Table 5.
Effects of sociodemographic predictors on transitions across W1-W4.
| Nonusers as reference a | Class membership at time t+1 | ||||||
| Class membership at time t | ECIG | SLT | CIG | ||||
| Beta | SE | Beta | SE | Beta | SE | ||
|
| |||||||
| Age | Nonusers | −0.11 b | 0.05 | 0.02 | 0.10 | 0.09 | 0.06 |
| ECIG | 0.39 c | 0.17 | 0.07 | 0.28 | 0.17 | 0.17 | |
| SLT | −0.42 | 0.31 | −0.16 | 0.25 | 0.45 | 0.39 | |
| CIG | −0.04 | 0.20 | −0.62 | 0.23 | 0.08 | 0.18 | |
| Gender (ref=male) | Nonusers | −0.02 | 0.08 | −1.22 | 0.26 | 0.37 | 0.11 |
| ECIG | 0.34 | 0.34 | −10.60 | 0.43 | −0.20 | 0.39 | |
| SLT | 10.25 | 0.84 | 0.73 | 0.49 | −0.06 | 0.84 | |
| CIG | −0.24 | 0.50 | 0.08 | 1.28 | −0.26 | 0.34 | |
| Parent education | Nonusers | 0.08 | 0.05 | −0.09 | 0.10 | −0.27 | 0.06 |
| ECIG | 0.13 | 0.19 | 0.42 | 0.36 | −0.01 | 0.30 | |
| SLT | 1.25 | 0.33 | −0.06 | 0.33 | 0.34 | 0.54 | |
| CIG | 0.29 | 0.26 | 0.81 | 0.21 | 0.23 | 0.23 | |
| Black (ref=White) | Nonusers | −0.25 | 0.16 | −0.51 | 0.40 | −0.15 | 0.19 |
| ECIG | −0.97 | 0.61 | −10.04 | 0.58 | −1.32 | 0.82 | |
| SLT | 2.00 | 1.65 | −1.42 | 0.80 | −9.64 | 0.77 | |
| CIG | −0.84 | 1.11 | 2.17 | 1.53 | −0.05 | 0.54 | |
| Hispanic (ref=White) | Nonusers | 0.05 | 0.09 | −0.63 | 0.28 | 0.11 | 0.12 |
| ECIG | −0.31 | 0.40 | −1.00 | 1.05 | 0.75 | 0.51 | |
| SLT | 2.13 | 1.68 | 0.49 | 0.76 | 1.01 | 0.96 | |
| CIG | −1.28 | 0.68 | −8.91 | 0.73 | −0.78 | 0.36 | |
| Other (ref=White) | Nonusers | 0.00 | 0.11 | −0.71 | 0.35 | −0.08 | 0.15 |
| ECIG | −0.61 | 0.51 | −0.46 | 1.09 | −0.50 | 0.57 | |
| SLT | −7.60 | 1.66 | 1.44 | 1.10 | 1.64 | 1.32 | |
| CIG | 0.03 | 0.69 | 2.17 | 1.50 | 0.15 | 0.43 | |
|
| |||||||
| ECIG users as reference d | Class membership at time t+1 | ||||||
| Class membership at time t | NON | SLT | CIG | ||||
| B | SE | B | SE | B | SE | ||
|
| |||||||
| Age | ECIG | −0.35 | 0.16 | −0.14 | 0.32 | 0.01 | 0.19 |
| Nonusers | 0.44 | 0.17 | 0.28 | 0.34 | 0.20 | 0.21 | |
| SLT | 0.88 | 0.35 | 0.54 | 0.41 | 1.07 | 0.43 | |
| CIG | 0.50 | 0.25 | −0.31 | 0.39 | 0.31 | 0.27 | |
| Gender (ref=male) | ECIG | −0.41 | 0.34 | −12.11 | 0.40 | −0.27 | 0.44 |
| Nonusers | 0.40 | 0.36 | 10.91 | 0.48 | 0.67 | 0.46 | |
| SLT | −10.07 | 0.93 | 1.15 | 0.89 | −9.90 | 1.09 | |
| CIG | 0.67 | 0.59 | 11.23 | 1.36 | 0.64 | 0.68 | |
| Parent education | ECIG | −0.24 | 0.20 | −0.20 | 0.40 | −0.45 | 0.34 |
| Nonusers | 0.18 | 0.21 | 0.02 | 0.41 | 0.10 | 0.35 | |
| SLT | −1.09 | 0.38 | −1.28 | 0.45 | −0.81 | 0.61 | |
| CIG | −0.14 | 0.32 | 0.54 | 0.47 | 0.03 | 0.42 | |
| Black (ref=White) | ECIG | 1.03 | 0.61 | −8.11 | 0.60 | −0.17 | 0.90 |
| Nonusers | −0.79 | 0.63 | 7.85 | 0.70 | 0.24 | 0.94 | |
| SLT | −2.78 | 1.81 | 4.44 | 1.71 | −11.70 | 1.71 | |
| CIG | 0.05 | 1.25 | 10.85 | 1.85 | 1.06 | 1.40 | |
| Hispanic (ref=White) | ECIG | 0.29 | 0.39 | −1.19 | 1.05 | −0.32 | 0.54 |
| Nonusers | −0.35 | 0.41 | 0.51 | 1.09 | 0.37 | 0.56 | |
| SLT | −2.47 | 1.75 | −1.13 | 1.93 | −0.75 | 1.82 | |
| CIG | 0.94 | 0.79 | −8.28 | 1.39 | 0.87 | 0.86 | |
| Other (ref=White) | ECIG | 0.67 | 0.50 | −0.42 | 1.11 | 0.08 | 0.66 |
| Nonusers | −0.68 | 0.52 | −0.29 | 1.17 | −0.15 | 0.68 | |
| SLT | 6.14 | 1.74 | 7.96 | 1.83 | 8.29 | 1.69 | |
| CIG | −0.70 | 0.86 | 1.85 | 1.88 | −0.04 | 0.92 | |
Betas (SEs) shaded in gray met the transition probability cutoff (>.10 across all waves), and thus are discussed in the text.
Betas for Nonusers at time t represent the effects of the predictor on transitioning to ECIG, SLT, or CIG at time t+1 (compared to remaining Nonusers) among those who were Nonusers at time t. Betas for ECIG, SLT, and CIG at time t represent the differences in the effects of the predictor on transitioning to ECIG, SLT, or CIG at time t+1 (compared to transitioning to Nonusers) among those who were ECIG, SLT, or CIG at time t.
As an example, this entry is interpreted as follows: older Nonusers (compared to younger Nonusers) were less likely to transition to ECIG use than to stay a nonuser; that is, for every one-unit increase in age, the log-odds of transitioning from Nonusers to ECIG (compared to remaining Nonusers) changed by −0.11.
As an example, this entry is interpreted as follows: compared to Nonusers, older ECIG users (compared to younger ECIG users) were more likely to continue ECIG use than to transition to nonuse; that is, for every one-unit increase in age, the difference in the log-odds of remaining ECIG (compared to transitioning from ECIG to Nonusers) changed by 0.39. This means that, for every one-unit increase in age, the log-odds of remaining ECIG (compared to transitioning from ECIG to Nonusers) changed by −0.11 + 0.39 = 0.28.
Betas for ECIG at time t represent the effects of the predictor on transitioning to Nonusers, SLT, or CIG at time t+1 (compared to remaining ECIG) among those who were ECIG at time t. Betas for Nonusers, SLT, and CIG at time t represent the differences in the effects of the predictor on transitioning to Nonusers, SLT, or CIG at time t+1 (compared to remaining ECIG) among those who were Nonusers, SLT, or CIG at time t.
Bolded values significant at p < .05.
Polytobacco use classes excluded from these analyses due to variable interpretations across waves and notably small sample sizes (<4% at most waves).