Examining latent change classes: An application of factor mixture modeling to change scores

Although change scores are used in a variety of statistical methods (e.g., analysis of variance and regression), there is a lack of application of latent variable modeling methods to change scores. This thesis provides a detailed description of two latent variable modeling methods applied to change scores: factor analysis of change scores and change score factor mixture modeling. To illustrate advantages of these methods, both were applied to change score data from undergraduates. Students responded to sense of identity items during a university-wide assessment day on two occasions, once as incoming freshmen and again as second-semester sophomores. Change scores were computed by subtracting sophomore item responses from freshmen item responses. Factor analysis results indicated sense of identity change scores were best represented by two factors, change in sense of self and purpose and development of morals and beliefs. Factor mixture modeling results suggested two latent classes underlying these factors. The classes differed in both factor means and factor variances, which implied two possible change patterns associated with development of sense of identity. One class contained students who were stable on the two change score factors (i.e. changed minimally on sense of self and purpose and morals and beliefs) and the other class contained students who were fluid on one of the two factors. Classes were somewhat replicated with a second, independent sample, in that two classes were detected, but class means and variances diverged from those in the first sample. Results across the two methods provided insightful information about change processes of sense of identity, particularly how development of sense of identity is not the same across students. The applied example highlights the advantages of applying these methods to change scores. Implications of the two methods are further discussed throughout the thesis

Purpose-Driven: Employee Engagement from a Human Flourishing Perspective

Employee engagement continues to be one of the most popular topics in the organizational sciences over the past few decades. Despite this popularity, however, the antecedents of employee engagement and its underlying motivational framework are still unclear and unavailable to guide organizational interventions (Macey & Schneider, 2008). Using data from a sample of 518 employees in a southeastern university, this study investigated the work environment antecedents of job demands-abilities fit, transformational leadership, and corporate social responsibility and found positive significant relationships with employee engagement. Additionally, in a time where an increasing number of workers are searching for more meaning and purpose from their jobs (Avolio & Sosik, 1999; Gallup, 2016), this study operationalizes a sense of purpose and demonstrates how fulfilling a sense of purpose at work relates to employee engagement and self-determination theory’s psychological need satisfaction (Deci & Ryan, 1985). Using a structural equation modeling approach, the results of this study found both a sense of purpose at work and psychological need satisfaction to be significant predictors of employee engagement. Additionally, adding an indirect effect between need satisfaction and engagement, through a sense of purpose, was found to be the best fitting model. This overall theoretical model provides initial support for a self-determination theory framework for the study of employee engagement with the addition of a sense of purpose at work

The Influence of Sense of School Community on Korean Students’ Life Satisfaction and Comparison of Sense of Community for Students’ Gender and High School Specialties

The purpose of this study was to compare the sense of community of South Korean high school students as measured by a Korean version of the Sense of Community Index 2 (KSCI2) and examine the effect of two factors of the KSCI2, reinforcement of needs and influence, on students’ life satisfaction as measured by the Satisfaction with Life Scale (SWLS). A total of 375 Korean high school students provided usable data from three vocational high schools specializing in agriculture, electronics and engineering, and business and marketing, and one Meister high school specializing in automotive. Descriptive statistics, independent t-test, one-way ANOVA, and path analysis with structural equation modeling (SEM), were performed to analyze data. Findings suggested that there were statistically significant differences in reinforcement of needs, influence, and life satisfaction, for the gender of students and school specialties. First, male students scored higher than female students on reinforcement of needs and influence. Next, male students were more satisfied with their lives than female students. For reinforcement of needs, students specializing in automotive scored higher than those specializing in business and marketing. For influence, students specializing in automotive scored higher than those specializing in all the other three specialties. Finally, both factors of reinforcement of needs and influence had positive effects on students’ life satisfaction. Implications and suggestions for further studies are discussed

Learning environment, interaction, sense of belonging and study success in ethnically diverse student groups

The purpose of this paper was to investigate a model for describing the relationships between the extent to which learning environments are activating and students' interaction with teachers and peers, sense of belonging, and study success. It was tested whether this model holds true for both ethnic minority students and ethnic majority students. A total of 523 students from four different universities completed a questionnaire. Structural equation modeling (Amos) was used to test the model. The model that best describes the relationships in the group of ethnic minority students (N = 145) was shown to be different than the model that best fits the group of majority students (N = 378). Ethnic minority students appeared to feel at home in their educational program if they had a good formal relationship with teachers and fellow students. Ethnic minority students' sense of belonging to the institution nevertheless did not contribute to their study progress. On the other hand, in majority students, informal relationships with fellow students were what led to a sense of belonging. In these students, the sense of belonging did further academic progress

Estimating Uncertainty of Bus Arrival Times and Passenger Occupancies

Travel time reliability and the availability of seating and boarding space are important indicators of bus service quality and strongly influence users’ satisfaction and attitudes towards bus transit systems. With Automated Vehicle Location (AVL) and Automated Passenger Counter (APC) units becoming common on buses, some agencies have begun to provide real-time bus location and passenger occupancy information as a means to improve perceived transit reliability. Travel time prediction models have also been established based on AVL and APC data. However, existing travel time prediction models fail to provide an indication of the uncertainty associated with these estimates. This can cause a false sense of precision, which can lead to experiences associated with unreliable service. Furthermore, no existing models are available to predict individual bus occupancies at downstream stops to help travelers understand if there will be space available to board. The purpose of this project was to develop modeling frameworks to predict travel times (and associated uncertainties) as well as individual bus passenger occupancies. For travel times, accelerated failure-time survival models were used to predict the entire distribution of travel times expected. The survival models were found to be just as accurate as models developed using traditional linear regression techniques. However, the survival models were found to have smaller variances associated with predictions. For passenger occupancies, linear and count regression models were compared. The linear regression models were found to outperform count regression models, perhaps due to the additive nature of the passenger boarding process. Various modeling frameworks were tested and the best frameworks were identified for predictions at near stops (within five stops downstream) and far stops (further than eight stops). Overall, these results can be integrated into existing real-time transit information systems to improve the quality of information provided to passengers