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. Author manuscript; available in PMC: 2017 Jan 1.
Published in final edited form as: Youth Violence Juv Justice. 2015 May 8;14(4):390–410. doi: 10.1177/1541204015585173

Table 2.

Logistic Regression Models Predicting Gang Involvement (Adjusted for Demographic Characteristicsa)

Gang
Involvementb
No Gang Involvement vs.
Membershipc Initiation Onlyc

OR RRR RRR

Family Characteristics
  Family member in gang 1.92 1.73 2.16
  Parent Education 0.77 0.79 0.75
  Per capita family income 0.90** 0.82** 0.97
  Low parental monitoring 1.21** 1.18 1.24*
School Adjustment
  Low school bonding 1.13 1.02 1.25*
  Trouble at school 2.36** 2.97* 1.18
Peer Relationships
  Peer delinquency 1.83** 1.43 2.38**
  Peer gang involvement 2.53** 2.07 3.11*
  Early dating 1.94* 1.67 2.34*
Individual Characteristics
  Negative life events 1.11* 1.06 1.19*
  Perceived racial discrimination 1.15*** 1.11* 1.19***
  Depressive symptoms 1.05** 1.05* 1.04*
  Anger 1.07 1.01 1.15*
  Hyperactivity/impulsivity 1.16** 1.20** 1.12
Early Delinquency
  General delinquency 1.15*** 1.16*** 1.15***
  Ever used tobacco 4.19*** 4.52*** 3.81***
  Ever used alcohol 4.80*** 3.52** 6.89***
  Ever used marijuana 3.47*** 1.45 7.48***
Cumulative Risk
  Total number of risk factors 1.26*** 1.22*** 1.32***
a

Adjusted for gender, age at the start of the study, living in a remote community, and living on/off reservation land

b

Binary Logistic Regression Models—Each risk factor was run as its own model with demographic controls

c

Multinomial Logistic Regression Models—Each risk factor was run as its own model with demographic controls (no gang involvement is the reference group)

Note: OR – Odds Ratio; RRR – Relative Risk Ratio

p < .10;

*

p < .05;

**

p < .01;

***

p < .001