Development and validation of a multidimensional Chemistry Resilience Scale for secondary level learners

Chemistry learning at the secondary level is cognitively demanding and increasingly shaped by digitally mediated instruction. However, most existing resilience measures are generic and do not capture the subject-specific and technology-related challenges faced by chemistry students. This study developed and validated the Chemistry Resilience Scale (CRS) as a multidimensional instrument for secondary-level learners. An initial pool of 60 items was generated from student open-ended responses and literature review and was refined through expert content validation. Exploratory factor analysis was conducted with 513 students, yielding a 42-item solution. Confirmatory factor analysis on an independent sample of 306 students supported a refined six-factor structure after merging two highly correlated technology-related dimensions. The final model demonstrated acceptable fit (χ²/df = 1.69, RMSEA = 0.047, SRMR = 0.057, CFI = 0.87, TLI = 0.86) and strong internal consistency (α = 0.91), with evidence of convergent and discriminant validity. Rural students reported significantly higher resilience than urban students, while no differences were observed by gender or academic stream. The CRS provides a psychometrically supported tool for assessing resilience in contemporary chemistry learning. Further validation across diverse educational contexts is recommended.

Authors

Institutions

Publication Details

Journal
Discover Education
Published
2026-05-30
DOI
https://doi.org/10.1007/s44217-026-01674-9
Primary Topic
Resilience and Mental Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and validation of a multidimensional Chemistry Resilience Scale for secondary level learners

Muhammad Siddique, Muhammad Shabbir Ali
Discover Education
Resilience and Mental Health
article

Development and validation of a multidimensional Chemistry Resilience Scale for secondary level learners

Muhammad Siddique, Muhammad Shabbir Ali
article en

Abstract

Chemistry learning at the secondary level is cognitively demanding and increasingly shaped by digitally mediated instruction. However, most existing resilience measures are generic and do not capture the subject-specific and technology-related challenges faced by chemistry students. This study developed and validated the Chemistry Resilience Scale (CRS) as a multidimensional instrument for secondary-level learners. An initial pool of 60 items was generated from student open-ended responses and literature review and was refined through expert content validation. Exploratory factor analysis was conducted with 513 students, yielding a 42-item solution. Confirmatory factor analysis on an independent sample of 306 students supported a refined six-factor structure after merging two highly correlated technology-related dimensions. The final model demonstrated acceptable fit (χ²/df = 1.69, RMSEA = 0.047, SRMR = 0.057, CFI = 0.87, TLI = 0.86) and strong internal consistency (α = 0.91), with evidence of convergent and discriminant validity. Rural students reported significantly higher resilience than urban students, while no differences were observed by gender or academic stream. The CRS provides a psychometrically supported tool for assessing resilience in contemporary chemistry learning. Further validation across diverse educational contexts is recommended.

Discover Education
University of Okara (PK), Superior University (PK), University of Education (PK)
Openalex Percentile: Top 4%
Resilience and Mental Health
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.