An integrated data analysis approach to investigating measurement equivalence of DSM nicotine dependence symptoms

Research identifying nicotine dependence (ND) symptoms most appropriate for measurement of adolescent ND and invariant across the range of smoking exposure is hampered by limited sample size and variability of smoking behavior within independent studies. Integrative data analysis, the process of pooling and analyzing data from multiple studies, produces larger and more heterogeneous samples with which to evaluate measurement equivalence across the full continuum of smoking quantity and frequency.

Data from two studies were pooled to obtain a large sample of adolescent and young adult smokers with considerable variability in smoking. We used moderated nonlinear factor analysis, which produces study equivalent ND scores, to simultaneously evaluate whether 14 DSM ND symptoms had equivalent psychometric properties (1) at different levels of smoking frequency and (2) across a continuous range of smoking quantity, after accounting for study differences.

Nine of 14 symptoms were equivalent across levels of smoking frequency and quantity in probability of endorsement at different levels of ND and in ability to discriminate between levels of ND severity. A more precise ND factor score accounted for study and smoking related differences in symptom psychometric properties.

DSM-IV symptoms may be used to reliably assess ND in young populations across a wide range of smoking quantity and frequency and within both nationally representative and geographically restricted samples with different study designs. Symptoms shared across studies produced an equivalently scaled ND factor score, demonstrating that integrating data for the purpose of studying ND in young smokers is viable.