Bayesian estimation and comparison of idiographic network models.

Idiographic network models are estimated on time series data of a single individual and allow researchers to investigate person-specific associations between multiple variables over time. The most common approach for fitting graphical vector autoregressive (GVAR) models uses least absolute shrinkage and selection operator (LASSO) regularization to estimate a contemporaneous and a temporal network. However, estimation of idiographic networks can be unstable in relatively small data sets typical for psychological research. This bears the risk of misinterpreting differences in estimated networks as spurious heterogeneity between individuals. As a remedy, we evaluate the performance of a Bayesian alternative for fitting GVAR models that allows for regularization of parameters while accounting for estimation uncertainty. We also develop a novel test, implemented in the tsnet package in R, which assesses whether differences between estimated networks are reliable based on matrix norms. We first compare Bayesian and LASSO approaches across a range of conditions in a simulation study. Overall, LASSO estimation performs well, while a Bayesian GVAR without edge selection may perform better when the true network is dense. In an additional simulation study, the novel test is conservative and shows good false-positive rates. Finally, we apply Bayesian estimation and testing in an empirical example using daily data on clinical symptoms for 40 individuals. We additionally provide functionality to estimate Bayesian GVAR models in Stan within tsnet. Overall, Bayesian GVAR modeling facilitates the assessment of estimation uncertainty which is important for studying interindividual differences of intraindividual dynamics. In doing so, the novel test serves as a safeguard against premature conclusions of heterogeneity. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Measuring the variability of personality traits with interval responses: Psychometric properties of the dual-range slider response format : Measuring variability with interval responses.

Measuring the variability in persons' behaviors and experiences using ecological momentary assessment is time-consuming and costly. We investigate whether interval responses provided through a dual-range slider (DRS) response format can be used as a simple and efficient alternative: Respondents indicate variability in their behavior in a retrospective rating by choosing a lower and an upper bound on a continuous, bounded scale. We investigate the psychometric properties of this response format as a prerequisite for further validation. First, we assess the test-retest reliability of factor-score estimates for the width of DRS intervals. Second, we test whether factor-score estimates of the visual analog scale (VAS) and the location of DRS intervals show convergent validity. Third, we investigate whether factor-score estimates for the DRS are uncorrelated between different personality scales. We present a longitudinal multitrait-multimethod study using two personality scales (Extraversion, Conscientiousness) and two response formats (VAS, DRS) at two measurement occasions (6-8 weeks apart) for which we estimate factor-score correlations in a joint item response theory model. The test-retest reliability of the width of DRS intervals was high ( ρ ^ ≥ . 73 ). Also, convergent validity between location scores of VAS and DRS was high ( ρ ^ ≥ . 88 ). Conversely, discriminant validity of the width of DRS intervals between Extraversion and Conscientiousness was poor ( ρ ^ ≥ . 94 ). In conclusion, the DRS seems to be a reliable response format that could be used to measure the central tendency of a trait equivalently to the VAS. However, it might not be well suited for measuring intra-individual variability in personality traits.

Measuring dysfunctional interpersonal beliefs: validation of the Interpersonal Cognitive Distortions Scale among a heterogeneous German-speaking sample.

BACKROUND: Dysfunctional interpersonal beliefs (DIBs) are a key symptom domain in numerous mental disorders. Because DIBs exert a strong influence on social experience and behavior, they play an important role in a mental disorder's development and progression. To date, only the Interpersonal Cognitive Distortions Scale (ICDS) captures DIBs independently of specific disorders, populations, or contexts. The present study's aim was to psychometrically evaluate and validate a German translation of the ICDS. METHODS: The ICDS was administered along with indicators of convergent (rejection sensitivity, depressive expectations, interpersonal trust, interpersonal problems, perceived social support), discriminant (self-efficacy, perseverative negative thinking, optimism), and clinical validity (psychopathology, perceived stress, well-being) to a pooled sample incorporating non-clinical (N = 114) and clinical (N = 94) participants. RESULTS: An exploratory factor analysis (EFA) suggested a five-factor solution (factor loadings: .44 to .85). Correlational analyses demonstrated acceptable convergent (ρ = -.29 to -.35, ρ = .27 to .59), suboptimal discriminant (ρ = -.27 to -.38, ρ = .52), and acceptable clinical validity (ρ = -.21, ρ = .36 to .44) at the total-scale level. However, results at the subscale level were mixed and required nuanced interpretation. Likewise, internal consistency was acceptable at the total-scale level (α = .76), but ranged from good to poor at the subscale level (α = .61 to .80). DIBs mediated the negative relationship between mental disorder onset and psychopathology levels. DISCUSSION: Our results imply DIBs' relevance to mental health and related outcomes. When working with the ICDS's German version, we recommend employing only the "insecurity" subscale, as this was the only scale revealing acceptable psychometric properties. Future studies should improve the construct validity of the ICDS (and its subscales), e.g., by adding more items to the respective subscales and further classes of DIBs.

The Dirichlet Dual Response Model: An Item Response Model for Continuous Bounded Interval Responses.

Standard response formats such as rating or visual analogue scales require respondents to condense distributions of latent states or behaviors into a single value. Whereas this is suitable to measure central tendency, it neglects the variance of distributions. As a remedy, variability may be measured using interval-response formats, more specifically the dual-range slider (RS2). Given the lack of an appropriate item response model for the RS2, we develop the Dirichlet dual response model (DDRM), an extension of the beta response model (BRM; Noel & Dauvier in Appl Psychol Meas, 31:47-73, 2007). We evaluate the DDRM's performance by assessing parameter recovery in a simulation study. Results indicate overall good parameter recovery, although parameters concerning interval width (which reflect variability in behavior or states) perform worse than parameters concerning central tendency. We also test the model empirically by jointly fitting the BRM and the DDRM to single-range slider (RS1) and RS2 responses for two Extraversion scales. While the DDRM has an acceptable fit, it shows some misfit regarding the RS2 interval widths. Nonetheless, the model indicates substantial differences between respondents concerning variability in behavior. High correlations between person parameters of the BRM and DDRM suggest convergent validity between the RS1 and the RS2 interval location. Both the simulation and the empirical study demonstrate that the latent parameter space of the DDRM addresses an important issue of the RS2 response format, namely, the scale-inherent interdependence of interval location and interval width (i.e., intervals at the boundaries are necessarily smaller).