Contemporary advanced statistical methods for the science of educational research: Principal components analysis versus L’ analysee factorielle des correspondances

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Paschalina Ntotsi
Sofia D Anastasiadou

Abstract

The current paper analyses two different statistical techniques: i.e., principal components analysis (PCA) and correspondence analysis (L’analysee factorielle des correspondances) (AFC). A survey was carried out using a structured questionnaire for a sample of 135 nurses which studied in the School of Pedagogical and Technological Education (ASPETE) in Greece. Tangibility, Reliability, Responsiveness, Assurance, Empathy and Associability subscales are related to Qualitative Services ASPETE offers. These subscales were measured by 24 items, rated on a seven-point Likert scale. The study focuses on the presentation of the two main types of clustering methods, PCA and AFC. Lee and Lin’s model contains a one-item scale developed to measure overall service quality and a one-item scale for customer satisfaction. The assessment of the students’ satisfaction degree is evaluated based on a seven-step on the Likert scale statement, investigating the extent that the respondents are satisfied from the experience they had with the specific tertiary education organisation (CSF).


Keywords: Advanced, statistical, methods, AFC, PCA

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How to Cite
Ntotsi, P., & Anastasiadou, S. D. (2019). Contemporary advanced statistical methods for the science of educational research: Principal components analysis versus L’ analysee factorielle des correspondances. New Trends and Issues Proceedings on Humanities and Social Sciences, 6(1), 246-255. https://doi.org/10.18844/prosoc.v6i1.4176
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