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OCR for page 96
4
Methodological Issues in
Criminal Career Research
This chapter examines some method-
ological issues that affect research on
criminal careers: of particular concern are
various aspects of the observational ap-
proaches user! to obtain data and the es-
timation techniques applier] to those data.
This chapter draws heavily on Cohen
(Appendix B), who provides a more cle-
tailed review of these issues. On the basis
of our examination, we propose various
suggestions for improver] research strate-
gies to reduce potential biases arising
from sampling anct measurement prob-
lems.
OBSERVATIONAL APPROACHES:
SELF-REPORTS AND OFFICL\L
RECORDS
The two main observational ap-
proaches for obtaining data on individual
criminal careers-self-reports and official
records of contacts with the criminal jus-
tice system-invoke longitudinal data for
individuals. Each approach is vuInera
iData on crimes committed could also be ob-
tained from reports by victims, from direct observa
96
ble to various sources of error that may
limit the accuracy of the derived esti-
mates of criminal career dimensions.
Self-Reports
The sources of distortion in self-report
surveys inclucle problems in design of
survey instruments, response errors, and
analytic problems in inferring career di-
~mensions from questionnaire responses.
The role of analytic problems was iTIus-
tratec! in the Chapter 3 discussion of al-
ternating spurts and quiescent periods in
offending: recognition that offending fre-
quencies during spurts are unlikely to
persist over periods as Tong as a year led
to downward revisions of as much as 25
percent in estimates of A.
Response errors may arise from prob
tion by researchers, and from information provided
by informants. Although such data might provide
rich information on the nature of the offense and
sometimes on the attributes of an offender, they are
usually linked to particular crime events and not to
individual offenders. Thus, it is often difficult if not
impossible to use such data to trace the develop-
ment of criminal careers for individual offenders.
OCR for page 97
METHODOLOGICAL ISSUES IN CRIMINAL CAREER RESEARCH
lems in questionnaire design or aciminis-
tration procedures, from intentional mis-
representation by respondents, or from
respondents' errors in the classification or
recall of events.2 An example of question-
naire design effects comes from the Rand
inmate surveys: the open-ended queries
about A in the first survey may have
yieldecI imprecise results for hi~h-fre-
quency offenclers, but the alternative ap-
proach used in the second survey a
complex series of closecI-ended ques-
tions apparently increased the rate of
ambiguous, unusable responses (see
Chapter 3 for cletaiTs).
One source of misclassification is re-
sponclent uncertainty about which events
are to be counted as arrests or police con-
tacts. The ambiguity for respondents may
increase as the number of interactions with
police increases and respondents are less
able to distinguish which of the interac-
tions involve an official charge or notation
in police records. There is also ambiguity
in classifying self-report items into official-
record crime categories: discrepancies may
arise because distinctions in the official
categories reflect considerations of strength
of evidence, criminal intent, ant! serious-
ness of the outcome, which are probably
not considered] in the sel£report categories.
For example, a self-reported burglary may
be noted in the offender's official record as
a burglary, a larceny, or possession of stolen
property, depencling on the circumstances
of the arrest.
Memory recall is likely to be affected
by the saliency of the events (with more
salient events more likely to be remem-
bered) and the recency of the events.
Two factors potentially affecting the sa-
liency of crimes are the seriousness and
the frequency with which they are com-
mitted. In general, more serious crimes-
with the greater risks they pose both for
2This discussion of errors in self-reports draws
heavily on material presented in Weis (Volume II).
97
victims ant! for offenders are expecter!
to be more salient. The more frequently
that crimes are committed, however, the
less salient any one ofthem is likely to be,
ant] so there will probably be errors in
counting the total number of crimes com-
mitted. These sources of memory errors
are likely to be of greatest concern for
high-rate, serious offenders.
Since recall is usually best for events
that occurred most recently, memory
problems are likely to increase with
longer recall periods and with greater
intervals between the recall period ant]
the survey ciate. These problems are most
typical of surveys that request reports of
lifetime frequencies. To reduce those
problems, most self-report surveys now
limit the recall period, for example, to the
year preceding the interview. But even
this period is subject to potentially seri-
ous recall errors for frequent ant! Tow-
saliency events. Further reducing the re-
call period to less than a year, however,
could jeopardize precision in estimates of
the number of reported criminal events,
especially for more serious offense types
that occur infrequently.
Defining the recall period in terms of
the interview ciate (e.g., during the year
preceding the interview)-although it
will enhance the recency of recalled
events may make the clata especially
vulnerable to bouncing errors: events
that occurred before the designated recall
period may be mistakenly attributed (or
"telescoped") to the recall period. Bound-
ing errors may be reduced by specifying a
recall period with more salient bound-
aries for respondents, such as the calen-
dar year or "age-year" (i.e., time between
birthdays). A more effective but far more
costly solution to the problem oftelescop-
ing is to administer two surveys, one at
the start of a recall period and the other at
the end of that period; events that are
reported in both surveys are then re-
moved from responses for the bounded
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98
period. Because of the extra cost ant] time
required for this approach, however,
bounding ot thiS sort is not usually done
for self-report surveys.
Even if a researcher exercises great
care, there will be ambiguous responses
that present cli£iculty in analyzing the
data. For example, on the basis of a
reanalysis of the data from the seconc!
Ranc3 survey, Visher (Volume II:Table
11) indicates that 35 to 40 percent of the
responses by inmates active in robbery or
burglary were ambiguous. The Rand re- Official Records
searchers tried to deal with that amb~gu
ity by computing minimum and maxi
mum estimates for each respondent
(Chaiken and Chaiken, 1982a). Visher
adopted an alternative strategy for deal
ing with ambiguous responses: rather
than cleveloping extreme estimates for all
respondents, she formulated rules for cle
riving a single reasonable estimate for
each indiviclual and excluclecl from the
analysis those few for whom no reason
able estimate couIc3 be computed. For
example, individuals who indicated that
they committed 1 to 10 crimes but did not
report the exact number were assigned a
single value in the range from 1 to 10 to
match the distribution of the responses by
the unambiguous respondents. She used
similar estimating strategies for other am
biguous responses. As indicated in Ta
bles 3-3 and 34 (in Chapter 3), Visher's
estimates are much closer to Rand's orig
inal minimum estimates than to the max
1 1e r.1 . . ~.
CRIMINAL CAREERS AND CAREER CRIMINALS
ally finds the reliability ant! validity of
responses reasonably insensitive to these
various administration conditions (Weis,
Volume II). Rather, the response errors
that are found in self-reports are due pri-
marily to the saliency, frequency, and
timing of criminal activities ant! to the
structure of the survey items; further cle-
velopment of survey instruments to bet-
ter acIdress these aspects might recluce
their effects significantly.
mums.
Various structural features in the acI-
ministration of self-report surveys may
potentially bias or limit the validity of
responses. Such features include cliffer-
ences between responses to self-admin-
isterec! questionnaires and interviews,
between anonymous and nonanonymous
surveys, and the effects of differences in
interviewer attributes. It is reassuring
that the research that has examined these
effects in self-reports of offending gener
Official records are also vulnerable to
important, but very different, errors that
affect the accuracy of estimates of individ-
ual offencling. The main sources of error
are the extent to which officially recorded
criminal events are limited only to crimes
that result in arrests or convictions, are
unreliably recorclecI, and are selective in
being more likely to recorc! arrests or
convictions for one population subgroup
or crime type than for another. There are
two main structural sources of recording
errors: misclassification of events and
nonrecorcling.
Classification errors can result from clif-
ferences among local agencies in their
classification of offense types, as in the
ambiguity over whether a purse snatch is
a larceny or a robbery. These cIassifica-
tion differences can also occur when To-
cally recorclecl criminal events, based on
the crime categories found in local stat-
utes, are transformed to some other crime
classification scheme in centrally main-
tained official records. Within a single
jurisdiction, classification is likely to be
fairly consistent, but inconsistencies may
be introduced in records that reflect cIas-
sifications from multiple jurisdictions or
. . . . ~ · · ~1
In comparisons across Jur~scl~ct~ons. ~ nus,
a high estimate of A for some crime type
in one jurisdiction might reflect a differ-
ence in classification rather than a (liffer-
ence in actual offending.
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METHODOLOGICAL ISSUES IN CRIMINAL CAREER RESEARCH
Nonrecording may occur because the
event does not meet reporting standards,
such as the requirement for a fingerprint
or disposition data, which may not be
available (Michigan State Police, 1983a,
b). Nonrecorcling is also likely to vary
across jurisdictions: jurisdictions that rely
especially heavily on state criminal his-
tory files are likely to be more thorough
in their reporting. Differences in the
strength of administrative ties between
local jurisdictions and the agencies main-
taining central records may also affect
recording.
Nonrecording of some events obvi-
ously understates the number of arrests
by sample members and thus contributes
to underestimates of arrest frequencies.
However, nonrecording can also lead to
overestimates when some arrestees are
missing from the arrest history data. Since
each subsequent arrest increases the
chance that a record will be created, low-
rate offenders with their smaller expected
number of arrests are likely to be dispro-
portionately missing from the arrest his-
tory ciata, contributing to overestimates of
arrest frequencies.
But however complete the recording
may be, officially recorded arrests still
account tor only a small portion of all
crimes committed. In addition to the
crimes clirectly associated with an arrest
(or a conviction), offenders usually com-
mit other crimes that do not result in
arrest. Arrest records can be used to infer
the volume of unobserved crimes com-
mitted. Such inferences require estimates
of the probability of arrest for a crime q
and assumptions about the nature of that
process, along with direct estimates of
individual arrest rates (,u) based on the
number of recorded arrests and the
length of time that an offender is free and
so at risk of arrest. For individuals with
arrest rate ,u and probability of arrest for a
crime q, A is equal to ~q.
One approach to estimating the proba
99
bility of arrest for a crime was proposed
by Blumstein and Cohen (19791. The es-
timate relies on readily available aggre-
gate data and starts with the ratio of the
reported per capita arrest rate, A, to the
reported per capita crime rate, C, in a
jurisdiction. The ratio A/C is then ad-
justed by the rate at which victims report
crimes to the police, r, to reflect total
crimes, including those not reported to
the police. Data on reporting rates are
available from annual victimization sur-
veys, and the adjustment should use re-
porting rates for the jurisdictions being
studied whenever possible. The estimate
of q is further adjusted by the average
number of offenders per crime incident,
O. also available from victimization sur-
veys. This correction adjusts for the fact
that the estimated risk of arrest per crime
for any inclividual offender is overstated
when the arrest data include arrests of
several different offenders who are in-
volvec! in the same crime incident but the
crime data do not. The resulting estimate
of the probability of arrest for a crime, q,
is (A/O)/(C/r).
There are various potential sources of
error in estimates of q and thus in the
associated estimates of crimes actually
committed. Reported numbers of arrests
and crimes are subject to nonrecording, as
discussed above. Both victims' reporting
rates and the number of multiple offend-
ers per crime are vulnerable to recall ant]
other response errors by the respondents
to victimization surveys. The multiple-
offenders factor is also subject to biases
arising from systematic differences be-
tween crimes for which victims know the
number of offenders (and report it in vic-
timization surveys) and those for which
the number of offenders is not known or
reported. The multiple-offenclers factors
estimated from the victimization surveys
will overstate the number of offencler-
crime incidents (C x O) and thus result in
underestimates of q if the number of of
OCR for page 100
100
fenders is more likely to be known in
crime incidents involving multiple of-
fenders.
Because of possible errors in compo-
nents involved in the estimation of q, it is
useful to perform sensitivity analyses to
assess the impact of those errors on esti-
mates of A. Based on available empirical
estimates of the various component val-
ues a range of A/C from .1 to .3 (Federal
Bureau of Investigation Uniform Crime
Reports, annual), values of O in the range
of 1.2S to 2.50 (Reiss, 1980b), and values
of r in the range of .25 to .75 (Bureau of
Justice Statistics, 1982a, bathe values of
q calculated from (A/O)/(Clr) range from
.010 to .180. This broad range of estimates
of q can be combined with a typical an-
nual value of ,u of .2 (Table 3-1) to gener-
ate estimates of A (A = u/q) ranging from
1 to 20 crimes committed annually. Be-
cause of the sensitivity of these A esti-
mates to variations in the factors compris-
ing q, better estimates are needed of the
range of error in the components of q.
Even if the average value of q is esti-
mated accurately, estimates of both indi-
vidual offending frequency (A) and partic-
ipation rates (~1) may be biased by
interactions between A and q at the indi-
vidual level. If individual crime rates and
arrest risks are negatively related to one
another, with high-rate offenders less
leery to ne arrested tor eacn crime, appli-
cation of a homogeneous q to all offenders
results in an underestimate of A. Corre-
spondingly, A wfll be overestimated if A
and q are positively related. If A and q
vary systematically with the attributes of
offenders, failure to adequately represent
that variation wfl] also distort the esti-
mates of A associated with different of-
fender subgroups. Without adequate con-
trols for variations in q, any estimate of A
derived from official records wfl! con-
found individual differences in A with
clifferential police practices reflected in q.
The limited research avaflable to date
... ~. ~. ~ r ~
CRIMINAL CAREERS AND CAREER CRIMINALS
generally tails to one systematic substan-
tial variations in q with differences in A or
in the demographic attributes of offend-
ers (see Cohen, Appenclix B). Further
research on this relationship should have
high priority. However, while stfl! pre-
liminary, the available results suggest
that the errors may be small in estimates
of average values of A that are derived
from arrest records made on the assump-
tion that A and q are independent.
Even in the face of heterogeneity in q,
estimates of individual crime rates would
be unbiased if the arrest risk for a crime
varied independently of individual crime
rates. Nevertheless, the potential for bias
in estimating dimensions of the crime
process from official data of the observed
arrest process highlights the importance
of empirically investigating the nature of
any variation in q, and especially in find-
ing any systematic variation in q with
changes in individual crime rates or with
attributes of offenders. To date, homoge-
neous qs have been used because official
records do not provide the information
necessary to indicate how arrest risks vary
across different offenders. Such informa-
tion, however, can be derived by combin-
ing self-reports of individual crime and
arrest experiences.
.~ ~ .~ ~ ~ . .
Potential for Synthesis of
Observational Methods
Self-reports and official records are cur-
rently the best avaflable methods for ob-
taining longitudinal data on individual
criminal careers. Because of the funda-
mental differences between them and
their sources of error, the two approaches
are often posed as competing alternatives.
This conflict between the methods has
been fueled by apparently substantial dif-
ferences in conclusions based on the two
data sources: most notably, early findings
based on official-record data showed im-
portant differences in criminal participa
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METHODOLOGICAL ISSUES IN CRIMINAL CAREER RESEARCH
tion by social class and race that were not
supported by data from self-reports. For
the most part, these early differences are
resolved when appropriate controls are
inclucled to ensure that the two data
sources are comparable in sample compo-
sition and in the seriousness threshold
used for criminal activities.
A more constructive approach is to
view the altemative methods as comple-
men~ and to search for mutually bene-
ficial ways of using them. The discussion
above highlighted some of the more im-
portant sources of error in the two ap-
proaches: because their sources of error
are very different, however, it is possible
to use the two approaches in concert to
ameliorate some of the error problems in
each. For example, one can compare es-
timates of individual offending patterns
that are derived independently from the
two methods. If the estimates are similar,
some confirmation of their accuracy is
provided. Since it is unlikely that the two
approaches would consistently result in
the same wrong estimates, similar find-
ings from independent analyses would
suggest that the errors in each approach
~^ homogeneous q. Self-report data provide
I01
Self-reports of crimes committed are
sometimes based on samples of inmates
in order to increase the number of respon-
dents who have a sufficient number of
criminal events. While providing a usable
number of reported events for analysis,
such samples are not representative ofthe
general offending population (see next
section on sampling). In these samples,
official records can be used to estimate
various statistics (e.g., the probabilities of
arrest for a crime, of conviction following
arrest, and of incarceration following con-
viction, and the average time served once
incarcerated) that describe the selection
process that led to the respondents' incar-
ceration. These statistics, which deter-
mine the chance that an offender with
particular characteristics will be found
among inmates, can be used to weight
observations in the inmate sample in or-
der to provide estimates applicable to
offenders who are not incarcerated.
Data from self-reports can also be used
to address one ofthe main sources of error
in analyses of official records the over-
simplified characterization of the arrest
process implied by the assumption of
are not grossly distorting. l he search tor
such convergent validity between results
is one way of using analyses of self-
reports and official records in concert.
The two approaches can also be used
together at intermediate levels in re-
search to try to deal explicitly with vari-
ous sources of error. Official records of
arrests or convictions are already used in
combination with self-reports of these
same officially recorded events as a means
of validating the accuracy of self-reports.
Official records might also be used to
help reduce response errors by invoking
events in the official record during self-
report interviews, both as a means of
triggering recall of events and time neri-
ods for respondents and of reducing re-
spondent inclinations to intentionally
misrepresent their criminal activities.
an opportunity to directly link self-re-
ported crimes to both self-reported ant]
official-record arrests for specific indivicl-
uals with known attributes. With these
data, it is possible to explicitly examine
the variability in q both with indiviclual
crime rates and with various attributes of
offenders. The patterns of variation that
are found would provide a basis for intro-
ducing heterogeneous estimates of q into
analyses of official-record clata.
SAMPLING ISSUES
Analyses of criminal careers involve
some important choices on the appropri-
ate sample design for generating offender
data. Central to problems of sample cle-
sign are tradeoffs between the represen
OCR for page 102
102
tativeness of offenders and obtaining a
sufficient number of active offenders for
analysis. The best sample choice in any
study varies with the career dimension
being measured.
At one extreme, a random sample of
people can be drawn from the general
population and their criminal careers fol-
lowed through self-reports or official-
record data. But since arrests and crimes
occur relatively infrequently in the gen-
eral population, the number of crime and
arrest events will be low, especially for
the more serious offense types. There-
fore, samples from the general population
are of value primarily for studying partic-
ipation in offending. Only for very minor
crime types, like truancy and smoking by
juveniles, which occur in large numbers,
do such samples permit analysis of active
offenders' careers.
Stratified sample designs can increase
CRIMINAL CAREERS AND CAREER CRIMINALS
all community-based samples exclude of-
fenders who are incarcerated at the time
of sampling, who tend to be dispropor-
tionately high-rate offenders. If high-rate
offenders are more likely to drop out of
school, they will be underrepresented in
school-based samples. High-rate offend-
ers who are free in the community are
hard for survey researchers to locate be-
cause they are likely to be "on the run" or
otherwise trying to avoid detection; those
with highly transient living arrangements
tend to be missed in household-based
samples. And high-rate offenders who are
located may be more likely than others to
be uncooperative, refusing to participate
in research.
The principal alternative to general
population samples is samples drawn
from populations of presumed offenders,
such as arrestees, convictees, or incarcer
- ~- - v ated people. The choice of a definition for
the yield of active offenders by oversam- sampling offenders involves a tradeoffbe
pling high-yield subpopulations, but at tween the degree of certainty about of
the cost of a reduced number of offenders fender status and the degree to which the
drawn from low-yield subpopulations. sample is representative of offenders
Therefore, stratification increases the pre- more generally. Sampling from convicted
cision of estimates of the career dimen- offenders greatly reduces any uncertainty
signs of active offenders in the high-yield about the actual criminal involvement of
subpopulation and usually in the aggre- sample members. However, convicted of
gate population, but decreases substan- fenders in prison, for example. are not
tially the precision of estimates for the
low-yield subpopulation. For example,
oversampling teenaged males from low
income neighborhoods will improve the
precision of A estimates for Hat subpop
ulation, but with a substantial loss of pre
cision in A estimates for young adults,
females, and residents of high-income
neighborhoods.
Regardless of stratification, samples
drawn from community-based sampling
frames like schools and households are
more likely to miss offenders than nonof-
fenders, and this problem is likely to be
most severe for high-rate offenders, lead-
ing to their disproportionate underrepre-
sentation in these samples. For example,
likely to be representative of all offend-
ers: they are presumably the most seri-
ous, the oldest, and perhaps the most
inept at avoiding detection.
The broadest sampling base among of-
ficially detected offenders is arrestees.
But selecting a sample of arrestees in-
volves potential errors of commission,
since some falsely arrested persons are
wrongly included among active offend-
ers. Selecting convictees, by contrast, is
more likely to involve errors of omission
because active offenders in the arrestee
population who are not convicted (and
often not prosecuted; see below) are ex-
cluded from the sample. In dealing with
dispositions for specific individuals, of
OCR for page 103
METHODOLOGICAL ISSUES IN CRIMINAL CAREER RESEARCH
course, the presumption of innocence
makes errors of commission unaccept-
able. In dealing with the empirical char-
acterization of criminal careers, however,
there must be a relative weighing of these
two types of errors. Fundamental to this
consideration is some assessment of the
factors contributing to the lack of a con-
viction after an arrest.
Empirical examinations of the reasons
for nonconviction suggest that nonconvic-
tion is by no mean synonymous with
innocence (Forst, Lucianovic, and Cox,
1977; Vera Institute of Justice, 1977;
Brosi, 1979; Boland et al., 1983; Feeney,
Dill, and Weir, 19831. The vast majority of
nonconvictions are the result of diver-
sions from adult criminal courts (to juve-
nile court or to pretrial diversion pro-
grams) and dismissals, rather than the
result of acquittals. The reasons for dis-
missal frequently have little to do with
the guilt or innocence of the defendant.
Instead, many cases are dismissed be-
cause of noncooperation by witnesses (of-
ten arising from the existence of a prior
relationship between the offender and
victim), inadmissibility of critical evi-
dence, and the lesser importance of the
case compared with other cases. Manv
less serious cases are also diverted prior
to trial. In view of the predominantly
procedural reasons that many arrests do
not result in a conviction, the errors of
commission associated with truly inno-
cent arrestees appear to be far less fre-
quent than the errors of omission that
wouIcI occur if the more stringent stan-
dard of conviction were user] as a basis for
sampling offenders.
An important consideration in using
samples of identified active offenders is
the clegree to which those samples are
biased toward high-rate offenders. High-
rate offenders wouIc} be overrepresented
among persons who have at least one
detected event (a crime, arrest, convic-
tion, or incarceration) during a sampling
103
period, since they are more likely to incur
the sampling event. This bias is greatest
when the sampling period is short com-
pared with the mean time between
events.3 These biases can be compen-
satecl analytically by accounting for the
differential sampling probabilities associ-
ated with offenders having different
event rates. Alternatively, one can in-
clude only offenders whose first recorcled
event occurs in a sampling period that is
short comparer} with career length. This
sample of starting offenders mirrors the
distribution of A among offenders.
There is also a greater concentration of
more serious offense types in convictee
and inmate samples than in other of-
fender samples. Processing cases through
the criminal justice system typically in-
volves increased filtering and selectivity
as many less serious cases are dropped.
Thus, the further into the criminal justice
system that samples are drawn, the less
likely samples are to be representative of
street offenders generally. From the per-
spective of ensuring greater representa-
tiveness, samples of self-reported offencl-
ers and arrestees are better than samples
of inmates or even of convictees.
It is possible to correct for biases aris-
ing from the sampling process. The cor-
rection involves reweighting the sample
to reflect the differences in the sampling
probabilities of sample members. Such a
weighting scheme wouIcl give greater
weight to offender types who are uncler-
represented in a sample and less weight
to those who are overrepresented. Suc-
cessful correction of sample biases using
3If events follow a Poisson process, an offender
with individual event rate ,u has a probability of
being sampled during a period of length ~ of Ps =
~ - e-~. If ~ is very short compared to Ilk, then Ps
is approximately At, and so higher-rate offenders are
seriously overrepresented. If ~ is very long com-
pared with 1/,u, then ps is close to unity, and the
sample is reasonably representative of the offender
sample.
OCR for page 104
104
this approach requires an adequate char-
acterization of the sampling process and
reasonable estimates of the sampling
probabilities.
General population and offender-basec!
sampling strategies, with their different
flaws, are each suited for clifferent pur-
poses. General population samples,
which include offenders and nonoffend-
ers, are more appropriate for estimating
participation rates. The undercount of
high-rate offenders in such samples will
understate participation, but this bias is
not likely to be substantial because of the
small numbers of high-rate offenders
among total offenders. However, general
population samples are inefficient for es-
timating frequency rates of active offend-
ers because of the low yield of active
offenders in such samples. The ineffi-
ciency is aggravated by the underrepre-
sentation of high-rate offenders in those
samples. Samples of arrestees or inmates
are better suites! for estimating A, but
corrections are required to adjust for the
overrepresentation of high-rate and more
serious offenders in these samples.
USE OF COHORT AND
CROSS-SECTIONAL DATA
Research on criminal careers involves a
study of the variation in an individual's
criminal activity during his life, inclucling
the ages at initiation and termination and
the pattern of offending between those
two points. Thus, all such research is
inherently longitudinal.
There are many ways in which such
longitudinal research can be pursued.
The most obvious wouIc! be to identify a
cohort at birth ant! to follow that cohort
prospectively for a Tong enough period of
time to inclucle the termination of most
criminal careers of cohort members. The
most important disadvantage of this ap-
proach is the Tong time required to
develop results. In addition to the consid
CRIMINAL CAREERS AND CAREER CRIMINALS
erable cost involved, the historical envi-
ronment in which the cohort is observed
may no longer be relevant at the time the
results become available. Thus, for exam-
ple, a cohort that reached maturity before
the sharp rise in drug use ofthe late 1960s
would yield no information on the influ-
ence of drug use on involvement in other
criminal activity.
A different approach to longitudinal re-
search is a retrospective Tongituclinal de-
sign. This approach avoids the Tong delay
associated with the prospective stu(ly by
defining a cohort and reconstructing its
prior criminal involvement. This is the
approach pursued by Wolfgang, Figlio,
and Sellin (1972), who first defined a
cohort all boys born in 1945 and resict-
ing in Philaclelphia from ages 10 to 10
and then retrospectively collected their
records of police contacts.
In the absence of longitudinal data,
annual cross-sectional data can be used to
synthesize a cohort by examining varia-
tions across age within a year as a proxy
for longitudinal age variations of a cohort.
However, the two may not be equivalent.
If there are important cohort effects, then
those cohort effects will be confounded
with age effects in a synthesized cohort. If
there is a positive association between
career length and A, for example, then a
cross-section cohort will display a larger
average A and more high-rate offenders
than a natural cohort; a negative associa-
tion will lead to the opposite effects. Fur-
thermore, a cross-section design pre-
cludes examining temporal sequences
within individuals, which is a main fea-
ture of longitudinal cohort designs.
A major problem with single-cohort de-
signs is that age effects are inextricably
confouncled with historical effects. Thus,
a cohort that happened to reach the high-
crime ages of the mid-teens at a time of
consi(lerable social turmoil wouIcl display
an amplified age effect in involvement in
crime compared with another cohort that
OCR for page 105
METHODOLOGICAL ISSUES IN CRIMINAL CAREER RESEARCH
reacher] those ages at a time of social
tranquility. Analysis of a single cohort
-C:7-
would not be able to isolate these effects.
One way to overcome this problem is by
drawing multiple cohorts and obtaining
longitudinal data on them. A variant of
this approach involves identifying a
cross-section sample of the population
(thus representing multiple cohorts) and
collecting longitudinal data-either pro-
spectively or retrospectively~n them.
Because of resiclential migration, how-
ever, the members of the cross-section
sample will cliffer from the birth cohorts
within the jurisdiction studied. Also, if
the cross-section sample is drawn from an
arrestee population (or some other of-
fencler sample), then the older members
of the cross-section sample wit! overrep-
resent individuals who have longer crim-
inal careers, and any estimates of relation-
ships with career length will be biased;
for example, factors positively associates!
with career length will be overrepresent-
ed.
The relative strengths ant! weaknesses
of the various design aspects consiclered
here suggest that an appropriate compro-
mise involves drawing samples from mul-
tiple jurisdictions ant! cleveloping multi-
ple overlapping cohorts, each observed
for a limited time through certain key
developmental age periods. For each of
these periods, prospective longitudinal
data on criminal activity and other related
events should be collected on cohort
members. Those data can and shouIc! be
augmented by retrospective longituclinal
data whenever available.
PROBLEMS OF CONFOUNDED
EFFECTS
A number of factors may confound es-
timates of criminal career dimensions. In
some cases, there are interactions among
various dimensions, which result in pos-
sible distortions in the separate estimates
105
for each dimension. The possibility of
career termination cLuring a follow-up pe-
riocI, for example, distorts estimates of A
for offenders who remain active, since
some offenders will ens! their criminal
activity during the follow-up period. If A
is calculated by assuming that all offend-
ers are active throughout the follow-up
period, failure to account for this short-
ened duration for some offenders will
result in a downward! bias in the estimate
of A.
Many criminal career analyses focus on
changes in offender behavior as their
criminal careers progress. For example,
such studies include analyses of trencis in
offense seriousness or in A as offenders
age or accumulate arrests. When such
trends are established they are often at-
tributec! to developmental changes as of-
fenders mature, to growth in criminality
as the career unfoIcts, or to consequences
of offenders' interactions with the crimi-
nal justice system. Another interpretation
of observed trencis that is rarely invoked,
however, involves none of these causal
explanations but derives from offender
heterogeneity. Uncler this interpretation,
different offender groups wilt display clif-
ferential persistence in their criminal ca-
reers. In such a situation, the more per-
sistent groups and their characteristics
increasingly dominate samples of offend-
ers who are observer! at later stages of
criminal careers. To the extent that of-
fencler heterogeneity is a factor in gener-
ating the observed trends and is not ade-
quately controllecl in the analysis ofthem,
the changing composition of the offender
population over the course of careers will
be incorrectly interpreted as changes in
the behavior of offenders.
Measurements of career dimensions of-
ten slider across studies and are some-
times characterizes] as presenting con-
flicting information. In many cases,
however, the differences in measure-
ments are attributable to (differences in
OCR for page 106
106
the scope of offenses consiclerecI or to the
composition of the population studied.
For example, participation rates wflT be
higher for all offenses than for violent
offenses, for cumulative lifetime partici-
pation (BL) than for participation by age
18 (Bit), and for samples of mates alone
than for samples of mates and females.
Therefore, any reporting of criminal ca-
reer measurements must indicate the ba-
sis of the measurements.
Variations in exposure time can also
affect measurements of criminal career
dimensions. For example, if an individual
initiates or terminates a career midway
through an observation period, then the
estimate of his offending frequency,
when distributed over the entire period,
would be only halfhis true rate cluring his
active periocl. A similar distortion could
occur in analyzing the effects of covari-
ates on criminal career dimensions. Con-
sider, for example, the relationships be-
tween precursor behaviors, such as
alcohol or marijuana use, ant! criminal
participation: alcohol use generally be-
gins at an earlier age than marijuana use,
and thus alcohol users have a longer ex-
posure time within any independently
established observation period than clo
marijuana users. Thus, even if both sub-
stances had the same influence in initiat-
ing delinquent careers, more of the alco-
hoT users would have had the opportunity
to begin offending within the observation
period than would marijuana users. Iso-
lating the relative influence of these two
covariates on participation requires ade-
quate controls for the differences in times
at risk (Robins and Wish, 19771.
Identifying the covariates of criminal
careers is especially important both for
improving theory on the causes of incli-
vidual criminality and for distinguishing
among offenders for various policy pur-
poses. The proportional hazards method!
is a statistical technique that permits si-
multaneous control for variations across
CRIMINAL CAREERS AND CAREER CRIMINALS
inclividuals in covariates of criminal ca-
reers and for variations in exposure times.
Its primary application in criminology has
been to data on time to recidivism (Barton
and Turnbull, 19811. By relying on time
to a first recidivist event as the clependent
variable, however, these models cannot
distinguish the separate effects of covari-
ate s on the career dimensions of fre-
quency and termination.
Maltz (1984) explores one approach to
disentangling the relationship of inde-
pendent covariates to separate career
dimensions. He proposes a model that
partitions recidivism between the proba-
bility of ever recidivating (which is re-
lated to career termination) and the fail-
ure rate of recidivists (a direct measure of
offending frequency for active offenders).
To examine the role of covariates on those
career dimensions, the data are parti-
tioned into groups that are reasonably
homogeneous with respect to the covari-
ates of interest, end the two dimensions of
recidivism are estimated separately for
each group. In an illustrative analysis of
the effects of one covariatc age at re-
leasc the estimated probability of ever
recidivating decreases as age at release
increases in three of four jurisdictions
examined, but there appears to be no
effect of age at release on failure rates for
those who do recidivate (Maltz, 1984:
131-1331. This approach, however, is still
preliminary, and considerable develop-
ment and testing are required to identify
the statistical properties of the technique.
The problem of identifying and control-
ling for the effects of independent
covariates on the various career dimen-
sions remains an important area for fur-
ther research development.
EXPLICIT MODELS OF OFFENDING
Virtually all estimates of criminal ca-
reer dimensions invoke some kind of im-
plicit model of individual offending. Be
OCR for page 107
METHODOLOGICAL ISSUES IN CRIMINAL CAREER RESEARCH
cause of the inherent clifficulties in
obtaining direct observations of crimes
committed by individual offenders, esti-
mates of career dimensions rest on other
observable data, like arrests and self-
reported crimes, as indirect indicators of
the underlying crime process. The vari-
ous estimation strategies that are applier!
to these data rest funclamentally on moc3-
els that characterize both inclividual of-
fending and the processes that give rise to
the observable data. The accuracy of the
estimates of criminal careers that emerge
clepencis on the adequacy of the assump-
tions in the models, which are usually
unstated.
Because the available observable data
are only indirect indicators of actual
crimes committed, improving the preci-
sion of measurement of those data is only
one part of needed work; estimates for the
underlying, but unobserved, crime proc-
ess must also be improved. This second!
step requires explicit models that link the
unobserved crime process with the ob-
servec] data. With explicit models, the
adequacy of estimates can be assesses] in
terms of the reasonableness of the as-
sumptions ant] the sensitivity of results to
those assumptions.
Models of incliviclual offending have
moved from treatments of offending
based on traditional aggregate measures
such as per capita crime rates and recidi-
vism rates to more detailed characteriza-
tions that partition offending levels
among the various aspects of a criminal
career. The initial models of criminal ca-
reers have relied on a number of simpli-
fying assumptions, principally that indi-
vidual careers are stationary over time
ant! homogeneous across offenders. This
simplest characterization underlies most
currently available estimates of career cli-
mensions. More recent developments
have begun to enrich the basic moclel to
better accommodate the complexities of
real careers. One issue of concern has
]07
been possible nonstationarities in offend-
ing frequencies during individual ca-
reers. Two forms of nonstationarity have
been addressed in recent research: spurts
in criminal activity as offenders move be-
tween active and quiescent periods and
changes in frequencies as offenders age.
Two approaches to addressing spurts in
criminal activity cluring a career are avail-
able. In reanalyzing the ciata from the
second Rand inmate survey, Chaiken and
Rolph (1985) fount! evidence that periods
of criminal activity cluring the observa-
tion period tended to be clustered near
the arrest that led to the current incarcer-
ation. The observation periods for respon-
clents with short street times are thus only
slightly longer than the periods of spurts
in activity. When offending rates during
these periods are treated as if they ap-
plied to both active and quiescent
periods, they lead to overestimates of of-
fenders' average annual frequency, A.
Chaiken ant] Rolph propose a mode! to
reflect this mixture of active and quies-
cent periods during a career and acldust
indiviclual frequencies downward to re-
flect this mixture. Lehoczky (Volume II)
proposes an alternative mocle! to accom-
modate spurts in criminal activity: indi-
viduals alternate between active ant! qui-
escent periods, and they commit crimes
en cl are arrested only during the active
periods. After each arrest, an offender
faces a possible transition to move into a
quiescent period (with probability a) or to
continue in the active state (with proba-
bflity 1 - a). In the Lehoczky model, sanc-
tions may also have an inhibiting effect on
future crimes as each arrest triggers a pos-
sible move to a quiescent periocl.
Other mocle} refinements address pos-
sible changes in A with age, like those
observer! in aggregate per capita arrest
rates, which increase rapidly into the late
teens and then decrease steadily for older
ages (see Figure 1-21. Similar clecTines
with age for adults have been observed in
OCR for page 108
108
recidivism rates and in frequency rates
for broac! aggregate offense categories,
such as "all offenses," or "all inclex of-
fenses" (see, for example, Peterson and
Braiker, 1980~.
Two different approaches to mocleling
these age effects are presented in papers
commissioner] by the panel. Flinn (Vol-
ume II) models indiviclual allocations of
time to criminal activity as a rational
choice baser! on the net expected returns
from legitimate ant! criminal activities. In
this model, declines in criminal activity
with age result when wage rates from
legitimate activity increase as inclividuals
accumulate more work experience and
when the expecter! cost per crime, mea-
surec3 by expected time spent incarcer-
ated, increases with the number of prior
incarcerations. The model by Lehoczky
(Volume II) captures the distinction be-
tween aging effects for inctiviclual crime
types and for the aggregate of several
crime types. An individual offender is
mocleled as having multiple careers, one
for each crime type. The careers in each
crime type are separate and operate incle-
pendently of one another. For any single
crime type i, an individual's frequency
rate (Ai) is fixed during his career in that
crime type, ant! that crime-specific career
terminates with some probability ,ll after
each crime committed. In this formula-
tion, the total value of A for an inclivid-
ual reflecting the sum over all crime
typesWeclines with age as active crime
types are gradually eliminated.
The Lehoczky mode] also incorporates
other refinements to the model of crimi-
nal careers, including variation across of-
fen(lers in their career (limensions. It also
CRIMINAL CAREERS AND CAREER CRIMINALS
permits covariates of the dimensions re-
flecting both fixed background character-
istics (such as sex, race, juvenile record,
and age at first encounter with the crimi-
nal justice system) and dynamic attributes
(such as drug use and employment sta-
tus). While the moclel has not been ap-
pliec3` to data, various techniques for esti-
mating the model parameters from
empirical clata have been proposed.
The mode! developments by Flinn
(Volume II), Lehoczky (Volume II), and
Chaiken and Rolph (1985) represent con-
ceptual advances over the simple model
of criminal careers representec! in Figure
1-2 that underlies most available esti-
mates of the various career dimensions.
As mo(lels of criminal careers are ex-
tenclec! to better reflect the underlying
behavioral and observational processes,
the estimates derived from those models
should be more valid. Application of the
enricher! models to improve estimates of
the distributions of career dimensions is
stfl] necessary. It wfl! also be useful to
compare the resulting estimates with sim-
flar estimates derived using the simple
model. Such comparisons will permit an
assessment of the error introduced by the
assumptions in the simpler moclels (e.g.,
homogeneous frequency rates across of-
fenders or the use of a single uniform
arrest probability per crime across offend-
ers). Such comparisons may indicate that
the assumptions of the simple moclel are
reasonable approximations that yield sat-
isfactory estimates and that adequately
account for important effects, or more de-
taflec3 sensitivity analyses may inclicate
which assumptions of the more elaborate
models are most important.
Representative terms from entire chapter:
criminal career
