This may be the author’s version of a work that was submitted/accepted
for publication in the following source:
af Wahlberg, Anders, Barraclough, Peter, & Freeman, James
(2017)
Personality versus traffic accidents; meta-analysis of real and method ef-
fects.
Transportation Research Part F: Traffic Psychology and Behaviour, 44, pp.
90-104.
This file was downloaded from: https://eprints.qut.edu.au/98734/
c
Consult author(s) regarding copyright matters
This work is covered by copyright. Unless the document is being made available under a
Creative Commons Licence, you must assume that re-use is limited to personal use and
that permission from the copyright owner must be obtained for all other uses. If the docu-
ment is available under a Creative Commons License (or other specified license) then refer
to the Licence for details of permitted re-use. It is a condition of access that users recog-
nise and abide by the legal requirements associated with these rights. If you believe that
this work infringes copyright please provide details by email to qut.copyright@qut.edu.au
License: Creative Commons: Attribution-Noncommercial-No Derivative
Works 4.0
Notice: Please note that this document may not be the Version of Record
(i.e. published version) of the work. Author manuscript versions (as Sub-
mitted for peer review or as Accepted for publication after peer review) can
be identified by an absence of publisher branding and/or typeset appear-
ance. If there is any doubt, please refer to the published source.
https://doi.org/10.1016/j.trf.2016.10.009
1
Personality versus traffic accidents; meta-analysis of real and
method effects
Running head: Personality versus traffic accidents
2
Abstract
Problem: The association between personality and traffic accident involvement has been extensively researched,
but the literature is difficult to summarize, because different personality instruments and statistics have been
used, and effect sizes differ strongly between studies.
Method: A meta-analysis of studies which had used measures of personality which could be converted into Big
Five dimensions, and traffic accidents as the dependent variable, was undertaken.
Analysis: Outlier values were identified and removed. Also, analyses on effects of common method variance,
type of instrument, dissemination bias and restriction of variance were undertaken.
Results: Outlier problems exist in these data, which prohibit any certainty in the conclusions. Each of the 5
personality dimensions were predictors of accident involvement, but the effects were small (r<.1), which is much
weaker than in a previous meta-analysis. Effect sizes were dependent upon variance in the accident variable, and
the true (population) effects could therefore be larger than the present estimates, something which could be
ascertained by new studies using high-risk samples over longer time periods. Newer studies and those using Big
Five instruments tended to have smaller effects. No effects of common method variance could be found.
Conclusions: Tests of personality are weak predictors of traffic accident involvement, compared to other
variables, such as previous accidents. Research into whether larger effects of personality can be found with
methods other than self-reports is needed.
Keywords: personality, accident, crash, common method variance
3
1. Introduction
1.1 Personality as predictor of traffic accident involvement
The present paper summarizes the literature on personality (in terms of the Big Five system)
as a predictor of traffic accident involvement in a meta-analysis. Several methodological
problems are considered, such as outliers, dissemination bias and conversion of data between
different personality systems.
Personality as a phenomenon is multi-faceted, but can usually be defined as the stable
behavioural tendencies of people over time, or the psychological traits which cause such
behaviours. This has been conceptualized in many different ways through the years, but today
it is agreed by most researchers that the most parsimonious description is by five dimensions;
Openness, Agreeableness, Conscientiousness, Neuroticism and Extraversion. Most other
systems map onto these dimensions, and results can therefore be converted between them.
Throughout the history of traffic safety, researchers have studied the influence of individual
differences in personality on accident record (although at first the term 'accident proneness'
was used; Greenwood & Woods, 1919; see also papers by Drake, 1940; Harris, 1950; Parker,
1953). Many researchers have proposed that certain personality features, in terms of recurrent
behaviours, cause accidents. In terms of the Big Five model (and its facets), Clarke and
Robertson (2005) summarized the theoretical basis for their traffic accident-causing properties
thus; people high on Extraversion tend to be poor on vigilance and take more risks. Those
high on Neuroticism have been suggested to be easily distracted, less likely to seek control of
the environment and prone to react to stress. Conscientiousness features several inter-related
concepts which are thought to make people safe, such as planning, self-control and decision-
making, while lack of Agreeableness is associated with accidents by the mechanism of
aggression in terms of emotion as well as behaviour. Finally, Openness has been suggested to
be positively correlated with accidents, due to the routine character of driving, where traits
such as experimentation and improvisation are not in accord with safe operation. However,
most researchers who investigate the link between personality and accidents refer to previous
significant associations reported, and describe the behaviours typical of a certain personality
dimension (e.g. Arthur et al., 2001; Begg, Langley & Williams, 1999; Burns & Wilde, 1995;
Clement & Jonah, 1984; Hartman & Rawson, 1992).
Many researchers also express a strong belief in the predictive capacity of tests of personality
versus accidents (e.g. Arthur et al., 2001; Brandau, Daghofer, Hofmann & Spitzer, 2011;
Hansen, 1988; Jonah, 1997). However, results, as always, have been mixed, and this belief
may therefore be unfounded. For example, Shaw and Sichel (1971; Shaw, 1965) reported
correlations between .4 and .7 for their personality tests and accidents for bus drivers, while
Carty, Stough and Gillespie (1998) found a strong negative association (-.212) instead of the
expected positive one for Extraversion, and many other such examples exist. Results are thus
very heterogeneous, which make interpretation difficult. A meta-analytic approach is
therefore needed, where the reasons for this apparent heterogeneity can be identified, and
estimates of the true (population) effects calculated.
Two meta-analyses of personality versus accidents have already been published; Arthur,
Barrett and Alexander (1991) and Clarke and Robertson (2005). However, there are several
reasons for why a new analysis of the personality-traffic accident association is needed. Apart
from now being outdated, the Arthur et al. study used a personality taxonomy which excluded
some available studies (e.g. Quenault, 1967; Andersson, Nilsson & Henriksson, 1970;
Jamison & McGlothlin, 1973). Similarly, the Clarke and Robertson study excluded many
available papers, while including some which used methodologies which were different from
those of the majority. Furthermore, moderator effects and dissemination bias were not
investigated in these studies.
4
We therefore wanted to undertake a new meta-analysis which used a very different approach
to the problem of meta-analysing personality as a predictor of traffic accident involvement,
taking into account not only the well-known problems of dissemination bias and
methodological moderator effects, but also effects which are probably peculiar to accident
prediction studies. The main aim of the study was therefore to estimate the population effect
while keeping known or suspected moderators constant, as will now be described.
1.2 Technical issues in meta-analysis; Heterogeneity and the population effect
This section describes some of the methodological problems associated with meta-analysing
data, under the general headings of trying to estimate a population effect, and the overall
problem of heterogeneous data, i.e. very different results in different studies. Also, possible
remedies are suggested.
In research on psychological mechanisms, it is usually the goal to infer from sampled data
what all people are like in a defined population. For example, are high levels of empathy
usually associated with low levels of aggression? In a meta-analytic context, it would
specifically be asked what the effect size is, i.e. how strong is the link between the two
concepts? When effect sizes from different studies are combined, however, it is important that
the data included is actually drawn from the same population, meaning those who share this
trait/mechanism. For example, the link between empathy and aggression might have different
strength in different cultures. If studies from different cultures are then combined, the
ensuing effect size will be slightly misleading, showing really the mean effect for two (or
more) different populations. When effect sizes from different populations are mixed, it is said
that the meta-dataset is heterogeneous, i.e. the numbers differ more between themselves than
could be expected by random sampling (which can be ascertained by statistical testing).
Heterogeneity can also be caused by differences in methodology. For example, it can be
expected that experiments and field studies will yield different effect sizes, although they are
ostensibly studying the same problem, because part of the effect is actually created by the
method used (e.g., a social science analogue to Heisenberg's uncertainty principle).
If heterogeneity is detected in the data, moderator
1
analysis should be applied to investigate
the causes of the variance. For example, the pooled effects for experiments versus field
studies can be compared, to see whether the estimated population effects differ significantly
between these two conditions. If they do, it can be concluded that the methods used have had
an influence on the results, a fact that needs to be considered when the true population effect
size is identified.
When meta-data has been gathered, an important operation is therefore to detect whether
effects differ more between themselves than could be expected from sampling error alone.
However, before moderator analysis is undertaken, data should be checked for outlying
values, i.e. values which differ very strongly from the majority, and could be suspected to be
due to errors in the research process. If a few such values are found, these deviating numbers
can be excluded (e.g. Bond & Smith, 1996; Eagly, Makhijani & Klonsky, 1992; de Winter &
Dodou, 2010; Groh et al., 2014; Fournier, Hass, Naik, Lodha & Cauraugh, 2010), although
most meta-analysts in social science do not proceed beyond concluding that there is
heterogeneity between effects (while in 'hard' sciences, such as physics, they do; Hedges,
1987).
1.3 Meta-analysis of personality as accident predictor
1
In meta-analytic jargon, a moderator is a variable which systematically influences the effect sizes in a set of
studies, for example differences in methodology in research on the same problem.