Social Expectancy Increases Skin Conductance Response in Mobile Instant Messaging Users.

BACKGROUND: Society is witnessing two parallel phenomena: an increase in the number of mobile phone users worldwide and a growing concern about problematic smartphone use. Leading explanatory models suggest that social reward may explain some problematic smartphone use. Given that experimental evidence about the impact of social variables on problematic smartphone use is scarce, the impact of social expectancy on emotional arousal measured with skin conductance response (SCR) was analysed during instant messaging. METHOD: A sample of 86 undergraduate students were randomly assigned to two groups. Experimental group participants were instructed to send a social expectation-generating message to their more active contacts in their preferred social network. After experiencing a virtual reality distraction environment, participants' SCR was measured when they were allowed to use the smartphone and when it was withdrawn. RESULTS: Participants in the experimental group showed a higher SCR response than the control group. Peaks analysis also showed that peak amplitude was higher in experimental participants when their smartphones were used and withdrawn. Experimental participants also showed a longer half recovery time when using the smartphone. CONCLUSIONS: Social expectancy is a critical variable in understanding problematic smartphone use and it should be considered in clinical contexts.

Attachment style and prenatal expectations from a Bayesian perspective.

BACKGROUND: The transition to motherhood is sometimes characterized by ambivalent emotions. This paper attempts to explore whether this transition may be triggering attachment insecurities in first-time pregnant women during their first trimester, and thus affecting their prenatal expectations and caregiving representations. METHOD: A sample of 100 first-time pregnant women during their first trimester was studied. Prenatal expectations were explored in terms of desire to have children (DTC), perceived ability to relate to children (PARC), personal meaning ascribed to motherhood, and preoccupations regarding becoming a mother. RESULTS: The estimated Bayesian network shows that attachment style is a predictor for prenatal expectations, either directly or indirectly. Results also suggest that PARC mediates the relationship between attachment and the preoccupations related to motherhood. Pregnant women with an insecure attachment style reported lower PARC and higher preoccupations about becoming a mother. CONCLUSION: Pregnant women with secure attachment have more positive pre-parenthood expectations and parenting representations than those with insecure attachment. These results may be useful in clinical settings to help women have a smoother transition to motherhood.

Deciding on Null Hypotheses using P-values or Bayesian alternatives: A simulation study.

BACKGROUND: The p-value is currently one of the key elements for testing statistical hypothesis despite its critics. Bayesian statistics and Bayes Factors have been proposed as alternatives to improve the scientific decision making when testing a hypothesis. This study compares the performance of two Bayes Factor estimations (the BIC-based Bayes Factor and the Vovk-Sellke p-value calibration) with the p-value when the null hypothesis holds. METHOD: A million pairs of independent data sets were simulated. All simulated data came from a normal population and different sample sizes were considered. Exact p-values for comparing sample means were recorded for each sample pair as well as Bayesian alternatives. RESULTS: Bayes factors exhibit better performance than the p-value, favouring the null hypothesis over the alternative. The BIC-based Bayes Factor is more accurate than the p-value calibration under the simulation conditions and this behaviour improves as the sample size grows. CONCLUSIONS: Our results show that Bayesian factors are good complements for testing a hypothesis. The use of the Bayesian alternatives we have tested could help researchers avoid claiming false statistical discoveries. We suggest using classical and Bayesian statistics together instead of rejecting either of them.