Rijeka Panel ------------ Our analyses of cross-lagged models featuring pornography use and indicators of psychological wellbeing among adolescents in the Zagreb indicated significant paths between wellbeing at T3 and pornography use at T5 in both men and women. However, controlling for trait impulsiveness appears to have entirely accounted for the effect among males. On the basis of this evidence it does not appear that pornography use is middle to late adolescence is associated with changes in psychological wellbeing, but it is possible that low wellbeing is associated with increases in subsequent pornography use, particularly for women. These results are inconsistent with assumed harms of pornography use among adolescents, but are consistent with one previous finding that indicated that lower life satisfaction was related to increases in subsequent pornography use (Peter & Valkenburg, 2011). Consequently, we expect directional effects to emerge between earlier wellbeing and later pornography use in similar cross-lagged analyses of the Rijeka panel data (H1). We further expect that controlling for trait level impulsiveness will reduce this direct effect in men but not women. In addition to these direct effects, we also found that pornography use at T5 was associated with wellbeing at T5 in males, r = -.27, p < .05, and pornography use at T4 was marginally associated with depression and anxiety at T4 among females, r = 0.11, p < .06. With these results in mind, it seems reasonable to expect that similar cross-sectional correlations may emerge in the Rijeka data (H2). As both of these associations emerged after controlling for preceding levels of pornography use and psychological wellbeing, it is unlikely that these cross-sectional correlations represent causal relationships between pornography use and psychological wellbeing. One alternative possibility is that these associations represent spurious correlations that emerged due to shared variance with an unobserved “third-variable.” We tested this in our exploratory study with respect to the link between pornography use and subjective wellbeing (Model 3), and found that controlling for impulsivity further reduced the marginal effect (from r = -.27, p < .05 to r = -.25 p < .06). Consequently, if cross-sectional correlations emerge, we will further scrutinize the models by controlling for impulsiveness. **Method** Adolescents in their 2nd year of secondary school were recruited from 14 out of 22 schools in Rijeka and Opatija in Croatia for a 6-wave panel study launched in 2015. Of the 1,675 2nd year students who were registered at the time, N = 1,291 were recruited into the first wave. Waves were separated by approximately 6 months and at the time of this registration, 5 waves of data have been collected. More information about the panel, including a list of full measures for this study can be found here: [https://osf.io/hf4k2/.][1] The following measures were used in this study, all of which were presented to participants in Croatian: **Pornography use.** The frequency of pornography use was assessed at each wave with the item, “How often have you used pornography during the last 6 months?” Scale response options included: 1 – “Not once”; 2 – “Several times”; 3 – “Once a month”; 4 – “2-3 times a month”; 5 – “Once a week”; 6 – “Several times a week”; 7 – “Every day or almost every day”; 8 – “Several times a day. ” Stability coefficients for the indicator were .74 and .85 (p < .001). In the questionnaire, pornography was defined for participants as “any material which openly (i.e., in an uncensored manner) depicts sexual activity; material which shows naked bodies but not sexual intercourse or other sexual activity does not belong to pornography as here defined.” **Depression and Anxiety.** Kroenke et al.’s (2009) 4-item measure of Depression and Anxiety was administered at all 5 waves. Following the stem “During the last two weeks, how often have you experienced...” participants responded to items such as “Feeling down, depressed, or hopeless” and “Feeling nervous, anxious or on edge” with 4-point scales that ranged from “Not at all” (1) to “Nearly every day” (4). Cronbach’s α coefficients for this measure ranged from .83 to .86. Stability coefficients of this state measure of depression and anxiety were in the r = .50 to r = .64 (p < .001) range. **Self-Esteem.** Self-esteem was measured with Marsh et al.’s (2014) 4-item inventory at all 5 waves. Participants were asked to “Estimate how the following statements relate to you” using responses that ranged from “It does not relate to me at all” (1) to “It relates to me completely” (5). Example items included “In general, I like myself the way I am” and “When I do something, I do it well.” Cronbach’s α coefficients for the scale ranged from .81 to .84, while stability coefficients ranged from r = .59 to r = .74 (p < .001). **Subjective Wellbeing.** Subjective Wellbeing was measured with an adapted 4-item version of the Personal Wellbeing Inventory – School Children (PWI-SC; Tomyn & Cummins, 2011) at waves T2 and T4. Participants were asked to indicate how satisfied they were with various facets of their life, such as their health, and what they had achieved so far. Responses were collected with 10-point scales that ranged from “Completely Unsatisfied” (1) to “Completely Satisfied” (10). Reliability of this measure was satisfactory (Cronbach’s α = .81 and .84), as was its stability across time (r = .66, p < .001). **Impulsiveness.** Impulsiveness was measured with an adapted 8-item version of the Barratt Impulsiveness Scale-Brief (Steinberg et al, 2013) at T2 and T3. Example items included “I do things without thinking” and “I am future oriented.” Responses were collected on 4-point scales ranging from 1 – “Never / Rarely” to 4 “Almost Always / Always”. For structural equation modeling, the items will be randomly paired in parcels. Reliability of this measure was satisfactory (Cronbach’s α = .75 and .77), as was its stability across time (r = .68, p < .001). **Analytic Plan** As with the Zagreb panel, 2 separate multi-group (male and female adolescent groups) cross-lagged path analytic models will be carried out. Model 1 will involve pornography use, self-esteem and depression and anxiety across all five time points (T1-T5) and will be limited to participants who provide data in at least 3 waves. Model 2 will involve pornography use and wellbeing across two time points (T2 & T4) and will be limited to participants who provide data at both waves. Subsequent models with impulsiveness added as a potential confounder will be considered if significant effects emerge in Models 1 or 2. Data analysis will be restricted to participants who provide data on at least three measurement occasions. In assessing the path analytic models, χ2/df ratio ≤ 2, comparative fit index (CFI) values ≥ .95 and root mean square error of approximation (RMSEA) values ≤ .05 (with the upper 90% CI value ≤ .08) will be used as indicators of good model fit to the data (Byrne, 2013). Time invariance by gender will be tested in progressively more restrictive steps (from configural to strong factorial invariance; cf. Little, 2013). Taking into account the large sample size, particularly in the case of female adolescents, the ∆CFI test will be used for comparing more and less constrained models—with values ≤ .002 indicating a non-significant model difference (Meade et al., 2008). Following the analysis of missing data patterns, full information maximum likelihood estimation will be used to handle missing values. [1]: https://osf.io/hf4k2/.