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Unit V Case Study
Weight: 10% of course grade
Grading Rubric
Due: Tuesday, 03/26/2019 11:59 PM (CST)
Man-Made Disaster
This case study requires that select a man-made disaster that has occurred in the past decade. You
should research in the CSU Online Library for any peer-reviewed journal articles related to the disaster
and how the community recovered. You may also do an Internet search for articles or information
regarding how the aftereffects of the disaster were mitigated through a community-wide effort.
Disaster suggestions include, but are not limited to:
Oklahoma City Federal Building bombing, or
Boston Marathon bombing.
Your case study should address at least three of these four main points:
disaster summary,
NGOs that provided assistance,
public utilities recovery, and
community recovery from the emergency.
Your case study should be well-organized and at least two pages in length, not including the title and
reference pages. You are required to have at least two sources, one of which must be from the CSU
Online Library. Your textbook is NOT required for this assignment, although you may choose to use it
when discussing concepts that apply to the disaster. All sources used, including the textbook, must be
referenced; paraphrased and quoted materials must have accompanying citations in accordance with
APA standards.
Vol. 30, No. 3, 539–549,
Threat perception after the Boston Marathon
bombings: The effects of personal relevance and
conceptual framing
Jolie Baumann Wormwood1, Spencer K. Lynn1, Lisa Feldman Barrett1,2, and
Karen S. Quigley1,3
Department of Psychology, Northeastern University, Boston, MA, USA
Department of Psychiatry and the Martinos Center for Biomedical Imaging, Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA
Edith Nourse Rogers Memorial VA Hospital, Bedford, MA, USA
(Received 18 August 2014; accepted 19 January 2015)
We examined how the Boston Marathon bombings affected threat perception in the Boston
community. In a threat perception task, participants attempted to “shoot” armed targets and avoid
shooting unarmed targets. Participants viewing images of the bombings accompanied by affectively
negative music and text (e.g., “Terror Strikes Boston”) made more false alarms (i.e., more errors
“shooting” unarmed targets) compared to participants viewing the same images accompanied by
affectively positive music and text (e.g., “Boston Strong”) and participants who did not view bombing
images. This difference appears to be driven by decreased sensitivity (i.e., decreased ability to
distinguish guns from non-guns) as opposed to a more liberal bias (i.e., favouring the “shoot”
response). Additionally, the more strongly affected the participant was by the bombings, the more
their sensitivity was reduced in the negatively framed condition, suggesting that this framing was
particularly detrimental to the most vulnerable individuals in the affected community.
Keywords: Threat perception; Threat accessibility; Framing; Terrorism; Signal detection theory.
On 15 April 2013, the 117th Boston Marathon was
brought to a violent end when two bombs exploded
near the finish line, killing three people and injuring
more than 250. A subsequent manhunt involved
further public bloodshed and a day-long city-wide
lockdown. Fed by media coverage, many residents
Correspondence should be addressed to: Jolie Wormwood, Department of Psychology NI-125, Northeastern University, 360
Huntington Avenue, Boston, MA 02115, USA. E-mail: [email protected]
This material is published by permission of the US Army Research Institute, operated by Northeastern University for the Department
of the Army under Contract Nos. W5J9CQ-12-C-0028, W5J9CQ-12-C-0049. The US Government retains for itself, and others
acting on its behalf, a paid-up, non-exclusive, and irrevocable worldwide license in said article to reproduce, prepare derivative works,
distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.
reported hypervigilance—perceiving ambiguous
objects, people and situations as threatening.
Consistent with this observation, previous research
has repeatedly demonstrated that as threat-relevant
cognitions become more accessible, people believe
they are more likely to encounter threats (Freedy,
Saladin, Kilpatrick, Resnick, & Saunders, 1994;
Johnson & Tversky, 1983; Lichtenstein, Slovic,
Fischhoff, Layman, & Combs, 1979; Slovic,
Fischhoff, & Lichtenstein, 1980). As media coverage in Boston shifted to emphasise resilience and
community cohesion (e.g., “Boston Strong”), however, hypervigilance seemed to decrease. This
observation suggests a novel hypothesis: how a
threat is framed may influence subsequent threat
perception. We hypothesised that negatively framing a real-world threat by focusing on death and
destruction would have a more pronounced impact
on subsequent threat perception than positively
framing the same information by focusing on
people’s heroic responses.
To test this hypothesis, members of the Boston
community were exposed to positively or negatively
framed audiovisual vignettes about the Boston
Marathon bombings in the months following the
tragedy. We then measured threat perception using
an in-lab shooting task. If threat accessibility alone
influences threat perception, we would expect more
false alarms (i.e., more errors mistakenly “shooting”
unarmed suspects) in both framing conditions
relative to a control condition in which no threatrelevant information was presented. If framing
matters, we would expect participants exposed to
negatively framed bombing information to make
more false alarms compared to participants exposed
to positively framed bombing information.
In addition, to help fill an important gap in the
existing literature, we utilised signal detection theory
to distinguish between two potential causal explanations for differences in false alarm rates. That is, we
examined whether any observed differences in false
alarm rates were driven by differences in biased
responding (i.e., favouring the “shoot” response over
the “don’t shoot” response) or decreased sensitivity
(i.e., decreased ability to distinguish threats from
non-threats). Understanding which underlying
mechanism is driving observed differences in false
alarm rates is crucial for developing successful
interventions aimed at reducing false alarm rates
following threats and, at a more basic level, understanding the observed phenomenon.
We report how we determined our sample size, all
data exclusions, all manipulations and all measures
in the study.
Eighty-one participants completed the experiment for $10 within two to four months of the
Boston Marathon bombings. Participants were
recruited from Northeastern University and the
surrounding Boston community through fliers
and advertisements. Target sample
size was based on previous experiments utilising a
similar threat detection task (e.g., Baumann &
DeSteno, 2010). Potential participants completed
the 8-item Patient Health Questionnaire (PHQ8; Kroenke et al., 2008) and those without
significant depressive symptomology (<10 on the PHQ-8) were eligible to participate. Five participants who misunderstood or disregarded task instructions were excluded from all analyses. Three participants were excluded because of computer failure. The final sample comprised 73 participants (29 males, 44 females; Mage = 27.2, SDage = 1.30 years). Materials Marathon recall survey Participants completed a Marathon Recall Survey in which they reflected on their experiences on the day of the bombings and rated how affected they were by the incident and how much exposure they felt they had to the incident on 7-point scales. Vignette stimuli Participants were randomly assigned to watch one of three 4.5 minute news-style vignettes (videos comprising still images set to music). The control THREAT PERCEPTION vignette (N = 24) used 36 neutrally rated images from the International Affective Picture System (IAPS; Lang, Bradley, & Cuthbert, 2008; norms: Mvalence = 4.9, Marousal = 3.2) displayed for five seconds each (plus a 2.5 s cross-fade transition to the next image). Audio for the control condition was an unidentified musical composition (available on the Interdisciplinary Affective Science Laboratory website: The two bombing vignettes utilised 28 identical images taken from newspaper sources that covered the bombings (e.g., The Boston Globe, The Chicago Tribune) displayed for seven seconds each (plus a 2.5-s cross-fade transition to the next image). Each bombing vignette used 10 short phrases taken from public officials’ speeches and newspaper headlines. Each phrase appeared for 4–10 seconds. Phrases included “Boston Strong” and “The People of Boston Refused to Be Intimidated” in the positively framed vignette (N = 24), and “Terror Strikes Boston” and “Not Since 9/11” in the negatively framed vignette (N = 25). Images were accompanied by affectively positive music in the positively framed vignette (Holst’s The Planets, Op. 32–4. Jupiter, The Bringer of Jollity) and affectively negative music in the negatively framed vignette (Beethoven’s Moonlight Sonata, Mvt. 1). Shooter bias task Threat perception was assessed via a modified version of the Shooter Bias Task (Correll, Park, Judd, & Wittenbrink, 2002, 2007). For each trial, participants were shown one to four randomly chosen images of background scenes (e.g., a park, a subway station) with variable duration (500– 1000 ms). The final image of each trial (the target image) was displayed for 1000 ms and was a repeat of the final background scene but contained a person. To the participant, this looked as if a person appeared in the final background scene. The individual in the target image was always a white male holding either a gun (black or silver) or a neutral object (i.e., wallet, camera, soda can, mobile phone). Participants were asked to decide within the 1000 ms display time whether or not the person shown was holding a gun. Participants responded using a realistic, wireless gun controller that simulated recoil with vibration and slide motion upon trigger pull. Participants pulled the trigger if they believed the person shown on screen was holding a gun and refrained from pulling the trigger if they believed the person was not holding a gun. Participants were instructed to leave the butt of the gun against a table top while pointing the muzzle of the gun at the screen, approximately 44.5 cm away. Participants were told to simply keep the gun pointing at the middle of the screen and not to aim at the individuals as they appeared. Participants did not receive feedback about their performance. A black screen was displayed between trials for 5000 ms. There were 10 target individuals, each shown four times: twice with a gun and twice with a neutral object, for a total of 40 trials. Ten practice trials preceded the task, using similar but not identical stimuli. Visual noise was added to the original images from the Shooter Bias Task (Correll et al., 2002, 2007) to increase the difficulty of the task. Original images from the Shooter Bias Task were reduced to a contrast range of 35–65% of the maximum luminance available, and then the RGB values at each pixel were altered by adding a multivariate normally distributed random RGB triplet (M = 0, SD = 17.5%, truncated at ±2 SDs). Each image was then gamma-corrected for the luminance nonlinearity of the monitor. Four versions of each image were created using this technique and the programme randomly sampled (with replacement) from the four versions for each stimulus presentation. Images were displayed on a 24” computer monitor at a resolution of 1024 × 768. Finally, because pilot data suggested a strong bias towards not shooting, all participants were told, “Although the number of armed and unarmed suspects can vary, there will be armed suspects in 40–60% of the trials”. Procedure After providing informed consent, participants completed a demographic questionnaire and the Marathon Recall Survey, received instructions for the Shooter Bias Task and completed the practice trials. Next, participants watched one of COGNITION AND EMOTION, 2016 541 WORMWOOD ET AL. the news-style vignettes, according to their randomly assigned condition, while listening to the accompanying music over noise-cancelling headphones. After the video, participants continued listening to the assigned music while completing the Shooter Bias Task. Next, participants completed a measure of their mood that asked them to rate how strongly they were currently experiencing 35 different emotions on 5-point Likert scales. Finally, participants completed several questionnaires as part of a separate experiment. RESULTS Mood differences Positive affect was measured as the mean rating across eight items: cheerful, delighted, happy, inspired, excited, proud, positive, confident (α = .88). Negative affect was measured as the mean rating across 15 items: disgusted, sad, afraid, negative, alone, blue, guilty, nervous, lonely, ashamed, scared, angry, downhearted, frightened and dissatisfied with self (α = .91). As expected, manipulation checks revealed significant differences in how much negative affect participants reported feeling across conditions, F(2, 70) = 9.62, p < .001. Post hoc comparisons revealed that participants in the control condition reported feeling significantly less negative affect (M = 1.24, SD = .39) than participants in the positively framed bombing condition (M = 1.98, SD = .73) and participants in the negatively framed bombing framing condition (M = 1.67, SD = .57), ps < .05. A one-way analysis of variance (ANOVA) did not reveal a significant difference in the extent of positive affect participants felt, F(2, 70) = 2.13, p > .05. However, post hoc tests suggested that
participants in the control condition felt marginally
more positive affect (M = 2.33, SD = 1.00) than
participants in both the positively framed bombing
condition (M = 1.92, SD = .67) and participants in
the negatively framed bombing condition (M =
1.88, SD = .78), ps < .10. The two bombing conditions did not differ significantly in reported positive or negative affect (ps > .05). A third oneway ANOVA failed to reveal differences in selfreported arousal (item: activated) across conditions,
F < 1. Thus, participants viewing images of the bombings felt similarly negative, regardless of the framing. We also conducted a one-way ANOVA to examine differences in self-reported fear (mean of three items: frightened, afraid and scared; α = .79) across conditions. As expected, the experience of fear differed significantly by condition, F(2, 70) = 5.13, p < .05. Consistent with the other mood results, this effect was driven by differences between the control condition and the two bombing conditions only. Participants in the control condition reported experiencing significantly less fear (M = 1.22, SD = 0.39) than participants in both the positively framed bombing condition (M = 1.75, SD = .79) and participants in the negatively framed bombing condition (M = 1.88, SD = .97), ps < .05. Participants in the two bombing conditions did not report experiencing different amounts of fear (p > .05) despite receiving the different
Threat perception performance by condition
We first explored whether viewing images of the
bombings would produce increased estimations of
threat, as well as whether framing would moderate
this effect, by examining participants’ false alarm
rates in the threat perception task (i.e., the proportion of trials with an unarmed target on which a
participant mistakenly decided to “shoot”). As
predicted, framing condition had a significant
effect on false alarm rates, F(2, 70) = 3.26, p < .05 (Figure 1). Post hoc comparisons revealed that participants in the negatively framed bombing condition had a significantly higher false alarm rate (M = .30, SD = .16) than participants in both the positively framed bombing condition (M = .21, SD = .12) and the control condition (M = .23, SD = .11), ps ≤ 05. Despite viewing images of the bombings, participants in the positively framed bombing condition did not have a higher false alarm rate than participants in the control condition, p > .05.
Next, we utilised signal detection theory to
explore two potential causal explanations for
the observed differences in false alarm rates
participants’ sensitivity, F(2, 70) = 2.87, p = .06.
Post hoc comparisons revealed that participants in
the negatively framed bombing condition had
significantly lower sensitivity (M = .25, SD =
.36), indicating that they were less able to distinguish guns from non-guns, compared to participants in the positively framed bombing condition
(M = .52, SD = .41, p < .05). Sensitivity in the control condition (M = .41, SD = .43) was intermediate and did not differ significantly from sensitivity in either of the two bombing conditions (ps > .05).
Considering personal relevance as a covariate
Figure 1. Mean (±1 SE) false alarm rate by condition.
(Macmillan & Creelman, 1991; Wickens, 2002).
In signal detection theory, an increased false alarm
rate can result from either a bias in responding (i.e.,
a tendency to respond as if a target is holding a gun
vs. a non-threatening object regardless of the
stimulus shown) or a decrease in sensitivity (i.e., a
reduced ability to distinguish whether a person is
holding a gun vs. a non-threatening object).Thus,
for each individual participant, we calculated estimates of both bias (c) and sensitivity (d′). Bias was
calculated as c = –0.5(zH + zF), where zH and zF
represent the inverse of the standard normal
cumulative distribution for the hit rate and false
alarm rate, respectively. Sensitivity was calculated as
d′ = zH – zF.
Across all participants, bias was significantly
greater than zero (M = .56, SD = .41), t(72) =
11.71, p < .05, indicating that participants had a significantly conservative bias (i.e., they favoured the “don’t shoot” response). A one-way ANOVA revealed that framing condition did not have a significant effect on response bias, F(2, 70) = 1.33, p > .05.
A one-way ANOVA revealed that framing condition had a marginally significant effect on
We then examined whether the extent to which
participants reported being affected by the bombings (from the Marathon Recall Survey) predicted
bias and sensitivity using multivariate regression.
Multivariate regression was utilised to explore
covariance instead of an ANCOVA as the data
violated the assumption of homogeneity of slopes.
Regression equations were of the general form:
y ¼ b0 þ b1 D1 þ b2 D2 þ b3 BMa
þ b4 ðD1 BMa Þ þ b5 ðD2 BMa Þ
where framing condition was dummy coded (variables D1 and D2), and the variable BMa represents how affected participants reported being by
the bombings on the Marathon Recall Survey.
The regression model with bias as the outcome
variable was not significant (R2 = .10, F(5, 67) =
1.50, p > .05) suggesting that how affected
participants were by the bombings did not predict
bias and did not interact with experimental
condition to predict bias.
The regression model with sensitivity as the
outcome variable was significant (R2 = .19,
F(5, 67) = 3.09, p < .05). The model revealed a significant interaction; the relationship between how affected participants were by the bombings and sensitivity significantly differed by framing COGNITION AND EMOTION, 2016 543 WORMWOOD ET AL. were by the bombings, participants in the negatively framed bo ... Purchase answer to see full attachment

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