Supplemental Material for Factor Mixture Modeling of Anxiety Sensitivity: Support for the Three-Class Solution in a Serbian Sample

2020 ◽  
Keyword(s):  
Anxiety Sensitivity ◽  
Mixture Modeling ◽  
Factor Mixture Modeling

Factor mixture modeling of anxiety sensitivity: Support for the three-class solution in a Serbian sample.

2020 ◽  
Vol 32 (10) ◽  
pp. 915-927
Author(s):  
Marija Volarov ◽  
Nicholas P. Allan ◽  
Ljiljana Mihić
Keyword(s):  
Anxiety Sensitivity ◽  
Mixture Modeling ◽  
Factor Mixture Modeling

Factor mixture modeling of anxiety sensitivity: A three-class structure.

2014 ◽  
Vol 26 (4) ◽  
pp. 1184-1195 ◽  
Author(s):  
Nicholas P. Allan ◽  
Kristina J. Korte ◽  
Daniel W. Capron ◽  
Amanda M. Raines ◽  
Norman B. Schmidt
Keyword(s):  
Anxiety Sensitivity ◽  
Mixture Modeling ◽  
Class Structure ◽  
Factor Mixture Modeling

scholarly journals Examining the latent structure of anxiety sensitivity in adolescents using factor mixture modeling.

2014 ◽  
Vol 26 (3) ◽  
pp. 741-751 ◽  
Author(s):  
Nicholas P. Allan ◽  
Laura MacPherson ◽  
Kevin C. Young ◽  
Carl W. Lejuez ◽  
Norman B. Schmidt
Keyword(s):  
Anxiety Sensitivity ◽  
Mixture Modeling ◽  
Latent Structure ◽  
Factor Mixture Modeling

Dimensional, Categorical, or Dimensional-Categories: Testing the Latent Structure of Anxiety Sensitivity Among Adults Using Factor-Mixture Modeling

2010 ◽  
Vol 41 (4) ◽  
pp. 515-529 ◽  
Author(s):  
Amit Bernstein ◽  
Timothy R. Stickle ◽  
Michael J. Zvolensky ◽  
Steven Taylor ◽  
Jonathan Abramowitz ◽  
...  
Keyword(s):  
Anxiety Sensitivity ◽  
Mixture Modeling ◽  
Latent Structure ◽  
Factor Mixture Modeling

DIF Detection With Zero-Inflation Under the Factor Mixture Modeling Framework

2021 ◽  
pp. 001316442110289
Author(s):  
Sooyong Lee ◽  
Suhwa Han ◽  
Seung W. Choi
Keyword(s):  
Total Sample ◽  
Mixture Modeling ◽  
Zero Inflation ◽  
Modeling Framework ◽  
Detection Power ◽  
Suggested Procedure ◽  
Response Data ◽  
Number Of Zeros ◽  
Factor Mixture Modeling ◽  
Study Results

Response data containing an excessive number of zeros are referred to as zero-inflated data. When differential item functioning (DIF) detection is of interest, zero-inflation can attenuate DIF effects in the total sample and lead to underdetection of DIF items. The current study presents a DIF detection procedure for response data with excess zeros due to the existence of unobserved heterogeneous subgroups. The suggested procedure utilizes the factor mixture modeling (FMM) with MIMIC (multiple-indicator multiple-cause) to address the compromised DIF detection power via the estimation of latent classes. A Monte Carlo simulation was conducted to evaluate the suggested procedure in comparison to the well-known likelihood ratio (LR) DIF test. Our simulation study results indicated the superiority of FMM over the LR DIF test in terms of detection power and illustrated the importance of accounting for latent heterogeneity in zero-inflated data. The empirical data analysis results further supported the use of FMM by flagging additional DIF items over and above the LR test.

Profiles of Psychological Resilience in College Students With Disabilities

2018 ◽  
Vol 37 (5) ◽  
pp. 635-651 ◽  
Author(s):  
Rahul Ganguly ◽  
Harsha N. Perera
Keyword(s):  
Students With Disabilities ◽  
Well Being ◽  
Mixture Modeling ◽  
The Other ◽  
Psychological Resilience ◽  
Academic Satisfaction ◽  
Factor Mixture Modeling ◽  
College Students With Disabilities ◽  
Career Optimism ◽  
Distal Outcome

The present article reports on research conducted to identify profiles of psychological resilience using factor mixture models. We also examine gender as a predictor of resilience profile membership and career optimism, academic satisfaction, and psychological well-being as outcomes of profile membership. Based on resilience data from university students with disabilities, factor mixture modeling revealed three distinct profiles of resilience (viz., “vulnerable,” “spirituality-dominant,” and “engaged-resilient”). Results also revealed that females were almost 4 times as likely to be in the spirituality-dominant profile than the vulnerable profile. Finally, distal outcome analyses revealed that career optimism, academic satisfaction, and well-being were higher in the engaged-resilient profile than the other profiles. Notably, spirituality-dominant and vulnerable individuals possessed about the same levels of career optimism, satisfaction, and well-being. The findings have important implications for the theory and assessment of resilience, suggesting the tenability of a person-centered assessment of psychological resilience.

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