Psychometric properties of Indonesian physics motivation questionnaire using Rasch model

<span>The transition to online learning during the pandemic has significantly impacted Indonesian students’ physics learning motivation. Recognizing the need for culturally relevant tools to assess this motivation, this study aims to adapt the physics motivation questionnaire (PMQ) to the Indonesian context. This research, which was conducted on 107 secondary and vocational school students, had a balanced gender distribution and used survey methodology. The PMQ, which was adapted from the biology motivation questionnaire (BMQ) through a rigorous forward-backward translation process, was analyzed for its psychometric properties using the Rasch model. This analysis focuses on the content aspects of construct validity, assessing content relevance, representativeness, and technical quality. The results show that the PMQ, using a 4-point Likert scale, effectively addresses these elements, thus confirming its reliability and applicability in the Indonesian educational environment. This study concludes that the culturally adapted PMQ is a valuable instrument for evaluating Indonesian students’ motivation to study physics, especially in the context of ongoing changes in the educational landscape.</span>

Authors

Institutions

Publication Details

Journal
International Journal of Evaluation and Research in Education (IJERE)
Published
2024-09-20
DOI
https://doi.org/10.11591/ijere.v13i6.29285
Primary Topic
Educational Methods and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Psychometric properties of Indonesian physics motivation questionnaire using Rasch model

Raden Oktova, Toni Kus Indratno, Zalik Nuryana, Moh. Irma Sukarelawan et al.
International Journal of Evaluation and Research in Education (IJERE)
Educational Methods and Outcomes
article

Psychometric properties of Indonesian physics motivation questionnaire using Rasch model

Raden Oktova, Toni Kus Indratno, Zalik Nuryana, Moh. Irma Sukarelawan, Jumadi Jumadi, Ariati Dina Puspitasari, Muhamad Farhan Ramadhan
article en

Abstract

<span>The transition to online learning during the pandemic has significantly impacted Indonesian students’ physics learning motivation. Recognizing the need for culturally relevant tools to assess this motivation, this study aims to adapt the physics motivation questionnaire (PMQ) to the Indonesian context. This research, which was conducted on 107 secondary and vocational school students, had a balanced gender distribution and used survey methodology. The PMQ, which was adapted from the biology motivation questionnaire (BMQ) through a rigorous forward-backward translation process, was analyzed for its psychometric properties using the Rasch model. This analysis focuses on the content aspects of construct validity, assessing content relevance, representativeness, and technical quality. The results show that the PMQ, using a 4-point Likert scale, effectively addresses these elements, thus confirming its reliability and applicability in the Indonesian educational environment. This study concludes that the culturally adapted PMQ is a valuable instrument for evaluating Indonesian students’ motivation to study physics, especially in the context of ongoing changes in the educational landscape.</span>

International Journal of Evaluation and Research in Education (IJERE)Vol. 13(6)
Yogyakarta State University (ID), Nanjing Normal University (CN), Universitas Ahmad Dahlan (ID), University of Muhammadiyah Mataram (ID)
Openalex Percentile: Top 78%
Educational Methods and Outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.