Psych

Doctoral degrees include Doctor of Philosophy (PhD) and other professional doctorates such as Engineering Doctorate (EngD), Doctor of Education (EdD), or Doctor of Clinical Psychology (DClinPsy). Unlike undergraduate- or postgraduate-taught students, doctoral study focuses on a single, autonomous piece of research. Research indicates a high occurrence of mental health problems in doctoral students. This paper describes the piloting and qualitative evaluation of a range of interventions designed to enhance the mental health support for doctoral students at one UK university. These interventions sought to target an array of known factors that affect the mental health of doctoral students, including individual capacity for coping with stress and social support availability.

Structured mixture item response models (StrMixIRMs) are a special type of constrained confirmatory mixture item response theory (IRT) model for detecting latent performance differences in a measurement instrument by characteristic item groups, and classifying respondents according to these differences. In light of limited software options for estimating StrMixIRMs under existing frameworks, this paper proposes reparameterizing it as a confirmatory mixture IRT model using interaction effects between latent classes and item groups. The reparameterization allows for easier implementation of StrMixIRMs with multiple software programs that have mixture modeling capabilities, including open-source ones. This widens the accessibility to these models to a broad range of users and thus can facilitate research and applications of StrMixIRMs. This paper serves two main goals: First, we introduce StrMixIRMs, focusing on the proposed reparameterization based on interaction effects and its various extensions. Second, we illustrate use cases of this novel reparameterization within the mirt 1.41 package in R by employing two empirical datasets. Detailed R code with notes are provided for the applications along with an interpretation of the outputs.

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