Analyses overview
We examined the relationship between Facebook use and affect using multilevel analyses to account for the nested data structure. Specifically, we examined whether T2 affect (i.e., How do you feel right now?) was predicted by T1–2 Facebook use (i.e., How much have you used Facebook since the last time we asked?), controlling for T1 affect at level-1 of the model (between-day lags were excluded). Note that although this analysis assesses Facebook use at T2, the question refers to usage between T1 and T2 (hence the notationT1–2). This analysis allowed us to explore whether Facebook use during the time period separating T1 and T2 predicted changes in affect over this time span.
When non-compliant cases were observed, we used participants' responses to the last text message they answered to examine the lagged effect of Facebook use on well-being to maximize power. So, if we were interested in examining whether T2–3 Facebook use predicted T3 Affect controlling for T2 Affect, but did not have data on T2 Affect, then we used T1 Affect instead. Excluding trials in which participants did not respond to the previous texts (rather than following the aforementioned analytical scheme) did not substantively alter any of the results we report.
Significance testing of fixed effects was performed using chi-squared distributed (df = 1) Wald-tests. All level-1 predictors were group-mean centered,