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# A note on the interpretation of factor analysis, or; Factor analysis: what good is it?

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LIBRARY

OF THE

MASSACHUSETTS INSTITUTE
OF TECHNOLOGY

IID28
.M414
no. 254-
67

WORKL
ALFRED P. SLOAN SCHOOL OF MANAGEMENT

A NOTE ON THE INTERPRETATION OF

FACTOR ANALYSIS
or
FACTOR ANALYSIS: WHAT GOOD IS IT?

J. Scott Armstrong
and Peer Soelberg

MASSACHUSETTS
INSTITUTE OF TECHNOLOGY
50 MEMORIAL DRIVE
â€¢AMBRIDGE, MASSACHUSETTS 02139

A NOTE ON THE INTERPRETATION OF

FACTOR ANALYSIS

or

FACTOR ANALYSIS: WHAT GOOD IS IT?

J. Scott Armstrong
and Peer Soelberg

254-67

(c) Copyright by Peer Soelberg and J. Scott Armstrong

This paper should not be reproduced in whole or in part,
by any process, without the authors' permission.

)

Dewey

I 1967

i . l ldr\/ utltS

ABSTRACT

Results of a survey are examined by a common factor analytic method.
The conclusion leads us to question the value of many recent papers that
have used factor analysis as their chief research method. Recommendations
are made for requirements that should be met by published articles employ-
ing factor analysis.

1752243

GENERAL BOOKBINDING CO.

CÂ£ 1

B3

QUALITY CONTROL MARK

FACTOR ANALYSIS: WHAT GOOD IS IT?

We have been concerned with certain methodological problems of factor
analysis. In order to get some feeling for the seriousness of the problems
that concerned us, we decided to study the following specific application.

We asked 50 employees to rate their supervisors on 20 traits. The
measuring instrument was a semantic differential ranging from +3 to -3.
An example is provided in Figure 1.

+3 +2 +1

very
aggressive

not aggressive
at all

Figure 1

The employees were all selected randomly from different work groups.
Analysis of the data showed that their responses to each trait were approx-
imately normally distributed. The responses were then factor analyzed in
order to determine whether underlying factors might be found that could
summerize the results in a meaningful fashion. Since this was to be an
exploratory study, we hoped to use any factors that emerged as our source
of hypotheses for further work on employee perception of supervisory be-
havior .

-2-

Pearson product-moment correlations were computed between all trait
variables. A principal components analysis was then carried out using unity
in the major diagonal. Following the recommendations of Kaiser (1960), we
selected all factors with eigenvalues greater than 1.0. These factors were
then rotated according to the varimax criterion. All calculations were done
with the California Biomedical 03M program at the MIT Computation Center.

Results

The results of the factor analysis seemed promising. The data did
fall into a pattern. In fact, we were able to summarize 71 percent of the
variance in the 20 traits with only 9 factors. The factors, using only

From Table 1 we see that very impressive loadings were obtained. The
factors also make good intuitive sense. Factors II, VI, and VIII came
through clearly, with only one significant variable in each factor: Sincer-
ity, Kindness, and Tactfulness, respectively. The other factors bear some
discussion .

Factor I seems to be a measure of Fascism. It is closely related to
the authoritarian personality described by Adorno , et .al . (1950). Super-
visors who score highly on this factor are low in sensitivity, low in demo-
cratic attitudes, but high on responsibility. The relationship to responsi-
bility also receives support from the "blind obedience" study by Milgram
(196A).

Factor III is a measure of Social Distance. People who score high on
this factor tend to be formal, somewhat unfair, and are low on humility.
This makes senje since, where social distance is high, the supervisor does
lem situations; as a result his decisions will seem unfair. In addition,
social distance can lead to the perception that the supervisor is making
unilateral decisions, which by itself may appear unfair and presumptuous to
employees in the American culture. The factor parallels Fiedler's descrip-
tion of social distance (1960).

Factor IV is a measure of Reliability. Supervisors high on this
factor are trusted by their employees. The loading of humbleness on both
Factor III and Factor IV is, however, not so easily explained. But it may
be that the term "humble" is eliciting two types of responses. In Factor
III it is a type of "aloofness", while in Factor IV it is more likely
associated with a non-evaluative attitude.

Factor V might be called Docility. Supervisors scoring high on this
factor are perceived as patient, unaggressive, and rather shallow.
Hertzberg et .al . (1959) have shown that this type of person is more inter-
ested in the hygiene conditions of his work rather than in the task itself.

Since this variable comes on in such a strong fashion, we have labelled
this factor a measure of Humor. The relationship of humor to aggressive-
ness is interesting, and fits rather well what we know about the "practical
joker".

Factor IX seems to be a measure of Social Leadership. It is closely
related to Bales (1950) concept of group maintenance. Supervisors who
score high on this factor are concerned about developing strong inter-
personal relationships. They have a high need to be social leaders in
their work groups.

As we had hoped, the above analysis opens up many interesting avenues
for further research. The factors not only seem to have a great deal of
face validity, but also agree well with other research on attitudes toward
supervisory behavior. Some of the earlier studies isolated two or three
underlying factors; however, different researchers have suggested differ-
ent factors. Our study, which was broad in scope, indicates that employee
perceptions of supervisors may be more complex than first thought to be the
case. We suggest that there are many factors which deserve consideration.

Discussion

The above study is not atypical of the factor analytic studies reported
in the literature. There is, however, one major difference. Our "employee
responses" were random data. We simply created trait responses for mythical
employees by drawing a sample of numbers from Rand's Table of Random Normal
Deviates , supplying each sample with an arbitrary variable name.

The moral of our story is that a reasonable person could easily con-
vince himself, and others, that the output of his factor analysis were mean-
ingful, unless he had set himself an a priori benchmark for evaluating his
results. A researcher could conceivably be "making sense" of completely
random data. Without such a benchmark reported, a factor analytic study
could thus be adding little more than noise to the literature.

At a minimum, we suggest that some measure of factor reliability be
made a publication requirement. Since statistical tests are not well dev-
eloped for factor analysis, and certainly not widely used, we shall summar-
ize three simple methods for checking factor reliability.

But, before we do this, let us see if our criticism of common uses of
factor analysis has merely been "beating a dead horse". We went through
recent issues of six academic journals and asked whether their papers util-
izing factor analysis did or did not provide the reader with some sort of
to include some measure of "validity" as an index of the usefulness of the
study.

This literature survey called for a fair amount of subjective evalua-
tion. Nevertheless, most of the cases were reasonably clear-cut, since we
were not evaluating how successful the author was at establishing the relia-
bility of his factors or the usefulness of his results. We were merely
interested in whether or not he had made an attemp_t to do so.

One wonders how many of the "delinquent" studies would have been pub-
lished had the reliability of the factors had been measured and reported. The results
of our preliminary survey were so impressive (or dismaying, depending on your

point of view) , that it seencd unnecessary for us to resort to a more rig-

2
orous literature survey in order to make our no inc.

-6-

Methods of Reliability Measurement

Three rather simple approaches to measuring factor reliability will be
considered - split samples, a priori analysis, and Monte Carlo simulation.

(a) Split Samples : The original data might be split into two
(or more) random sub-samples. Separate factor analyses can
then be run for each sub-set, and a comparison made of the
solutions, for example, by correlating the factor scores as
Suggested by Burt (1948). We would recommend that the results
from each sub-sample be published separately, so that "eye-ball"
comparisons may be made, since the statistical tests for deter-
mining differences among factor structures are poorly developed
(Harmon, 1960). In the studies that were surveyed in Table 2,
the sample sizes were generally large enough relative to the
number of variables evaluated, that it would have been easy

for the authors to have split their samples.

(b) A Priori Analysis : Before collecting or looking at the data,
the researcher should work out, in as much detail as possible,
the structure of the solution that he expects to find. He
might, for example, postulate the number of factors he expects
to appear, which variables should load together, relationships
which should exist among factors, or what variables he expects
will dominate which factors. His predictions could be based on
behavioral models, previous findings reported in the literature,
or merely on "well educated" hunches. Few of the studies that
we examined made any attempt to develop an a priori model.

-7-

(c) Monte Carlo Simulation : In some cases, sample sizes are so small
that is is not practical to split the sample. In addition, one
may have very weak prior information about the underlying behav-
ioral processes. For such cases we propose that researchers should
try to simulate their results by factor analyzing suitable samples
of random data. By "suitable samples" we mean sets of random data
chosen to conform to the actual data in terms of sample size, num-
ber of variables, and assumed underlying distributions. The tyoe of fac
tor analysis should obviously also conform to that used on the
actual data.

The reliability analysis would parallel our "study" above,
except that it would be replicated many times in order to ob-
tain distributions on the various factor statistics. By com-
paring the results based on actual data with the results from
Monte Carlo simulations one could get an idea, based on sample
frequencies, whether the former appeared to be "significantly"
different from the latter. For example, comparisons could be made
in terms of the following statistics:

(1) Number of factors having eigenvalues greater than 1.0;

(3) Percent of variance explained by a given number of factors .

If the actual results yielded fewer factors, had consistently higher
data, the investigator would gain increased support for a claim that his
factor analysis provided a valid way of summarizing the data. On the other

hand, if actual results did not differ substantially from simulated results,
one would have reason to question the value of the reported factor analysis.
Several of the papers included in our survey reported results that, at face
value, seemed no more impressive than the outcome of our random number
experiment .

Measure of Usefulness

In addition to the measures of reliability mentioned above, it would be
desirable if a researcher provided some measure of the usefulness of his
factor analysis. Authors often claim that their study has been useful in
an "exploratory sense". But how is such a statement to be interpreted? If
the study merely provides the author with an "intuitive understanding", then
it is not always clear what the next step should be. If there are other
payoffs, say for prediction or control, then perhaps an author should
somehow attempt to demonstrate how useful his factor analysis seems to be.

In order to judge the usefulness of an exploratory study it may, for
example, be helpful to specify at least one "dependent" variable, the be-
havior of which the factor analysis was designed to help explain or pre-
dict. But in many of the reports that we read it appeared as if factor
analysis had been run when the investigator finally saw no other way of
massaging his data.

The Influence of the Analyst

Factor analysis, despite its apparent mathematical sophistication re-
quires much interpretive artistry by the analyst. A researcher must make
man decisions in order to reduce his factor analysis to a manageable size.

â– 9-

The rationale for making these decisions often remains unstated in published
reports. In some cases in our survey, there was not even enough information
for another researcher to be able to replicate the study.

Following are some of the decisions that face a researcher using factor
analysis - the first four of which will obviously be common to any multi-
variate analysis:

(a) How should the variables be measured?

(b) How many variables should be included in each analysis?

(c) How large should the sample size be?

(d) What types of base matrix should be operated on (e.g. Pearson
product-moment correlation, a variance-covariance matrix, or
a set of non-parametric correlations)'.

(e) What estimates should^be used for the communalities (e.g. 1.0,
highest r, multiple r , subjective estimates)?

(f) What method should be used for extracting factors?

(g) How many factors should be extracted'^

(h) Should the factors be othogonal or oblique?
(i) What types of rotation should be performed?

In view of the diversity and scope of these methodological decisions it
is not surprising that it can be difficult to evaluate the results of a fac-
tor analysis in which the bases for making these decisions remain unstated.
To illustrate this we ran further Monte Carlo simulations on samples of
normally distributed random deviates, considering "all other things equal"
in each factor analysis, even to the extent of using the same sets of input
data wherever possible. The following generalizations seem reasonable:

-10-

(a) As the number of variables increases, the number of significant
factors increases. For our data, of course, each variable is
theoretically independent of the others, so this result is not
surprising. Table 3 illustrates this effect.

(b) As the sample size increases, the percent of variance explained
(by a given number of factors) decreases. Table 4 provides an
idea as to the size of this effect.

(c) As the communality estimates become smaller, the number of
factors with eigenvalues greater than 1.0 decreases. Once
again, this is an obvious result since, as the values in the
major diagonal approach zero the rank of the matrix approaches
zero. Therefore, with sufficient patience and experimentation,
one could obtain almost any number of factors he might desire.
See Table 5.

-11-

Conclusions

Our concern has been how to evaluate the results of factor analysis.
We focussed on the questions of reliability and usefulness. Another use-
ful and complementary approach would have been to examine the assumptions
upon which the factor analytic model is based. This, however, has already
been done by Harmon (1960). Guilford (1952) also provides a brief but
informative summary of the factor analytic assumptions, with special refer-
ence to psychological testing.

With computers, factor analysis has become a relatively inexpensive
technique. As a consequence, the number of published studies employing
this method of analysis is rapidly increasing. Solomon (1960) reports that
over 1000 papers on factor analysis were published between 1900 and 1960,
and the rate of publication has increased steadily. We found that many of
ological information to enable their results to be evaluated by other
researchers. To rely on the face validity of factor loadings is clearly
not enough. We presented a simple illustration of how, on the basis of com-
pletely random data, one could produce factor analytic results that would
seem both "reasonable" and "interesting".

The conclusion is that all factor analytic studies should provide a
benchmark or measure of their reliability. Available statistical tests
for testing the significance of a set of factors are, unfortunately, neither
adequate nor well known. Those that exist are^ in any case not often used.
We have indicated three approaches that might be used for checking on the
reliability of a factor analytic result: split samples, a priori models

â€¢12-

and Monte Carlo simulation. While arguments may be raised against the use
of split samples or the use of a priori models in certain situations, there
seem to be few cases in which a Monte Carlo approach would not be feasible.

-13-

Table 1

Nine Factors from Principal Components Solution

II

Sensitive - .72

Democratic -.72

Responsible .69

Variable

Sincere

-.85

III

Formal .84

Fair -.65

Humble -.54

IV

Trustworthy .80
Humble .55

VI

VII
Humorous -.88
Aggressive -.54

Patient

.69

Shallow

.59

Aggressive

-.53

VIII

Tactful

.81

Kind

.82

IX

Warm

.72

Active

.71

Enthusiastic

.64

Ambitious

.55

-14-

SUMMARY OF LITERATURE SURVEY

Journal

Years

Educational and

Psychological

Measurement

Journal of
Applied Psychology

Journal of
Consulting
Psychology

Journal of
Marketing Research

Journal of
Personality

Number of
Studies

1964,1965,1966
1963,1965

1964,1965,1966
1964,1965,1966

1964,1966
Personnel Psychology 1962,1963,1065

11

15

1

Number of

Studies

Without a Without

Reliability Measure

Measure of

Number of Number of
Studies Studies

11

1

3

With
Neither
Reliability
Usefulness Nor Useful-
ness Measures

10

1
6

1
5

Totals

46

30

27

21

â– 15-

Table 3
Effects of Variation in Number of Variables
For Principal Components - n = 50

Nun'cer of Variables Number of Factors with Eigenvalues > 1.0

20 8

40 16

Table 4
Effects of Variation in Sample Size
For Principal Components - 20 variables 8 factors

n Percent of Variance Explained
30 72.8

50 65.1

100 58.0

Table 5

Effects of Variation in Communality Estimates
For Principal Components - 20 variables; n = 50

Communality Estimates Number of Factors with Eigenvalues > 1.0
1.0 8

multiple r 4

highest r in row 3

â– 16-

FOOTNOTES

1. Factor analytic reports excluded from the survey were those studies
that explicitly set out to test, or refine, the reliability of some previ-
ously reported, factor-derived scaling of items (like the MMPI scale), or
to replicate, or test, the predictive validity of some previously reported
set of factors.

2. The articles surveyed are not listed in the bibliography. However,
anyone wishing to know more about which particular articles were chosen,
or how they were coded, is invited to write to either author.

-17-

BIBLIOGRAPHY

Adorno , T.W., Else Frenkel -Brunswick, D.J. Levinson and R.N. Sanf ord ,
The Authoritarian Personality , New York: Harper, 1950.

Bales, R.F., Interaction Process Analysis; A Method for the Study of
Small Groups , Cambridge, Massachusetts: Addison-Wesley , 1950.

Burt, C, "The Factorial Study of Temperamental Traits", British Journal
of Psychology , Statistical Section, 1 (1948), 178-203.

Dixon, W.J. (ed.), Biomedical Computer Programs , Revised Sept. 1, 1965,
U. of California at Los Angeles.

Fiedler, F.E., "The Leader's Psychological Distance and Group Effective-
ness," in D. Cartwright and A. Zander (eds.), Group Dynamics ,
Chicago: Row Peterson, 1960.

Guilford, J. P., "When Not to Factor Analyze", Psychological Bulletin ,
48 (1952), 26-37.

Hertzberg, F., B. Mausner and B.B. Snyderman, The Motivation to Work ,
New York: John Wiley, 1959.

Harmon, H. , Modern Factor Analysis , U. of Chicago Press, 1960.

Kaiser, H.F., "The Application of Electronic Computers to Factor Analysis",
Educational and Psychological Measurement , 20 (1960), 141-151.

Milgram, S., "Behavioral Study of Obedience", in W.G. Bennis et .al . (eds.),
Interpersonal Dynamics , Homewood , Illinois: Dorsey (1964) 108-121.

Rand Corporation, A Million Random Digits with 100,000 Normal Deviates ,
Free Press of Glencoe , 1955.

Solomon, H. , "A Survey of Mathematical Models in Factor Analysis", in

H. Solomon (ed.), Mathematical Thinking in the Measurement of Behavior ,
Free Press of Glencoe, 1960.

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