SOCIOLOGY EDUCATION - American Sociological Association

Another Way Out 381
trolled), upper-bound estimates of the net
impact of arrest (models not shown).
Model 2 adds the prior semester’s
absences, which shrinks the valid sample to
2,056 (164 arrestees). 19 Predictably, high
school absences strongly influence final
dropout. A standard deviation (13.74)
increase in average absences predicts a 450
percent increase in the odds of dropout.
Although its effects appear markedly reduced,
arrest predicts a 368 percent increase (versus
682 percent in Model 1 estimated for this
subsample).
These estimates of the effect of arrest are
large by any standard. However, disruptive life
changes may inflate this association if they
disproportionately affect arrested youths in
the interim between participation in the survey
and first arrest or between arrest and
eventual dropout. 20 Compressing these lags
reduces the threat of selection bias.
Accordingly, Model 3 estimates the impact of
arrests during Year 1 or Year 2 on dropout by
the start of Year 3. The sample of arrested
youths with valid outcomes increases from
270 to 354, and 264 nonarrested cases are
added. Earlier outcome measurement (and a
lower base rate) sharply reduces the odds
ratio for the effect of arrest to 3.65 (and 5.61
in the baseline). Twelve youths whose institutionalization
censored their dropout status
were excluded.
Adding prior absenteeism, as shown in
Model 4, shrinks the valid sample to 2,260
(216 arrestees). A standard deviation increase
in absences predicts a 142 percent increase in
the odds of dropout. Nonetheless, arrest still
predicts a 200 percent increase (versus a 343
percent increase in Model 3 estimated for this
subsample). 21
Early Dropout
As is suggested by the inclusion of school
absences, presecondary survey controls do
not capture all the proximate differences
between arrested and nonarrested youths
that may account for the differential risk of
dropout. The first quasi-experimental design
reduces this problem. Models of early
dropout were first estimated on the entire
sampling pool, so both Year 1 arrestees and
nonarrested youths could be compared to
Year 2 arrestees (the reference category). The
latter comparisons are important because differences
in early dropout between Year 2
arrestees and nonarrestees would suggest
that some omitted factor helps drive variation
in both arrest and dropout. Model 1 conservatively
retains all the individual-level control
variables, including many that do not distinguish
Year 1 and Year 2 arrestees. Youths who
were arrested in Year 1 still have more than
twice the odds of early dropout compared to
those who were arrested in Year 2 (O.R. =
2.18), whereas nonarrestees are as likely to
drop out. Several robust predictors of middropout,
including acting out, substance use,
station adjustments, and hanging out, have
null effects on early dropout. Likelihood ratios
suggest no significant improvement in model
fit over the unconditional model.
The unconditional model shows that variation
in early dropout by neighborhood is not
significant (χ 2 = 405.29); 73 percent of the
sampled census tracts contained no early
dropouts, and only 5 percent had more than
one. Accordingly, the addition of the contextual
factors in Model 2 does not diminish the
estimated impact of arrest (O.R. = 2.35).
Models 1 and 2 conservatively estimate the
impact of ninth-grade arrest on early dropout
because they retain all control variables.
Furthermore, a preponderance of nonarrested
cases reduces the outcome variation that
ninth-grade arrest status could explain. A
more accurate picture of the impact of ninthgrade
arrest, therefore, can be obtained by
limiting the sample to Year 1 arrestees and
Year 2 arrestees. Model 3 demonstrates that
even when all the variables in the full model
are included, along with binary indicators of
violence or weapons charges (p < .10) and
spring semester arrest (p < .001), arrest
increases the odds of early dropout by a factor
of 2.60 (p = .016). No other variables,
except age, significantly predict early dropout
among this sample (p < .05). 22
To cross-validate this atypical matching
strategy, I paired the 226 Year 1 arrestees with
the nonarrestees with the closest predicted
risk of arrest. Propensity scores for the entire
early dropout sample were derived from a
logistic (selection) model of Year 1 arrest that
included variables associated with arrest status
in Year 1 and, secondarily, early dropout. 23
The caliper value should optimize intergroup
covariate balance and sample size (Rubin and