AIAA 13th Aerospace Sciences Meeting
Pasadena, Calif. / January 20-22, 1975
AIAA paper 75 - 43
Extrinsic Factors in UFO-Reporting
by
D.R. Saunders
The University of Chicago
Chicago, Illinois
Abstract
Using the method of stepwise multiple correlation analysis,
five factors are identified having empirically-demonstrable
effects on the production of UFO-reports; other factors have
been simultaneously set aside as irrelevant. In order to
maximize the number of reports, it is helpful (a) to assemble a
large number of potential witnesses, (b) to educate them at
least through high school, (c) to station them where they can
see (d) to give them a place to report, and (e) to provide one
or more examples of such reports. Data on these factors alone
suffice to provide a multiple correlation of 0.82 with actual
numbers of UFO-reports produced in US counties, and they come at
least very close to accounting for the statistical reliability
of this criterion. Several hypotheses predicated on alternative
models of the UFO-reporting process are affirmatively rejected
by the data reported here.
Introduction
Irrespective of the eventual disposition of UFOs themselves,
it is an unmistakable fact that UFO-reports do exist. By August
15, 1947, as one result of a major wave of UFO-reports peaking
in July of that year, Gallup (4) was able to report that 90
percent of the American public had heard of "flying saucers."
And by the spring of 1966, extrapolating from new survey data,
Gallup (5) estimated that more than 5 million Americans would
claim to have seen a ''flying saucer.'' Independent data
gathered by Lee (6) in 1968 also suggest the existence of
several million potential UFO-reports. Quite obviously,
flying-saucer-seeing has been a widespread phenomenon.
However the bulk of these potential reports are inaccessible.
Lee's data indicate that only about one witness in ten even
tried to render a UFO-report in the first place, either to the
local press, the local constabulary, or the local military.
These recipients, in turn, have apparently rejected 80-90
percent of their UFO input, and have created a record that
accounts for only about 1 percent of the estimated original
population of potential reports. It would seem foolish to
assume that the remnant 1 percent is a random sample from the
larger population, but it is still a large body of material that
may be analyzed on its own merits.
A variety of speculative hypotheses have been advanced for
UFO-reporting behavior, and some of these have been given wide
circulation without having been in any way tested against data.
The primary purposes here are (1) to demonstrate one way in
which such tests may be carried out and (2) to present the
resulting substantive findings.
Method and Procedures
The basic method used in this study is stepwise multiple
correlation. The criterion variable was the frequency of
recorded UFO-reporting for each of a series of well-defined
demographic units. This total frequency was also broken down
according to a rudimentary classification of types of reports.
The predictor variables were various other measurable attributes
of the demographie units, such as population, area, etc. The
demographic units themselves were the counties of the United
States (*), which were regarded as a sample from a statistical
population of possible counties.
(* The exact number of recognized counties varies slightly
from census to census. The analyses to follow were based on
3053 counties which were consistently defined in 1950 and 1960
and for which complete data could be assembled. Approximately
fifty counties, including all of Alaska, were excluded by these criteria.)
The main analysis whose results will be discussed in detail
was approached in several stages. In the first stage, only three
predictors were considered, and these were examined only in
relation to a criterion measure based on a relatively limitad
collection of UFO-reports. Since these results were
encouraging, additional predictors were developed successively,
until there were fourteen predictors altogether. At the same
time, the collection of UFO-reports was steadily enlarged,
leading to more reliable determinatian of the reporting activity
of each county and, in the final analysis, to a fourfold
breakdown of that activity.
It was anticipated, and verified in the first stage analysis,
that the best results would be obtained by transforming the raw
variables of the study prior to their intercorrelation. The
standard multiple regression model postulates additive
contributions from each of the independent predictor variables.
However, if this additive model is applied to the logarithms of
the raw variables, the effect is to fit a model in which each
raw predictor actually makes a multiplicative contribution.
Such a multiplicative model seemed conceptually appropriate for
the data to be analyzed, and its use led to substantial
enhancements in the zero-order correlations. For further
discussion of this issue, see Bartlett (1) or any textbook on econometrics.
The fourteen predictor variables became available to this
study in the following chronological order:
- X1 - Population of the county according to the 1950 census
tabulation. This was taken as the logarithm for purposes of correlation.
- X2 - Population of the county according to the 1960 census
tabulation. This was taken as the logarithm for purposes of
correlation. Both the 1950 and 1960 census data were included
so that the difference (ratio) between them would become
implicitly available to the multiple correlation analysis; the
difference in the logarithms may be interpreted as the logarithm
of the population growth rate of the county.
- X3 - Area of the county in square miles. This was taken as
the logarithm for purposes of correlation.
As the result of the first-stage analysis, all three of these
predictors were found to make statistically significant,
independent contributions to the prediction of UFO-reporting
activity. Warren's (15) study of 'status inconsistency' and
UFO-reporting was published at about this time, and motivated
the selection of the next group of three predictors.
- X4 - Proportion of non-white population according to the 1960
census tabulation (2). For purposes of correlation, this was
transformed to the logarithm of (1-p)/p; note that this not only
alleviates the boundary effects at both ends of the scale of
proportion but also effectively reverses the direction of this
variable so that high numbers are associated with a high
proportion of 'white' population.
- X5 - Proportion of family incomes exceeding $10.000/yr
according to the 1960 census tabulation (2). For purposes of
correlation, this was converted to the logarithm of p/(1-p).
- X6 - Proportion of the adult population (over age 25) with at
least a high school education, according to the 1960 census
tabulation (2). For purposes of correlation, this was
transformed to the logarithm of p/(1-p).
Considered separately, each of these six predictors is
positively and significantly correlated with the criterion of
UFO-reporting. However, when they were first tried in
combination, X1, X4, and X5 were all found to be completely
redundant with other variables. X2 and X6 emerged as the major
predictors, both with positive weights. X3 emerged as a
marginal predictor, still statistically significant but with
less weight than it had after the first stage analysis. The
next two predictors were now included with the hope of forcing
X3 to nonsignificance, as had already been accomplished for X1.
X7 - Longitude of the county, measured in degrees west of
Greenwich. Specifically, this was taken as the coordinate of
the county seat as given by the Times of London World Atlas
(10). This was correlated without any further transformation.
X8 - Latitude of the county, measured in degrees north of the
equator. The comments under X7 apply.
Both of these new measures yielded positive zero-order
correlations with the criterion. In multiple correlation, X8
was completely redundant, while X7 behaved as a 'parallel form'
of X3 - while overlapping appreciably, neither X3 nor X7 was
able to supersede the other. Two more simple-to-define
geographical measures were added next.
X9 - Proximity to salt water. This variable was scored 1 if
the county is adjacent to salt water (an ocean or the Great Salt
Lake), and otherwise 0.
X10 - Proximity to fresh water. This variable was scored 1
if the county is adjacent to (or contains) a large natural body
of fresh water, and otherwise 0.
Both of these new measures yielded positive zero-order
correlations with the criterion, and each provided a small but
statistically significant increment to the multiple correlation.
These results supported the idea that an important geographical
variable exists, but also indicated that this variable had not
yet been found. The remaining four measures were added to the
study all at one time, but for varying reasons.
X11 - Proportion of population over age 65, according to the
1960 census tabulation (3). For purposes of correlation, this
was transformed to the logarithm of (1-p)/p, effectively
reversing the variable. Thus, positive correlations may show an
effect of (comparative) youth. No measure of age had been
included among the previous predictors.
X12 - Proportion of population with at least five years of
education, according to the 1960 census tabulation (3). This
was transformed to the logarithm of p/(1-p). In view of the
consistent importance of education through the previous stages
of analysis, it seemed desirable to explore at least one more
variant of the education measure. Since variables X6 and X12 are
correlated only 0.816, they evidently do reflect different
aspects of the educational process.
X13 - Number of distinct newspapers currently published in
the county, as counted in a compilation prepared for this
purpose from several sources. For purposes of correlation, this
was taken as the logarithm of (N+1).
X14 - Number of newspaper editions currently published in the
county per week, as counted in the same compilation used for
X13. For purposes of correlation, this was also taken as the
logarithm of (N+1). Since variables X13 and X14 are correlated
only 0.856, they apparently do measure different aspects of the
availability of newspaper coverage.
Once again, all the new measured yielded positive zero-order
correlations with the criterion of total UFO-reporting. The
complete matrix of zero-order correlations for all fourteen
predictors is included in Table 1. This table also gives the
correlations between these predictors and five variants of the
criterion variable; these must now be defined.
Y0 - Total UFO-reporting. All the variants of the criterion
variable are based on the UFOCAT catalog of UFO-reports, and
were tallied from a version of this catalog containing 59237
total entries (10). This total count included numerous
duplicate reports of the same events, as well as many reports of
events occurring outside the usable counties of the United
States. Thus, only 18122 entries were used to generate the
criterion measures now being described. When these 18122 UFO-
reports were distributed among the 3053 usable counties, the
number of reports per county ranged from 0 (numerous instances)
to 598 (Montgomery County, Ohio - the home of USAF Project Blue
Book). For purposes of correlation, these counts were
transformed to the logarithm of (N+1).
Each of the 18122 usable UFOCAT entries is also classifiable
as to the 'Type of Event,' on a scale which roughly
characterizes the 'strangeness' of the report. These types may
be indicated as follows:
Type 0 - Not reported as a UFO.
Type 1 - Reports of non-moving objects.
Type 2 - Reports of continuously moving objects.
Type 3 - Reports involving motion with one discontinuity.
Type 4 - Reports involving motion with multiple discontinuities.
Type 5 - Reports of ob@ects entering the witness' frame-of-reference
Type 6 - Landing reports.
Type 7 - Occupant reports.
Type 8 - Contact reports.
Type 9 - Reports involving post-encounter effects.
More complete definitions of these types of report are given in
the UFOCAT Codebook (10). For purposes of this study, Type 0
reports were completely excluded and the remaining types were
grouped into four broader categories, as follows:
Y1 - Simple reports, based on the distribution of 7411 events coded as Type 1 or Type 2.
Y2 - Strange reports, based on the distribution of 9142
events coded as Type 3 or Type 4 or uncoded as to type.
Y3 - Encounter reports, based on the distribution of 1254
events coded as Typ 5 or Type 6.
Y4 - Interaction reports, based on the distribution of 315
events coded as Typ 7 or Type 8 or Type 9.
For purposes of correlation, each of these sub-criterion
measures was transformed to the logarithm of (N+1).
Before taking any logarithms, it is true for each county that
Y0 = Y1 + Y2 + Y3 + Y4. However, in scanning the UFOCAT file
for the purpose of generating these criterion data, a
restriction was enforced so that no more than one event per
county per day could be counted toward Y0. If more than one
otherwise countable entry was found in the file, only the one
with the highest code for Type of Report was used. This
procedure introduces a small negative bias affecting the
correlations between the Y's. This procedure avoids a larger
problem associated with differences in reporting practices of
different sources underlying UFOCAT, since some of these sources
do and some do not tend to detail a cluster of reports from one
county on one date.
Results
The main results of this study are contained in Table 2, which
lists the predictor variables in the order of their selection
for each of the five possible multiple correlation runs. Each
list stops with the last predictor having a remarkability (*)
exceeding 5 bits. The listed values for 'partial r' are
obtained by holding all previously chosen predictors constant,
but the 'multiple r' includes the contribution of the predictor
listed on the same line.
(* Remarkability is net information favoring rejection of the
null hypothesis. The calculation of remarkability allows for
the explicit selection effects involved in basing the stepwise
regression on the best available predictor at each step, as well
as for implicit selection of the sign of each partial
correlation and for sample size (11). On each line of Table 2,
the odds are 2ER to 1 against obtaining such a large partial
correlation from chance alone.)
Discussion
1. Population, vintage 1960, is the only predictor variable
to be chosen in all five of the stepwise regression solutions,
and in every case it is the first predictor to be chosen.
Almost every UFO-report involves at least one witness. It comes
as no surprise, therefore, that the counties containing more
potential witnesses have produced more reports. The magnitude of
this correlation is undoubtedly enhanced by the wide range of
populations represented in our sample of counties, the ratio of
the largest to the smallest being well over 10,000 to 1. The
1960 population data work better than the 1950 population data
because 1960 is more nearly the midpoint of the period during
which the bulk of the UFO-reports were produced - 1947 through
1972. When 1960 population is held constant, the partial
correlations between 1950 population and the criteria are
consistently negative; this may be interpreted as a positive
relationship between growth-rate and UFO-reporting, but one that
disappears as soon as educational effects are accounted for.
2. Education, measured in terms of the proportion of adults
who have completed high school, is the second most powerful
predictor found in this study; it is chosen immediately after
population in four of the five analyses, but it is not chosen at
all to help predict Y4. Whenever it is chosen its weight is
positive, that is, more reports per capita are produced by the
better-educated counties. The differential role of education in
predicting the subcriteria is quite interesting. Education
appears to be maximally relevant in connection with Y2, which is
the subcriterion for the strangest remote reports. On the
other hand, education is simply immaterial in relation to Y4,
which is the subcriterion for the strangest nearby reports.
All these effects could be anticipated, however, starting from
the simple-minded assumption that UFOs have physical reality.
Education, as measured by completion of the 5th grade, is
unable to contribute anything to these analyses that is not
already better contributed by the 12th grade education measure.
Evidently, if there is any threshold effect associated with the
contribution of education to UFO-reporting, the threshold is not
below the 12-year point. The census volumes do not contain any
data with which to explore other possible thresholds.
3. Area is the third most powerful predictor found in this
study, appearing in four of the five analyses: subcriterion Y4
is again the exception. The contribution of area is always
positive, that is, at any particular level of education, more
reports per capita are produced by bigger counties. If it is
assumed that UFOs have physical reality, this could simply mean
that there are more of them waiting to be seen in bigger
counties. A second possible explanation for this predictor is
that area is merely acting as an indirect measure of seeing
conditions; large counties in the US do tend to be associated
with desert areas, where visibility is relatively good. Poher
(8) has displayed a correlation between seeing conditions in
France, measured as the number of hours of sunshine per year,
and numbers of UFO-reports per department. Both longitude, which
was a meaningful predictor in some of our preliminary analyses,
and proximity to salt water, which now appears in all the same
analyses as area, may also be interpreted as indirect measures
of seeing conditions.
4. If we regard X13 and X14 both as measures of the
availability of newspaper coverage for a UFO-report, we may
observe that one or the other of them does appear in four of the
five analyses. Again, the weights are always positive, and the
general interpretation is easy. The vast majority of
UFO-reports that have been collected by UFOCAT were originally
published in newspapers, even though UFOCAT's immediate source
for a report is typically some secondary or tertiary
publication. Thus, we might easily argue that the contributions
of X13 and X14 are simply artifacts of the process used to
collect the criterion data. This would be true, but the
argument really applies equally to all the measures used in this
study; this is why we refer to them collectively as 'extrinsic
factors in UFO-reporting.'
The differential validity of X13 and X14 is interesting, and
provides a further indication that the breakdown of the
criterion is meaningful. The pattern of relations suggests that
reports classified here as Types 1-4 are being treated by the
press as 'filler', which may or may not be published depending
on the availability of space. The more editions that are being
published in a county, the more likely such space is to be
available. On the other hand, reports classifled here as Types
5-9 are 'news' in the full journalistic sense; space can always
be found for these reports, but they will be first be subjected
to a much more careful editorial review, so that their
likelihood of actual publication is primarily a function of the
number of editors who have an independent opportunity to say
Yes. The latter effect is particularly pertinent for Types 7-9,
which underlie subcriterion Y4.
5. Only two other predictors appear anywhere in Table 2.
These are income and race and they appear only with marginal
significance and only in connection with Y0, Y1, and Y2 - the
subcriteria based on the largest amounts of data. It seems most
likely that these measures are acting as indirect substitutes
for one or more other variables not included in the study.
Perhaps the most important thing to note is that any direct
contributlon that these variables may make must still be
positive, that is, it is the 'good' end of each of these
variables that contributes to increased UFO-reporting. Nowhere
in this study do we find even an indirect derogatory implication
concerning the UFO-reporter. Nowhere in this study do we find
any suppressor variable with sufficient remarkability to require our notice.
6. The largest multiple correlation reported in Table 2 is
0.806 for the prediction of Y0. The largest multiple
correlation obtainable from Table 1 is 0.819, which may be
obtained for the prediction of Y2 when Y1 is included as the
first predictor. The latter compares with only 0.774 which is
available for the prediction of Y2 from the extrinsic predictors
alone. Similarly, Y3 and Y4 are better predicted by Y2 alone
than they are by any combination of the extrinsic predictors -
for Y3, 0.589 (Table 1) is greater than 0.548 (Table 2), and for
Y4, 0.360 (Table 1) is greater than 0.330 (Table 2). Each of
these differences is an indication that there is more
reliability in the criterion than we have accounted for with the
predictors. The magnitudes of these differences suggest that
one or even two important sources of variance remain to be
discovered; alternatively, there may be a much larger number of
individually less important missing predictors.
7. On the basis of an examination of the French landing wave
of 1954, Vallee (14) has proposed that what he calls Type I
reports are most common in regions of low population density,
and that this is a sign of intelligence on the part of the UFOs.
Type I on Vallee's scale is almost identical with Types 5-9 on
our scale of strangeness; actually, the French cases considered
by Vallee as supporting his hypothesis are mainly Types 6 and 7.
If population density is population per unit area, then Vallee's
hypothesis predicts a negative weight for population and/or a
positive weight for area under the conditions of the present
study. The population aspect of this prediction is strongly
rejected by our results, but the area aspect cannot be
unambiguously rejected. However, if area in the US is really
acting as an indirect measure of seeing conditions, then it will
disappear from the stepwise regression analysis as soon as an
adequate measure of seeing conditions is provided; in such an
event the area aspect of Vallee's hypothesis will also be rejected.
In view of the relatively small variance associated with
either the populations or the areas of the French departments
(leaving Paris and its suburbs out of consideration), as
compared with the populations and areas of the US counties, it
does not seem likely in any case that Vallee's hypothesis can
hope to provide a sufficient explanation for the observed
distribution of French reports in 1954. Even if area is
retained as a predictor in its own right, its weight is not big
enough to do the job. Noting that X13 rivals X2 in its
zero-order validity for Y4, and that it makes a demonstrable
independent contribution to the prediction of both Y3 and Y4, it
is reasonable to suspect that X11 may be the crucial agent
leading to the data interpreted by Vallee as an effect of
population density. This explanation would simply require that
the number of newspaper editors per capita or per square
kilometer be substantially higher in rural France than in
metropolitan France.
It may be observed, of course, that the inability to support
Vallee's hypothesis of low population density is a function of
the use of a relatively coarse geographic grid (whole
counties). Since we are aware of no report that a UFO has landed
on the same precise spot where a person was standing, it could
be argued in the limit that all landings are in places with zero
population density. The interesting problem, then, is to
determine on how large a scale Vallee's principle can still be supported.
8. On the basis of a reinterpretation of Gallup Poll data,
Warren (15) has proposed that 'status inconsistency,' a concept
that he attributes to Lenski (7), is directly operative in the
production of UFO-reports. Perhaps the strangest thing about
Warren's paper on this subject is that it is totally devoid of
any attempt to assess statistical significance, even though it
is identified with a laboratory and published in a journal which
are ordinarily quite statistically sophisticated. When this
deficiency is corrected, it becomes apparent that Warren's data
provide no more support for his thesis than could reasonably be
expected from tables of random numbers. Certainly, there is
nothing in Warren's published data which necessarily motivates
such a complicated theory of UFO-reporting as the theory of
status inconsistency. The non-scientific ramifications of this
situation need not be explored here.
Nevertheless, it is at least an interesting methodological
exercise to look for effects in the present data that might be
attributed to status inconsistency. Reduced to its essentials,
Warren's theory argues that interaction effects corresponding to
combinations of the present variables X4, X5, and X6, are
primarily responsible for UFO-reporting. We have already seen
that X6 (education) does play a major predictive role in its own
right, whereas X4 (race) and X5 (income) make little or no
independent contribution. We may look for the interaction
effects simply by trying to use the product-terms X4X5, X4X6,
X5X6 and X4X5X6 as additional predictors in the multiple
correlation; status inconsistency will be supported if a
significant negative weight appears for any of these
product-term predictors. In effect, status inconsistency
proposes that X4, X5, and X6 act as moderator variables (9) for
one another, with particular sign relationships.
When the indicated product-terms were computed and tried as
predictors, no significant multiple correlation improvement
associated with a negative weight was produced for any of the
five criterion variants. However, the product-term X5X6 did
yield a positive weight in all five analyses and was supported
by as much as 17.0 bits of remarkability (in the prediction of
Y1); thus, status consistency with respect to income and
education may be a meaningful positive predictor.
Again, it may be argued that the theory of status
inconsistency is supposed to apply to individuals and not
necessarily to averages for whole counties. For this reason,
this test of the theory was not a very crucial one.
Nevertheless, it remains true that there is no empirical support
for this theory at either the county or the individual level.
9. Also basing his reasoning on the French UFO wave of 1954,
Toulet (13) has proposed an epidemiological model to account for
the numbers of reports yielded by the various departments. The
important feature of this model is that reporting is assumed to
be facilitated by the existence of other reports; the exact
mechanism of facilitation proposed by Toulet is a secondary
feature. While his analysis does employ certain simplifying
assumptions, Toulet does adduce supporting data which are
incompatible with the simpler hypothesis that reports are
independently produced.
It is possible to analyze the data of this study in a way
which displays a similar autocatalytic effect. This was
accomplished by supplying the power-terms, X2E2 [= X2X2] and
X2E3 [= X2X2X2], as possible predictors along with the other
fourteen extrinsic measures. Given the choice between X2, X2E2,
and X2E3, the stepwise algorithm invariably chooses X2E3 as the
best single predictor of UFO-reporting; then X2 and X2E2 either
do not appear, or appear later with negative (suppressor)
weights. X2E3 is enough better than X2 to account for about
one-half of the previously unpredictable reliability of the
criterion, which was discussed in paragraph 6. Also, when X2E3
is used in place of X2, even the marginal utility X4 and X5,
which was discussed in paragraph 5, disappears. The net effect
is an appreciably higher coefficient of multiple correlation
based on a smaller number of extrinsic predictors. However, the
multiple correlations that were available by including the other
subcriteria as predictors are not enhanced by X2E3; the ceiling
is still 0.82.
The effects just described are strongly remarkable, and
provide compelling evidence for a curvilinear dependence of
UFO-reporting on population; the number of reports is a
positively accelerated function of the population, which is
consistent with the facilitation of some reports by other
reports. There is still more than one way to conceptualize this
pattern. Toulet, who borrowed his mathematical model from the
field of public health, writes as if he were discussing the
contagious process of a mental illness. Perhaps so.
Alternatively, perhaps we are merely observing the reluctance of
a UFO-witness to report his experience until another witness has
'broken the ice.' Or perhaps we are observing an effect of non-
random sampling in which the same witness is more likely to
make a report if he has already made a previous report. These
possibilities cannot be distinguished by the present data.
10. One obviously important factor that this study has
neither controlled nor assessed is the effect of local UFO-
investigative groups. The most extreme example of this effect
is provided by Montgomery County, Ohio, where the
well-publicized local presence of Project Blue Book has elicited
several times the number of UFO-reports that would otherwise be
predicted for such a county. The effect of any such local
enterprise will be to enhance the correlations between the
subcriteria without enhancing their extrinsic predictability. It
seems entirely possible that this effect is of sufficient
magnitude to account for the remaining unpredictable reliability
of the criteria used in this study.
References
- 1. Bartlett, M.S.: The use of transformations. in: Biometrics 3, 39-52, 1947.
- 2. Bureau of the Census, US Department of Commerce: County and city data book, 1962. Washington: USGPO, 1962.
- 3. Bureau of the Census, US Department of Commerce: County and city data book, 1967. Washington: USGPO, 1967.
- 4. Gallup, George: Nine out of ten people heard of flying saucers. in: Public Opinion News Service. Princeton: August 15, 1947.
- 5. Gallup, George: More than 5 million Americans claim to have seen 'Flying Saucers.' in: Gallup Poll. Princeton: May 8, 1966.
- 6. Lee, Aldora: Public attitudes toward UFO phenomena. Chapter 7 in 'Scientific Study of Unidentified Flying Objects,' D.S. Gillmor, Ed. New York: Bantam, 1969.
- 7. Lenski, G.E.: Status crystallization: A non-vertical dimension of social status. in: American Sociological Review 19, 405-413, 1954
- 8. Poher, Claude: Etudes statistiques portant sur 1000 temoignages d'observation d'U.F.O. Toulouse: Author, 1972.
- 9. Saunders, D.R.: Moderator variables in prediction. in: Educational and Psychological Measurement 16, 209-222, 1956.
- 10. Saunders, D.R.: The UFOCAT Codebook. Boulder: Author, 1973.
- 11. Saunders, D.R.: The Meaning of Data. New York: Academic Press, 1975. In press
- 12. The Times (of London). Atlas of the World. Boston: Houghton-Mifflin, 1967.
- 13. Toulet, Francois: L'orthotenie n'est-elle qu'une hypothese ? in: Phenomenes Spatiaux 26, 3-11, 1970.
- 14. Vallee, Jacques: The pattern behind the UFO landings. in: Flying Saucer Review Special Issue 1, 8-27, 1966.
- 15. Warren, D.I.: Status inconsistency theory and flying saucer sightings. in: Science 170, 599-603, 1970
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