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\title{DOSPERT+M: A survey of medical risk attitudes in the United States}

\author{Adam Rosman\thanks{Department of Medical Education, University of Illinois at Chicago} \and
    Maggie Garcia\footnotemark[1] \and
    Sam Lee\footnotemark[1] \and
    Shoshana Butler\footnotemark[1] \and
    Alan Schwartz\thanks{Department of Medical Education (mc 591), University of Illinois at Chicago, 808 S. Wood St., 986 CME, Chicago, IL 60612. E-mail: alansz@uic.edu.}}

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\newcommand{\jref}{http://journal.sjdm.org/vol8.4.html}
\newcommand{\jhead}{Judgment and Decision Making, Vol.~8, No.~4}
\newcommand{\jdate}{July 2013}
\pagestyle{myheadings} \markright{\protect\small \href{\jref}{\jhead}, \jdate \hfill MEDICAL RISK ATTITUDES \qquad}
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\href{\jref}{\jhead}, \jdate, pp. 470-481
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Background: The Domain-Specific Risk Taking scale (DOSPERT) has been
recommended as a tool for measuring risk attitudes in medical studies,
but does not contain items specific to health care. Butler, et al. (2012)
developed a medical risk domain subscale for DOSPERT.

Objective: To characterize medical risk attitudes in a
nationally-representative U.S. sample using the full DOSPERT scale with
the medical risk domain add-on (DOSPERT+M), and examine associations
with other risk domains.

Methods: Members of a nationally-representative online panel
(KnowledgePanel\textregistered) were randomized to complete pairs of DOSPERT+M tasks
(risk attitude, risk perception, expected benefits). We explored
relationships among domains through correlational and factor analysis;
we tested the hypothesis that the medical risk domain and DOSPERT's
health/safety domains were not highly correlated.

Participants: Three hundred forty-four panelists.

Results: The medical risk domain subscale had low inter-item
reliability in the risk-taking task and moderate inter-item reliability
in the other tasks.  Medical risk domain scores were poorly correlated
with the DOSPERT health/safety domain.  Exploratory factor analysis
largely recovered the expected DOSPERT domain structure.

Conclusion: Attitudes toward risky medical activities may constitute a
distinct domain from those measured by the standard DOSPERT items.
Additional work is required to develop a medical risk subscale with
higher inter-item reliability.


\smallskip
\noindent
Keywords: DOSPERT, DOSPERT+M, risk attitude, risk perception, medical decision making
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{\renewcommand{\thefootnote}{}
\footnotetext{

The work reported here was funded by U.S. National Science Foundation grant SES-1023831 to Alan Schwartz.

Copyright: \copyright\ 2013.
The authors license this article under the terms of the
\href{http://creativecommons.org/licenses/by/3.0/}{Creative Commons
  Attribution 3.0 License.}
}}

\saythanks
\section{Introduction}

The Domain-Specific Risk Taking scale (DOSPERT) (Blais \& Weber,
2006; Blais \& Weber, 2009; Weber, Blais, \& Betz, 2002) is a widely
used instrument that measures risk attitudes (willingness to engage),
risk perceptions, and expected benefits for a set of potentially risky
activities organized into five domains: ethical, financial, health/safety,
recreational, and social risk taking. DOSPERT is one of a small number
of instruments recommended in Harrison, et al.'s (2005) review of
instruments for assessing health-related or clinical risks. However,
DOSPERT does not include a domain that focuses on health care activities;
the health/safety scale is limited to preventive behaviors.

In recognition of this limitation and other concerns with the
health/safety subscale (Blais \& Weber, 2009; Young et al., 2008), Butler,
et al. (2012) developed and provided initial psychometric evidence for
an add-on medical risk domain consisting of six activities, modeled
after the other DOSPERT domains. Adding the new domain's items to the
standard DOSPERT items produces a 36-item instrument that we refer to
as DOSPERT+M. Table 1 lists the medical risk domain items.

%Table 1
\begin{table}[t]
\caption{DOSPERT+M medical risk domain subscale items.}
\begin{tabular}{L{.3in}L{2.7in}@{}}\toprule
 & Item\\\midrule
M1 & Donating one kidney to a patient you do not know\\
M2 & Giving blood\\
M3 & Participating in a clinical trial to determine whether a new drug is effective\\
M4 & Taking daily medication to relieve allergy symptoms\\
M5 & Knee replacement surgery to treat arthritis\\
M6 & Receiving general rather than local anesthesia when having a wisdom tooth removed\\\bottomrule
\end{tabular}
\end{table}


The goal of this study was to characterize risk attitudes, including
medical risk attitudes, in a nationally-representative U.S. sample using
DOSPERT+M, and to examine the associations among domains. We specifically
hypothesized that the DOSPERT health/safety scale would not be strongly
associated with the DOSPERT medical scale.

\section{Method} % example of a heading

\subsection{Instrument}

The 36 DOSPERT+M items, each describing a risky activity, can be presented
in any of the three standard DOSPERT tasks  (risk-taking, risk perception,
or expected benefit). In the risk-taking task, participants indicate their
likelihood of participating in the activity on a 1-7 scale from "extremely
unlikely" to "extremely likely". In the risk perception task, participants
indicate how risky they believe the activity is on a 1-7 scale from
"not at all risky" to "extremely risky". In the expected benefit task,
participants indicate the benefits they believe they would receive from
each activity on a 1-7 scale from "no benefits at all" to "great benefits".
To reduce respondent fatigue, each participant was randomized to receive
two of these three tasks presented in a randomized order.

\subsection{Participants}

DOSPERT+M was administered using the web-enabled
KnowledgePanel\textregistered, a probability-based panel
designed to be representative of the United States population.
KnowledgePanel\textregistered consists of approximately 50,000
members aged 18 and older and includes persons living in cell phone
only households as well as non-internet households. All participants
completed the DOSPERT+M survey online; participants without computers or
internet access complete the survey using computers provided by Knowledge
Networks (now GfK). Prior to taking the survey each participant was asked
a series of demographic questions related to age, gender, education level,
geographic location, marital status, and socioeconomic status. Data
collection took place between December 2, 2011 and December 18, 2011.

\subsection{Data analysis}

We examined the relationship among risk-taking, risk perception, and
expected benefits in each domain by computing correlations among domain
subscale scores in each task. Because participants were randomized to
complete two of three tasks, each intra-domain (inter-task) correlation
is based on a different group of participants.

To explore the hypothesis that the medical domain subscale measures a
different construct than the health/safety domain subscale, we tested the
correlation between subscale scores on the two domains in each task. In
addition, we fit three confirmatory factor analysis models to the 12 items
of the two scales in each task. The first model ("one factor") assumed
all items loaded onto a single "health and medical" factor. The second
model ("two distinct factors") assumed the health/safety items loaded
onto a health factor and the medical items loaded onto a medical factor,
with the possibility of correlation between the factors. The third model
("two entangled factors") also assumed two factors, but allowed all of
the items but one in each domain to potentially load onto either factor
(one item in each domain was fixed to each factor to make the model
identifiable). The goal of the confirmatory factor analysis was to
compare the goodness of fit of these three models; if the medical domain
subscale and health/safety domain subscales are distinct constructs,
we expect to see better fit of the "two distinct factors" model than
the (nested) "one factor" model, but little additional fit improvement
from the (non-nested) "two entangled factors" model.
We examined RMSEA and AIC for each model and compared
nested models using chi-squared tests of the difference in likelihood
ratios (MacCallum, Browne, \& Sugawara, 1996; Schreiber, et al., 2006).

Finally, we conducted exploratory factor analyses for each task using all
36 DOSPERT+M items, extracting six or seven factors by maximum likelihood
methods and using an oblimin rotation, and examined the factor pattern
matrix and inter-factor correlations (Fabrigar, et al., 1999).  The goal
of the exploratory factor analyses was to further investigate how the
medical risk items were related to other DOSPERT items.

We also conducted three multiple linear regressions to determine whether
variance in the medical domain task scores (risk-taking, risk perception,
and expected benefits) was associated with demographic factors. Age,
gender, ethnicity (dummy coded to compare White non-Hispanic, Black
non-Hispanic, Hispanic, and Multiracial/Other non-Hispanic), cohabitation
(married/living with partner or not), and education (some college or no
college) were included as predictors.

Analyses were conducted using R 2.15 for factor analyses with the sem,
semPlot,
psych, and GPArotation packages (R Core Team, 2013; Fox, Nie, \& Byrnes,
2013; Epskamp, 2013; Bernaards \& Jennrich, 2005; Revelle, 2013)

\section{Results}

\subsection{Participants}

The overall within-survey response rate for KnowledgePanel\textregistered is 
65\% with some minor variation depending on survey length and topic. The
DOSPERT+M study sampled 538 participants of which 350 completed the survey
for a response of 65\%. Of those 350 completed surveys, 344 were considered
sufficiently complete for inclusion. The six removed were excluded due
to the fact that they refused to answer a majority (19 or more) of the
substantive survey questions. The median time for completing the survey
was nine minutes.  Table 2 presents the demographic characteristics of
study participants.

%Table 2
\begin{table}[t!]
\caption{Demographic characteristics.}
\begin{tabular}{@{}L{1.9in}R{.5in}R{.5in}}\toprule
Characteristic & N & \% \\\midrule
Gender  & & \\
\hskip .4cm    Male & 169 & 49 \\
\hskip .4cm    Female & 175 & 51 \\\midrule
Age (years) &  &  \\
\hskip .4cm 18-29 & 56 & 16 \\
\hskip .4cm 30-44 & 75 & 22 \\
\hskip .4cm 45-59 & 99 & 29 \\
\hskip .4cm 60+ & 114 & 33 \\\midrule
Race/Ethnicity &  &  \\
\hskip .4cm White/Non Hispanic & 246 & 72 \\
\hskip .4cm Black/Non Hispanic & 32 & 9 \\
\mbox{\hskip .4cm Other~or~Multiracial/Non-Hispanic} & 25 & 7 \\
\hskip .4cm Hispanic & 41 & 12 \\\midrule
Marital Status &  &  \\
\hskip .4cm Married & 195 & 57 \\
\hskip .4cm Widowed & 20 & 6 \\
\hskip .4cm Divorced & 27 & 8 \\
\hskip .4cm Separated & 3 & 1 \\
\hskip .4cm Never Married & 73 & 21 \\
\hskip .4cm Living with Partner & 26 & 7 \\\midrule
Education &  &  \\
\hskip .4cm < High School & 29 & 8 \\
\hskip .4cm High School & 86 & 25 \\
\hskip .4cm Some College & 107 & 31 \\
\hskip .4cm > Bachelors & 122 & 36 \\\bottomrule
\end{tabular}
\end{table}

\begin{figure}[t!]
\caption{Medical subscale item distributions - risk-taking task}
\includegraphics[width=\columnwidth]{rt-panels.eps}
\end{figure}
\begin{figure}[t!]
\caption{Medical subscale item distributions - risk perception task}
\includegraphics[width=\columnwidth]{rp-panels.eps}
\end{figure}
\begin{figure}[t!]
\caption{Medical subscale item distributions - expected benefits task}
\includegraphics[width=\columnwidth]{bp-panels.eps}
\end{figure}

Twenty-five respondents were excluded from analysis based on response
patterns: 11 respondents indicated that giving blood was riskier than
donating a kidney, and 14 (different) respondents gave the same response
to every item in at least one task. Repeating the analyses with these
respondents included did not result in substantial differences except
where the inclusion of respondents with no variance interfered with
model fitting.

\subsection{Medical risk subscale item-level statistics}

Table 3 presents the range, median, mean, and standard deviation of
responses for each of the medical risk subscale items in each task. There
was considerable variance in the responses for each item in each task,
with the exception of risk perceptions for giving blood (M2) which heavily
clustered around the low end of the scale. Figures 1-3 show histograms of
each item in each task on the main diagonals, with pairwise scatterplots
(lower triangle) and correlations (upper triangle).

\subsection{Relationships among tasks}

Within the medical subscale, risk-taking scores were not significantly
correlated with risk perception scores (r=-.12) in the subsample that
completed those two tasks. Risk taking was significantly positively
correlated with expected benefits scores (r=.69, p<.01) in the subsample
that completed those two tasks. Benefit and risk perception scores were
negatively correlated (r=-.41, p<.01) in the subsample that completed
those two tasks, which may suggest evaluation based on a common underlying
feeling (Alhakami \& Slovic, 1994). A similar pattern of associations was
found for the financial and social subscales; for recreation, ethical,
and health/safety subscales, risk-taking and risk perception were
also significantly negatively correlated (r=-.28, r=-.49, and r=-.46,
respectively).

% Table 3
\begin{table*}[t]
\caption{Medical risk subscale item-level statistics.}
\begin{center}
\begin{tabular}{L{1.6in}C{.8in}C{.8in}C{.8in}C{.8in}C{.8in}}\toprule
 & n & Range & Median & Mean & Standard deviation\\\midrule
Risk-taking task  \\
M1 & 213 & 1-7 & 3 & 3.09 & 1.68 \\
M2 & 210 & 1-7 & 5 & 4.76 & 1.98 \\
M3 & 214 & 1-7 & 4 & 3.36 & 1.85 \\
M4 & 211 & 1-7 & 5 & 4.76 & 1.95 \\
M5 & 211 & 1-7 & 4 & 4.09 & 1.81 \\
M6 & 214 & 1-7 & 4 & 3.52 & 1.90 \\
Subscale mean & 214 & 1.33-6.33 & 4.00 & 3.92 & 0.99 \\\midrule
Risk perception task \\
M1 & 198 & 1-7 & 4 & 4.34 & 1.63 \\
M2 & 199 & 1-7 & 1 & 1.65 & 1.00 \\
M3 & 198 & 1-7 & 4 & 4.28 & 1.59 \\
M4 & 200 & 1-7 & 2 & 2.00 & 1.22 \\
M5 & 196 & 1-7 & 3 & 3.43 & 1.40 \\
M6 & 197 & 1-7 & 3 & 3.21 & 1.63 \\
Subscale mean & 200 & 1.00-5.50 & 3.00 & 3.15 & 0.85 \\\midrule
Expected benefits task \\
M1 & 223 & 1-7 & 4 & 3.63 & 1.82 \\
M2 & 223 & 1-7 & 5 & 4.98 & 1.79 \\
M3 & 224 & 1-7 & 3 & 3.29 & 1.70 \\
M4 & 222 & 1-7 & 5 & 4.69 & 1.79 \\
M5 & 224 & 1-7 & 4 & 4.35 & 1.88 \\
M6 & 222 & 1-7 & 3 & 3.13 & 1.86 \\
Subscale mean & 224 & 1.00-6.67 & 4.17 & 4.01 & 1.16 \\\bottomrule
\end{tabular}
\end{center}
\end{table*}

\subsection{Relationships among domains}

Table 4 presents the correlations among domain subscale scores in each
task. Medical domain scores were least strongly correlated with the
health/safety and ethics domain scores, and most strongly correlated
with social domain scores.

% Table 4
\begin{table*}[t!]
\caption{Correlations among domains and Cronbach's $\alpha$ for each domain.}
\begin{center}
\begin{tabular}{l@{}*{6}{d{2.6}}}\toprule
& \mc{6}{c}{Risk-taking task}\\ \cmidrule{2-7}
& \mc{1}{c}{Medical} & \mc{1}{c}{Ethical} & \mc{1}{c}{Financial} &
\mc{1}{c}{Health/Safety} & \mc{1}{c}{Recreational} & \mc{1}{c}{Social}\\\midrule
Medical & .49 & .16* & .33{**} & .12 & .28{**} & .41{**} \\
Ethical &  & .60 & .41{**} & .63{**} & .30{**} & .21{**} \\
Financial &  &  & .66 & .30{**} & .41{**} & .28{**} \\
Health/Safety &  &  &  & .71 & .45{**} & .25{**} \\
Recreational &  &  &  &  & .77 & .33{**} \\
Social &  &  &  &  &  & .69 \\\midrule
& \mc{6}{c}{Risk perception task}\\ \cmidrule{2-7}
& \mc{1}{c}{Medical} & \mc{1}{c}{Ethical} & \mc{1}{c}{Financial} &
\mc{1}{c}{Health/Safety} & \mc{1}{c}{Recreational} & \mc{1}{c}{Social}\\\midrule
Medical & .63 & .27{**} & .24{**} & .25{**} & .38{**} & .46{**} \\
Ethical &  & .75 & .54{**} & .72{**} & .54{**} & .26{**} \\
Financial &  &  & .76 & .53{**} & .45{**} & .18{**} \\
Health/Safety &  &  &  & .78 & .61{**} & .26{**} \\
Recreational &  &  &  &  & .80 & .29{**} \\
Social &  &  &  &  &  & .71 \\\midrule
& \mc{6}{c}{Expected benefit task}\\ \cmidrule{2-7}
& \mc{1}{c}{Medical} & \mc{1}{c}{Ethical} & \mc{1}{c}{Financial} &
\mc{1}{c}{Health/Safety} & \mc{1}{c}{Recreational} & \mc{1}{c}{Social}\\\midrule
Medical & .72 & .07 & .34{**} & .00 & .25{**} & .53{**} \\
Ethical &  & .61 & .41{**} & .63{**} & .31{**} & .20{**} \\
Financial &  &  & .79 & .32{**} & .43{**} & .43{**} \\
Health/Safety &  &  &  & .66 & .40{**} & .21{**} \\
Recreational &  &  &  &  & .80 & .44{**} \\
Social &  &  &  &  &  & .72 \\\bottomrule\addlinespace[.3ex]
\mc{7}{l}{* \textit{p} {\textless} .05, ** \textit{p} {\textless} .01}\\
\mc{7}{p{4.5in}}{Note: Cronbach's $\alpha$ on main diagonals, correlations on
off-diagonals.}
\end{tabular}
\end{center}
\end{table*}

\subsection{Confirmatory factor analysis}

In each task, the "two distinct factors" model provided a significantly
better fit than the "one factor" model (likelihood ratio difference
test $\chi\textsuperscript{2}$(1)=56, 103, and 146, 
for risk-taking, risk perception, and
expected benefits, respectively, p<.001 in all cases; AIC=157, 162,
145, respectively). The "two entangled factors" model did not improve
significantly on the "two distinct factors" model (AIC=159 vs. 157 for
risk-taking, AIC=174 vs. 162 for risk perception, and AIC=156 vs. 145
for expected benefits). Figure 4 shows the standardized path coefficients
(factor loadings) for the "two distinct factors" model for each task. The
RMSEA for the "two distinct factors" models varies from 0.06 to 0.08
across the tasks, representing acceptable but not excellent fit of
the models to the data (risk-taking RMSEA=0.07, 95\% CI [0.05-0.09],
risk perception RMSEA=0.08 [0.06-0.10], expected benefit RMSEA=0.06
[0.04-0.08]). Item M6 (general rather than local anesthesia for wisdom
tooth removal) contributed most to misfit of the model, particularly
in the risk-taking task. Correlations among the latent factors for the
medical and health/safety items were low.

\subsection{Exploratory factor analysis}

Tables 5-7 present the pattern matrices from exploratory factor analyses
of each task extracting 6 factors; similar tables for 7-factor extraction
appear in the Appendix. RMSEA values for the 6-factor solutions (with
95\% confidence intervals) were: risk-taking 0.04 [0.03-0.05], risk
perception 0.04 [0.03-0.05], expected benefits 0.05 [0.04-0.06]. RMSEAs
for the 7-factor solution were not significantly lower (risk-taking 0.04
[0.03-0.05], risk perception 0.04 [0.03-0.05], expected benefits 0.04
[0.03-0.05]).

In the risk-taking task (Table 5), each DOSPERT+M domain's items
largely loaded together on different factors, with the ethics domain
items spread across two factors, one of which also included most of the
financial domain items. Medical and health/safety domain items did not
load onto the same factor. Item M6 (general rather than local anesthesia)
did not load onto any factor.

%Table 5
\begin{table}[t!]
\caption{Exploratory factor analysis of the risk-taking task.}
\begin{center}
\begin{tabular}{l*{6}{d{3.2}}}\toprule
& \mc{6}{c}{Factor}\\ \cmidrule{2-7}
& 1 & 2 & 3 & 4 & 5 & 6 \\\midrule
R1 & \cellcolor{lightgray}0.47 &  &  &  &  & \\
R2 & \cellcolor{lightgray}0.41 &  &  &  &  & \\
R3 & \cellcolor{lightgray}0.59 &  &  &  &  & \\
R4 & \cellcolor{lightgray}0.81 &  &  &  &  & \\
R5 & \cellcolor{lightgray}0.76 &  &  &  &  & \\
R6 & \cellcolor{lightgray}0.50 &  &  & -0.24 & 0.25 & \\
E1 &  & \cellcolor{lightgray}0.36 &  &  &  & \\
E2 &  &  &  &  & \cellcolor{lightgray}0.55 & \\
E3 &  & \cellcolor{lightgray}0.48 &  &  & 0.33 & \\
E4 &  & 0.28 &  & 0.22 & 0.28 & 0.25 \\
E5 &  & 0.22 &  & 0.27 &  & \\
E6 &  & 0.24 &  & 0.27 &  & \\
F1 &  & \cellcolor{lightgray}0.72 &  &  &  & \\
F2 & 0.24 &  & \cellcolor{lightgray}0.46 &  & -0.23 & \\
F3 &  & \cellcolor{lightgray}0.65 &  &  &  & \\
F4 &  & \cellcolor{lightgray}0.46 & 0.30 &  &  & \\
F5 &  & \cellcolor{lightgray}0.53 &  &  &  & \\
F6 & 0.23 & 0.31 & \cellcolor{lightgray}0.48 &  & -0.21 & \\
S1 &  &  & \cellcolor{lightgray}0.57 &  &  & \\
S2 &  &  & \cellcolor{lightgray}0.50 &  &  & \\
S3 &  &  & \cellcolor{lightgray}0.58 &  &  & \\
S4 &  &  & \cellcolor{lightgray}0.66 &  &  & \\
S5 &  &  & 0.24 &  & 0.27 & \\
S6 &  &  & 0.31 &  &  & \\
H1 &  & 0.24 &  & 0.28 &  & \\
H2 &  &  &  & \cellcolor{lightgray}0.56 &  & \\
H3 &  &  &  & \cellcolor{lightgray}0.55 &  & \\
H4 & 0.32 &  &  & \cellcolor{lightgray}0.47 &  & \\
H5 & 0.27 & 0.21 &  & \cellcolor{lightgray}0.47 &  & \\
H6 &  &  &  &  & \cellcolor{lightgray}0.70 & \\
M1 &  &  &  & -0.32 &  & 0.26 \\
M2 & 0.21 &  & 0.23 &  &  & 0.21 \\
M3 &  & 0.29 &  &  &  & \cellcolor{lightgray}0.36 \\
M4 &  &  &  &  & -0.24 & \cellcolor{lightgray}0.42 \\
M5 &  &  &  &  &  & \cellcolor{lightgray}0.64 \\
M6 &  &  &  &  &  & \\\bottomrule
\mc{7}{p{3in}}{Note: Loadings $\geq$0.2 shown; loadings $\geq$0.35 shaded.}
\end{tabular}
\end{center}
\end{table}

%Table 6
\begin{table}[t!]
\caption{Exploratory factor analysis of the risk perception task.}
\begin{center}
\begin{tabular}{l*{6}{d{3.2}}}\toprule
& \mc{6}{c}{Factor}\\ \cmidrule{2-7}
& 1 & 2 & 3 & 4 & 5 & 6 \\\midrule
E1 & \cellcolor{lightgray}0.60 &  &  &  &  &  \\
E2 & \cellcolor{lightgray}0.35 &  & 0.26 &  &  &  \\
E3 & \cellcolor{lightgray}0.62 &  &  &  &  &  \\
E4 & \cellcolor{lightgray}0.57 &  &  &  &  &  \\
E5 & 0.24 &  & 0.30 &  &  &  \\
E6 & \cellcolor{lightgray}0.60 &  &  &  &  &  \\
R1 &  & \cellcolor{lightgray}0.48 &  &  &  &  \\
R2 &  & \cellcolor{lightgray}0.42 &  &  &  &  \\
R3 & 0.33 & \cellcolor{lightgray}0.49 &  &  &  &  \\
R4 &  & \cellcolor{lightgray}0.67 &  &  &  &  \\
R5 &  & \cellcolor{lightgray}0.67 &  &  &  &  \\
R6 &  & \cellcolor{lightgray}0.63 &  &  &  &  \\
H1 & \cellcolor{lightgray}0.64 &  &  &  &  &  \\
H2 & \cellcolor{lightgray}0.43 &  &  &  &  &  \\
H3 & 0.22 & 0.20 &  & 0.28 & -0.21 &  \\
H4 &  & \cellcolor{lightgray}0.35 & 0.21 &  & -0.20 &  \\
H5 & 0.29 & 0.27 &  & 0.23 &  &  \\
H6 & 0.27 & 0.29 &  &  &  &  \\
F1 &  &  & \cellcolor{lightgray}0.86 &  &  &  \\
F2 &  &  &  &  &  & \cellcolor{lightgray}0.62 \\
F3 &  &  & \cellcolor{lightgray}0.82 &  &  &  \\
F4 &  &  &  &  &  & \cellcolor{lightgray}0.49 \\
F5 &  &  & \cellcolor{lightgray}0.85 &  &  &  \\
F6 &  &  &  &  &  & \cellcolor{lightgray}0.68 \\
S1 &  &  &  & \cellcolor{lightgray}0.65 &  &  \\
S2 &  &  &  & \cellcolor{lightgray}0.49 &  &  \\
S3 & -0.25 &  &  & \cellcolor{lightgray}0.45 &  &  \\
S4 &  &  &  & \cellcolor{lightgray}0.63 &  &  \\
S5 &  &  &  & 0.33 & 0.21 &  \\
S6 &  &  &  & \cellcolor{lightgray}0.53 &  &  \\
M1 &  &  &  &  & \cellcolor{lightgray}0.39 &  \\
M2 &  &  &  &  & \cellcolor{lightgray}0.38 &  \\
M3 &  &  &  &  & \cellcolor{lightgray}0.36 &  \\
M4 &  &  &  &  & 0.33 &  \\
M5 &  &  &  &  & \cellcolor{lightgray}0.65 &  \\
M6 &  & 0.23 &  & 0.21 & 0.33 &  \\\bottomrule
\mc{7}{p{3in}}{Note: Loadings $\geq$0.2 shown; loadings $\geq$0.35 shaded.}
\end{tabular}
\end{center}
\end{table}

%Table 7
\begin{table}[t!]
\caption{Exploratory factor analysis of the expected benefit task.}
\begin{center}
\begin{tabular}{l*{6}{d{3.2}}}\toprule
& \mc{6}{c}{Factor}\\ \cmidrule{2-7}
& 1 & 2 & 3 & 4 & 5 & 6 \\\midrule
M1 & \cellcolor{lightgray}0.47 &  &  &  &  &  \\
M2 & \cellcolor{lightgray}0.46 &  &  &  &  &  \\
M3 & \cellcolor{lightgray}0.52 &  &  &  &  &  \\
M4 & \cellcolor{lightgray}0.52 &  &  &  &  &  \\
M5 & \cellcolor{lightgray}0.58 &  &  &  &  &  \\
M6 & \cellcolor{lightgray}0.35 &  &  &  &  &  \\
R1 &  & \cellcolor{lightgray}0.53 &  &  &  &  \\
R2 &  & \cellcolor{lightgray}0.40 &  &  &  &  \\
R3 &  & \cellcolor{lightgray}0.77 &  &  &  &  \\
R4 & 0.25 & \cellcolor{lightgray}0.65 &  &  &  &  \\
R5 &  & \cellcolor{lightgray}0.72 &  &  &  &  \\
R6 &  & \cellcolor{lightgray}0.56 &  &  &  &  \\
F1 &  &  & \cellcolor{lightgray}0.84 &  &  &  \\
F2 & \cellcolor{lightgray}0.53 &  &  &  &  &  \\
F3 &  &  & \cellcolor{lightgray}0.85 &  &  &  \\
F4 & \cellcolor{lightgray}0.45 &  &  &  &  &  \\
F5 &  &  & \cellcolor{lightgray}0.80 &  &  &  \\
F6 & \cellcolor{lightgray}0.52 &  & 0.22 &  &  &  \\
H1 &  &  &  &  & \cellcolor{lightgray}0.60 &  \\
H2 &  &  &  &  & \cellcolor{lightgray}0.50 &  \\
H3 &  &  &  & \cellcolor{lightgray}0.61 &  &  \\
H4 &  & 0.29 &  & 0.22 &  &  \\
H5 &  &  &  & \cellcolor{lightgray}0.42 &  &  \\
H6 &  &  &  & \cellcolor{lightgray}0.55 &  &  \\
E1 &  &  & 0.23 & 0.26 &  &  \\
E2 &  &  &  &  & \cellcolor{lightgray}0.58 &  \\
E3 &  &  &  &  & \cellcolor{lightgray}0.68 &  \\
E4 &  &  &  & \cellcolor{lightgray}0.75 &  &  \\
E5 &  &  &  & \cellcolor{lightgray}0.38 &  &  \\
E6 & 0.22 &  &  &  & 0.31 & -0.21 \\
S1 &  &  &  &  &  & \cellcolor{lightgray}0.44 \\
S2 &  &  &  &  &  & \cellcolor{lightgray}0.68 \\
S3 & \cellcolor{lightgray}0.37 &  &  & -0.20 &  & 0.29 \\
S4 &  &  &  &  &  & \cellcolor{lightgray}0.74 \\
S5 & 0.31 &  &  &  & 0.30 &  \\
S6 & \cellcolor{lightgray}0.57 &  &  &  &  &  \\\bottomrule
\mc{7}{p{3in}}{Note: Loadings $\geq$0.2 shown; loadings $\geq$0.35 shaded.}
\end{tabular}
\end{center}
\end{table}

\begin{figure*}[t!]\centering
\caption{Confirmatory factor analysis model for risk-taking}
\includegraphics[width=4in,clip=true]{rt-cfa.eps}
\end{figure*}
\begin{figure*}[t!]\centering
\caption{Confirmatory factor analysis model for risk perception}
\includegraphics[width=4in,clip=true]{rp-cfa.eps}
\end{figure*}
\begin{figure*}[t!]\centering
\caption{Confirmatory factor analysis model for expected benefit}
\includegraphics[width=4in,clip=true]{bp-cfa.eps}
\end{figure*}

In the risk perception task (Table 6), the health domain items shared
variance with the ethics and recreational items, and the financial items
were spread across two factors representing gambling vs. investing
items. Medical and health/safety domain items did not load onto the
same factor; the correlation between one health/safety factor (factor
1, with ethics items) and the medical factor was 0.48; the correlation
between the other health/safety factor (factor 2, with recreational
items) and the medical factor was -0.29. Item M6 (general rather than
local anesthesia) had weaker loadings.

In the expected benefit task (Table 7), the medical domain items shared
variance with the financial and social items, and the financial and
ethical items were spread across two factors (with financial representing
investing vs. gambling). Medical and health/safety domain items did
not load onto the same factor; the correlation between the medical and
health/safety factors was 0.29.

\subsection{Demographic predictors of medical domain scores}

The regression models for medical risk-taking and medical risk
perception scores did not account for significant variance in the scores
(F(7,213)=0.89, p=0.5, and F(7,199)=1.5, p=0.2, respectively). For
expected benefit of medical activities, the regression model reached
significance (F(7,223)=2.6, p=0.014), and three demographic predictors
were associated with expected benefit scores. Black non-Hispanic
respondents had higher scores for expected benefit (B=0.60, SE=0.27,
p=.028), as did female respondents (B=0.32, SE=0.15, p=.039). Older
respondents also reported higher expected benefit (B=0.010 per year
of age, SE=0.004, p=.026), contrary to the findings in the development
study (Butler, et al., 2012). Table 8 compares the demographic predictors
of medical domain scores with those for the other DOSPERT domains in each
task, and suggests that associations between demographic predictors and
the medical risk subscale are different than those for other subscales.

\section{Discussion}

\subsection{Summary of results}

In this study using a U.S-representative panel sample, the DOSPERT+M
items largely (although not perfectly) cluster into the expected domains,
providing additional evidence of DOSPERT's fundamental assumption of
domain specificity in risk attitude. The pattern is similar to that
reported in factor analyses of the original DOSPERT scale (Blais \&
Weber, 2009).

DOSPERT+M medical domain scores were not associated with health/safety
domain scores. The items in these two domains did not load onto a
common factor in either confirmatory or exploratory factor analysis
of any task. DOSPERT+M risk-taking and risk perception scores were
not associated with demographic factors; DOSPERT+M expected benefit
scores were higher for women, Black respondents, and older respondents,
on average. 

The internal consistency of the medical domain subscale for the
risk-taking task was unacceptably low for practical use as a measure
of willingness to engage in medical risks. Although the consistency
was acceptable in the risk perception and expected benefits tasks,
a feature of DOSPERT is the use of the same items in all three tasks,
and items in the subscale should be replaced or revised before it is
applied as a risk measure in practice. The dental anesthesia item (M6)
appears to be a clear candidate for replacement.

%Table 8
\begin{table*}[t!]
\caption{Demographic predictors of domain scores.}
\begin{center}
\begin{tabular}{lcccccc}\toprule
& \mc{6}{c}{Risk-taking task}\\ \cmidrule{2-7}
& Medical & Health/safety & Financial & Ethical & Recreational & Social \\\midrule
Age    & & (-)  & (-)  &   & (-)  & (-)  \\
Female    & &   &   &   & (-)  & (-)  \\
Cohabitating    & &   &   &   &   &   \\
College    & &   &   &   & (+)  & (+)  \\
Ethnicity (vs. White, Non-Hispanic)    & &   &   &   &   &   \\
\hskip .4cm	Black, Non-Hispanic    & &   &   &   &   &   \\
\hskip .4cm	Other, Non-Hispanic    & &   &   & (+)  &   &   \\
\hskip .4cm	Hispanic    & &   &   &   & (-)  &   \\
\hskip .4cm	Multiracial, Non-Hispanic    & &   &   &   &   &   \\\midrule
& \mc{6}{c}{Risk perception task}\\ \cmidrule{2-7}
& Medical & Health/safety & Financial & Ethical & Recreational & Social \\\midrule
Age    & & (+)  & (+)  &   & (+)  &   \\
Female    & & (+)  & (+)  & (+)  & (+)  &   \\
Cohabitating    & &   &   &   &   &   \\
College    & &   &   &   &   &   \\
Ethnicity (vs. White, Non-Hispanic)    & &   &   &   &   &   \\
\hskip .4cm	Black, Non-Hispanic    & &   &   & (+)  & (+)  &   \\
\hskip .4cm	Other, Non-Hispanic    & &   &   &   &   &   \\
\hskip .4cm	Hispanic    & &   &   &   & (+)  &   \\
\hskip .4cm	Multiracial, Non-Hispanic    & &   &   &   &   &   \\\midrule
& \mc{6}{c}{Expected benefit task}\\ \cmidrule{2-7}
& Medical & Health/safety & Financial & Ethical & Recreational & Social \\\midrule
Age  & (+)   & (-)  & (-)  &   & (-)  &   \\
Female  & (+)   &   &   &   & (-)  & (-)  \\
Cohabitating    & &   & (-)  &   &   &   \\
College    & &   &   &   & (+)  & (+)  \\
Ethnicity (vs. White, Non-Hispanic)    & &   &   &   &   &   \\
\hskip .4cm	Black, Non-Hispanic  & (+)  &   &   &   &   & (+)  \\
\hskip .4cm	Other, Non-Hispanic    & &   &   &   &   &   \\
\hskip .4cm	Hispanic    & &   &   &   &   &   \\
\hskip .4cm	Multiracial, Non-Hispanic    & &   &   &   &   &   \\\bottomrule
\mc{7}{l}{(+) = significant (\textit{p} {\textless} .05) positive association, (-) = significant negative association}\\
\end{tabular}
\end{center}
\end{table*}

\subsection{Limitations}

We did not measure actual decision behavior, and we cannot conclude
whether medical domain subscale scores are associated with behavior
by patients facing actual medical decisions. This remains an area for
future investigation.

Out of concern for survey fatigue, we did not present all three DOSPERT
tasks to each participant, limiting our ability to conduct within-subject
comparisons of responses to each task. Past studies using DOSPERT have
often omitted the expected benefits scale for similar reasons, but we
preferred to collect information on benefit perception.

\subsection{Implications}

DOSPERT, and the health/safety domain in particular, have been recommended
as measures of risk attitude for use in studies of medical decisions. As
responses to the health/safety domain do not appear to be related to
responses to the DOSPERT+M medical domain, we recommend that studies
seeking to measure risk attitudes in medical decisions include
medical domain items as well as the health/safety items and
assess their value in explaining or informing medical decisions.


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\begin{hangparas}{1em}{1}

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\end{hangparas}

\newpage
\section*{Appendix}

Table A1. 7-Factor exploratory factor analysis pattern matrix in the
risk-taking task.\smallskip

\noindent
\begin{tabular}{l*{7}{d{2.2}}}\toprule
& \mc{7}{c}{Factor}\\ \cmidrule{2-8}
& 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\midrule
R1 & \cellcolor{lightgray}0.37 &  &  &  &  &  & 0.25 \\
R2 & \cellcolor{lightgray}0.39 &  &  &  &  &  & \\
R3 & \cellcolor{lightgray}0.54 &  &  &  &  &  & \\
R4 & \cellcolor{lightgray}0.82 &  &  &  &  &  & \\
R5 & \cellcolor{lightgray}0.75 &  &  &  &  &  & \\
R6 & \cellcolor{lightgray}0.49 &  &  & -0.21 & 0.25 &  & \\
F1 &  & \cellcolor{lightgray}0.70 &  &  &  &  & \\
F2 & 0.26 &  & \cellcolor{lightgray}0.49 &  &  &  & \\
F3 &  & \cellcolor{lightgray}0.66 &  &  &  &  & \\
F4 &  & \cellcolor{lightgray}0.43 & \cellcolor{lightgray}0.36 &  &  & -0.23 & \\
F5 &  & \cellcolor{lightgray}0.55 &  &  &  &  & \\
F6 &  & 0.28 & \cellcolor{lightgray}0.53 &  &  &  & \\
S1 &  & -0.22 & \cellcolor{lightgray}0.54 &  &  &  & \\
S2 &  &  & \cellcolor{lightgray}0.45 &  & 0.29 &  & \\
S3 &  &  & \cellcolor{lightgray}0.58 &  &  &  & \\
S4 & -0.21 &  & \cellcolor{lightgray}0.61 &  & 0.23 &  & \\
S5 & 0.22 &  &  &  & 0.34 & 0.21 & \\
S6 &  &  & 0.29 &  &  &  & \\
H1 &  & 0.22 &  & 0.28 &  &  & \\
H2 &  &  &  & \cellcolor{lightgray}0.54 & 0.20 &  & \\
H3 &  &  &  & \cellcolor{lightgray}0.63 &  &  & \\
H4 & 0.25 &  &  & \cellcolor{lightgray}0.53 &  &  & \\
H5 &  &  &  & \cellcolor{lightgray}0.57 &  &  & \\
H6 &  &  &  &  & \cellcolor{lightgray}0.68 &  & \\
E1 &  & 0.34 &  &  &  &  & \\
E2 &  &  &  &  & \cellcolor{lightgray}0.65 &  & \\
E3 &  & \cellcolor{lightgray}0.46 &  &  & 0.29 &  & \\
E4 &  & 0.28 &  &  & 0.33 & 0.26 & \\
E5 &  & 0.23 &  &  &  &  & \\
E6 &  & 0.22 &  & 0.27 &  &  & \\
M1 &  &  &  &  &  &  & \cellcolor{lightgray}0.68 \\
M2 &  &  & 0.26 &  &  &  & \cellcolor{lightgray}0.51 \\
M3 &  & 0.27 &  &  &  & 0.29 & \\
M4 &  &  &  &  &  & \cellcolor{lightgray}0.54 & \\
M5 & 0.21 &  &  &  &  & \cellcolor{lightgray}0.56 & 0.21 \\
M6 &  &  &  &  &  &  &  \\\bottomrule
\mc{8}{p{3in}}{Note: Loadings $\geq$0.2 shown; loadings $\geq$0.35 shaded.}
\end{tabular}

\newpage

Table A2. 7-Factor exploratory factor analysis pattern matrix in the
risk perception task.\smallskip

\noindent
\begin{tabular}{l*{7}{d{2.2}}}\toprule
& \mc{7}{c}{Factor}\\ \cmidrule{2-8}
& 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\midrule
E1 & \cellcolor{lightgray}0.59 &  &  &  &  &  & \\
E2 & 0.33 & 0.26 &  &  &  &  & \\
E3 & \cellcolor{lightgray}0.59 &  &  &  &  &  & \\
E4 & \cellcolor{lightgray}0.52 &  &  &  &  &  & \\
E5 & 0.21 &  &  &  &  &  & \\
E6 & \cellcolor{lightgray}0.62 &  &  &  &  &  & \\
F1 &  & \cellcolor{lightgray}0.85 &  &  &  &  & \\
F2 &  &  &  &  &  &  & \cellcolor{lightgray}0.62 \\
F3 &  & \cellcolor{lightgray}0.82 &  &  &  &  & \\
F4 &  &  &  &  &  &  & \cellcolor{lightgray}0.50 \\
F5 &  & \cellcolor{lightgray}0.84 &  &  &  &  & \\
F6 &  &  &  &  &  &  & \cellcolor{lightgray}0.67 \\
R1 &  &  & \cellcolor{lightgray}0.44 &  &  &  & \\
R2 &  &  & \cellcolor{lightgray}0.43 &  &  &  & \\
R3 & 0.31 &  & \cellcolor{lightgray}0.49 &  &  &  & \\
R4 &  &  & \cellcolor{lightgray}0.66 &  &  &  & \\
R5 &  &  & \cellcolor{lightgray}0.66 &  &  &  & \\
R6 &  &  & \cellcolor{lightgray}0.62 &  &  &  & \\
S1 &  &  &  & \cellcolor{lightgray}0.61 &  &  & \\
S2 &  &  &  & \cellcolor{lightgray}0.54 &  &  & \\
S3 & -0.21 &  &  & \cellcolor{lightgray}0.50 &  &  & \\
S4 &  &  &  & \cellcolor{lightgray}0.60 &  &  & \\
S5 &  &  &  & 0.34 &  &  & \\
S6 &  &  &  & \cellcolor{lightgray}0.53 &  &  & \\
H1 & \cellcolor{lightgray}0.59 &  &  &  &  &  & \\
H2 & \cellcolor{lightgray}0.39 &  &  &  &  &  & \\
H3 &  &  &  &  & \cellcolor{lightgray}1.00 &  & \\
H4 &  &  & 0.23 &  & \cellcolor{lightgray}0.51 &  & \\
H5 & 0.27 &  & 0.25 &  &  &  & \\
H6 & 0.28 & 0.22 & 0.29 &  &  &  & \\
M1 &  &  &  &  &  & \cellcolor{lightgray}0.44 & \\
M2 &  &  &  &  &  & \cellcolor{lightgray}0.40 & \\
M3 &  &  &  &  &  & \cellcolor{lightgray}0.39 & \\
M4 &  &  &  &  &  & 0.32 & \\
M5 &  &  &  &  &  & \cellcolor{lightgray}0.72 & \\
M6 &  &  & 0.25 & 0.24 &  & 0.28 &  \\\bottomrule
\mc{8}{p{3in}}{Note: Loadings $\geq$0.2 shown; loadings $\geq$0.35 shaded.}
\end{tabular}

\newpage

Table A3. 7-Factor exploratory factor analysis pattern matrix in the
expected benefit task.\smallskip

\noindent
\begin{tabular}{l*{7}{d{2.2}}}\toprule
& \mc{7}{c}{Factor}\\ \cmidrule{2-8}
& 1 & 2 & 3 & 4 & 5 & 6 & 7 \\\midrule
R1 & \cellcolor{lightgray}0.50 &  &  &  &  &  & 0.21 \\
R2 & \cellcolor{lightgray}0.35 &  &  &  & 0.21 &  & \\
R3 & \cellcolor{lightgray}0.73 &  &  &  &  &  & \\
R4 & \cellcolor{lightgray}0.66 &  &  &  &  &  & \\
R5 & \cellcolor{lightgray}0.78 &  &  &  &  &  & \\
R6 & \cellcolor{lightgray}0.53 &  &  &  &  &  & \\
F1 &  & \cellcolor{lightgray}0.84 &  &  &  &  & \\
F2 &  &  &  & \cellcolor{lightgray}0.62 &  &  & \\
F3 &  & \cellcolor{lightgray}0.81 &  &  &  &  & \\
F4 &  &  &  & \cellcolor{lightgray}0.62 &  &  & \\
F5 &  & \cellcolor{lightgray}0.80 &  &  &  &  & \\
F6 &  &  &  & \cellcolor{lightgray}0.83 &  &  & \\
M1 &  &  & \cellcolor{lightgray}0.41 &  &  &  & \\
M2 &  &  & \cellcolor{lightgray}0.55 &  &  &  & \\
M3 &  &  & \cellcolor{lightgray}0.53 &  &  &  & \\
M4 &  &  & \cellcolor{lightgray}0.69 &  &  &  & \\
M5 &  &  & \cellcolor{lightgray}0.59 &  &  &  & \\
M6 &  &  & 0.33 &  &  &  & \\
E1 &  & 0.22 &  &  &  & 0.24 & \\
E2 &  &  &  &  & \cellcolor{lightgray}0.56 &  & \\
E3 &  &  &  &  & \cellcolor{lightgray}0.70 &  & \\
E4 &  &  &  &  &  & \cellcolor{lightgray}0.71 & \\
E5 &  &  &  &  & 0.21 & 0.34 & \\
E6 &  &  &  & 0.26 & 0.34 &  & -0.20 \\
H1 &  &  &  &  & \cellcolor{lightgray}0.59 &  & \\
H2 &  &  &  &  & \cellcolor{lightgray}0.51 &  & \\
H3 &  &  &  &  &  & \cellcolor{lightgray}0.65 & \\
H4 & 0.31 &  &  &  &  & 0.27 & \\
H5 &  &  &  &  &  & \cellcolor{lightgray}0.46 & \\
H6 &  &  &  &  &  & \cellcolor{lightgray}0.52 & \\
S1 &  &  &  &  &  &  & \cellcolor{lightgray}0.44 \\
S2 &  &  &  &  &  &  & \cellcolor{lightgray}0.69 \\
S3 &  &  & 0.21 & 0.22 &  & -0.22 & 0.31 \\
S4 &  &  &  &  &  &  & \cellcolor{lightgray}0.73 \\
S5 &  &  &  & 0.24 & 0.31 &  & \\
S6 &  &  & \cellcolor{lightgray}0.35 & 0.31 &  &  & \\\bottomrule
\mc{8}{p{3in}}{Note: Loadings $\geq$0.2 shown; loadings $\geq$0.35 shaded.}
\end{tabular}

\end{document}
