Problematic smartphone use and relations with negative affect, fear of missing out, and fear of negative and positive evaluation.
TL;DR: Results demonstrated that FoMO was most strongly related to both problematic smartphone use and social smartphone use relative to negative affect and fears of negative and positive evaluation, and these relations held when controlling for age and gender.
Abstract: For many individuals, excessive smartphone use interferes with everyday life In the present study, we recruited a non-clinical sample of 296 participants for a cross-sectional survey of problematic smartphone use, social and non-social smartphone use, and psychopathology-related constructs including negative affect, fear of negative and positive evaluation, and fear of missing out (FoMO) Results demonstrated that FoMO was most strongly related to both problematic smartphone use and social smartphone use relative to negative affect and fears of negative and positive evaluation, and these relations held when controlling for age and gender Furthermore, FoMO (cross-sectionally) mediated relations between both fear of negative and positive evaluation with both problematic and social smartphone use Theoretical implications are considered with regard to developing problematic smartphone use
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TL;DR: The study findings suggest that compulsive media use significantly triggered social media fatigue, which later result in elevated anxiety and depression.
439 citations
01 Oct 2019-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: It is concluded that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors and conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.
Abstract: Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite a growing body of resources that can quantify smartphone use, research within psychology and social science overwhelmingly relies on self-reported assessments. These have yet to convincingly demonstrate an ability to predict objective behavior. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviors derived from Apple's Screen Time application. While correlations between psychometric scales and objective behavior are generally poor, single estimates and measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favorably with subsequent behavior. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviors. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.
187 citations
TL;DR: Investigation of self-reported levels of PSU, depression, anxiety, and daily depressive mood relate to objectively measured smartphone use over one week found depression and anxiety severity were not related to screen time minutes, but negatively correlated with frequency of phone screen unlocking.
178 citations
TL;DR: Negative affectivity may be a key mechanism by which FOMO may drive PSU, but future research should clarify the directionality among these variables.
170 citations
TL;DR: The literature studying relations between problematic smartphone use (PSU) and anxiety symptom severity is examined, and an own theoretical model of how PSU is specifically related to anxiety is presented.
164 citations
References
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TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...
76,383 citations
TL;DR: Two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS) are developed and are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period.
Abstract: In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented.
34,482 citations
Book•
17 Jan 2008TL;DR: In this paper, the authors introduce the statistical, methodological, and conceptual aspects of mediation analysis applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout Singlemediator, multilevel, and longitudinal models are reviewed.
Abstract: This volume introduces the statistical, methodological, and conceptual aspects of mediation analysis Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout Single-mediator, multilevel, and longitudinal models are reviewed The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations
Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions) The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, MPLUS, and CALIS, and a program to simulate the model The notation used is consistent with existing literature on mediation in psychology
The book opens with a review of the types of research questions the mediation model addresses Part II describes the estimation of mediation effects including assumptions, statistical tests, and the construction of confidence limits Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions
Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology Some exposure to a graduate level research methods or statistics course is assumed The overview of mediation analysis and the guidelines for conducting a mediation analysis will be appreciated by all readers
4,473 citations
3,216 citations
"Problematic smartphone use and rela..." refers background in this paper
...In contrast, fear of negative evaluation involves apprehension that others will negatively evaluative oneself, and associated distress (see Watson and Friend, 1969)....
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TL;DR: The present research presents three studies conducted to advance an empirically based understanding of the fear of missing out phenomenon, the Fear of Missing Out scale (FoMOs), which is the first to operationalize the construct.
1,598 citations
"Problematic smartphone use and rela..." refers background in this paper
...FoMO is a pervasive apprehension that others might be having rewarding experiences from which one is absent (Przybylski et al., 2013)....
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...Furthermore, Przybylski et al. (2013) and Alt (2015) found that FoMO related to increased social media use....
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...Additionally, FoMO was associated with increased social smartphone use in college and community participants (Przybylski et al., 2013; Alt, 2015)....
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