Contemporary technological advances have led to a significant increase in using mobile technologies. Recent research has pointed to potential problems as a consequence of mobile overuse, including addiction, financial problems, dangerous use (i.e. whilst driving) and prohibited use (i.e. use in forbidden areas). The aim of this study is to extend previous findings regarding the predictive power of psychopathological symptoms (depression, anxiety and stress), mobile phone use (i.e. calls, SMS, time spent on the phone, as well as the engagement in specific smartphone activities) across Generations X and Y on problematic mobile phone use in a sample of 273 adults. Findings revealed prohibited use and dependence were predicted by calls/day, time on the phone and using social media. Only for dependent mobile phone use (rather than prohibited), stress appeared as significant. Using social media and anxiety significantly predicted belonging to Generation Y, with calls per day predicted belonging to Generation X. This finding suggests Generation Y are more likely to use asynchronous social media-based communication, whereas Generation X engage more in synchronous communication. The findings have implications for prevention and awareness-raising efforts of possibly problematic mobile phone use for educators, parents and individuals, particularly including dependence and prohibited use.

This is a draft of a chapter that has been accepted for publication by Oxford University Press in the forthcoming book Handbook of methodological approaches to community-based research:Qualitative, quantitative, and mixed methods, edited by L. A. Jason and D. S. Glenwick and due for publication in 2016.2 Latent class analysis (LCA) and latent profile analysis (LPA) are powerful techniques that enable researchers to glean insights into "hidden" psychological experiences to create typologies and profiles to provide better-informed community-based policies and practice. These analytic methods have been used in a variety of domains, such as: psychosis symptomatology in the general population (Kibowski & Williams, 2012;Shevlin, Murphy, Dorahy, & Adamson, 2007); substance abuse (Cleveland, Collins, Lanza, Greenberg, & Feinberg, 2010;James, McField, & Montgomery, 2013), peer victimization (Nylund, Bellmore, Nishina, & Graham, 2007), and anti-social/self-defeating behavior (Rosato & Baer, 2010). LCA and LPA are versatile methods of dealing with data of interest to community-based researchers in a deep and psychologically grounded way. This chapter will address the nuances of how and when to use LCA and LPA. Case studies of LCA and LPA will also be presented to illustrate the applicability of these techniques. Introduction to Latent Class AnalysisThe main aim of LCA is to split data that are apparently homogeneous overall into subclasses of two or more different homogeneous groups or classes. Study participant responses to a questionnaire, structured interview, or behavioral checklist would be used as the basis for making probabilistic assessments of the likelihood of each participant being assigned to one of these classes. A participant's likelihood of belonging to any of the other latent classes would also be calculated, and then decisions would be made as to the ultimate class membership that each respondent would assume. The beneficial role that LCA can have is that, once class membership has been assigned to each participant in relation to the pattern of responses or behaviors, this class membership can be used to inform policies and practice-based interventions aimed at targeting a specific latent class that has emerged from the analysis. An example of the 3 potential for this method can be seen in a study of the transportation-related attitudes and experiences of workers (Williams, Murphy, & Hill, 2008). In this study, latent class analysis was deployed to examine the role of multimodality (i.e. using more than one mode of transportation) versus single transport mode use on commuters' psychological well-being.Other community-level analyses have utilized LCA to investigate how to encourage sections of the population to engage more in community-based arts activities (Biggins, Cottee, & Williams, 2012). LCA is also helpful for testing population-wide phenomena and epidemiological trends, such as the potential existence of psychosis symptom experiences being measured along a continuum throughout the general populati...

The dark triad (DT) traits–psychopathy, narcissism and Machiavellianism–have collectively been linked to reduced empathy and increased aggression; however, their association with distinct empathic subtypes remains unclear; and unique links to indirect relational aggression (IRA) have not been delineated. Moreover, whether dark traits should be conceptualized individually, as a dyad or as a triad with a dark core centered around the absence of empathy is debated. The current study examines (i) whether impaired empathy indeed represents a common “dark core” binding Machiavellianism, narcissism, and psychopathy, and (ii) this core explains associations between the dark traits and IRA. Participants (N = 301, 262 F/39 M) completed measures of the DT traits, cognitive and affective empathy components and IRA (Social Exclusion, Malicious Humor and Guilt Induction). The individual traits model without links between narcissism and IRA showed the best fit, suggesting that, at least in the context of IRA, the DT traits are best viewed as three independent personality traits. Distinct cognitive and affective empathy deficits and capacities are seen in the DT. Peripheral responsivity was the only type of empathy deficit associated with all dark traits, but unrelated to IRA. Psychopathy was the strongest indicator of impaired empathy and all IRAs; however, only online simulation, an affect-related cognitive empathy facet, partially mediated the relationships of psychopathy and Machiavellianism with IRA. Whilst the unique pathways for the dark triad traits suggest stronger alignment of psychopathy and Machiavellianism in their empathic deficits and indirect aggression; the data do not support the notion that an unempathic dark core underpinning all three traits drives indirect aggression. This is the first paper delineating the specific empathic deficits involved using a facet approach and their link to indirect forms of aggression. Results therefore inform theoretical models of aggression in the DT and offer some clarity on the debates surrounding the unempathic dark core in the DT.

Memory experts, the police and the public completed a memory questionnaire containing a series of statements about autobiographical memory. The statements covered issues such as the nature of memory, determinants of accuracy and the relation of emotion and trauma to memory, and respondents indicated their agreement/disagreement with each of the statements. The police and public were found to share a ‘common sense’ memory belief system (CSMBS) in which memories were like videos/photographs, and accuracy was determined by the number of details recalled and also by their vividness. In direct contrast, the scientific memory belief system, held by memory researchers, largely based on scientific evidence, was the opposite of the CSMBS and memories were judged to be fragmentary, number of details and their nature did not predict accuracy, and memories and their details could be in error and even false. The problematic nature of the CSMBS, which is pervasive in society, in raising the probability of flawed judgments of memory evidence is considered and, by way of illustration, applied to the (very high) attrition rate in complaints of rape.

Research suggests that maladaptive perfectionism impedes the development of self-2 compassion, a self-attitude with numerous biopsychosocial benefits. The precise relationship between these constructs remains unclear, but accurate modelling could foster an understanding of the barriers that perfectionists experience to self-compassion, enabling focused interventions to be developed. 6 This study used structural equation modelling within a convenience-sampled, general, 7 population (n=428; ̅ age=34.3 yrs, SD=12.1) to analyze how multidimensional perfectionism related to multidimensional self-compassion. The maladaptive perfectionism dimensions 9 (Concern over Mistakes; Discrepancy) predicted lower levels of self-compassion and its positive dimensions (Self-kindness; Common Humanity; Mindfulness). Adaptive perfectionism also predicted higher levels of Self-judgment. 12 Findings were discussed theoretically, and their utility for developing populationtailored, dual-focused, interventions aimed at reducing perfectionism and increasing selfcompassion was explored.