Subjects: Psychology >> Developmental Psychology Subjects: Psychology >> Other Disciplines of Psychology submitted time 2024-01-20
Shunsen Huang
Xiaoxiong Lai
Cai Zhang
Xinmei Zhao
Xinran Dai
Mengdi Qi
Huanlei Wang
Wenrong Wang
Yun Wang
Abstract： Adolescents frequently encounter elevated levels of digital stress by exposure to digital media (e.g., smartphone stress). Their ongoing brain development increases adolescents’ susceptibility to digital stress, making them more vulnerable to its adverse effects. Among digital devices, smartphones are the most widely used ones by adolescents and a primary source of digital stress. The current study aims to investigate the robust association between digital stress, specifically smartphone stress, and adolescent mental health. The study also aims to investigate the underlying mechanisms of this association.
In Study 1, a multiverse-style analysis was employed to investigate the robust relationship between smartphone stress and mental health (depression and well-being) in a large sample of adolescents (N=74,178, male=39,129). This method was chosen for its robustness of various data manipulations to test the effect of interest, and median β and NSRPD (number of significant results in predominant direction) were used as statistical inference indicators of the effect. In Study 2, we conducted an intensive longitudinal design to examine the mechanism of how smartphone stress affects mental health among adolescents (N=477, female=214, Mage=12.67±.31). Before intensive longitudinal design, we assessed smartphone stress, well-being, and depression (T1). Subsequently, daily rumination (consecutive 17 days, T2) and daily negative mood (consecutive 18 days, T3) were assessed over a 35-day period. Upon intensive longitudinal design, we once again measured well-being and depression (T4). We found that rumination, negative emotion (NE), and rumination-NE (serial mediation) mediate the link between smartphone stress and mental health (smartphone stress-depression model, smartphone stress-well-being model).
Study 1 indicated that over half of adolescents (52.6% of grade 4 students and 78.2% of grade 8 students) experienced smartphone stress. Furthermore, smartphone stress strongly and robustly predicted depression (Median β = 0.37, p < 0.001, NSRPD = 160/160, p < 0.001, partial r2 = 0.172) and well-being (Median β = -0.14, p < 0.001, NSRPD = 160/160, p < 0.001, partial r2 = 0.011). Effect sizes from both outcomes (partial r2 > .010) are capable to inform policy and the public sphere. Study 2 revealed that rumination intensity, negative emotion intensity, and rumination-negative emotion intensity mediate the relationship between smartphone stress and depression. However, no mediation was found for rumination or negative emotion fluctuation. In smartphone stress-well-being model, negative emotion intensity and rumination-negative emotion intensity, but not rumination intensity, mediated the association between smartphone stress and well-being. Moreover, negative emotion and rumination-negative emotion fluctuation, but not rumination fluctuation, mediated the association between smartphone stress and well-being. Therefore, the intensity and fluctuation of rumination and negative emotion are common mediators in the relationship between smartphone stress and depression/well-being, while the effects of mechanisms are outcome-dependent.
The findings pinpoint the significant and robust effect of smartphone stress on depression and well-being among adolescents. The mediation of rumination and negative emotion in the relationship between smartphone stress and mental health probes into the mechanism of this relationship. These results support classic theories (e.g., the Emotional Cascade Model) and confirm and enrich the recent Media use-Digital stress-Mental health model. These findings could also inform future interventions for mental health problems related to smartphone stress.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Multiverse-style analysis (e.g., the vibration of effects, multimodel analysis, multiverse analysis, specification curve analysis) proposes to report combinations of multiple analysis strategies during data analysis and to test the robustness of effects between relevant variables in all analytic strategies. The basic principles and applications of the multiverse-style analysis are described, and the operational steps are presented as an example of the relationship between smartphone use and smartphone stress. The strengths and limitations of the method are discussed, as well as future directions. Searching the Web of Science on the topic of multiverse-style analysis (e.g., the vibration of effects), we found that the number of papers rose from 9 in 2015 to 40 in 2021. Multiverse-style analysis is gradually being applied in psychology, behavioral sciences, neuroscience, psychiatry, and other fields. Most of these studies used self-reported data. Some neuroscience and biology-related studies used objective data (e.g. physiological indicators such as brain imaging data). Few studies combined self-reported and objective data. Most studies used cross-sectional designs. A few studies used longitudinal or cohort designs. In addition, multiverse-style analysis is gradually being combined with other psychological methods. For example, some researchers have combined it with mediation analysis to determine the robustness of mechanisms among variables. It has also been used with network analysis to reduce the instability of network centrality. People have combined multiverse-style analysis with meta-analysis to form the “combinational meta-analysis”. Finally, different studies have different preferences in the choice of combinations of analytic strategies. For example, some focus on different measurement approaches (self-report or objective measures), while others focus on different estimation methods or concentrate on the diversity of the datasets. In combining multiverse-style analysis with other methods, researchers usually emphasize the strengths of multiverse-style analysis to compensate for the weaknesses of other methods. Advantages of multiverse-style analysis: (1) It can include multiple data sets, multiple measurement methods and estimation methods, and then perform effect tests. (2) Multiverse-style analysis can be used to resolve controversial issues. Multiverse-style analysis not only has the advantages of meta-analysis but also can be applied to emerging areas where empirical studies are scarce and meta-analysis is not appropriate. Limitations of multiverse-style analysis: (1) As the combination of analysis strategies and sample size increases, it becomes more time consuming to make statistical inferences. (2) The method is still essentially a subjective selection process by the researchers. As such, there may be a potential risk of leading to the problem of "truly arbitrariness". (3) The statistical inference indicators of multiverse-style analysis are not stable. Conflicting results between different statistical indicators may arise. (4) It is difficult for the researcher to report all possible combinations of analytical strategies for an effect based on the available dataset. It is necessary to select the appropriate combination of analytical strategies and build an appropriate dataset based on the available theory and evidence before data analysis. Future directions: (1) Most existing studies demonstrate the robustness of interesting effects by simply describing all outcomes. Future applied research should consider implementing statistical inference. (2) Deepening the integration of multiverse-style analysis with other research methods, e.g. developing different criteria when integrating multiverse-style analysis with different methods. (3) Select stable statistical inference indicators, give more consideration to parameter estimation (e.g., BIC, AIC) and model estimation methods (e.g., Bayes, Monte-Carlo) when constructing combinations of analytic strategies, and include statistical inference in analytical software or software packages. (4) Combining multiple channels to jointly address the reproducibility crisis (e.g. future research could incorporate multiverse-style analysis during data analysis and pre-registration before data collection). (5) Hold a critical sight towards the different outcomes of different combinations of analytical strategies. There may not be a single standard law in the field of human psychology and behavior, which is influenced by multiple factors (e.g. genes, groups, environment, culture, etc.).
Subjects: Psychology >> Statistics in Psychology submitted time 2022-07-09
Abstract：
Selective analysis and selective report are one of the main triggers of the replicability crisis in psychological science. In recent years, researchers have proposed a new method—multiverse-style analysis, which includes multiple data analytic decisions to reduce the subjective selectiveness and arbitrariness and performs robustness to increase the reliability of results. This manuscript introduces the multiverse-style analysis and related steps by using the example of exploring the relationship between smartphone use and smartphone stress. The multiverse-style analysis method has been applied in fields such as psychology and cognitive neuroscience. Future research should continue to develop and improve the statistic inference of multiverse-style analysis, so that it can be applied to more sorts of data and broader research fields.
Peer Review Status:
Awaiting Review
Hits
YES
Hits
Downloads
Comment