Table 3 |
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|
Results of linear regression predicting alcohol consumption in high school a (n = 1,100). |
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| Bivariate Models |
Multivariate Modelc |
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| b |
SE |
t(df) |
sr2 |
p |
b |
SE |
t(df) |
sr2 |
p |
||
|
|
|||||||||||
| Parental Monitoring Score |
-.13 |
.01 |
-10.49 (1,194) |
.08 |
<.0001 |
-.12 |
.01 |
-9.26 (1,092) |
.07 |
<.0001 |
|
| Sex [Reference = Female] |
.97 |
.16 |
6.14 (1,249) |
.03 |
<.0001 |
.69 |
.16 |
4.32 (1,092) |
.01 |
<.0001 |
|
| Race [Reference = Non-White] |
1.37 |
.17 |
7.91 (1,246) |
.05 |
<.0001 |
1.26 |
.18 |
7.02 (1,092) |
.04 |
<.0001 |
|
| Religiosityb [Reference = Slightly/Not Important] |
-.56 |
.16 |
-3.46 (1,243) |
.01 |
.0006 |
-.10 |
.16 |
-.59 (1,092) |
<.01 |
.56 |
|
| R2 |
.14 |
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| F (df, df) p |
24.82 (7, 1,092) p < .0001 |
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|
|
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|
Effects were evaluated using the null hypothesis test of b = 0 (tested as: b/SE) which evaluates the unique contribution of a variable in a regression equation. a High school alcohol consumption was defined as the typical number of drinks per drinking day during the past year at the screener. b Religiosity was dichotomized into a binary variable (i.e., extremely/moderately vs. slightly/not). c As a proxy for socioeconomic status, the effect of mother's education was held constant in the multivariate model. Effect size (sr2) for each explanatory variable was as follows: parental monitoring score (.07), sex (.01), race (.04), religiosity (<.01), mother's education (<.01). |
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|
Arria et al. Substance Abuse Treatment, Prevention, and Policy 2008 3:6 doi:10.1186/1747-597X-3-6 |
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