Preferred Name
Riley Herr
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Date of Graduation
5-9-2024
Semester of Graduation
Spring
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Department of Graduate Psychology
Advisor(s)
John Hathcoat
Sara J. Finney
Joseph M. Kush
Abstract
The ISSAQ-SS is a new scale developed for the purpose of measuring students on twelve noncognitive skills, all of which are theoretically predictive of academic success in higher education. Each of the twelve noncognitive skills measured via the ISSAQ-SS are represented by distinct subscales which contain five to nine items. Students respond to each item using a 4-point Likert scale. Given that the ISSAQ-SS is still new, further validity evidence is needed to support the use of this scale. For example, prior to comparing ISSAQ-SS subscale scores across groups of interest, it is necessary to establish that the subscales exhibit measurement invariance. To evaluate the extent to which three ISSAQ-SS subscales (Self-Efficacy, Goal Commitment, and Engagement) function equivalently across samples of first-generation and continuing generation college students, I intended to test configural, metric, and scalar invariance across these groups. However, inadequate fit at the configural stage prevented further measurement invariance testing. Efforts to improve model-data fit at the configural level (e.g., removing poor functioning items) resulted in more favorable but ultimately inadequate fit index values. In addition, confirmatory factor analysis models were tested using continuous and categorical methodology to investigate potential differences in fit. Across all conditions, the categorical models fit worse than the continuous models.
Recommended Citation
Herr, Riley, "Examining the psychometric properties of ISSAQ-SS subscales and testing measurement invariance across first-generation status" (2024). Masters Theses, 2020-current. 274.
https://commons.lib.jmu.edu/masters202029/274
