The Development and Psychometric Analysis of The Social Media Engagement Adolescent Scale (SMEAS) in Indonesia

This study aims to develop a Social Media Engagement Adolescent Scale (SMEAS) that can comprehensively capture the culture of social media use among Indonesian adolescents and its implications for educational psychology. This study employs a quantitative approach and uses self-report for data collection. A total of 435 participants aged between 17-21 years were involved in this study, and the data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The results of this study showed that the validated Social Media Engagement Adolescent Scale (SMEAS) consists of 24 statement items divided into three dimensions: affective engagement, behavioral engagement, and cognitive engagement based on social media engagement theory from Ni et al. Reliability testing using Cronbach’s α showed that each dimension in SMEAS has good reliability. Item discrimination analysis revealed that all items in SMEAS could differentiate individuals with high and low levels of engagement. Based on the results, SMEAS can be considered a reliable and valid scale for measuring Social Media Engagement among adolescents in Indonesia.

Authors

Publication Details

Journal
Jurnal Kependidikan Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan Pengajaran dan Pembelajaran
Published
2025-03-20
DOI
https://doi.org/10.33394/jk.v11i1.14387
Citations
1
Primary Topic
Educational Methods and Impacts
Type
article
Field-Weighted Citation Impact
8.93
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Development and Psychometric Analysis of The Social Media Engagement Adolescent Scale (SMEAS) in Indonesia

Nadatuljannah Alza, Dewi Maulina, Rizqa Nur Fajar, Farah Almira Nu'ma et al.
1 citations
Jurnal Kependidikan Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan Pengajaran dan Pembelajaran
Educational Methods and Impacts
8.93
article

The Development and Psychometric Analysis of The Social Media Engagement Adolescent Scale (SMEAS) in Indonesia

Nadatuljannah Alza, Dewi Maulina, Rizqa Nur Fajar, Farah Almira Nu'ma, Nurani Fitriyah, Raudhah Mutiara Riyandra
article en
1 citations

Abstract

This study aims to develop a Social Media Engagement Adolescent Scale (SMEAS) that can comprehensively capture the culture of social media use among Indonesian adolescents and its implications for educational psychology. This study employs a quantitative approach and uses self-report for data collection. A total of 435 participants aged between 17-21 years were involved in this study, and the data were analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The results of this study showed that the validated Social Media Engagement Adolescent Scale (SMEAS) consists of 24 statement items divided into three dimensions: affective engagement, behavioral engagement, and cognitive engagement based on social media engagement theory from Ni et al. Reliability testing using Cronbach’s α showed that each dimension in SMEAS has good reliability. Item discrimination analysis revealed that all items in SMEAS could differentiate individuals with high and low levels of engagement. Based on the results, SMEAS can be considered a reliable and valid scale for measuring Social Media Engagement among adolescents in Indonesia.

Jurnal Kependidikan Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan Pengajaran dan PembelajaranVol. 11(1)
Openalex Percentile: Top 5%
Educational Methods and Impacts
8.93
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.