undergraduate dissertation - does intelligence affect susceptibility to anchoring?
TRANSCRIPT
School of Economics
University of Nottingham
L13500 Dissertation 2014-15
Does Intelligence Affect Susceptibility to Anchoring?
Louis Adams
Student ID: 4173657
Supervisor: Fabio Tufano
Word Count: 7,492
This Dissertation is presented in part fulfilment of the requirement for the completion of an undergraduate degree in the School of
Economics, University of Nottingham. The work is the sole responsibility of the candidate.
I give permission for my dissertation proposal to be made available to students in future years if selected as an example of good
practice
Louis Adams 1
Abstract
The anchoring effect is now an accepted phenomenon in behavioural economics that leads to bias in decision-
making. The literature has progressed to examine the factors that affect susceptibility to anchoring, with a
small number of papers coming to contrasting conclusions on whether intelligence is one such factor. This
paper describes an online experiment conducted to explore whether people of higher cognitive ability are less
influenced by an arbitrary anchor number when making price judgement decisions of standard consumer
products. Firstly, it finds that subjects’ stated willingness to pay and willingness to accept decisions are affected
by the uninformative anchor. Secondly, it does not find evidence to suggest that cognitive ability is related to
susceptibility to anchoring.
Contents
1. Introduction .......................................................................................................................................................... 2
1.1 Research motivation .................................................................................................................................... 2
1.2 Research question ......................................................................................................................................... 2
1.3 Hypotheses ....................................................................................................................................................... 3
2. Literature review................................................................................................................................................. 3
2.1 Anchoring ........................................................................................................................................................ 3
2.2 Price judgement and anchoring ............................................................................................................... 5
2.3 Cognitive ability ............................................................................................................................................. 6
2.4 Testing of cognitive ability ......................................................................................................................... 6
3. Experimental design .......................................................................................................................................... 7
3.1 Valuation tasks .............................................................................................................................................. 7
3.2 Cognitive ability test .................................................................................................................................... 9
3.3 Demographic questionnaire ....................................................................................................................10
3.4 Subject pool ...................................................................................................................................................10
4. Results and discussion ....................................................................................................................................10
4.1 Cognitive ability ...........................................................................................................................................10
4.2 Hypothesis 1 ..................................................................................................................................................11
4.3 Hypothesis 2 ..................................................................................................................................................14
4.4 Discussion ......................................................................................................................................................17
5. Evaluation ............................................................................................................................................................18
5.1 Experimental procedure ...........................................................................................................................18
5.2 Further research ..........................................................................................................................................20
5.3 Conclusion ......................................................................................................................................................21
6. References ............................................................................................................................................................22
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1. Introduction
1.1 Research motivation
Since Tversky and Kahneman’s (1974) pioneering research on judgement under uncertainty,
the anchoring effect, the phenomenon whereby exposure to an irrelevant number influences an
individual’s subsequent judgement of a quantity or valuation, has become an accepted psychological
tendency. Ariely et al. (2003) (henceforth ALP) were the first to investigate the anchoring effect in
the economic context of valuing standard consumer products, such as a bottle of wine, by exposing
subjects to an arbitrary price prior to asking them to state the most they would pay for the good.
They found strong evidence of its existence, which has important economic implications as it
suggests that people do not always reveal, or even know, their true preferences, and that their price
judgements are not based on fundamental underlying value. This in turn draws into question the
neoclassical theory that individuals are rational economic decision makers.
More recently, research has progressed to study the factors that affect an individual’s
susceptibility to anchoring; in other words, whether certain types of people are more influenced than
others. An area of the literature that is attracting new attention is the investigation of whether
intelligence plays a part. In 2010, Bergman et al. (henceforth BEJS) replicated ALP’s first experiment,
and extended it to find that subjects with higher cognitive ability (CA), a measurement of “analytical
intelligence” (Furnham, 2011, p.6), were less susceptible to anchoring. Oechssler et al. (2009) and
Stanovich and West (2008), however, do not find a significant link between the two. The very few
papers investigating this relationship and the discord among them suggest that further research into
the area would be worthwhile.
1.2 Research question
This paper has two aims. Firstly, it looks to replicate the findings of BEJS and ALP that
anchoring impacts upon an individual’s price judgements; secondly, it aims to reaffirm the
robustness of BEJS’s finding that there exists a relationship between CA and anchoring. They,
however, only consider this in the context of buying goods (willingness to pay) so I will extend the
investigation to check whether their results also hold in a selling (willingness to accept) scenario.
The potential finding that people with higher CA are less susceptible to anchoring is
important in an economic context because it implies that such people are more resistant to the
irrelevant anchor and therefore behave more rationally, displaying a lower disparity between their
revealed and true preferences.
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1.3 Hypotheses
As will be explained in Section 3, I conducted an online experiment with a between-subject
design in which each participant was put in either a buying or selling situation. They were then
exposed to a high (£17) or low (£3) anchor before giving price judgements of four products.
Following this, all subjects took a CA test. The following hypotheses are proposed in order to address
the aforementioned research questions;
Hypothesis 1: Within both the willingness to pay (WTP) and willingness to accept (WTA)
treatments, the group of subjects exposed to the high anchor will, on average, value the products
higher than the low anchor group. I therefore test the null hypothesis that the high and low anchor
groups’ mean valuations are not significantly different.
Hypothesis 2: Drawing from BEJS’s findings, subjects who score higher in the CA test will be
less influenced by the anchor. I test the null hypothesis that the difference in anchoring effect
between subjects of high and low CA is not statistically significant.
2. Literature review
2.1 Anchoring
Tversky and Kahneman (1974) found that an irrelevant anchor between 1 and 99, randomly
generated from a wheel of fortune, had a significant impact on people’s subsequent estimates of the
percentage of African countries in the United Nations. For example, average estimates of 25% and
45% were given by those who landed on 10 and 65 on the wheel, respectively. To check the
robustness of their findings, however, the behavioural bias has since been investigated in a variety of
alternative contexts. Plous (1989) found evidence of the anchoring effect when asking students to
indicate their probability estimates of the outbreak of a nuclear war, and Carlson (1990) found it to
be robust in the context of selling three-outcome gambles.
Epley and Gilovich (2001) investigated whether people were influenced by an irrelevant
anchor when giving responses to general knowledge questions such as “When was George
Washington elected president?” but they extended their study to consider two different types of
anchor simultaneously: those generated by the subject (self-generated) and those given by the
experimenter (externally-provided). They found that anchoring plays a part in both cases.
Over time the anchoring effect has thus shown itself to exist in a range of contexts and is
described by Furnham and Boo (2011) as “one of the most robust cognitive heuristics” (2011, p. 35),
a heuristic being a cognitive rule of thumb. However, it is perhaps unsurprising that individuals are
influenced by an anchor when asked to make a judgement decision in an area in which they have no
expertise or experience. This is what Northcraft and Neale (1987) investigated in an innovative field
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experiment in which they asked both students (amateurs) and real estate agents (professionals) to
value properties, manipulating the listing price as the anchor. The finding that both groups were
significantly affected shows, firstly, that even people with expertise are susceptible to anchoring, and,
secondly, that the anchoring bias is not simply a “function of the contrived nature of the laboratory
setting” (1987, p.84), and therefore has external validity in the real world.
As discussed in Section 1, one of the first and most instrumental studies to consider
anchoring in the context of behavioural economics was that of ALP. They found strong evidence of
the effect across a range of WTP and WTA valuation tasks of both familiar consumer goods and
hedonic experiences. The results of their first experiment, which involved valuing consumer goods,
showed that people are susceptible to the effect even when they have experience of a product. Their
findings underline the economic relevance of anchoring, showing that arbitrary values can affect
individuals’ decision-making and preferences.
There are two primary psychological explanations as to why the anchoring heuristic exists.
The first, proposed by Tversky and Kahneman (1974), is the anchoring and adjustment hypothesis,
which explains that individuals initially pin their estimate to the irrelevant anchor and then fail to
sufficiently adjust away from it towards their true judgement. However, it is doubtful that this
hypothesis can be applied to every case of anchoring as it assumes that people actively use the
anchor as a reference point, which would be illogical in cases when the anchor is clearly
uninformative.
In response to this shortcoming, a second mechanism of “selective accessibility” was put
forward by Strack and Mussweiler (1997; Mussweiler and Strack, 1999), suggesting that people first
consider whether the anchor is plausible and then look for ways in which their own judgement is
similar, thus naturally resulting in bias towards it. The implication of this is that if the anchor is
implausible or irrelevant, it will not significantly affect the subsequent judgement, which is
supported by Sugden et al. (2013) in their study into different types of anchor.
The literature is divided in its support for each model, but Epley and Gilovich (2001, 2005)
conducted a series of experiments to show that the psychological mechanism employed is not the
same in every case, and can in fact depend on the type of anchor involved. Their papers argue, and
their results imply, that when an anchor is self-generated, there is no reason for an individual to
believe it could be perfectly correct, as they have derived it themselves from a typically irrelevant
source, so they employ the anchoring and adjustment process. On the other hand, when the anchor is
provided by the experimenter, it could feasibly be informative because the subject does not know of
its true origins, and so the process of selective accessibility takes places. Ultimately there is no single
mechanism that fully accounts for the anchoring phenomenon and so further research is required to
develop an all-encompassing model.
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More recently, the literature has moved on to consider the factors that affect susceptibility to
anchoring, which usually fall into one of two brackets: the type of anchor, and individual human
traits. The type of anchor refers to whether it is self-generated or externally-provided and whether it
is feasible or not; the relevance of each has already been discussed.
Regarding individual differences, it has been found that different types of people are
influenced by anchoring to varying degrees. In finding that introverts with high agreeableness are
more susceptible to anchoring, Eroglu and Croxton (2010) showed that personality plays a part,
while McElroy and Dowd (2007) concluded that the same could be said for people who are open to
new experiences. This area of the literature is developing and this paper aims to add to it by
considering the relationship between CA and anchoring. The small number of studies that have
already explored the link will be discussed in Section 2.3, but first the literature on anchoring in a
price judgement scenario will be covered.
2.2 Price judgement and anchoring
Asking individuals to state their maximum WTP or minimum WTA are two different ways of
establishing their valuation of a good. As Simonson and Drolet (2004) posit, the study of WTA is
becoming more important with the growth of online peer-to-peer market places such as Ebay.com,
because more consumers are becoming sellers as well.
Contrary to standard economic theory, behavioural economics has found that “willingness to
accept is usually substantially higher than willingness to pay” (Horowitz and McConnell, 2002, p.
426). The primary explanation for this, supported by Thaler (1980) and Kahneman et al. (1990), is
the “endowment effect”, the principle that a good is more valuable to somebody when they already
own it, due primarily to loss aversion.
In the literature studying the effects of anchoring on WTP and WTA judgements there is
inconsistency in results. Finding that anchoring has a greater effect on WTA than WTP, Sugden et al.
(2013) offer a convincing argument as to why this is the case. They explain that experiment
participants are more accustomed to buying than selling; people have less experience in accepting
payments for goods and are thus more influenced by the irrelevant anchor when making WTA selling
decisions.
Interestingly, Simonson and Drolet (2004) found the opposite to be true, concluding that
anchoring had a greater effect on WTP. In their WTA treatment, however, they asked participants to
imagine they had received the item as a gift and wanted to sell it. This introduces a potential framing
effect, whereby subjects are less likely to experience the endowment effect with a good that they
have neither bought themselves nor used, thus possibly explaining why the results are unusual.
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2.3 Cognitive ability
The effect of CA on economic preferences and behavioural biases has been explored in a
number of recent studies, and it is often found that more intelligent people behave “more like the
textbook model of ‘economic man’” (Bergman et al., 2010). Benjamin et al. (2006) and Dohmen et al.
(2007) looked into the effect of CA on small-scale risk aversion and impatience, respectively, finding
that higher CA correlated with lower susceptibility to these “anomalous preferences” (Benjamin et al.,
2006, p. 35). While the criticism could be made that the experiment in Benjamin et al. (2006) only
tested students, a very particular demographic, similar results were found by Dohmen et al. (2007),
who tested around 1000 German adults, implying that the homogeneous subject pool did not
influence results. Furthermore, Frederick (2005) had similar findings when running a lottery
experiment in hypothetical fashion.
Literature investigating whether CA is related to the anchoring bias, however, continues to be
underdeveloped and inconclusive. There is stark contrast between the results of BEJS on the one
hand, and of Oechssler et al. (2009), Stanovich and West (2008), and Furnham et al. (2012) on the
other. Initially, BEJS followed a very similar experimental design to the first ALP experiment, asking
participants for their maximum WTP for some familiar consumer goods. Subjects then completed a
CA test, with the results showing that those who scored higher on it were less susceptible to
anchoring. BEJS were simply testing for the existence of the relationship and no attempt is made to
explain why it is observed.
The other three papers, however, found no significant correlation between CA and anchoring
but this could be due to the way they measured CA. Whilst BEJS incorporated a professionally
developed 44-question intelligence test, Oechssler et al. (2009) used only a short three-question
cognitive reflection test, and Stanovich and West (2008) used self-reported SAT results, which
provide a much less reliable measure. Aside from this, it is worth noting that BEJS is the only one of
these papers to have considered anchoring in a price judgement scenario; the other three focused on
arbitrary topics such as the height of a redwood tree (Stanovich and West, 2008) and the population
of Ukraine (Furnham et al., 2012). This could explain the disparity in their results but further
research would be required to test this.
2.4 Testing of cognitive ability
In arguing that further research is required into the effects of CA on decision-making,
Frederick (2005) developed a simple three-question cognitive reflection (CR) test. These questions
are designed so that the immediately obvious answer is wrong, thus ensuring that a correct answer
requires the overcoming of the spontaneous “System 1 process” (Frederick, 2005, p. 27). Thus, the
test distinguishes between people who tend to make spontaneous decisions, and those who are more
reflective.
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There are two considerable drawbacks to Frederick’s test, however. The first is that CR
represents just one aspect of CA, and BEJS found it to have little effect on anchoring. The second is
that with only three questions, it is difficult to differentiate effectively between participants of
varying ability. To overcome these issues, Sousa (2010) developed a 12-question test, split into four
sections on sequential, verbal, and quantitative reasoning, as well as Frederick’s CR. While perhaps
not as effective as a full intelligence quotient (IQ) test, it does provide an improved general measure
of CA compared with Frederick’s test.
One further way of measuring CA is with self-reported SAT scores, as used by Stanovich and
West (2008). This is convenient for both the experimenter and the subject, however, as noted in
Kuncel et al. (2005), self-reported grades are “less construct valid than many scholars believe” (2005,
p. 63) and “unlikely to represent accurately the actual scores of students with…low ability” (2005, p.
74).
3. Experimental design
I conducted an incentivised experiment to test the aforementioned hypotheses using the
online survey platform Survey Gizmo, although the format was similar to that of a laboratory
experiment. It was made up of three sections: valuation tasks, a CA test, and a demographic
questionnaire.
3.1 Valuation tasks
As with BEJS and ALP, the valuation tasks were used to test for anchoring and were of
between-subject design. Each participant firstly was placed at random into either the WTP or WTA
treatment, and then into a high anchor treatment or low anchor treatment. Subjects therefore took
part in any one of four possible treatments that shall be called WTP High, WTP Low, WTA High, and
WTA Low. Those in the High treatments were exposed to a £17 anchor figure, and those in the Low
treatments were exposed to a £3 anchor. Participants then valued four standard consumer products:
a bottle of wine, a box of Belgian chocolates, a recipe book, and an 8GB USB memory stick.
Before the valuation tasks commenced, instructions were given, and questions were asked to
check participants’ understanding of the task and payoff procedure. The instructions for the WTP and
WTA treatments were framed in a similar style, but different in terms of key points. Those in the WTP
procedure were first asked to state ‘yes’ or ‘no’ on whether they would pay a certain amount for the
product. The fact that this amount, either £3 or £17, was being used as an anchor was not explicitly
stated, so as not to inform the subject of the nature of the test. Subjects were then asked to give their
maximum WTP, which, according to neoclassical theory, should have represented the point at which
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they were indifferent between buying and not buying. On the other hand, subjects in the WTA
procedure were asked to imagine that they owned the product, and then state the minimum amount
for which they would be willing to sell (the WTA decision). Figure 1 shows the decisions faced by a
WTA High subject regarding the bottle of wine.
Figure 1 Bottle of wine, WTA High treatment
In line with BEJS, to incentivise the experiment, one participant was selected at random for
each product. A coin was flipped to determine whether their yes/no decision or their valuation
decision would be taken into account. In the WTA treatment, if the yes/no decision was selected then
it was implemented and the subject either kept the product (if their answer was ‘no’) or sold it to me
for the anchor amount (if their answer was ‘yes’). If the minimum WTA decision was implemented,
the Becker-DeGroot-Marschak procedure (Becker et al., 1964) was used and an integer price between
£0 and £20 was drawn at random; if it was higher than the subject’s minimum WTA then they sold
the product to me for that price, and if it was lower, no transaction took place. This process was
similar for the WTP decision, except that when a transaction should have taken place, instead of
forcing the participant to buy the product, it was simply offered to them at the price, in order to avoid
unwanted real losses. The purpose of this incentive structure was to encourage participants to
behave truthfully.
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The use of an externally generated anchor is worthy of discussion. This study did not
replicate BEJS’s use of a self-generated anchor (derived from the subject’s social security number)
partly due to technical restraints but equally to ascertain whether their results held when using a
different type of anchor. As both types have been found to induce the anchoring effect, this was not
expected to significantly impact the results. Reasonable anchor prices of £3 and £17 were used in
light of Sugden et al.’s (2013) findings that anchors are more effective when considered to be feasible
values.
The products displayed in this experiment were similar to those in BEJS and ALP: standard
consumer goods with which almost all participants would have had some experience. BEJS and ALP
asked subjects to consider six products but reducing this to four was sufficient for my analysis; ALP
were also investigating relative valuations, hence their use of two bottles of wine of differing quality
and two different types of chocolates. To control for any framing effect and thus allow for comparison
across treatments, every participant saw exactly the same products, with the same neutral
description.
3.2 Cognitive ability test
The valuation tasks were followed by the CA test developed by Sousa (2010), which was
made up of 12 questions testing four different categories of CA: Frederick’s (2005) cognitive
reflection, quantitative reasoning, verbal reasoning, and sequential reasoning. This test gave a better
general measure of CA than Frederick’s three-question test, and the 12 questions allowed for a wide
enough range of scores to differentiate participants effectively, which was crucial for the statistical
analysis.
Figure 2 Example cognitive ability question
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Subjects were restricted to 12 minutes to complete the test, increasing the likelihood that
scores were based on ability and not on the willingness to spend additional time on it. Participants
were reminded of their anonymity and specifically asked not to cheat; however there was no way of
controlling for this and so a certain degree of goodwill is assumed. The questions were randomised to
avoid systematic bias, and, as specified by Sousa (2010), subjects were not told which category of CA
was being tested in each question.
3.3 Demographic questionnaire
The demographic questionnaire was administered at the end of the experiment, and asked
participants to disclose their gender, age, level of education, and income. Age and income were given
in ranges to increase anonymity. These details were acquired as such exogenous factors could
potentially impact an individual’s price judgement in the valuation tasks and therefore integrating
them into my regression analysis as control variables would isolate the effect of the anchor.
3.4 Subject pool
My subject pool was initially made up of willing family and friends, but a number of them
subsequently passed the link onto others to broaden the group. Almost all previous laboratory
experiments investigating the anchoring heuristic have been undertaken solely by university
students. I therefore looked to increase the heterogeneity of my subjects by asking people of a wide
range of ages and incomes to take part, thus adding to the external validity of the results.
4. Results and discussion
In total 203 people participated in my experiment; 58 in the WTP High treatment, 53 in WTP
Low, 47 in WTA High, and 45 in WTA Low. Participants were evenly split in terms of gender. 50% of
subjects were aged 18-24 and 41% were aged 45-64, reflecting the fact that the majority of
participants were either my peers or from my parents’ generation. 81.59% of participants were
undertaking or had undertaken higher education studies.
The statistical analysis consists of a combination of nonparametric and regression analyses to
test the two hypotheses. Firstly, however, the results of the CA test shall be considered, as they play a
key part in the second hypothesis.
4.1 Cognitive ability
Scores in the CA test ranged from 2 to 12 (out of 12), with a mean of 7.28, a median of 8 and a
standard deviation of 2.6. Scores across all four treatments were not significantly different. Figure 3
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shows the full distribution of scores, with some skew towards the upper end, but a fairly even
distribution overall.
The cognitive reflection, quantitative reasoning and verbal reasoning questions had mean
scores (out of 3) of 1.43, 1.94, and 1.42, respectively, as well as relatively even distributions. The
scores of the sequential reasoning, however, had a mean of 2.48, and their distribution was heavily
skewed towards the higher end. This implies that the sequential reasoning questions were not as
successful as the other three types in distinguishing between people of different abilities, especially
considering 57% of participants scored 3 out of 3.
Nevertheless, the test as a whole was clearly effective in displaying a wide range of overall
ability.
4.2 Hypothesis 1
To test the first hypothesis, that the anchoring effect is present in my data, nonparametric statistics
are used initially. Table 1 shows the difference in average WTP between the WTP High and WTP Low
groups for each product individually and as an average across all four products. Mann-Whitney U
tests are run to test the null hypothesis that both groups are drawn from the same distribution, as
was done by Fudenberg et al. (2012) when comparing a high anchor group against a low anchor
group in their paper investigating the anchoring effect on WTP and WTA decisions. Due to the nature
of my high/low anchor binary variable, I felt this to be a more appropriate nonparametric test than
the Pearson Correlation used by BEJS. Table 2 displays the same information but for the WTA
treatments. Both tables of results give clear evidence of anchoring: in both the WTP and WTA
treatments, the high anchor groups gave higher valuations for every product.
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The ratios between low anchor maximum WTP and high anchor maximum WTP vary from 1.36 to
1.90 for each product, and the overall ratio is 1.56, showing that those in the WTP High treatment
valued the products at 56% higher than in the WTP Low treatment. These ratios are shown to be
significant by the Mann-Whitney U test, which allows me to reject the null hypothesis of homogeneity
across the two groups at the 1% significance level in all cases except that of the USB stick, where it
can be rejected at the 5% level.
The story is similar regarding subjects’ minimum WTA with ratios ranging from 1.83 for the
chocolates to 2.37 for the recipe book. The overall ratio is 2.08, showing that subjects exposed to the
£17 anchor stated minimum WTAs of more than double those of their low anchor counterparts. As
seen with the WTP treatment, the Mann-Whitney U test statistic is significant at the 1% level across
the board. These results therefore support the first hypothesis that the difference in price
judgements between the High and Low treatments is statistically significant and suggest that this
experiment has indeed induced the anchoring effect.
Table 1 Average WTP in GBP sorted by anchor and product
Anchor Wine Chocolates Recipe Book USB Stick Average for all products
Low (£3) (n=53) 6.32 6.28 5.49 5.74 5.96
High (£17) (n=58) 8.62 9.65 10.41 8.39 9.27
Ratio 1.36 1.54 1.90 1.46 1.56
Mann-Whitney test stat
z -2.802 -4.169 -4.575 -2.149 -4.540 (p-value) (0.005) (<0.001) (<0.001) (0.032) (<0.001)
Table 2 Average WTA in GBP sorted by anchor and product
Anchor Wine Chocolates Recipe Book USB Stick Average for all products
Low (£3) (n=47) 6.79 6.14 5.43 6.22 6.15
High (£17) (n=45) 14.1 11.26 12.86 12.91 12.78
Ratio 2.06 1.83 2.37 2.08 2.08
Mann-Whitney test stat
z -5.752 -5.253 -5.775 -4.810 -6.660
(p-value) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)
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To check the robustness of these findings, regression analysis was implemented, allowing for
the control of certain exogenous factors affecting subjects’ price judgements. A number of OLS
regressions were run, with the dependent variables being the maximum WTP or minimum WTA
valuations (depending on the treatment). Logarithmic transformations were applied to the
dependent variables for two reasons: firstly, in logs, the distributions were closer to normality, and
secondly, this allowed for a percentage change interpretation of the independent variable
coefficients, which was more effective for comparison across treatments and between products. The
independent variable of interest was a high/low anchor dummy variable, the coefficient of which
represents the anchoring effect. The control variables were a gender dummy, a higher education
dummy, income category dummies and age category dummies. Table 3 displays the anchor dummy
coefficients that were extracted from the OLS regressions run.
These results represent the percentage difference in valuation between high and low anchor
subjects, when gender, education level, income and age are held constant. Ceteris paribus, the average
WTP for all products is 42.3% higher for those people who were exposed to the £17 anchor than
those exposed to the £3 anchor. The equivalent figure in the WTA treatment is 78.3%. The above
results are all statistically significant when tested at the 1% level, except for the wine WTP, which is
significant at the 5% level and the USB WTP, which is not significant. No other independent variables
in the regressions were found to be statistically significant.
The reason Table 3 includes fewer observations than Tables 1 and 2 is because not all
experiment participants were willing to disclose their demographic characteristics and thus their
data were dropped from the regressions. These results support the previous nonparametric findings
that the anchor does have a significant effect on a person’s subsequent price judgement and therefore
I am able to reject the null hypothesis of Hypothesis 1, providing further evidence to support the
findings of ALP and BEJS.
Comparing WTP and WTA, it is clear that the anchoring effect is greater when making selling
decisions; the WTP anchoring effects range from 26.8% to 75.2% whereas the WTA range is 72.3% to
Table 3 Anchoring effects (regression coefficients) in the WTP and WTA treatments
Wine Chocolates Recipe Book USB Stick Average for all products
WTP Coefficient 0.268 0.468 0.752 0.257 0.423 (n=101) (p-value) (0.012) (<0.001) (<0.001) (0.124) (<0.001)
WTA Coefficient 0.756 0.723 1.119 0.748 0.783 (n=87) (p-value) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)
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111.9%. The overall effect is 78.3% for WTA compared with only 42.3% for WTP. These results
support the findings of Sugden et al. (2013), who offer the convincing explanation that this difference
is due to the fact that subjects have more experience in purchasing goods than selling them, and are
therefore less influenced by the arbitrary anchor when in a buying scenario.
A further observation of interest from my results is that the anchoring effect is clearly
stronger on certain products than others. Following on from Sugden et al.’s (2013) argument, it is
reasonable to assume that most participants have had more transaction experience with wine and
chocolate than with a recipe book, so it is not surprising that the effect is greater on the recipe book.
This, however, would not explain why the anchoring effect is insignificant on WTP for the USB stick. A
second possible explanation could therefore be that in reality the price range of a typical recipe book
is wider than that of a USB stick, and so the range of perceivable values is narrower for the USB stick,
leading to a less pronounced anchoring effect. Further research would be required to explore these
points.
4.3 Hypothesis 2
Having established that the results support the existence of the anchoring effect, the study moves on
to consider the second hypothesis that more intelligent people are less susceptible to it. To test this,
the subjects were split into high and low CA groups and regressions were run in the same format as
those described above, so as to allow for comparison of the anchoring effect regression coefficient
between the two ability groups. Those who scored below the median, from 2 to 7 (out of 12), were
placed in the low CA group and those who scored the median or above, from 8 to 12, were placed in
the high CA group.
Table 4 WTP anchoring effects (regression coefficients) for low CA and high CA groups
Wine Chocolates Recipe Book USB Stick Average for all products
Low CA Coefficient 0.381 0.490 0.816 0.184 0.443 (n=51) (p-value) (0.024) (0.001) (0.001) (0.416) (0.003)
High CA Coefficient 0.201 0.493 0.674 0.014 0.377 (n=50) (p-value) (0.237) (0.008) (0.061) (0.961) (0.014)
Difference 0.180 0.003 0.142 0.170 0.066 (p-value) (0.442) (0.991) (0.734) (0.632) (0.749)
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Table 4 displays the results for the WTP treatments, which would not appear to support my
hypothesis. The differences between the coefficients are of small magnitude and, more importantly, a
number of the coefficients themselves are not statistically significant. For both the wine and the USB
stick, whilst the difference in coefficients is noticeable (0.18 and 0.17 respectively), only the low CA
wine result is significant. On the other hand, both Belgian chocolates anchoring coefficients are
significant but they are almost exactly the same, implying no difference between high and low CA.
However, to test the differences formally, Chow tests were conducted. This involved
introducing a high/low CA dummy variable and interacting it with all other independent variables,
which allowed me to test the null hypothesis that the two coefficients are not statistically different.
The coefficient of the CA/anchor interaction term represents the difference, which can be analysed in
line with its p-value. Not a single difference is found to be statistically significant, and most
importantly the overall difference for all products is only 0.066 and not significant (p=0.749). These
results lead to the conclusion that when making a purchasing price judgement, anchoring has a
similar effect on all participants, regardless of their CA.
The WTA results give a little more support to the second hypothesis, but not overly so. Shown
in Table 5, the anchoring effect regression coefficient for the average WTA of all products is 0.57 for
the high intelligence group, and 0.91 for the low intelligence group, with a difference of 0.34 that is
very close to significance at the 10% level, with a p-value of 0.102. Although all anchoring coefficients
are significant for the four individual products, only one difference is significant at the 10% level, and
that is of the chocolates (p=0.098). The USB treatment displays no difference at all across the two
intelligence levels.
Considering these results contradict those of BEJS, I decided to investigate further by
excluding from my regressions the participants who found themselves in the central 25% of CA
Table 5 WTA anchoring effects (regression coefficients) for low CA and high CA groups
Wine Chocolates Recipe Book USB Stick Average for all products
Low CA Coefficient 0.876 0.903 1.153 0.744 0.907 (n=42) (p-value) (<0.001) (<0.001) (<0.001) (0.001) (<0.001)
High CA Coefficient 0.538 0.447 0.932 0.754 0.566 (n=45) (p-value) (0.004) (0.074) (0.004) (0.020) (0.001)
Difference 0.338 0.456 0.221 -0.01 0.341 (p-value) (0.186) (0.098) (0.541) (0.977) (0.102)
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scores. In removing scores of 7 or 8 on the test, I could compare participants at more extreme ends of
the CA spectrum. Tables 6 and 7 summarise the findings for WTP and WTA respectively.
Regarding the WTP treatment, the differences do increase noticeably but it remains the case
that they are not significant. It should also be noted that only one of the high CA coefficients is
significant, which could imply that in the high intelligence group, the anchoring effect is not present;
however this is more likely to be the result of a smaller sample size. The average difference across all
products is still only 0.21, and found not to be significant by the Chow test.
The second set of WTA findings are not dissimilar from those found in Table 5. The overall
difference for all products has hardly changed, but it is further from significance with a p-value of
0.190. All ten anchor coefficients are significant, showing that subjects of all intelligence levels are
Table 6 WTP anchoring effects (regression coefficients), with CA test scores of 7 and 8 excluded Wine Chocolates Recipe Book USB Stick Average for all
products
Low CA Coefficient 0.398 0.420 0.995 0.070 0.423 (n=37) (p-value) (0.065) (0.026) (0.006) (0.823) (0.045)
High CA Coefficient 0.070 0.350 0.521 -0.132 0.210 (n=39) (p-value) (0.664) (0.061) (0.199) (0.646) (0.123)
Difference 0.328 0.069 0.474 0.202 0.213 (p-value) (0.206) (0.784) (0.368) (0.632) (0.365)
Table 7 WTA anchoring effects (regression coefficients), with CA test scores of 7 and 8 excluded
Wine Chocolates Recipe Book USB Stick Average for all products
Low CA Coefficient 0.941 0.883 1.169 0.739 0.911 (n=33) (p-value) (<0.001) (<0.001) (<0.001) (0.016) (<0.001)
High CA Coefficient 0.442 0.554 1.051 0.815 0.553 (n=31) (p-value) (0.049) (0.091) (0.028) (0.068) (0.024)
Difference 0.499 0.329 0.118 -0.076 0.358 (p-value) (0.122) (0.336) (0.804) (0.878) (0.190)
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influenced by the irrelevant £3 or £17 price but, again, no differences are significant. Having said this,
there is a clear contrast between certain products: the difference for wine is large and approaching
significance, whilst the differences for the recipe book and the USB stick are arbitrary and therefore
far from significance. This will be discussed in section 4.4.
Having conducted further analysis on both treatments, it is clear that the results of this
experiment fail to support the findings of BEJS in both the buying and selling contexts. Therefore, it is
not possible to reject the second null hypothesis that there is no significant difference in
susceptibility to anchoring across the different levels of CA.
4.4 Discussion
This paper set out to test for the existence of a relationship between CA and anchoring, and it
has ultimately found no significant link. While I may speculate below that a weak relationship does
exist, the overall findings of my experiment suggest that it does not, and this has certain economic
and psychological implications. In contrast to BEJS and the initial predictions, I find that, in the
context of price judgement anchoring, more intelligent people do not necessarily “behave more like
the textbook model of ‘economic man’” (BEJS, 2010, p.67). Participants of all levels of CA are
influenced by the anchor in a significant way, regardless of whether they are making a buying or
selling decision, showing that more intelligent people are not more likely to reveal their true
preferences. In psychological terms, my results suggest that a person of higher CA is no more capable
of detaching themselves from the arbitrary anchor or referring to past experience when valuing a
good than someone of lower CA.
Furthermore, when conducting my statistical analysis, I checked whether any of the
individual types of CA, such as cognitive reflection or quantitative reasoning, were significantly
related to susceptibility to anchoring and found them not to be in all cases; however to test this
properly more than three questions per type would be required to elicit a good measure of each
individual ability. What my results are therefore showing is that the psychological processes that
affect how a person behaves in the presence of an anchor are not related to intelligence, and certainly
are not captured by the CA test implemented here.
On the face of it, my results would appear to completely contradict those of BEJS. The
variation in our results could potentially be explained by the variation in our experimental
procedure, especially regarding the CA test. BEJS argue that Oechssler et al. (2009) and Stanovich and
West (2008) find no relationship between CA and anchoring because they use poor proxies for
intelligence, and they would probably extend the same argument to my 12-question test, which was
not as comprehensive as their 44-question professionally developed psychometric test of general
intelligence. Perhaps if the same test had been used, higher significance would have been found.
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Having said this, our findings are not necessarily as different as they may first appear. For the
average of all products, BEJS did have a statistically significant difference of 0.550, but the differences
for three of their six individual products were not significant, showing that their results were neither
definitive nor entirely convincing. It could be that BEJS’s paper is an example of a “false positive”
(Maniadis et al., 2013, p.1), whereby their data reveal a relationship that does not actually exist.
In most cases, however, I did find a notable difference in anchoring effect between those of
high and low intelligence, though the Chow test found it not to be significant. My results would imply
that CA is not entirely irrelevant, but that further research is required to explore the relationship in
more depth. This is especially true considering that in both treatments the difference in anchoring
coefficient was typically larger and closer to significance when valuing the wine and chocolates, yet
non-existent when valuing the USB stick. The fact that the effect of intelligence varies between
products implies that it does play some part in certain cases. Similarly, while not statistically
significant, the differences are clearly larger in the WTA treatment than in the WTP treatment. Again,
this would superficially imply that the difference in intelligence has more of an effect in the selling
scenario, but this would need to be investigated further.
In summary, despite the speculation that a weak relationship may exist, the lack of
significance in the results fails to support the findings of BEJS. In running an experiment of similar
structure, I found strong evidence for the existence of anchoring and thus support for the first
hypothesis. However, the results do not support the robustness of BEJS’s conclusion that intelligence
and anchoring are negatively linked in either the buying context, as BEJS investigated, or the selling
context; even though the differences in the WTA treatment were bigger than in the WTP treatment,
they still were not significant. This leads me to conclude that my findings instead support those of
Oechssler et al. (2009), Stanovich and West (2008), and Furnham et al. (2012).
5. Evaluation
5.1 Experimental procedure
The results are quite unambiguous in rejecting the second hypothesis. However, considering
the experimental procedure departed from the standard practice in a number of ways, the external
validity of these findings must be evaluated.
The primary issue in this regard is the incentive structure of the valuation tasks. While I
believe the payoff mechanism was sound for the WTA treatments, the WTP mechanism could have
been conducted differently. Rather than ensuring the buying transaction took place at the
appropriate price, subjects were only offered the opportunity to buy at that price. The purpose of this
was to avoid subjects making real losses, however this could have been accounted for if the relevant
participants had been given endowments from which they could have purchased the product. Had
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the incentive structure been stronger, the experiment may have induced more truthful responses in
the WTP treatments. Perhaps the findings would have then been more significant regarding the
second hypothesis, as subjects with higher CA scores may have applied more effort and concentration
in the tasks, and thus may have been less influenced by the anchor.
The first hypothesis would not appear to have been similarly affected, considering the WTP
treatments induced less anchoring, which does fall in line with the findings of past literature.
Additionally, the payment structure was correct for the WTA treatment and yet the Hypothesis 2
findings were still not significant when subjects were selling.
A further factor is that the CA test was not incentivised and I was unable to control the
amount of effort or time people were willing to put into it, which raises the pertinent question of
whether the test was effective in measuring intelligence. It is possible that someone of high
intelligence scored a low mark simply because they completed the experiment at a busy time or
without due attention. I believe this is the point that most draws into question the validity of the
results. The benefit of not incentivising such a test in an online environment is that there is no
resulting incentive to cheat for the purposes of monetary gain. The downside is that there is no
reason, aside from goodwill, for a participant to try and do the best they can, especially knowing that
the test was taken anonymously. While I do not think participants would have cheated, it is unlikely
that every person’s score perfectly reflects the best score they could have got, which has implications
for my results.
Whether the test was a good proxy for intelligence is another matter. Ideally, subjects would
have completed a full IQ test, as BEJS’s subjects did, but considering the extra time this would have
taken, the test used was a best alternative. In just 12 questions it measured four types of skill and
gave a good general measure of CA. That said, the difficulty of the sequential reasoning questions
could be increased due to reasons that have already been discussed: the mean score for these three
questions was 2.48 and 57% of subjects answered all three correctly, showing that they did not
successfully distinguish between people of different ability.
A more general problem with the experimental procedure is that it was conducted online,
which inevitably brings a lack of control when compared to a laboratory experiment. While the
approach was designed to control for as many relevant factors as possible, it could not fully account
for exogenous factors such as distractions, cheating, or even disinterest in the task. This is an
unfortunate side-effect of conducting research in this way.
Despite the above points, the experiment was considered successful in several areas.
Standard procedure was followed regarding the test for anchoring and as a result I found the
phenomenon to be present in the data. Additionally, products and anchor values remained constant
across all treatments, allowing proper comparisons to be made. Although the results do not support
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BEJS’s findings, they do have interesting implications and show that the area will benefit from more
attention.
Furthermore, the use of an online platform allowed for a subject pool that was more
heterogeneous than in the majority of the literature. Most previous anchoring experiments have
subject pools comprised entirely of students, representing a very specific demographic group,
typically with low incomes. Half of participants in my experiment were aged over 24, and incomes
ranged from £0 to £100,000+, making my subjects more closely representative of the general
population than is usually the case. This would therefore add to the external validity of my findings.
I should note that a total sample size larger than 203 would have been desirable to eliminate
the possibility that the lack of significance of my results is simply caused by too few participants. This
is especially the case regarding the second set of regressions that excluded all subjects who scored 7
or 8 in the test, seeing as a number of the differences were large but still not considered significant by
the Chow test.
5.2 Further research
This paper is unique in exploring the relationship between intelligence and anchoring in a
selling context and as a result experimental replication is required to check the robustness of the
findings. As Maniadis et al. (2013) argue, “a few independent replications dramatically increase the
chances that a given original finding is true” (2013, p.1). The next step in this area of research would
therefore be to conduct an experiment similar to this one, in a more controlled laboratory setting,
taking into account the points I outlined above, including a revised incentive structure designed to
elicit responses that reflect real world behaviour.
Replication is especially important considering the contrast between these results and those
of BEJS. If it is found that there is indeed no relationship between intelligence and anchoring in a
price judgement scenario, then there would be two directions in which the literature could move. The
first would be to check whether these results hold in other anchoring contexts such as when giving
probability estimates. The second would be to continue investigating the individual differences, other
than CA, that do influence susceptibility to anchoring, such as personality and expertise.
The results do suggest, however, that intelligence has a small effect on anchoring for certain
products but not for others, and that this effect is different for WTP and WTA. I think these two points
are worthy of further investigation
Finally, this paper makes no attempt to distinguish between the different types of CA because
it was taking into account a general measure. Further research might replicate this experiment whilst
testing each type of CA individually and more comprehensively, to establish whether a relationship
does in fact exist, if only in certain cases.
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5.3 Conclusion
This paper set out to test the robustness of BEJS’s findings, and to extend their research
question through an investigation into whether more intelligent people are also less susceptible to
the anchoring effect when stating their minimum WTA. An online experiment was run comprising
four valuation tasks of standard consumer products, followed by a 12-question test of CA. Having
found strong evidence of the anchoring heuristic within the data, I proceeded to check for the
existence of a relationship between intelligence and anchoring in order to test the second hypothesis.
In both the WTP and WTA treatments, the results do not support those of BEJS. Evidence was
not found to suggest that intelligence and anchoring are significantly related, implying that when
making a price judgement, people of higher CA are just as influenced by an arbitrary number as those
of lower CA. This in turn suggests that more intelligent people are no more likely to act rationally or
reveal their true preferences in this context. Whilst it could be that the lack of significant results is
due to issues with experimental procedure, it is also possible that these findings are robust and
externally valid; further research is required to establish whether this be true.
This paper contributes to the literature by drawing into question the findings of BEJS, in both
a buying and a selling context. In the few papers that explore the relationship between intelligence
and anchoring, there is already discord, giving support to the argument that further investigation is
required to develop a deeper and more nuanced understanding of the matter.
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6. References Ariely, D., Loewenstein, G., Prelec, D. (2003). Coherent arbitrariness: stable demand curves without
stable preferences. Quarterly Journal of Economics, 118, 73-105.
Benjamin, D., Brown, S., Shapiro, J. (2006), Cognitive ability and anomalous preferences. Mimeo, Harvard University.
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Economics Letters, 107 66-68.
Carlson, B. W. (1990). Anchoring and adjustment in judgments under risk. Journal of Experimental Psychology: Learning, Memory and Cognition, 16, 665-676.
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Influence of Anchoring Cues. Journal of Individual Differences, 33(2), 89–93.
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Appendix A: Questionnaire
A.1 Introduction
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A.2 WTP Explanation page
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A.3 WTP Questions to check understanding
The text in blue appeared after the corresponding question had been answered.
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A.4 WTA Explanation page
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A.5 WTA Questions to check understanding
The text in blue appeared after the corresponding question had been answered.
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A.6 Wine valuation task
This is an example from the WTP High treatment.
Values are entered for demonstration purposes; subjects had blank spaces in which to answer.
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A.7 Belgian chocolates valuation task
This is an example from the WTP Low treatment.
Values are entered for demonstration purposes; subjects had blank spaces in which to answer.
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A.8 Recipe book valuation task
This is an example from the WTA High treatment.
Values are entered for demonstration purposes; subjects had blank spaces in which to answer.
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A.9 8GB USB stick valuation task
This is an example from the WTA Low treatment.
Values are entered for demonstration purposes; subjects had blank spaces in which to answer.
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A.10 Cognitive ability test
Questions were randomised.
Correct answers are entered here for demonstration purposes.
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A.11 Demographic questionnaire
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Appendix B: Regression output tables
All regressions took the same format, so one WTP and one WTA example are given.
B.1 Regression output table for ‘WTP: Average for all products’ (found in Table 3)
Number of obs = 101 F (14, 86) = 2.66 Prob > F = 0.003 R² = 0.302 Adj R² = 0.189
Average WTP (logs) Coefficient Standard Error t P > │t│ [95% confidence interval]
High anchor .4228681 .0946679 4.47 0.000 .2346746 .6110616
Male .0286447 .0941634 0.30 0.762 -.1585461 .2158354
Higher education
.0554399 .1323124 0.42 0.676 -.2075885 .3184682
Income
Group 2 -.0962399 .1822303 -0.53 0.599 -.4585017 .2660219
Group 3 -.048309 .2014169 -0.24 0.811 -.4487126 .3520945
Group 4 .0531143 .3686012 0.14 0.886 -.6796406 .7858693
Group 5 .0337771 .2391853 0.14 0.888 -.4417076 .5092618
Group 6 -.520335 .2936453 -1.77 0.080 -1.104082 .0634124
Group 7 -.2499797 .233069 -1.07 0.286 -.7133055 .213346
Age
Group 2 .2493917 .2550043 0.98 0.331 -.25754 .7563234
Group 3 -.1318741 .2797127 -0.47 0.639 -.6879245 .4241763
Group 4 .0068182 .2055535 0.03 0.974 -.4018086 .4154451
Group 5 -.0570527 .1924786 -0.30 0.768 -.4396874 .325582
Group 6 .0894002 .3037076 0.29 0.769 -.5143505 .6931508
Constant 1.713019 .1465369 11.69 0.000 1.421713 2.004324
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B.2 Regression output table for ‘WTA: Average for all products’ (found in Table 3)
Number of obs = 87 F (14, 86) = 5.96 Prob > F = 0.0000 R² = 0.5366 Adj R² = 0.4465
Average WTA (logs) Coefficient Standard Error t P > │t│ [95% confidence interval]
High anchor .7829722 .0930785 8.41 0.000 .5974235 .9685209
Male .0650118 .0916341 0.71 0.480 -.1176573 .247681
Higher education
-.0895331 .1345627 -0.67 0.508 -.357779 .1787128
Income
Group 2 -.2058362 .219582 -0.94 0.352 -.643565 .2318925
Group 3 -.2613322 .1763257 -1.48 0.143 -.612831 .0901666
Group 4 -.3138622 .2294593 -1.37 0.176 -.7712811 .1435566
Group 5 .083074 .2100037 0.40 0.694 -.3355607 .5017088
Group 6 -.0271214 .2450597 -0.11 0.912 -.515639 .4613962
Group 7 -.0985883 .2125234 -0.46 0.644 -.522246 .3250694
Age
Group 2 -.0272748 .2770223 -0.10 0.922 -.5795085 .524959
Group 3 .7028294 .4623622 1.52 0.133 -.2188728 1.624532
Group 4 .0818781 .1815544 0.45 0.653 -.280044 .4438001
Group 5 .0242351 .1890821 0.13 0.898 -.3526931 .4011633
Group 6 .1121862 .4582636 0.24 0.807 -.8013456 1.025718
Constant 1.7946 .1492053 12.03 0.000 1.497165 2.092036