Everyday Memory: An Expanded View Of Autobiographical Memory Functions

The current study investigated an expanded set of everyday autobiographical memory (AM) functions as proposed by developers of the 7-function Child-Caregiver Reminiscence Scale (CRS) (Kulkofsky & Koh, 2009). The current study adapted the theoretical CRS for use with diverse, adult samples. Participants (N = 1841) from a large, urban university completed the CRS-A online over the course or two academic semesters. Validation analyses included EFA using principal axis factoring, CFA, MGCFA invariance testing, and MTMM tests of construct validity (convergent and discriminant) and method effects. Results yielded evidence for a 6- function (Conversation, Perspective-Taking, Relationship Maintenance, Behavioral Control/Teaching/Problem-Solving, Emotion Regulation, and Self) model that demonstrated invariance across time, gender, and ethnicity/race. Provisional evidence showed that the Conversation and Behavioral Control/Teaching/Problem-Solving functions were used differentially across Caucasian and African-American/Black groups. Implications, limitations, and future directions are discussed

Supervised classification and mathematical optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.Ministerio de Ciencia e InnovaciónJunta de Andalucí

Superwoman schema: using structural equation modeling to investigate measurement invariance in a questionnaire

Evaluating the psychometric properties of a newly developed instrument is critical to understanding how well an instrument measures what it intends to measure, and ensuring proposed use and interpretation of questionnaire scores are valid. The current study uses Structural Equation Modeling (SEM) techniques to examine the factorial structure and invariance properties of a newly developed construct called Superwoman Schema (SWS). The SWS instrument describes the characteristics of a superwoman (strong woman) which consists of 35 items representing five subscales: obligation to present an image of strength, obligation to suppress emotions, resistance to being vulnerable, intense motivation to succeed, and obligation to help others. Multigroup confirmatory factor analysis (CFA) and a multiple indicators multiple causes (MIMIC) model were the SEM approaches used to examine measurement invariance in the SWS instrument. Specifically in the multigroup CFA analyses, configural invariance, metric invariance, intercept invariance, residual variance invariance, and latent mean invariance are examined between a group of young (18-39 years old) women and middle-aged (40-65 years old) women. In the MIMIC model, the hypothesized model of the SWS was used to investigate the group differences in the young and middle-aged women. Both SEM techniques provided a didactic discussion about the findings of the study, which confirmed that the SWS instrument could be broadly used (i.e., invariance held) to compare young and middle-aged African American women on superwoman characteristics. Further research is needed to better understand the possible contextual factors (i.e., racial or gender stereotyping, oppression, spiritual values, etc.) that may contribute to group differences on the SWS subscales and minor violation to invariance

Supervised Classification and Mathematical Optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data

Unveiling AI Aversion: Understanding Antecedents and Task Complexity Effects

Artificial Intelligence (AI) has generated significant interest due to its potential to augment human intelligence. However, user attitudes towards AI are diverse, with some individuals embracing it enthusiastically while others harbor concerns and actively avoid its use. This two essays\u27 dissertation explores the reasons behind user aversion to AI. In the first essay, I develop a concise research model to explain users\u27 AI aversion based on the theory of effective use and the adaptive structuration theory. I then employ an online experiment to test my hypotheses empirically. The multigroup analysis by Structural Equation Modeling shows that users\u27 perceptions of human dissimilarity, AI bias, and social influence strongly drive AI aversion. Moreover, I find a significant difference between the simple and the complex task groups. This study reveals why users avert using AI by systematically examining the factors related to technology, user, task, and environment, thus making a significant contribution to the emerging field of AI aversion research. Next, while trust and distrust have been recognized as influential factors shaping users\u27 attitudes towards IT artifacts, their intricate relationship with task characteristics and their impact on AI aversion remains largely unexplored. In my second essay, I conduct an online randomized controlled experiment on Amazon Mechanical Turk to bridge this critical research gap. My comprehensive analytic approach, including structural equation modeling (SEM), ANOVA, and PROCESS conditional analysis, allowed me to shed light on the intricate web of factors influencing users\u27 AI aversion. I discovered that distrust and trust mediate between task complexity and AI aversion. Moreover, this study unveiled intriguing differences in these mediated relationships between subjective and objective task groups. Specifically, my findings demonstrate that, for objective tasks, task complexity can significantly increase aversion by reducing trust and significantly decrease aversion by reducing distrust. In contrast, for subjective tasks, task complexity only significantly increases aversion by enhancing distrust. By considering various task characteristics and recognizing trust and distrust as vital mediators, my research not only pushes the boundaries of the human-AI literature but also significantly contributes to the field of AI aversion

A Structural Equation Model Analysis of Computing Identity Sub-Constructs and Student Academic Persistence

This dissertation explores the impact of computing identity sub-constructs on the academic persistence of computing students. This research provides models, quantified relationships, and insights to increase the number of graduates with the intention of pursuing a career in computing. Despite the growing significance of computer science and all the projected new jobs in computer science, many university and college programs suffer from low student persistence rates. One theoretical framework used to better understand persistence in other STEM disciplines is disciplinary identity. Disciplinary identity refers to how students see themselves with respect to a discipline. This study examines the effects of computing identity sub-constructs (performance/competence, recognition, interest, and sense of belonging) on the academic persistence of computing students. A quantitative analysis with three phases was performed for this study. First, confirmatory factor analysis (CFA) and structural equation model (SEM) analysis were performed to validate and explore the relationship between sub-constructs in the computing identity model. Second, a multigroup SEM was performed to estimate the impact of the identity sub-constructs on persistence for students with diverse demographics in this case by gender and level of education. Third, a time-series SEM were used to investigate the impact of identity development on computing persistence over time. The findings indicated that students\u27 academic persistence was directly influenced by their interest as the most significant factor. In addition, performance, competence, recognition, and sense of belonging contributed to students’ identity development and academic persistence. Results of the second analysis indicated identity sub-constructs contributed differently to academic persistence among freshman and senior students; however, no significant differences were found between male and female students. Ultimately, the last analysis with time-series data indicated that interest and competence/performance, as individual factors had the strongest direct impacts on persistence over time. Considering student identity in understanding academic persistence in computing programs may provide a meaningful lens of analysis for institutes and their curriculum and extracurricular planning methods. In addition, the development of students’ self-beliefs provides ways for increasing the number of graduates with increased likelihood of pursuing computing careers

Educational Considerations, vol. 35(2) Full Issue

Educational Considerations, vol. 35(2)-Fall 2008-Full issu

Diversity of thought in the blogosphere: implications for influencing and monitoring image

A blog, a shortened form of weblog, is a website where an author shares thoughts in posts or entries. Most blogs permit readers to add comments to posts and thereby be a conversational mechanism. One way that companies have started to use blogs is to monitor their corporate image (in this dissertation, the term image is used in reference to corporate, brand and/or product image). This study focuses on how common socio-psychological processes mediate consumers’ revelation of corporate image in the blogosphere. Centering resonance analysis, a means of measuring similarity between two bodies of text, is used in conjunction with multidimensional scaling to locate text as cognitive objects in a space. Clusters are then detected and measured to quantify diversity in the thoughts expressed. Detected patterns are studied from a social process theory perspective, where complex phenomena are hypothesized to be the result of the interaction of simpler processes. A majority of blog commenters compromise the expression of their thoughts to gain social acceptance. This study identifies the most extreme of such people so companies who monitor blogs can assign less weight to image indications gained from them as they may be merely expressing thoughts that are intended to maintain social acceptance. It was also found that single-theme blogs attract a readership with similarly narrow interests. The boldest and most diverse thinkers among comment writers have the most impact because of their ability to provoke the thinking of others. However, commenters who repeat the same ideas have little effect, suggesting that introducing shills is unlikely to shift the sentiment of a blog’s readership. People participate in blog communities for reasons (e.g., need for community) that may undermine thought diversity. However, there may be value in serving those needs even though no valuable insights are provided into image or directions for product development. Members of homogeneous-thinking communities were observed to more actively participate, with greater longevity. This may increase loyalty to the company hosting the blog

Self-Regulation, Emotion Regulation, & Social Problem-Solving: Common & Distinct Pathways to Depression

The present study examined the relationships among three psychological constructs: self-regulation (SR), emotion regulation (ER), and social problem-solving (SPS), and their connection to depressive symptomology. SR, ER, and SPS arose from independent, well-established literature bases and each has demonstrated links to psychopathology. The theories underlying these constructs, however, suggest overlap in their operationalization and measurement. Despite these concerns, no empirical investigations to date have examined the measurement and predictive validity of measures of SR, ER, and SPS in the context of one another. Undergraduate students aged 18-29 (N = 592) completed three self-report measures each of the constructs interest, as well as a measure of depressive symptoms. First, a confirmatory factor analysis (CFA) was conducted, and four rival CFAs reflecting differing levels of convergence and divergence were tested against one another. Then, the best fitting measurement model was used to test a latent variable structural equation model (SEM). Findings from the first-order CFA model indicated that seven out of nine measures loaded on to their intended factors. Contrary to prediction, the bifactor model was identified as the best-fitting CFA model. This suggests that each construct is comprised of distinct variance, as well as common variance that is shared among all nine measures. Interestingly, only the common factor variance and distinct variance of ER significantly predicted depressive symptoms in the final SEM model. This study was the first to demonstrate and explore the high levels of convergence among SR, ER, and SPS as commonly measured in practice. Overall, the results indicated a substantial amount of shared variance and offered a complicated picture of construct validity. It appears that measures often used to assess these constructs are capturing more common features than investigators may be aware of, which has notable implications for the interpretation of findings. Future investigations that include a multitrait-multimethod examination of common and distinct pathways from SR, ER, and SPS to depressive symptoms would serve to further clarify these relationships