Table 2.

Effects of symptom knowledge and detection behavior on the outcome measures

Predictor/outcome measuresSymptom knowledge
Detection behavior
Knowledge* Detection
BSEPBSEPBSEP
Manipulation checks recognition0.240.030.0000.100.030.0010.110.030.000
    If passive0.130.040.001
    If less known−0.010.040.850
    If active0.350.040.000
    If well known0.220.040.000
Verifiability−0.040.020.0980.120.020.000−0.030.020.171
Perceived threat0.160.020.000−0.130.030.0000.100.020.000
    If passive0.070.030.011
    If less known−0.230.030.000
    If active0.260.030.000
    If well known−0.040.030.197
Adaptive coping0.100.010.000−0.040.020.0260.040.020.020
    If passive0.060.020.001
    If less known−0.070.020.001
    If active0.130.020.000
    If well known−0.000.020.891
Maladaptive coping−0.100.020.0000.070.020.001−0.010.020.407
  • NOTE: This table displays the nonstandardized regression coefficient (B), SE, and P. Because our design is balanced and both within-subject factors are coded (−1, 1) instead of (0, 1), the interaction term symptom knowledge × detection behavior is orthogonal to the main effects. Consequently, the main effects are interpretable and testable even in the presence of a nonsignificant interaction term in the model (see first row for each outcome measure). In cases where a significant interaction of symptom knowledge and detection behavior on one of the outcome measures was found, simple effects of one factor are reported for each level of the other factor. This means that the second row for that outcome measure indicates the simple effect of symptom knowledge for passively detected symptoms; the third row, the simple effect of detection behavior for less-known symptoms; the fourth row, the simple effect of symptom knowledge for actively detected symptoms; and the fifth row, the simple effect of detection behavior for well-known symptoms. Note that each main effect equals 2B, not B, again because of the (−1, 1) coding. The test statistic (Z) per effect is equal to B / SE and the confidence interval for each effect is equal to B ± c × SE, where c = 1.96 if α = 0.05 and c = 2.58 if α = 0.01.