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Measuring Collective Intelligence in Groups: A Reply to Credd and Howardson

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Measuring Collective Intelligence in Groups:
A Reply to Credé and Howardson
Anita Williams Woolleya, Yeonjeong Kimb, Thomas W. Maloneb
a Tepper School of Business, Carnegie Mellon University
awoolley@cmu.edu
b MIT Sloan School of Management
{yeonkim, malone}@mit.edu
Abstract. Recent work by Woolley, Chabris, Pentland, Hashmi, & Malone
(2010) and their colleagues finds evidence for a general collective intelligence
factor that predicts a group’s performance on a wide variety of tasks, like the
general intelligence factor does for individuals. Credé and Howardson (2017)
argue that there is not yet sufficient evidence to conclude such a collective
intelligence factor exists. Specifically, C&H suggest that the general factor is not
strongly enough correlated with all the tasks examined, but we point out
problems with their interpretation of the evidence presented. C&H also suggest
that the effort and abilities of the group members are “statistical artifacts” that
may have inflated the correlations observed, but we demonstrate that the data do
not support that interpretation. We concur with C&H about the importance of
improving measures of collective intelligence, and we elaborate upon their
suggestions for future research to better understand the boundaries and the causal
mechanisms of this phenomenon.
Keywords. collective intelligence, group performance, construct validity
1. Introduction
Organizations increasingly rely upon small groups and teams as the locus of work
(Mathieu, Hollenbeck, Van Knippenberg, & Ilgen, 2017). However, we are often
surprised by which teams perform well and which do not, since we lack good tools for
predicting group performance. By contrast, for over 100 years, psychology has had
good tools for predicting many kinds of individual performance, including
especiallyindividual intelligence tests (Schmidt & Hunter, 1998). Despite
controversy about what causes individual intelligence or whether intelligence tests are
systematically biased against certain groups, the ability of individual IQ scores to
predict an individual’s future performance on a wide range of mental tasks remains one
of the most replicated results in all of psychology (Deary, 2012).
Motivated by this work, Woolley, Chabris, Pentland, Hashmi and Malone (2010)
explored the question of whether something analogous to individual intelligence exists
for groups. Is there a similar factor for groups that predicts how well a group will
perform a wide range of different tasks? In a series of studies, the researchers found
evidence for a single statistical factor for a group that explains approximately 30-50%
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of the variance in the group’s performance on a variety of different tasks. By analogy
with individual intelligence (g), they called this factor c for “collective intelligence.
(Woolley et al., 2010). Credé and Howardson (C&H; 2017) argue that the inference of
a general collective intelligence construct is unwarranted because this c factor doesn’t
correlate strongly with all the tasks performed by groups and because of other
“statistical artifacts in the study design and resulting data.
In this paper, we will first clarify how we define collective intelligence (CI) and
summarize the accumulated evidence for the validity of the CI construct. Then we will
respond specifically to C&H’s criticisms of the previous work on collective
intelligence. Finally, we will discuss how to further develop the CI construct to
strengthen its measurement.
2. What is collective intelligence?
Whether it is valid to say that collective intelligence exists depends, of course, on how
we define collective intelligence. There are a number of definitions of CI for which it
is obvious that collective intelligence exists, even without any special testing (e.g., see
Malone, 2018; Malone & Bernstein, 2015). But in this paper, we focus on the definition
of collective intelligence used by Woolley et al (2010):
the general ability of a group to perform a wide variety of tasks.
This is one way of describing what we might call “general factor intelligence” in
which a single general factor predicts the performance of members of a whole
population across a wide range of tasks. With IQ tests of individual humans, for
example, there is a large group of tasks (“mental tasks”) and a population of
individuals (humans) for which the individuals’ abilities to perform the tasks are
predicted by a single statistical factor.
In order to determine whether general factor intelligence exists for a given population
and range of tasks, systematic statistical analysis is needed. The first person to do this
for individual intelligence was Spearman (1904). Using an early equivalent of what
we now call factor analysis, he (and many later researchers) found that (a) a single
statistical factor predicted a substantial part of the variance in individual performance
on a wide range of mental tasks, (b) no other factor predicted nearly this much, (c)
most of the tasks loaded strongly on the first factor, and (d) most of the tasks were
positively correlated with one another (Deary, 2000).
It is important to realize that factor analysis does not always lead to a result like this.
For example, standard personality variables measuring the characteristics of an
individual do not load onto a single general factor. People’s tendency to be
extraverted, for instance, appears to be uncorrelated with their tendency to be
agreeable (Digman, 1990; McCrae & Costa, 1987). And even within the domain of
mental tasks, recent research suggests that certain kinds of face recognition tasks are
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not correlated with the kind of general intelligence measured by IQ tests (Shakeshaft
& Plomin, 2015).
In other words, it is an empirical question whether general factor intelligence exists
for human groups, not just for individuals, and if so, what is the domain of tasks over
which it extends. That is the question that Woolley et al (2010) set out to answer.
2.1. How is the c factor related to previous measures used in organizational
research on groups and teams?
The kind of collective intelligence measured by Woolley et al (2010) differs in at least
two important ways from previous measures used to evaluate teams in organizational
research.
First, CI is broader in scope than most existing measures used to evaluate teams. Most
self-report surveys, for instance, aim to measure relatively narrowly defined aspects
of group functioning with highly overlapping items, and in these cases,
psychometricians look for very high average inter-item correlations. Measures of CI,
on the other hand, are focused on using a more diverse array of tasks to predict a
group’s ability to perform a wide range of different tasks together. Thus the overlap
among items will be considerably less than one would observe with the more focused
measures typically used in teams research. The construct of CI is also much broader
than the single-task measures of team performance typically used in laboratory-based
research (such as creativity or decision making tasks). At the same time, CI is
narrower than more general concepts of team effectiveness, which typically
encompass not only team performance but also the well-being of members and the
socioemotional processes of the group (Hackman, 1987; Mathieu, Maynard, Rapp, &
Gilson, 2008).
Second, in addition to the predictive validity of CI, recent studies have also begun to
explore its convergent and discriminant validity vis-à-vis other commonly studied
group-based states and processes. Across a series of studies, some published and others
under review, researchers have observed a dissociation between CI and indices of group
climate or group member relationships. They have found no correlation between CI and
group satisfaction (Chikersal, Tomprou, Kim, Woolley, & Dabbish, 2017; Engel,
Woolley, Jing, Chabris, & Malone, 2014), relationship quality (Woolley & Aggarwal,
under review), or psychological safety (Glikson, Harush, Kim, Woolley, & Erez, 2016;
Woolley et al., 2010). By contrast, researchers do see a strong association between CI
and transactive memory systems (Kim, Aggarwal, & Woolley, 2016) and some forms
of group learning (Aggarwal, Woolley, Chabris, & Malone, 2015; Woolley &
Aggarwal, under review). Past research on groups has drawn distinctions between the
task vs. socio-emotional processes of a group (Hackman, 1987; Marks, Mathieu, &
Zaccaro, 2001; Mesmer-Magnus & DeChurch, 2009), and given the evidence in the
literature thus far, it would appear that CI is much more deeply connected to task and
cognitive processes in groups than to its socio-emotional processes, further
distinguishing CI from more general constructs of group effectiveness (Hackman,
1987; Mathieu et al., 2008)
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3. Responses to critiques of research on collective intelligence
3.1. Do the relationships among group task scores support the existence of a
general factor?
C&H argue that the correlations reported so far among tasks measuring CI are not
strong enough to support the existence of a general factor. In particular, they claim that
task factor loadings on the CI factor, the average task variance explained by the factor,
and the internal consistency among tasks are all too low, and consequently there is no
evidence of collective intelligence. To respond to these concerns, we first make a broad
conceptual point about the stage of development of current measures of collective
intelligence. Then we respond to the detailed statistical arguments C&H make by
showing how they are inappropriate in this context.
3.1.1. Stage of development of collective intelligence measurement instruments
The most important limitation of this part of C&H’s argument is that they are using
criteria that are appropriate for evaluating well-developed psychometric instruments,
but they are applying these criteria to the very first investigations of whether the
construct in question even exists. In a sense, the measurement of CI is now at a stage
similar to where the measurement of individual intelligence was soon after Spearman
made his initial observations.
For instance, Woolley et al (2010) designed the initial studies to intentionally sample
very different kinds of tasks, and it was not known at the time those studies were
designed which, if any, of the tasks would be associated with a single collective
intelligence factor. In spite of this exploratory nature of the initial studies, a factor
analysis of all the groups’ scores showed that the first factor accounted for 43% of the
variance in performance across all the different tasks. This is consistent with the 30
50% of variance typically explained by the first factor in many well-developed batteries
of individual cognitive tasks (Chabris, 2007).
C&H are correct to note that it is possible to have a first factor that explains a substantial
amount of variance in a number of variables, even when some of those variables are
essentially uncorrelated with the factor. But they are incorrect to assume that when
some of the variables in a study don’t correlate with the first factor that means no
general factor exists. It may simply mean that some of the variables included in the
study are not in the domain of variables to which the general factor applies. For
instance, in the hypothetical data C&H created for their Table 2, the first four tasks do
appear to have a common general factor even though the last four variables are not
included in the domain of tasks this factor predicts.
This is often the case in early studies of a new construct, and it would be erroneous to
interpret such relationships as evidence that a factor (or the construct it measures) does
not exist. For example, if Spearman had included certain kinds of face recognition tasks
in his original studies, he would probably have found very low correlations between
those tasks and the tasks that loaded heavily on g (Shakeshaft & Plomin, 2015). If that
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had occurred, and he had concluded that g did not exist as a result, it would have been
an enormous loss to the field of psychology.
It should also be noted that over three decades passed between Spearman’s initial
observations supporting the existence of the intelligence factor and the publication of
the first individual intelligence tests that served as accepted and enduring measures of
the construct. Modern intelligence tests are the result of additional decades of
psychometric refinements that, among other things, involved developing a large
repository of test items and selecting those that maximize the validity and reliability of
the test.
Despite its relatively nascent stage of development, the initial findings supporting the
existence of CI have subsequently been replicated in a number of studies, both
published and under review, in both laboratory and field settings, where researchers
find consistent evidence of a strong first factor accounting for 30%-50% of the variance
in the data (Chikersal et al., 2017; Engel et al., 2015, 2014; Kim et al., 2017; Woolley
& Aggarwal, under review; see Woolley, Riedl, Kim, & Malone, 2017 for a meta-
analysis).
In addition, researchers also find evidence of the predictive validity of this factor for
predicting performance on other kinds of tasks not included in the original estimation
of the factor. Establishing the predictive validity of a construct is at least as important
as examining internal consistency and reliability. For example, CI has been shown to
predict future performance on more complex tasks such as those performed in software
programming teams (Engel et al., 2015), in student course projects (Engel et al., 2015;
Glikson et al., 2016; Kim et al., 2016), and in online video game teams (Kim et al.,
2017) months later.
Together the accumulated findings of all these studies, demonstrating reasonable
reliability of the developing CI measure as well as its predictive validity, affirm the
initial conclusions that the construct is one worth further investigation. Over time, as
more CI tasks are developed and tested, researchers will develop a much better
understanding of exactly what kind of tasks are included in the domain of tasks for
which a collective intelligence factor exists.
3.1.2. Statistical concerns with C&H’s arguments
Even if C&H were evaluating results from measurement instruments that had already
undergone many generations of testing and refinement, rather than the early stage
exploration of Woolley et al. (2010), the statistical criteria they apply to the collective
intelligence data are inappropriate.
First, C&H’s sole reliance on shared variance to evaluate validity is problematic. It is
well-established in psychometric theory that solely maximizing the shared variance
among items almost invariably produces a scale that is too narrow in content, and thus
undermines construct validity (i.e., the classic attenuation paradox in psychometrics;
Loevinger, 1954, 1957). Therefore, researchers should not construct or evaluate
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measures solely based on factor analysis or the examination of correlation matrices
(Bollen & Lennox, 1991; Briggs & Cheek, 1986; Epstein, 1983). Given the breadth of
the CI construct, the researchers intentionally sampled a variety of tasks, in order to
cover various aspects of CI. Moreover, despite this task heterogeneity, as described
above, they found that CI predicts various criteria, such as group future performance.
Thus, C&H’s argument, based only on shared variance, is too narrow and does not
nullify the predictive validity of CI.
Second, C&H’s criteria for the sizes of correlations between tasks and for task factor
loadings are much too high and thus inappropriate for a broad construct like CI. C&H
argue that a .20 average correlation is too weak to conclude that tasks measure the same
latent construct. However, in the literature on psychometrics and task development, it
is well-established that moderate inter-item correlations are better than very strong
correlations, and that the optimal sizes of inter-item correlations depend on the breadth
of the target construct. Typically, inter-item correlations between .20 and .40 are
considered optimal (e.g., Bollen & Lennox, 1991; Briggs & Cheek, 1986; Cattell, 1965;
Epstein, 1983), as explained by Epstein (1983):
What is often not realized is that the average inter-item correlation for
most intelligence tests is between .20 and .30. If items in a scale were
more highly correlated with each other, they would be too redundant to
sample efficiently the breadth of broad personality variables such as
intelligence, honesty, or extraversion. An ideal item in a test that
measures a broad trait is one that has a relatively high correlation with
the sum of all items in the test (minus itself) and a relatively low average
correlation with the other items (p. 366).
Third, C&H also argue that task factor loadings should be greater than .70 (i.e., at least
50% of the variance explained by the general factor). However, in the literature on
psychometrics and task development, factor loadings greater than .40 are considered
acceptable (Clark & Watson, 1995; Cliff & Hamburger, 1967; Osborne & Costello,
2009; Santor et al., 2011).
We also note that papers that C&H cited as satisfying their criteria used a hierarchical
factor model. In those papers, several latent factors were introduced and used to derive
a second-order general factor (in contrast to the CI studies, which only modeled a single
general latent factor). Therefore, the papers containing these hierarchical factor models
that C&H cited are not comparable with the current CI studies. A more reasonable and
appropriate source from which to derive criteria thresholds would be the literature on
general intelligence. As already discussed, the construct of general intelligence is itself
based on the observation that the first factor derived from a battery of cognitive tasks
typically explains 30-50% of the variance (Chabris, 2007). Given that the first factor
derived from the tasks used to measure CI explain a similar level of variance (i.e., 30-
50%), it would seem there is initial evidence for a general factor of collective
intelligence.
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Finally, we note that C&H’s estimates of the shared variance are biased downward by
their use of data from sample 6, a conference paper by Barlow & Dennis (2014), which
has several problems. First of all, the study only used 3 tasks to measure CI (which is
inherently less reliable than the larger sets of tasks used in the other samples). However,
C&H included another task, the criterion task in that study (that was administered
separately and differently from the other 3 group tasks), in the measure of CI. As a
result, the factor model that C&H calculated yields weaker estimates for sample 6. This
is important, since most of the low correlations (r 0) that C&H report in Figure 1 are
derived from sample 6. When dropping sample 6, the average correlation among tasks
in published articles is .24, and well within recommended ranges for broad scope
measures discussed above.
3.2. Is collective intelligence a statistical artifact caused (or inflated) by low effort
responding?
C&H argue that some groups in the studies they reviewed might be exceedingly low in
motivation, which could lead to inflated correlations among CI tasks. In principle, they
are correct that if (a) some groups have generally low motivation, and (b) low
motivation decreases a group’s scores on most tasks, then (c) this cluster of consistently
low scoring groups together with other groups with uncorrelated task scores would lead
to a falsely inflated correlation among the variables where no relationship exists.
However, there is strong evidence this hypothesis does not explain what is happening
in the studies of CI.
To examine this argument, we conducted a simulation to generate a dataset that
conforms to the characteristics that C&H assert are true of the studies of CI. Two
clusters of data were generated and for each group, two variables were generated to
have almost zero correlation. For the majority group (N=500), each of two variables
(i.e., task 1 and 2) was generated from normal distribution with mean of 50 and standard
deviance of 15. The minority group (N=50), each of two variables was generated to
follow normal distribution with mean of 5 and standard deviation of 1.5.
First, and most important, if the pairwise correlations among task scores are inflated by
very low effort by some groups, we would expect to see a pattern similar to that from
the simulated data shown in Figure 1a; a cluster of scores in the bottom left corner and
a second cloud of uncorrelated points in the middle. This indicates that some teams
have exceedingly low scores on both tasks, thus inflating the correlations. But, both in
our original data and now with the accumulated data of 758 teams, we see no evidence
of this (see Figure 1b). Thus the empirical data does not support the conclusion that the
observed correlations are inflated by a cluster of low effort groups.
Second, there is additional empirical evidence that low motivation of group members
did not cause low group CI scores. In Study 2 of Woolley et al, 2010, group members
completed a validated measure of motivation (Wageman, Hackman, & Lehman, 2005)
to indicate how strongly they were motivated for their team to perform well. As
reported in the Supplementary Online Materials for that study, the correlation between
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Figure 1a. Simulated data that fit the pattern argued for by Crede and Howardson (2017)
Figure 1b. Pairwise scatterplots from 758 teams in our studies.
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the average of this measure for all group members and the group’s collective
intelligence score was -0.01, ns. If C&H’s theory about low effort responding had been
correct, this correlation should have been strongly, and significantly, positive.
Third, C&H assume that very low scores result from lack of effort. However, they
overlook key details in the scoring in Woolley et al. (2010) and subsequent studies that
provide a more plausible explanation for these scores. For example, points are deducted
when groups performing the typing task skip words or when groups performing the
brainstorming task repeat ideas that have already been given. In these cases, a score of
zero can be evidence of poor coordination, not, as C&H assumed, lack of effort.
Finally, as another form of evidence that some group members had very low
motivation, C&H noted the instance of low Wonderlic Personnel Test (WPT) scores as
evidence of low effort, and calculated expected variance of WPT using the central limit
theorem (CLT). We realized in reviewing their argument that one of the tables in the
supplement to Woolley et al. (2010) had a misleading label, and the range of scores
reported were for individuals in the sample, not teams. The WPT scores for individuals
in the Woolley et al. (2010) sample have a median of 24, and standard deviation of
6.52. These scores fall between the comparable scores in the norming samples reported
by Wonderlic (2002) for the adult working population (median = 22, SD = 7.6) and
college graduates (median = 30, SD = 6.3), and these are the two populations from
which the Woolley et al. (2010) sample was drawn. Thus, there is nothing to suggest
that the Woolley et al. (2010) sample is not representative of the population, or
exhibiting exceedingly low effort.
Furthermore, in evaluating the distribution of WPT scores, C&H made assertions about
the non-random assignment of participants to teams using calculations based on CLT.
However, their calculations are problematic. The CLT states that given a sufficiently
large sample drawn from a population (e.g., n > 30), the mean of independent and
identically distributed (iid) samples from the same population will approximate the
mean of the population. The sample size of n = 4, which C&H used in their calculation,
is not sufficiently large, and the samples in this context cannot be approximated by iid
considering the sampling without replacement in a small, finite sample (Casella &
Berger, 2002).
3.3 Is collective intelligence a statistical artifact caused (or inflated) by the
influence of nested data?
C&H further argue that the conclusions of a CI factor are inflated by nested data. To
demonstrate their argument, they simulated individual-level data and conducted multi-
level factor analysis. C&H’s simulations demonstrate a well-known phenomenon, the
ecological fallacy (Hox, Moerbeek, & van de Schoot, 2010), which occurs when
individuals are nested in groups, and when we cannot correctly infer relationships
among variables at the group level because the outcome of interest is measured
fundamentally at the individual-level (e.g., achievement scores of students, who are
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nested within school). These issues are a common problem in organizational research,
which is inherently multi-level (Rousseau, 1985).
However, C&H’s simulations do not show that this phenomenon explains the collective
intelligence results in the studies upon which they are commenting. Instead, C&H
merely assume that there are no group level influences in performance and then
simulate what happens under that assumption. More specifically, they assume that the
groups determine their answers by having each group member perform the tasks
independently and then “averaging” their answers to produce the group answer.
However, there are two major flaws with the reasoning underlying this argument.
First, this is far from an accurate model of how groups complete most of the tasks used
in the studies they are modeling. Of the full set of ten tasks used in study 2 of Woolley
et al. (2010), at least half cannot be readily disaggregated into individual components
that could be merely assembled without group interaction, as in a nominal group
paradigm (Thorndike, 1938). As described above, for instance, the group typing task
requires group members to not only type different sections of the text passage, but also
to coordinate their work so no one types the same segment of the text twice and so that
there are no gaps between the segments typed. The moral reasoning task requires that
groups come to a consensus that balances the interests of different parties to the conflict.
And in the brainstorming task, members must not only generate possible answers, but
also coordinate their work to avoid duplicates. Under such conditions, some groups will
do well less than the sum of their parts because of process loss, due to the failure to
coordinate well (Steiner, 1972) while others might exhibit significant synergistic gains
(Larson, 2010) and outperform even their best members.
In other words, if groups could in fact disaggregate tasks into individual components,
there are some groups (we would argue those that are low in CI) that would perform
better if they completed the tasks individually, since working together reduced their
aggregate ability! Meanwhile, high CI groups have been shown to consistently operate
above the capability of even their best members (Woolley & Aggarwal, under review).
However, because some groups gain from interaction while others lose, statistically the
numbers could look similar to what C&H produced under their assumption of no
interaction, but this does not capture the underlying phenomenon.
A second problem with the assumptions underlying C&H’s simulated data is that if the
results of Woolley et al. (2010) and the other studies reviewed are merely an aggregate
of individual inputs, then features of group interaction should not matter at all to the
quality of what groups produce. However, some of the strongest associations observed
in Woolley et al. (2010) and subsequent studies relate to the relationship between group
communication patterns and CI, as well as individual characteristics that affect the
quality of interpersonal interaction such as social perceptiveness. Multiple studies find
that the amount and distribution of communication are strong predictors of collective
intelligence, even in groups collaborating online via text chat (Engel et al., 2014;
Woolley et al., 2010). Social perceptiveness, a characteristic describing how effective
individuals are at interpreting subtle cues from interaction partners also ends up being
a strong predictor of CI whether groups are working face-to-face or online (Chikersal
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et al., 2017; Engel et al., 2014; Kim et al., 2017; Woolley, Riedl, et al., 2017). The
consistency of these findings suggests that team interaction does play an important role
in shaping CI, and thus that C&H’s hypotheses to the contrary are not supported by the
data
C&H recommend that future research on collective intelligence should measure both
the individual-level and group-level performance on the same tasks, so that researchers
can apply multi-level models. That is an interesting idea, and worth considering; it
would enable researchers to estimate what portion of CI is due to individual ability vs.
group coordination, a common approach to conceptualizing intelligence in
organizations and other larger systems in other disciplines (Knott, 2008). This could
enable exploration of the degree to which CI enables synergistic gains in groups
(Curşeu, Meslec, Pluut, & Lucas, 2015; Larson, 2007) which research in progress
suggests is a good possibility (Woolley & Aggarwal, under review).
2 Conclusions and Moving Forward
We believe we have shown that the arguments C&H made do not, in fact, refute the
conclusion that a collective intelligence factor exists. However, the process of devising
measurements of the CI factor is still in an early stage, and we completely agree that
there is much additional work to be done.
For example, more research is clearly needed on the development and strengthening of
measures of CI. In doing so, it will be important to maximize the content validity of
measurement by sampling different group task types, while balancing the competing
need to maintain an adequate level of internal consistency and reliability. The work to
date has focused on sampling from the task domains identified by teams researchers in
the past (Larson, 2010; McGrath, 1984; Steiner, 1966). Thus far, studies have been
measuring a single factor. As the work evolves, however, it is likely that a two-level
factor structure like that often used in individual intelligence (e.g., Deary, 2012) will
become useful. In the case of groups, the lower level factors might separately predict
performance on several of the major task types identified in existing team task
taxonomies, in essence identifying some specialized intelligences in groups.
As researchers continue to develop tasks, they can also more systematically investigate
how many tasks should be used to evaluate each task type. The Platform for Online
Groups Studies (POGS; Engel et al., 2015; Kim et al., 2017) that has been made
available as open source software to other researchers (pogs.mit.edu) facilitates the
development and testing of new group-based tasks in different environments. We hope
that this instrument can be used and refined by many other researchers.
Another important aspect of continuing to develop our understanding of the CI
construct is to evaluate how stable CI is in teams, which is both an important theoretical
question as well as a measurement question. There is preliminary evidence, not yet
published (Woolley, Kim, Kim, & Malone, 2017) to suggest that CI is relatively stable.
In ongoing studies, the observed test-retest reliability of the CI is .88 in teams of
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strangers completing two sets of tasks one hour apart, .75 in teams of strangers
completing them 2 weeks apart (with no interaction in between), and .73 in student
teams from academic courses taking them 6-8 weeks apart (with frequent interaction in
between). By comparison, some estimates of the test-retest reliability for commonly
used measures of individual IQ range between .57 and .94 (Goodman, Streiner, &
Woodward, 1974; Snow, Tierney, Zorzitto, Fisher, & Reid, 1989).
In summary, the work on collective intelligence is still at an early stage but shows
promising signs of providing a vehicle for the field to conceptualize and measure the
collective ability of groups or even larger entities to work together. We are hopeful that
we and many other researchers can continue to accumulate data and conduct analyses
which will further develop our understanding of the construct, its measurement, and its
utility in advancing the science of groups.
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... Stage4: The final form of intelligence that results from the chaotic fusion of all intelligences in crowd cyber is at the highest level of intelligence, and here we name it "Super minds", referring to the work of Woolley et al. (2018). ...
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