Symptom Presence and Symptom Severity as Unique Indicators of Psychopathology: An Application of Multidimensional Zero-Inflated and Hurdle Graded Response Models

2021 ◽  
pp. 001316442110618
Author(s):  
Brooke E. Magnus ◽  
Yang Liu
Keyword(s):  
Symptom Severity ◽  
Latent Variables ◽  
Latent Variable ◽  
Suicide Attempts ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Response Models ◽  
Explanatory Variables ◽  
Graded Response ◽  
Scale Scores

Questionnaires inquiring about psychopathology symptoms often produce data with excess zeros or the equivalent (e.g., none, never, and not at all). This type of zero inflation is especially common in nonclinical samples in which many people do not exhibit psychopathology, and if unaccounted for, can result in biased parameter estimates when fitting latent variable models. In the present research, we adopt a maximum likelihood approach in fitting multidimensional zero-inflated and hurdle graded response models to data from a psychological distress measure. These models include two latent variables: susceptibility, which relates to the probability of endorsing the symptom at all, and severity, which relates to the frequency of the symptom, given its presence. After estimating model parameters, we compute susceptibility and severity scale scores and include them as explanatory variables in modeling health-related criterion measures (e.g., suicide attempts, diagnosis of major depressive disorder). Results indicate that susceptibility and severity uniquely and differentially predict other health outcomes, which suggests that symptom presence and symptom severity are unique indicators of psychopathology and both may be clinically useful. Psychometric and clinical implications are discussed, including scale score reliability.

Bayesian Adaptive Lasso for Ordinal Regression With Latent Variables

2015 ◽  
Vol 46 (4) ◽  
pp. 926-953 ◽  
Author(s):  
Xiang-Nan Feng ◽  
Hao-Tian Wu ◽  
Xin-Yuan Song
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Social Research ◽  
Ordinal Regression ◽  
Adaptive Lasso ◽  
Simultaneous Estimation ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Explanatory Variables ◽  
Ordinal Regression Model

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct simultaneous estimation and variable selection. Nice features including empirical performance of the proposed methodology are demonstrated by simulation studies. The model is applied to a study on happiness and its potential determinants from the Inter-university Consortium for Political and Social Research.

scholarly journals Accounting for Item Response Process and Response Styles Using the Unfolding Item Response Tree (UIRTree) Model

2021 ◽  
Author(s):  
Zhaojun Li ◽  
Bo Zhang ◽  
Mengyang Cao ◽  
Louis Tay
Keyword(s):  
Item Response ◽  
Response Styles ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Response Process ◽  
Monte Carlo Simulation Study ◽  
Graded Response ◽  
Extreme Response ◽  
Generalized Graded Unfolding Model ◽  
Unfolding Models

Many researchers have found that unfolding models may better represent how respondents answer Liker-type items and response styles (RSs) often have moderate to strong presence in responses to such items. However, the two research lines have been growing largely in parallel. The present study proposed an unfolding item response tree (UIRTree) model that can account for unfolding response process and RSs simultaneously. An empirical illustration showed that the UIRTree model could fit a personality dataset well and produced more reasonable parameter estimates. Strong presence of the extreme response style (ERS) was also revealed by the UIRTree model. We further conducted a Monte Carlo simulation study to examine the performance of the UIRTree model compared to three other models for Likert-scale responses: the Samejima’s graded response model, the generalized graded unfolding model, and the dominance item response tree (DIRTree) model. Results showed that when data followed unfolding response process and contained the ERS, the AIC was able to select the UIRTree model, while BIC was biased towards the DIRTree model in many conditions. In addition, model parameters in the UIRTree model could be accurately recovered under realistic conditions, and wrongly assuming the item response process or ignoring RSs was detrimental to the estimation of key parameters. In general, the UIRTree model is expected to help in better understanding of responses to Liker-type items theoretically and contribute to better scale development practically. Future studies on multi-trait UIRTree models and UIRTree models accounting for different types of RSs are expected.

scholarly journals The Q-Matrix Anchored Mixture Rasch Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Ming-Chi Tseng ◽  
Wen-Chung Wang
Keyword(s):  
Rasch Model ◽  
Latent Variable ◽  
Latent Class ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Latent Classes ◽  
Modeling Framework ◽  
Class Invariant ◽  
Common Scale ◽  
Q Matrix

Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds. Under this modeling framework, students can be characterized by both their location on a continuous latent variable and by their latent class membership according to Students’ responses. It is important to identify anchor items for constructing a common scale between latent classes beforehand under the mixture IRT framework. Then, all model parameters across latent classes can be estimated on the common scale. In the study, we proposed Q-matrix anchored mixture Rasch model (QAMRM), including a Q-matrix and the traditional mixture Rasch model. The Q-matrix in QAMRM can use class invariant items to place all model parameter estimates from different latent classes on a common scale regardless of the ability distribution. A simulation study was conducted, and it was found that the estimated parameters of the QAMRM recovered fairly well. A real dataset from the Certificate of Proficiency in English was analyzed with the QAMRM, LCDM. It was found the QAMRM outperformed the LCDM in terms of model fit indices.

scholarly journals New Estimators of the Bayes Factor for Models with High-Dimensional Parameter and/or Latent Variable Spaces

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 399
Author(s):  
Anna Pajor
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Bayes Factor ◽  
High Dimensional ◽  
Model Parameters ◽  
Posterior Odds ◽  
Dimensional Parameter ◽  
Data Set ◽  
Multivariate Stochastic Volatility ◽  
Competing Models

Formal Bayesian comparison of two competing models, based on the posterior odds ratio, amounts to estimation of the Bayes factor, which is equal to the ratio of respective two marginal data density values. In models with a large number of parameters and/or latent variables, they are expressed by high-dimensional integrals, which are often computationally infeasible. Therefore, other methods of evaluation of the Bayes factor are needed. In this paper, a new method of estimation of the Bayes factor is proposed. Simulation examples confirm good performance of the proposed estimators. Finally, these new estimators are used to formally compare different hybrid Multivariate Stochastic Volatility–Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MSV-MGARCH) models which have a large number of latent variables. The empirical results show, among other things, that the validity of reduction of the hybrid MSV-MGARCH model to the MGARCH specification depends on the analyzed data set as well as on prior assumptions about model parameters.

scholarly journals Optimizing Detection of True Within-Person Effects for Intensive Measurement Designs: A Comparison of Multilevel SEM and Unit-Weighted Scale Scores

2020 ◽  
Author(s):  
Jonathan Rush ◽  
Philippe Rast ◽  
Scott Michael Hofer
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Simulated Data ◽  
Variable Model ◽  
Alternative Approach ◽  
Scale Scores ◽  
Weighted Model ◽  
The Impact

Intensive repeated measurement designs are frequently used to investigate within-person variation over relatively brief intervals of time. The majority of research utilizing these designs rely on unit-weighted scale scores, which assume that the constructs are measured without error. An alternative approach makes use of multilevel structural equation models (MSEM), which permit the specification of latent variables at both within-person and between-person levels. These models disaggregate measurement error from systematic variance, which should result in less biased within-person estimates and larger effect sizes. Differences in power, precision, and bias between multilevel unit-weighted and MSEM models were compared through a series of Monte Carlo simulations. Results based on simulated data revealed that precision was consistently poorer in the MSEM models than the unit-weighted models, particularly when reliability was low. However, the degree of bias was considerably greater in the unit-weighted model than the latent variable model. Although the unit-weighted model consistently underestimated the effect of a covariate, it generally had similar power relative to the MSEM model due to the greater precision. Considerations for scale development and the impact of within-person reliability are highlighted.

scholarly journals Are we on the same page? Latent variable modeling suggests different nomothetic and idiographic factor structures for momentary affect

2019 ◽  
Author(s):  
MohammadHossein Manuel Haqiqatkhah ◽  
francis tuerlinckx
Keyword(s):  
Item Response ◽  
Factor Structure ◽  
Latent Variable ◽  
Goodness Of Fit ◽  
Sampling Method ◽  
Daily Diaries ◽  
Response Models ◽  
Graded Response ◽  
Confirmatory Factor ◽  
Best Fitting

Objective: This study investigated the factor structure underlying affect and whether it differs within and between persons. Method: In an exploratory study, 12 datasets of self-reported momentary emotions collected via experience sampling method (11 datasets) and daily diaries (one dataset) were analyzed using graded response models based on item response theory. Before doing so, two pools of datasets were extracted so that the intraindividual (sampling all measurements of a person) and interindividual (sampling measurement occasions of different individuals) variations are disentangled. Various factor models were then fitted to each pool via an alternative-model confirmatory factor analysis. The candidate models were compared according to their relative goodness of fit. The best-fitting models were defined using a vote-counting method. Results: The intrapersonal momentary variations of affect can be best explained by a unidimensional factor structure resembling valence while individual differences are explained by a two-dimensional construct similar to positive and negative activation constructs. Conclusions: The structure of change in affect is not the same between and within persons. This implies that the nomothetic and idiographic analyses do not converge, providing evidence for construct nonequivalence between the two levels of analysis.

scholarly journals Revealing unobserved factors underlying cortical activity using a rectified latent variable model applied to neural population recordings

2016 ◽  
Author(s):  
Matthew R. Whiteway ◽  
Daniel A. Butts
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Large Population ◽  
Component Analysis ◽  
Latent Variable Model ◽  
Neural Population ◽  
Model Parameters ◽  
Variable Model ◽  
Sensory Stimuli ◽  
External Stimuli

ABSTRACTThe activity of sensory cortical neurons is not only driven by external stimuli, but is also shaped by other sources of input to the cortex. Unlike external stimuli these other sources of input are challenging to experimentally control or even observe, and as a result contribute to variability of neuronal responses to sensory stimuli. However, such sources of input are likely not “noise”, and likely play an integral role in sensory cortex function. Here, we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs non-negative (rectified) latent variables, and is able to be much less restrictive in the mathematical constraints on solutions due to the use an autoencoder neural network to initialize model parameters. We show the RLVM outperforms principal component analysis, factor analysis and independent component analysis across a variety of measures using simulated data. We then apply this model to the 2-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as non-stimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function, and demonstrates novel methods for leveraging large population recordings to this end.

Multiple-Response Surface Approach: Modeling and Optimization

2011 ◽  
Vol 339 ◽  
pp. 321-325
Author(s):  
Bin Nie ◽  
Dan Liao ◽  
Jing Ding ◽  
Yao Dong He
Keyword(s):  
Operating Conditions ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Multiple Response ◽  
Response Models ◽  
Modeling And Optimization ◽  
Response Data ◽  
Model Average ◽  
Response Optimization ◽  
Single Response

Many products and processes have multidimensional characteristics and criteria. As these responses involve common parameters and processes, the response data are correlated. The quality and reliability improvement of such products and processes will typically involve multiple-response optimizations to find optimal operating conditions. Many of the current multiple-response optimization approaches assume a single-response uncertainty in the response models, and the uncertainty in the parameter estimates of the models. In this paper, we consider a Bayesian Model Average (BMA) approach to the modeling and optimization of variability of the predictions and the uncertainty of the model parameters. We further propose a Mahalanobis distance (MD) approach to account for the correlations among the response and the variation in the estimation of the response model.

scholarly journals Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings

2017 ◽  
Vol 117 (3) ◽  
pp. 919-936 ◽  
Author(s):  
Matthew R. Whiteway ◽  
Daniel A. Butts
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Large Population ◽  
Component Analysis ◽  
Latent Variable Model ◽  
Neural Population ◽  
Model Parameters ◽  
Variable Model ◽  
Sensory Stimuli ◽  
External Stimuli

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not “noise” and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end. NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control.

scholarly journals Estimating Parameters of Dichotomous and Ordinal Item Response Models with Gllamm

2007 ◽  
Vol 7 (3) ◽  
pp. 313-333 ◽  
Author(s):  
Xiaohui Zheng ◽  
Sophia Rabe-Hesketh
Keyword(s):  
Item Response ◽  
Latent Variable ◽  
Rating Scale ◽  
Ordinal Scale ◽  
Response Models ◽  
Item Response Models ◽  
Explanatory Variables ◽  
Test Items ◽  
Measurement Models ◽  
Latent Traits

Item response theory models are measurement models for categorical responses. Traditionally, the models are used in educational testing, where responses to test items can be viewed as indirect measures of latent ability. The test items are scored either dichotomously (correct–incorrect) or by using an ordinal scale (a grade from poor to excellent). Item response models also apply equally for measurement of other latent traits. Here we describe the one- and two-parameter logit models for dichotomous items, the partial-credit and rating scale models for ordinal items, and an extension of these models where the latent variable is regressed on explanatory variables. We show how these models can be expressed as generalized linear latent and mixed models and fitted by using the user-written command gllamm.

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