C Appendix C: Improving Data

Between-Person Correlations: Results of a Random-Intercept Cross Lagged Panel Model Specification Curve Analysis relating social media use and life satisfaction. Each point on the x-axis represents a different combination of analytical decisions (i.e. life satisfaction domain, gender, number of waves, estimator, data imputation and control variables). The dashboard shows all the analytical decisions.

Figure C.1: Between-Person Correlations: Results of a Random-Intercept Cross Lagged Panel Model Specification Curve Analysis relating social media use and life satisfaction. Each point on the x-axis represents a different combination of analytical decisions (i.e. life satisfaction domain, gender, number of waves, estimator, data imputation and control variables). The dashboard shows all the analytical decisions.

Within-Person Effect of Social Media Use predicting Well-Being: Results of a Random-Intercept Cross Lagged Panel Model Specification Curve Analysis relating social media use and life satisfaction. Each point on the x-axis represents a different combination of analytical decisions (i.e. life satisfaction domain, gender, number of waves, estimator, data imputation and control variables). The dashboard shows all the analytical decisions.

Figure C.2: Within-Person Effect of Social Media Use predicting Well-Being: Results of a Random-Intercept Cross Lagged Panel Model Specification Curve Analysis relating social media use and life satisfaction. Each point on the x-axis represents a different combination of analytical decisions (i.e. life satisfaction domain, gender, number of waves, estimator, data imputation and control variables). The dashboard shows all the analytical decisions.

Within-Person Effect of Well-Being predicting Social Media Use: Results of a Random-Intercept Cross Lagged Panel Model Specification Curve Analysis relating social media use and life satisfaction. Each point on the x-axis represents a different combination of analytical decisions (i.e. life satisfaction domain, gender, number of waves, estimator, data imputation and control variables). The dashboard shows all the analytical decisions.

Figure C.3: Within-Person Effect of Well-Being predicting Social Media Use: Results of a Random-Intercept Cross Lagged Panel Model Specification Curve Analysis relating social media use and life satisfaction. Each point on the x-axis represents a different combination of analytical decisions (i.e. life satisfaction domain, gender, number of waves, estimator, data imputation and control variables). The dashboard shows all the analytical decisions.

Number of observations (participants) for each specification analysed in the Random Intercept Cross Lagged Panel Model. This graph shows the between-person correlation. Red dots indicate when the specification was non-significant, while black dots show significant specifications.

Figure C.4: Number of observations (participants) for each specification analysed in the Random Intercept Cross Lagged Panel Model. This graph shows the between-person correlation. Red dots indicate when the specification was non-significant, while black dots show significant specifications.

Number of observations (participants) for each specification analysed in the Random Intercept Cross Lagged Panel Model. This graph shows the within-person effect of social media use predicting well-being. Red dots indicate when the specification was non-significant, while black dots show significant specifications.

Figure C.5: Number of observations (participants) for each specification analysed in the Random Intercept Cross Lagged Panel Model. This graph shows the within-person effect of social media use predicting well-being. Red dots indicate when the specification was non-significant, while black dots show significant specifications.

Number of observations (participants) for each specification analysed in the Random Intercept Cross Lagged Panel Model. This graph shows the within-person effect of well-being predicting social media use. Red dots indicate when the specification was non-significant, while black dots show significant specifications.

Figure C.6: Number of observations (participants) for each specification analysed in the Random Intercept Cross Lagged Panel Model. This graph shows the within-person effect of well-being predicting social media use. Red dots indicate when the specification was non-significant, while black dots show significant specifications.