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Selected KIP Survey Results Fall 2003. Robert J. Illback, PsyD & Daniel Sanders, PhD REACH of Louisville. KIP Survey. Measures ATOD use and related factors for students in Grades 6, 8, 10, & 12 Originated in KY with CSAP-funded KIP projects in 1999
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Selected KIP Survey ResultsFall 2003 Robert J. Illback, PsyD & Daniel Sanders, PhD REACH of Louisville
KIP Survey • Measures ATOD use and related factors for students in Grades 6, 8, 10, & 12 • Originated in KY with CSAP-funded KIP projects in 1999 • Items derived from on Hawkins & Catalano’s “Student Survey of Risk and Protective Factors and Prevalence of Alcohol, Tobacco, & Other Drug Use” (1998) • Originally intended to allow comparisons among SIG states, given in fall and spring • Current plan is to administer bi-annually
Administration & Scoring • Administered at four grade levels within each participating district • Training of district coordinators • Voluntary participation • Protections for anonymity • “Passive consent” model • About 40 minutes to administer • Sent to service bureau for scanning and data verification
Scale Components • Demographics – Age, gender, race, zip, achievement, grades, SES, school attendance, family constellation, urban/suburban/rural • ATOD Use (personal & peer) – 30 day, past year, lifetime, onset • ATOD-related problems/antisocial behavior • ATOD accessibility • Values (personal, parental) • School safety • Family harmony/conflict
Demographics of the 2003 Sample • Not representative of the state as a whole • Across the 38 school districts (plus School for the Blind), 28,372 students completed the survey • Equal distribution by gender (50%) • About 1/3 participate in the free/reduced school lunch program (proxy for SES)
30-Day Substance Use • Exploratory factor analysis, principal components, varimax rotation, Kaiser normalization (Eigenvalue set at 1.0) • Two factor solution emerges across 11 substances • Factor 1 – alcohol, tobacco, marijuana • Factor 2 – all other substances • Accounts for 58.1% of the variance
Predicting ATOD Abuse and Related Problems(Some Preliminary Analyses) • Step-wise logistic regressions for: • Heavy smoking use (1/2 pack per day or more in last 30 days) • Drunkenness (1 or more times in last 30 days) • Regular marijuana use (3 or more times in last 30 days) • Predictors – age, gender, family conflict, SES, perceived risk, perceived availability, peer use, personal disapproval, parental disapproval
Heavy Smoking in Last 30 days • Logistic regression (forward, stepwise) of demographic and smoking-related items • Salient predictors • Friends who smoke (#11a) • Parental disapproval (#41b) • Personal disapproval (#40b) • Grades (#9) • R2 (Nagelkerke) = .517 • Variables that did not contribute appreciably greater predictive power: age, gender, family conflict, risk perception, availability, age of 1st use Classifies 92.9% of cases
Drunk in Last 30 Days • Logistic regression (forward, stepwise) of demographic and alcohol-related items • Salient predictors • Friends who drink (#11b) • Personal disapproval (40a) • R2 (Nagelkerke) = .483 • Variables that did not contribute appreciably greater predictive power: age, gender, grades, family conflict, parental disapproval, risk perception, availability, age of 1st use Classifies 85.8% of cases
Regular Marijuana Use in Last 30 Days • Logistic regression (forward, stepwise) of demographic and marijuana-related items • Salient predictors • Friends who drink (#11c) • Personal disapproval (40c) • R2 (Nagelkerke) = .582 • Variables that did not contribute appreciably greater predictive power: age, gender, grades, family conflict, parental disapproval, risk perception, availability, age of 1st use Classifies 93.7% of cases