Date of Award

Fall 12-1-2017

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Psychology

Committee Chair

Randolph Arnau

Committee Chair Department

Psychology

Committee Member 2

Brad Green

Committee Member 2 Department

Psychology

Committee Member 3

Michael Anestis

Committee Member 3 Department

Psychology

Committee Member 4

Richard Mohn

Committee Member 4 Department

Educational Research and Administration

Abstract

Prior studies examining PTSD subtypes have yielded mixed results, likely stemming in part from the use of divergent samples and measurement techniques. This study aimed to expand upon these findings by utilizing a large nationally-representative sample in combination with sophisticated statistical analyses. Utilizing a sample of 2496 adults with a diagnosis of PTSD, latent profile analysis was used to determine the optimal number and composition of latent classes of individuals diagnosed with PTSD, and then taxometric analysis was utilized to determine whether these classes differed not only in degree, but in kind.

Finally, class relationships with a number of external variables were compared in order to evaluate the external validity and clinical utility of the latent class model. Results indicated five classes of individuals diagnosed with PTSD. One of these classes was characterized by the highest endorsement of symptoms from each of the four symptom clusters of PTSD and was named the “Complex” class. Taxometric analyses indicated categorical differences between this class and all other classes. Further, the Complex class differed categorically from a group comprised of all other participants combined. The Complex class was characterized by a higher likelihood of experiencing more severe types of traumatic events and demonstrated stronger relationships with the most negative outcomes, including suicide attempts and inpatient hospitalization. Overall, the current study appears to have provided evidence of the ability of taxometric analysis to provide further validation of classes identified through latent profile analysis.

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