Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Design
2.2. Instruments
2.3. Study Setting, Sample Size, and Sampling
2.4. Study Participants
2.5. Data Analyses
2.5.1. Descriptive Statistics, Reliabilities, and Correlation
2.5.2. Rasch Measurement Modeling
2.5.3. Bentler & Liang’s Maximum Likelihood Estimation Method
- Step 1: Perform a Single Conventional Confirmatory Factor Analysis
- Step 2: Estimation of Between-Group Variation
- Step 3: Obtain Fitness of MCFA
- Step 4: Estimate Within-Level Model
- Step 5: Estimate Between-Level Model
3. Results
3.1. Descriptive Statistics
3.2. Rasch Measurement Model
3.3. Wright’s Map of IES-R
3.4. Multilevel Confirmatory Factor Analysis for the IES-R
3.4.1. Step 1: Performing Conventional CFA for the Total Sample Covariance Matrix
3.4.2. Step 2: Estimation of Between-Level Variations
3.4.3. Step 3: Fitness of Multilevel CFA for the IES-R
3.4.4. Step 4: Estimation of Within-Level Model
3.4.5. Step 5: Estimation of Between-Level Models
3.5. Examining for Measurement Invariance across Levels in the Analysis
4. Discussion
Strengths and Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AIC | Akaike information criterion |
CFA | Confirmatory factor analysis |
CFI | Comparative Fit Index |
GFI | Goodness of Fit Index |
ICC | Intraclass correlation |
IES-R | Impact of Event Scale-Revised |
IFI | Incremental fit index |
MCFA | Multilevel confirmatory factor analysis |
NFI | Normed Fit Index |
NNFI | (Non) Normed Fit Index |
PVUNE | Proportion of Variance Unexplained |
PTSD | Post-traumatic stress disorder |
RMSEA | Root mean square error of approximation |
RMR | Root mean square residual |
SRMR | Standardized root mean square residual |
References
- Wang, C.; Horby, P.W.; Hayden, F.G.; Gao, G.F. A novel coronavirus outbreak of global health concern. Lancet 2020, 395, 470–473. [Google Scholar] [CrossRef]
- Kang, L.; Li, Y.; Hu, S.; Chen, M.; Yang, C.; Yang, B.X.; Wang, Y.; Hu, J.; Lai, J.; Ma, X. The mental health of medical workers in Wuhan, China dealing with the 2019 novel coronavirus. Lancet Psychiatry 2020, 7, e14. [Google Scholar] [CrossRef]
- Khatatbeh, M.; Al-Maqableh, H.O.; Albalas, S.; Al Ajlouni, S.; A’aqoulah, A.; Khatatbeh, H.; Kasasbeh, M.A.; Khatatbeh, I.; Albalas, R.; Ala’a, B. Attitudes and commitment toward precautionary measures against COVID-19 amongst the Jordanian population: A large-scale cross-sectional survey. Front. Public Health 2021, 9, 745149. [Google Scholar] [CrossRef] [PubMed]
- Al-Amer, R.; Darwish, M.; Malak, M.; Ali, A.M.; Al Weldat, K.; Alkhamees, A.; Alshammari, K.S.; Abuzied, Y.; Randall, S. Nurses experience of caring for patients with COVID-19: A phenomenological study. Front. Psychiatry 2022, 13, 922410. [Google Scholar] [CrossRef]
- Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; McIntyre, R.S.; Choo, F.N.; Tran, B.; Ho, R.; Sharma, V.K.; et al. A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain Behav. Immun. 2020, 87, 40–48. [Google Scholar] [CrossRef]
- Alkhamees, A.A.; Alrashed, S.A.; Alzunaydi, A.A.; Almohimeed, A.S.; Aljohani, M.S. The psychological impact of COVID-19 pandemic on the general population of Saudi Arabia. Compr. Psychiatry 2020, 102, 152192. [Google Scholar] [CrossRef]
- Mahase, E. China coronavirus: WHO declares international emergency as death toll exceeds 200. Br. Med. J. 2020, 368, m408. [Google Scholar] [CrossRef]
- WHO. COVID-19 Weekly Epidemiological Update; WHO: Geneva, Switzerland, 2022; Volume 7, 21p.
- Abiddine, F.Z.E.; Aljaberi, M.A.; Gadelrab, H.F.; Lin, C.-Y.; Muhammed, A. Mediated effects of insomnia in the association between problematic social media use and subjective well-being among university students during COVID-19 pandemic. Sleep Epidemiol. 2022, 2, 100030. [Google Scholar] [CrossRef]
- Al-Tammemi, A.a.B.; Barakat, M.; Al Tamimi, D.a.; Alhallaq, S.A.; Al Hasan, D.M.; Khasawneh, G.M.; Naqera, K.A.; Jaradat, R.M.; Farah, F.W.; Al-Maqableh, H.O.; et al. Beliefs Toward Smoking and COVID-19, and the Pandemic Impact on Smoking Behavior and Quit Intention: Findings from a Community-Based Cross-Sectional Study in Jordan. Tob. Use Insights 2021, 14, 1179173X211053022. [Google Scholar] [CrossRef]
- Al-Tammemi, A.a.; Nheili, R.; Jibuaku, C.; Tamimi, D.a.A.; Aljaberi, M.; Khatatbeh, M.; Barakat, M.; Al-Maqableh, H.; Fakhouri, H. A Qualitative Exploration of University Students’ Perspectives on Distance Education in Jordan: An Application of Moore’s Theory of Transactional Distance. Front. Educ. 2022, 7, 960660. [Google Scholar] [CrossRef]
- Mohammed, L.A.; Aljaberi, M.A.; Amidi, A.; Abdulsalam, R.; Lin, C.-Y.; Hamat, R.A.; Abdallah, A.M. Exploring Factors Affecting Graduate Students’ Satisfaction toward E-Learning in the Era of the COVID-19 Crisis. Eur. J. Investig. Health Psychol. Educ. 2022, 12, 1121–1142. [Google Scholar] [CrossRef] [PubMed]
- Folayan, M.O.; Ibigbami, O.; ElTantawi, M.; Abeldaño, G.F.; Ara, E.; Ayanore, M.A.; Ellakany, P.; Gaffar, B.; Al-Khanati, N.M.; Idigbe, I.; et al. Factors associated with COVID-19 pandemic induced post-traumatic stress symptoms among adults living with and without HIV in Nigeria: A cross-sectional study. BMC Psychiatry 2022, 22, 48. [Google Scholar] [CrossRef]
- Ellakany, P.; Zuñiga, R.A.A.; El Tantawi, M.; Brown, B.; Aly, N.M.; Ezechi, O.; Uzochukwu, B.; Abeldaño, G.F.; Ara, E.; Ayanore, M.A.; et al. Impact of the COVID-19 pandemic on student’ sleep patterns, sexual activity, screen use, and food intake: A global survey. PLoS ONE 2022, 17, e0262617. [Google Scholar] [CrossRef] [PubMed]
- Garbóczy, S.; Szemán-Nagy, A.; Ahmad, M.S.; Harsányi, S.; Ocsenás, D.; Rekenyi, V.; Al-Tammemi, A.a.B.; Kolozsvári, L.R. Health anxiety, perceived stress, and coping styles in the shadow of the COVID-19. BMC Psychol. 2021, 9, 53. [Google Scholar] [CrossRef]
- Ali, A.M.; Kunugi, H. Physical Frailty/Sarcopenia as a Key Predisposing Factor to Coronavirus Disease 2019 (COVID-19) and Its Complications in Older Adults. BioMed 2021, 1, 11–40. [Google Scholar] [CrossRef]
- Kukreti, S.; Ahorsu, D.K.; Strong, C.; Chen, I.-H.; Lin, C.-Y.; Ko, N.-Y.; Griffiths, M.D.; Chen, Y.-P.; Kuo, Y.-J.; Pakpour, A.H. Post-Traumatic Stress Disorder in Chinese Teachers during COVID-19 Pandemic: Roles of Fear of COVID-19, Nomophobia, and Psychological Distress. Healthcare 2021, 9, 1288. [Google Scholar] [CrossRef]
- Suo, X.; Zuo, C.; Lan, H.; Pan, N.; Zhang, X.; Kemp, G.J.; Wang, S.; Gong, Q. COVID-19 vicarious traumatization links functional connectome to general distress. NeuroImage 2022, 255, 119185. [Google Scholar] [CrossRef]
- Jiang, W.; Ren, Z.; Yu, L.; Tan, Y.; Shi, C. A Network Analysis of Post-traumatic Stress Disorder Symptoms and Correlates During the COVID-19 Pandemic. Front. Psychiatry 2020, 11, 568037. [Google Scholar] [CrossRef]
- Ali, S.; Maguire, S.; Marks, E.; Doyle, M.; Sheehy, C. Psychological impact of the COVID-19 pandemic on healthcare workers at acute hospital settings in the South-East of Ireland: An observational cohort multicentre study. BMJ Open 2020, 10, e042930. [Google Scholar] [CrossRef]
- Ali, A.M.; Hori, H.; Kim, Y.; Kunugi, H. The Depression Anxiety Stress Scale 8-Items Expresses Robust Psychometric Properties as an Ideal Shorter Version of the Depression Anxiety Stress Scale 21 Among Healthy Respondents From Three Continents. Front. Psychol. 2022, 13, 799769. [Google Scholar] [CrossRef]
- Ali, A.M.; Alkhamees, A.A.; Abd Elhay, E.S.; Taha, S.M.; Hendawy, A.O. COVID-19-Related Psychological Trauma and Psychological Distress Among Community-Dwelling Psychiatric Patients: People Struck by Depression and Sleep Disorders Endure the Greatest Burden. Front. Public Health 2022, 9, 799812. [Google Scholar] [CrossRef] [PubMed]
- Ali, A.M.; Al-Amer, R.; Kunugi, H.; Stănculescu, E.; Taha, S.M.; Saleh, M.Y.; Alkhamees, A.A.; Hendawy, A.O. The Arabic Version of the Impact of Event Scale-Revised: Psychometric Evaluation among Psychiatric Patients and the General Public within the Context of COVID-19 Outbreak and Quarantine as Collective Traumatic Events. J. Pers. Med. 2022, 12, 681. [Google Scholar] [CrossRef] [PubMed]
- Al-Amer, R.; Malak, M.Z.; Burqan, H.M.R.; Stănculescu, E.; Nalubega, S.; Alkhamees, A.A.; Hendawy, A.O.; Ali, A.M. Emotional Reaction to the First Dose of COVID-19 Vaccine: Postvaccination Decline in Anxiety and Stress among Anxious Individuals and Increase among Individuals with Normal Prevaccination Anxiety Levels. J. Pers. Med. 2022, 12, 912. [Google Scholar] [CrossRef]
- Ali, A.M.; Alkhamees, A.A.; Hori, H.; Kim, Y.; Kunugi, H. The Depression Anxiety Stress Scale 21: Development and Validation of the Depression Anxiety Stress Scale 8-Item in Psychiatric Patients and the General Public for Easier Mental Health Measurement in a Post COVID-19 World. Int. J. Environ. Res. Public Health 2021, 18, 10142. [Google Scholar] [CrossRef] [PubMed]
- Akour, A.; AlMuhaissen, S.A.; Nusair, M.B.; Al-Tammemi, A.a.B.; Mahmoud, N.N.; Jalouqa, S.; Alrawashdeh, M.N. The untold story of the COVID-19 pandemic: Perceptions and views towards social stigma and bullying in the shadow of COVID-19 illness in Jordan. SN Soc. Sci. 2021, 1, 240. [Google Scholar] [CrossRef]
- Al-Tammemi, A.a.B.; Tarhini, Z. Beyond equity: Advocating theory-based health promotion in parallel with COVID-19 mass vaccination campaigns. Public Health Pract. 2021, 2, 100142. [Google Scholar] [CrossRef]
- Arinah, W.; Musheer, J.; Juni, M.H. Health Care Provision and Equity. Int. J. Public Health Clin. Sci. 2016, 3, 2289–7577. [Google Scholar]
- Pramukti, I.; Strong, C.; Sitthimongkol, Y.; Setiawan, A.; Pandin, M.G.R.; Yen, C.-F.; Lin, C.-Y.; Griffiths, M.D.; Ko, N.-Y. Anxiety and Suicidal Thoughts During the COVID-19 Pandemic: Cross-Country Comparative Study Among Indonesian, Taiwanese, and Thai University Students. J. Med. Internet Res. 2020, 22, e24487. [Google Scholar] [CrossRef]
- Chang, K.-C.; Strong, C.; Pakpour, A.H.; Griffiths, M.D.; Lin, C.-Y. Factors related to preventive COVID-19 infection behaviors among people with mental illness. J. Formos. Med. Assoc. 2020, 119, 1772–1780. [Google Scholar] [CrossRef]
- Zhao, S.Z.; Luk, T.T.; Wu, Y.; Weng, X.; Wong, J.Y.H.; Wang, M.P.; Lam, T.H. Factors Associated With Mental Health Symptoms During the COVID-19 Pandemic in Hong Kong. Front. Psychiatry 2021, 12, 617397. [Google Scholar] [CrossRef]
- Al-Tammemi, A.a.B.; Tarhini, Z.; Akour, A. A swaying between successive pandemic waves and pandemic fatigue: Where does Jordan stand? Ann. Med. Surg. 2021, 65, 102298. [Google Scholar] [CrossRef]
- Ali, A.M.; Kunugi, H. Skeletal Muscle Damage in COVID-19: A Call for Action. Medicina 2021, 57, 372. [Google Scholar] [CrossRef]
- Ali, A.M.; Kunugi, H. COVID-19: A pandemic that threatens physical and mental health by promoting physical inactivity. Sports Med. Health Sci. 2020, 2, 221–223. [Google Scholar] [CrossRef]
- Folayan, M.O.; Ibigbami, O.; Brown, B.; El Tantawi, M.; Aly, N.M.; Ezechi, O.C.; Abeldaño, G.F.; Ara, E.; Ayanore, M.A.; Ellakany, P. Factors associated with experiences of fear, anxiety, depression, and changes in sleep pattern during the COVID-19 pandemic among adults in Nigeria: A cross-sectional study. Front. Public Health 2022, 10, 779498. [Google Scholar] [CrossRef] [PubMed]
- Fares, Z.E.A.; Al-Tammemi, A.B.A.; Gadelrab, H.F.; Lin, C.-Y.; Aljaberi, M.A.; Alhuwailah, A.; Roubi, M.L. Arabic COVID-19 Psychological Distress Scale: Development and initial validation. BMJ Open 2021, 11, e046006. [Google Scholar] [CrossRef]
- Mamun, M.A.; Sakib, N.; Gozal, D.; Bhuiyan, A.K.M.I.; Hossain, S.; Bodrud-Doza, M.; Al Mamun, F.; Hosen, I.; Safiq, M.B.; Abdullah, A.H.; et al. The COVID-19 pandemic and serious psychological consequences in Bangladesh: A population-based nationwide study. J. Affect. Disord. 2021, 279, 462–472. [Google Scholar] [CrossRef]
- Pakpour, A.H.; Griffiths, M.D.; Lin, C.-Y. Assessing Psychological Response to the COVID-19: The Fear of COVID-19 Scale and the COVID Stress Scales. Int. J. Ment. Health Addict. 2021, 19, 2407–2410. [Google Scholar] [CrossRef]
- Lau, J.T.; Yang, X.; Pang, E.; Tsui, H.; Wong, E.; Wing, Y.K. SARS-related perceptions in Hong Kong. Emerg. Infect. Dis. 2005, 11, 417–424. [Google Scholar] [CrossRef] [PubMed]
- Rubin, G.J.; Potts, H.; Michie, S. The impact of communications about swine flu (influenza A H1N1v) on public responses to the outbreak: Results from 36 national telephone surveys in the UK. Health Technol. Assess. 2010, 14, 183–266. [Google Scholar] [CrossRef] [PubMed]
- Brooks, S.K.; Webster, R.K.; Smith, L.E.; Woodland, L.; Wessely, S.; Greenberg, N.; Rubin, G.J. The psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Lancet 2020, 395, 912–920. [Google Scholar] [CrossRef]
- Weiss, D.S.; Marmar, C. The impact of event scale-revised. In Assessing Psychological Trauma and PTSD: A Handbook for Practitioners; Wilson, J.P., Keane, T.M., Eds.; Guilford Press: New York, NY, USA, 1997; Volume 19, pp. 399–411. [Google Scholar]
- Weiss, D.S. The impact of event scale: Revised. In Cross-Cultural Assessment of Psychological Trauma and PTSD’; Wilson, J.P., Tang, S.-K., Catherine, C., Eds.; Springer: New York, NY, USA, 2007; pp. 219–238. [Google Scholar]
- Cusack, K.; Spates, C.R. The cognitive dismantling of eye movement desensitization and reprocessing (EMDR) treatment of posttraumatic stress disorder (PTSD). J. Anxiety Disord. 1999, 13, 87–99. [Google Scholar] [CrossRef]
- Pfefferbaum, B.; Seale, T.W.; McDonald, N.B.; Brandt Jr, E.N.; Rainwater, S.M.; Maynard, B.T.; Meierhoefer, B.; Miller, P.D. Posttraumatic stress two years after the Oklahoma City bombing in youths geographically distant from the explosion. Psychiatry 2000, 63, 358–370. [Google Scholar] [CrossRef]
- Sim, K.; Chan, Y.H.; Chong, P.N.; Chua, H.C.; Soon, S.W. Psychosocial and coping responses within the community health care setting towards a national outbreak of an infectious disease. J. Psychosom. Res. 2010, 68, 195–202. [Google Scholar] [CrossRef] [PubMed]
- Odriozola-Gonzalez, P.; Planchuelo-Gomez, A.; Irurtia, M.J.; de Luis-Garcia, R. Psychological effects of the COVID-19 outbreak and lockdown among students and workers of a Spanish university. Psychiatry Res. 2020, 290, 113108. [Google Scholar] [CrossRef] [PubMed]
- Creamer, M.; Bell, R.; Failla, S. Psychometric properties of the impact of event scale—Revised. Behav. Res. Ther. 2003, 41, 1489–1496. [Google Scholar] [CrossRef]
- Tan, B.Y.; Chew, N.W.; Lee, G.K.; Jing, M.; Goh, Y.; Yeo, L.L.; Zhang, K.; Chin, H.-K.; Ahmad, A.; Khan, F.A. Psychological impact of the COVID-19 pandemic on health care workers in Singapore. Ann. Intern. Med. 2020, 173, 317–320. [Google Scholar] [CrossRef]
- Zhang, M.W.; Ho, C.S.; Fang, P.; Lu, Y.; Ho, R.C. Usage of social media and smartphone application in assessment of physical and psychological well-being of individuals in times of a major air pollution crisis. JMIR Mhealth Uhealth 2014, 2, e16. [Google Scholar] [CrossRef]
- Zhang, M.W.; Ho, C.S.; Fang, P.; Lu, Y.; Ho, R. Methodology of developing a smartphone application for crisis research and its clinical application. Technol. Health Care 2014, 22, 547–559. [Google Scholar] [CrossRef]
- Matsuishi, K.; Kawazoe, A.; Imai, H.; Ito, A.; Mouri, K.; Kitamura, N.; Miyake, K.; Mino, K.; Isobe, M.; Takamiya, S. Psychological impact of the pandemic (H1N1) 2009 on general hospital workers in Kobe. Psychiatry Clin. Neurosci. 2012, 66, 353–360. [Google Scholar] [CrossRef]
- Tan, W.; Hao, F.; McIntyre, R.S.; Jiang, L.; Jiang, X.; Zhang, L.; Zhao, X.; Zou, Y.; Hu, Y.; Luo, X. Is returning to work during the COVID-19 pandemic stressful? A study on immediate mental health status and psychoneuroimmunity prevention measures of Chinese workforce. Brain Behav. Immun. 2020, 87, 84–92. [Google Scholar] [CrossRef]
- Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; Ho, C.S.; Ho, R.C. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int. J. Environ. Res. Public Health 2020, 17, 1729. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hao, F.; Tan, W.; Jiang, L.; Zhang, L.; Zhao, X.; Zou, Y.; Hu, Y.; Luo, X.; Jiang, X.; McIntyre, R.S. Do psychiatric patients experience more psychiatric symptoms during COVID-19 pandemic and lockdown? A case-control study with service and research implications for immunopsychiatry. Brain Behav. Immun. 2020, 87, 100–106. [Google Scholar] [CrossRef] [PubMed]
- Puigcerver, M.J.B.; Soler, E.V.; Mateo, M.A.B.; Badia, M.C.R.; Reig, R. Propiedades psicométricas de la versión española de la Escala Revisada de Impacto del Estresor (EIE-R). Análisis Modif. Conducta 2001, 27, 581–604. [Google Scholar]
- Rodriguez-Rey, R.; Garrido-Hernansaiz, H.; Collado, S. Psychological impact of COVID-19 in Spain: Early data report. Psychol. Trauma Theory Res. Pract. Policy 2020, 12, 550. [Google Scholar] [CrossRef]
- Zhang, Y.; Ma, Z.F. Impact of the COVID-19 pandemic on mental health and quality of life among local residents in Liaoning Province, China: A cross-sectional study. Int. J. Environ. Res. Public Health Rep. 2020, 17, 2381. [Google Scholar] [CrossRef]
- Beck, J.G.; Grant, D.M.; Read, J.P.; Clapp, J.D.; Coffey, S.F.; Miller, L.M.; Palyo, S.A. The Impact of Event Scale-Revised: Psychometric properties in a sample of motor vehicle accident survivors. J. Anxiety Disord. 2008, 22, 187–198. [Google Scholar] [CrossRef]
- Craparo, G.; Faraci, P.; Rotondo, G.; Gori, A. The Impact of Event Scale—Revised: Psychometric properties of the Italian version in a sample of flood victims. Neuropsychiatr. Dis. Treat. 2013, 9, 1427–1432. [Google Scholar] [CrossRef]
- Qiu, J.; Shen, B.; Zhao, M.; Wang, Z.; Xie, B.; Xu, Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations. Gen. Psychiatry 2020, 33, e100213. [Google Scholar] [CrossRef]
- Asukai, N.; Kato, H.; Kawamura, N.; Kim, Y.; Yamamoto, K.; Kishimoto, J.; Miyake, Y.; Nishizono-Maher, A. Reliabiligy and validity of the Japanese-language version of the impact of event scale-revised (Ies-RJ): Four studies of different traumatic events. J. Nerv. Ment. Dis. 2002, 190, 175–182. [Google Scholar] [CrossRef]
- Norhayati, M.; Aniza, A. Psychometric properties of the Malay version of Impact of Event Scale-Revised (IES-R). Int. J. Collab. Res. Intern. Med. Public Health 2014, 6, 39–51. [Google Scholar]
- Brunet, A.; St-Hilaire, A.; Jehel, L.; King, S. Validation of a French version of the impact of event scale-revised. Can. J. Psychiatry 2003, 48, 56–61. [Google Scholar] [CrossRef]
- Baumert, J.; Simon, H.; Gundel, H.; Schmitt, C.; Ladwig, K.H. The Impact of Event Scale-Revised: Evaluation of the subscales and correlations to psychophysiological startle response patterns in survivors of a life-threatening cardiac event: An analysis of 129 patients with an implanted cardioverter defibrillator. J. Affect. Disord. 2004, 82, 29–41. [Google Scholar] [CrossRef] [PubMed]
- Fan, C.-W.; Chang, K.-C.; Lee, K.-Y.; Yang, W.-C.; Pakpour, A.H.; Potenza, M.N.; Lin, C.-Y. Rasch Modeling and Differential Item Functioning of the Self-Stigma Scale-Short Version among People with Three Different Psychiatric Disorders. Int. J. Environ. Res. Public Health 2022, 19, 8843. [Google Scholar] [CrossRef] [PubMed]
- Mamun, M.A.; Alimoradi, Z.; Gozal, D.; Manzar, M.D.; Broström, A.; Lin, C.-Y.; Huang, R.-Y.; Pakpour, A.H. Validating Insomnia Severity Index (ISI) in a Bangladeshi Population: Using Classical Test Theory and Rasch Analysis. Int. J. Environ. Res. Public Health 2022, 19, 225. [Google Scholar] [CrossRef] [PubMed]
- Lin, C.-Y.; Pakpour, A.H. Using Hospital Anxiety and Depression Scale (HADS) on patients with epilepsy: Confirmatory factor analysis and Rasch models. Seizure—Eur. J. Epilepsy 2017, 45, 42–46. [Google Scholar] [CrossRef]
- Yen, C.-F.; Huang, Y.-T.; Potenza, M.N.; Tsai, T.-T.; Lin, C.-Y.; Tsang, H.W.H. Measure of Internalized Sexual Stigma for Lesbians and Gay Men (MISS-LG) in Taiwan: Psychometric Evidence from Rasch and Confirmatory Factor Analysis. Int. J. Environ. Res. Public Health 2021, 18, 13352. [Google Scholar] [CrossRef]
- Poorebrahim, A.; Lin, C.-Y.; Imani, V.; Kolvani, S.S.; Alaviyoun, S.A.; Ehsani, N.; Pakpour, A.H. Using Mindful Attention Awareness Scale on male prisoners: Confirmatory factor analysis and Rasch models. PLoS ONE 2021, 16, e0254333. [Google Scholar] [CrossRef]
- Broström, A.; Ulander, M.; Nilsen, P.; Lin, C.-Y.; Pakpour, A.H. Development and psychometric evaluation of the Motivation to Use CPAP Scale (MUC-S) using factorial structure and Rasch analysis among patients with obstructive sleep apnea before CPAP treatment is initiated. Sleep Breath. 2021, 25, 627–637. [Google Scholar] [CrossRef]
- Lin, C.-Y.; Imani, V.; Griffiths, M.D.; Pakpour, A.H. Psychometric Properties of the Persian Generalized Trust Scale: Confirmatory Factor Analysis and Rasch Models and Relationship with Quality of Life, Happiness, and Depression. Int. J. Ment. Health Addict. 2021, 19, 1854–1865. [Google Scholar] [CrossRef]
- Raudenbush, S.W.; Bryk, A.S. Hierarchical Linear Models: Applications and Data Analysis Methods, 2nd ed.; Sage: Chicago, IL, USA, 2002; Volume 1. [Google Scholar]
- Ross, J.; Murphy, D.; Armour, C. A network analysis of DSM-5 posttraumatic stress disorder and functional impairment in UK treatment-seeking veterans. J. Anxiety Disord. 2018, 57, 7–15. [Google Scholar] [CrossRef]
- Manuel, A. A classification system for research designs in psychology. An. Psicol. 2013, 29, 1038–1059. [Google Scholar]
- Montero, I.; León, O.G. A guide for naming research studies in Psychology. Int. J. Clin. Health Psychol. 2007, 7, 847–862. [Google Scholar]
- Horowitz, M.; Wilner, N.; Alvarez, W. Impact of Event Scale: A measure of subjective stress. Psychosom. Med. 1979, 41, 209–218. [Google Scholar] [CrossRef]
- Luke, D.A. Multilevel Modeling; SAGE Publications, Incorporated: Thousand Oaks, CA, USA, 2004; Volume 143. [Google Scholar]
- Rice, N.; Leyland, A. Multilevel models: Applications to health data. J. Health Serv. Res. Policy 1996, 1, 154–164. [Google Scholar] [CrossRef] [PubMed]
- Selig, J.P.; Card, N.A.; Little, T.D. Latent variable structural equation modeling in cross-cultural research: Multigroup and multilevel approaches. In Multilevel Analysis of Individuals and Cultures; Psychology Press Ltd.: London, UK, 2014; pp. 93–119. [Google Scholar]
- Brown, T.A. Methodology in the Social Sciences. Confirmatory Factor Analysis for Applied Research, 2nd ed.; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
- Byrne, B.M. Structural Equation Modeling with EQS: Basic Concepts, Applications, and Programming, 2nd ed.; Routledge: New York, NY, USA, 2006. [Google Scholar]
- Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
- Field, A. Discovering Statistics Using IBM SPSS Statistics; Sage: Chicago, IL, USA, 2013. [Google Scholar]
- Nunnally, J.C.; Bernstein, I. Psychometric Theory (McGraw-Hill Series in Psychology); McGraw-Hill: New York, NY, USA, 1994; Volume 3. [Google Scholar]
- Stevens, J.P. Applied Multivariate Statistics for the Social Sciences; Routledge: Abingdon, UK, 2012. [Google Scholar]
- Tabachnick, B.; Fidell, L. Using Multivariate Statistics, 6th ed.; Pearson New International Edition; Pearson Education: London, UK, 2007. [Google Scholar]
- Bond, T.G.; Fox, C.M. Applying the Rasch Model: Fundamental Measurement in the Human Sciences, 3rd ed.; Routledge: New York, NY, USA, 2015. [Google Scholar]
- Linacre, J.M. WINSTEPS Rasch Measurement Computer Program. Chicago. 2006. Available online: https://www.winsteps.com/index.htm (accessed on 23 January 2021).
- Bentler, P.M. EQS 6 Structural Equations Program Manual; Multivariate Software Inc.: Encino, CA, USA, 2006; Volume 6, p. 422. [Google Scholar]
- Cheung, M.W.-L.; Au, K. Applications of multilevel structural equation modeling to cross-cultural research. Struct. Equ. Modeling 2005, 12, 598–619. [Google Scholar] [CrossRef] [Green Version]
- Schumacker, R.E.; Lomax, R.G. A Beginner’s Guide to Structural Equation Modeling, 3rd ed.; Routledge: New York, NY, USA, 2010. [Google Scholar]
- Aljaberi, M.A.; Juni, M.H.; Al-Maqtari, R.A.; Lye, M.S.; Saeed, M.A.; Al-Dubai, S.A.R.; Kadir Shahar, H. Relationships among perceived quality of healthcare services, satisfaction and behavioural intentions of international students in Kuala Lumpur, Malaysia: A cross-sectional study. BMJ Open 2018, 8, e021180. [Google Scholar] [CrossRef]
- Noman, S.; Shahar, H.K.; Rahman, H.A.; Ismail, S.; Aljaberi, M.A.; Abdulrahman, M.N. Factor structure and internal reliability of breast cancer screening Champion’s Health Belief Model Scale in Yemeni women in Malaysia: A cross-sectional study. BMC Women’s Health 2021, 21, 437. [Google Scholar] [CrossRef]
- Uzir, M.U.H.; Al Halbusi, H.; Thurasamy, R.; Thiam Hock, R.L.; Aljaberi, M.A.; Hasan, N.; Hamid, M. The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country. J. Retail. Consum. Serv. 2021, 63, 102721. [Google Scholar] [CrossRef]
- Alareqe, N.A.; Hassan, S.A.; Kamarudin, E.M.E.; Nordin, M.S.; Ashureay, N.M.; Mohammed, L.A.; Al-Jaberi, M.A. Validity of Adult Psychopathology Model Using Psychiatric Patients Sample from a Developing Country: Confirmatory Factor Analysis. Res. Sq. 2021, 1–20. [Google Scholar] [CrossRef]
- Little, T.D. Longitudinal Structural Equation Modeling; The Guilford Press: New York, NY, USA, 2013. [Google Scholar]
Countries | Sample Size | Percentage |
---|---|---|
Malaysia | 282 | 28.2 |
Yemen | 111 | 11.1 |
Indonesia | 73 | 7.3 |
Nigeria | 62 | 6.2 |
Sri Lanka | 65 | 6.5 |
Tunisia | 43 | 4.3 |
Pakistan | 27 | 2.7 |
Somalia | 24 | 2.4 |
Syria | 22 | 2.2 |
Saudi Arabia | 22 | 2.2 |
Vietnam | 23 | 2.3 |
Bangladesh | 29 | 2.9 |
China | 23 | 2.3 |
India | 20 | 2.0 |
Iraq | 34 | 3.4 |
Egypt | 22 | 2.2 |
Algeria | 24 | 2.4 |
Guinea | 21 | 2.1 |
Afghanistan | 25 | 2.5 |
Others | 47 | 4.7 |
Total | 999 | 100.0 |
n | % | |
---|---|---|
Gender | ||
Female | 554 | 55.5 |
Male | 445 | 44.5 |
Marital status | ||
Single | 464 | 46.4 |
Married | 496 | 49.6 |
Engaged | 27 | 2.7 |
Divorced | 12 | 1.2 |
Employment status | ||
Students | 551 | 55.2 |
Healthcare workers | 53 | 5.3 |
Educational profession | 230 | 23.0 |
Administrative professional | 56 | 5.6 |
Other | 109 | 10.9 |
Education level | ||
High school equivalent | 31 | 3.1 |
Bachelor | 285 | 28.5 |
Diploma | 79 | 7.9 |
Master | 367 | 36.7 |
PhD | 237 | 23.7 |
Items | Mean ± SD | Skewness (≤−/+3) | Kurtosis (≤−/+7) | Corrected Item Total Correlation | Squared Multiple Correlation (≥0.30) | McDonald’s ω (≥0.70) |
---|---|---|---|---|---|---|
Q1_Int | 1.09 ± 1.048 | 0.872 | 0.208 | 0.610 | 0.424 | 0.873 |
Q2_Int | 0.87 ± 1.116 | 1.249 | 0.750 | 0.568 | 0.337 | 0.877 |
Q3_Int | 1.23 ± 1.099 | 0.746 | −0.159 | 0.714 | 0.559 | 0.862 |
Q6_Int | 0.98 ± 1.072 | 0.991 | 0.304 | 0.703 | 0.515 | 0.863 |
Q9_Int | 0.86 ± 1.079 | 1.194 | 0.664 | 0.684 | 0.506 | 0.865 |
Q14_Int | 0.86 ± 1.043 | 1.136 | 0.608 | 0.615 | 0.418 | 0.872 |
Q16_Int | 0.91 ± 1.059 | 1.093 | 0.551 | 0.757 | 0.593 | 0.857 |
Q20_Int | 0.41 ± 0.890 | 2.364 | 5.056 | 0.528 | 0.293 | 0.880 |
Overall Intrusion | 7.21 ± 6.23 | 0.883 | ||||
Q5_Avo | 1.29 ± 1.249 | 0.707 | −0.521 | 0.534 | 0.292 | 0.856 |
Q7_Avo | 0.93 ± 1.149 | 1.165 | 0.492 | 0.493 | 0.251 | 0.860 |
Q22_Avo | 0.94 ± 1.128 | 1.102 | 0.414 | 0.661 | 0.474 | 0.842 |
Q8_Avo | 0.99 ± 1.179 | 1.054 | 0.175 | 0.608 | 0.383 | 0.849 |
Q11_Avo | 1.20 ± 1.189 | 0.780 | −0.304 | 0.712 | 0.527 | 0.836 |
Q12_Avo | 0.99 ± 1.070 | 0.973 | 0.286 | 0.612 | 0.402 | 0.848 |
Q13_Avo | 1.03 ± 1.077 | 0.895 | 0.128 | 0.554 | 0.336 | 0.854 |
Q17_Avo | 0.98 ± 1.174 | 1.070 | 0.217 | 0.709 | 0.527 | 0.836 |
Overall Avoidance | 8.35 ± 6.58 | 0.864 | ||||
Q4_Hyp | 0.97 ± 1.080 | 1.014 | 0.268 | 0.666 | 0.470 | 0.799 |
Q18_Hyp | 1.11 ± 1.213 | .913 | −0.147 | 0.701 | 0.527 | 0.792 |
Q10_Hyp | 0.79 ± 1.034 | 1.231 | 0.753 | 0.600 | 0.385 | 0.814 |
Q15_Hyp | 1.04 ± 1.241 | 1.051 | 0.029 | 0.601 | 0.413 | 0.812 |
Q19_Hyp | 0.55 ± 0.961 | 1.831 | 2.701 | 0.599 | 0.385 | 0.815 |
Q21_Hyp | 1.04 ± 1.226 | 1.027 | 0.064 | 0.486 | 0.251 | 0.833 |
Overall Hyperarousal | 5.50 ± 5.00 | 0.837 | ||||
IES-R Overall | 21.06 ± 16.30 |
Items | Point Measure Correlation (≥0.03) | Infit Mean Squares (≥0.60–≤1.60) | Outfit Mean Squares (≥0.60–≤1.60) | S.E. | Logits Scores | Ordered Rank |
---|---|---|---|---|---|---|
Q1_Int | 0.56 | 0.97 | 1.27 | 0.04 | −0.21 | 18 |
Q2_Int | 0.55 | 1.13 | 1.10 | 0.04 | 0.10 | 6 |
Q3_Int | 0.64 | 0.78 | 0.86 | 0.04 | −0.40 | 21 |
Q6_Int | 0.63 | 0.78 | 0.82 | 0.04 | −0.05 | 11 |
Q9_Int | 0.57 | 0.97 | 0.89 | 0.04 | 0.12 | 5 |
Q14_Int | 0.60 | 0.84 | 0.84 | 0.04 | 0.13 | 4 |
Q16_Int | 0.64 | 0.71 | 0.71 | 0.04 | 0.04 | 7 |
Q20_Int | 0.44 | 1.45 | 1.04 | 0.05 | 1.05 | 1 |
Q5_Avo | 0.58 | 1.23 | 1.33 | 0.04 | −0.47 | 22 |
Q7_Avo | 0.49 | 1.41 | 1.43 | 0.04 | 0.02 | 8 |
Q22_Avo | 0.57 | 1.06 | 1.05 | 0.04 | 0.00 | 9 |
Q8_Avo | 0.53 | 1.30 | 1.41 | 0.04 | −0.08 | 14 |
Q11_Avo | 0.60 | 1.03 | 1.07 | 0.04 | −0.36 | 20 |
Q12_Avo | 0.63 | 0.78 | 0.82 | 0.04 | −0.07 | 13 |
Q13_Avo | 0.58 | 1.00 | 1.05 | 0.04 | −0.13 | 15 |
Q17_Avo | 0.61 | 0.96 | 0.86 | 0.04 | −0.06 | 12 |
Q4_Hyp | 0.61 | 0.85 | 0.88 | 0.04 | −0.04 | 10 |
Q18_Hyp | 0.63 | 0.93 | 0.92 | 0.04 | −0.24 | 19 |
Q10_Hyp | 0.60 | 0.85 | 0.76 | 0.04 | 0.24 | 3 |
Q15_Hyp | 0.56 | 1.25 | 1.32 | 0.04 | −0.14 | 16 |
Q19_Hyp | 0.53 | 1.06 | 0.80 | 0.05 | 0.69 | 2 |
Q21_Hyp | 0.54 | 1.31 | 1.40 | 0.04 | −0.15 | 17 |
Indices | Acceptable Criteria | Conventional CFA | Multilevel CFA | Multilevel CFA | Constraint MCFA | Differences | ||
---|---|---|---|---|---|---|---|---|
Original Model (22 Items) | Re-Specified Model (18 Items) * | Re-Specified Model (16 Items) ** | Original Model (22 Items) | Re-Specified Model (16 Items) *** | ||||
χ2 | - | 1940.189 | 911.344 | 943.261 | 1944.818 | 889.038 | 936.667 | 47.629 |
DF | - | 206 | 132 | 264 | 412 | 202 | 215 | 13 |
p | >0.05 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
NFI | 0.839 | 0.900 | 0.902 | 0.846 | 0.900 | 0.900 | 0.000 | |
NNFI | ≥0.90 | 0.835 | 0.900 | 0.918 | 0.860 | 0.902 | 0.908 | 0.006 |
CFI | ≥0.90 | 0.853 | 0.911 | 0.930 | 0.876 | 0.917 | 0.917 | 0.000 |
IFI | ≥0.90 | 0.854 | 0.911 | 0.930 | 0.876 | 0.917 | 0.918 | 0.001 |
GFI | ≥0.90 | 0.842 | 0.900 | 0.923 | 0.867 | 0.918 | 0.917 | 0.001 |
RMR | ≤0.08 | 0.071 | 0.059 | 0.039 | 0.047 | 0.044 | 0.044 | 0.00 |
SRMR | ≤0.08 | 0.058 | 0.048 | 0.048 | 0.052 | 0.047 | 0.079 | 0.032 |
RMSEA | ≤0.08 | 0.092 | 0.077 | 0.067 | 0.083 | 0.080 | 0.078 | 0.002 |
90% CI RMSEA | ≤0.08 | (0.088–0.096) | (0.072- 0.082) | (0.062–0.072) | (0.080–0.087 | 0.075–0.086 | 0.073–0.084 | 0.002–0.002, |
Model AIC | Less | 1528.189 | 647.344 | 371.878 | 1075.934 | 485.038 | 506.667 |
Items | β | S.E | z-Value * | Loading | R. Square | PVUNE | Wald Tests | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Within | Between | Within | Between | Within | Between | Within | Between | Within | Between | Within | Between | Within | Between | |
Intrusion | ||||||||||||||
Q3_INT | 0.710 | 0.934 | - | - | - | - | 0.710 | 0.934 | 0.504 | 0.872 | 0.704 | 0.358 | - | - |
Q6_INT | 0.746 | 0.972 | 0.046 | 0.216 | 22.486 | 4.950 | 0.746 | 0.972 | 0.557 | 0.945 | 0.666 | 0.234 | 16.22 | 4.50 |
Q9_INT | 0.741 | 0.976 | 0.045 | 0.263 | 22.334 | 5.159 | 0.741 | 0.976 | 0.549 | 0.952 | 0.671 | 0.219 | 16.47 | 3.71 |
Q14_INT | 0.684 | 0.969 | 0.044 | 0.189 | 20.638 | 4.307 | 0.684 | 0.969 | 0.467 | 0.939 | 0.730 | 0.247 | 15.55 | 5.13 |
Q16_INT | 0.815 | 0.952 | 0.045 | 0.184 | 24.466 | 4.604 | 0.815 | 0.952 | 0.664 | 0.907 | 0.580 | 0.305 | 18.11 | 5.17 |
Q20_INT | 0.589 | 0.949 | 0.038 | 0.166 | 17.808 | 4.950 | 0.589 | 0.949 | 0.347 | 0.900 | 0.808 | 0.317 | 15.5 | 5.72 |
Avoidance | ||||||||||||||
Q5_AVO | 0.580 | 0.832 | - | - | - | - | 0.580 | 0.832 | 0.337 | 0.692 | 0.815 | 0.555 | - | - |
Q22_AVO | 0.714 | 0.960 | 0.065 | 0.304 | 17.127 | 3.627 | 0.714 | 0.960 | 0.509 | 0.922 | 0.701 | 0.279 | 10.98 | 3.16 |
Q8_AVO | 0.613 | 0.981 | 0.063 | 0.433 | 15.461 | 3.743 | 0.613 | 0.981 | 0.376 | 0.962 | 0.790 | 0.196 | 9.73 | 2.27 |
Q11_AVO | 0.730 | 0.982 | 0.069 | 0.344 | 17.382 | 3.785 | 0.730 | 0.982 | 0.533 | 0.963 | 0.683 | 0.191 | 10.58 | 2.85 |
Q12_AVO | 0.690 | 0.874 | 0.061 | 0.312 | 16.746 | 3.395 | 0.690 | 0.874 | 0.475 | 0.764 | 0.724 | 0.486 | 11.31 | 2.80 |
Q17_AVO | 0.775 | 0.973 | 0.068 | 0.420 | 18.022 | 3.893 | 0.775 | 0.973 | 0.601 | 0.946 | 0.632 | 0.232 | 11.4 | 2.32 |
Hyperarousal | ||||||||||||||
Q4_HYP | 0.654 | 0.822 | - | - | - | - | 0.654 | 0.822 | 0.428 | 0.675 | 0.756 | 0.822 | - | - |
Q10_HYP | 0.721 | 0.969 | 0.051 | 0.378 | 20.372 | 3.554 | 0.721 | 0.969 | 0.520 | 939 | 0.693 | 0.969 | 14.14 | 2.56 |
Q19_HYP | 0.684 | 0.961 | 0.047 | 0.443 | 19.472 | 3.548 | 0.684 | 0.961 | 0.468 | 0.923 | 0.729 | 0.961 | 14.55 | 2.17 |
Q21_HYP | 0.583 | 0.300 | 0.060 | 0.371 | 16.915 | 0.969 | 0.583 | 0.300 | 0.340 | 0.090 | 0.812 | 0.300 | 9.72 | 0.81 |
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Aljaberi, M.A.; Lee, K.-H.; Alareqe, N.A.; Qasem, M.A.; Alsalahi, A.; Abdallah, A.M.; Noman, S.; Al-Tammemi, A.B.; Mohamed Ibrahim, M.I.; Lin, C.-Y. Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic. Healthcare 2022, 10, 1858. https://doi.org/10.3390/healthcare10101858
Aljaberi MA, Lee K-H, Alareqe NA, Qasem MA, Alsalahi A, Abdallah AM, Noman S, Al-Tammemi AB, Mohamed Ibrahim MI, Lin C-Y. Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic. Healthcare. 2022; 10(10):1858. https://doi.org/10.3390/healthcare10101858
Chicago/Turabian StyleAljaberi, Musheer A., Kuo-Hsin Lee, Naser A. Alareqe, Mousa A. Qasem, Abdulsamad Alsalahi, Atiyeh M. Abdallah, Sarah Noman, Ala’a B. Al-Tammemi, Mohamed Izham Mohamed Ibrahim, and Chung-Ying Lin. 2022. "Rasch Modeling and Multilevel Confirmatory Factor Analysis for the Usability of the Impact of Event Scale-Revised (IES-R) during the COVID-19 Pandemic" Healthcare 10, no. 10: 1858. https://doi.org/10.3390/healthcare10101858