scholarly journals The COVID-19 Multifaceted Threat Scale

2021 ◽  
Author(s):  
Joel Anderson ◽  
Adrian Lueders ◽  
Sindhuja Sankaran ◽  
Eva Green ◽  
Emanuele Politi
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Thematic Analysis ◽  
Qualitative Methodology ◽  
The United States ◽  
Preliminary Evidence ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Psychometric Evidence

The COVID-19 pandemic represents an unprecedented threat for individuals worldwide. This paper reports the initial psychometric properties for the recently developed COVID-19 Multifaceted Threat Scale. Across three studies the construction and initial psychometric evidence is presented. In Study 1 (n = 194, 11 national groups), we adopted an inductive qualitative methodology to elicit participants’ concerns, worries, or fears about the corona pandemic. A thematic analysis revealed 10 consistent themes around threat, from which we constructed a pool of 100 potential items. In Study 2, a sample from the United States (n = 322) provided data for an exploratory factor analysis which reduced the 100 items to 30 items across the 10 hypothesised dimensions sub-factors. In Study 3, these findings were then ratified in samples from the United States (n = 471) and India (n = 423) using a multi-group confirmatory factor analysis. We also present reliability estimates (internal consistency: Studies 2-3) and preliminary evidence of the validity for the scale across two national groups (United States and India). The evidence presented suggests that the COVID-19 Multifaceted Threat Scale is a psychometrically sound measure and can be used to explore current and long-lasting effects of the pandemic on individuals and societies.

A Comparison of Reliability Estimation Based on Confirmatory Factor Analysis and Exploratory Structural Equation Models

2021 ◽  
pp. 001316442110089
Author(s):  
Yuanshu Fu ◽  
Zhonglin Wen ◽  
Yang Wang
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Structural Equation ◽  
Reliability Estimation ◽  
Model Fit ◽  
Equation Modeling ◽  
Factor Loadings ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Composite Reliability

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is often found to be substantially better than that of ICM-CFA. The present study first illustrated the method used to estimate composite reliability under ESEM and then compared the difference between ESEM and ICM-CFA in terms of composite reliability estimation under various indicators per factor, target factor loadings, cross-loadings, and sample sizes. The results showed no apparent difference in using ESEM or ICM-CFA for estimating composite reliability, and the rotation type did not affect the composite reliability estimates generated by ESEM. An empirical example was given as further proof of the results of the simulation studies. Based on the present study, we suggest that if the model fit of ESEM (regardless of the utilized rotation criteria) is acceptable but that of ICM-CFA is not, the composite reliability estimates based on the above two models should be similar. If the target factor loadings are relatively small, researchers should increase the number of indicators per factor or increase the sample size.

Reasons for attending support groups and organizational preferences: A replication study using the North American Scleroderma Support Group Survey

2019 ◽  
Vol 4 (3) ◽  
pp. 173-186 ◽  
Author(s):  
Mia Pépin ◽  
Linda Kwakkenbos ◽  
Marie-Eve Carrier ◽  
Sandra Peláez ◽  
Ghassan El-Baalbaki ◽  
...  
Keyword(s):  
Social Support ◽  
Factor Analysis ◽  
Systemic Sclerosis ◽  
North America ◽  
Confirmatory Factor Analysis ◽  
Support Groups ◽  
Support Group ◽  
Factor Model ◽  
The United States ◽  
Confirmatory Factor

Peer-facilitated support groups are an important source for receiving disease-related information and support for people with systemic sclerosis (or scleroderma). A recent survey explored reasons for attending systemic sclerosis support groups in Europe and Australia and used exploratory factor analysis to group reasons for attendance into three main themes: (1) interpersonal and social support, (2) disease treatment and symptom management strategies, and (3) other aspects of living with systemic sclerosis. The objective of the present study was to replicate this study in a sample of patients from North America using confirmatory factor analysis. A 30-item survey was used to assess reasons for attendance and organizational preferences among systemic sclerosis patients in Canada and the United States. In total, 171 members completed the survey. In the confirmatory factor analysis, the three-factor model showed good fit to the data (χ2(399) = 646.0, p < 0.001, Tucker–Lewis index = 0.97, comparative fit index = 0.97, root mean square error approximation = 0.06). On average, respondents rated 22 (73%) of 30 items as “important” or “very important” reasons for attending support groups. Among organizational preferences, respondents emphasized the importance of the ability to share feelings and concerns, as well as educational aspects. Findings of our study suggest that reasons for attending support groups are similar for patients from Europe, Australia, and North America and that support groups should facilitate social support as well as disease education. These results inform the development of training programs for current and future systemic sclerosis support group leaders across the globe.

Measurement Development and Validation in Research

2015 ◽  
pp. 435-462
Author(s):  
Lihua Xu
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Social Research ◽  
The United States ◽  
Statistical Technique ◽  
Measurement Development ◽  
Procedural Approach ◽  
Confirmatory Factor ◽  
Research And Education ◽  
K 12

This chapter describes the importance of measurement in social research and education research. In order to validly compare across groups, whether it is age, gender, ethnicities, or cultures, measurement invariance needs to be established. This is accomplished through single-group and multi-group confirmatory factor analysis. The procedural approach is presented with a detailed illustration from real research in servant leadership in K-12 principals in the United States. Second-order confirmatory factor analysis is described due to its popularity. Procedural steps are cited, and an example is given for illustration. As a major statistical technique in instrument development, exploratory factor analysis is discussed and illustrated at the beginning of this chapter.

Investigating the Psychometric Properties of School Counselor Self-Advocacy Questionnaire

2011 ◽  
Vol 15 (1) ◽  
pp. 2156759X1101500 ◽  
Author(s):  
Elysia V. Clemens ◽  
Adria Shipp ◽  
Tyler Kimbel
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Psychometric Properties ◽  
School Counselors ◽  
Concurrent Validity ◽  
School Counselor ◽  
Self Advocacy ◽  
Reliability Estimates ◽  
Confirmatory Factor ◽  
Alpha Reliability

This article reports on the development and the exploration of the underlying psychometric properties of the School Counselor Self-Advocacy Questionnaire, a measure of skills school counselors can use to advocate for their roles and programs. An exploratory factor analysis (N = 188) suggested a unidimensional model, and a confirmatory factor analysis indicated the overall model robustly explains the data, accounting for 80% of the variance. Cronbach's alpha reliability estimates ranged from .84 to .87 for the questionnaire and the estimates of concurrent validity were promising. Implications for school counselor advocacy practice are also included.

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