Baron and Kenny's (1986) causal steps approach has been widely used by researchers in mediation analyses. Yet, recently, some researchers have begun to argue that Baron and Kenny's approach is not an appropriate method for mediation analysis, and that contemporary or new methods that are based on bootstrapping would yield more valid and reliable results in mediation analysis. The aim of the current study was to discuss basic assumptions between the causal steps approach and new approach in mediation analysis and show statistical differences between the two approaches by using real data set. As a result, it can be stated that the validity of the causal steps approach, which has been used extensively in the analysis of mediation models until today, is controversial, and the use of a new approach based on the bootstrap technique in psychology and behavior research may bring more valid results.
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Mediators generally find mediation of hierarchical workplace conflicts difficult, as it often involves structural power imbalances. This dissertation seeks to increase knowledge of how hierarchical conflict affects how parties and mediators perceive mediation across dyads and across time. Three questions are central to this: (a) How effective in the long-term is the mediation of hierarchical workplace conflicts? (b) How does perceived situational power in supervisor-subordinate dyads relate to mediation effectiveness? (c) Do supervisors and subordinates differ in their emotional experiences during mediation, and are mediators able to perceive these emotions accurately? To answer these questions, we rely on the literature on power, emotions, mediation, and conflict management. We introduce our research via a heuristic model (chapter one). We then present our quantitative empirical research in three chapters based on survey data we collected from supervisors, subordinates, and
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Introduction: Illness Perceptions (IPs) may play a role in the management of persistent low back pain. The mediation and/or moderation effect of IPs on primary outcomes in physiotherapy treatment is unknown. Methods: A multiple single-case experimental design, using a matched care physiotherapy intervention, with three phases (phases A-B-A’) was used including a 3 month follow up (phase A’). Primary outcomes: pain intensity, physical functioning and pain interference in daily life. Analyzes: linear mixed models, adjusted for fear of movement, catastrophizing, avoidance, sombreness and sleep. Results: Nine patients were included by six different primary care physiotherapists. Repeated measures on 196 data points showed that IPs Consequences, Personal control, Identity, Concern and Emotional response had a mediation effect on all three primary outcomes. The IP Personal control acted as a moderator for all primary outcomes, with clinically relevant improvements at 3 month follow up. Conclusion: Our study might indicate that some IPs have a mediating or a moderating effect on the outcome of a matched care physiotherapy treatment. Assessing Personal control at baseline, as a relevant moderator for the outcome prognosis of successful physiotherapy management of persistent low back pain, should be further eplored.
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Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
