering.resulted in the identification of 45 clu'sters.which seemed to have.reasonable homogeneity.

A subjective comparison and ,a satatistied analysis of e results ofthese two clustering procedures give the impression that theclusters resulting from the BC-TRY program were somewhat more homogeneouthat those resulting from the CODAP program. However, this dif-ference may more likely be associated with the differences in thenature of the job dimensions used in the two instances (those basedon the various "divisions" of the PAQ, as contrasted with thegeneral or (G) dimensions), rather than being associated with theclustering procedures as such.

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TABLE OF CONTENTS --.

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INTRODUCTION.1

Bases for Gro.t..ng Jobs2

Statistical Methods of %..ruL,ping Jobs2

BACKGROUND OF PReSENT STUDY3

STUDY 1: JOB CLUSTERING WITH THE BC TRY PROGRAM 5

Sample of Jobs5Job Dimensions Used5

Execution of BC-TRY Program5

RESULTS5

"STUDY 2: JOB CLUSTERING WITH CODAP PROGRAM 6

Job Samples6

Job Dimension Used6

Execution of CODAP Program6

RESULTS6

DISCUSSION7

REFERENCES . . .

11

APPENDIX A: Tables of Data12

APPENDIX B: JOB CLUSTERS RESULTING PROM BC-TRY PROGRAM. 31

APPENDIX C: JOB CLUSTERS RESULTING FROM CODAP PROGRAM . . . . 65

TABLE OF CONTENTS (cont.)

LIST OF TABLES9

TablePage,

1. Job Dimensions Used With .BC -TRY Program 13

2. Job Dimensions Used With CODAP Program 14

3. Means, Standard Deviations, and HomogeneityIndexes of Dimensions for BC-TRY Clusters 15

4. Means, Standard Deviations, and HomogeneityIndexes of Dimensions for CODAP Clusters 19

5. Average Homogeneity Indexes for BC-TRY Clusters 29

6. Average Homogeneity for CODAP Clusters. . 30

1

Over the years various types of job classification systems havebeen developed and used for a variety of purposes. Among the mostwidely used systems are those of the United States Bureau of theCensus for reflecting the occupational characteristics of the laborforce, and that used by the United States Training and EmploymentService for the purpose of classification of applicants for jobs andof positions available. Job classification systaas have also been used byindustrial organizations, such as for the establishment of rates ofcompensation, and by vocational and counseling agencies, for thepurpose of providing aounselees with some insight into various relatedfields of work. In the case of most job classification systems thathave been developed the classification catergories, sub-classes, andin some instances sub-sub classes, have typically been based on thejudgement of those responsible for the system in terms of thestructure that is judged to be most appropriate for serving the pur-poses of the system. Individual jobs that have been grouped to-gether in various classes and sub-classes typically have been sortedor grouped on the basis of judgements of their job similarities anddifferences in terms of the basis of the classification system. Theuse of various classification systems in certain contexts probablyis some reflection of the fact that such systems have been reason-ably satisfactory in serving their intended purposes.

Although most classification system probably are of an a priorinature, there have been various efforts to develop job classificationsystems on the basis of statistically-determined relationships betweenand among jobs.. Such approaches would be predicated upon the use of'certain common "units" of analysis of jobs that provide for chara-cterizing jobs either in terms of quantitative values on each such"unit," or in terms of the presence or absence of specific character-istics. The values associated with these units in the case of in-dividual jobs might in some instances be based on reasonably objectivedata (such as observations or film-records of work activities) or onthe basis of judgements (such as ratings of the "importance" of somehuman attribute to performance on various jobs.

It would seem that for certain purposes the formation of groupingof jobs based on statistical analysis of similarities 4f,job character.»istics could be very useful. The range of such uses covers the con-ventional functions involved in personnel activities, such as personnelselection and placement, training, personnel evaluation, job evaluation,etc., and in vocational couseling.

The statistical grouping of jobs to form job families is pre-dicated on two primary considerations, namely the basis for the group-ing, and the method of grouping

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Bases for Grouping Jobs

Jobs can of course be characterized in terms of any of a number ofdifferent types of descriptors (McCormick, 1974). Among such possibledescriptors are the tasks or activities that are performed, the "humanbehaviors" that are involved in the work activit3ss (such as sensing,psycho - motor activitiest.etc.),.and the human "qualities" that arerelevant for successful performance (such as arithmetic reasoning,finger dexterity, personality characteristics, etc.). In the forma-tion of job families for any particular purpose, obviously the de-scriptors that are selected for use should be those which, if actuallyused in the grouping of jobs, would result in groupings that are suit-able for the purpose in mind. The use of task inventories by theUnited States Air Force, for example, has provided for the identif i-cation of "job types" which reflect similarities in the combinationsof work activities that are actually performed by personnel in 'theAir Force. (Morsh, June 1966). In turn, the study carried out by Mc-Cormick, Finn, Scheips (1957) was based on ratings of the personnel"requirements" for jobs, and the resulting groupings of jobs then re-flected groupings of jobs that were similar in the kinds of personalqualities required for successful performance.

There is of course no single basis for the formation of jobfamilies that could be expected to serve all purposes. Rather; thebasis for any such grouping (as reflected by the descriptors thatare used in the grouping process) needs to be selected to serve thepurposes in question. In this regard, however, Thorndike many yearsago (1953) raised the provocative question of "Who belongs in thefamily?" Although the techniques, of job and occupational analysisand of statistical manipulations have been improved during the inter-vening years, such developments provide no solid answers to the ques-tion Thorndike raised, which deals more with the logic that would beinvolved in making judgements about the appropriate bases for bundlingjobs together for the purpose at hand.

Statistical Methods of Grouping Jobs

There are various procedures that have been used or that could beused in the grouping of jobs. These include factor and principal com-ponents analysis and various types of cluster analysis procedures, along,with other multi-dimensional procedures. In the use of factor and prin-ciple components analysis procedures a Q-type analysis would be thetype that would result in groupings of jobs on the basis of the vari-ables used. Such a procedure was used, for example, by Impala: in thecase of a sample of executive positions.

In comparing the possible relevance of factor and principal ''. /

components analysis procedures, as contrasted with various clusteringprocedures, an argument can be made in favor of some clustering approachsince it takes into account the "elevation" of the variables thatcharacterize the profileforany given case, as well as the "AWcifile"

3

itself. In case the interrelationships between individual jobs arereflected by altoefficient c.f correlation (as in the case of factor aprincipal components analyses) one can have a very high correlationbetween, let us say, two positions, but one might have higher valuesgenerally across the several variables 1.e., a descriptor) where-as the other migtct have systematically lower values. Most clusteringprocedures, on the: other hand, are based on some index of similaritybetween pairs of jobs that does take into account the comparative"elevation" of the profiles.

.

There is one special problem however, in connection with the useof at least certain clustering procedures, that is determination of thenumber of clusters to use. This is, for example, a special problem inconnection with the use of. the hilrachical grouping procedure devel-oped by Ward (1961). This method provides for the grouping of jobs ac-,;:ording to profile similarity in such a way that the.number of groupsis reduced by one in each of a series of iterations. Thus, with agiven number of jobs, one starts with N groilps (one job in each group),then prodeeds to N-1, N-2, N-3, etc., to one group (in which all casesare put together). There presumably are no definitive guidelines fordetermining the number of clusters that would be most suitable for thepurpose at hard.

BACKGROUND OF PRESENT STUDY

The present study is a part of a broader research program in-volving the use of a structured job analysis questinnaire called thePosition Analysis Questionnaire (PAQ). The PAQ includes provision forthe analysis of jobs in terms of 187 job elements that are organizedinto the following 6 divisions: (1) Information input, (2) Mentalprocesses, (3) Work output, (4) Relationships with other persons, (5)Job context, (6) Other job characteristics. For most job, elements asix-point rating scale is provided for use in rating the relevance ofthe items to any given job. There are different types of scales, suchas importance, frequency, and Ame spent, and certain special scales.The stale used with any given job element is the one that is consideredto be most appropriate for that job element. In the case of a fewelements (those relating to the work schedule and the job context) adichotomous scale is used to indicate whether the element does or doesnot apply to the job,

A series of principal components analyses were carried out withdata from a reasonably representative sample of 3700 jobs, separateanalyses being carried out with the job elements within the separatedivisions (with two such analyses being carried out for the elementsin Division 6, these being done separately for the job elements forwhich the six-point rating scale is used.versus the dichotomous -scale).

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In addition, a principal components analysis was carried out with theentire pool of job elements (excluding certain ones such as those thatwere of a "write-in" nature).

These analyses resulted in the identification of 30 componentsbased on the "divisional" analyses and 14 based on an overall or gen-eral (G) analysis, (Marquardt and McCormick, Report No. 1, 1973;Marquardt and McCormick Report No. 5, June 1974). These are referredto as job dimensions.

On the basis of previous studies with PAQ-based data, there issubstantial evidence to indicate that such data might be used as thebasis for the establishment of the job component validity of tests(i.e., the establishment of aptitude requirements directly from jobdata)-without the need for conventional text validation. In addition,such data seem to be potentially useful for establishing compensationrates for jobs, thereby possibly eliminating the conventional jobevaluation procedures.

In part because of evidence of the potential utility of the PAQfor such purposes, it would seem that the PAQ might serve as the basisfor grouping jobs into job families, with the thought that such jobfamilies might have certain practical utility. Such families concei-vably colld be formed of those jobs which have reasonably similar pro-files of job dimension scores. Given such job families, each of whichhopefully would be reasonably homogeneous, it would be conceivablethat one could determine separately the profile of aptitude require-ments for the jobs within any given family. If such a procedure werepossible, and if the aptitude profiles so identified would be reasonablyvalid for the jobs wf.thin any given family, it would then be possiblewith a new job to compare its PAQ picfile to those of the variousfamilies in order to allocate it to the family with-it most nearly matched.In such a case one could then apply to that job the aptitude profile forthe family iv question with reasonable assurance that the profile wouldhave acceptable validity for the purpose of personnel selection for thejob in question. Similarly, it would be conceivable that the com-pensation rates for jobs in individual families might be found to bereasonably similar. In such a case it would then be possible, in asimilar fashion, to match a job to a family and to apply to it a klom,pensation rate which was appropriate for that job family. Otherpossible applications, of such job families come to mind as well, as invocational counseling.

This study was then directed toward the use of PAQ based data asthe basis for the identification of job families.

In this investigation two separate studies were carried out, oneinvolving a hierarchical grouping technique as the basis for the cluster-ing of jobs, and the other involving the BC-TRY cluster analysis procedure(Tryon and Bailey, 1970).

5

STUDY 1: JOB CLUSTERING WITH THE BC-TRY PROGRAM

One of the clustering procedures used was the BC-TRY cluster an..alysis procedures. This consists essentially of two phases (Tryon andBailey, 1970). The first phase, a V-analysis, consists of discovering,the general properties of "objedts," and the second phase, an 0-typeanalysis, consists of the discovery of the general types into which the"objects" can be classed.

Sample of Jobs

The jobs used in this procedure consisted of the total sample of3700 jobs used by. Marquardt and McCormick (Report No. 4, June 1914) ina series of principal components analyses. This sample was relativelyrepresentative of employed workers in the labor force in terms ofmajor occupational categories.

Job Dimensions Used

One of the principal components analyses carried out by Marquardtand McCormick (Report No. 4, June 1970 was an "overall" or "general"analysis, in which 168 of the job elements of the PAQ were pooled to-gether. This resulted in the identification of 14 (G) principle com-ponents, (job dimensions). The scores of the 3700 jobs on these 14job dimensions served as the input to the BC-TRY program. Then 14job dimensions then comprised the V-analysis data. (The names of these14 dimensions are given in Appendix A).

Execution of BC-TRY Program

The application of the 0-type analysis of the 1T-TRY program wascarried out by Peter Lenz, Portland, Oregon.

RESULTS

The program resulted in the identification of 33 job clusters.These clusters are described primarily in terms of the mean scores onthe 14 job dimensions and of the variability of the scores in thesedimensions. The profiles of the clusters are given in Appendix B, theprofile for each cluster representing the mean values on the 14 jobdimensions. The standard diviations of the mean job dimension scoresare also given with the profile for each cluster.

In addition an index of homogeneity (H) is given for each jobdimension for each cluster, This is a quantitiative index of theamount of similarity of the values on each dimension. If they wereall identical the H value would be 1.00. The index is derived by thefollowing formula (Tryon and Bailey):

6

"" variance of:flaka......H (homogeneity) = 1 mg total variance for all jobs

The hierarchical grouping portion of the CODAP program has beenadapted under the direction of Dr. William J. Cunningham, NorthCarolina State University, for use with occupational data such as thatresulting from the PAQ.

STUDY 2: JOB CLUSTERING WITH CODAP PROGRAMThe other procedure involved in the development of job families

was based on a hierarchical grouping technique developed by Ward (1961).As indicated earlier, this procedure is based on an index of job sim-ilarity between all possible pairs of jobs, and by an iterative processthe jobs are formed into groups ranging from N down to 1. This pro-cedure has been incorporated in the Comprehensive Occupational DataAnalysis Programs (CODAP) of the United States Air Force for processingtask inventory data.

Job Samples

The jobs used in this clustering procedure were part of a sample of3700 jobs used with the BC-TRY program, Since the capacity of.theprogram was limited to a sample of 2,000, and since the computer costsrise exponentially with numbers of cases, a subsample of 800 jobs wasselected from the 3700 jobs.

Job Dimension Used

In this study 21 of the 30 "divisional" job dimensions were used,these being the ones which were considered to be potentially more re-levent as the basis for the grouping. (The names of these job dimensionsare given in Appendix A).

Executiol!ILEINUMM

The CODAP program requires the use of an index of similarity of theprofiles of each pair of cases (in this instance for each pair of the 800jobs). For this purpose a d2 index was used. The execution of the pro-gram was carried out under th... direction of Dr. William J. Cunningham,North Carolina State University.

RESULTS

Since the hierarchical grouping portion of the CODAP program is aniterative one, and since there are no generallv recognized objectivecriteria for relecting the "optimum" iterati,Jn, the investigator must usehis judgement in making such determination. In the present instance itwas considered desirable, in terms of the possible future use of jobclusters, to have a reasonably limited number, compatable with the addl..tional desirability of having each cluster include job.

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that are reasonably homogeneous. In making such a judgement with thedata from this analysis, listings of tha jobs that formed the clusterswere used.

On the basis of an examination of the jobs that fell into thevarious clusters at various iterations, it appeared that the interation

at which 42 clusters were formed would reflect a reasonable com-promise between the contradictory objectives of having a small numberof clusters and still having each cluster represent what was con-sidered to be a relatively homogeneous groups of jobs.

The profiles of these 42 clusters are given in Appendix B, alongwith the means of the job dimension scores that characterize eachprofile, the standard deviations of those means, and the homogenuity(11) values.

DISCUSSION

In the case of the job clusters resulting from the two proceduresused, no effort was made to label the clusters, other than by a com-bination letter (T for the BC-TRY clusters, and C for the CODA? cluster)followed by a number (1, 2, 3, etc.) .

The ultimate evaluation of the job clusters developed by. any givenprocedure would have to be in terms of some judgement regarding the ex-tent to which they might serve their intended purposes. The primaryobjective in developing these job clusters was that of ultimutelyusing them as the possible basis for the establishment of apititude re-quirements for individual jobs. The evaluation of the suitability ofthe clusters for this purpose would depend essentially upon the ham&geneity of the actual aptitude requirements of jobs that fall in thevarious clusters. (A planned continuation of research with the PAQhopefully would result in some data that would shed light on this).A secondary objective relates to the possible use of job clusters asthe basis for the establishment of compensation rates for jobs. Thefulfillment of this objective would depend similarly upon the homo-geneity of rates of pay for the jobs and the various clusters. (Here,again, further planned research might result in data that would con..tribute to the evaluation of job clusters for this purpose).

A a

In the absence of organized data to serve either of these objectivei,a present assessment of the job clusters must be based in part of. thecomputer *Ut-put available for the clusters, such as the,. can jobension scores of the jobs in each cluster, the standard d40.4001044those means, and the homogenuity (H) values. In addition, some,!.A.0:0,,:,

ment can be made on the basis of the jobs that form the dusters..

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A comparison of the mean values of the job dimension scores of theclusters generated by the two procedures indicates that there is morevariability in those mean values for the clusters resulting from theBC-TRY procedure than from the CODAP procedure. Since the job dimen-sion scores of the jobs are in standard score or (with a mean of zeroand a standard deviation of about 1) the means of the scores on anygiven job dimension can be compared with those for other dimensions.The BC-TRY clusters show, for example, more such mean values about 1.00(or at values just below that, such as .90, .80 or .70) than in theclusters resulting from the CODAP program. This suggests that the BC-TRYclusters by-and-large are more "discriminating" than the CODAP clustersin that the profiles are more easily characterized in terms of severaldominant dimensions on which the jobs in each cluster scored high.

Further, the standard deviations and homogeneity (H) values forthe BC-TRY clusters generally reflect less variability for the jobs ineach of the clusters than did the CODAP clusters. In this regard, theoverall mean H value (for all clusters) was:

.75 for the BC-TRY clusters andl

.45 for the CODAF clusters, thus confirming this difference.

Subsequent to the completion of the CODA? clustering procedures Dr.Cunningham at North Carolina State University in a personal communication[1974] reported that they had made a comparison of various "distance"metrics as used with the CODAP program. On the basis of this compar-ison it was considered that the d index was not as appropriate for usein clustering of jobs as were certain other indexes such as precept over-lap or a coefficent of correlation. Since the CODAP clusters reportedin this study were based on a d2 index it is very possible that thisaccounts in part for the lack of homogeneity in the CODAP clusters, ascontrasted with those derived from the BC-TRY program. The resultsshould be interpreted with this in mind.

In connection with the various job clusters, several observationsare in order. For example, a number of the job clusters seem to becharacterized dominantly by a limited number of job dimension, in that themean scores on only a few dimensions deviated markedly above, or below,the central range of mean dimension values for all jobs. In other words,in the case of a number of clustes that mean scores on several or manyof the job dimensions fall somewhere around the midpoint, and seem not toserve as "discriminators" of the dimensions. Although the BC-TRYclusters generally have more job dimensions with higher mean values than110110311011111.110111

1. Due to the nature of the formula for computing homogeneity, it ispossible to get a negative value. It was decided, however, for thesake of conceptual clarity to report only positive values and toassign a value of zero for all others. This only happened once inthe case of the BC-TRY clusters but repeatedly in the case of theCODAP clusters. These average values reflect this policy.

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do the CODAP clusters, the same pattern applies to the CODAP clusters butin more moderate degree. Thus, in reviewing the profiles for individualclusters it is suggested that the reader might look at the job dimensionvalues that are particularly high or particularly low to get a "feel"for the dimensions that tend to earmark the individual clusters. Further,within each of the two sets of the clusters there are certain clustersthat tend to have relatively similar profiles in job dimension scores interms of at least certain of the dimensions, but that differ appreciablyfrom each other in terms of only one or a very limited number of thedimensions.

It should also be ,noted that, although the mean values on thedifferent dimensions tend to characterize the clusters, the variabilityof the job dimension scores on certain of the dimensions are in somecases rather great. This is reflected by the standard deviations(the higher standard deviations of course indicating greater variability)and by the homogeneity (H) values (the lower scores indicating greatervariability). The implication of this is that the profile of thedominant job dimension scores that characterize a given cluster is nota universally applicable profile for all of the jobs within a cluster.In other words, on certain of the dimensions the jobs within a clusterare more variable than they are on the dimensions which presumablyhave served as the dominant basis for the idenWication of the.cluster.

It should be noted that the differences in the results from theBC-TRY and CODAP analyses are not necessarily the consequence of theprocedures as such, but may rather be attributed, at least in part,tothe nature of the job dimensions used as the input data. Those used inthe BC-TRY program were the overall or general (G) dimensions, where..as those used ulth the CODAP program were the "divisional" dimensions(these generally being characterized by job elements of the same general"class," such as input, mediation and output activities).

In connection with clustering procedures generally, one additionalreflection will be made, this being admittedly an impression without anysupporting data to back it up. In the typical clustering procedures (asthose used in this study) the variables used (in this instance job dim-ensions) are given equal statistical weight as the basis for thegrouping of jobs into the clusters. It is suggested that if the "truth"were known,it might be that certain variables (in this instance jobdimensions) should receive more weight or importance in the clusteringthan others. However, the basis for any such weighting of coursewould present a serious problem.

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An examination of many of the jobs that fall within the individualclusters seems generally to make rational sense, but at the same time,at least in certain of the clusters, there are a number of jobs that seemsubjectively not to "fit" with the jobs which tend to characterize thecluster in general. These seem to fall into two general classes. Inthe first place, although some combinations of jobs seem to representstrange bedfellows Jn terms of the overt job-related activities thatare involved, it should be recalled that the basis for the clusteringis in terms of job dimension scores. In this regard, jobs that involverather obviously different kinds of overt behaviors might still havejob dimension scores that are reasonably comparable. This kind of

grouping is probably to be expected since the clustering process re-sults in the reduction of a large number of jobs into a seasonablylimited number of classes, with those with relatively similar jobdimension score profiles falling into the same cluster.

In the second place, however, there are certain clusters whichinclude jobs that might be thought of as diStinct odd balls, and itwould be difficult to envision them falling within. the same class asthe other jobs. . A ready explanation for this is not immediatelyapparent, although in certain instances this could be 'a function ofthe initial PAQ analysis for such. jobs. (In this regard, it shouldbe noted that many of the jobs were analyzed by single analyses, whomay not have had much experience with the PAQ, and therefore may havemisinterpreted certain of the job elements, resulting in unreliableanalysis).

As indicated before, the ultimate test of any job clusteringwould depend upon its ultimate use for certain practical objectives.It is probable that subsequent planned research efforts will atleast provide partial inklings about the utility of clusters developedby the two procedures used in this particular study.

11

REFERENCES

Marquardt, L. D., and"McCormick, E. J. Attribute ratings and profilesof the job elements of the Position Analysis Questionnaire (PAQ).Department of Psychological Sciences, Purdue University, ReportNo. 1, June 1972 (under Contract No. N00014-67-A-0226-0016).

Marquardt, L. D., and McCormick, E. J. The job dimensions underlyingthe job elements of the Position Analysis Questionnaire (PAQ)(Form B). Department of Psychological Sciences, Purdue University,Report No. 4, June 1974 (under Contract No. N00014-67-A-0226-0016).

Marquardt, L. D., and McCormick, E. J. The utility of job dimensionsbased on Form B of the Position Analysis Questionnaire (PAQ) in ajob component validation model. Department of PsychologicalSciences, Purdue University, Report No. 5, July 1974 (underContract No. N00014-67-A-0226-0016).

McCormick, E. J. Job information: its development and applications,Chapter 4.2, in ASPA Handbook of Personnel and Industrial Relations,edited by Dale Yoder and Herbert G. Heneman, The Bureau of NationalAffairs, Inc., Washington, D. C. 1974.

McCormick, E. J., Finn, R. H., and Scheips, C. D. Patterns of jobrequirements, Journal of Applied Psychology, 1957, 14, 358-364.

Morsh, J. E. Job types identified with an inventory constructed byelectronics engineers. PRL-TR-66-6. Lackland Air Force Base,Texas: Personnel Research Laboratory, Aerospace Medical Division,Air Force Systems Command, USAF, June 1966.

Thorndike, R. L. Who belongs in the family? Psychometrika, 1953,18$.267-276.

Tryon, R. C., Bailey, D. G. Cluster Analysis. New York: McGraw-HillBook Co., Inc. 1970.

Ward, J. H. Hierarchical grouping to maximize payoff. WADD-TN-61-29.Lackland Air Force Base, Texas: Personnel Laboratory, WrightAir Development Division, Air Research and Development Command,USAF, March 1961.

1

Appendix A

Tables of Data

19

13

Table 1

Job Dimensions Used With BC-TRY Program

JG- 1 Decision/Communication/Social ResponsibilitiesJG- 2 Environmental Demands/General Body ControlJG- 3 Equipment/Machine OperationJG- 4 Environmental AwarenessJG- 5 Manual Control ActivitiesJG- 6 Office/Related ActivitiesJG- 7 Evaluation of Sensory InputJG- 8 General/Public Related Personal ContactJG- 9 Use of Technical/Related MaterialsJG-10 Geperal Physical Activities versus Sedentary ActivitiesJG-11 Hazardous/Personally Demanding SituationsJG-12 Attentive/Vigilant Work ActivitiesJG-13 Routine/Controlled Work. ActivitiesJG-14 Supervision/Coordination

20:11,11P1410t r ""'"'"';'"

,...au

14

Table 2

Job Dimensions Used with CODAP Program

Jl- 1 Perceptual InterpretationJ1- 2 Evaluation of Sensory InputJl- 3 Visual Input from Deices/ MaterialsJl- 4 Input from Representational SourcesJ2- 6 Decision MakingJ2- 7 Information ProcessingJ3- 8 Manual/Control ActivitiesJ3- 9 Physical Coordination in Control/Related Activities33-10 General Body Activity versus Sedentary ActivitiesJ3-11 Manipulating/Handling ActivitiesJ3-12 Adjusting/Operating Machines/EquipmentJ3-13 Skilled/Technical ActivitiesJ3-14 Use of Miscellaneous Equipment/DevicesJ4-15 Interchange of Ideas/Judgements/Related InformationJ4-16 Supervisory/Staff ActivitiesJ4-18 Communicating Instructions/Directions/Related Job Information34-19 General Personal ContactJ5-23 Personally Demanding SituationsJ6-24 Attentive Job DemandsJ6-25 Vigilant/Discriminating Work ActivitiesJ6-26 Structured versus Unstructured Work Activities

4.4.a.aIWILM w 1, A, AMOS JOSIAM .41,1C44 aditu..laft,.1.111141116...

21.

**Min

Table 3

Means, Standard Deviations, and

Homogeneity

Indexes of Dimensions for BC-TRYClusters

Job Dimension

12

34

56

78

910

11

12

13

14

Clabcer-

1 If

-1.30

- .06

- .57

.49

.65

- .28

- .37

.11

- .19

.52

- .29

.57

- .23

.07

SD

.36

.53

.48

.57

.42

-.54

.35

.56

.46

.61

.40

.55

.44

.55

U.93

.85

.88

.80

.90

.82

.93

.52

.89

.77

.91

.81

.90

...i8

Cluster

2 M

- .98

.40

-1.12

- .28

- .51

.58

2.31

.36

.19

.36

- .86

1.35

.40

- .37

SD

.45

,78

.71

.60

.73

.53

.72

..75

.65

.94

.55

.87

.60

.55

IL

.88

'.63

.70

.78

.67

.83

.68

.63

.76

.24

.82

.39

.79

.77

Cluster

3 n

- .71

1.67

- .25

.72

- .16

- .13

- .91

- .44

.04

- .16

.32

.43

.27

-1.13

SD

.41

.71

.84

.50

.51

.83

.50

.85

.90

.83

.73

.86

-.57

.71

H.90

.70

.54

.85

.85

.48

.86

.47

.42

.40

.66

.41

.81

;(1

...,,

Cluster

4 N

- .75

- .44

- .47

.16

.56

- .09

.27

.03

- :10

.32

- .33

- .59.

.35

.97

52

.48

.45

.49

.61

.64

.55

.57

.66

.72

.65

.63

.59

.55

.53

Et

.87

.90

.87

.76

.76

.81

.81

.73

.69

.74

.76

.73

.83

.75

i-

Cluster

5It

- .72

- .99

.50

- .81

- .21

- .68

.01

- .24

- .01

.75

.

- .58

.aS

.02

-1.31

u,

SD

.46

.42

.79

.78

.47

.78

.44

.63

.81

.70

.60

.77

.58

.67

II

.88

.91

.61

.57

.88

.57

.69

.75

.57

.68

.79

.58

.81

.65

Cluster

6.II

-1.Z4

- .11

1.29

- .02

.32

.93

- .44

- .1S

.82

1.32

- .63

- .21

.10

- .47

SD

.42

.72

.73

.66

.51

.48

.40

.64

.67

.83

.57

.63

.49

.65

if

.90

..69

.69

.72-

.85

.86

.91

.75

.74

.50

.31

.75

.87

.63

Cluster

CM

7DI

- .72

- .64

1.20

.52

.07

- .30

.- .56

- .45

'

.20

.38

-- .03

.23,

.21

.52

Tr'

GO

SD

.47

.44

.64

.50.

.44

.59

.33

.60

.64

-

.68

.57

.61

..47

.59

*--1

II

.87

.90

.77.

.85

.89

.78

.94

.78

.76

.71

.61

.77

.88

.74

CDC,

"a

Cluster

8 M

-1.03

.73

- .02

.92

.26

- .73

.25

.54

- .48

- .64

- .45

.38

.10

.44

-ic

SD

.34

.70

.62

.53

.50

.48

.70

.67

.58.

.62

.49

.66

.55

.55

am

K.94

:71

.79

.64

.86

.86

.70

.72

.81

.77

.87

...72

.83

.7S

NC

Cluster

9 11

.0Z

- .79

- .70

- .21

- .17

1.4

- .60

.07

.20

-1.36

- .24

- .32

-.22

- .55

SD

.66

.51'

.63.

.66

.67

.68

.49

.65

.80

.64

.57

.59

.63

.79

sa

.73

.86

'.78

.72

.73

.70

.87

.74

.59

.74

.81

.78

.77

.43

M =

Mea

nSD

= S

tand

ard

Dev

iatio

nH

= H

omog

enei

ty

L

Table 3. (cont.)

Job Dimension.

12

-3

45

67

89

10

11

12

13

14

Cluster

10 M

- .19

- .35

- .28

- .29

- .32

- .15

- .51

-1.01

- .47

-1.09

1.50

- .68

- .07

.20

SD

.57

.71

.71

.72

.48

,57

.50

.77

.72

.68

.63

.54

.71

.75

II

.81

.70

.70

.66

.87

.S0

.86

.61

.69

.71

.77

.82

.70

.51

Cluster

11 M

.05

- .09

-1.34

- .39

- .06

1.75

- .31

.64

- .50

..,

.24

- .39

-1.49

.26

.90

SD

.57

:74

.51

.73

.57

.76

.51

.80

.80

.65

.54

.70

.69

.77

R.81

.67

.86

.64

.82

.60

.85

:56

.60

.73

.83

.67,

.71

.43

Cluster

12 M

- .23

- .22

- .46

-1.30

- .53

- .54

- .38

.46

- .50

.39

- .21'

- .03

.06

- .01

SD

.48

.33

.43

.56

-.49

.31

.34

.43

.49

.59

.59

.64

.52

.56

R.87

.94

.90

.81

.87

.94

.94

.39

.87

.79

.30

.74

.25

.77

Cluster

13 M.7- .26

.91

1.11

.60

- .62

- .97

1.49

.40

- .76

- .41

.20

- .85 .

!-- .22

.30

SD

.57

.85

.74

.61

.62

.66

.73

.88

.78

.63

.70

.68

.71

.59

.81

.53

.67

.77

.77

.71

.67

.41

.62

.75

.70

.70

.70

.74

Cluster

14 M

- .78

.17

- .87

.32

.04

.56

- .16

.29

- .89

- .95

1.38

-1.49

- .99

- .55

SD

.34

.51

.44

.53

.40

.87

.45

.43

.S6

.56

.51

.60

.53

.73

R.94

.86

.90

.83

.91

.33

.89

.90

.24

.S2

.85

.77

.84

.55

Cluster

15 M

- .71

- .05

- .21

.15

.02

- .78

- .20

- .31

.77

-1.98

- .17

.31

.24

.19

SD

.49

.66

.67

.62

.73

.53

.55

.64

.76

.76

.62

.63

.49

.62

a.86

.75

..74

.76

.70

.83

.83

.75

.64

.62

.77

.70

.86

.71

Cluster

16

Di ED

.84

.53

- .20

.36

- .60

.43

- .01

.57

- .46

.56

- .42

.33

- .58

.36

.46

.51

- .54

.55

.26

.56

- .01

.65

.60

.54

- .00

.69

.34

.53

II

.83

.93

.90

.80

.82

.94

.93

.85

.83

.81

.74

.82

.77

.80

Cluster

17

11

.01

- .72

- .15

- .26

- .36

- .67

.31

.32

.21

.25

2.00

.16

.16

.32

SD

.61

.51

.71

.63

.53

.48

.67

.61

.78

.62

.63

.68

.60

a.77

.86

.70

.75

.84

.86

.73

.78

'

.62

.77

.79

.75

.72

.73

Cluster

18

ht SD

.90

.68

- .10

.60

- .62

.69

.42

.70

- .15

.73

- .43

.50

- .51

.55

- .17

.70'

1.39

.77

.00

.63

.06

.75

- .12

.70

.94

.79

.40

.74

H%71

.80

.72

.67

t67

.85

.82-

.69

.63

.75

64

.68

.60

.54

Wib

Table 3 (cont.)

Job Dimension

12

34

56

78

910

11

12

13

14

Cluster

19 a

- .08

.87

.42

- .13

- .27

.27

1.40

- .45

.79

.15

- .45

-..65

.42

.79

SD

.56

.75

.76

.55

.71

.65

.64

.93

.63

.66

.63

.S2

.S5

.62

H.82

.66

.66

.82

.69

.73

.76

.78

.73

.73

.76

.50

.51

.70

Cluster

20 M

- .28

- .71

- .37

.69

- .50

- .31

1.87

.18

- .28

.01

- .16

- .06

- .36

- .03

SD

.66

.59

.59

.56

.57

.44

.63

.67

.72

..65

.67

.85

.81

.50

.11

.73

:81

.81

.81

.31

.88

.77

.72

.69

.74

.73

.44

.56

.F.,

Cluster

21 M

- .66

- .52

- .40

- .83

- .17

1.38

- .59

- .77

- .21

- .24

.54

.73

.12

- .60

SD

.35

.61

.56

.44

.53

.64

.41

.61

.60

.65

.63

.71

.48

.72.

H.93

.79

.83

.89

.84

.74

.91

.77

.79

.74

.76

.67

.37

.56

Cluster

22 M

- .12

- .36

1.25

.35

- .69

1.19

- .34

- .80

- .07

- .40

.37

.92

.02

-1

- .74

SD

.46

.82

.66

.61

.49

.67

.50

.77

.77

.85

'

.{2

.77

.60

.72

11

.83

.57

.75

.76

.S7

.70

.36

.60

.63

.48

.77

.53

.79

.57

Cluster

23 M

.41

1.70

.97

- .64

.40

1.15

.09

.97

.51

- .04

.14

.27

- .16

- .06

SD

.61

.83

.73

.76

.84

.69

.64

.70

.80

.69

.64

.66

.70

.79

.:-

11

.77

.56

.68

.59

.51

.69

.71

.69

.59

.70

.75

.71

.70

.43

Cluster

7,

...4M

.20

.36

1.13

.19

- .35

.31

- .14

.24

-1.02

.17

.99

- .78

- .07

.94

SD

.55

.76

.59

.59

.50

.67

.53

.67

.70

.65

.66

.71

.61

.62

H.82

.64

.80

.78

.86

.71

.81

.72

.70

.74

.74

.66

.7S

.71

Cluster

25 M

.12

.46

1.47

.00

- .61

.71

1.33

.68

- .50

- .77

- .21

.84

.30

.47.

SD

.50

.62

.61

.70

.70

.61

.85

.85

.77

'

.82

.72

.64

.59

.64

H.86

.78

.79

.68

.70

.76

.49

.47

.63

.53

.67

.74

.S0

.63

Cluster

26 M

- .11

- .40

'.99

.10

- .03

1.43

- .19

.47

.65

1.00

.46

- .23

.17

.35

SD

.54

.75

.69

.56

.52

.56

.74

.70

.79

.62

.65

.65

.56

.72

H.83

.66

.72

.31

.85

.81

.66

.69

.61

.77

.75

.72

.82

.57

Cluster

27 M

.08

1.90

1.20

.17

- .70

.38

- .36

- .55

.23

.10

.32

.56

.27

.48

SD

.41

.73

.62

.77

.59

.74

.67

.78

.85

.60

.63

.73

.68

.68

if

.91

.69

.78

.59

-.80

.62

.73

.59

.52

.79

.77

.57

'.72

-

.63

Tab

le 3

(co

nt.)

Job Dimension

12

34

56

78

910

11

12

13

14

Cluster

28

II SD

.32

.56

- .49

.43

.15

.56

-1.19

.69

.27

.73

- .46

.52

- .43

.39

- .15

.61

- .53

.79

- .51

.74

- .68

.59

- .38

.69

.81

.64

- .29

.69

H.82

.90

.83

.68

.67

.84

.92

.78

.60

.64

.80

.68

.76

.62

Cluster

29 M

1.29

.59

- .98

- .02

.27

.09

- .44

.68

.90

.70

1.59

.46

.45

.33

SD

.43

.72

.67

.67

.83

.61

.48

.67

.90

.58

.59

.78

.77

.62

1!

.90

.69

.74

.71

.53

.76

.87

.72

.42

-83

.S3

.56

.62

.70

Cluster

30 H

.97

- .63

- .27

.35

-1.05

- .29

- .46

.16

.98

- .43

-1.29

- .52

-1.25

- .23

SD

.5o

.47

.46

.74

.47

.56

.49

.64

.89

.55

.57

.60

.65

.74

H.93

.86

.89

.63

.88

.81

..o6

.75

.43

.82

.81

.77

.75

.S4

CIuste,-

31

X1.4S

-r?

...)..

- .27

1.01

.04

- .33

- .36

- .00

- .(2

.38

- .S1

.40

-1.61

-0-

SL

.5

.38

.43

.60

.80

.40

.61

.75

.69

.57

.69

.61

.F0

..:.:

ii

.S/

.92

.90

.7S

.57

.91

:78

.53

.72

.S1

.71

.76

.59

J.1

Clust:Ir

I-.

32

11

1.17

- .52

- .73

.79

.65

- .63

1.67

- .58

.24

.29

.54

- .36

1.29

- .62

oo

SD

.54

.61

.54

.52

.84

.58

.g..)

.81

.81

.65

.9S

.S3

.84

.::.;-.

li

.83

.79

.84

.S4

.52

.79

.71

.55

.53

.74

.4S

.52

.65

Cluster

33

M.

1.87

- .29

- .35

.84

.67

- .52

- .35

- .23

- .62

.25

- .66

- .30

.56

- .53

en

.47

.45

.47

.64

.da

.42

.60

.62

.71

.63

.67

.66

.76

.CG

E.

.87

.90

.88

.74

.44

.90

.79

.77

.69

.76

.73

.72

.64

.73

Table 4

Means, Standard Deviations, and Homogeneity

Indexes of Dimensions for CODAP Clusters

I2

34

6

Cluster

1FL

.35

SD

1.18

HO

N 'M

M.

.04

1.00

.36

1.07

- .26

.99

- .18

.80

.62

1Cluster

2 M

.34

- .41

.30

- .27

- .17

SD

1.04

.12

.54 -

.74

1.11

H- --

.63

.83

.66-

Cluster

3 M

.15

- .49

.17

- .33

- .21

SD

.97

.76

1.21

.97

.85

H.27

.57

.20

.55

CIuster

4 M

.16

- .22

.21

- .32

- .33

NI.

SD

.98

.73

.92

1.00

1.03

al

H.20

.62

_33-

Cluster

5 M

.37

- .12

.46

- .28

- .05

SD

1.08

1.03

1.13

.91

H.39

.39

_-----

----

Cluster

6 M

.54

- .18

.54

- .40

.08

SD 1.15

1.05

.92

.86

.97

a.35

.41

.31

Cluster

7 M

.37

- .18

.23

- .20

- .05

SD 1.28

.95

1.04

1.13

1.23

_____

Cluster

Cluster

8 H - .02

SD

1.03

.08

.93

- .06

.92

.13

1.04

-.08

1.05

H.38

.34

---

Cluster

9 M

.10

SD 1.26

.24

1.12

- .41

.74

- .34

1.13

- .11

1.15

H--

.66-

-M = Median

SD = Standard Deviation

H = Homogeneity

Job Dimension

78

- .22

-

:1604

.99

-- ---

.30

- .52

- .51

.55

.96

.83

.25

- .35

- .23

.87

1.22

.47

--- --

- .73

- .62

.60

1.02

.79

-

:899

-1.210

.42

MII

- .52

- .81

.97

1.29

.19

.20

.33

1.05

.76

.64

1.09

.16

- .06

.97

.18

-----

- .77

.81

.9119

--

910

11

1.36

-- - --

-1:(03;

.15

.93

.37

--- --

- .43

.64

.77

.30

.43

.90

- .47

.56

.83

- .10

.67

.74

.56

1.27

.02

1.10

- .31

.55

.14

.85

.52

- .38

.78

.63

.27

1.13

-.32

.61

- .06

:9.:;-

'

.02

1.20

.23

.86

.50

.03

1.03

.0/0

.00

.03

.77

.11

1.09

.18

1.14

-1:loti

_ ---iii

__-_

_

- .13

1.04-

.-..1::t

.36

1.02

- .29

12

13

14

1.734

-....:

-

.870

--- ---

.07

.59

.34

.07

- .65

.72

.54

.41

.69

.83

.91

.01

1.n6

- .01

- .05

.66

.97

-.73

.20

- .03

.90

.43

- .45

.13

.87

.81

.43

.58

.53

- .12

1.15

1.01

- .03

1.27

.40

.01

- .01

1.05

1.00

2.13

All

- .02

.07

.00

Co

1.01

.93

1.26

au'il

.6

c'-

.11

-

:t011;.

C I

---i3

1.33

.32

.231 NC.<

.54

.22

5;

CU

-1::017

-

.7.F4

1.07

;;;

.57

AIM

III.II

MIM

.11

1.11

0

15

16

Job

18

Table.4 (cont.)

DImenstm

19

'23

24

25

26

Cluster

I M

.24

- .21

.15

.19

- .22

.19

- .39

.29

SD .72

.71

1.01

.54

.78

.92

1.10

.88

M.72

.68

----

.82

.63

.35

.27

Cluster

.

2 M

.23

- .46

.29

.26

- .55

,.33

- .05

- .06

SD .76

.47

.84

.57

.57

1.05

1.13

.48

H.67

.87

.54

.80

.32

-.83

Cluster

3 M

.23

- .13

.03

- .31

- .28

.30

- .12

- .09

SD .75

.79

1.01

1.69

1.18

1.03

.91

.97

H.

.63

7----

+.43

.32

Cluster

4 M

.55

- .17

- .14

.20

- .35

.30

.21

- .03

SD .38

.53

1.16

.i6

.78

.37

.98

.93.

H .93

.80

.'I

.64

.47

.25

.43

---

Cluster

5 M .17

.07

.33

- .21

- .09

.40

- .35

.11

SD .84

.83

.96

1.52

1.26

1.05

- 90

.98

H .40

.52

.25

---__

-- - --

.45

.30

Cluster

!.:

.

6 M .23

- .08

.40

.07

- .23

-.13

- .31

.11

SD .62

.99

1.02

.91

.94

.62"

-

.94

.88

H .80

.29

.38

.77

.38

.52

-Cluster

7 M .11

- .00

- .02

- .07

.12

.17

- .11

- .28

SD .31

1.24

1.11

.87

1.21

1.02

..97

1.09

0 ___

.42

.28

--..

Cluster

$ M .20

- .06

- .12

- .20

.01

.03

,.07

.39

SID.89

.89

.95

1.10

.85

.78

'

1.04

.94

H .50

.41

.31

-_---

.55 '.

.62

.40

---_-

Cluster

9li .35

.57

- .12

- .25

- .27

.49

- .16

.15

91.0.70

1.00

1.32

.76

.88

1.06

.84

.96

R .73

-----

.58

.50

.55

.36

-----

-.

Table 4 (cont.)

Job Dimension

12

34

67

,8

910

1112

13

14

ElriatUr

10

1:

.15

.19

- .10

- .09

.11

- .14

- .12

.49

.12

- .14

.09

- .10

- .05

SD

.56

1.03

.85

.96

1.16

.93

1.08

1.C9

.97

.93

1.00

1.12

1.01

H.30

.50

.21

.33-

.24

.20

*111

.1.

......

--

----

-----

Cluster

11

11

.11

- .22

.35

- .12

- .19

- .19

- .26

.01

.02

.01

.24

- .15

- .25

SD

.96

.76

.94

1.06

.96

.79

.98

.90

1.09

.99

.96

.94

.96

H.29

.58'

.27

.35

.60

.11

.42

.15

.28

.25

.25

----

Cluster

12 X

.07

- .55

- .25

.09

- .11

- .61

'

- .28

..16

- .03

- .32

- .10

- .30

- .53'

SD

.56

.39

.79

1.03

1.17

.70

.88

-.88

.80

.44

.71

1.05

.54

H.83

.91

.59

.70

.46

.60

.90

.71

.8-f.

------

Chmter

v.,

13 M

.12

.19

.19

.31

1.06

- .24

- .12

.41

- .19

- .20

- .16

.41

.14

SD

1.02

.96

1.17

.40

.82

.79

.94

.82

.71

.96

.70

.1.27

.98

al,

CM

H.91

.59

.60

.30

.57

.70

.27

.72

.12

---

Cluster

14 M SD

- .22

.89

- .17

.85

.05

.97

.34

.84

.29

.72

- .09

.89

- .14

.68

- .12

.72

- .10

1.14

- .24

.64

- .56

.72

.38

1.14

.23

.80

H.46

.40

.16

.52

.71

.42

.72

.69

VO

NIN

..7

7.69

.53

-Cluster

.

15 M

-1.04

- .67

- .14

.71

.12

.57

.86

.01

1c

.60

- .75

.96

.01

SD

.58

.19

1.16

.47

.60

1.30

.55

.64

.29

1.47

.33

.93

.17

H..82

.89

.88

.81

.83

.77

.96

....94

.29

.98

--.

C/uster

16

M.

.18

.27

.45

.85

.98

.64

"

.01

- .18

- .01

- .52

..12

1.00

.24

SD

1.06

1.03

.69

.62

.91

.72

1.02

.70

.90

.94

.86

.78

1.14

K--

,-

.42

.33

.50

.59

Cluster

.71

.78

.45

.67

.71

17 M

- .81

1.01

- .25

1.12

.85

1.09

.54

- .11

- .33

- .40

- .24

.28

.36

SD

.41

.82

1.22

.37

.62

.93

.35

.58

.45

.56

.70

.44

.43

H.91

..47

-----

.93

.79

.32

.93-

.81

.89

.83

.71

.89

.90

Cluster

18 M

- .29

.08

- .57

.24

- .06

.32

.46

- .03

- .37

.07

- .42

- .O1

- .08

.SD

.68

.78

.67

.91

1.03

.96

.70

.98

.84

.90

.61

.84

.60

.H

.74

.55

.73

.39

.21

.70

.18

,.53

.43

.79

.50

.79

-Cluster

19 M

- .20

- .05.-

- .30

.38

- .17

.37

:40

- .37

- .60

.40

.29

- .09

- .13

SD

.65

.62

1.00

.85

1.10

.88

1.00

.44

.93

1.16

1.19

.79

.44

a.76

.75

.52

.45

.90

.37--

.58

.89

--

-

Table 4 (coat.)

Job Dimension

15

16

18

19

23

24

25

26

Cluster

10 M - .07

.24

.21

- .13

.46

- .01

-.44

- .10

SD 1.11

1.14

1.16

1.07

1.33

1.06

.96

.98

.31

.29

Cluster

12 M

.23

- .03

.07

.23

- .33

.21

.08

.20

SD

.78

.76

.90

.62

.75

.90

1.11

1.06

H.65

.62

.43

.77

.67

.41-

Cluster

12 M

.50

.01

.12

.17

.09

.23

- .00

.24

SD

.49

.45

.51

.20

.86

.82

.90

.74

a.88

-39

.86

.98

.53

.55

.46

..69

Cluster

13 M - .68

.13

.12

.09

.10

- .45

.02

- .38

SD 1.14

1.08

.97

.99

.74

.58

.68

1.40

.22

.68

.81

.74

a-

Cluster

14 M - .14

- .50

.19

.14

- .13

- .33

.48

.10

241

SD

.76

1.01

.55

.64

.55

1.00

.99

CAO

.94

H.40

.6?

.82

.78

.83

.25

-Cluster

15 M - .45

- .86

.66

.49

'

.24

-1.13

1.10

.13

SD

.79

.57

1.19

.54

.63

.52

.79

.84

a.64

.81

.82 _

.79

.85

.62

.57

Cluster

16 H -1.36

.11

.08

.05

.68

- .65

- .36

- .56

SD 1.16

1.48

.99

1.33

1.17

.75

.95

.97

------

.64

.35

.33

a --

-.10

Cluster

17

EL -1.39

.76

.00

.32

.82

-1.12

.16

- .55

SD

.84

.91

.42

.48

.96

.54

.99

1.00

a.57

.36

.91

.87

.32

.84

.22

.22

Cluster

18

Di - .07

- .03

- .36

.11

.06

- .31

.41

- .02

SD 1.05

.77

.79

1.00

'.85

.87

.92

1.08

.61

.61

.55

.46,

.41

II. -

----

Claster

:

19 H

.06

- .16

-

- .67

.05

- .34

- .43

.85

-

.09

SD

.98

.34

.66

.166

.56

.80

.92

1.23

H.30

.94

.75

.72

.8:a.

.59.

.41

-----

1

Tab

le 4

(co

at.)

Job Dimension

12

34

67

89

10

11

12

13

14

Cluster

i'

20 M - .32

.42

.- .07

- .36

.58

- .08

- .19

.03

.22

- .44

- .55

- .21

- .19

SD

.58

1.27

.38

1.43

.92

1.00

.92

.96

.34

.56

.16

.63

.40

H.82

-----

.92

.42

.37

.26

.94

.83

.99

.7'5

.92

-.

---

Cluster

21 M - .20

.24

.10

- .18

- .59

.16

.01

- .03

-.40

.99

.37

.01

- .24

SD

.90

.92

1.02

.66

.42

1.15

.82

1.02

1.36

1.38

1.25

.45

.83

H.43

.16

.74

.91

.55

.86

.54

----

--- --

Cluster

22 x - .26

.06

- .45

.34

.32

.04

.26

- .28

- .19

.01

- .36

.22

.22

SD

.°O

1.00

.61

.90

.93

.81

.63

-

.89

.84

.82

.73

.78

.93

.H

.44

.78

.41

.28

.57

.77

.44

.54

.57

.68

.60

.34

---_-

Cluster

23 M - .89

.49

.24

.47

.67

.19

.38

- .32

- .32

- .37

- .15

.40

- .11

SD

.64

.95

.93

.75

.88

.79

.71

.54

.69

.79

1.08

.94

.38

V.)

H.77

.35

.65

..51

.60

.70

.84

.72

.61

'

.23

.92

-----

0Cluster

24 M - .04

.18

.09.

..32

.21

.14

.51

- .01

.52

.37

.33

- .03

.33

SD

.64

.91

1.03

.91

.73

.91

.67

1.05

1.02

1.16

1.17

.26

R.77

.21

.79

.59

.45

.67

.39

.74

.96

-.

-----

-----

Cluster

25 M - .42

.02

- .70

.05

- .40

.42

- .17

'.03

- .23

- .02

- .77

- .21

.10

SD

.47

.85

.72

.70

.58

1.12

.86

.94

.76

1.08

.56

.75

.51

Ii

.88

.41

.68

.70

.82

.48

.34

.65

1.00

.83

.63

.85

-Cluster

26 M

.21

- .47

.03

.09

.15

.47

- .09

- .28

- .58

- .06

- .59

- .16

- .2?

SD

.70

.92

.77

.73

.71

.46

.86

.32

1.11

1.35

.55

.62

.42

R.72

.17

.62

.67

.72

.88

.48

.95

-.84

.77

.91

-Cluster

..

to27 M - .75

4..01

- .03

.10

7 .31

.17

.04

.06

- .24

.31

z.- .29

- .41

- .06

m GOSD

.27

.67 ..

.78

.85

1.05

.97

.75

1.26

:.13

1.05

.78

.74

.78

111

.96

.70

.60

.51

.14

.65

____-

.63

.64

.62

cm-----

_----

-_-_-

Cluster

CP

'47

28

M.17

..74

- .28

.47

- .07

.17

.53

- .31

- .50

.11

.28

; .67

- .26

=NC

SD

.86

...99-

.53

.79

-.57)

1:08:

.90

.30

.81

.87

1.19

.60

.56

..1:m.

li=

II

.52

.84

.60

.83

.41

.95

.58

.49

-----

.78

.82

:mr

-----

-----

Cluster

Ell:

29 M - .77

.34

-.56

- .06

- .38

.53

;49

- .51

'

- .63'

.84

- .45

- .25

- .04

CCI

SD

.17

.83

.64

.72

.74

.98

.49

.34

-

.89

1.08

.94

.41

.23

gm;

H.99

.46

.75

.68-

.69

.12

.87

.94

.46

.35

.90

.97

-----

Table 4 (cont.)

Job Dimension

15

16

18

19

23

24

25

26

Cluster

20

11

.45

SD

H.85

.60

1.18

.22

1.05

.29

.81

.53

- .02

1.22

L----

.09

.54

.84

-

.:91 -1.%

.60

.27

-----

Cluster

21 x

.49

- .44

- .10

.22

- .17

- .10

- .06

.84

SD

.29

.44

.91

.46

.96

.64

1.00

.68

il

.96

.89

.41

.F8

.31

.76

.14

.75

Cluster

22

11 - .41

.34

- .31

-.09

.25

- .20

.28

- .20

-SD 1.21

1.15

.96

.72

1.02

2 J9

1.00

.S5

.28

.65

.18

.56

Cluster

23 M

0.49

.67

.40

.47

- .53

.60

- .72

SD 1.32

1.21

.43

.75

1.01

.67

.79

1.03

.90

.61

.03

.74

.63

HCluster

24 M- .51

-.24

-.08

.69

- .02

- .60

.09

.3S

SD 1.44

1.34

.50

.48

.62

1.19

.63

.55

.86

.87

.79

.78

.85

U..l

uste

r25 M

.01

.22

- .01

.51

a

.01

- .01

.45

- .32

SD 1.11

1.34

.93

.52

.61

.95

.71

.79

.36

.84

.79

.26

.71

.64

H -----

Cluster

26 N

.00

- .27

- .55

- .03

.06

- .49

.62

.08

SD 1.10

.53

.84,

.49

.93

.54

.67

1.02

.84

.53-

.51

.40

.84

.75

.12

Cluster

27 M

.20

- .25

- .63

.00

- .42

.29

.28

.19

SD

.53

.46

.89

.34

.32

.77

.66

.92

H.86

.88

.45

.93.

.95

.63

.76

.44

Cluster

-28 M- .28

.31

- .39

- .09

- .04-

- .63

.59

.13

SD

.96

.66

.93

.88

.59

1.04

.60

1.38

R.35

.74

.37

.39

.81

.80

Cluster '

29 Mi .17

- .10

- .44

'-.03

.01

- .32

.98

.25

SD

.51

.29

.90

.77

.79

.76

.78

1.13

H: .87-

.95

.43

.59

.63

.64

.64

Table 4 (cont.)

Job Dimension

12

34

67

89

10

11

12

13

14

Cluster

30 It

- .31

- .05

- .87

.69

- .27

1.02

.71

- .27

,- .65

.40

- .81

.20

.03

SD

.97

.68

.49

.32

.81

.67

.13

.40

.56

.78

.17

.50

.32

H.25

.69

.87

.95

.61

.74

.99

.91

.83

.62

.99

.86

.95

Cluster

3/

IL

- .64

.03

- .97

.13

- .31

.19

.52

- .50

- .65

.20

- .53

- .44

.32

SD

.09

.75

.34

.57

.49

.73

.37

.22

.57

.62

.51

.67

.34

H1.00

.59

.94

.82

.88

.67

.93

.98

.82

.79

.86

.72

.94

Cluster

.

-

32

Di

- .34

.26

- .81

...:.46

- .21

- .42

.38

- .45

.11

-. .01

- .26

- .04

- .23

SD

.18

1.12

.41

1.19

1.07

1.38

.66

.56

1.22

1.37

.30

.45

.50

H.98

--___

.91-

.74

.83

.95

.89

.86-

--

-____

Custer

33 M

.05

.11

- .91

- .98

- .10

.45

.25

'1.03

- .31

- .83

- .15

.01

- .24

Ca

IN;

SD 1.10

Ii

.90

.27

.50

.86

.99

.....-

1.10

.67

.74

.48

.87

1.42

-

.96

.26

.51

.86

.67

.74

1.02

.67

.73

--

-.....-

Cluster

34

DI

- .16

.22

- .39

- .29

- .43

.46

.18

- .40

- .04

1.03

- .27

.23

- .40

SD 1.18

1.25

.69

1.26

.99

1.00

.61

.45

1.02

"1.58

.70

1.25

.65

H-

.71

.23

.79

.89

.71

.75

-Cluster

35

EL

- .15

- .33

- .30

.33

- .10

- .02

.28

:.01

- .24

.38

- .20

- .19

.02

SD

.63

.66

.92

.85

.85

1.00

.84-

1.07

.99

.83

1.22

.96

1.02

H.73

.71

.34

.35

.55

.48

.12

.55

.15

-Cluster

36 M - .42

- .28

- .93

.40

- .00

.38

.27

- .53

- .88

.23

- .87

- .04

- .14

SD

.30

.42

.36

.68

-1.02

.97

.39

.14

,.62

1.05

.23

.33

.22

H.95

.90

.93

.73

.09

.19

.92

.99

' .78

.97

.94

.97

-Cluster

37 M - .69

.27

- .58

.11

.05

.88

.64

.11

.29

.01

- .62

.06

.02

SD

.32

.93

.49

1.00

1.50

1.01

.43

.61

1.06

1.40

.30

.53

.70

H.95

.08

.87

-____

.90

.79

.95

.83

.70

--___

----

___--

-_---

Cluster

38 m - .58

- .20

.31

.78

.27

.58

- .06

- .39

- .31

.48

- .48

- .41

.03

SD

.65

.78

.96

.73

.74

.97

.67

.53

.76

1.17

.63

1.30

.97

H -----

.74

.47

.85

.92

.58

/.83

.69

.34

.97

-....-

-em

.

Cluster

39 M - .18

.74

- .57

.05

.17

.29

.39

.45

- .80

.57

- .36

.09

- .09

SD 1.00

.64

.57

.76

1.00

.50

.48

'-

.87

'

.37

.98

.80

.63

.60

H.73

.81

.64.

.21

.86

.88

.49

.93

.21

.60

.76

.79

___--

-

.

CaCa

Table 4 (cont .)

-I

ft

Cluster SD

.76

1.91

.64

.49

1.10

.96

.10

1.23

30 M

.05

.53

- .53

.44

.33

- .57

.28

.30

H.68

IMI

.76

.96

.24

1.00

Cluster

31 M

.62

.71

.24

SD

.41

1.01

.03

- .37

- .10

- .60

- .22

.87

.91

.32

.59

.51

.75

H.92 -

.48

.30

.95

.80

.87

.68

Cluster

32 M

.60

.61

- .85

.C2

- .36

.21

.66

.36

SD

.27

.96

.42

.72

.66

1.44

.33

1.55

H.97

.13

.91

'.65

-

.76

-.95

Cluster

33 M - .30

- .48

.17

.19

- .15

.16

.14

.04

SD

1.21

.41

1.24

.48

.60

1.27

1.34

.39

H ___--

.91

_.86

.80

.92

Cluster

-34 M - .19

.20

.28

- .84

.06

- .29

.45

.25

SD

.63

.90

1.11

1.50

.94

.91

.87

.86

H.79

.38 - -

.37

.39

.52

.55

Cluster

35 M

.23

- .03

- .05

.28

- .07

.-- .14

.32

.10

SD

.88

.63

..84

.50

.67

.89

.73

1.07

H.51

.77

.54

.85

.75

.43

.69

!

-Cluster

.,

.36 M - .10

.61

-1.21

.39

- .40

- .51

.70

- .04

SD

.56

1.15

.29

.37

.68

.57

.42

1.23

-II

.84

.96

.92

.74

.81

.91

Cluster

--___

37- ti

- .99

.11

- .04

.28

'1.05

- .95

.77

- .05

iSD

1.16

1.33

.51

.74

1.11

.66

.74

1.05

iClusterH

.86

.64

-- .

.75

.68

SD 1.20

1.11

.75

.63

.82

.68

.07-

- .20

.72

.9'

.01

38 M - .99

- .40

- .12

.29

- .60

.

H -

.80

.39

.93

.78

.64

---

-----

Job.Dimension

15

16

18

19

23

24

25

26

Cluster

39

it

- .06

.22

- .63

- .43

1.01

- .68

'

.46

.34

SD

1.31

1.02

.47

1.56

1.04

.91

.78

1.44

-.88

.38

.64

Table 4 (cont.).

Job Dimension

12

34

67

89

10

11

Cluster

40 M

- .00

- .12

1.25

.24

.51

.28

-1.17

- .09

- .49

.22

SD

1.20

.63

.86

.52.

.40

.80

1.22

1.02

.55

.73

11

.74

.47

.85

.92

.58

-.83

.69

--

Cluster

41 M

1.75

- .43

- .39

.06

- .01

- .24

- .75

2.43

- .33

.20

SD

.30

.30

1.06

1.03

.71

.30

1.36

.30

.18

.33

.96

.95

!.72

.95

41M

.95

.98

.94

-Cluster

42 M

- .16

- .02

1.

- .14

.21

.07

.19

.27

- .14

- .04

- .C1

SD

.95

.82

.91

.94

.95

:98

.84

.94

1.01

.90

-11

.32

.49

.37

.29

.36

.02

.30

.34

.44

-----

12

13

14

- .12

.37

.16

1.03

.91

.22

-.34

.97

:370

- .28

-:118'

1.0C

.99

.96

1.00

-

:63:46

-17

.29

.25

.74

Ciubte,..

15

16

18

Tab

le 4

(co

nt.)

Job Dimension

19'

23

24

25

26

40

24

- .62

- .66

.38

.66

.36

- .75

.55

4...23

SD

1.07

.59

.92

.35

.63

.75

1.03

1.07

H.80

.39

.93

.78

--

-Cluster

.64

41

H- .0G

- .49

.72

.76

- .40

.81

-1.55

- .26

SD

.43

.12

1.04

.49

.74

.72

.02

.14

.H

.91

.99

.86

.68

.68

1.00

.99

ME

MN

ON

,

'Cluster

42

M- .18

- .17

- .26

.09

- .12

- .04

.23

- .18

SD

1.05

.93

.80

.84

.84

1.02

.97

.94

H.27

.60

.47

.55

.30

.39

29

Table 5

Average Homogeneity Indexes for BC-TRY Clusters

Cluster Cluster1 .86

2 .70

3 .67

4 .79

5 .74

6 .77

7 .82

8 .80

9 .74

10 .74

11 .72

12 .86

13 .69

14 .79

15 .75

16 .84

17 .76

r.

18 .70

19 .68

20 .74

21 .80

22 .71

23 .66

24. .75

25 .69

26 .74

27 .70.

28 .76

29 .71

30 .78

31 .77

32 .67.

33 .76

Overall Average = .75

30

Table 6

Average Homogeneity for CODAP Clusters

ClusterCluster

221 .26

.39

2 .6123 .47

3 .2524 .43

4 .4525 .57

5 .20.. 26 .50

6 .2427 .61

7 .1128 .49

8 .2429 .62

9 .26 30 .66

10 .1131 .76

11 .3432 .50

12 .62 33 .42

13 .34 34 .34

14 .4735* .41

15 .6436 .69

16 .3137 .43

17 .65 38 .48

18 .4439 .47

19 .46 40 .47

20 .48 41 .74

21 .44 42 .32

37

Overall Average Ps .45

Appendix B

Job Clusters Resulting from BC-TRY Program

Note: The three sets of values for each dimension (as shown for eachcluster) represent the following:

1. Top Value Mean2. Middle Value Standard Deviation3. Lower Value Homogeneity Index

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to

BEST COPY AVAILABLE

Appendix.0

Job Clusters Resulting froM CODAP Program

Note : The three sets of values for each dimension (as shown for each

cluster) represent the following:

1. Top Value Mean2, Middle Value Standard Deviation

3. Lower Value Homogeneity Index

PROFILES BYDIVISIONAL DIMENSION

SCORES

CODAP CLUSTERNUMBER--

al01

11P

14Vv

yrtf

.

..35

.36

- .18

- .10

- .02

1.18

1.07

.80

.94

1.09

.62

.30

--__

.,

.04,

- .26

- .22.

.14

'

1.0O'

.99-

.99

1.36

...11

.15

.93

.37

:60

1.13

- .17

.80

.59

.24

.72-

.72

- .21

.71

.68

.15

1.01

-_--_

.19

.54

.82

- .22

.78

.63

.19

.92

.35 - .39

1.10

-- - --

.?9

.88

_27

- .36

'.96

..07

PROFILES BY DIVISIONAL

DIMENSION SCORES

CODAP CLUSTER NUMBER--

2

-.34

1.04

.30

.54

.83

- .17

1.11

- .51

.96

.25

.30

.43

.90

- .41

- .27

- .52

- .43

.72

.74

.55

.64

.63

.66

.83

.77

.34

.72

.69

- 46

- .65

47

.41

..57

.91

.80

.33

1.05

.23

.29

- .55

- .47

.07

.76

.84

.57

.56

.c4

.67

.54

.8")

.83

.83

- .06

.48

.88

co

rn

td*

CDC.

n2

am

ca

en

- .05

1.13 ,,,

PROFILES BY DIVISIONAL DIMENSION

SCORES

CODAP CLUSTER

NUMBER--

.15

.17

- .21

- .23

.56

.97

1.21

-

.85

1.22

1.27

..27

_---

.55

...-....

"..._

- .49

- .33

- .35

- ;10

.76

.97

.87

.67

.57

.20

.47

.74

.02

1.10

.01

- .05

1.06

.97

.20

- .01

.66

.73

.28

.75

.68

.03

1.01

- .31

1.69 - .28

1.18

.30

- .09

1.03

.97

.32

- .12

,91

.43

ski

.PROFILES BY DIVISIONAL DIMENSION SCORES


4

.16

.21

- .33

- .62

.14

.98

.92

1.08

1.02

.85

.20

.33

.52

- .22'

-. .32

- .73

- .31

.73

1.00

.60

.83

.62.

.55

-----

.79

- .03

.90

.43

- .38

- .45

.78

.87

.63

..43

'

.13

.81

.58

- .17

.58

.80

.20

.56

.81

.80

.87

.47

- .03

.93

.43

.55

38

.93

- .14.

1.16

D.

II=

- .35

.78

.64

.21

.98

.25

'72

2w

3a

CU


SCORES


5

.37

.46

- .05

- .26

.32

.53

- .03'

1.08

1.13p

.94

1.10

.79

1.15

.1..27

.....--

39

-----

.61

- .28

- .29

.27

- .06

- .12

.17

- .12

.91

.89

1.13

.92

1.01

.84

_..1.03

.39

____

.-..._

.42

.,39

.40

r

.07

- .21

.40

.83

1.52

1.05

.52

.33

- .09

-

.96

1.26

.25

.35

.90

.45

.11 -

.93

.30

PROFILES BY

DIVISIONALDIENSION SCORES

CODAP CLUSTER NUMBER-

6

.54

.08

- .81

.92

.97

1.29

.35

.31

- .40

- .52

.02

.86

.97

1.20

.49

.19 -

. 23

.40

.86

1.05

. 50

.03

.01

1.03

1.00

- .01

- -GS

2.13

.99

.91

.29

.23

.40

-.62

1.02

.80

.13

.21

.62

.77'

.52

.23

- .31

.94

.94

.38

.33

3


SCORES

CODAP CLUSTER NUMBER-

7

a .

40

.37

1.28

----,

.23

1.04.

- .05

1.23

-----i

- .33

.76

.64'

........---

.t

41'aili

.951 . -

.- .20'

1.13.

M- .20

1.05

14

.18

- .02

.00

- .00

1.14.

1.01

1.26

1.24

18

- .07

.17

.87

1.02

------

-----

.42

.03

.77

.64

- .12

1.05

---

.07

..93

.26

.11

.31

-_-

- .02

1.11

-----

.12-----

1.21

.

e.!

- .28

1.09

.11

.97

.28

1


SCORES


8

- .06;

.08

.92

1.05

.06

.97

.18

.08

.13

.16

.11

.93'-

1.04:

1.09

1.09

...38

1111

1.1

- .13

- .11

1.04

.78

.32

- .06

1.33

.89

.63

.41

- .10

- .04

.20

.96

.81

.39

.28

.54

.50

- .20

.03

.39

1.10

.78

.94

.

01

.62

.40

.12

-.07

.95

.85

1.04

.31

.55

----

PROFILES BYDIVISIONAL prmilsic14

ECOR"

CODAP CLUSTER

NUMBER--

..111

111.

.10

- .41

- .11

1.26

.74

1.15

.66-

.24

- .3427

1.12

1.13

1111

1

.04

.36

.99

1.02

77----

.81

1.05

.57

. 01

- -29

.81

.66

- .26

1.01 :.

.22

.57

'-

1.07

1.00

.59-.35 -----

.12

.64

.70

.57

'.73

1.32

.25

.49

.15

.76

1.06

.96

.58

-----

.36

.27

- .16

.88

.84

.50

.55

PROFILES BY DIVISIONAL DIMENSION SCORES

I


$

10

.15

- .10

.11

- .12

.12-

.09

- .05

.24

- .13

- .01

- .10

.96

.85

1.16

-

1.08

.97

1.00

1.01

1.14

1.07

1.06.

98

.30

..50

-.24

-----

_-_

_____

_____

.19.

- .09

- .14

.49

- .14

- .10

- .07

.21

.46

- .44

1.03:

.96

.93

1.09

.98

1.12

.1.11

1.16

1.33

.9C,

-.a.

.21

.33 -

.20

.31

.1 .

PROFILES BY DIVISIONAL DINENSION

SCORES

11.

CODAP CLUSTER NUNBER--

.35

.26

.24

- .25

.96

.96

.94

.96

.98

1.09

.96

.96

.28

..25

.29

.27

.35

-.11.

.76

,6

.79

.90

.9

.94

- .22

2- .19

.01

.58

.60

.42

.15

.25

Nit

.78

.65

.76

.61.07

.3

.23

.62

.77- .33

.75

.67

.90

1.06

.41

.03

1.11

PROFILES BYDIVISIONAL

DIMENSION SCORES


12

yvv

vvvr

vii12

*

.07

- .25

- AI

- .28

- .03

- .10

.5i

.56

.79

1.17

.88

.80

.71

.54

.83

.59

---

.46

.60

.71

.84

- .55

.09

- .61

.16

- .32

- .30

.50

.39

1.03

.70

.88

.44

.1.05

.88

.49

.91

----

..70

.46

..90

----

.01

.17

.20

.89

.98

.12

.51

.86

.09

.S6

.53

.23

.82

.55

.24

.74

.69

PROFILES BY DIVISIONAL.DIMENSION

SCORES


13

.12

1.02 _

.19

1.17

.31

..40

.91

1.06

- .12

.82

.94

.59

.30

- .24

.79

.60

.41

.82

.57

- .19

- .16

.71

.70

.70

.72

- .20

.40

.96

1.27

.27 - .

.14

.98

.12 -- -68

1.14

.13

1.08

.97

.22

.09

.99

- .45

.5S

.81

.10

.81

.74

.68

- .1L

1.4:,

.02_____

.63

.74

-.19

.96

41-.

,..-

......

..w.


SCORES


14

.22

.05

.89

.97

.46 - .17

.34

.85

.84

.40

.52

.29

_.72

.71 - .09

.89

.42

.14

- .13

- .56

.23

- .50

.14

- .33

.10

.68

1.14

.72

.80

.76

.55

.55

.99

.72

_-___

.69

.58

.63

.82

.83

.25

- .12

.24

.38

- .14

i.19

- .13

.43

.72

.64

1.14

.94

1.01

.64

1.00

.69

.77

____

.4G -

.78



15

-1.04

- .14

.12

.86

- .59

- .75

-58

1.16

.60

.55

.29

.33

.82

.19

.47

1.30

.64

.96

-94

1.47

.63

.67

.71

.57

.01

.81

.89

.88

.77

.

.ni

- .86

7

.49

-1.13

.13

.17

.57

.52

.84

.96

.98_

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.24

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.93

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.29

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17

4

- -81

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.85

.54

- .33

- .24 -

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1.22!

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.58

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.32

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CODAP CLUSTER'NUMBER--

12

- .20

.65

.76- .05

.62

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1.00

- .17

.40

- .60

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1.10

1.00

.93

.1.19

.37

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.37

- .37

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.85

.88

.44

1.16

.52

.45

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---

- .13

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.79

.98

.58

.30

- .16

.34

.94

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.66

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1.23

.72

.59

- .67

- .34

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.66

.56

.92

.75

.83

1.41

CD


CODAP CLUSTER NUMBER --

.

20

1Z

14

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.32

.07

.58

- .19

.09

- .27

- .55

- .19

.60

.29

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.42-

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.54

1.10

.53:

.38

.92'

.92

.34.

.40

1.18

.81.

.42'

.36'

- .08'

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----.21-

.45

.22

- .02

- .49

.37

.94.

.99'

..92.

.53

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.26.

.83.

.8

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.85

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1.22

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1.05

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AO

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+DISTRIBUTION LIST

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document resume ce 003 330 - eric · the 95-page appendix contains the tables of data (15 pages)...

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