Matching Items (25)

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Associations between dyadic coping and interaction quality: the mediating effect of couples' language use during real-time conversations

Description

Stress in romantic relationships is an all-too-common phenomenon that has detrimental effects on relationship well-being. Specifically, stress can increase partners’ negative interactions, ultimately decreasing effective communication and overall relationship functioning.

Stress in romantic relationships is an all-too-common phenomenon that has detrimental effects on relationship well-being. Specifically, stress can increase partners’ negative interactions, ultimately decreasing effective communication and overall relationship functioning. Positive dyadic coping (DC) occurs when one partner assists the other in coping with stress (e.g. empathizing or helping the partner problem-solve solutions to their stress), and has been proposed as a method of buffering the deleterious effect of stress on interaction quality. One possible mechanism between the positive associations between DC and interaction quality could be how partners verbally express their support (e.g., more we-talk) during discussions about external stress. Using real-time interaction data from 40 heterosexual couples, this project examined whether observed positive and negative DC was associated with greater (or lesser) levels of perceived interaction quality. Further, language use (i.e., pronouns, emotion words, cognition words) was assessed as mediators in the associations between DC and interaction quality. Overall, results suggested that language did not mediate the effect of DC on interaction quality; however, there were several interesting links between DC, language, and interaction quality. Implications of these findings for relationship researchers and mental health clinicians working with couples are discussed.

Contributors

Agent

Created

Date Created
  • 2017

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The co-construction of moral emotions and employee treatment in the workplace

Description

ABSTRACT

This study examines the ways in which employees experience moral emotions that violate employee treatment and how employees co-construct moral emotions and subsequent expressions of dissent. This qualitative study

ABSTRACT

This study examines the ways in which employees experience moral emotions that violate employee treatment and how employees co-construct moral emotions and subsequent expressions of dissent. This qualitative study consisted of 123 full-time employees and utilized open-coding, content analysis, constant comparison analysis, and concept mapping. The analysis revealed that employees expressed dissent laterally as a series of sensemaking processes, such as validation of feelings, moral assessments, and assessing the fear of moral transgressions. Employees also expressed dissent as a series of risk assessments that overlapped with the ways in which employees made sense of the perceived infraction. Employees' lateral dissent expression manifested as a form of social support which occasionally led to co-rumination. Employees expressed dissent upwardly when seeking a desired action or change. Circumvention was utilized as a direct reflection to the type and degree of moral transgression related to the person responsible for the mistreatment. Results indicated that experiencing moral emotions that led to expressing dissent with a designated audience was determined by where employees were situated in the cyclical model of communicating moral emotions and in relation to the co-construction of both the infraction related to employee mistreatment and the experience of moral emotions. Results contribute to the existing body of literature on dissent and emotions. A discussion synthesizing the findings and analysis is presented, in addition to the implications for future research.

KEYWORDS: Emotion, Dissent, Moral Emotions, Sensemaking, Risk-Assessment, Social Support, Co-Rumination

Contributors

Agent

Created

Date Created
  • 2015

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Using the theory of planned behavior to predict college students' communication of affirmative sexual consent

Description

Sexual violence is a problem on college campuses across that United States. In the past few years, federal and state legislation has been drafted in order to address campus

Sexual violence is a problem on college campuses across that United States. In the past few years, federal and state legislation has been drafted in order to address campus sexual violence. A main feature of this legislation addresses an important communicative construct related to students’ sexual behavior: sexual consent. Colleges and universities are adopting an affirmative-standard of consent, which emphasizes that consent for sexual activity be communicated verbally or via unambiguous actions, mutual, voluntary, enthusiastic, and ongoing throughout the sexual encounter. Literature has explored how college students communicate and interpret sexual consent, but antecedents to sexual consent behaviors, particularly affirmative consent, are largely unknown.

The current investigation seeks to longitudinally explore the antecedents to college students’ affirmative sexual consent behaviors (i.e., nonverbal, initiating, verbal). Using the Theory of Planned Behavior (TPB) as a theoretical framework, hypotheses predicted that at Time 1 (T1) attitudes, norms, and perceived behavioral control would positively and significantly predict students’ (T1) intentions to communicate affirmative consent to their partner. Then, it was predicted that at Time 2 (T2)—thirty days later—intentions to communicate consent from T1 would positively and significantly predict college students’ communication of affirmative consent to their partner during their most recent sexual encounter. The final matched (i.e., completed T1 and T2 surveys) sample included two hundred twenty-five (N = 225) college students who had engaged in sexual activity during the 30 days between survey distributions. Results from the path analyses support the theoretically driven hypotheses for all three affirmative consent behaviors, and demonstrate that subjective norms and perceived control are important and strong determinants of students’ communication of affirmative sexual consent. Furthermore, multi-group invariance tested the potential moderating effects of three individual, two dyadic, and two environmental/contextual variables on the strength of path coefficients between TPB constructs for all three sexual consent behaviors. Only individual and environmental/contextual variables significantly moderated relationships within the TPB for the three models. Results are discussed with regard to theoretical implications as well as practical implications for university health educators and other health professionals. Additionally, limitations and future directions are noted.

Contributors

Agent

Created

Date Created
  • 2016

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Making thin data thick: user behavior analysis with minimum information

Description

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million

With the rise of social media, user-generated content has become available at an unprecedented scale. On Twitter, 1 billion tweets are posted every 5 days and on Facebook, 20 million links are shared every 20 minutes. These massive collections of user-generated content have introduced the human behavior's big-data.

This big data has brought about countless opportunities for analyzing human behavior at scale. However, is this data enough? Unfortunately, the data available at the individual-level is limited for most users. This limited individual-level data is often referred to as thin data. Hence, researchers face a big-data paradox, where this big-data is a large collection of mostly limited individual-level information. Researchers are often constrained to derive meaningful insights regarding online user behavior with this limited information. Simply put, they have to make thin data thick.

In this dissertation, how human behavior's thin data can be made thick is investigated. The chief objective of this dissertation is to demonstrate how traces of human behavior can be efficiently gleaned from the, often limited, individual-level information; hence, introducing an all-inclusive user behavior analysis methodology that considers social media users with different levels of information availability. To that end, the absolute minimum information in terms of both link or content data that is available for any social media user is determined. Utilizing only minimum information in different applications on social media such as prediction or recommendation tasks allows for solutions that are (1) generalizable to all social media users and that are (2) easy to implement. However, are applications that employ only minimum information as effective or comparable to applications that use more information?

In this dissertation, it is shown that common research challenges such as detecting malicious users or friend recommendation (i.e., link prediction) can be effectively performed using only minimum information. More importantly, it is demonstrated that unique user identification can be achieved using minimum information. Theoretical boundaries of unique user identification are obtained by introducing social signatures. Social signatures allow for user identification in any large-scale network on social media. The results on single-site user identification are generalized to multiple sites and it is shown how the same user can be uniquely identified across multiple sites using only minimum link or content information.

The findings in this dissertation allows finding the same user across multiple sites, which in turn has multiple implications. In particular, by identifying the same users across sites, (1) patterns that users exhibit across sites are identified, (2) how user behavior varies across sites is determined, and (3) activities that are observed only across sites are identified and studied.

Contributors

Agent

Created

Date Created
  • 2015

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Predictive Control of Interpersonal Communication Processes in Civil Infrastructure Systems Operations

Description

Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS

Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.

This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.

This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.

Contributors

Agent

Created

Date Created
  • 2020

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Learning from task heterogeneity in social media

Description

In recent years, the rise in social media usage both vertically in terms of the number of users by platform and horizontally in terms of the number of platforms per

In recent years, the rise in social media usage both vertically in terms of the number of users by platform and horizontally in terms of the number of platforms per user has led to data explosion.

User-generated social media content provides an excellent opportunity to mine data of interest and to build resourceful applications. The rise in the number of healthcare-related social media platforms and the volume of healthcare knowledge available online in the last decade has resulted in increased social media usage for personal healthcare. In the United States, nearly ninety percent of adults, in the age group 50-75, have used social media to seek and share health information. Motivated by the growth of social media usage, this thesis focuses on healthcare-related applications, study various challenges posed by social media data, and address them through novel and effective machine learning algorithms.

The major challenges for effectively and efficiently mining social media data to build functional applications include: (1) Data reliability and acceptance: most social media data (especially in the context of healthcare-related social media) is not regulated and little has been studied on the benefits of healthcare-specific social media; (2) Data heterogeneity: social media data is generated by users with both demographic and geographic diversity; (3) Model transparency and trustworthiness: most existing machine learning models for addressing heterogeneity are considered as black box models, not many providing explanations for why they do what they do to trust them.

In response to these challenges, three main research directions have been investigated in this thesis: (1) Analyzing social media influence on healthcare: to study the real world impact of social media as a source to offer or seek support for patients with chronic health conditions; (2) Learning from task heterogeneity: to propose various models and algorithms that are adaptable to new social media platforms and robust to dynamic social media data, specifically on modeling user behaviors, identifying similar actors across platforms, and adapting black box models to a specific learning scenario; (3) Explaining heterogeneous models: to interpret predictive models in the presence of task heterogeneity. In this thesis, novel algorithms with theoretical analysis from various aspects (e.g., time complexity, convergence properties) have been proposed. The effectiveness and efficiency of the proposed algorithms is demonstrated by comparison with state-of-the-art methods and relevant case studies.

Contributors

Agent

Created

Date Created
  • 2019

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The effect of text messaging preferences and behavior on romantic relationship satisfaction

Description

Proponents of cues-filtered-out approaches to communication suggest that the quality of person-to-person interaction is diminished when that interaction is mediated by technology. This postulation has implications for communication given the

Proponents of cues-filtered-out approaches to communication suggest that the quality of person-to-person interaction is diminished when that interaction is mediated by technology. This postulation has implications for communication given the surging popularity of text messaging in the United States. It is important to examine the degree to which text messaging may inhibit successful communication due to the detriments of technologically mediated communication. The relation between text messaging and romantic relationship satisfaction in individuals ages 18-45 was investigated because successful communication is widely known by researchers and lay individuals to be an integral aspect of healthy intimate relationships. The Relationship Assessment Scale (RAS) (Hendricks, 1988) and an inventory of text messaging behavior was administered to graduate students (n = 22), undergraduate students (n = 24), and people not affiliated with universities (n = 104). Using responses on these inventories, whether or not (1) frequency of text messaging and (2) preference for a particular method of communication are related to romantic relationship satisfaction were evaluated. It was hypothesized that (1) a higher frequency of text messaging will be inversely related with romantic relationship satisfaction and (2) that a participant indicating a preference for verbal phone communication over text messaging communication will be positively correlated with romantic relationship satisfaction. The lack of statistically significant results prevented the drawing of conclusions about relationships between text messaging frequency or preference for voice communication over texting and romantic relationship satisfaction.

Contributors

Agent

Created

Date Created
  • 2016

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Conceptualizing and operationalizing empathetic expressions: scale development, validation, and message evaluation

Description

The goals of this dissertation were to develop a measurement called the

Empathetic Expressions Scale (EES) for Negative and Positive Events, to evaluate expressions of empathy from the receiver perspective, and

The goals of this dissertation were to develop a measurement called the

Empathetic Expressions Scale (EES) for Negative and Positive Events, to evaluate expressions of empathy from the receiver perspective, and to provide initial evidence for empathetic expressions as a separate construct from the empathy experience. A series of studies were conducted using three separately collected sets of data. Through the use of Exploratory Factor Analysis (EFA), the EES for Negative Events and the EES for Positive Events were created from the emerged factors. A five-factor structure emerged for the EES for Negative Events, which include Verbal Affirmation, Experience Sharing, Empathetic Voice, Emotional Reactivity, and Empathetic Touch. This scale was found to have good convergent and discriminant validity through the process of construct validation and good local and model fit through Confirmatory Factor Analysis (CFA). A four-factor structure and two-factor structure emerged for the EES for Positive Events. The four factors include Verbal Affirmation, Experience Sharing, Empathetic Voice, and Emotional Reactivity. The two factors in the second structure include Celebratory Touch and Hugs.The final study focused on evaluating different empathetic expressions from the receiver perspective. From the receiver perspective, the participants rated five types of empathetic expressions in response to negative or positive events disclosure. According to the findings, Emotional Reactivity was rated as the most effective empathetic expression in negative events on both levels of supportiveness and message quality scales whereas Verbal Affirmation received the lowest ratings on both criteria. In positive events, Experience Sharing was evaluated as the most supportive and highest quality message whereas Verbal Affirmation was evaluated the lowest on both criteria. Taken together, the series of studies presented in this dissertation provided evidence for the development and validity of the EES for Negative and Positive Events.

Contributors

Agent

Created

Date Created
  • 2016