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Whirlwinds and Break-Ins: Evidence Linking a New Orleans
Tornado to Residential Burglary
Kelly Frailinga, Thomas Zawiszab and Dee Wood Harpera
aDepartment of Criminology and Justice, Loyola University
New Orleans, New Orleans, LA, USA; bDepartment of
Justice Studies, Lasell University, Newton, MA, USA
ABSTRACT
This study examines the number and location of residential
burglaries before
and after a tornado that struck New Orleans, Louisiana in
February 2017.
Using calls for service to the New Orleans Police Department,
Weather Service
data and geospatial referencing, we found that the number of
residential
burglaries increased in the short-term aftermath of the tornado
and that the
increase in suitable targets caused by the tornado appears to be
an important
predictor of post-tornado burglary in that timeframe. We
conclude with
implications for policy and practice that stem from our !ndings.

ARTICLE HISTORY
Received 21 April 2020
Accepted 9 June 2020
KEYWORDS
Burglary; tornado; New
Orleans; concentrated
disadvantage; routine
activities
Introduction
The study of disasters has long had its home in sociology
(Dynes Dynes 1970; Dynes, De Marchi, and
Pelanda 1987; Dynes and Tierney 1994; Fischer 2008; Mileti
1987; Quarantelli 1978, 1987; Rodriguez,
Quarantelli, and Dynes 2007; Wenger 1987). Systematic
disaster research beginning in the middle of
the 20th century revealed post-disaster reactions characterized
by altruism, cooperation, and ration-
ality and not by antisocial or criminal behavior. In light of these
empirical realities, theories of
collective behavior were modi!ed to include focus on disruption
of the existing social structure by
a precipitating event such as a disaster and on norms and
behaviors that emerge in the wake of such
an event (Wenger 1987). These revisions had a dual e"ect: they
provided a new framework for
understanding behavior in disaster and they paved the way for
the persistent claim that criminal
behavior was a rarity in disaster. According to Dynes (1970),
disasters do not create disorganization.
Rather, they create organization in which the emergent norms
support prosocial behavior. Barton
(1969) called this the informal mass assault, which refers to the
prosocial behavior that emerges in

the wake of disaster to solve shared problems, such as tending
to the injured and removal of the
deceased, as part of the therapeutic community. Similarly,
Drabek (1986) !nds that while disaster
survivors do experience fear, they nevertheless act in a
composed, rational, and adaptive way in the
wake of disaster that includes providing assistance to other
survivors. Moreover, the desire to help is
not limited to survivors – those who are not directly impacted
by a disaster have been observed
going in droves to the a"ected area to provide relief.
A robust sociological literature !nds that looting is rare in the
wake of disaster (Drabek 1986, 2010;
Dynes and Quarantelli 1968a; Dynes 1968b, 1968c; Quarantelli
and Dynes 1970; Quarantelli 1994,
2008; Quarantelli and Dynes 1970). The prevailing belief that
looting inexorably follows disasters is
presumed to be just one aspect of disaster mythology
(Quarantelli 2008; Wenger et al. 1975); other
mythical antisocial behaviors thought to accompany disasters
are panic #ight (Johnson, Feinberg,
CONTACT Kelly Frailing [email protected] Department of
Criminology and Justice, Box 55, Loyola University New
Orleans, New Orleans, LA 70118, USA
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and Johnston 1994), mass hysteria (Stallings 1994), and price
gouging (Fischer 2008). The over-
arching conclusion of disaster sociology is that disasters
engender a therapeutic community among
survivors, which serves to minimize antisocial behavior, such as
crime.
However, major disasters of the late 20th and early 21st
centuries demanded a reexamination of
these long-held conclusions drawn by disaster sociologists.
Widespread looting in the wakes of
Hurricanes Hugo (1989) and Katrina (2005) led some schol ars
to theorize that pre-disaster conditions,
especially those related to social strati!cation and crime, were
important in understanding why the
behavioral response to these disasters was so di"erent from what
had been previously observed,
namely the emergence of a therapeutic community characterized
by altruism and prosocial helping
behavior (Akimoto 1987; Albala-Bertrand 1993; Barsky,
Trainor, and Torres 2006; Brown 2012; Drabek
2010; Quarantelli 2006, 2007; Tierney, Bevc, and Kuligowski
2007). These two disasters in particular
and the theorizing around them paved the way for disaster
criminology, a new !eld that examines
criminal and other antisocial behavior in the wake of disasters.
Relying on the theories and methods
of criminology, disaster criminologists have argued that
property crime, interpersonal violence, and
fraud increase in the wake of some disasters (Frailing and
Harper 2017). Most of the empirical work
on property crime, particularly burglary, in the wake of disaster
so far has focused on Hurricane
Katrina (Frailing and Harper 2007, 2010a, 2010b, 2015a;
Frailing, Harper, and Serpas 2015b, Frailing

and Harper 2016a), and !nds that certain social structural
indicators, including population loss, high
unemployment, low wages, family disruption, and a segregated
school system are associated with
increases in the burglary rate in the month after Katrina in New
Orleans as compared to the month
before. Empirical work focused on property crime and other
disasters bears out similar conclusions
(Leitner and Helbich 2011; Siman 1977; Teh 2008; Walker,
Sim, and Keys-Mathews 2014; Yu et al.
2017; Zahnow et al. 2017; Zhou 1997, but see Breetzke, King,
and Fabris-Rotelli 2018; Zahran et al.
2009).
Applicable theories for disaster criminology
Criminologists who study disaster (e.g., Frailing and Harper
2017) have typically applied two theories
to understand crime in the wake of disaster, particularly
residential burglary. The !rst of these is
routine activity theory. Routine activity theory is part of the
environmental criminology paradigm,
which ‘is a family of theories that share a common interest in
criminal events and the immediate
circumstances in which they occur’ (Wortley and Mazerolle
2011, p. 1). Routine activity theory (Cohen
and Felson 1979) holds that three elements – motivated
o"enders, suitable targets, and the absence
of capable guardianship, either formal or informal, must be
present together in time and space for
crime to occur. Disasters may create suitable targets, i ncrease
the number of motivated o"enders,
and may diminish especially formal guardianship; they may also
change people’s routine activities so
that they become suitable targets in the presence of motivated
o"enders and the absence of

capable guardianship.
As noted above, disaster criminologists have also investigated
the macro-level social structural
indicators in the areas impacted by disaster. This is in line with
the social disorganization theory,
which holds that poverty, residential instability, and ethnic
heterogeneity (Shaw and McKay 1942) as
well as family disruption (Sampson 1986) are important
neighborhood-level characteristics asso-
ciated with crime in the area. Social disorganization theory has
also taken into account the notion of
concentrated disadvantage. Concentrated disadvantage is a
concept aimed at capturing deprivation
and is typically comprised of indicators such as poverty,
unemployment, female-headed households,
and receipt of public assistance. Research has shown
concentrated disadvantage is important in
predicting crime at the neighborhood level (Krivo and Peterson
1996; Sampson, Raudenbush, and
Earls 1997; Wilson 1987).
The legitimacy of disaster criminology as a sub!eld is
predicated on the continued testing of its
propositions as laid out in Frailing and Harper (2017), namely
that some crimes increase after
a disaster and that these increases are in part predictable by
criminological theory. Here, we examine
2 K. FRAILING ET AL.
burglary before and after the February 2017 tornado in New
Orleans, Louisiana in order to test three
of Frailing and Harper (2017) hypotheses. The !rst of these

hypotheses is that concentrated
disadvantage is associated with pre-disaster burglary. The
second is that burglaries increase in the
short-term aftermath of a disaster and then return to pre-disaster
levels, and the third is that areas
characterized by concentrated disadvantage will see the greatest
increases in post-disaster burglary.
The New Orleans tornado
Though New Orleans is no stranger to hurricane impacts,
tornados are relatively rare. However,
despite this statistical pattern, on 7 February 2017 six tornados
hit southeastern Louisiana and three
hit the New Orleans metro area. The most serious of them was
the tornado that hit New Orleans East
in the morning at approximately 11:12 am. This EF-3 tornado
lasted 20 minutes, had a maximum
wind speed of 150 miles an hour, a width of 600 yards and a
path length of 10.1 miles. It damaged
638 homes and 40 businesses, about half of which were
considered total losses (NWS 2017a).
Pre-tornado burglary and concentrated disadvantage
New Orleans East is comprised of six neighborhoods, three of
which, Plum Orchard, Read Boulevard
East, and Read Boulevard West, were in the path of the tornado,
whereas Pines Village, Little Woods,
and West Lake Forest were not.
In order to determine the number of burglaries before and after
the tornado, we utilized the New
Orleans Police Department’s (NOPD’s) publicly available calls
for service database, which includes the
location of each call for service by latitude and longitude

(NOPD 2017). We retrieved all the calls for
service for residential burglaries in the NOPD’s Seventh
District, which covers the three neighbor-
hoods under investigation here, from 1 December 2016 to 30
April 2017. This timeframe allowed us
to examine residential burglaries across all six neighborhoods
as far as 2 months before and 2
months after the tornado.
As seen in Table 1, commonalities across the neighborhoods
impacted by the tornado include
population loss, majority African American population, an
increase in percent female-headed house-
hold, a decrease in average household income, and an increase
in the percent of vacant properties.
Importantly, Table 1 also includes characteristics associated
with concentrated disadvantage.
We created a concentrated disadvantage measure for the 2015
data comprised three key vari-
ables: (1) percent of vacant property, (2) percent female-headed
households, and (3) percent Black.
While these variables are a somewhat atypical construction of
concentrated disadvantage, they
loaded on a single factor with an Eigenvalue of 4.082 with a
Cronbach’s alpha of.901, above the
thresholds of 1 and .800, respectively. We then employed a
negative binomial regression analysis to
test the measure’s ability to predict pre-disaster burglary by
neighborhood. Table 2 presents the
results of the negative binomial regression (NBR) analysis for
only those neighborhoods within the
path of the tornado (Plum Orchard, Read Boulevard East, and
Read Boulevard West) and for the time
period of 2 months after the tornado. As shown, our measure of
concentrated disadvantage was not

a signi!cant predictor of burglary for these three neighborhoods.
Similarly, Table 3 shows the results
of the negative binomial regression for those neighborhoods
that were not within the path of the
tornado. Again, our measure of concentrated disadvantage was
not a signi!cant predictor of
burglaries for the two-month period following the tornado.
Subsequent analyses (not shown)
were conducted for the number of burglaries 1 week, 2 weeks,
and 1 month before and after the
date of the tornado. This included separate analyses for both
clusters of neighborhoods (those
impacted by the tornado and those not) and all neighborhoods
together. Like our !rst two analyses,
our measure of concentrated disadvantage was not a predictor of
burglary counts.
We further explored the possibility of an association between
concentrated disadvantage and
number of burglaries by conducting bivariate correlations
between counts for time periods and
concentrated disadvantage. Table 4 presents the results for the
association between the number of
JOURNAL OF CRIME AND JUSTICE 3
Ta
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burglaries 2 months prior to the tornado and concentrated
disadvantage and the number of

burglaries 2 months after the tornado and concentrated
disadvantage. There was no signi!cant
association between concentrated disadvantage and the total
number of burglaries in either time-
frame. Subsequent analyses (not shown) were conducted for 2
weeks, 1 month, and 2 months before
and after the tornado. Results for these analyses also indicated
non-signi!cant associations between
the number of burglaries and concentrated disadvantage.
Burglaries before and after the New Orleans tornado
As seen in Table 5, there was an overall increase in residential
burglaries 1 week, 2 weeks, and 1
month after the tornado for the area in question. The increase in
residential burglaries was not
uniformly spread over neighborhoods, though. The Plum
Orchard, Read Boulevard West, and Read
Boulevard East neighborhoods, the three neighborhoods directly
impacted by the tornado, saw an
increase in residential burglaries at each of the time peri ods.
Nor was the increase in residential
burglaries uniformly spread over time. The increase in
residential burglaries is largely con!ned to the
!rst month after the tornado. By the 2-month mark, the number
of residential burglaries had
returned to (and even dipped slightly below) the pre-tornado
number.
In order to determine where residential burglaries occurred
before and after the tornado, we
created dot maps showing the location of residential burglaries
before and after the tornado using
geospatial referencing in ArcMap. Included in each of these dot
maps is the path of the tornado
(NWS 2017b); inclusion of the path allowed us to investigate

the association between the occurrence
of the tornado and changes in residential burglary. Figures 1–4
show the location of residential
burglaries 1 week, 2 weeks, 1 month, and 2 months before and
after the tornado, as well as the path
of the tornado itself. Plum Orchard appears to retain its pre-
disaster burglary patterns after the
tornado. However, residential burglaries concentrated around
the path of the tornado in the Read
Boulevard West and Read Boulevard East neighborhoods in
particular beginning within the week
after the tornado; this was a stark change from pre-tornado
burglary patterns.
Table 2. NBR Predicting Burglary Counts for Neighborhoods in
the Tornado Path.
Coe!cient Std. Error Z p
Intercept 4.477 1.883 2.378 0.017
Concentrated Disadvantage "0.054 0.044 "1.223 0.221
Source: The authors.
Table 3. NBR Predicting Burglary Counts for Neighborhoods
outside of the Tornado Path.
Coe!cient Std. Error Z p
Intercept 26.26 14.49 1.812 0.070
Concentrated Disadvantage "0.481 0.296 "1.627 0.104
Table 4. Correlations Between Concentrated Disadvantage (CD)
and Burglaries.

Two Months Before Two Months After
CD 0.350 "0.006
p 0.4961 0.991
Applying criminological theory
We believe the routine activity theory is potentially useful in
helping to understand this change.
Routine activity theory (Cohen and Felson 1979) holds that
three elements – motivated o"enders,
suitable targets, and the absence of capable guardianship, either
formal or informal, must be present
together in time and space for crime to occur. As the theor y
itself does, we put aside the notion of
Table 5. Number of Residential Burglaries Before and After the
New Orleans Tornado by Neighborhood.
Before After Total Before Total After
Time Period
One Week 8 23
Little Woods 7 4
Pines Village 0 2
West Lake Forest 0 1
Plum Orchard* 0 3
Read Blvd E* 0 4
Read Blvd W* 1 3

Two Weeks 17 34
Little Woods 10 13
Pines Village 2 2
Plum Orchard* 1 4
Read Blvd E* 1 5
Read Blvd W* 1 4
One Month 31 50
Little Woods 15 18
Pines Village 2 3
Plum Orchard* 2 4
Read Blvd E* 1 10
Read Blvd W* 2 5
Two Months 84 83
Little Woods 39 38
Pines Village 6 5
Plum Orchard* 4 9
Read Blvd E* 4 12
Read Blvd W* 4 5
* indicates a neighborhood directly impacted by the tornado.
Source: Adapted from NOPD (2017).
Figure 1. Tornado Path Burglaries One Week Before and After.
motivated o"enders and focus on target suitability and capable

guardianship to explain the !ndings
in the Read Boulevard neighborhoods. The tornado may have
created a number of suitable targets –
as noted above, over 600 homes were damaged by the tornado –
and the usual guardianship that
prevents homes from being suitable targets for burglary,
namely, the presence of their residents, was
presumably absent in the wake of the tornado, especially for
those homes that sustained great or
total damage, which as noted above was about half of the
impacted structures. In other words, the
tornado may have increased the number of suitable targets and
decreased the capable guardianship
of those targets, facilitating an increase in burglary in those
neighborhoods especially.
Discussion
Our !ndings do not provide support for the !rst hypothesis that
concentrated disadvantage would
be associated with pre-disaster burglary. This may be because
there is relatively little variation
among the three neighborhoods of interest in terms of
characteristics that comprised the concen-
trated disadvantage index. The neighborhoods are too similar on
these characteristics for any of
them to show an impact on burglary. We did !nd support for the
second hypothesis that residential
Figure 2. Tornado Path Burglaries Two Weeks Before and
After. Source: The authors.
Figure 3. Tornado Path Burglaries One Month Before and After.

burglaries would increase in the immediate aftermath of the
tornado, then return to pre-disaster
levels. This !nding is inconsistent with conclusions drawn by
disaster sociologists, which as seen
above, tend to reveal the emergence of a therapeutic community
that serves to keep antisocial
behavior low. These !ndings are very likely due to the
timeframe of the study and the methodolo-
gical techniques used for measuring crime; as Frailing and
Harper (2017) argue, the criminological
approach is preferred when determining the type and extent of
antisocial behavior after a disaster.
We did not !nd support for our third hypothesis that indicators
of concentrated disadvantage
would explain post-disaster burglary. Here, and in conjunction
with better understanding the
temporary increase in post-disaster burglary, it is useful to draw
on routine activity theory as
described above. It is presumable that the tornado created
suitable targets and facilitated the
absence of capable guardianship in the Read Boulevard
neighborhoods in particular. The otherwise
rare burglary neighborhoods of Read Boulevard West and
especially Read Boulevard East experi-
enced an increase in residential burglaries that were
concentrated near the path of the tornado. In
fact, these two neighborhoods largely drove the post-disaster
increase in residential burglary in New
Orleans East as a whole for the !rst 2 months and especially the
!rst month after the disaster. In
a quite meaningful sense, these two neighborhoods could be
considered ‘hot spots,’ areas where

crime regularly and predictably occurs. (Sherman, Gartin, and
Buerger 1989).
Limitations
Like any study, this one is not without limitations. Probably the
most important of these is the nature
of our data. Calls for service only represent those incidents
reported to the police. It could be that there
were more residential burglaries than are re#ected in the calls
for service. It could also be that calls for
service for residential burglaries, particularly those in the short-
term wake of the tornado, were actually
losses due to the tornado itself. Moreover, it is important to
note that the number of burglaries in the
timeframe is relatively low, which means that changes could be
due to chance, and the timeframe
itself may be too short to account for longer term variations in
burglary that could be independent of
the tornado. In other words, relying on calls for service data to
determine the number, timing, and
location of residential burglaries before and after a disaster is
imperfect at best. Nevertheless, we can
presume enough accuracy in these data to draw the
aforementioned conclusions, at least tentatively.
Another important limitation is our designation of the variables
that indicate concentrated
disadvantage. It could be the case that our selected indicators,
namely poverty, female-headed
households, and renting, do not fully capture the e"ects of
concentrated disadvantage and therefore
Figure 4. Tornado Path Burglaries Two Months Before and
After. Source: The authors.

do not permit the e"ects of concentrated disadvantage on pre- or
post-disaster burglary (if any) to
be observed. It could also be the case that the indicators from
2015 are too dated to reveal the e"ects
of concentrated disadvantage in 2017. Moreover, examining
these indicators at the census tract level
rather than the block level, which we were unable to do, may
have obscured the e"ects (if any) of
concentrated disadvantage on burglary. While we believe our
selected indicators are valid and
thorough, we nevertheless acknowledge these potential
shortcomings.
Finally, it could be the case that either target suitability or the
absence of capable guardianship is
far more important than the other in determining where post-
disaster burglaries will occur. We simply
do not have enough information on the extent of the damage to
each of the 84 burglarized residences
out of the over 600 that were damaged to determine whether the
residence was a suitable target but
ostensibly had capable guardianship (i.e., residents were still
able to live there), or whether both
elements of routine activity theory were in play. Moreover, we
do not have information about target
suitability or capable guardianship within the neighborhoods in
this study that preceded the tornado
and how the tornado impacted them, if at all. We believe we
have su$cient evidence to presume that
both suitable targets and the absence of capable guardianship
are important in understanding post-
disaster burglary, but acknowledge that this presumption may be

incomplete.
Conclusion
As noted, the legitimacy of disaster criminology as a discipline
hinges on empirical tests like the one
described here. We believe our !ndings mostly support the
contentions disaster criminologists have
laid out (i.e., Frailing and Harper 2017), and have potentially
useful policy and practice implications.
For example, disaster response plans for local law enforcement
should include the provision of
guardianship in disaster-stricken areas, particularly those where
typical informal guardianship is
temporarily unavailable as a result of the disaster. Importantly,
the guardianship provided by law
enforcement should last well into the post-disaster period
(Frailing and Harper 2016b). Of course,
due to the dynamic nature of disasters and the damage they
cause, law enforcement may be
consumed with search and rescue operations and unable to
provide formal guardianship, especially
in the immediate aftermath of a disaster (Harper 2016). One
way to supplement the guardianship
provided by law enforcement is with clearly marked and
weather-resistant crime cameras, such as
those recently installed throughout New Orleans. Through their
ubiquity, these cameras are
designed to deter crime, including post-disaster crime (Stein
2018; La Vigne et al. 2011).
Finally, we believe further and more nuanced investigations
similar to this one are important
so that the circumstances, both those that precede and those that
follow disasters, which
facilitate crime can be better understood so that disaster crime

can be reduced or even
prevented.
Disclosure statement
No potential con#ict of interest was reported by the authors.
Notes on contributors
Kelly Frailing earned her doctorate in Criminology from the
University of Cambridge and is currently an Associate
Professor and Graduate Program Coordinator in the Department
of Criminology and Justice at Loyola University New
Orleans. She is the coeditor of Criminalization of Mental
Illness: A Reader, and of all three editions of Crime and
Criminal
Justice in Disaster. She is the coauthor of both editions of
Fundamentals of Criminology: New Dimensions and of Toward
a Criminology of Disaster: What We Know and What We Need
to Find Out.
Thomas Zawisza is an assistant professor at Lasell University.
His main research interests include using eye-tracking
technology as a medium to study burglar target selection,
investigating distance and direction of crime and victimiza-
tion, and how non-disastrous natural phenomenon a"ects crime
patterns. His most recent works appeared in the
Journal of Police and Criminal Psychology, Crime Prevention
and Community Safety, and in the Journal of Contemporary
Criminology.

Dee Wood Harper, Jr. (Ph.D., LSU, 1967) Emeritus Professor of
Sociology, Criminology and Justice at Loyola University,
New Orleans has published extensively on the problem of crime
and disaster since Hurricane Katrina. Beginning with
a session at the Southern Sociological Society meetings in New
Orleans in the Spring of 2006 on crime and policing
during Katrina, a chapter in The Sociology of Katrina through
three editions of Crime and Criminal Justice in Disaster, and
more recently, Toward a Criminology of Disaster. Currently, we
(with Kelly Frailing) are laying the groundwork for testing
theories focusing on fraud and other criminal behavior linked to
the COVID-19 Pandemic in the United States.
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Advanced Research Methods
Question Set #1 (10 points total)
You are a counselor assigned with the duty to help inmates cope
with everyday life in prison. As a graduate of Dr. Z’s School of
Awesomeness University, you learned a number of new
techniques aimed to reduce stress and violence. You believe
that the techniques you have learned at DZSA-U are far superior
to the techniques learned at other, well-known institutions. To
test whether or not these techniques were successful, you
randomly assigned new inmates to one of two groups. The first
group, A, receives the traditional counseling sessions used
within the prison. The second group, B, receives the counseling
sessions that you have learned at DZSA-U. Once each person
has completed the session, you gather the number of infractions
made by each person for two weeks. The number of infractions
are shown below. Test to see whether or not the mean of each
group differs from 4.5 infractions.
Group A: 2 1 2 3 3 5 4 5 6 1 2
3 5 4 2
Group B: 6 8 9 7 8 9 7 5 9 8 7

5 4 6 8
1. State H0 and H1 for both A and B. (2 points)
2. Calculate the measures of center and spread for each group (2
points)
3. What are your findings? (3 points)
4. Which of the programs do you recommend? (2 points) Why?
(1 point)
Question Set #2 (10 points total)
You are a researcher for Dr. Z’s Wonder Drug Corp. and you
have synthesized a drug that grants its users heightened senses
for 3 hours. The drug you have created has an infinite number
of uses, but you are really interested in whether or not people
are able to correctly identify offenders in a line-up. Test to see
if each group is able to correctly identify more than 7 suspects.
Control: 3 4 5 5 4 5 6 4 2 4

100mg: 2 4 6 5 2 1 4 5 3 5
1. State H0 and H1 for both the control group and 100mg group.
(2 points)
2. Calculate measures of center and dispersion for both groups.
Report your findings below
3. What is the Independent Variable in this study? Dependent
Variable? Explain your answers.
3. Now that you have analyzed the data, what can you conclude?
Explain
Question Set #3 (10 points)
It is my personal belief that students do not study, on average, 3
hours a day. I randomly surveyed 25 students on their studying
habits. Test to see whether or not the average number of hours
students spend studying is different than 3 hours. Data is on

CANVAS under final project stuff.
1. What is the null and alternative hypothesis? (2 point)
2. Do you think that students’ study approximately 3 hours a
day? Explain your reasoning based on the results from the data.
3. What might influence the number of hours studied (4
points)?

Question #4 (30 points)
Using the article provided on CANVAS, please answer the
following questions.
1) What was the purpose of this research? Explain your answer
(4 points).
2) Identify a null and alternative hypothesis for this study. It is
not explicitly stated so you will have to deduce it from the
article. (4 points)
3) What is the dependent variable? What is / are the
independent variable(s)? (4 points)
4) Explain where the sample came from. (2 points)
5) Of all the types of sampling, what type of sampling frame did
the authors use? (e.g. random sampling, systematic random
sampling, etc.) (3 points)
6) Was this an experiment? Using your book, explain your
reasoning. (3 points)
7) What were the major findings of the article? (3 points)
8) Was there a relationship between disadvantage and burglary?
(2 points)
9) What can you conclude about tornados and burglary? (2
points)

Question #5 (20 points)
Using the teen birth data found on CANVAS conduct a
regression predicting violent crime rates using teen births.
Provide a write up of your results using the video I posted as a
guide.

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