Development and Validation of Social Media Mirage Toolkit

This study aimed to develop and validate a scale for measuring social media addiction. The research used mixed-method design, involving semi-projective approach, in-depth interviews, followed by exploratory and confirmatory analyses (EFA & CFA). Themes emerging from semi-projective test were about psychological dependence & emotional effects, social comparison & validation seeking, critical awareness & risk recognition, and intentional & functional use. The sample for objective test is (N=400), EFA and CFA was run on (n=200) & (n=200) separate data. These themes served as the foundation for item generation and final 12-items SMAS scale revealed three empirically and theoretically supported subscales: Psychosocial Disruption, Affective Social Comparison, and Techno-compulsion. Exploratory factor analysis supported a three-factor solution explaining 52.54% of variance, with a KMO of .75 and significant Bartlett's test. CFA confirmed model fit (x²/df=2.80, df =51, SRMR=.071, GFI=.90) and internal consistency was good (Cronbach’s Alpha α = 0.87 full scale). Grounded in the Psychosocial Model and Components of Addiction Theory, this scale serves as psychometrically reliable and valid tool to assess and evaluate social media addiction. It has important and practical implications for education, prevention programs, media studies and policies, behavioral studies, and for future researches.

Authors

Publication Details

Journal
ACADEMIA International Journal for Social Sciences
Published
2025-09-15
DOI
https://doi.org/10.63056/acad.004.03.0781
Primary Topic
Impact of Technology on Adolescents
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 Social Media Mirage Toolkit

Muhammad Abubakar, Stamatia Bibi, Andleeb Zahra, Anisah Azhar et al.
ACADEMIA International Journal for Social Sciences
Impact of Technology on Adolescents
article

Development and Validation of Social Media Mirage Toolkit

Muhammad Abubakar, Stamatia Bibi, Andleeb Zahra, Anisah Azhar, Tanzeela Rubab, Laraib
article en

Abstract

This study aimed to develop and validate a scale for measuring social media addiction. The research used mixed-method design, involving semi-projective approach, in-depth interviews, followed by exploratory and confirmatory analyses (EFA & CFA). Themes emerging from semi-projective test were about psychological dependence & emotional effects, social comparison & validation seeking, critical awareness & risk recognition, and intentional & functional use. The sample for objective test is (N=400), EFA and CFA was run on (n=200) & (n=200) separate data. These themes served as the foundation for item generation and final 12-items SMAS scale revealed three empirically and theoretically supported subscales: Psychosocial Disruption, Affective Social Comparison, and Techno-compulsion. Exploratory factor analysis supported a three-factor solution explaining 52.54% of variance, with a KMO of .75 and significant Bartlett's test. CFA confirmed model fit (x²/df=2.80, df =51, SRMR=.071, GFI=.90) and internal consistency was good (Cronbach’s Alpha α = 0.87 full scale). Grounded in the Psychosocial Model and Components of Addiction Theory, this scale serves as psychometrically reliable and valid tool to assess and evaluate social media addiction. It has important and practical implications for education, prevention programs, media studies and policies, behavioral studies, and for future researches.

ACADEMIA International Journal for Social SciencesVol. 4(3)
Openalex Percentile: Top 66%
Impact of Technology on Adolescents
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.