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7
An Assessment of the Dictionary of
Occupational Titles as a Source of
Occupational Information
INTRODUCTION
In the preceding chapter, procedures used to compile the most recent
edition of the Dictionary of Occupational Titles are described, and several
concerns are raised about the quality and characteristics of the fourth edition in
light of the way it was produced. To fulfill the committee's charge to make
recommendations about whether future editions of the DOT should be produced
and what kinds of occupational research should be conducted to produce them,
an evaluation of the quality and characteristics of the DOT is presented in this
chapter. The results of this assessment, coupled with knowledge about use, have
helped to inform us as to how well the data contained in the DOT meet the
purposes for which they are intended and/or used. This assessment is also a
basis for the committee's recommendations about whether data collection and
analysis activities used in compiling future editions of the DOT should differ
substantially from what has been done in the past.
Establishing the quality and characteristics of data contained in the DOT is
not a straightforward task. First, as already mentioned, data collection
procedures were not well documented. As a result the possibilities are
Pamela S.Cain had primary responsibility for the preparation of this chapter.
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limited for systematic secondary analysis of the procedures themselves or of
their implications for the resulting data. Second, most of the data contained in
the DOT are unique, so no readily available bench marks exist against which to
compare and assess them. In fact, a great deal of occupational research takes the
DOT as the bench mark or standard of comparison, a fact that makes the
assessment of DOT data even more important. In this chapter we present the
results of several analyses that were designed to explore in detail and
systematically the nature of the process by which the DOT was produced and the
quality and characteristics of the resulting data.
SAMPLING PROCEDURES
As described in chapter 6, the industry designations developed by the
occupational analysis program provide the “sampling frames” from which
establishments are selected for on-site visits. The underlying assumptions of the
procedure are that jobs vary by industry, by region, and by size (i.e., number of
employees) and that these criteria provide the soundest basis for achieving
reasonable coverage of all jobs and for discovering significant variations among
jobs within occupations. Within the establishments chosen, emphasis is put on
analyzing those jobs that appear to be unique to the work performed in
establishments of the type that the selected one represents.
No bench mark data on the “population” of jobs exist, and the procedures
by which specific choices were made about which jobs to study are not well
documented. Consequently, it is not possible to establish whether the DOT
provides comprehensive and representative information about jobs in the U.S.
economy. Nevertheless, certain aspects of the procedures and their outcomes
raise serious questions about the success in attaining representative coverage.
A total of 232 industry designations are used to delineate the “universes”
from which sample establishments are chosen. As we have noted in chapter 2,
several of these, notably the designation clerical and kindred workers, are not in
fact industries, and their use carries the implicit assumption that such
occupations do not vary significantly in content among establishments of
different types. As a consequence of this treatment of a number of
nonproduction occupations the majority of the 232 industry designations that
provide the universes from which establishments are selected are in the
manufacturing sector. In contrast, the current version of the Standard Industrial
Classification denotes 1,005 industries at its most detailed level, and less than
half are in manufacturing. Viewed in this context then, the DOT cannot be said to
be based on job analyses
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conducted in establishments representing the entire spectrum of U.S. industry
types.
Comparable establishment-level data for the DOT and the U.S. economy can
be used to yield a crude indicator of the direction in which the job analysis
efforts for the fourth edition DOT were channeled. In themselves these data do
not constitute an evaluation of the DOT's coverage, since the critical issue, under
the assumptions of the procedure currently used, is the variety of types of
establishments rather than the number of establishments (or the number of
employees). Nevertheless, comparison of the two distributions reinforces the
impression of a disproportionate emphasis on manufacturing.
Data for DOT establishments were obtained from a set of staffing schedules
that were recently computerized and made available to us by the national office
of the Division of Occupational Analysis. As noted in chapter 6, in the course of
fourth edition production, staffing schedules were not prepared for all
establishments entered or for all jobs analyzed. Furthermore, computerization of
the schedules had not yet been completed at the time of the committee's study.
Thus the data employed in our analysis cover only 2,063 establishments;
schedules for an estimated 1,100 to 1,200 establishments are still outstanding.1
The characteristics of establishments in which staffing schedules were not
completed or of establishments whose schedules had not yet been computerized
cannot be determined. As far as we can ascertain, there is no reason to believe
that there are marked differences between the characteristics of establishments
for which data are and are not available, especially since analysts were
supposed to complete staffing schedules for every establishment in which they
analyzed a significant number of jobs. Given the procedures by which staffing
schedules were filled out and their purpose, however, we conjecture that
analysts may have been more likely to complete the schedules in larger, more
bureaucratic establishments, especially those with personnel offices.
Data on the national population of establishments were obtained from
tables in County Business Patterns, 1974 (U.S. Bureau of the Census, 1977).
This publication is compiled by the Census Bureau using data from the
administrative records of the Internal Revenue Service and the Social Security
Administration. Information is available on establishments, payroll, and
employment by industrial classification, size class, and county for all types of
employment covered by the Federal Insurance Contributions Act. In 1974 these
data covered approximately 90 percent of U.S.
1This information was obtained through personal communication with staff at the
national office and the North Carolina field center.
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establishments and 75 percent of the employed population. Not covered were
some government employees; self-employed persons; and certain types of farm,
domestic service, and railroad workers.
In order to compare the DOT data with the published data on the national
population of establishments, the staffing schedules were receded to the
categories used in County Business Patterns. The DOT establishments in public
administration (N=59) were excluded from tabulations, as were establishments
for which data were missing. These exclusions resulted in the loss of 113
establishments and a final total of 1,950 establishments in the DOT sample.
Table 7-1 presents a comparison of the percentage distribution of DOT and
U.S. establishments by SIC major industry division. The two distributions exhibit
marked dissimilarities. The largest discrepancy occurs in the manufacturing
category: 67 percent of the DOT establishments are in manufacturing industries,
although this category accounts for only 8 percent of all U.S. establishments
and for 32 percent of total employment.
Underrepresentation is most pronounced in the retail trade and services
divisions. Retail trade accounts for a mere 4 percent of the DOT establishments,
although nationally, it includes 29 percent of establishments and employs 20
percent of the labor force. Only 7 percent of the DOT establishments are in the
services division, an industry division that accounts for 27 percent of all U.S.
establishments and for 20 percent of U.S. employment. Both retail trade and
services include establishments engaged in a great variety of activities. It seems
highly improbable that the disparity in coverage between these major industry
divisions and the manufacturing division reflects a real difference in the
heterogeneity of occupations.
As previously noted, the wide disparity between the two distributions
cannot be interpreted as conclusive evidence; but it does suggest that the
procedures used to select establishments for the fourth edition DOT resulted in an
overrepresentation of establishments in manufacturing industries. This
overrepresentation occurred primarily at the expense of the retail trade and
service industries, which include 40 percent of all workers. Moreover, the
comments and observations of field center personnel lend additional support to
the general impression that job analysis activities have tended to place emphasis
on manufacturing industries.
Size was another important criterion of establishment selection according
to the occupational analysts, one for which national data are also available from
County Business Patterns, 1974 (U.S. Bureau of the Census, 1977). In
Table 7-2 the percentage distribution of establishments by size class (number of
employees) is presented for the DOT and for the U.S.
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population of establishments. This comparison also reveals discrepancies
between the DOT sample and the national population; the discrepancy is
particularly large in the smallest size class. Establishments employing one to
four workers made up 59 percent of all U.S. establishments but only 6 percent
of the DOT establishments. Generally, small establishments with fewer than 20
employees were underrepresented in the DOT sample, while intermediate (20 to
249 employees) and large (250 or more employees) establishments were
overrepresented in relation to the U.S. distribution of establishments. There is a
rather close correspondence, however, between the DOT distribution of
establishments and the distribution of U.S. employment.
TABLE 7-1 Percentage Distribution of Establishments by SIC Industry Division:
Comparison of DOT Samplea and U.S. Labor Forceb
Establishments
SIC Division DOT, N DOT, U.S., U.S. Labor
Force,c
percentage percentage
percentage
Agricultural 161 8.3 0.9 0.3
services, forestry,
fisheries
Mining 27 1.4 0.6 1.1
Contract 52 2.4 9.1 6.2
construction
Manufacturing 1,309 67.2 7.6 32.1
Transportation and 95 4.9 3.5 6.4
utilities
Wholesale trade 40 2.1 8.7 7.0
Retail trade 82 4.2 29.0 19.6
Finance, 44 2.3 9.0 6.8
insurance, real
estate
Services 140 7.2 26.8 19.6
Nonclassifiabled 0 0.0 4.8 0.9
1,950 100.0 100.0 100.0
TOTAL
aDOT: data taken from establishment staffing schedules. For purposes of comparison with U.S. data,
establishments in public administration were eliminated from tabulation.
bSOURCE: County Business Patterns, 1974 (U.S. Bureau of the Census, 1977: Table 1B).
cWorkers employed in the establishments covered, not the employed civilian labor force.
dIncluded in this category are establishments that could not be classified because of insufficient
information. Typically, these were new businesses.
Once again, we point out that the implications of these results for the
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coverage of jobs are not straightforward. If the assumption that industry type is
the proper basis for sampling establishments is correct, then an important first
step might be to revise the industry list so that it provides coverage of all unit
items in the SIC. In this frame of reference the number of establishments in each
industry would not be relevant, since the objective would be to obtain adequate
minimum coverage for each separate type of establishment. On the other hand,
if jobs in manufacturing are more diverse than those in other sectors, then
oversampling of manufacturing enterprises is quite appropriate. The DOT
analysts would be expected to devote more of their attention to establishments
(and presumably jobs) in these industries. Furthermore, if jobs tend to be similar
in large and small establishments, undersampling small establishments and
oversampling large estabishments would be justified on grounds of cost
effectiveness.
TABLE 7-2 Percentage Distribution of Establishments by Employment-Size Class:
Comparison of DOT Samplea and U.S. Labor Forceb
Establishments
U.S. Labor Force,c
Size DOT, N DOT, percentage U.S., percentage
percentage
1–4 125 6.4 58.7 7.2
5–9 149 7.6 18.0 8.2
10–19 200 10.3 11.3 10.4
20–49 367 18.8 7.5 15.3
50–99 277 14.2 2.4 11.4
100–249 338 17.3 1.4 13.6
250–499 216 11.1 0.4 9.6
500–999 120 6.2 0.2 8.3
1,000+ 158 8.1 0.1 16.0
1,950 100.0 100.0 100.0
TOTAL
aDOT data taken from establishment staffing schedules. For purposes of comparison with U.S. data,
establishments in public administration were eliminated from tabulation.
bSOURCE: County Business Patterns, 1974 (US. Bureau of the Census, 1977: Table 1B).
cWorkers employed in the establishments covered, not the employed civilian labor force.
The difficulty is that there is no evidence at all regarding the relationship
between type of establishment and the variability of job content. We do not
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know whether manufacturing jobs are more heterogeneous than other jobs or
whether jobs in small establishments differ from ostensibly similar jobs in large
establishments or in other small establishments.
In addition to considering the types and sizes of establishments providing
the base data for the DOT, it is also possible to compare the distribution of
occupational units in the DOT with the distribution of workers. This approach
also has very obvious limitations, since some occupational units include large
numbers of workers and others include relatively few. Nevertheless, the data
presented in Table 7-3, in which DOT coverage and labor force employment by
major occupational category are shown, reveal very marked discrepancies.
Some 60 percent of all base titles fall in the processing, machine trades, and
benchwork categories, although these categories include only about 12 percent
of the labor force. Taken in conjunction with the finding (documented in
Table 7-5 below) that a substantial proportion of occupational titles are
supported by one (or no) job analysis schedule, the skewness of the distribution
in Table 7-3 raises the conjecture that the choice of jobs for analysis has a major
impact on the number of occupations identified and that therefore the
concentration of attention on manufacturing establishments has an important
impact on the entire classification structure. To state this more explicitly, if
there is a strong tendency for each job analysis to result in the identification of a
separate occupation (as Table 7-5 seems to imply), the selection of job analysis
sites and of the jobs to be analyzed at these sites becomes the crucial decision of
the occupational analysis program.
As noted above, the procedures for selecting sites for job analysis were not
carefully developed. Analysts drew heavily on the third edition DOT to guide
their job analysis activities. This practice might well have led them to
concentrate more on jobs in established manufacturing industries (which were
well represented in earlier editions) and to devote less attention to jobs in newly
emerging or rapidly growing sectors of the economy, such as services or retail
trade. In addition, it was clear to us in talking with the analysts that many were
oriented almost exclusively toward the study of production jobs. Undoubtedly,
this orientation is a historical outgrowth of the program, rooted in tradition, but
other reasons may be salient, such as the ease of access to manufacturing
establishments. Similarly, the emphasis on large establishments may have come
about because of the relative efficiency of analyzing many jobs in a few large
establishments versus a few jobs each in many small ones.
For whatever reasons the concentration on manufacturing and relatively
large establishments came about, and whatever its implications are for the
coverage of jobs, the results of the foregoing comparisons raise questions about
exactly how sampling for the DOT should proceed. Previous practices were
relatively unsystematic, virtually uninformed by empirical
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data, and resulted in relative inattention to several sectors that include large
proportions of workers. The distributions of workers or of establishments that
we have had to use as crude indicators are not the basic relevant criteria, of
course; a more desirable goal would be the identification of the types of
organizations that have unique types of jobs, with at least minimum coverage of
these unique types of jobs.
TABLE 7-3 Comparison of Percentage Distributions of DOT Titles and Labor Force
by DOT Occupational Categories
DOT Occupational Category Percentage of Base Percentage of Labor
Titles (N=12,099) Force
Professional, technical, and 12 25
managerial
Clerical and sales 8 25
Service 4 16
Agriculture, fishing, and 2 4
forestry
Processing 23 2
Machine trades 18 6
Benchwork 19 4
Structural work 7 9
Miscellaneous 7 8
100 99
TOTAL
SOURCE: Labor force data derived from April 1971, Current Population Survey; sample
(N=60,441) includes currently employed workers and experienced unemployed for
whom a census code could be assigned. Excluded are 12 percent of sample for whom
DOT codes could not be assigned. Data on distribution of DOT titles by category provided
by the Department of Labor occupational analysis program.
A sampling strategy that would ensure adequate coverage of the job
content of the American economy will not be easy to develop, but it is essential
that work on this problem be initiated immediately if the DOT is to serve the
many demands that are made of it.
SOURCE DATA
Chapter 6 observes that the amount and type of source data supporting DOT
titles and definitions vary and that the quality of the data appears to be uneven.
These conclusions were based on examination of the source data, on reports
from analysts involved in writing definitions, and on findings of the Booz,
Allen & Hamilton, Inc. (1979) management review. In this section a more
systematic and detailed inquiry into the quality of
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source data is undertaken to determine the extent to which departures from
standard procedures occurred and whether such departures vary by period or
across certain types of jobs. As is evident from the discussion in chapter 6, there
are numerous points at which departures could have occurred. The nature of
these departures is important to the extent that they have deleteriously affected
the quality and comparability of the data in the DOT.
To assess the quality of DOT documentation, we used a set of data collected
by Booz, Allen & Hamilton as part of its management review. Because the only
information available on the procedures by which the DOT was produced is
anecdotal and impressionistic, Booz, Allen & Hamilton conducted a special
study of DOT source data in November 1978. Analysts at the North Carolina
field center were requested to record information on the documentation
available for a sample of 307 DOT base titles. The sample was systematically
selected by choosing every fortieth title in the DOT. However, there was an
occasional departure from this procedure. If the title selected was not a base
title, a substitution was made, but the procedure by which this was done is
unclear.
Even though the sample is slightly unsytematic, the difficulties of
conducting another similar study justify the use of these data to get an idea of
the quality of DOT documentation. As a check on the Booz, Allen & Hamilton
sample, the percentage distribution of base titles by DOT major occupational
categories for the sample was compared with that of the DOT. The comparison,
in Table 7-4, reveals that the two distributions are very similar. Hence on this
criterion at least, the sample appears to be quite representative of the population
from which it was drawn.
The distribution of DOT titles by the kind of documentation available for
each is shown in Table 7-5. The summary information at the end of the table
shows that 11 percent of the DOT titles had no supporting documentation other
than the third edition definition, which was based on job analyses conducted
prior to 1965. Seventy-one percent of titles were supported by job analysis
schedules only, 8 percent by schedules and occupational code requests, and the
remaining 10 percent by other combinations of data. Thus job analysis
schedules constituted the bulk of the data base for the DOT, other types of
information making up a relatively small percentage of the source data.
The quality of the definitions for the 11 percent of titles lacking any sort of
documentation other than the third edition is particularly questionable, since
there is no way of knowing whether and to what extent changes in the content
of these jobs occurred between the third and fourth editions. The quality of
definitions based solely (5 percent) or in part (14 percent) on information other
than job analysis schedules may also be questionable.
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Occupational code requests, for example, are essentially employers' job orders,
which are taken over the phone and may not be verified on site. As a result the
job specifications contained in code requests probably reflect hiring
requirements rather than the functional requirements of jobs, as would have
been determined via on-site analysis. Similarly, information obtained through
letters from trade associations (which are, in part, advocacy groups) is perhaps
more likely to depict the ideal job than the average or typical one. For both
sources of information, skill and other requirements of the job may be inflated
or biased upward, in relation to what would have been determined through on-
site analysis. If these data continue to be used to support DOT definitions, steps
should probably be taken to determine their properties and possible biases and
their comparability to data obtained via on-site observations and interviews.
TABLE 7-4 Percentage Distribution of DOT Titles by Major Group: The DOT
versus the Booz, Allen & Hamilton Sample
Category Booz, Allen & Hamilton Sample
DOT
0–1 12 13
2 8 9
3 4 5
4 2 1
5 23 21
6 18 18
7 19 19
8 7 8
9 7 6
100 100
TOTAL
N (12,099) (307)
Table 7-5 shows the distribution of titles by the number of job analysis
schedules available for each. Sixteen percent of DOT occupations are
unsupported by job analysis schedules (11 percent of these are completely
unsupported, and 4.5 percent are supported by other types of information). Of
the total number of occupations an additional 29 percent are supported by only
one schedule, 19 percent by two schedules, and the remaining 37 percent by
three or more schedules.
The small number of jobs analyzed per title raises additional questions
about the inclusiveness and accuracy of the occupational information
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TABLE 7-5 Percentage Distribution of DOT Titles by Number and Type of
Supporting Documentation
Documentation Percentage
Number of job analysis schedules (JAS)
0 16
1 29
2 19
3 8
4 7
5 3
6 4
7 2
8+ 13
101
TOTAL
Number of occupational code requests (OCR)
0 90
1 6
2 2
3+ 2
100
TOTAL
Number of othera sources
0 89
1 8
2 2
3+ 1
100
TOTAL
All forms of documentation
None 11
JAS only 71
OCR only 1
Other only 4
JAS and OCR 8
JAS and other 5
JAS, OCR, and other 1
101
TOTAL
TOTAL N 307
aOther includes comments from trade associations, job descriptions from employees, etc.
SOURCE: Tabulated using data from Booz, Allen & Hamilton study of DOT documentation.
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Factor
Variableb 1 2 3 4 5 6
Eigenvalue 10.86 4.98 2.18 1.20 1.09 0.63
Percentage variance 49.30 22.60 9.90 5.40 4.90 2.90
Cumulative percentage 49.30 72.00 81.90 87.30 92.20 95.10
aFactor loadings greater than or equal to .4 are in boldface.
bWhere necessary, scores on variables were reflected so that high scores represent high levels of the
trait.
relationship in the DOT between the substantive complexity of occupations
and their managerial responsibilities.
The fifth and sixth factors account for 5 and 3 percent of the shared
variance in the matrix, respectively. Factor 5, which is composed of only 4
items, might be labeled “interpersonal skills.” An inspection of the items'
content reveals that this dimension involves working with feelings and ideas
and sensory or judgmental criteria and that it involves influencing people and
dealing with their social welfare. The sixth factor, although it accounts for only
3 percent of the variance, is readily interpretable as reflecting undesirable
aspects of the working conditions of occupations.
By and large, the results of this factor analysis are straightforward. Several
variables did load on more than one factor: as noted, there is some overlap
between factors 1 and 4; factors 1 and 2 also share two items in common. Only
five variables (COLORDIS, PUS, COLD, WET, and NOISE), failed to load significantly
on any of the factors. Of these five variables, all but COLORDIS are dichotomous
variables with limited variance. The variable COLORDIS (occupations requiring an
aptitude for color discrimination) appears to tap a unique occupational
dimension. Presumably, many occupations require similar special aptitudes, but
since each aptitude is probably required of only a few occupations, it would be
preferable to include such information as part of the occupational definition.
These results can be interpreted in two ways. The most straightforward
interpretation is simply that there is a great deal of redundancy among DOT
indicators. Alternatively, the factor patterns just presented could result from the
procedures used in making DOT ratings. In rating occupations for these traits,
occupational analysts might have forced consistency among them. It is true that
many of the functions and traits appear to tap nearly identical phenomena (e.g.,
GED and INTELL). However, it is also the case that the way in which the ratings
were made—
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TABLE 7-14 Factor Analysis of Fourth Edition DOT Occupational Characteristics:
Items and Loadings for Six Major Factors
Variable Label Description Loading
Factor 1: substantive complexity, 49.3 percenta
general educational development .86
GED
specific vocational preparation .86
SVP
intelligenceb .83
INTELL
complexity of functioning with datab .81
DATA
repetitive or continuous processes .81
REPCON
numerical aptitudeb .78
NUMER
verbal aptitudeb .76
VERBAL
abstract and creative versus routine, concrete activities .68
ABSTRACT
measurable or verifiable criteria .64
MVC
clerical perceptionb .64
CLERICAL
spatial perceptionb .55
SPATIAL
complexity of functioning with peopleb .47
PEOPLE
form perceptionb .46
FORM
talking .44
TALK
direction, control, and planning .43
DCP
variety and change .42
VARCH
communication of data versus activities with things .41
DATACOM
Factor 2: motor skills, 22.6 percenta
finger dexterityb .69
FINGDEX
motor coordinationb .68
MOTOR
manual dexterityb .67
MANDEX
complexity of functioning with thingsb .66
THINGS
form perceptionb .52
FORM
spatial perceptionb .47
SPATIAL
seeing .43
SEE
reaching .42
REACH
set limits, tolerances, or standards .37
STS
activities involving processes, machines versus social .33
MACHINE
welfare
Factor 3: physical demands, 9.9 percenta
outside working conditions .67
LOCATION
stooping, kneeling, crouching, crawling .53
STOOP
eye-hand-foot coordinationb .52
EYEHAND
climbing, balancing .49
CLIMB
lifting, carrying, pulling, pushing .48
STRENGTH
Factor 4: management, 5.4 percenta
dealing with people .78
DEPL
direction, control, planning .74
DCP
complexity of functioning with peopleb .70
PEOPLE
talking .64
TALK
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Variable Label Description Loading
−.63
activities resulting in tangible satisfaction versus
TANGIBLE
prestige
−.57
scientific, technical activities versus business contact
SCIENCE
communication of data versus activities with things .49
DATACOM
complexity of functioning with datab .44
DATA
Factor 5: interpersonal skills, 4.9 percenta
sensory or judgmental criteria .51
SJC
feelings, ideas, facts .41
FIF
influencing people .41
INFLU
−.37
activities involving processes, machines versus social
MACHINE
welfare
Factor 6: undesirable working conditions, 2.9 percenta
hazardous conditions .52
HAZARDS
fumes, odors, dust, poor ventilation .42
ATMOSPHR
extreme heat .37
HEAT
aPercentage of common variance explained.
bSign reflected on this variable.
all ratings assigned at one time by a single analyst—could have inflated the
degree of consistency among the scores for each occupation and hence the
degree of correlation between variables measured over occupations. This is
called a “halo effect,” the tendency of one judgment to be affected by another. It
is well known that when several ratings are made at a single time by a single
judge, they tend to be more consistent than when the ratings are made
independently of one another (Selltiz et al., 1959:351– 352).
Evidence that the rating procedure itself is an important source of the high
degree of interrelationship among the DOT variables is offered by the results of a
similar factor analysis performed by using third edition data (Barker, 1969). For
the third edition, different analysts rated each of the traits: one analyst rated
occupations for aptitudes, another for temperaments, etc., a procedure that
would mitigate the tendency to force consistency among the ratings. In an
analysis of third edition ratings, Barker found that 11 factors emerged and that
the factor loadings, commonalities, and percentage of common variance
explained were all much lower than the estimates presented here. Although
other reasons could account for the differences between his findings and ours
(e.g., differences in the underlying distribution of occupations), the suspicion is
strong that the differences are attributable to the change in the rating
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procedures from the third to fourth edition, that is, that the high covariation
among the worker functions and worker traits is an artifact at least in part of the
procedures used to rate DOT occupations. If this is so, these findings suggest that
a modification of current rating procedures is needed along with a careful
examination of the content of the items themselves.
These results suggest that the more reliable indicators of the features of
occupations tapped by the worker traits and worker functions variables could be
created by developing factor-based multiple-item scales to represent the various
dimensions revealed by the factor analysis. Such scales would have the
advantage of greater internal reliabiilty and consistency than single indicators or
scales created by simple summing of items without knowledge of their factor
structure. In Appendix F we present scores for scales constructed in this way for
the categories of the 1970 U.S. Census detailed occupational classification.
SEX BIAS IN THE RATING OF OCCUPATIONS
Recently, the DOT has come under attack for alleged sex bias. It has been
claimed that in the third edition DOT both the occupational descriptions and the
ratings of occupational characteristics undervalued jobs held mainly by women
(Witt and Naherny, 1975). In particular, it has been asserted that third edition
ratings of the complexity of work in relation to data, people, and things reflect
traditional stereotypes regarding the relative complexity of the kinds of jobs
typically held by women and those typically held by men (Witt and Naherny,
1975). Consideration of a few examples is sufficient to legitimate the charge of
sex bias in the third edition. In it the DATA, PEOPLE, and THINGS variables included
as the lowest response level a judgment that an occupation had “no significant
relationship” to data, people, or things. Typist, a job held mainly by women,
was coded as having no significant relationship to things, whereas Typesetting-
Machine Tender, a job held mainly by men, was coded at a higher level of
complexity. Such jobs as Nursery School Teacher and Practical Nurse were
coded as having minimal or no significant relationship to data, people, and
things, while such jobs as Dog Pound Attendant were rated as functioning at a
higher level of complexity.
According to informants in the national office the no significant
relationship category for the worker functions was dropped in the fourth edition
in response to the charge of sex bias in the third edition. Occupations that had
been scored at the lowest complexity levels in the third edition were assigned
new worker function scores. In addition, in
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some instances other scores were changed, presumably to reflect changes in job
content or to correct other errors in the third edition.
In order to document the changes made between the third and fourth
editions and to determine whether the ratings of occupations commonly pursued
by women had been upgraded as claimed, we conducted an analysis of third and
fourth edition worker function ratings. This was done by utilizing the April
1971 Current Population Survey (CPS) of a representative sample of the labor
force. This data set contains, among other variables, both the third and fourth
edition DOT codes for the job held at the time of the survey and the sex of each
worker. The CPS data set includes data for 60,441 members of the labor force.
Third edition DOT codes were assigned to each occupational response by trained
occupational analysts in the occupational analysis field centers. Fourth edition
codes were subsequently added to the data, using a map prepared by the
Division of Occupational Analysis that related fourth edition DOT codes to third
edition codes. By comparing third and fourth edition scores on the DATA, PEOPLE,
and THINGS variables separately for men and women, we can determine the
effect of scoring changes between the third and fourth editions on the relative
status of male and female workers. Note that our sample for this analysis is
composed of workers, not jobs. However, neither workers nor jobs changed,
only the classification of jobs in the DOT scheme and hence the scoring of the
worker function variables. An analysis of the nature of these changes permits an
indirect inference about the extent of sex bias remaining in the fourth edition DOT.
We begin by considering the labor force as a whole (see Table 7-15). In
1971, about a third of both the male and female labor force were in occupations
that were judged in the third edition to have no significant relationship to data.
In contrast, a much larger proportion of men than women were in occupations
having no significant relationship to people, and a much larger proportion of
women than men were in occupations with no significant relationship to things.
The second line of the table, which gives the mean fourth edition score for
occupations with “no significant relationship” in the third edition, shows what
happened to these occupations in the fourth edition. On average, the
occupations held by men and those held by women were assigned similar scores
on the DATA and PEOPLE variables, but on the THINGS variable the occupations
held by women were judged to be more complex than the occupations held by
men. In short, the major effect of the abolition of the no significant relationship
category was to upgrade substantially the complexity in relation to things of
occupations held by women. This conclusion is also evident in the “difference
in means” row, which shows the difference in the average score between the
third and fourth editions. Since a low score
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means greater complexity, the fact that all the numbers in the row are negative
indicates an average upgrading of complexity levels between the third and
fourth editions. The only change of substantive importance, however, is the
upgrading of occupations held by women on the THINGS variable.
The remaining point to note concerning the total labor force is that except
for changes required by the abolition of the no significant relationship codes,
there were few changes in ratings between the third and fourth editions. More
than 90 percent of the scores remained unchanged between the two editions, as
perhaps was to be expected, given the way in which DOT occupational data were
generated.
Inspection of the second section of Table 7-15 allows us to identify a
major source of change in the THINGS ratings: the upgrading of clerical and sales
jobs held by women. Most clerical and sales jobs (whether held by men or
women) were identified in the third edition as having no significant relationship
to things. However, the occupations held by women were coded substantially
differently on the THINGS variable in the fourth edition from those held by men;
on average, the clerical and sales occupations held by women were judged as
having much greater complexity than those held by men. No doubt this reflects
the greater propensity of female clerical and sales workers than male clerical
and sales workers to operate office machines. Whereas in the third edition the
task of typing was rated as not involving a significant relationship to things
(level 8), in the fourth edition it was rated as involving the “operating-
controlling” of things (level 2). The same sort of coding change was made for a
large number of positions involving the operation of office machines. Hence
while both clerical and sales occupations held by women and those held by men
tended to be upgraded in the fourth edition, the upgrading was much greater for
the jobs held by women. Thus on the basis of fourth edition scores the average
female clerical and sales worker is scored as doing more complex work in
relation to things than the average male clerical and sales worker.
In contrast to the clerical and sales sector the service and benchwork
sectors—included here because they are also large employers of women— do
not exhibit radically different patterns of upgrading for jobs held by men and
those held by women, although they do show significant differences in the
proportion of occupations in the third edition with no significant relationship to
data, people, and things.
What do these results tell us about sex bias in the fourth edition DOT?
Although no definitive judgment is possible in the absence of an external
criterion of job complexity against which to assess the DOT ratings, the relative
similarity in the mean scores for male and female workers is
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certainly consistent with an inference that these variables are largely bias free.
For the total labor force, the means for the DATA variable vary by only about half
a point, and the means for PEOPLE and THINGS by even less. Although the means
are lower for men, indicating that they work in occupations with greater
complexity than those held by women, the size of the differences is within what
would be expected from well-known patterns of occupational segregation by
sex. Hence there is no reason to believe that the kind of work women do is
undervalued in the fourth edition DOT, at least with respect to the worker
function ratings. Of course, the possibility exists that the work that women do is
overvalued and that if unbiased scores were available, the mean difference
between male and female workers would be even greater. However, this is
unlikely, given other evidence demonstrating that men and women are equally
well educated on the average and hold jobs with similar average prestige
(Treiman and Terrell, 1975a, b), that the average GED levels of the jobs held by
men and by women are virtually identical (the means are 3.14 and 3.20), and
that the average SVP levels of the jobs held by men and by women differ by only
about a half a point (the means are 4.70 and 4.14). These results imply that the
worker function ratings in the fourth edition—but not the third edition—can be
used to assess sex differences in occupational attainment without undue
distortion (see chapter 4 for a discussion of such analyses).
CONCLUSION
This chapter deals with two major issues, the adequacy of the source data
used to create the DOT and the adequacy of the data on occupational
characteristics created in conjunction with the DOT. These issues are, of course,
not unrelated, since the adequacy of the source data determines, in part, the
adequacy of the resulting occupational characteristics scales. Still, it is useful to
consider them separately.
The chapter documents the very uneven coverage of the labor force in the
basic data collection process. First, the DOT includes many more production
process occupations, relative to the number of individuals in the labor force
employed in such occupations, than clerical, sales, and service occupations.
While it may be that production process occupations are, in fact, more finely
differentiated in the economy than are other occupations, there is no evidence
that this is so. An equally plausible explanation is that DOT data collection
procedures, which tend to concentrate on manufacturing plants, create a bias
toward more detailed coverage of production process occupations than of other
types of work. At present, there is no way of resolving this question, since there
exist no principles for determining the boundaries of occupations and hence no
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TABLE 7-15 Changes in the Scoring of DATA, PEOPLE, and THINGS Between the Third and Fourth Editions of the DOTa
DATA PEOPLE THINGS
Male Female Male Female Male Female
Total labor force
Percent no significant relationship in 3rd edition 34.4 33.2 60.6 37.9 39.9 72.6
Mean in 4th edition, of those with no significant relationship in 3rd edition 5.41 5.34 7.23 7.38 6.21 4.76
Percent in lowest category in 4th edition, of those with no significant relationship in 3rd edition 75.8 68.1 61.6 68.9 92.7 82.9
Percent upgraded—3rd to 4th editionb 3.6 5.0 3.2 1.8 2.6 3.1
Percent constant—3rd and 4th edition 95.1 93.1 94.2 97.6 96.1 95.7
Percent downgraded—3rd to 4th edition 1.4 1.1 2.6 .6 1.3 1.2
Total mean—3rd editionc 3.97 4.51 6.58 6.28 5.13 6.92
Total mean—4th edition 3.10 3.63 6.14 6.22 4.39 4.55
Difference in means (4th minus 3rd) −.87 −.88 −.44 −.06 −.74 −2.37
Clerical and sales
Percent no significant relationship in 3rd edition 7.5 4.7 32.6 38.2 83.8 91.4
Mean in 4th edition, of those with no significant relationship in 3rd edition 4.50 5.03 7.10 7.08 5.87 3.60
SOURCE OF OCCUPATIONAL INFORMATION
Percent in lowest category in 4th edition, of those with no significant relationship in 3rd edition 24.1 51.9 55.5 54.5 75.7 31.9
Percent upgraded— 3rd to 4th editionb 8.4 7.4 8.2 .7 .2 0.0
Percent constant— 3rd and 4th edition 88.7 91.7 91.5 98.9 94.4 99.2
Percent downgraded— 3rd to 4th edition 3.1 .9 .3 .4 5.5 .8
Total mean—3rd editionc 3.40 3.61 6.16 6.68 7.68 7.63
Total mean—4th edition 3.07 3.40 5.77 6.32 5.94 3.61
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Difference in means (4th minus 3rd) −.33 −.21 −.39 −.36 −1.74 −4.02
Service
Percent no significant relationship in 3rd edition 75.5 82.9 55.7 29.1 37.9 64.0
Mean in 4th edition, of those with no significant relationship in 3rd edition 5.54 5.18 7.32 7.54 5.63 5.90
Percent in lowest category in 4th edition, of those with no significant relationship in 3rd edition 81.3 62.2 65.9 76.8 62.1 63.9
Percent upgraded—3rd to 4th editionb 0.0 0.0 1.8 1.5 0.0 .5
Percent constant—3rd and 4th edition 99.3 99.9 97.9 98.4 100.0 99.3
Percent downgraded—3rd to 4th edition .7 .1 .2 .1 0.0 .2
Total mean—3rd editionc 6.56 7.03 7.18 7.17 6.09 6.67
Total mean—4th edition 4.72 4.71 6.78 7.02 5.19 5.32
Difference in means (4th minus 3rd) −1.84 −2.32 −.40 −.15 −.90 −1.35
Benchwork
Percent no significant relationship in 3rd edition 55.9 77.7 92.1 97.7 .3 0.0
Mean in 4th edition, of those with no significant relationship in 3rd edition 5.74 5.84 7.90 7.83 7.00 −
Percent in lowest category in 4th edition, of those with no significant relationship in 3rd edition 88.2 94.2 95.1 91.6 100.0 −.
Percent upgraded—3rd to 4th editionb 4.3 8.1 0.0 0.0 7.1 7.1
SOURCE OF OCCUPATIONAL INFORMATION
Percent constant—3rd and 4th edition 91.3 85.7 96.2 100.0 90.7 89.8
Percent downgraded—3rd to 4th edition 4.4 6.2 3.8 0.0 2.2 3.0
Total mean—3rd editionc 5.48 6.68 7.64 7.92 3.32 3.47
Total mean—4th edition 4.33 5.38 7.55 7.76 3.18 3.37
Difference in means (4th minus 3rd) −1.15 −1.30 −.09 −.16 −.14 −.10
aData from an April 1971 Current Population Survey of a representative sample of the adult labor force, N=60, 441.
bPercent assigned a higher complexity level (a lower score) in the 4th edition than in the 3rd edition, excluding those with no significant relationship in the 3rd edition. Percent
constant and percent downgraded similarly defined.
cMean is computed on entire sample, including those with no significant relationship, a score of 7 or 8 on DATA, 7 on PEOPLE, and 8 on THINGS.
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unambiguous procedures for aggregating jobs into occupations. The
development of such principles and of procedures for using them in the data
collection process should be given high priority in preparation for future
editions of the DOT.
Second, some occupations in the fourth edition DOT were analyzed many
times, while others were not analyzed at all. Given the heterogeneity of jobs
included within a single occupational category (which is confirmed by the
substantial “job description” effect on the reliability of worker trait and worker
function ratings), procedures need to be developed to ensure a more even
sampling of jobs within occupations in order to be certain that each
occupational description is based on data from a sufficient number of job
analyses to produce representative data.
What constitutes an “occupation”—and how much heterogeneity in the
content of a set of jobs justifies a single occupational title—is a difficult
question. Historically, the DOT has tended to define occupations by their titles
rather than by their content. Jobs with similar titles have been grouped unless
the evidence strongly indicated that they differed in content, and occupations
with different titles have been defined as being different, regardless of similarity
in content. At the same time, each job analysis tends to produce a new DOT
occupation, while jobs with titles similar to titles already existing in the DOT
tend not to be analyzed at all, making it impossible to determine their degree of
similarity. Occupational titles are also used inconsistently in the DOT to define
very specific or very heterogeneous groups of jobs. Branch manager, for
example, describes a wide variety of jobs, all of which involve coordination and
control functions but vary enormously in terms of the specific tasks performed.
Tool and Die Maker, by contrast, describes basically the same job regardless of
where tool and die makers are employed.
Consideration should be given to developing a clear and unambiguous way
of defining occupations.
The analysis in this chapter also raises serious questions regarding the
adequacy of the worker trait and worker function variables. First, it is unclear
whether the 46 variables on which data are collected adequately represent the
kind of information needed by users within and outside the Employment
Service. Our conjecture is that they do not. Many of the DOT variables,
especially the aptitudes, interests, and temperaments, are not heavily used, as
we have seen in chapters 3 and 4. Oddly, other information collected on job
analysis schedules but never subsequently recorded, i.e., information on
promotion ladders and lateral transfer routes, is often mentioned by users
outside the Employment Service as a major lack in the DOT. Obviously,
consideration should be given to the inclusion of such information in the DOT
occupational descriptions. More
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generally, a careful conceptual review should be undertaken of the sort of
information needed for matching workers with jobs (e.g., data on the
transferability of skills), for counseling job applicants about occupational
requirements, for assessing the comparability of occupations for the resolution
of equal employment opportunity disputes (better data on the responsibilities
entailed in occupational performance, for example), and for occupational
research of various kinds. Once the major dimensions of occupations on which
data are needed are identified, scales measuring these dimensions should be
developed following standard psychometric practices. In particular,
consideration should be given to the development of factor-based multiple-item
scales, the use of which would go a long way toward overcoming the reliability
problems identified in Appendix E and summarized in this chapter.
Despite the deficiencies in the fourth edition worker trait and worker
function variables identified here, they remain the most comprehensive set of
occupational characteristics currently available. As such, their use should be
encouraged. To facilitate this use, Appendix F provides data on eight DOT
variables aggregated to match the categories of the 1970 U.S. Census detailed
occupational classification and four factor-based scales derived from the DOT
variables. Researchers should find these data a useful supplement to data on the
average characteristics of workers that can be derived from census occupational
statistics. Moreover, one potential major threat to the usefulness of these data
can be discounted on the basis of our analysis: so far as we can tell, the fourth
edition worker function variables do not undervalue occupations held mainly by
women as the third edition worker function variables apparently did.
Representative terms from entire chapter:
third edition
