Browsing by Author "Savla, Jyoti Shital"

Now showing 1 - 2 of 2
  • Thumbnail Image
    The order in which you cope matters: An examination of the moderating role of coping sequence on the impact of stressor type on affect
    Minton, Brandon Tyler (Virginia Tech, 2023-05-08)
    To date, few studies have sought to investigate whether the sequence in which individuals engage in coping strategies could impact the effectiveness of those strategies. The present study utilizes an EMA data collection approach to obtain a sample of N = 93 student participants to investigate this potential impact. I analyzed the data with a type of multilevel structural equation model (MSEM) called a cross-lagged panel model (CLPM), where the individual served as the higher level and surveys collected at various time points (three per day for five consecutive days) served as the lower level nested within those individuals. Autoregressive, cross-lagged, and moderation paths were tested to see which constructs at time point T-1 were significantly related to positive affect and negative affect at time point T. Findings indicated more significant relationships for positive affect at time point T than negative affect at time point T. Among these were moderation effects of coping strategy on the relationship between the presence of an interpersonal stressor and positive affect, such that emotion-focused coping buffers that effect and problem-focused coping amplifies it.
  • Thumbnail Image
    Student Satisfaction, Perceived Employability Skills, and Deep Approaches to Learning: A Structural Equation Modeling Analyses
    Kapania, Madhu Bala (Virginia Tech, 2023-06-05)
    This study explored the relationship of Deep Approaches to Learning (DAL) with overall students' satisfaction and perceived employability skills in the field of Science, Technology, Engineering and Mathematics (STEM) for the undergraduate seniors in the U.S. The study also aimed to investigate whether there is a difference between students in STEM and non-STEM fields on the relationship of DAL to overall student satisfaction and students' perceived employability skills. The data for the analysis was taken from the National Study of Student Engagement (NSSE) data. The Structural Equation Modeling (SEM) analysis was applied to explore the relationship between students' Deep Approaches to Learning (DAL), overall students' satisfaction and their perceived employability skills. The measurement invariance testing explored whether estimated factors are measuring the same constructs for STEM and non-STEM groups. The findings of the study show that HO and RI construct was found to have statistically significant positive total (direct and indirect) effect on overall student satisfaction. Further, the results show that HO and RI learning activities were identified as the statistically significant factors in predicting students' perceived employability skills for STEM students. The HO and RI have a statistically significant positive effect on perceived employability skills for STEM and the non-STEM students. The STEM students have a higher effect of HO learning activities on perceived employability skills than the non-STEM students. Further, the direct effect of perceived employability skill on overall student satisfaction is also positive for both the groups. The findings of the study confirmed the indirect effect of employability on overall students' satisfaction for both STEM and non-STEM students. This study has created strong groundwork for future researchers to use the measurement models and the hypothesized full structure model for invariance testing among the groups of STEM and non-STEM in higher education in the U.S. Thus, this measurement model has a strong generalizability to both STEM and non-STEM groups. The implications and limitations of study are further discussed.