Confirmatory factor analysis of posttraumatic stress symptoms assessed by the Impact of Event Scale-Revised in Chinese earthquake victims: Examining factor structure and its stability across sex

Wang, Li; Zhang, Jianxin; Shi, Zhanbiao; Zhou, Mingjie; Huang, Duan; Liu, Ping

doi:10.1016/j.janxdis.2010.10.011

Cited by 32 publications

(55 citation statements)

References 44 publications

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“…Research indicates that the value of 33 points in the IES-R represents the best cutoff for a probable diagnosis of PTSD (Creamer et al, 2003;Wang et al, 2011). In this sample, this value allocated 86 possible PTSD cases.…”

Section: Descriptive Resultsmentioning

confidence: 88%

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Factors Contributing to Posttraumatic Growth and Its Buffering Effect in Adult Children of Cancer Patients Undergoing Treatment

Teixeira

Pereira

2013

Journal of Psychosocial Oncology

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This study examined relationships among demographic, clinical, and psychosocial variables in adult children of cancer patients. Two hundred and fourteen participants completed measures of posttraumatic growth (PTG), distress, posttraumatic stress disorder (PTSD) symptoms, social support, and family functioning. Significant gender differences in all PTG dimensions were found, as well as associations among PTG, gender, parental dependency, distress, PTSD, and family functioning. Social support was not a mediator in the relationship between gender and PTG. Gender, education, disease duration, dependency, distress, and family flexibility predicted PTG. Finally, PTG had a moderating effect in the relationship between distress and PTSD/social support. These results may guide psychosocial interventions in this population.

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Section: Descriptive Resultsmentioning

confidence: 88%

“…In this sample, Cronbach's alphas were .93 for hyperarousal, .74 for intrusion, .70 for avoidance, and .93 for the total scale. A cutoff of 33 was used for a probable diagnosis of PTSD (Creamer, Bell, & Failla, 2003;Wang et al, 2011).…”

Section: Methodsmentioning

confidence: 99%

Factors Contributing to Posttraumatic Growth and Its Buffering Effect in Adult Children of Cancer Patients Undergoing Treatment

Teixeira

Pereira

2013

Journal of Psychosocial Oncology

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“…Previous researchers have interpreted the poor fit of these items as indicating a separate sleep-disturbance factor (Morina et al, 2010;L. Wang, Zhang, Shi, Zhou, Huang, et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Properties of Swedish posttraumatic stress measures after a disaster

Arnberg

Michel

Johannesson

2014

Journal of Anxiety Disorders

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This study evaluated the properties of Swedish versions of self-report measures of posttraumatic stress disorder (PTSD), with emphasis on the Impact of Event ScaleRevised (IES-R). Survey data from adult survivors 1, 3, and 6 years after the 2004 Indian Ocean tsunami (n= 1,506) included the IES-R (from which the IES-6 was derived) and the 12-item General Health Questionnaire (GHQ-12). The PTSD Checklist (PCL) was included in one survey. A structured clinical interview was performed after 6 years (n = 142). Factor analyses of the IES-R and PCL indicated that a dysphoric-arousal model provided good fit invariant across assessments. Both measures were accurate in excluding PTSD while all measures provided poorer positive predictive values. The IES-R, but not the IES-6 and GHQ-12, evidenced stability across assessments. In conclusion, the Swedish IES-R and PCL are sound measures of chronic PTSD, and the findings illustrate important temporal aspects of PTSD assessment.

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“…The questionnaire was composed of five parts (22 items) according to the HBM: 5 indicators for SUS; 4 indicators for SER; 5 indicators for BEN; 4 indicators for BAR; and 4 indicators for CTA. The questionnaire was developed by modifying and expanding on the operational indicators used in earlier HBM studies [24] (see Table 1 for item mapping). Each item was rated on a 5-point scale using anchors between 1 and 5 (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree).…”

Section: Methodsmentioning

confidence: 99%

“…A confirmatory factor analysis of the classical HBM was made with data from the post-intervention survey by using Lisrel 8.70 [24], since the sample size of the survey was larger. The maximum likelihood estimation in the analysis and the factors were permitted to correlate.…”

Section: Methodsmentioning

confidence: 99%

Health belief model based evaluation of school health education programme for injury prevention among high school students in the community context

2014

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BackgroundAlthough multifaceted community-based programmes have been widely developed, there remains a paucity of evaluation of the effectiveness of multifaceted injury prevention programmes implemented in different settings in the community context. This study was to provide information for the evaluation of community-based health education programmes of injury prevention among high school students.MethodsThe pre-intervention survey was conducted in November 2009. Health belief model (HBM) based health education for injury prevention started in January 2010 and stopped in the end of 2011 among high school students in the community context in Shanghai, China. A post-intervention survey was conducted six weeks after the completion of intervention. Injury-related health belief indicators were captured by a short questionnaire before and after the intervention. Health belief scores were calculated and compared using the simple sum score (SSS) method and the confirmatory factor analysis weighted score (CFAWS) method, respectively.ResultsThe average reliability coefficient for the questionnaire was 0.89. The factor structure of HBM was given and the data fit HBM in the confirmatory factor analysis (CFA) very well. The result of CFA showed that Perceived Benefits of Taking Action (BEN) and Perceived Seriousness (SER) had the greatest impact on the health belief, Perceived Susceptibility (SUS) and Cues to Action (CTA) were the second and third most important components of HBM respectively. Barriers to Taking Action (BAR) had no notable impact on HBM. The standardized path coefficient was only 0.35, with only a small impact on CTA. The health belief score was significantly higher after intervention (p < 0.001), which was similar in the CFAWS method and in the SSS method. However, the 95% confidential interval in the CFAWS method was narrower than that in the SSS method.ConclusionsThe results of CFA provide further empirical support for the HBM in injury intervention. The CFAWS method can be used to calculate the health belief scores and evaluate the injury related intervention. The community-based school health education might improve injury-related health belief among high school students; however, this preliminary observation needs to be confirmed in further research.

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Confirmatory factor analysis of posttraumatic stress symptoms assessed by the Impact of Event Scale-Revised in Chinese earthquake victims: Examining factor structure and its stability across sex

Cited by 32 publications

References 44 publications

Factors Contributing to Posttraumatic Growth and Its Buffering Effect in Adult Children of Cancer Patients Undergoing Treatment

Factors Contributing to Posttraumatic Growth and Its Buffering Effect in Adult Children of Cancer Patients Undergoing Treatment

Properties of Swedish posttraumatic stress measures after a disaster

Health belief model based evaluation of school health education programme for injury prevention among high school students in the community context

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