AI Summary & Key Takeaways
AI InsightThe findings indicate the conditions required for measurement invariance in each stage, or in other words, the longitudinal invariance of the scale was achieved.
📌 3 Key Takeaways
Abstract
As with all fields of social sciences, open and distance learning studies frequently use self-reports in their collection of data. Similarly, as with all measurement tools, proof of construct validity through statistical processes is important for testing hypotheses based on the findings, making decisions, and essentially ensuring the tools and methods used are fit for purpose. During construct validity testing processes, the reporting of findings regarding measurement invariance of a scale is an important element that is often neglected. This study aims to portray the longitudinal invariance of the Social Presence Scale (Cakmak, Cebi & Kan, 2014), which is frequently used to determine the social presence in open and distance learning environments research and has confirmed construct validity in the literature, through repeated measurements obtained in an experimental study. This research was conducted on 280 learners in a 3-month interval. The data gathered was analyzed for measurement invariance using the Mplus 7.0 software package in accordance with the four stages described in the literature. The measurement invariance tested in each stage were studied for ΔCFI, and ΔRMSEA values in addition to likelihood chi-square. The findings indicate the conditions required for measurement invariance in each stage, or in other words, the longitudinal invariance of the scale was achieved. The findings of this study may provide a precedent for similar studies in the future.