Doctor of Philosophy, University of Akron, 2024, Psychology-Industrial/Organizational

Negative feedback, when done right, can be a powerful learning and performance enhancing tool. Yet, despite the inherent instrumental value in negative feedback, negative feedback interventions are not always effective. Building off existing research on the feedback appraisal process, I asserted that negative feedback being perceived as a threat gets in the way of people being able to learn and grow from negative feedback. By elaborating on existing process models of feedback, I identified the anticipation stage as a key point during which these threat perceptions may be impacted. Specifically, I theorized that two antecedents, a recipient's anxiety and self-enhancement motive, at this stage will influence how much the negative feedback message is appraised as a threat. Further, I asserted that these threat-related cognitions and affect may be minimized by the experience of state mindfulness. Practicing mindfulness prior to receiving a negative feedback message may help a feedback recipient perceive less threat from the feedback, enhancing their ability to learn and grow from the feedback. I theorized that being in a more mindful state prior to receiving feedback will lead to less experienced anxiety and ego-driven motives, facilitating more positive feedback reactions. The main purpose of this dissertation was to test if a short-dose virtually administered mindfulness induction could be leveraged to improve people's reaction to negative feedback. An experiment was designed and conducted to test if a short-dose virtually administered mindfulness intervention can enhance the effectiveness of negative feedback. Results provided preliminary support for the ability of a short-dose virtually administered mindfulness induction to positively impact people's motivation to use feedback to improve. Participants who participated in the mindfulness induction prior to feedback showed more motivation to use the feedback to improve in the next round. Unfortunately, the mindfulne (open full item for complete abstract)

Ph.D., Antioch University, 2024, Antioch New England: Marriage and Family Therapy

The double bind of Black womanhood has been long documented in Black feminist literature. This dissertation seeks to greatly contextualize how Black women experience make sense of, and heal from, sexual trauma given the nature of gendered racism in the United States (U.S). Utilizing a convergent parallel design grounded in Black Feminist Theory and hermeneutic phenomenology, the lived experiences of 98 Black women from across the U.S. were investigated. Regression analyses revealed that the frequency of gendered racial microaggressions and the associated appraisal were not significant predictors of participants' PTSD symptoms. Additionally, PTSD symptoms were inversely predictive of current perceptions of healing and healing progress. A mediation effect of negative alterations in cognitions and mood on the relationship between sexual objectification, specific to Black women, and progress in healing was also evident. Gendered racism did moderate the relationship between PTSD symptoms and perceived healing at the lowest point but not healing progress. The major themes that emerged from the interviews included “Negative Consequences of Sexual Assault,” “Pathways of Healing,” “Barriers to Help and Justice-Seeking,” “Dimensions of Racial-Ethnic Socialization,” and “Dimensions of Gendered Racial Socialization.” Combined, these findings highlight the unique sociocultural and historical context of Black female survivorship and amplify the necessity for clinicians to integrate Black feminist therapeutic praxis to inform treatment assessment, goal, and intervention. This dissertation is available in open access at AURA (https://aura.antioch.edu/) and OhioLINK ETD Center (https://etd.ohiolink.edu).

Doctor of Education , University of Dayton, 2024, Educational Administration

To build a safe and supportive community in school climates, we first need to prevent bullying through healthy relationships and safety procedures, which will begin in the classroom. My theory is to start early on in school settings such as Kindergarten. I hope to build a plan to ensure an autonomous mindset that gives students, teachers, and staff a pure and wholesome thought process. This thinking will take some years to develop a nuanced expression. During this starting phase of Kindergarten, I hope to broaden the opportunity for students in elementary school to be role models for students of the same age and younger; this begins with weekly training for them. The design is that this learning will transition into the middle school setting to facilitate a bridge in learning about the middle school student climate. The design continues to bridge students' transition into high school, hoping to eliminate bullying in the high school climate. The mindset structure is the tool to reduce and eliminate bullying, with a nuanced mindset to pivot from daily bullying in school settings. The intent is for this process to govern higher education and into future employment. This is a small piece of the planning process and implementation process with the management of a programmatic solution to building safer school environments.

PHD, Kent State University, 2024, College of Communication and Information

Personalization for users can be a desired outcome of their interactions with social media algorithms (e.g., liking certain content to suggest they want more of it). This level of interaction can depend on users' awareness and affective evaluations of the algorithm. However, considering these two contextual influences, how do users perceive and subsequently act in response to social media algorithms predicting private information about the user that they do not wish the algorithm to know or understand? Over the course of two online survey studies, this dissertation integrates the Communication Privacy Management (CPM) theoretical framework (Petronio, 2002; 2013) into the human-machine communication (HMC) context. Users' experiences of privacy breakdowns and recalibration strategies with social media algorithms collected in the first study were used to develop and test two preliminary measures: the algorithmic privacy breakdown measure and the algorithmic privacy repair measure. We argued that, in addition to the preliminary measure, users' awareness of and attitudes towards social media algorithms, both positive and negative, play a crucial role in predicting their interaction behavior, regulating their desired co-ownership (i.e., granting the algorithm access to private information), and determining how the degree of co-ownership influences their breakdown experiences and the strategies they employ to rectify these breakdowns. Results suggest that greater positive attitudes predict greater co-ownership of private information with the algorithm. However, greater awareness and negative attitudes predict the inverse. Those with more awareness and negative attitudes are less likely to allow private information to be known by social media algorithms. Breakdown experiences involving targeted ads related to intimate personal information led to greater use of recalibration practices that adjust their platform settings, resemble human-algorithm interplay, and severe pulling (open full item for complete abstract)

Master of Science, The Ohio State University, 2024, Psychology

Across a wide array of substantive research areas within psychology, many researchers make use of the regression modeling framework. A central assumption of this framework is that the relation between the independent variable (IV) and the dependent variable (DV) is linear, and the current diagnostic criterion for this assumption is a visual inspection of a scatter- or residual plot for the absence of systematic nonlinearity. The current work endeavors to create an additional avenue for examination of the linearity assumption that goes beyond graphical analysis. Borrowing inspiration from the ANOVA framework, contrast analyses were applied to simulated data as a way to detect linear and quadratic trends as well as violations of monotonicity (MT) via an ordinal analysis approach. We explore the use of categorized data in conjunction with contrasts for the IV for two reasons: first, to enhance the robustness of the analysis in that local deviations from monotonicity and linearity would not determine the results and second, to be more user-friendly that more sophisticated approaches such as splines and generalized additive models. Type I errors, power, and comparison of findings with additional methods are discussed, as well as limitations and directions for future research.

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Psychology/Industrial-Organizational

Within cross-cultural analyses, specifically measurement invariance studies, cultures are often compared at the national level (e.g., invariance across French and German cultures). However, the presence of cultural variation within nations, on factors such as region, level of urbanization, gender, or ethnic roots, presents an added level of complexity that is often ignored in these analyses The current study used mixture model item response theory (MMIRT) to determine the adequacy of nationality as a proxy for culture in the United States, Indian, and Austrian contexts using a combined American, Indian, and Austrian sample collected from multiple data sources (N = 986). More specifically, this study asks if more granular variables such as gender or individual-level cultural beliefs are more strongly related (compared to country) to response patterns using Howard's (2017) Workplace Courage Scale. This was achieved through examining significant covariates of latent class membership within MMIRT models with a mixed American, Indian, and Austrian sample. Covariates examined include Hofstede's (2001) cultural dimensions (at the individual level of analysis), demographic variables, autonomy, employee voice, and leader-member exchange, which were also subjected to measurement invariance testing (barring demographics) specifically across the American and Indian samples. Power distance orientation and uncertainty avoidance orientation emerged as statistically significant drivers of response pattern, while no demographic variables, including nation, emerged.

Doctor of Philosophy, The Ohio State University, 2024, Educational Studies

The latent growth model has been pivotal in understanding developmental processes for several decades. While most researchers have focused on growth factors' mean structure and variability, the within-subject residual variance-covariance structure has not received as much attention. The present study proposes a novel procedure for specifying a linear latent growth model's optimal within-subject residual variance-covariance structure. The method is based on the following ideas: approximating the true variance-covariance structure of the data, generating a series of replications with parameter values of the most probable within-subject residual variance-covariance structures within the data, and employing classification machine learning algorithms for prediction. A simulation examined the feasibility of the procedure predicting the true within-subject residual variance-covariance structure. Additionally, the performance of the proposed procedure was compared to the traditional approach of selecting models based on information criteria using the same replications. The results showed that the proposed method effectively detected true first-ordered autoregressive and banded main diagonal structures. In contrast, the information criteria, specifically the Bayesian Information Criterion and Sample-Size Adjusted BIC, effectively detected true identity and banded main diagonal structures, respectively. Based on these findings, suggestions were provided for researchers to consider when specifying the optimal within-subject structure of their data.

Ph.D., Antioch University, 2023, Antioch Seattle: Counselor Education & Supervision

This study examines counselor professional identity and burnout for clinical mental health counselors. The population of focus included licensed or license-eligible Clinical Mental Health Counselors, who were post-grad (N=53). Participants then completed the Professional Identity Scale in Counseling - Short Form and the Maslach Burnout Inventory–Human Services Survey. When examining the findings regarding the relationship between Counselor Professional Identity and Burnout for this study, the initial observation revealed the validity and applicability of the MBI-HSS to clinical mental health counselors. Though a relationship between Burnout and Counselor Professional Identity was not identified, relationships between sub-scale items were noteworthy. Implications for counselor education and supervision are presented. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu).

Doctor of Philosophy, The Ohio State University, 2023, Psychology

This dissertation concerns methods to investigate measurement invariance (MI) violation in a confirmatory factor analytic framework when the number of groups is large. The focus of the dissertation is on the implementation of the proposed methods and application with real datasets. Four methods, (1) multilevel confirmatory factor analysis, (2) 𝑘-means clustering, (3) hierarchical clustering, (4) MI-tree models are proposed to explore scalar invariance violation among groups. One simulated dataset was used to demonstrate the four methods and two real datasets were used as application

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2023, Psychology/Industrial-Organizational

Language understanding plays a crucial role in psychological measurement and so it is important that semantic cues should be studied for more effective and accurate measurement practices. With advancements in computer science, natural language processing (NLP) techniques have emerged as efficient methods for analyzing textual data and have been used to improve psychological measurement. This dissertation investigates the application of NLP embeddings to address fundamental methodological challenges in psychological measurement, specifically scale development and validation. In Study 1, a word embedding-based approach was used to develop a corporate personality measure, which resulted in a three-factor solution closely mirroring three dimensions out of the Big Five framework (i.e., Extraversion, Agreeableness, and Conscientiousness). This research furthers our conceptual understanding of corporate personality by identifying similarities and differences between human and organizational personality traits. In Study 2, the sentence-based embedding model was applied to predict empirical pairwise item response relationships, comparing its performance with human ratings. This study also demonstrated the effectiveness of fine-tuned NLP models for classifying item pair relationships into trivial/low or moderate/high empirical relationships, which provides preliminary validity evidence without collecting human responses. The research seeks to enhance psychological measurement practices by leveraging NLP techniques, fostering innovation and improved understanding in the field of social sciences.

Master of Science (MS), Wright State University, 2023, Human Factors and Industrial/Organizational Psychology MS

Item Response Trees are a type of item response model that incorporates information about conditional responding to items using a rooted tree graph structure. Researchers have used item response trees for common measurement tasks and for testing novel hypotheses. Previous simulation studies investigating item response trees either lack generalizability to the broad domain of their use or lack thorough investigation and reporting of the results. I conducted a simulation study to explore how sample size, test length, item characteristics, and tree structure affect both item and person parameter recovery for 1PL and 2PL models. The results suggested that, as with any item response model, item response tree models are unbiased. However, large samples and long test lengths are needed to minimize estimate uncertainty. Issues of sample size and test length are compounded by the conditional structure incorporated in item response tree models. In particular, the depth of the tree and low item endorsement can pose severe estimation issues when sample sizes are not large and test lengths are not long. I used posterior predictive simulations to provide the reader with a practical understanding of the limitations of item response trees in the context of item and personnel selection and prediction of external variables.

Specialist in Education (Ed.S.), University of Dayton, 2023, School Psychology

Physical classroom space can influence a student's sense of interconnectivity and can support learning. Social effects of the physical space have been infrequently researched regarding the role it has on student collaboration and therefore is not well understood by school personnel. This thesis shares results of a mixed method content analysis of data collected across three new law school buildings in the United States of America. Students from each law school completed a survey to determine the effects the new law school building had on their perceptions of the space, their ability to collaborate with peers and faculty, and the overall difference between their experience in the new building compared to the old law school building.

Doctor of Philosophy, The Ohio State University, 2023, Human Ecology: Human Development and Family Science

Clear associations exist between the intersections of socioeconomic factors, relationship processes, and relationship outcomes. Though romantic relationships are predictive of positive outcomes across multiple life domains (i.e., mental health, physical health, financial health, relational health), maintaining a satisfying romantic relationship can be challenging for partners of low-income statuses given systemically induced stressors. Not only is this population growing, but these families continue to navigate economic, health, and intervention disparities. Previous relationship intervention and prevention efforts have largely produced little-to-no sustainable gains for couples of lowincome statuses, despite the need and potential benefits of services for this population. Scholars posit that the ineffectiveness of these interventions is due, in part, to the lack of client-driven and tailored interventions, as previous initiatives were directly transferred from middle- and higher-income participants. Building a strong foundation of basic science is essential for working towards accessible, sustainable, and effective evidence-based interventions for couples of low income statuses. In addition, the area of relationship maintenance continues to be integral to relational satisfaction and commitment; however, this area is understudied in terms of how maintenance associates with relationship outcomes across different levels of socioeconomic factors. As such, the aims of this dissertation were two-fold: 1) Investigate the longitudinal associations between relationship maintenance behaviors, socioeconomic factors, and relationship satisfaction; 2) Explore latent profiles of dyadic maintenance behavior use and their associations with socioeconomic factors, relationship satisfaction, and commitment using actor and partner data. Data were drawn from the German Family Panel Analysis of Intimate Relationships and Family Dynamics (pairfam). For Aim 1, associ (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Psychology

In social science research, statistical power is emphasized as the “gold standard” to justify sample size in hopes that it promotes transparency in study design, addresses concerns about the replicability of findings, and limits questionable research practices. However, statistical power can result in a sample size that is unattainable for researchers to collect, and the practical uses of power are often misinterpreted beyond their scope. This dissertation consists of three projects that address statistical power and its limitations, as well as investigates alternative methods to justify sample size. The first project is a meta-science study that examines elements of study design and statistical analyses used in research published in Psychological Science. Results indicate the most common type of effect size used as an input in power analysis is sourced from previously collected data. However, use of point estimated effect sizes does not account for sampling variability and can result in estimates that are imprecise. Therefore, the second project in this dissertation empirically evaluates the performance of several sample size determination methods that expand upon classical power analysis by accounting for sampling variability of the effect size. In general, the sample sizes calculated from these modern methods have the potential to be unrealistically large for researchers to collect, which could deter from their use. To address this issue, the third project moves away from statistical power and focuses on using the sequential probability ratio test (SPRT) for study design and analysis instead. Two new extensions of SPRT are proposed that also account for sampling variability of the effect size. Evaluations of these approaches suggest that they offer a beneficial alternative to power analysis, especially for researchers that need to limit sample size.

Master of Science, The Ohio State University, 2023, Psychology

Accurately categorizing items requires humans to selectively attend to stimulus dimensions that are relevant to a task. However, learning to direct attention toward the relevant dimensions is often achieved through trial and error. Therefore, category learning models should seek to describe a neurally plausible account of how humans adjust their attention over time. The Adaptive Attention Representation Model (AARM) attempts to describe this dynamic process by employing a between-trial attention updating function in the form of a feedback-based error gradient. To provide neural validation for AARM's attentional mechanisms we conducted a simulation study, fit AARM to behavioral data from Mack et al (2016), and conducted three model-based fMRI analyses. The simulation demonstrated a priori expectations of the model's behavior in the context of the Shepard VI paradigm. The behavioral fits showcased AARM's capacity to capture choice accuracy and attentional dynamics in a complex learning environment. The fMRI analyses revealed a brain-wide system that supports flexible attention updating. This neural system includes areas believed to support attention orienting (prefrontal and parietal cortices), visual perception (visual pathways), memory encoding and retrieval (hippocampus and MTL), prediction error (basal ganglia), and goal maintenance (PFC). These results support AARM's specification of attentional tuning as a dynamic property of distributed cognitive systems.

Doctor of Philosophy, The Ohio State University, 2023, Psychology

Researchers interested in understanding causal relationships must not only test if X causes Y, but how and/or when X causes Y. Mediation analysis is a tool that allows researchers to identify the mechanism(s) by which one variable causes another, whereas moderation analysis allows researchers to detect when one variable's effect is heterogenous across levels of another variable (or multiple variables). Although these analyses lead to a deeper understanding of an observed relationship, they are still often too simplistic in isolation to properly model real-world effects. Combining mediation and moderation into a single analysis allows one to study conditional indirect effects—that is, when an indirect effect of X on Y is variable across the levels of a moderator. Methodological researchers have paid much attention on how to test for conditional indirect effects. However, considerably less work has been devoted to evaluating the performance of these proposed methods or interpreting the results of these tests. A review of the simulation studies that have been done reveals that current testing methods have relatively poor performance except for the most optimistic combinations of effect and sample size. Despite this, many substantive researchers continue to use these methods and rely on them for dichotomous decisions about and interpretations of such effects. In this dissertation, I aim to clarify the best way(s) for researchers to specify, test, and interpret conditional indirect effects. In Chapter 1, I introduce the concepts of mediation, moderation, and conditional indirect effects and conduct a literature review to learn how methodological and substantive researchers think about and apply conditional indirect effects. In Chapter 2, I introduce the math underlying mediation, moderation, and conditional indirect effects and step through substantive examples of each. I also introduce a graphical presentation of effect size to aid in the interpretation of conditi (open full item for complete abstract)

Master of Arts, University of Toledo, 2022, Psychology - Experimental

STEM (science, technology, engineering, and mathematics) communication that fosters understanding is as crucial today as it is lacking. More than ever, there is a need for STEM communication that goes beyond 'nudging' the average layperson toward a target behavior, or simply bombarding them with complex and ill-constructed information. The prevailing ‘nudge-or-bombard' strategies can result in subject knowledge that is at best incomplete and easily forgotten, and at its worst is impoverished, eliciting short-term compliance that can result in distrust of experts and policymakers. Additionally, empirically based communication techniques that go beyond disseminating rote facts to achieving insight are imperative in an oversaturated communication environment wherein laypeople are flooded with more information than they can achieve expertise in, or even comprehend (Scheufele, 2006). The present study aimed to extend existing findings of evidence-based communication grounded in a dual-process model of cognition called Fuzzy-Trace Theory (FTT) into the realm of STEM communication. It also sought to lend further evidence to the use of a new computational textual measurement tool based on FTT that informs the development of effective textual information via assisting individuals in the formation of an overall bottom-line understanding of a text. In the present study, 201 participants were presented with one of two versions of a text on a complex STEM subject matter. Texts were edited systematically using the FTT-based computational methodology to produce either a dense information presentation or one that was manipulated with the goal of increasing understanding by helping participants ‘get the gist' of the text. Participants then completed two measures that tested their knowledge and comprehension of the text. Additionally, risk perception questionnaires and multiple decision intention tasks were administered that were associated with preparedness for the risks presented i (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2022, Clinical Psychology (Arts and Sciences)

ADHD symptoms are associated with dysfunctional automatic thoughts that increase avoidance, impairment, and distress; however, little is known about factors that increase the likelihood of these thoughts. The current study developed an Experimental Vignette Measure of Automatic Thoughts for ADHD (VMATA-E), which was used to test how task-specific factors (Immediacy, Aversiveness, and History of Failure) and ADHD Status influenced automatic thoughts. This study systematically manipulated components of the vignettes, using a 23-1 between-subjects fractional factorial design. After randomization to an experimental condition, 320 participants read three brief vignettes and rated automatic thoughts in response to each one. EFA was used to develop the measure, convergent validity was examined via correlations. MANOVAs were used to examine main effects of task-specific factors and interaction with ADHD status. The VMATA-E demonstrated a three-factor structure which supports the stability of negative (NAT), rationalizing (RAT), and adaptive (GOAT) thoughts found in prior work. Task-specific factors of Immediacy and Aversiveness were found to have effects on NAT and GOAT. Compared to non-ADHD participants, individuals with clinically significant ADHD symptoms reported higher levels of dysfunctional automatic thoughts and lower levels of adaptive automatic thoughts (i.e., GOAT), even after controlling for prior mood disorder diagnoses. In contrast to expectations, there were no interactions between ADHD status and any task-specific factor. The findings of the current study have theoretical and practical implications for research on the role of automatic thoughts in ADHD-related impairment.

Doctor of Philosophy, University of Akron, 2022, Counseling Psychology

The purpose of this study was to explore the relationship between microaggressions, Black male masculinity, and psychological adjustment. Also, the study aimed to assess whether racial socialization is related to how Black male masculinity affects the relationship between microaggressions and psychological adjustment. The study tested the following hypotheses. Hypothesis A stated that the relationship between microaggressions, and psychological adjustment would be negative. Hypothesis B stated that Black male masculinity would mediate the relationship between microaggressions and psychological adjustment. Hypothesis C stated that racial socialization will have a moderating effect on the relationship between microaggressions and Black male masculinity. To test the stated hypotheses, 107 Black men over the age of 18 participated in the study. A bivariate correlation and a moderated mediation were conducted. The results of the moderated mediation model were non-significant. However, there were significant correlational relationships across constructs. The correlational relationship between racial socialization and Black male masculinity was novel and would benefit from further exploration. Post-hoc analysis was conducted employing a mediation model with the five racial socialization subscales used as mediators. Two of the subscales, alertness to discrimination and cultural endorsement of the mainstream were found to be significant. Speculation was made around the interpretation of the significant findings. The implications for research and practice were discussed.

Doctor of Philosophy, The Ohio State University, 2022, Psychology

The diagnosis of attention-deficit hyperactivity disorder (ADHD) in younger adults is rising. Laboratory tests (also known as neurocognitive testing) have been used to understand ADHD-specific characteristics in cognition, complementing clinical questionnaires and interviews. It is important to better characterize ADHD because affected people have diverse symptoms, and they often require different treatments. However, current research using neurocognitive testing has limitations: 1) it focuses on ADHD subgroups such as boys; 2) the sensitivity of existing tests to detect clinical characteristics is in question; 3) results are analyzed with summary statistics unsuitable for the study of individual differences and the entire ADHD spectrum; 4) current reviews suggest that conflict processing is a promising but understudied domain for understanding ADHD-specific characteristics. Computational psychiatry is a growing field of research offering new tools to link physiological data with behavioral data derived from neurocognitive testing. The objectives of this dissertation are to explore an improved test environment for ADHD. Specifically, developing and implementing cognitive and social-cognitive tests which tap into the domain of conflict processing; which integrate research in cognitive psychology; and which are suitable for the application of computational modeling. The aims are to: 1) characterize decision-making processes of younger adults with ADHD; 2) study how individual differences relate to symptom severity; and 3) link test performance to physiological measures collected with eye-tracking. The developed test environment, as part of this dissertation, consisted of a cognitive and social-cognitive computerized test that tapped into the processing of perceptual and motivational conflict, respectively. Sixty-eight adults (aged 18-35, gender balanced, nADHD=34, ncontrols=34) completed these tests, while I collected eye-tracking measures. I used computational mod (open full item for complete abstract)