The influence of language
on ambiguity aversion
Vincent van Noort
349138
1
Contents
Introduction
3
Sapir-Whorf Hypothesis
4
Questionnaire
6
Results
8
Language
9
Vienna versus The Hague
13
Gender
13
CRT Scores
14
Age
15
Degree
16
Other Regressions
17
Conclusions
19
Discussion
20
Bibliography
22
Appendix A: Questionnaire
23
Appendix B: Results
28
2
Introduction
In a very well known experiment Daniel Ellsberg showed in 1961 that people dislike
ambiguous situations. He proved that in some situations people have the tendency to
behave irrational. Using data from responses under non-experimental conditions he showed
that people violate the Savage axioms when they are dealing with uncertainties. These
responses came on questions in which there are two urns; the first urn had fifty red balls and
fifty black balls. The second urn also contained hundred balls but with unknown distribution
between the two colours. Ellsberg showed that most respondents prefer the urn with known
distribution, no matter on which colour the bet. This suggests that they think the second urn
contains less red balls and less black balls than the first urn. Since the second urn also
contains hundred balls this is impossible. Ellsberg explained this by saying that people prefer
the risk of the first urn over the uncertainty of the second urn. He called this ambiguity
aversion (Ellsberg, 1961). Ambiguity is now often defined as uncertainty in the relevant
information or completely unknown relevant information. This information is often
regarding the probabilities or payoffs. Ambiguity can however also refer to information that
is misunderstood. As previous research has proven a lot of factors influence ambiguity such
as the context or the way information is obtained. Three different kinds of ambiguity can be
distinguished: uncertain probability, uncertain pay-off and situations were both are
uncertain. This research will only focus on situations where the probability is unknown.
Since Ellsberg published his findings there has been a lot of research regarding ambiguity
attitudes. Part of this research is that people are so averse to ambiguity that they would be
willing to pay to avoid ambiguous situations if a real payoff was involved (Becker &
Brownson, 1964). Besides the consequences of ambiguity aversion it has also been
investigated what factors influence ambiguity aversion. According to Fox and Tversky (1995)
ambiguity is also influenced by the context. Ambiguity aversion is strong in case of a
comparative context but disappears in the absence of a comparison (Fox & Tversky, 1995).
Others found that the way people get (ambiguous) information also influences decisions.
People who see a representative sequence seem to be less averse to the ambiguity than
when a verbal description is given (Bleaney & Humphrey, 2006).
3
Because there already has been a lot research regarding ambiguity aversion this is not just a
research to show how people react to ambiguity. It will be tested whether people have a
different attitude to ambiguity in different languages. One of the reasons that it is possible
language could change the attitude comes from Sapir-Whorf hypothesis. This hypothesis
named after linguist Edward Sapir and his student Benjamin Lee Whorf states that the
language that someone speaks influences our decision making. Another reason to believe
that ambiguity attitude is affected by language is the fact that it has already been proven
that language can influence decisions. It has for instance been tested that in a foreign
language the framing effect disappears and that people tend to be less averse to losses in a
foreign language. Usually the framing effect makes it possible that the same people give
different answers to the same problem but framed differently. Because most people are risk
seeking for losses and risk averse for gains they answer differently when the problem is
framed as losses compared to the same problem framed as gains. Most famous framing
problem is the Asian disease problem. This problem is also used in a bilingual study. This
experiment showed that in the native language framing does work, leading to different
answers for the same problem. In a foreign language however this framing effect disappears.
The same study also shows that bets with positive expected value are more likely accepted
and people are less averse to losses in a foreign language than in the native language. The
writers argue that this is because the native language causes more emotional reactions
leading to biased decisions (Keysar, Hayakawa, & An, 2012).
Two questionnaires will be used to test whether this is the case with ambiguity attitudes.
Those two questionnaires will be exactly the same except one will be in Dutch and one in
English. In this questionnaire the respondents will be asked four questions regarding their
attitude to ambiguity. These questions illustrate situations in which the probability of
winning is unknown compared to situations where this probability is known. With the results
from these questionnaires it will be tested whether there is a significant different between
the answer from the Dutch questionnaire and the answers from the English questionnaire.
Sapir-Whorf Hypothesis
As said the ambiguity attitude can be influenced by for instance the context and the way of
obtaining information. Another factor that could perhaps influence ambiguity attitude is the
4
language in which information is given. As said earlier ambiguity is also information that is
misunderstood. Information given in a second language could be more easily misunderstood
making people more averse to ambiguity. There is however a more important reason to
believe why the language could influence ambiguity attitudes. Some linguistics believe that
the language spoken influences our view of the world and more important our decision
making. The hypothesis these linguistics believe in is called the Sapir-Whorf hypothesis (Kay
& Kempton, 2009). This hypothesis consists of two parts:
1. Structural differences between language systems will, in general, be paralleled by
non-linguistic cognitive differences, of an unspecified sort, in the native speakers of
the two languages.
2. The structure of anyone’s native language strongly influences or fully determines the
world-view he will acquire as he learns the language (Kay & Kempton, 2009).
For this research the focus will be on the first part. This part could mean for this research
that different languages will be accompanied by different decision making. Meaning that the
results from the Dutch questionnaire could be very different from the English questionnaire.
The hypothesis does not state how each language influences the view of the world or
decision making. Therefore the results can go both ways, the Dutch results can be more
averse to ambiguity but it could also be that the English results show more ambiguity
aversion. This is dependent on the actual influences both languages have. This first part of
the Sapir-Whorf hypothesis is also called linguistic determinism. It can also be further
divided in strong and weak determinism. Strong determinism is the believe that what is said
is responsible for what is seen by the mind (Badhesha, 2002). In an Australian experiment
with deaf children it is shown that strong determinism can hold. After a doll is put in a box
with a marble the doll is removed first and after that the marble is removed. When asked
where the doll will look for the marble children with parents fluent in sign language
answered correct. The children growing up in a family with non deaf parents who are not
fluent in sign language answered incorrectly (Peterson & Siegal, 2006). Although in this
situation strong determinism holds most linguistics reject the view of strong determinism.
The version with weak determinism however is a lot more accepted. Weak determinism still
means that the language someone speaks influences our view of the world or our decisions.
But were strong determinism says that language defines this strictly; weak determinism
means that there are still other factors that influence our view or decisions (Badhesha,
5
2002). Therefore weak determinism is a lot more probable than strong determinism. But
because weak determinism states that language certainly has an influence it could still lead
to differences in our decisions. These differences can perhaps also occur between a native
language in which people are fluent (Dutch) or a second language which is probably not
spoken completely fluent (English). So it is very well possible that people will show different
attitudes to ambiguity in different languages.
Questionnaire
To test whether language really affects ambiguity attitudes a questionnaire is used. Or rather
two questionnaires: one in English and one in Dutch (Appendix A). The questions in both
questionnaires are exactly the same and phrased in the same way to avoid other factors
such as framing. At first friends, family and some colleagues were asked to fill in one of the
questionnaires. Later also other students were asked to fill in one. This gives a group of
respondents that differ a lot from each other. Although a lot of respondents are in the early
twenties there are also respondents with an age in the thirties, forties or even higher. It also
gives a lot of variety in the field of study or profession despite most of the fellow students
study economics. Thanks to the other respondents there will also be enough differences
here. It was decided that all the respondents should speak Dutch as their first language.
Therefore all the respondents speak Dutch as their first language the Dutch questionnaire
gives results for first language and the English gives the results for the second language.
There are a couple of reasons why only Dutch respondents were used. First reason is that
this way it is sure that there are roughly the same amounts of results for first language as
there are for second language. Otherwise it could have happened that only people speaking
English as their native language filled in the English questionnaire. Since fluency in the
language can have an effect this could influence results. The second reason is that the switch
from Dutch to English could be different than for instance from Italian to English. This could
influence the results. Another important decision was to let every respondent fill in only one
of the questionnaires instead of both. The main reason for this was to avoid the will to be
consistent. Because the questions are exactly the same in both questionnaires respondents
would probably want to be consistent and use the same answers without thinking about it.
6
Table 1: Question 1 (Red ball)
Number of Balls
in Urn K
K
€100:
€0:
Red
Black
0
100
Urn K
10
90
Urn K
20
80
Urn K
30
70
Urn K
40
60
Urn K
50
50
Urn K
60
40
Urn K
70
30
Urn K
80
20
Urn K
90
10
Urn K
100
0
Urn K
U
Number of Balls in
Urn U
€100:
€0:
Red
Black
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown Unknown
The first question in the questionnaires looks like the questions used by Ellsberg. There are
two urns with black and red balls. The distribution in the first urn is known and the
distribution in the second urn in unknown. Respondents have to pick an urn to draw a ball
from after they have chosen a colour. Table 1 shows the table the respondents have to fill in
if they chose a red ball. If a black ball is chosen the same table is given but with the colours
switched. The second question also uses two urns but they are filled with five different
colours. This gives the chance to see whether the ambiguity attitude stays the same when
the probability of winning in Urn K gets lower. Questions three and four are also used to
measure ambiguity attitudes. In both questions respondents have the choice between a
certain amount of money or a bet on the weather in Vienna or The Hague respectively. With
these questions it will also be tested whether a city far away (Vienna) changes the ambiguity
attitude in comparison to a city nearby (The Hague). Table 2 shows the Vienna question.
Table 2: Question 3 (Vienna)
Option A
Get €0 for sure
Get €2 for sure
Get €4 for sure
Get €6 for sure
Get €8 for sure
Get €10 for sure
Get €12 for sure
Get €14 for sure
Get €16 for sure
Get €18 for sure
Get €20 for sure
A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
7
Option B
€20 if it rains
in Vienna
on Monday
The questionnaire will also make use of a cognitive reflection test (CRT). Three questions will
be used to measure the cognitive ability of the respondents. The same test that Shane
Frederick used in 2005 to research the relation between cognitive ability and decision
making is also used in this research. His research proved that cognitive ability influences the
time and risk preferences. Respondents having a higher cognitive ability tend to be more
patient, more risky for gains and take less risk when it involves losses (Frederick, 2005). At
last there will be asked a couple of demographic questions. This is to make sure possible
differences are not caused by factors such as gender, age or education. The native language
of the respondents is also asked to make sure whether the results belong to first or second
language. In the English questionnaire respondents are also asked to rate their English. This
way it is also possible to remove certain results if their English is very bad and they do not
seem to understand the questions.
Results
In the end a total number of 54 respondents were reached. This was divided in 30 for the
English questionnaire and 24 for the Dutch questionnaire. However due to several reasons
some answers could not be used for research. Therefore I ended up with 21 answers to the
English questionnaire and 23 for the Dutch. First the answers given to the questions
regarding the ambiguity aversion were given numerical values. If for instance one choose a
red ball in the first question and switched between 30 red balls in Urn K and 40 red balls in
Urn K this would be given a value of 35. This was done in a similar way for the questions with
five different colours and the weather in Vienna and The Hague. Also every respondent was
given a CRT score. This varies between zero and three depending on how many CRT
questions were answered right. This research will mainly focus on possible significant
differences caused by language. This can be because of the different languages or the
proficiency in English. But it will also be tested whether other factors such as CRT scores,
gender, age or degree has an influence on ambiguity aversion.
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Table 3: Descriptive Statistics
Language
2 colours
5 colours
18
18
21
21
3
3
0
0
Mean
45,00
28,33
6,667
7,905
Median
45,00
25,00
7,000
7,000
Std. Deviation
8,402
12,367
4,2348
4,6250
Minimum
25
15
1,0
1,0
Maximum
55
55
20,0
20,0
N
23
23
22
22
0
0
1
1
Mean
45,00
32,83
8,136
8,818
Median
45,00
25,00
9,000
9,000
Std. Deviation
7,977
13,803
4,3017
4,6561
Minimum
25
15
1,0
1,0
Maximum
55
55
20,0
19,0
English N
Valid
Missing
Dutch
Valid
Missing
Vienna
The Hague
Language
Now the results can be analyzed. First the descriptive statistics in table 3 are examined. The
statistics have been separated by the language of the questionnaire. In the question with
two colours respondents with a value below fifty are averse to ambiguity. Higher than fifty
means they are ambiguity seeking. It is remarkable to see that most statistics for this first
question are the same in both languages. In line with ambiguity aversion the means (and
median) of both groups are below fifty. However there are apparently some respondents
who seem to be ambiguity seeking as can be seen from the maximum of 55. More
surprisingly are the results from question with urns containing five different colours. In this
case ambiguity aversion should be indicated by a value below twenty. The means are
however both above twenty and in case of the Dutch questionnaire even above the thirty. If
you take a closer look at the frequencies of this question you will see that in the English
questionnaire 72.2% of the respondents is ambiguity seeking. In the Dutch questionnaire this
percentage is even higher at 78.3% (Appendix B, Table 1). The questions about the weather
in Vienna and The Hague however give the expected results. Whether it is ambiguity
aversion or ambiguity seeking depends on the belief of the raining probability of each
respondent. Because this is not asked it is impossible to know whether respondents are
9
averse to ambiguity or seeking ambiguity. But at both questions a higher value means less
aversion to ambiguity so it is possible to test whether language influences the attitude. It is
remarkable though that both have a maximum of twenty (or nineteen in the Dutch The
Hague question). This means there are respondents that prefer to win €20 only if it rains in
one of the cities above the opportunity to win €20 with certainty. This is in contrast to every
behavioural theory and more importantly also the opposite of common sense. Therefore it
could be doubted whether this person read the question well or answered it seriously. At
the last three questions there are slight differences in means between the languages. And in
the last two questions the median differs also. However whether these differences are
significant remains to be seen.
Normally an independent samples t-test is used to compare means between two groups. But
because the sample size is quite small and the distribution is not normal, it is better to use
the Wilcoxon rank sum test in this situation. This tests the difference between the groups in
distribution by comparing the sum of the ranks. The null hypothesis for this test is that there
is no difference between both groups. As the sample size is small and may be poorly
distributed it is possible that the asymptotic significance level gives a wrong indication.
Therefore the exact significance levels (2 sided) will be used to determine whether the
differences are significant or not. For these tests a confidence level of 95% will be used.
Because the tests are two sided it will be significant if the p-value is below 0.025 (0.05/2).
Now the analysis of the influence of the language of the questionnaire can start. First the
differences in answers on the first question will be tested. As is already shown in table 3
there is no difference in means or medians between both languages for this question. The
results of the Wilcoxon rank sum test support this (Appendix B, table 2 and 3). The mean
rank of the Dutch questionnaire is slightly higher. So the respondents from the Dutch
questionnaire have a higher rating meaning that they are a little less averse to ambiguity.
But with a (exact) p-value of 0.990 there is no evidence of a significant difference between
both languages for this question (Appendix B, table 3). This was expected after seeing the
descriptive statistics. For the second question those descriptive statistics do show a
difference between languages. The medians are still the same but the mean of the Dutch
questionnaire is higher than that of the English (32.83 and 28.33 respectively). This could
10
indicate that the Dutch language leads to less ambiguity aversion, or in this case more
ambiguity seeking. The Wilcoxon test however still shows no significance in the differences.
Although the ranks show that the average rank of the Dutch questionnaire is higher
(Appendix B, table 4) there is still no significance to be found (Appendix B, table 5). This is
shown by a p-value of 0.327 which is still well above 0.025. Last the two questions about the
weather in Vienna and The Hague will be evaluated. The descriptive statistics show that for
both questions the mean of the Dutch questionnaire is higher, again indicating less aversion
to ambiguity. But as opposed to the first two questions there is also a difference in the
medians at the last two questions. Both medians are seven in English and nine in Dutch. The
difference at the Vienna question seems to be bigger which is also showed in the mean
ranks. The difference in mean rank for the Vienna question is five, bigger than at all the
other questions (Appendix B, table 6). This difference is however also not significant
according to the Wilcoxon test (Appendix B, table 7). Although the p-value (0.188) is getting
lower it is still not past the critical value. The differences in the The Hague question are a
little smaller (Appendix B, table 8 and 9). Besides with a p-value of 0.489 it is also not
significant. So at none of the questions the difference is significant. But all the statistics
indicate that the respondents of the Dutch questionnaire are less averse to ambiguity than
those of the English questionnaire. And as said earlier the sample size is quite small. If this
was bigger it could well be that the differences become significant. So it is too early to say
that there is no evidence consistent with the Sapir-Whorf hypothesis.
So far it is tested whether language has an influence on the decisions made by the
respondents. But as said earlier it is also possible language has another effect on ambiguity
aversion. This effect could be that information in another language is more easily
misunderstood and therefore leads to more ambiguity aversion. To test this, respondents of
the English questionnaire were asked to give an indication of their skill in the English
language on a scale from zero to ten. Zero would mean the respondent speaks no English at
all and a ten means fluent in English. The answers on this question ranged from a six to ten.
To test whether there is a relationship between proficiency in the English language and
ambiguity attitude the Spearman correlation test is used. If the result of this test is 1 or -1
there is complete correlation between both and if the result is 0 there is no correlation at all.
A positive correlation between both variables is expected, because a better proficiency of
11
the English language should lead to fewer misunderstandings and a higher score on the
questions about ambiguity attitude indicates less aversion to ambiguity.
Table 4: Correlations, 2 colours, Proficiency English
2
colours
prof.
English
1,000
0,478
.
0,045
41
18
Correlation
Coefficient
0,478
1,000
Sig. (2-tailed)
0,045
.
18
21
Spearman's 2 colours Correlation
rho
Coefficient
Sig. (2-tailed)
N
prof.
English
N
The results of the Spearman correlation test for the question with two different colours are
shown in table 4. As can be seen there is a correlation of 0,478 between both variables.
Although the strength of this relationship is not very strong it should definitely not be
ignored. Besides the p-value of 0.045 makes this correlation significant at a 5% level. This
indeed means that a higher proficiency in the English language leads to less ambiguity
aversion. However to say that there is definitely a relationship between ambiguity attitude
and English proficiency there should also be correlation with the other three question. But
this is not the case. None of the correlations between proficiency in English and the last
three questions are significant. The question of the urns with five colours has a correlation of
0.345 which is a little weaker than the first question. But the p-value of 0.161 makes it
insignificant (Appendix B, table 10). The question about Vienna is already much weaker at
0.138 and the p-value indicates also here insignificance (Appendix B, table 11). The question
about The Hague is the most extreme though. Besides being insignificant (p-value of 0.954) it
also has a correlation coefficient of only -0.013 (Appendix B, table 12). Having a negative
correlation means that in this situation a lower proficiency in English leads to more
ambiguity aversion. The correlation is however so close to zero that one could say there is
no relationship at all. The reason that the correlation at these last two questions was so low
could be that the respondents first have to decide how big the probability of rain is. These
probabilities can have a big range between all the respondents which will probably also lead
to a big range of answers. This makes it less likely there is a correlation.
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Vienna versus The Hague
Besides the possible influence of language it is also interesting to see whether ambiguity
attitude differs in the questions about the weather in Vienna and The Hague. Again a nonparametric test is used because of the small sample size. In this situation the Wilcoxon
ranked sign test is used. The results of this test are significant if the exact p-value is lower
than 0.025. For this test every respondent’s answers of the questions about Vienna and The
Hague are compared. A negative rank is given if the value of the Vienna question exceeds
that of the question about The Hague. If the opposite is true a positive rank is given. In this
case there are five negative ranks and eighteen positive ranks (Appendix B, table 13). The
higher mean rank for positive ranks indicates that it is more likely that the value of The
Hague is higher than that of Vienna. The results of the Wilcoxon ranked sign test show that
there is a significant difference. The p-value is 0.001 which is way lower than the critical
value of 0.005 and therefore indicates significance (Appendix B, table 14). Because the Zvalue is negative (-3.141) and based on negative ranks this actually means that the answers
on the The Hague question were significantly higher than those on the Vienna question. So
respondents show less aversion to ambiguity (or in some cases more ambiguity seeking)
when asked about the weather in The Hague compared to the weather in Vienna. This is
possibly because all respondents were Dutch and for them there is ambiguity regarding
when it rains in Vienna. However it is also possible that the respondents think it is more
likely to rain in The Hague than it is in Vienna.
Gender
Now it will be examined whether there is a difference in ambiguity aversion between men
and women. To test this the Wilcoxon rank sum test will again be used. Again results will be
significant if the p-value is lower than 0.025. At the two colours question there is quite some
difference between men and women in the ranks (Appendix B, table 15). The mean rank of
the men is more than seven higher than that of the women. This shows that at least for this
question men are less averse to ambiguity. The results of the Wilcoxon test show that these
differences are insignificant, it is however very close to significant (Appendix B, table 16).
The p-value is with 0.058 closer to significance than most of the other test so far. The results
for the second question are completely the opposite. At this question women showed less
aversion to ambiguity than men although the difference is not as big as in the first question.
13
This is displayed in the mean ranks of both (Appendix B, table 17). But these results are not
as close to significance as with the first question. In this case the p-value is only 0.441, not
even close to the critical value (Appendix B, table 18). The results for the third question show
bigger differences again. Here there is a difference of almost seven in mean ranks however.
And like in the previous question women also show here less aversion towards ambiguity
(Appendix B, table 19). Compared to the second question these differences are much closer
to significance but are still insignificant. This is shown by the p-value of 0.103 (Appendix B,
table 20). The results of the last question about The Hague look very similar to the previous
question about Vienna. Women are again less averse to ambiguity but the difference in
mean ranks is with six slightly smaller (Appendix B, table 21). And as could be expected from
that it is also insignificant. The p-value is with 0.143 a little but not significant (Appendix B,
table 22).
CRT scores
Next research is regarding the CRT scores. As said the cognitive ability is determined by three
questions at the end of the questionnaires. The number of questions answered right
determines the score. If all three questions are answered right this results in a score of
three. None good is a score of zero. First it could be interesting to see whether language has
an influence on the scores. The Wilcoxon rank sum test is again used to determine this. It
turns out that the mean rank for the English questionnaire is higher than that of the Dutch
questionnaire (Appendix B, table 23). So the respondents who filled in the English
questionnaire got higher CRT scores. A reason for this is perhaps that a foreign language, in
which respondents are not fluent, forces them to make more deliberate decisions. The
native language makes it easier for respondents to answer such questions quickly without
really thinking about it. The difference is however not significant according to the p-value of
0.371 (Appendix B, table 24). To test whether there is a relationship between CRT scores and
ambiguity attitude the Spearman correlation test is used again. The analysis will again be
started with the question with two colours. The correlation between this question and CRT
scores is 0.068 (Appendix B, table 25). This is so close to zero that it can be said that there is
no relationship at all between this question and CRT scores. Besides it has a p-value of 0.674
so it is insignificant as well. Between CRT scores and the second question with five colours
there seems to be a relation. Although the test shows the correlation is only -0.226
14
indicating that it is still a very weak relation (Appendix B, table 26). Being negative it means
that someone with a higher CRT score shows more aversion to ambiguity than someone with
a lower CRT score. Also this relationship seems to be insignificant judging from the p-value of
0.155. CRT is also very weakly correlated to the answers on the question regarding the
weather in Vienna. The correlation of -0.238 is similar to the previous correlation (Appendix
B, table 27). Again a higher CRT score indicates more ambiguity aversion. However this
correlation is also insignificant because of the p-value of 0.125. The Spearman’s test with the
question about The Hague also gives similar results. The correlation is even a little bit weaker
though at -0.182 (Appendix B, table 28). But also this has a p-value of 0.243 and is therefore
insignificant. So the relation between CRT scores and ambiguity attitude looks to be a
negative one. Respondents scoring high on the CRT test tend to be more averse to
ambiguity. It is all insignificant but this could perhaps be due to the small sample size.
Age
Although most of the respondents are aged in the early twenties there are enough older
respondents to test whether age has an influence on ambiguity aversion. The ages varied
between twenty and sixty. To test the influence of age regressions will be used. In these
regressions the four questions which indicate ambiguity attitude will be the dependent
variables. Further there will be a constant and the independent variable will of course be
age. The calculated coefficient will show which influence age has on the ambiguity attitude.
Of course the p-value will indicate whether this is significant or not. In the first regression
the dependent variable are the answers on the question with two different colours. The
results this regression show that age does not have much influence (Appendix B, table 29).
The coefficient of the constant is 44.043. This means that a child of zero would on average
give this score on the first question. Remember that in this question a value below fifty
indicates aversion to ambiguity. The coefficient for age in this regression is 0.031. So with
every year someone grows older he becomes a little bit less averse to ambiguity. This
coefficient is however so small that it is negligible. The p-value of 0.755 also shows that this
coefficient is insignificant. In the second regression the question with five colours is the
dependent variable. In this question a value below twenty indicates ambiguity aversion and
above twenty ambiguity seeking. At this question the influence of age looks a bit bigger. The
coefficients are this time 21.709 for the constant and 0.298 for age (Appendix B, table 30).
15
So in this case someone of zero years is already ambiguity seeking and this ambiguity
seeking becomes bigger with every year. The p-value of age is this time also quite close to
being significant with 0.064. The last two regressions will be made with the questions about
Vienna and The Hague as dependent variables. In these questions ambiguity aversion is
showed by a value below ten. Above ten means that person is ambiguity seeking. The results
of the regression with Vienna as dependent variable are similar to the first regression
(Appendix B, table 31). The constant coefficient of 6.762 displays that the respondents are
probably averse to ambiguity. Or they just think it will not rain in Vienna. Age however does
not seem to have any influence with a coefficient of only 0.021. That this is insignificant
according to the p-value of 0.684 does not matter much. Even if it was significant the
influence is so small that it would barely be noticed. Also the last regression shows
similarities to this regression. As is discussed earlier respondents show less aversion to
ambiguity in the The Hague question compared to the Vienna question. This is showed by
the constant coefficient of 7.708 (Appendix B, table 32). The age coefficient is however
exactly the same at these last two regression, so also here it is 0.021. So almost no influence
of age on the attitude towards ambiguity on this occasion as well. As could be expected the
p-value is 0.702. But as said earlier that does not matter much which such small coefficients.
Degree
The tests for differences caused by the highest degree received were done in a similar way
to the age. All respondents were divided into four groups again. The first group consisted of
all respondents with a high school degree or no degree at all. The second and third group
were respectively the so called MBO and HBO. And the last group consisted of everyone with
a university degree. In the last two groups there is no difference made between a bachelor
and a master. The regressions were again made with the ambiguity questions as dependent
variable and this time degree as independent variable. People without any degree would
have a value equal to the constant. The first regression gives a coefficient of 0.418 for degree
(Appendix B, table 33). That would mean that respondent with a higher degree are less
averse to ambiguity. The constant coefficient of 43.909 shows that this is really about
ambiguity aversion and not ambiguity seeking. The coefficient of degree is however not
significant with a p-value of 0.666. When a look is taken at the second regression it is seen
that at this question it is about ambiguity seeking. This is showed by the coefficient of the
16
constant of 30.721 (Appendix B, table 34). This was the question with five colours so there is
ambiguity seeking at a value higher than twenty. However, more importantly the coefficient
of degree is only 0.051. This is very low so degree has barely any impact on the ambiguity
attitude in this question. Besides the very low coefficient it is also insignificant according to
the p-value (0.974). The influence of degree is already much bigger at the question about
Vienna. Although it is insignificant (p-value of 0.448) the coefficient for degree is in this
regression 0.390 (Appendix B, table 35). The constant of 6.376 indicates that the
respondents do not like ambiguity or that they think there is a very small probability that it
will rain in Vienna. Finally the last regression gives similar results regarding the influence of
degree. The coefficient of 0.409 shows this, as was the case with previous regression also
this coefficient is insignificant with the p-value at 0.460 (Appendix B, table 36). As expected
in this question about The Hague the aversion to ambiguity is a little less compared to
Vienna. This is displayed by the constant of 7.279. Although all the coefficients are
insignificant it is remarkable that in all regressions the coefficient for degree was positive. So
respondents with a higher degree showed less aversion to ambiguity at all of the four
questions.
Other Regressions
So far it is tested whether all the factors influence ambiguity attitudes one for one. It could
be interesting to see whether some of the influences found so far would change if a
regression is made with all the factors in it. In these regressions the four different questions
about ambiguity are of course still the dependent variable. But this time the language of the
questionnaire, gender CRT scores, age and degree will all be used as independent variables.
The factors language and gender will enter these regressions as dummies. These will get a
value of one respectively if the language of the questions was English and if the respondent
is female. This will mean that if the Dutch language makes people less averse to ambiguity
(or more ambiguity seeking) the coefficient should be negative. If the coefficient of gender is
positive then women are less averse to ambiguity.
17
Table 5: Regressions
Dependent
2 colours
5 colours
Vienna
The Hague
Constant
44.132 (0.000)
28.769 (0.003)
7.644 (0.018)
8.381 (0.017)
Language
0.159 (0.953)
-2.684 (0.527)
-1.280 (0.338)
-0.699 (0.628)
Gender
-5.161 (0.089)
0.748 (0.874)
1.604 (0.292)
1.824 (0.271)
CRT
-0.415 (0.816)
-3.629 (0.203)
-0.892 (0.291)
-0.988 (0.282)
Age
0.073 (0.487)
0.289 (0.087)
0.010 (0.839)
0.010 (0.853)
Degree
0.399 (0.697)
1.024 (0.528)
0.633 (0.239)
0.670 (0.251)
Variable
The results of the four regressions can be seen in table 5 above. The p-value for each
coefficient is in between the brackets. The first regression with the question with two
colours as dependent variable does give some remarkable results. The coefficient of 0.159
for language is not in line with the first tests. First tests showed that at this question the
Dutch questionnaire had higher ranks indicating less aversion to ambiguity. In this regression
is the coefficient however positive. As said this indicates that the English questionnaire leads
to less aversion to ambiguity. Compared to earlier results there is also a switch in the
influence of CRT scores at this question. The correlation between this question and CRT was
positive, in this regression it has a negative coefficient (-0.415) though. All the other
variables show the same effect in this regression as in the other tests. Gender has a very big
influence in this regression with a coefficient of -5.161. But the difference in ranks in the first
tests was also very big at this question so this is not a very big surprise. Another result that is
seen earlier is the fact that none of these coefficients are significant. All the p-values are well
above 0.025. The second regression with the question with five different colours as
dependent variable gives no surprising results. As all other tests for this question showed
this is the only question where the respondents display ambiguity seeking behaviour. This is
showed by a constant that is with 28.769 well above the twenty. All the other results are in
line with results obtained with previous tests. Language is in this regression negative
indicating that indeed the Dutch questionnaire leads to more ambiguity seeking. The value
of -2.684 is perhaps bigger than expected but the p-value of 0.527 makes it insignificant
anyway. Also the coefficients of CRT (-3.269) and degree (1.024) are bigger than was
expected from previous tests. However these coefficients are also insignificant (p-values of
18
0.203 and 0.528 respectively). Next is the regression with the question about the weather in
Vienna as dependent variable. As with the previous regression there are here also no
unexpected signs before the coefficient. But this time also the size of the coefficients is no
surprise. Language and Gender seem to have the biggest influence on ambiguity attitude.
Earlier test with this question indeed did show the biggest differences with these variables.
When age was tested earlier it showed to have very little effect and this continues in this
regression. The coefficient of only 0.010 shows that even if this was significant it still would
have almost no influence at all on ambiguity attitude. As could be expected all the p-values
are well above 0.025 and are therefore insignificant. About the last regression with The
Hague as dependent variable can be said the same. No really surprising coefficients or pvalues in this regression. Gender has also in this regression the biggest coefficient (1.824).
But this was expected as in the first gender test there was a quite big difference in mean
ranks already. Age has again no effect at all with a coefficient of 0.010. In this last regression
CRT also seems to have a reasonable influence on ambiguity attitude. The coefficient is
-0.988 although earlier test showed a very weak correlation between CRT and this question.
As was the case with all these regressions also here there are no significant variables since all
p-values are above 0.025.
Conclusions
This research focused mainly on differences in ambiguity aversion caused by language. This
could have been caused because language determines our view of the world and more
importantly it influences our decision making as stated by the Sapir-Whorf hypothesis. It
could also have been caused by the fact that information in another language is more easily
misunderstood. Results of mean comparing and correlation tests however show that
language has no influence in either way. Only one comparison resulted in a significant effect
which could have been caused by the proficiency in the English language. Since that was the
only significant difference it seems to be a coincidence. So language does not seem to have
any relationship with ambiguity aversion. But as said multiple times before the sample size
was very small. Perhaps bigger sample sizes lead to more significant results. So it is too early
to conclude that language has no influence at all. Main reason for this is the fact that in all
the Wilcoxon tests the Dutch questionnaires led to less ambiguity aversion (or more
seeking). Also the correlations between the proficiency in English and the several questions
19
were mostly similar. The last question about the weather in The Hague gave a negative
correlation, but this was so small that it could be said that there was no correlation at all.
The other three questions all had positive correlation and were more importantly much
bigger. This could indicate that when respondents speak better English they show less
aversion to ambiguity. Besides language it was also tested whether gender, CRT scores, age
or degree had any influence on ambiguity aversion. These tests all resulted in the same
conclusion. None of these factors seemed to influence ambiguity aversion in any way.
However the same thing that can be said of the influence of language can also be said of the
variables age and degree. In all the tests they had the same positive effect. This would mean
that when people grow older or have a higher degree this results in less aversion of
ambiguity. The results of age however where so small that this does not change much.
Discussion
Despite these conclusions there should be made some side notes regarding this research.
Most important is that the research sample was not big enough to make any definite
conclusions. As is said a couple of times already the small sample size could be the cause
some of the relations turned out to be insignificant. Therefore there could be hypotheses
wrongly accepted. To make any real conclusions a way bigger number of respondents should
be use.
Also the conclusions of this research are based on tests between only two different
languages. Both are an even a Germanic language which means there are probably quite
some similarities between both languages. Besides that English is a language that is taught at
every high school in the Netherlands. This makes that most of the respondents spoke English
reasonable well. This could be seen in the grades each respondent had to give for his or her
English. Most respondents indicated that this was a nine or even a ten. Since misunderstood
information is a part of ambiguity this could possibly influence the results. If respondents
speak better English less information will be misunderstood, which could influence the
attitude towards ambiguity. Future research could be done to see whether conclusions
would change if completely other languages were added to the list, such as Italian or even
Chinese. These languages are really completely different and could therefore lead to other
results. People would not speak these languages especially well which could give other
20
attitudes to ambiguity. Because more information would be misunderstood, or the
respondents would think they understand less. Also according to the Sapir-Whorf hypothesis
the results could be different with other languages. Each language has its own influence on
decision making. Since English and Dutch are both Germanic and possibly have some
similarities the differences in decision is not that big. This could be enlarged by adding a
completely different language.
Besides that this research also did not include respondents with different native language. It
is possible that people think in their native language anyway, despite reading or hearing
information in another language. If this is the case then decisions should not change
according to the Sapir-Whorf hypothesis. Asking people these questions only in their native
language makes it possible investigate the possible influence of the Sapir-Whorf hypothesis
even better. Of course you would need enough respondents from different countries with
different languages. This was impossible to do for this research. So although this research
shows no relation between language and ambiguity aversion there are enough reasons to do
more research before the possibility is completely rejected.
21
Bibliography
Badhesha,
R.
S.
(2002).
Sapir-Whorf
Hypothesis.
Retrieved
from
http://zimmer.csufresno.edu/~johnca/spch100/4-9-sapir.htm
Becker, S. W., & Brownson, F. O. (1964). What Price Ambiguity? Or the Role of Ambiguity in
Decision-Making. Journal of Political Economy, 62-73.
Bleaney, M., & Humphrey, S. J. (2006). An Experimental Test of Generalized Ambiguity
Aversion using Lottery Pricing Tasks. Theory and Decision, 257-282.
Ellsberg, D. (1961). Risk, Ambiguity, and the Savage Axioms. The Quarterly Journal of
Economics, 643-669.
Fox, C. R., & Tversky, A. (1995). Ambiguity Aversion and Comparative Ignorance. The
Quarterly Journal of Economics, 585-603.
Frederick, S. (2005). Cognitive Reflection and Decision Making. The Journal of Economic
Perspectives, 25-42.
Kay, P., & Kempton, W. (2009). What is the Sapir-Whorf Hypothesis? American
Anthropologist, 65-79.
Keysar, B., Hayakawa, S. L., & An, S. G. (2012). The Foreign Language Effect: Thinking in a
Foreign Tongue Reduces Decision Biases. Psychological Science, 661-668.
Peterson, C. C., & Siegal, M. (2006). Deafness, Conversation and Theory of Mind. The Journal
of Child Psychology and Psychiatry, 459-474.
22
Appendix A: Questionnaire
1
Imagine that there are two different urns, Urn K and Urn U. Both urns contain 100 balls with
two different colours: red and black. The proportion of the 2 colours are always Known in Urn K.
They are always Unknown in Urn U, to both you and the experimenter. The unknown Urn U has been
prepared by a third party. You have to pick one colour which you would like to bet on and then you
can choose one of the urns to draw a ball from. You get €100 if the colour of the drawn ball is the
same as the colour you bet on.
The left two columns describe the proportions of the coloured balls in Urn K. The first left column
specifies the number of balls of the colour you choose, and the second column specifies the number
of balls of the other colour. The total number of balls in Urn K is always 100.
As regards the two columns to the right, the proportions are always unknown for Urn U. The total
number of balls in Urn U is always 100 also.
Each row represents a choice scenario with two options: Urn K and Urn U. You can indicate your
preference between Urn K and Urn U for each row by circle “Urn K” or “Urn U” in the middle two
columns in each row. Please indicate your preferences for all rows.
You can choose the colour you prefer to bet on. If you bet on a red ball you can indicate your
preferences in the first table. If you bet on a black ball you can skip the first table and use the
second table.
If you choose to bet on Red:
Number of Balls in Urn K
€100:
€0:
Red
Black
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
K
U
Number of Balls in Urn U
€100:
€0:
Red
Black
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
23
Unknown
If you choose to bet on Black:
Number of Balls in Urn K
€100:
€0:
Black
Red
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
K
U
Number of Balls in Urn U
€100:
€0:
Black
Red
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
Unknown
2
Again imagine two urns, Urn K and Urn U. Both contain 100 balls but this time there are five
different colours: black, red, blue, yellow and green. The proportion in Urn K will again be known to
everyone and the proportion in Urn U is unknown to you and the experimenter. Pick a colour to bet
on and pick an urn to draw a ball from. If the colour of your bet matches to colour of the drawn ball
you get €100.
In the tables below the columns on the far left indicate how much balls of the colour you bet on are
in Urn K. The second column from the left shows how much balls of the four other colours combined
are in Urn K. The two columns on the right show the distribution of colours in Urn U which is
unknown. You can indicate your preference for each row by circling either “Urn K” or “Urn U” in the
two columns in the middle. Please give your preference for all rows. You only have to do this in the
table that corresponds with the colour you would like to bet on.
If you choose to bet on Red:
Number of Balls in Urn K
€100:
€0:
Red
Other colours
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
K
U
Number of Balls in Urn U
€100:
€0:
Red
Other colours
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
24
Unknown
If you choose to bet on Black:
Number of Balls in Urn K
€100:
€0:
Black
Other colours
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
If you choose to bet on Blue:
Number of Balls in Urn K
€100:
€0:
Blue
Other colours
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
If you choose to bet on Yellow:
Number of Balls in Urn K
€100:
€0:
Yellow
Other colours
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
K
U
Number of Balls in Urn U
€100:
€0:
Black
Other Colours
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
K
U
Number of Balls in Urn U
€100:
€0:
Blue
Other colours
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
K
U
Number of Balls in Urn U
€100:
€0:
Yellow
Other colours
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
25
Unknown
Unknown
Unknown
If you choose to bet on Green:
Number of Balls in Urn K
€100:
€0:
Green
Other colours
0
100
10
90
20
80
30
70
40
60
50
50
60
40
70
30
80
20
90
10
100
0
K
U
Number of Balls in Urn U
€100:
€0:
Green
Other colours
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn K
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Urn U
Unknown
26
Unknown
3
Consider two options you can choose from, Option A and Option B. Option A assures that you
get some money. Option B is that you get €20 if it rains in Vienna on Monday.
The column on the far left states the amount of money you will get if you choose Option A. The
column on the right describes Option B which is the same in each occasion.
Each row gives the choice between Option A and Option B. You can indicate your preference
between both options for each row by circle “Option A” or “Option B” in the middle two columns in
each row. Please indicate your preferences for all rows.
Option A
A
B
Option B
Get €0 for sure
Get €2 for sure
Get €4 for sure
Get €6 for sure
Get €8 for sure
Get €10 for sure
Get €12 for sure
Get €14 for sure
Get €16 for sure
Get €18 for sure
Get €20 for sure
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
€20 if it rains
in Vienna
on Monday
4
Consider the same situation as in the previous question. But this time Option B means that
you get €20 if it rains in The Hague on Monday.
Which option do you prefer now? Please indicate your preferences for all rows.
Option A
A
B
Option B
Get €0 for sure
Get €2 for sure
Get €4 for sure
Get €6 for sure
Get €8 for sure
Get €10 for sure
Get €12 for sure
Get €14 for sure
Get €16 for sure
Get €18 for sure
Get €20 for sure
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option A
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
Option B
€20 if it rains
in The Hague
on Monday
27
5
A bat and a ball cost €1,10 in total. The bat costs €1,00 more than the ball. How much does
the ball cost? …………………………..
6
If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to
make 100 widgets?
…………………………..
7
In a lake, there’s a patch of lily pads. Every day, the patch doubles in size. If it takes 48 days
for the patch to cover the entire lake, how long would it take for the patch to cover half of the lake?
…………………………..
At last I would like you to answer some questions about yourself:
Gender:
o Male
o Female
Age:
…………………………..
First Language:
…………………………..
How good is your English on a scale from 0 to 10?
(0 means that you don’t speak English and 10 means that English is your first language):
…………………………..
Education (Highest degree received): …………………………..
Field of study/Profession:
…………………………..
Appendix B: Results
Table 1: Frequincies, 5 colours
Language
English Valid
Frequency
5
23,8
27,8
27,8
25
7
33,3
38,9
66,7
35
2
9,5
11,1
77,8
45
3
14,3
16,7
94,4
55
1
4,8
5,6
100,0
18
85,7
100,0
3
14,3
21
100,0
15
5
21,7
21,7
21,7
25
7
30,4
30,4
52,2
35
1
4,3
4,3
56,5
45
8
34,8
34,8
91,3
55
2
8,7
8,7
100,0
23
100,0
100,0
Missing System
Total
Valid
Valid Percent
15
Total
Dutch
Percent
Cumulative
Percent
Total
28
Table 2: Ranks, 2 colours
Language
2 colours
N
Mean Rank
Sum of Ranks
English
18
20,92
376,50
Dutch
23
21,07
484,50
Total
41
Mean Rank
Sum of Ranks
Table 3: Test Statisticsa, 2 colours
2 colours
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
205,500
376,500
-0,045
0,965
Exact Sig. (2-tailed)
0,990
0,522
0,020
Exact Sig. (1-tailed)
Point Probability
a. Grouping Variable: Language
Table 4: Ranks, 5 colours
Language
5 colours
N
English
18
18,94
341,00
Dutch
23
22,61
520,00
Total
41
Table 5: Test Statisticsa, 5 colours
5 colours
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
170,000
341,000
-1,010
0,312
0,327
0,165
0,013
a. Grouping Variable: Language
Table 6: Ranks, Vienna
Language
Vienna
N
Mean Rank
Sum of Ranks
English
21
19,45
408,50
Dutch
22
24,43
537,50
Total
43
29
Table 7: Test Statisticsa, Vienna
Vienna
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
177,500
408,500
-1,325
0,185
0,188
0,094
0,002
a. Grouping Variable: Language
Table 8: Ranks, The Hague
Language
The Hague
N
Mean Rank
Sum of Ranks
English
21
20,64
433,50
Dutch
22
23,30
512,50
Total
43
Table 9: Test Statisticsa, The Hague
The Hague
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
202,500
433,500
-0,704
0,481
0,489
0,244
0,003
a. Grouping Variable: Language
Table 10: Correlations, English Proficiency and 5 colours
Spearman's rho
prof. English
prof. English
5 colours
1,000
0,345
.
0,161
21
18
Correlation Coefficient
0,345
1,000
Sig. (2-tailed)
0,161
.
18
41
Correlation Coefficient
Sig. (2-tailed)
N
5 colours
N
30
Table 11: Correlations, English Proficiency and Vienna
prof. English
Spearman's rho
prof. English
Correlation Coefficient
1,000
0,138
.
0,550
21
21
Correlation Coefficient
0,138
1,000
Sig. (2-tailed)
0,550
.
21
43
Sig. (2-tailed)
N
Vienna
Vienna
N
Table 12: Correlations, English Proficiency and The Hague
Spearman's rho
prof. English
Correlation Coefficient
Sig. (2-tailed)
N
The Hague
Correlation Coefficient
Sig. (2-tailed)
N
prof. English
The Hague
1,000
-0,013
.
0,954
21
21
-0,013
1,000
0,954
.
21
43
Table 13: Ranks, The Hague, Vienna
Positive Ranks
Mean Rank Sum of Ranks
7,80
39,00
5
13,17
237,00
18b
Ties
20c
Total
43
N
a
The Hague - Vienna Negative Ranks
a. The Hague < Vienna
b. The Hague > Vienna
c. The Hague = Vienna
Table 14: Test Statisticsa, The Hague, Vienna
The Hague - Vienna
Z
Asymp. Sig. (2-tailed)
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
-3,141b
0,002
0,001
0,001
0,000
a. Wilcoxon Signed Ranks Test
b. Based on negative ranks.
31
Table 15: Ranks, 2 colours
Gender
2 colours
N
Mean Rank
Sum of Ranks
Female
12
15,96
191,50
Male
29
23,09
669,50
Total
41
Mean Rank
Sum of Ranks
Table 16: Test Statisticsa, 2 colours
2 colours
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. [2*(1-tailed
Sig.)]
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
113,500
191,500
-1,958
0,050
0,083b
0,058
0,033
0,005
a. Grouping Variable: Gender
b. Not corrected for ties.
Table 17: Ranks, 5 colours
Gender
5 colours
N
Female
12
23,25
279,00
Male
29
20,07
582,00
Total
41
Table 18: Test Statisticsa, 5 colours
5 colours
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. [2*(1-tailed
Sig.)]
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
147,000
582,000
-0,804
0,421
0,453b
0,441
0,225
0,023
a. Grouping Variable: Gender
b. Not corrected for ties.
32
Table 19: Ranks, Vienna
Gender
Vienna
N
Mean Rank
Sum of Ranks
Female
12
26,96
323,50
Male
31
20,08
622,50
Total
43
Mean Rank
Sum of Ranks
Table 20: Test Statisticsa, Vienna
Vienna
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. [2*(1-tailed
Sig.)]
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
126,500
622,500
-1,642
0,101
0,108b
0,103
0,052
0,002
a. Grouping Variable: Gender
b. Not corrected for ties.
Table 21: Ranks, The Hague
Gender
The Hague
N
Female
12
26,46
317,50
Male
31
20,27
628,50
Total
43
Table 22: Test Statisticsa, The Hague
The Hague
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. [2*(1-tailed
Sig.)]
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
132,500
628,500
-1,473
0,141
0,149b
0,143
0,072
0,002
a. Grouping Variable: Gender
b. Not corrected for ties.
33
Table 23: Ranks, CRT scores
Language
CRT
N
Mean Rank
Sum of Ranks
English
21
24,21
508,50
Dutch
23
20,93
481,50
Total
44
Table 24: Test Statisticsa, CRT scores
CRT
Mann-Whitney U
Wilcoxon W
Z
Asymp. Sig. (2-tailed)
Exact Sig. (2-tailed)
Exact Sig. (1-tailed)
Point Probability
205,500
481,500
-0,928
0,354
0,371
0,182
0,016
a. Grouping Variable: Language
Table 25: Correlations, CRT and 2 colours
2 colours
Spearman's rho
2 colours
Correlation Coefficient
1,000
0,068
.
0,674
41
41
Correlation Coefficient
0,068
1,000
Sig. (2-tailed)
0,674
.
41
44
Sig. (2-tailed)
N
CRT
CRT
N
Table 26: Correlations, CRT and 5 colours
CRT
Spearman's rho
CRT
Correlation Coefficient
Sig. (2-tailed)
N
5 colours
Correlation Coefficient
Sig. (2-tailed)
N
34
5 colours
1,000
-0,226
.
0,155
44
41
-0,226
1,000
0,155
.
41
41
Table 27: Correlations, CRT and Vienna
CRT
Spearman's rho
CRT
Correlation Coefficient
Vienna
1,000
-0,238
.
0,125
44
43
-0,238
1,000
0,125
.
43
43
Sig. (2-tailed)
N
Vienna
Correlation Coefficient
Sig. (2-tailed)
N
Table 28: Correlations, CRT and The Hague
CRT
Spearman's rho
CRT
Correlation Coefficient
The Hague
1,000
-0,182
.
0,243
44
43
-0,182
1,000
0,243
.
43
43
Sig. (2-tailed)
N
The Hague
Correlation Coefficient
Sig. (2-tailed)
N
Table 29: Regression 2 colours, Age
Unstandardized Coefficients
Model
1
B
(Constant)
Std. Error
Beta
44,043
3,305
0,031
0,099
Age
Standardized
Coefficients
0,050
t
Sig.
13,326
0,000
0,314
0,755
a. Dependent Variable: 2 colours
Table 30: Regression 5 colours, Age
Unstandardized Coefficients
Model
1
B
(Constant)
Age
Std. Error
Standardized
Coefficients
Beta
21,709
5,191
0,298
0,156
a. Dependent Variable: 5 colours
35
0,292
t
Sig.
4,182
0,000
1,909
0,064
Table 31: Regression Vienna, Age
Unstandardized Coefficients
Model
1
B
Std. Error
Standardized
Coefficients
Beta
(Constant)
6,762
1,733
Age
0,021
0,050
0,064
t
Sig.
3,903
0,000
0,410
0,684
a. Dependent Variable: Vienna
Table 32: Regression The Hague, Age
Unstandardized Coefficients
Model
1
B
Std. Error
Standardized
Coefficients
Beta
(Constant)
7,708
1,865
Age
0,021
0,054
0,060
t
Sig.
4,134
0,000
0,385
0,702
a. Dependent Variable: The Hague
Table 33: Regression 2 colours, Degree
Unstandardized Coefficients
Model
1
B
(Constant)
Std. Error
Beta
43,909
2,813
0,418
0,962
Degree
Standardized
Coefficients
0,069
t
Sig.
15,607
0,000
0,435
0,666
a. Dependent Variable: 2 colours
Table 34: Regression 5 colours, Degree
Unstandardized Coefficients
Model
1
B
(Constant)
Std. Error
Beta
30,721
4,626
0,051
1,581
Degree
Standardized
Coefficients
0,005
t
Sig.
6,641
0,000
0,032
0,974
a. Dependent Variable: 5 colours
Table 35: Regression Vienna, Degree
Unstandardized Coefficients
Model
1
B
Std. Error
Standardized
Coefficients
Beta
(Constant)
6,376
1,512
Degree
0,390
0,509
a. Dependent Variable: Vienna
36
0,119
t
Sig.
4,217
0,000
0,766
0,448
Table 36: Regression The Hague, Degree
Unstandardized Coefficients
Model
1
B
Std. Error
Standardized
Coefficients
Beta
(Constant)
7,279
1,627
Degree
0,409
0,548
a. Dependent Variable: The Hague
37
0,116
t
Sig.
4,473
0,000
0,746
0,460