Variety in mate choice /
Lenton & Francesconi
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ELECTRONIC SUPPLEMENTARY MATERIAL
ESM Table 1. Variety-quality correlations (p-value) by attribute and sex of option set.
Attribute
Female options
Male options
.12
.32
(.11)
(.01)
.07
.03
(.49)
(.82)
.05
.05
(.58)
(.66)
.03
.01
(.88)
(.93)
.06
.07
(.48)
(.44)
.06
.01
(.62)
(.87)
.03
-.07
(.76)
(.35)
Average correlation (s.d.)
.06 (.57)
.06 (.57)
Average correlation (s.d.) excluding
“age”
.02 (.66)
.05 (.65)
Age
Education
Occupation
Smoking
Religion
Height
BMI
Note: We performed the supplemental analyses shown in this table in order to determine
whether the finding of a negative relationship between variety (variance) and number of
proposals might be explained, instead, by an a priori relationship between variety and optionset quality; i.e., perhaps additional variety brings with it lower-quality options and it is for
this reason that people make fewer proposals in events with more variety.
For this argument to work, one has to assume some consensus on the part of choosers as to
what constitutes “quality” for each attribute. We therefore had to do the same in our analysis.
To that end, we used prior speed-dating findings to help us code the “quality” of the attribute
levels. For example, with respect to height (a continuous attribute), “taller is better”. Thus,
the average height of the option sets (male, female) was obtained for each of the 84 speed-
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dating events. The event with the tallest average height was ranked 1, and the event with the
shortest average height was ranked 84 (or N, as ties permitted).
With respect to occupation (a categorical attribute), we distinguished amongst the three
categories described in the primary analysis: professional/managerial occupations, skilled
non-manual occupations, and all of the other occupations. For each event and by sex, we then
computed the proportion of options in professional/managerial occupations vs. all the other
occupations (p1), the proportion of skilled non-manual occupations vs. all other occupations
(p2) and the proportion of other occupations vs. professional/managerial and skilled nonmanual occupations (p3=1-p1-p2). For the initial rankings, we treated each attribute level
separately. That is, the event with the highest p1 was ranked 1 and the event with the lowest
was ranked 84 (or N, as ties permitted). Based on this ranking we computed the variety-p1ranking correlation (r1). We repeated the same exercise for skilled non-manual occupations,
for which the event with the highest p2 was ranked 1 and the event with the lowest p2 was
ranked 84 (or N). (This is based on previous speed-dating research, which established that
more professional and more skilled, non-manual is better).The same was repeated for “other
occupations”, but this time the event with the lowest p3 was ranked 1 and the event with the
highest p3 was ranked 84 (or N). We finally computed the overall variety-occupation
correlation as the weighted average of r1, r2 and r3, where the weights are given by the sample
proportions p1, p2 and p3, respectively.
These two approaches to operationalizing quality were replicated for the remaining
continuous and categorical attributes, respectively. The full set of event codings were as
follows: Age: youngest= 1, oldest= N; Height: tallest=1, shortest=N; Occupation (3
categories; see text for explanation): most professional/skilled, non-manual =1, least
professional/unskilled manual =N; Education: most educated=1, least educated = N; BMI:
Women: lightest = 1, heaviest = N; Men (a non-linear preference, where normal is preferred
to heaviest is preferred to lightest): normal weight = 1, lightest = N; Smoking: nonsmoker =
1; smoker = N; Religion: nonreligious = 1, religious = N.
As readers will note, nearly all of the correlations are very weak and nonsignificant. The
significant correlation for age in the case of male options likely had something to do with the
design of speed-dating events: the company organises events according to age bands (e.g.,
“participants for this event should be men and women between the ages of 18 and
24”).Variety in age distribution within events appears to come from the attendance of
participants who are at the older side of each age band. If we exclude age as an attribute, the
average variety-quality correlations reduce to negligible values. Overall, the evidence
suggests that there is not a prior association between variety and option quality across
attributes and, as such, this does not seem to us a viable alternative explanation for the
results.
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ESM Table 2. Partial correlations (p-values) between chooser attribute variety (variance) and
option attribute variety (variance), controlling for number of speed-daters attending the event.
Attribute
Age
Partial correlation
.29
(.04)
Education
.06
(.59)
Occupation
.07
(.70)
Smoking
.15
(.18)
Religion
.10
(.38)
Height
-.08
(.52)
BMI
.14
(.18)
Average partial correlation (s.d.)
.26 (.05)
Average partial correlation (s.d.) excluding “age”
.18 (.25)
Note: We performed the supplemental analyses shown in this table in order to determine
whether chooser variety correlated with option variety, i.e., whether events in which the
women were diverse were also those in which the men were diverse. This might be an
alternative explanation for the result that choosers made fewer proposals at events in which
the array of options had greater variety. That is, if one assumes that choosers’ preferences are
determined, at least in part, by their own attributes, then attribute variability amongst the
choosers would be indicative of preference variability. And, further, if there is a correlation
between chooser variability and option variability, then the reduced number of proposals
made by choosers facing higher variability option sets could be explained by choosers in this
context encountering fewer options that meet their more diverse preferences, the result of
which is that, as a set, they make fewer offers .
As can be seen above, age was the only attribute for which there was a reliable relationship
between the variability in the chooser set and the variability in the option set: the more
diverse the choosers were in age, the more diverse were the options in age. Again, the speed-
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dating company organises events according to age bands (e.g., “participants for this event
should be men and women between the ages of 18 and 24”). This is the most likely
explanation for age being the only variable for which we observe a statistically reliable
association between degree of variety amongst choosers and degree of variety amongst the
to-be-chosen. Excluding this outlying attribute, there is little evidence to support the idea that
an a priori relationship between chooser preferences and option variety explains our findings.
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ESM Table 3. Regression results: proportion of choosers making 0 proposals.
Predictor
Constant
coefficient
p-
effect size
(s.e.)
value
(ηp2)1
alternative
coefficient2
(s.e.)
1.08
.001
--
.44
(.04)
Variety (Variance)
.04
(.10)
.001
.04
(.01)
Number of options
-.01
(.01)
.23
.01
(.01)
Sex of chooser set
.26
-.00
.001
.10
.01
.95
.00
.00
.79
.00
.04
.004
(.02)
.82
.00
(.02)
Variety × Number of options × Chooser sex
-.001
(.01)
(.02)
Number of options × Chooser sex
.26
(.04)
(.01)
Variety × Chooser sex
-.03
(.02)
(.06)
Variety × Number of options
.03
.01
(.08)
.37
.02
(.04)
.04
(.02)
Note: for all t-tests, degrees of freedom = 167.
1
Partial eta-squared indicates the proportion of variability attributable to a given predictor,
over and above the other predictors.
2
This column reports coefficients (s.e.) from a regression with raw (unstandardized,
uncentred) predictors.
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ESM Table 4. Regression results: proportion of choosers selecting the top-ranked oppositesex speed dater.
Predictor
coefficient
p-value
(s.e.)
Constant
.50
.001
effect size
(ηp2)1
alternative
coefficient2
(s.e.)
--
.54
(.01)
Variety (Variance)
-.03
(.14)
.03
.03
(.01)
Number of options
.01
(.01)
.02
.03
(.003)
Sex of chooser set
.18
.001
.001
.25
.02
.67
.00
.01
.37
.00
.001
(.01)
.02
(.03)
.42
.00
(.01)
Variety × Number of options × Chooser sex
.001
(.004)
(.02)
Number of options × Chooser sex
.19
(.03)
(.002)
Variety × Chooser sex
.01
(.003)
(.02)
Variety × Number of options
-.03
.01
(.01)
.91
.00
.001
(.01)
Notes: for all t-tests, degrees of freedom = 167. Because the probability of choosers making 0
proposals was moderately correlated with the probability of choosers selecting the top-ranked
opposite sex speed dater (r = -.35), it is conceivable that the reason variety appears to yield a
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negative effect on the likelihood of choosing the top-ranked opposite sex speed dater is
because variety makes it more likely that choosers will choose no one at all. To substantiate
the independence of these effects, we re-ran the above analysis, but where the sample was
restricted only to those participants who made at least one proposal. That is, speed daters
making 0 proposals were excluded. If the significant (negative) relationship between variety
and the likelihood of choosing the top-ranked speed dater was due to the significant (positive)
effect of variety on the likelihood of choosing no one at all, then in this new analysis we
should have found that the relationship between variety and the dependent variable was
nonsignificant. This is not, however, what the analysis showed: the effect of variety on the
likelihood of choosing the top-ranked speed dater remained significantly negative (b = -.03,
se = .014, p = .025, ηp2 = .04). The size of the effect even increased (albeit slightly). Thus, we
are confident that ESM Tables 3 and 4 are reporting unique effects of variety on chooser
behaviour.
1 Partial eta-squared indicates the proportion of variability attributable to a given predictor,
over and above the other predictors.
2
This column reports coefficients (s.e.) from a regression with raw (unstandardized,
uncentred) predictors.