ArticlePDF Available

Mental Illness and the Left

Authors:
  • Ulster Institute for Social Research
Article

Mental Illness and the Left

Abstract and Figures

It has been claimed that left-wingers or liberals (US sense) tend to more often suffer from mental illness than right-wingers or conservatives. This potential link was investigated using the General Social Survey cumulative cross-sectional dataset (1972-2018). A search of the available variables resulted in 5 items measuring one's own mental illness (e.g., ”Do you have any emotional or mental disability?”). All of these items were weakly associated with left-wing political ideology as measured by self-report, with especially high rates seen for the “extremely liberal” group. These results mostly held up in regressions that adjusted for age, sex, and race. For the variable with the most data (n = 11,338), the difference in the mental illness measure between “extremely liberal” and “extremely conservative” was 0.39 d. Temporal analysis showed that the relationship between mental illness, happiness, and political ideology has existed in the GSS data since the 1970s and still existed in the 2010s. Within-study meta-analysis of all the results found that extreme liberals had a 150% increased rate of mental illness compared to moderates. The finding of increased mental illness among left-wingers is congruent with numerous findings based on related constructs, such as positive relationships between conservatism, religiousness and health in general.
Content may be subject to copyright.
MANKIND QUARTERLY 2020 60:4 487-510
487
Mental Illness and the Left
Emil O. W. Kirkegaard*
Ulster Institute for Social Research, London, UK
*Email: emil@emilkirkegaard.dk
It has been claimed that left-wingers or liberals (US sense) tend
to more often suffer from mental illness than right-wingers or
conservatives. This potential link was investigated using the General
Social Survey cumulative cross-sectional dataset (1972-2018). A
search of the available variables resulted in 5 items measuring one's
own mental illness (e.g., ”Do you have any emotional or mental
disability?”). All of these items were weakly associated with left-wing
political ideology as measured by self-report, with especially high
rates seen for the “extremely liberal” group. These results mostly
held up in regressions that adjusted for age, sex, and race. For the
variable with the most data (n = 11,338), the difference in the mental
illness measure between “extremely liberal” and “extremely
conservative” was 0.39 d. Temporal analysis showed that the
relationship between mental illness, happiness, and political
ideology has existed in the GSS data since the 1970s and still
existed in the 2010s. Within-study meta-analysis of all the results
found that extreme liberals had a 150% increased rate of mental
illness compared to moderates. The finding of increased mental
illness among left-wingers is congruent with numerous findings
based on related constructs, such as positive relationships between
conservatism, religiousness and health in general.
Key Words: Mental illness, Mental health, Happiness, Life
satisfaction, Political ideology, Left-wing, Liberalism, Right-wing,
Conservatism
It has been reported that left-wingers or liberals (US sense) tend to more
often suffer from mental illness than right-wingers or conservatives (Bullenkamp
& Voges, 2004; Duckworth et al., 1994; Guhname, 2007; Howard & Anthony,
MANKIND QUARTERLY 2020 60:4
488
1977; Kelly, 2014; Unorthodox Theory, 2020). This suggestion is consistent with
other research showing that religiosity predicts both mental and physical health
(AbdAleati et al., 2016; Cotton et al., 2006; Dutton et al., 2018; Moreira-Almeida
et al., 2006; Seeman et al., 2003; VanderWeele, 2017), given the known strong
relationship between political conservatism and religiousness (Koenig &
Bouchard Jr., 2006; Ludeke et al., 2013). Furthermore, political conservatism has
been found to be associated with longevity (Kannan et al., 2019).
In a recent series of tweets, Lemoine (2020) analyzed data from the Slate
Star Codex (SSC) 2020 reader survey1 (n = 8,043; Alexander, 2020), and showed
that self-rated political ideological position (1-10 scale) and self-rated far-left
labels were related to mental health. Since his work was not published in
academic format, we reproduce his main result in Figure 1.
Figure 1. Self-reported mental health and self-reported political label.
Reproduced from Lemoine (2020).
1 The Slate Star Codex is a popular blog by Scott Alexander, a Silicon Valley-based
philosopher and psychiatrist. The reader survey is a public survey composed by
Alexander and colleagues and was freely available to take via a link from his blog.
https://slatestarcodex.com/
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
489
However, the SSC survey is far from representative, being mainly limited to
readers of a particular blog that attracts mainly European-descent, highly
intelligent readers (Karlin, 2018). Thus, there was a need to replicate the analysis
in more representative samples. Hence, the aim of the article was to examine the
links between mental health and political ideology in the General Social Survey
(GSS, https://gss.norc.org/), a public access large-scale survey with relevant
data.
Data
We used data from the cumulative cross-sectional file 1972-2018 (release 1)
available for public use at https://gssdataexplorer.norc.org/pages/show?page=
gss%2Fgss_data. This has a total sample size of 64,814, but not all items
(questions) were asked in every wave, or given to all respondents in each wave.
We searched the database for items relating to mental illness.2 Five items were
found with at least a sample size of 1,000, shown in Table 1. Four of these were
binary/dichotomous, and one was numeric. Political ideology was measured by a
1-7 scale going from extremely liberal to extremely conservative, which was
available for all subjects. Figure 2 shows the distribution of political ideology by
survey year.
Table 1. Survey questions concerning mental health.
Question
Response
n
Years
Do you have any emotional or mental
disability?
Yes/no 2,777 2006
Now thinking about your mental health, which
includes stress, depression, and problems
with emotions, for how many days during the
past 30 days was your mental health not
good?
Numeric
[0-30] 11,338
2002, 2004,
2006, 2010,
2012, 2014,
2016, 2018
First, thinking about health related matters,
did any of the following happen to you since
[12 months ago]? Underwent counseling for
mental or emotional problems.
Yes/no 2,345 1991, 2004
Have you ever felt you had a mental health
problem?
Yes/no 1,053 1996
Have you personally ever received treatment
for a mental health problem?
Yes/no 1,413 2006
2 We used search terms such as “mental”, “illness”, “disorder”, and “depression”.
MANKIND QUARTERLY 2020 60:4
490
Figure 2. Distribution of political ideology by survey year.
Moderates are the largest group at roughly 40% of the population. People
with extreme views represent approximately 10% of the population in 2018 but
only about 5% in 1975. Thus, there is a long running tendency towards more
ideological extremism, at least insofar as this self-report measure is concerned.
The increase in recent years is consistent with the pattern from other sources
about the Great Awokening (Goldberg, 2019; Kaufman, 2019; Winegard &
Winegard, 2018; Yglesias, 2019).
Results
First, we plotted the average of each outcome by political ideology and sex.
We included a split by sex because sex relates to politics3, and sex relates to
3 Sex/gender relates to politics in that women vote more for left-wing parties. This,
however, is not a constant finding through history. For research, see e.g. (Abendschön
& Steinmetz, 2014; Edlund & Pande, 2002; Inglehart & Norris, 2000). However, even
before women voted for left-wing parties (roughly, prior to the 1970s), their influence
on politics was to increase state spending on welfare, i.e. a left-wing/big government
influence (Abrams & Settle, 1999; Lott & Kenny, 2015). Evidence for this comes from
countries and subnational governments that implemented women’s suffrage (vote) at
different times.
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
491
mental illness (women higher), and ignoring sex would thus lead to some
confounding. Figures 3a-e show the results.
Figure 3a. Mental health outcomes by political ideology and sex: treatment for
mental health. Error bars are 95% analytic confidence intervals.
Figure 3b. Mental health outcomes by political ideology and sex: ever had mental
health problem.
MANKIND QUARTERLY 2020 60:4
492
Figure 3c. Mental health outcomes by political ideology and sex: counseling last
year.
Figure 3d. Mental health outcomes by political ideology and sex: days in poor
health.
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
493
Figure 3e. Mental health outcomes by political ideology and sex:
emotional/mental disability.
So we see that for mental illness outcomes, left-wing political ideology, in
particular “extremely liberal”, predicts worse mental health. The results also hold
across sexes, though the sample sizes are not large for the extreme groups, and
the predictor did not always have p<.05 in a regression model. Are the results
large enough to care about? One approach is calculating Cohen’s d values for
the group gaps. This can only properly be done when the outcome data is
continuous. Table 2 shows the results for the “days of poor mental health” variable
with which we have the most data and which is a continuous outcome.
For the two most extreme groups, the gap is of moderate size, at least as
measured by this single item. Another way to quantify it is to convert political
ideology into numeric form (1-7) and correlate it with the variable (Pearson
correlation). This produces a correlation of only .07 that is nevertheless highly
significant statistically (p = 3e-10). Hence, overall, the relationship between the two
is quite weak and reaches a notable size only for extreme groups.
To examine whether age was a confound, we fit a regression model for each
outcome, logistic models for the dichotomous and OLS for the continuous (days
of poor health). Each model included political ideology, age (as a natural spline),
and sex as predictors, without any interactions as the sample size did not provide
sufficient statistical power. For each model, we projected the predicted levels of
MANKIND QUARTERLY 2020 60:4
494
mental illness from the models using the ggeffects package’s ggpredict() function
(Lüdecke, 2018), shown in Figures 4a-e.
Table 2. Cohen’s d gaps for “days of poor mental health last 30 days” by political
ideology. Positive values indicate more days of poor health compared to the less
liberal or more conservative group. For example, moderates had 0.28 standard
deviations fewer days of poor mental health than extremely liberals, and 0.03
standard deviations more than those considering themselves slightly liberal.
Extr.
lib.
Lib.
Slight.
lib.
Mod.
Con.
Extr.
Con.
Extr.
lib.
0.21 0.25 0.20 0.28 0.36 0.39
Lib.
0.21
0.04
-0.01
0.15
0.18
Slight.
lib.
0.25 0.04 -0.05 0.03 0.12 0.14
Mod.
0.20
-0.01
-0.05
0.16
0.19
Slight.
con.
0.28 0.07 0.03 0.08 0.09 0.03
Con.
0.36
0.15
0.12
0.16
0.03
Extr.
con.
0.39 0.18 0.14 0.19 0.11 0.03
Extr. = Extremely; Lib = Liberal; Slight. = Slightly; Mod. = Moderate; Con. = Conservative.
Figure 4a. Model projections of mental health by political ideology, controlling for
age and sex (covariates are held at average values): treatment for mental health.
Error bars are 95% prediction intervals.
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
495
Figure 4b. Model projections of mental health by political ideology, controlling
for age and sex (covariates are held at average values): ever had mental health
problem.
MANKIND QUARTERLY 2020 60:4
496
Figure 4c. Model projections of mental health by political ideology, controlling for
age and sex (covariates are held at average values): counseling last year.
Figure 4d. Model projections of mental health by political ideology, controlling
for age and sex (covariates are held at average values): days in poor health.
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
497
Figure 4e. Model projections of mental health by political ideology, controlling for
age and sex (covariates are held at average values): emotional/mental disability.
As before, the results show that there is some relationship between mental
health and political ideology such that left-wingers have worse outcomes. The
confidence intervals are fairly wide, however. For people reporting having an
emotional mental disorder or not, there was seemingly no pattern except that
“extremely liberal” reported this higher than everybody else. As a robustness test,
we ran models on whites only to avoid any potential confound with race and
mental illness (Coleman et al., 2016; Maura & Weisman de Mamani, 2017).
However, the results were very similar and are not shown here. Full statistical
output and code can be found in the supplementary materials
(https://osf.io/fhpxm/).
Reverse indicators: happiness and political ideology
Another option is to use reverse items that instead of measuring mental
illness measure the opposite, happiness or high life satisfaction. The GSS has
two items asking about happiness based on the questions: 1) “Taken all together,
how would you say things are these days would you say that you are very
happy, pretty happy, or not too happy?”, and 2) “If you were to consider your life
in general, how happy or unhappy would you say you are, on the whole? [from
completely unhappy to completely happy, 7 levels]”. The sample sizes for these
were 60,054 and 2,444, respectively. Figures 5a and b show the mean happiness
level by sex and political ideology.
MANKIND QUARTERLY 2020 60:4
498
Figure 5a. Mean happiness level by sex and political ideology: would you say
that you are very happy, pretty happy, or not too happy? N = 60,054. Error bars
are 95% analytic confidence intervals.
Figure 5b. Mean happiness level by sex and political ideology: how happy or
unhappy would you say you are, on the whole? N = 2,044.
We see the same pattern in reverse, in that the more conservative groups
have higher happiness levels on both questions. In terms of Cohen’s d, the effect
sizes between the extreme groups are 0.20 and 0.56, respectively; in terms of
Pearson correlations, they are .06 and .11, respectively. It’s unclear why the
second should produce substantially larger gaps when the questions are quite
similar, but it may just be sampling error.
Temporal pattern
Two of the items used have been asked for many years (bad days per month
and happiness question 1), allowing for analysis of any temporal pattern. Because
large samples are required to detect weak patterns, the waves of data were
grouped at the decadal level. Figure 6 shows the results.
The relationships persisted across all waves of data, beginning in the 1970s
and into the end of the 2010s. Cohen’s d was also calculated for the same
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
499
comparisons shown in the figure. Although there were some differences across
decades, they were all numerically consistent in direction and probably not
significantly different in most cases if formally tested. The full numerical output
can be found in the statistical output.
Figure 6. Temporal pattern in relationship between political ideology, mental
illness and happiness. Error bars are 95% analytic confidence intervals.
Party affiliation as alternative political measure
At the request of a reviewer, we replicated the main result in Figure 3 using
the party affiliation question. This question is similar to the political ideology one,
but instead asks people which party they are affiliated with, ranging from strong
Democrat to strong Republican. There is also an option for third parties, but we
removed this due to incompatibility with the other results, as well as limited
sample size which makes the results too imprecise to interpret. Figure 7 shows
the results.
With the four binary measures, the pattern is roughly the same as the prior
analyses. However, for the continuous outcome (days poor health), we see a non-
monotonic pattern such that Independents actually score higher. Even in this
subanalysis, however, strong Republicans are quite far below their counterpart.
MANKIND QUARTERLY 2020 60:4
500
The main issue here is that the outcome measures are not on the same scale
(dichotomous vs. quasi-continuous), and even those on the same scale have
different base rates. Thus, to facilitate comparison, the data were converted into
relative risk (RR) relative to the moderates.4
Figure 7. Party affiliation and mental illness measures. Error bars are 95%
analytic confidence intervals.
Meta-analysis
The individual estimates of mental illness measures are somewhat unreliable
owing to the small number of persons in the extreme groups, and the low base
rates. To overcome this limitation, we can meta-analyze the findings from Figure
3a-e. Bootstrapped standard errors were computed by resampling individuals and
then computing all downstream statistics 1000 times. This was done because it
was unclear how to calculate analytic error bars for these data, which were
converted into RR and originating from different scales, as well as having some
4 One could also have computed them relative to the overall mean. The latter would
remove any changes related to the changing of the distribution of political ideology, as
was shown to exist in Figure 2.
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
501
degree of non-independence between values due to multiple questions asked in
the same wave in some cases. Figure 8 shows the results.
Figure 8. Meta-analysis of mental illness indicators on relative risk scale
compared to moderates. Error bars are 95% bootstrapped confidence intervals.
The error bar for moderates has 0 height because it is the reference group. Total
sample sizes shown at the bar bottoms.
The results confirm the general pattern from before, namely that there is a
strongly elevated risk for mental illness among the extreme liberals (+150%), a
small increase among the liberals and slightly liberals (+29 to 32%), and
somewhat lower rates among conservatives and extreme conservatives (-17 to
24%). Breaking the pattern, slightly conservatives had a marginally increased rate
(+6%). A variant of this analysis was also carried out by including the happiness
metrics reverse-coded. This produced materially the same pattern, but was
weaker since the happiness items had a weaker relationship with political
ideology than the mental illness variables.
Discussion
The present study investigated a large dataset of representative adult
Americans to see whether there was a relationship between political ideology and
mental health. Prior research and media claims had indicated these variables
were related such that left-wing ideology was associated with worse mental health
MANKIND QUARTERLY 2020 60:4
502
(Bullenkamp & Voges, 2004; Duckworth et al., 1994; Guhname, 2007; Howard &
Anthony, 1977; Kelly, 2014; Lemoine, 2020; Unorthodox Theory, 2020). The
results of the present study are in line with previous claims, in particular
concerning people who reported being “extremely liberal”, though this is a small
minority of persons in the study (about 5% in 2018, cf. Figure 2). It is notable that
the question based on the largest sample size (n = 11,338, spanning the years
2002-2018, days of poor mental health last 30 days) showed one of the most
consistent patterns, both in the simple averages by sex and when adjusting for
age. The effect size between the two extreme groups was 0.39 d, thus of
moderate size. As both variables are single item measures which have limited
reliability, the true score effect size would perhaps be around 0.50 assuming
about 0.70 test-retest reliability (Kim & Abraham, 2016; Littman et al., 2006;
Spörrle & Bekk, 2014). On the other hand, the correlation between the variables
is only .07 (p = 3e-10).
So, is the effect large enough to care about? It may depend on whether one
is interested in people with extreme political views, roughly in the top 10% of
extremism (5% on either side, cf. Figure 2). There is evidence that most political
discourse and activism is done by highly interested, generally intelligent people
(Kalmoe, 2020), who also tend to be more ideologically consistent and thus more
represented among the extreme groups (Kalmoe, 2020). Thus, one might expect
that among such people, the left-wing political activists would tend to be more
mentally ill than the equally extreme right-wing political activists.
With regards to etiology, this kind of cross-sectional study is not highly
informative. Both mental illness and political ideology are substantially heritable
(Brikell et al., 2018; Hatemi et al., 2014; Kirkegaard, 2018; Neumann et al., 2016),
and both are moderately to strongly related to broader personality differences
(Fatke, 2017; Ksiazkiewicz & Friesen, 2019). A good start would be doing a
multivariate behavioral genetic study to assess the degree to which the relations
are due to common genetic variance or shared environmental (which includes
upbringing). Based on prior findings (Kirkegaard, 2018), it is unlikely that the
shared environment contributes substantially to the relationship, and the
covariance of these traits probably mostly reflects common genetic pathways.
However, even finding common genetic variation would not necessarily be
terribly informative regarding causality. It is possible that mentally ill people select
into extreme left-wing views, or that being in extreme left-wing contexts results in
mental illness (i.e. left-wing contexts promote mental illness). It is also likely that
both are caused to some degree by other factors not measured or even
mentioned here. With regard to overlap between contexts, it is well-known that
academics lean extremely to the left (especially the softer fields), and show high
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
503
rates of mental illness (Duarte et al., 2015; Kinman & Wray, 2013; Langbert,
2018). There are also reports of increasing rates of mental illness among students
and PhD students in particular (Levecque et al., 2017; Puthran et al., 2016;
Rotenstein et al., 2016; Twenge et al., 2010).
This suggests that there is perhaps something about being in university that
is causal for mental illness and probably also encourages people with poor mental
health to self-select into it. A promising route would be to look at people who were
somehow randomized to attend college or not, or to become a PhD student or
not, perhaps as a result of a lottery for scholarships. This would remove the
possibility of self-selection, and thus be informative about the degree of causality
from university attendance or employment to mental illness. It would also be
informative to locate datasets with longitudinal data between the variables to see
if mental illness in children or teens predicts later entry into academic
employment, thus suggesting self-selection rather than causal effects of the
academic environment. Similarly, one could do a longitudinal study to see if
people leaving academia tend to become happier.
Limitations and suggestions
The study has a number of limitations. First, since we were limited to single
items, there is a question of whether these tap into the construct of mental illness
properly (construct validity). Generally, research on single item measures of
mental health finds that they are useful (Ahmad et al., 2014). Some prior research
on the topic employed stronger methods such as looking at the voting pattern of
people who are institutionalized or hospitalized for mental illness, and also found
a left-wing association (Bullenkamp & Voges, 2004; Duckworth et al., 1994;
Howard & Anthony, 1977; Kelly, 2014). Thus, construct validity does not seem a
plausible issue. As the reliability of single items is usually estimated to be around
.70, the observed relationships would be somewhat stronger if adjusted for this
measurement error. For instance, the correlation of .07 between bad mental
health days and political ideology would become .09, while the Cohen’s d
between extreme liberals and extreme conservatives of 0.39 would become 0.56.
Second, the sample sizes were not always sufficient to estimate differences
with confidence for the extreme groups. We used all the available data as of this
time. Our use of meta-analysis across items uses the available data in an optimal
way to increase statistical certainty. Research should attempt to find large
surveys that use better measures of mental illness, and include a measure of
political ideology. Another alternative is to scrape data from political activists
online (e.g. Twitter) and examine it for indicators of mental illness (Coppersmith
et al., 2014, 2018; Nadeem, 2016; Reece et al., 2017). A third option would be to
MANKIND QUARTERLY 2020 60:4
504
sample persons already known to have severe mental illness issues and examine
their political views, as already done in a number of small studies of voting
behavior among patients in mental health facilities (Bullenkamp & Voges, 2004;
Duckworth et al., 1994; Howard & Anthony, 1977; Kelly, 2014). This could be
done via the internet since various online survey companies allow targeting of
specific subgroups, such as those with a diagnosis of mental illness.
Third, a thorny issue is whether there is measurement invariance by group.
In the case of single items, one cannot conduct measurement invariance (MI)
testing since differential item functioning (DIF) tests rely upon other items to
estimate their parameters. Measurement bias could be examined using standard
methods such as multi-group confirmatory factor analysis (MGCFA) and DIF,
especially if one used a heterogeneous set of items or tests. A number of studies
have examined other groups where one might expect measurement bias exists
and found it lacking (ethnic groups Hoe & Brekke, 2008; natives vs. immigrants
Iliceto et al., 2013; across sexes T.-H. Wu et al., 2015). Finally, one might look at
objective or other-ratings of mental illness. One reason to believe there might be
measurement bias is that left-wing political views are on the whole better
disposed towards people with mental illness (Gonzales et al., 2017; Parcesepe &
Cabassa, 2013). Thus left-wing individuals with mental health problems may be
more willing to seek help, get diagnosed, get treatment, and even admit their
problems to themselves (Alexander, 2020); or, those with mental health problems
are attracted to left-wing ideology because left-wing ideology and policies are
more supportive of people with psychiatric problems, as suggested by
Bullenkamp & Voges (2004).
Among objective indicators, the suicide rate could be useful as it represents
a concrete action that is difficult to misinterpret. Various research shows that
conservative and religious people have much lower suicide risk, suggesting lower
rates of mental illness among conservative and religious people (Stack &
Wasserman, 1992; A. Wu et al., 2015). However, the same argument could be
made here that suicides are a faulty indicator because Abrahamic religions have
laws against them, which results in fewer suicides despite equal rates of mental
illness. While measurement invariance issues cannot at present be ruled out, they
seem implausible considering that every available indicator examined has the
same direction of effect.
Fourth, similar to the use of a single item to measure mental illness, the use
of a single item to measure political ideology is questionable. Various factor
analytic studies of politics find that one can in general not easily summarize the
views of the general population into a simple one-dimensional scale like typically
done in studies, including the present (Carl, 2015; Carmines et al., 2012; Feldman
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
505
& Johnston, 2014; Kalmoe, 2020; Kirkegaard et al., 2017; Swedlow, 2008). Many
researchers advocate two- or three-dimensional approaches. It is likely that the
pattern found in this study will turn out to be more complicated if such measures
were used. In order to keep the analyses simple here, such more advanced
measurements were not attempted, but left for future studies. At the request of a
reviewer, we did conduct an extra analysis using the party affiliation measure,
and this showed weaker relationships than the political ideology measure, and in
one case, a different non-monotonic pattern (Figure 7). The reasons for this are
unknown but deserve investigation.
Supplementary materials
Study analysis code, full statistical output (R notebook), and data are
available at https://osf.io/fhpxm/.
References
AbdAleati, N.S., Mohd Zaharim, N. & Mydin, Y.O. (2016). Religiousness and mental
health: Systematic review study. Journal of Religion and Health 55: 1929-1937.
https://doi.org/10.1007/s10943-014-9896-1
Abendschön, S. & Steinmetz, S. (2014). The gender gap in voting revisited: Women’s
party preferences in a European context. Social Politics: International Studies in
Gender, State & Society 21: 315-344. https://doi.org/10.1093/sp/jxu009
Abrams, B.A. & Settle, R.F. (1999). Women’s suffrage and the growth of the welfare
state. Public Choice 100: 289-300. https:/doi.org/10.1023/A:1018312829025
Ahmad, F., Jhajj, A.K., Stewart, D.E., Burghardt, M. & Bierman, A.S. (2014). Single item
measures of self-rated mental health: A scoping review. BMC Health Services Research
14(1): 398. https://doi.org/10.1186/1472-6963-14-398
Alexander, S. (2020, January 21). SSC Survey results 2020. Slate Star Codex.
https://slatestarcodex.com/2020/01/20/ssc-survey-results-2020/
Brikell, I., Larsson, H., Lu, Y., Pettersson, E., Chen, Q., Kuja-Halkola, R., … & Martin, J.
(2018). The contribution of common genetic risk variants for ADHD to a general factor of
childhood psychopathology. Molecular Psychiatry: 1-13. https://doi.org/10.1038/s41380-
018-0109-2
Bullenkamp, J. & Voges, B. (2004). Voting preferences of outpatients with chronic
mental illness in Germany. Psychiatric Services (Washington, D.C.) 55: 1440-1442.
https://doi.org/10.1176/appi.ps.55.12.1440
MANKIND QUARTERLY 2020 60:4
506
Carl, N. (2015). Cognitive ability and political beliefs in the United States. Personality
and Individual Differences 83: 245-248. https://doi.org/10.1016/j.paid.2015.04.029
Carmines, E.G., Ensley, M.J. & Wagner, M.W. (2012). Political ideology in American
politics: One, two, or none? The Forum 10(3). https://doi.org/10.1515/1540-8884.1526
Coleman, K.J., Stewart, C., Waitzfelder, B.E., Zeber, J.E., Morales, L.S., Ahmed, A.T.,
… & Simon, G.E. (2016). Racial/ethnic differences in diagnoses and treatment of mental
health conditions across healthcare systems participating in the Mental Health Research
Network. Psychiatric Services (Washington, D.C.) 67m(7): 749-757.
https://doi.org/10.1176/appi.ps.201500217
Coppersmith, G., Dredze, M. & Harman, C. (2014). Quantifying mental health signals in
Twitter. Proceedings of the Workshop on Computational Linguistics and Clinical
Psychology: From Linguistic Signal to Clinical Reality, 51-60.
https://doi.org/10.3115/v1/W14-3207
Coppersmith, G., Leary, R., Crutchley, P. & Fine, A. (2018). Natural language
processing of social media as screening for suicide risk. Biomedical Informatics Insights
10: 1178222618792860. https://doi.org/10.1177/1178222618792860
Cotton, S., Zebracki, K., Rosenthal, S.L., Tsevat, J. & Drotar, D. (2006).
Religion/spirituality and adolescent health outcomes: A review. Journal of Adolescent
Health 38: 472-480.https:/doi.org/10.1016/j.jadohealth.2005.10.005
Duarte, J.L., Crawford, J.T., Stern, C., Haidt, J., Jussim, L. & Tetlock, P.E. (2015).
Political diversity will improve social psychological science. Behavioral and Brain
Sciences 38: e130. https://doi.org/10.1017/S0140525X14000430
Duckworth, K., Kingsbury, S.J., Kass, N., Goisman, R., Wellington, C. & Etheridge, M.
(1994). Voting behavior and attitudes of chronic mentally ill outpatients. Hospital &
Community Psychiatry 45m: 608-609. https://doi.org/10.1176/ps.45.6.608
Dutton, E., Madison, G. & Dunkel, C. (2018). The mutant says in his heart, “There Is No
God”: The rejection of collective religiosity centred around the worship of moral gods is
associated with high mutational load. Evolutionary Psychological Science 4h(3): 233-
244. https://doi.org/10.1007/s40806-017-0133-5
Edlund, L. & Pande, R. (2002). Why have women become left-wing? The political
gender gap and the decline in marriage. Quarterly Journal of Economics, 117: 917-961.
https://doi.org/10.1162/003355302760193922
Fatke, M. (2017). Personality traits and political ideology: A first global assessment.
Political Psychology 38: 881-899. https://doi.org/10.1111/pops.12347
Feldman, S. & Johnston, C. (2014). Understanding the determinants of political
ideology: Implications of structural complexity. Political Psychology 35w: 337-358.
https://doi.org/10.1111/pops.12055
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
507
Goldberg, Z. (2019, June 6). America’s white saviors. Tablet Magazine.
https://www.tabletmag.com/jewish-news-and-politics/284875/americas-white-saviors
Gonzales, L., Chan, G. & Yanos, P.T. (2017). Individual and neighborhood predictors of
mental illness stigma in New York state. Stigma and Health 2[(3): 175-181.
https://doi.org/10.1037/sah0000043
Guhname, R. (2007, July 4). Thirty percent of really liberal people have a history of
mental illness. Inductivist. http://inductivist.blogspot.com/2007/07/4th-makes-me-think-
of-politics-and.html
Hatemi, P.K., Medland, S.E., Klemmensen, R., Oskarsson, S., Littvay, L., Dawes, C.T.,
… & Martin, N.G. (2014). Genetic influences on political ideologies: Twin analyses of 19
measures of political ideologies from five democracies and genome-wide findings from
three populations. Behavior Genetics 44: 282-294. https://doi.org/10.1007/s10519-014-
9648-8
Hoe, M. & Brekke, J.S. (2008). Cross-ethnic measurement invariance of the Brief
Symptom Inventory for individuals with mental illness. Social Work Research 32: 71-78.
https://doi.org/10.1093/swr/32.2.71
Howard, G. & Anthony, R. (1977). The right to vote and voting patterns of hospitalized
psychiatric patients. Psychiatric Quarterly 49: 124-132.
https://doi.org/10.1007/BF01071660
Iliceto, P., Pompili, M., Candilera, G., Borges, G., Lamis, D.A., Serafini, G. & Girardi, P.
(2013). Suicide risk and psychopathology in immigrants: A multi-group confirmatory
factor analysis. Social Psychiatry and Psychiatric Epidemiology 48: 1105-1114.
https://doi.org/10.1007/s00127-012-0608-4
Inglehart, R. & Norris, P. (2000). The developmental theory of the gender gap: Women’s
and men’s voting behavior in global perspective. International Political Science Review
21: 441-463. https://doi.org/10.1177/0192512100214007
Lott, J.R. & Kenny, L.W. (2015). Did women’s suffrage change the size and scope of
government? Journal of Political Economy 107: 1163-1198.
https://doi.org/10.1086/250093
Kalmoe, N.P. (2020). Uses and abuses of ideology in political psychology. Political
Psychology. https://doi.org/10.1111/pops.12650
Kannan, V.D., Brown, T.M., Kunitz, S.J. & Chapman, B.P. (2019). Political parties and
mortality: The role of social status and personal responsibility. Social Science &
Medicine 223: 1-7. https://doi.org/10.1016/j.socscimed.2019.01.029
Karlin, A. (2018, January 8). Coffee salon demographics. Russian Reaction.
https://www.unz.com/akarlin/salon-demographics/
Kaufman, E. (2019). Whiteshift: Populism, Immigration and the Future of White
MANKIND QUARTERLY 2020 60:4
508
Majorities. Penguin Books.
Kelly, B.D. (2014). Voting and mental illness: The silent constituency. Irish Journal of
Psychological Medicine 31: 225-227. https://doi.org/10.1017/ipm.2014.52
Kim, H.-J. & Abraham, I. (2016). Psychometric comparison of single-item, short, and
comprehensive depression screening measures in Korean young adults. International
Journal of Nursing Studies 56: 71-80. https://doi.org/10.1016/j.ijnurstu.2015.12.003
Kinman, G. & Wray, S. (2013). Higher stress: A survey of stress and well-being among
staff in higher education. London, UK: University and College Union.
Kirkegaard, E.O.W. (2018, September 19). Vertical cultural transfer effectsPlausible
but mostly not real. Clear Language, Clear Mind. https://emilkirkegaard.dk/en/?p=7370
Kirkegaard, E.O.W., Bjerrekær, J.D. & Carl, N. (2017). Cognitive ability and political
preferences in Denmark. Open Quantitative Sociology & Political ScienceE 1(1).
https://openpsych.net/paper/51
Koenig, L.B. & Bouchard Jr., T.J. (2006). Genetic and environmental influences on the
traditional moral values triadauthoritarianism, conservatism, and religiousness—as
assessed by quantitative behavior genetic methods. In: P. McNamara (ed.), Where God
and Science Meet: How Brain and Evolutionary Studies Alter Our Understanding of
Religion (Vol 1): Evolution, Genes, and the Religious Brain, pp. 47-76. Praeger
Publishers/Greenwood Publishing Group.
Ksiazkiewicz, A. & Friesen, A. (2019). The higher power of religiosity over personality on
political ideology. Political Behavior. https://doi.org/10.1007/s11109-019-09566-5
Langbert, M. (2018). Homogenous: The political affiliations of elite liberal arts college
faculty. Academic Questions 31: 186-197. https://doi.org/10.1007/s12129-018-9700-x
Lemoine, P. (2020). Tweets series about mental health and political ideology.
https://threadreaderapp.com/thread/1227338353101672450.html
Levecque, K., Anseel, F., De Beuckelaer, A., Van der Heyden, J. & Gisle, L. (2017).
Work organization and mental health problems in PhD students. Research Policy 46:
868-879. https://doi.org/10.1016/j.respol.2017.02.008
Littman, A.J., White, E., Satia, J.A., Bowen, D.J. & Kristal, A.R. (2006). Reliability and
validity of 2 single-item measures of psychosocial stress. Epidemiology (Cambridge,
Mass.) 17: 398-403. https://doi.org/10.1097/01.ede.0000219721.89552.51
Lüdecke, D. (2018). ggeffects: Tidy data frames of marginal effects from regression
models. Journal of Open Source Software 3(26): 772.
https://doi.org/10.21105/joss.00772
Ludeke, S., Johnson, W. & Bouchard, T.J. (2013). “Obedience to traditional authority”: A
heritable factor underlying authoritarianism, conservatism and religiousness. Personality
KIRKEGAARD, E.O.W. MENTAL ILLNESS AND THE LEFT
509
and Individual Differences 55: 375-380. https://doi.org/10.1016/j.paid.2013.03.018
Maura, J. & Weisman de Mamani, A. (2017). Mental health disparities, treatment
engagement, and attrition among racial/ethnic minorities with severe mental illness: A
review. Journal of Clinical Psychology in Medical Settings 24(3): 187-210.
https://doi.org/10.1007/s10880-017-9510-2
Moreira-Almeida, A., Lotufo Neto, F. & Koenig, H.G. (2006). Religiousness and mental
health: A review. Brazilian Journal of Psychiatry 28: 242-250.
https://doi.org/10.1590/S1516-44462006005000006
Nadeem, M. (2016). Identifying depression on Twitter. ArXiv:1607.07384 [Cs, Stat].
http://arxiv.org/abs/1607.07384
Neumann, A., Pappa, I., Lahey, B.B., Verhulst, F.C., Medina-Gomez, C., Jaddoe, V.W.,
… & Tiemeier, H. (2016). Single nucleotide polymorphism heritability of a general
psychopathology factor in children. Journal of the American Academy of Child &
Adolescent Psychiatry 55: 1038-1045. https://doi.org/10.1016/j.jaac.2016.09.498
Parcesepe, A.M. & Cabassa, L.J. (2013). Public stigma of mental illness in the United
States: A systematic literature review. Administration and Policy in Mental Health 40:
384-399. https://doi.org/10.1007/s10488-012-0430-z
Puthran, R., Zhang, M.W.B., Tam, W.W. & Ho, R.C. (2016). Prevalence of depression
amongst medical students: A meta-analysis. Medical Education 50: 456-468.
https://doi.org/10.1111/medu.12962
Reece, A.G., Reagan, A.J., Lix, K.L.M., Dodds, P.S., Danforth, C.M. & Langer, E.J.
(2017). Forecasting the onset and course of mental illness with Twitter data. Scientific
Reports 7(1): 1-11. 13006. https://doi.org/10.1038/s41598-017-12961-9
Rotenstein, L.S., Ramos, M.A., Torre, M., Segal, J.B., Peluso, M.J., Guille, C., Sen, S. &
Mata, D.A. (2016). Prevalence of depression, depressive symptoms, and suicidal
ideation among medical students: A systematic review and meta-analysis. Journal of the
American Medical Association 316: 2214-2236.
https://doi.org/10.1001/jama.2016.17324
Seeman, T.E., Dubin, L.F. & Seeman, M. (2003). Religiosity/spirituality and health: A
critical review of the evidence for biological pathways. American Psychologist 58: 53-63.
https://doi.org/10.1037/0003-066X.58.1.53
Spörrle, M. & Bekk, M. (2014). Meta-analytic guidelines for evaluating single-item
reliabilities of personality instruments. Assessment 21: 272-285.
https://doi.org/10.1177/1073191113498267
Stack, S. & Wasserman, I. (1992). The effect of religion on suicide ideology: An analysis
of the networks perspective. Journal for the Scientific Study of Religion 31: 457-466.
JSTOR. https://doi.org/10.2307/1386856
MANKIND QUARTERLY 2020 60:4
510
Swedlow, B. (2008). Beyond liberal and conservative: Two-dimensional conceptions of
ideology and the structure of political attitudes and values. Journal of Political Ideologies
13: 157-180. https://doi.org/10.1080/13569310802075969
Twenge, J.M., Gentile, B., DeWall, C.N., Ma, D., Lacefield, K. & Schurtz, D.R. (2010).
Birth cohort increases in psychopathology among young Americans, 19382007: A
cross-temporal meta-analysis of the MMPI. Clinical Psychology Review 30: 145-154.
https://doi.org/10.1016/j.cpr.2009.10.005
Unorthodox Theory (2020, February 15). Authoritarianism and correlates: Behavior,
attitudes, personality. Race & Conflicts.
https://raceandconflicts.home.blog/2020/02/15/authoritarianism-and-correlates-
behavior-attitudes-personality/
VanderWeele, T.J. (2017). Religion and health: A synthesis. In: M. Balboni & J. Peteet
(eds.), Spirituality and Religion within the Culture of Medicine: From Evidence to
Practice, pp. 357-401. Oxford University Press.
Winegard, B. & Winegard, B. (2018, September 21). The preachers of the Great
Awokening. Quillette. https://quillette.com/2018/09/21/the-preachers-of-the-great-
awokening/
Wu, A., Wang, J.-Y. & Jia, C.-X. (2015). Religion and completed suicide: A meta-
analysis. PLoS ONE 10(6): e0131715. https://doi.org/10.1371/journal.pone.0131715
Wu, T.-H., Chang, C.-C., Chen, C.-Y., Wang, J.-D. & Lin, C.-Y. (2015). Further
psychometric evaluation of the Self-Stigma Scale-Short: Measurement invariance
across mental illness and gender. PLoS ONE 10(2): e0117592.
https://doi.org/10.1371/journal.pone.0117592
Yglesias, M. (2019, March 22). The Great Awokening. Vox.
https://www.vox.com/2019/3/22/18259865/great-awokening-white-liberals-race-polling-
trump-2020
... In addition, liberalism has been found to be associated with mental instability and depression while conservatism has been found to be associated mental stability and feeling that life has meaning (Schlenker et al., 2012;Kirkegaard, 2020;Bernardi, 2021;Kwon, 2022). This may be because if a person feels negative feelings strongly -in particular a sense of unfairness and envy -they will agitate for radical change. ...
Article
Full-text available
There is solid evidence that human populations have been selecting against intelligence-related genetic variants since the mid to late 1800s. The selection is generally weak, but varies by ethnic group and sex. Since religious teachings usually include strong pro-natalist components, we investigated whether this might also affect the selection for intelligence among different religious groups. We found that Latter-day Saints in the USA show slightly positive selection for intelligence, whereas all other religious groups examined did not robustly differ from the average. We similarly found that conservatives, in general, show a weaker selection against intelligence than do liberals.
Research
Full-text available
Purpose-This is a meta-theoretical analysis of the origins and prospects of real estate economic activity. To balance the usual business oriented and technical documentations, a broad view of the real estate economy is applied, involving behavioural and political aspects. The focus is on social innovation and place safety. Design/methodology/approach-Critical literature review together with the author's own experiences of rhetorical juxtapositions; focus on behaviourism and political economy. Findings-Identification of the relevance of social innovation and place safety for real estate economic activity at large. Originality/value-An inquiry into various overlooked assumptions in real estate (economic) analysis.
Article
Full-text available
Two streams of research, culture war and system justification, have proposed that religious orientations and personality, respectively, play critical roles in political orientations. There has been only limited work integrating these two streams. This integration is now of increased importance given the introduction of behavior-genetic frameworks into our understanding of why people differ politically. Extant research has largely considered the influence of personality as heritable and religiosity as social, but this view needs reconsideration as religiosity is also genetically influenced. Here we integrate these domains and conduct multivariate analyses on twin samples in the U.S. and Australia to identify the relative importance of genetic, environmental, and cultural influences. First, we find that religiosity’s role on political attitudes is more heritable than social. Second, religiosity accounts for more genetic influence on political attitudes than personality. When including religiosity, personality’s influence is greatly reduced. Our results suggest religion scholars and political psychologists are partially correct in their assessment of the “culture wars”—religiosity and ideology are closely linked, but their connection is grounded in genetic predispositions.
Article
Full-text available
Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social media networks—offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have “opted in” for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention—we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?
Article
Full-text available
Results of regression models, like estimates, are typically presented as tables that are easy to understand. Sometimes pure estimates are not helpful and difficult to interpret. This is especially true for interaction terms in logistic regression or even more complex models, or transformed terms (quadratic or cubic terms, polynomials, splines), where the estimates are no longer interpretable in a direct way. In such cases, marginal effects are far easier to understand. In particular, the visualization of marginal effects makes it possible to intuitively get the idea of how predictors and outcome are associated, even for complex models. ggeffects is an R-package that aims at easily calculating marginal effects for a broad range of different regression models. This is achieved by three core ideas that describe the philosophy of the function design: 1) Functions are type-safe and always return a data frame with the same, consistent structure; 2) there is a simple, unique approach to calculate marginal effects for many different models; 3) the package supports "labelled data" (Lüdecke 2018), which allows human readable annotations for graphical outputs. This means, users do not need to care about any expensive steps after modelling to visualize the results.
Article
Full-text available
Common genetic risk variants have been implicated in the etiology of clinical attention-deficit/hyperactivity disorder (ADHD) diagnoses and symptoms in the general population. However, given the extensive comorbidity across ADHD and other psychiatric conditions, the extent to which genetic variants associated with ADHD also influence broader psychopathology dimensions remains unclear. The aim of this study was to evaluate the associations between ADHD polygenic risk scores (PRS) and a broad range of childhood psychiatric symptoms, and to quantify the extent to which such associations can be attributed to a general factor of childhood psychopathology. We derived ADHD PRS for 13,457 children aged 9 or 12 from the Child and Adolescent Twin Study in Sweden, using results from an independent meta-analysis of genome-wide association studies of ADHD diagnosis and symptoms. We estimated associations between ADHD PRS, a general psychopathology factor, and several dimensions of neurodevelopmental, externalizing, and internalizing symptoms, using structural equation modeling. Higher ADHD PRS were statistically significantly associated with elevated neurodevelopmental, externalizing, and depressive symptoms (R2 = 0.26–1.69%), but not with anxiety. After accounting for a general psychopathology factor, on which all symptoms loaded positively (mean loading = 0.50, range = 0.09–0.91), an association with specific hyperactivity/impulsivity remained significant. ADHD PRS explained ~ 1% (p value < 0.0001) of the variance in the general psychopathology factor and ~ 0.50% (p value < 0.0001) in specific hyperactivity/impulsivity. Our results suggest that common genetic risk variants associated with ADHD, and captured by PRS, also influence a general genetic liability towards broad childhood psychopathology in the general population, in addition to a specific association with hyperactivity/impulsivity symptoms.
Article
Full-text available
Industrialisation leads to relaxed selection and thus the accumulation of fitness-damaging genetic mutations. We argue that religion is a selected trait that would be highly sensitive to mutational load. We further argue that a specific form of religiousness was selected for in complex societies up until industrialisation based around the collective worship of moral gods. With the relaxation of selection, we predict the degeneration of this form of religion and diverse deviations from it. These deviations, however, would correlate with the same indicators because they would all be underpinned by mutational load. We test this hypothesis using two very different deviations: atheism and paranormal belief. We examine associations between these deviations and four indicators of mutational load: (1) poor general health, (2) autism, (3) fluctuating asymmetry, and (4) left-handedness. A systematic literature review combined with primary research on handedness demonstrates that atheism and/or paranormal belief is associated with all of these indicators of high mutational load.
Article
Full-text available
Mounting evidence indicates that there are mental health disparities in the United States that disadvantage racial/ethnic minorities in medical and mental health settings. Less is known, however, about how these findings apply to a particularly vulnerable population, individuals with severe mental illness (SMI). The aim of this paper is to (1) provide a critical review of the literature on racial/ethnic disparities in mental health care among individuals with SMI; (2) identify factors which may contribute to the observed disparities; and (3) generate recommendations on how best to address these disparities. Specifically, this article provides an in-depth review of sociocultural factors that may contribute to differences in treatment engagement and rates of attrition from treatment among racial/ethnic minorities with SMI who present at medical and mental health facilities. This review is followed by a discussion of specific strategies that may promote engagement in mental health services and therefore reduce racial/ethnic disparities in SMI.
Article
Full-text available
OBJETIVO: A relacao entre religiosidade e saude mental tem sido uma perene fonte de controversias. O presente artigo revisa a evidencia cientifica disponivel sobre a relacao entre religiao e saude mental. METODO: Os autores apresentam os principais estudos e as conclusoes de uma revisao sistematica abrangente dos estudos sobre a relacao religiao-saude mental. Utilizando-se de varias bases de dados, a revisao identificou 850 artigos publicados ao longo do seculo XX. O presente artigo tambem inclui uma breve contextualizacao historica e metodologica, alem de uma atualizacao com artigos publicados apos 2000 e a descricao de pesquisas conduzidas no Brasil. DISCUSSAO: A ampla maioria dos estudos de boa qualidade encontrou que maiores niveis de envolvimento religioso estao associados positivamente a indicadores de bem estar psicologico (satisfacao com a vida, felicidade, afeto positivo e moral mais elevado) e a menos depressao, pensamentos e comportamentos suicidas, uso/abuso de alcool/drogas. Habitualmente, o impacto positivo do envolvimento religioso na saude mental e mais intenso entre pessoas sob estresse (idosos, e aqueles com deficiencias e doencas clinicas). Mecanismos teoricos da conexao religiosidade-saude mental e as implicacoes clinicas destes achados sao discutidos. CONCLUSOES: Ha evidencia suficiente disponivel para se afirmar que o envolvimento religioso habitualmente esta associado a melhor saude mental. Atualmente, duas areas necessitam de maior investimento: compreensao dos fatores mediadores desta associacao e a aplicacao deste conhecimento na pratica clinica.
Article
Ideology is a central construct in political psychology. Even so, the field's strong claims about an ideological public rarely engage evidence of enormous individual differences: a minority with real ideological coherence and weak to nonexistent political belief organization for everyone else. Here, I bridge disciplinary gaps by showing the limits of mass political ideology with several popular measures and components—self‐identification, core political values (egalitarian and traditionalism's resistance to change), and policy indices—in representative U.S. surveys across four decades (Ns ~ 13 k–37 k), plus panel data testing stability. Results show polar, coherent, stable, and potent ideological orientations only among the most knowledgeable 20–30% of citizens. That heterogeneity means full‐sample tests overstate ideology for most people but understate it for knowledgeable citizens. Whether through top‐down opinion leadership or bottom‐up ideological reasoning, organized political belief systems require political attention and understanding to form. Finally, I show that convenience samples make trouble for ideology generalizations. I conclude by proposing analytic best practices to help avoid overclaiming ideology in the public. Taken together, what first looks like strong and broad ideology is actually ideological innocence for most and meaningful ideology for a few.
Article
Previous research findings across a variety of nations show that affiliation with the conservative party is associated with greater longevity; however, it is thus far unclear what characteristics contribute to this relationship. We examine the political party/mortality relationship in the United States context. The goal of this paper is two-fold: first, we seek to replicate the mortality difference between Republicans and Democrats in two samples, controlling for demographic confounders. Second, we attempt to isolate and test two potential contributors to the relationship between political party affiliation and mortality: (1) socioeconomic status and (2) dispositional traits reflecting a personal responsibility ethos, as described by the Republican party. Graduate and sibling cohorts from the Wisconsin Longitudinal Study were used to estimate mortality risk from 2004 to 2014. In separate Cox proportional hazards models controlling for age and sex, we adjusted first for markers of socioeconomic status (such as wealth and education), then for dispositional traits (such as conscientiousness and active coping), and finally for both socioeconomic status and dispositional traits together. Clogg's method was used to test the statistical significance of attenuation in hazard ratios for each model. In both cohorts, Republicans exhibited lower mortality risk compared to Democrats (Hazard Ratios = 0.79 and 0.73 in graduate and sibling cohorts, respectively [p < 0.05]). This relationship was explained, in part, by socioeconomic status and traits reflecting personal responsibility. Together, socioeconomic factors and dispositional traits account for about 52% (graduates) and 44% (siblings) of Republicans' survival advantage. This study suggests that mortality differences between political parties in the US may be linked to structural and individual determinants of health. These findings highlight the need for better understanding of political party divides in mortality rates.