Latent Response Research Articles

Protective role of TRPM7 knockdown in ulcerative colitis via blocking NLRP3 inflammasome-mediated pyroptosis

Transient receptor potential melastatin 7 (TRPM7) has been emerged as a potent drug target for immunomodulation with ion conductance and kinase activities. The research is projected to characterize the influences of TRPM7 on the course of ulcerative colitis (UC) and dissect the latent response mechanisms. The in vivo murine model and in vitro cell model of UC were both stimulated by DSS. RT-qPCR and western blotting tested the abundance of TRPM7. Colonic damage was estimated by Hematoxylin-eosin staining, calculation of colon length, measurement of DAI and MPO assay kit. CCK-8 method and TUNEL staining severally ascertained cell activity and apoptosis. ELISA method assayed the inflammatory levels and relevant assay kits determined oxidative stress levels. FITC-dextran flux, immunohistochemistry, TEER as well as western blotting evaluated intestinal barrier function. Immunofluorescence staining and western blotting appraised NLR family pyrin domain containing 3 (NLRP3)-dependent pyroptosis. Depleted TRPM7 retarded inflammation, oxidative damage as well as intestinal barrier damage both in vitro and in vivo. TRPM7 reduction repressed the pyroptosis mediated by NLRP3 inflammasome. NLRP3 agonist nigericin partly abolished the protection elicited by TRPM7 silencing against inflammation, oxidative damage as well as intestinal barrier damage in vitro. Collectively, TRPM7 deletion might possess the therapeutic potential in UC, the working mechanism of which might involve the inactivation of NLRP3-dependent pyroptosis.

Relating violations of measurement invariance to group differences in response times.

Measurement invariance is an assumption underlying the regression of a latent variable on a background variable. It requires the measurement model parameters of the latent variable to be equal across the levels of the background variable. Item-specific violations of this assumption are referred to as differential item functioning and are ideally substantively explainable to warrant theoretically valid and meaningful results. Past research has focused on developing statistical approaches to explain differential item functioning effects in terms of item- or person-specific covariates. In this study, we propose a modeling approach that can be used to test if differences in item response times can be used to statistically explain differential item functioning. To this end, we operationalize a latent response process factor and test if item-specific group differences on this factor can account for the observed differences in item scores. We investigate the properties of the model in a simulation study, and we apply the model to a real data set. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian Semiparametric Longitudinal Inverse-Probit Mixed Models for Category Learning.

Understanding how the adult human brain learns novel categories is an important problem in neuroscience. Drift-diffusion models are popular in such contexts for their ability to mimic the underlying neural mechanisms. One such model for gradual longitudinal learning was recently developed in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). In practice, category response accuracies are often the only reliable measure recorded by behavioral scientists to describe human learning. Category response accuracies are, however, often the only reliable measure recorded by behavioral scientists to describe human learning. To our knowledge, however, drift-diffusion models for such scenarios have never been considered in the literature before. To address this gap, in this article, we build carefully on Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021), but now with latent response times integrated out, to derive a novel biologically interpretable class of 'inverse-probit' categorical probability models for observed categories alone. However, this new marginal model presents significant identifiability and inferential challenges not encountered originally for the joint model in Paulon et al. (J Am Stat Assoc 116:1114-1127, 2021). We address these new challenges using a novel projection-based approach with a symmetry-preserving identifiability constraint that allows us to work with conjugate priors in an unconstrained space. We adapt the model for group and individual-level inference in longitudinal settings. Building again on the model's latent variable representation, we design an efficient Markov chain Monte Carlo algorithm for posterior computation. We evaluate the empirical performance of the method through simulation experiments. The practical efficacy of the method is illustrated in applications to longitudinal tone learning studies.

Supervised probabilistic latent semantic analysis with applications to controversy analysis of legislative bills

Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text analysis technique that models each word in a document as a sample from a mixture of topics. PLSA is the precursor of probabilistic topic models including Latent Dirichlet Allocation (LDA). PLSA, LDA and their numerous extensions have been successfully applied to many text mining and retrieval tasks. One important extension of LDA is supervised LDA (sLDA), which distinguishes itself from most topic models in that it is supervised. However, to the best of our knowledge, no prior work extends PLSA in a similar manner sLDA extends LDA by jointly modeling the contents and the responses of documents. In this paper, we propose supervised PLSA (sPLSA) which can efficiently infer latent topics and their factorized response values from the contents and the responses of documents. The major challenge lies in estimating a document’s topic distribution which is a constrained probability that is dictated by both the content and the response of the document. To tackle this challenge, we introduce an auxiliary variable to transform the constrained optimization problem to an unconstrained optimization problem. This allows us to derive an efficient Expectation and Maximization (EM) algorithm for parameter estimation. Compared to sLDA, sPLSA converges much faster and requires less hyperparameter tuning, while performing similarly on topic modeling and better in response factorization. This makes sPLSA an appealing choice for latent response analysis such as ranking latent topics by their factorized response values. We apply the proposed sPLSA model to analyze the controversy of bills from the United States Congress. We demonstrate the effectiveness of our model by identifying contentious legislative issues.

Experiences of hospital rotation from family medicine residents’ points of view an empirical holistic study

Objective Trainees or medical residents’ experiences of hospital rotations and training have not been sufficiently studied. More empirical holistic studies of experiences of General Practice/Family Medicine (GP/FM) residents in Sweden are needed. The purpose of this study was to describe experiences of hospital rotation during residency. Design Empirical-holistic study. Setting GP/FM residents were invited by email to participate in the study. They could describe their experiences anonymously by answering two questions via an esMaker internet survey. Analyses of the responses were carried out with content analysis as the analytical methodology. Both manifest and latent responses were analyzed. Subjects Fifty-nine GP/FM residents participated in the study. Main Outcome Measure The results identified four main topics: structure, resources, effects, and constructive supervision. Results GP/FM residents experienced hospital rotations as effective when there was a structured schedule and adequate time allotted for introduction and meeting patients. Hospital rotations that lacked, or had unstructured, supervision caused uncertainty and insecurity, which led to rotations being experienced as less beneficial, which was, from a GP/FM perspective, not constructive. Conclusion The study suggests that family medicine residents required a structured and planned schedule during hospital rotations. This study may contribute to increased quality of hospital rotations during residency as a family physician.

Nonignorable Consequences of (Partially) Ignoring Missing Item Responses: Students Omit (Constructed Response) Items Due to a Lack of Knowledge

In recent literature, alternative models for handling missing item responses in large-scale assessments have been proposed. Based on simulations and arguments based on psychometric test theory, it is argued in this literature that missing item responses should never be scored as incorrect in scaling models but rather treated as ignorable or handled based on a model. The present article shows that these arguments have limited validity and illustrates the consequences in a country comparison using the PIRLS 2011 study. It is argued that students omit (constructed response) items because they do not know the correct item answer. A different treatment of missing item responses than scoring them as incorrect leads to significant changes in country rankings, which induces nonignorable consequences regarding the validity of the results. Additionally, two alternative item response models are proposed based on different assumptions for missing item responses. In the first pseudo-likelihood approach, missing item responses for a particular student are replaced by a score that ranges between zero and a model-implied probability computed based on the non-missing items. In the second approach, the probability of a missing item response is predicted by a latent response propensity variable and the item response itself. The models were applied to the PIRLS 2011 study, demonstrating that country comparisons change under different modeling assumptions for missing item responses.

Using Structural Equation Modeling to Reproduce and Extend ANOVA-Based Generalizability Theory Analyses for Psychological Assessments

Generalizability theory provides a comprehensive framework for determining how multiple sources of measurement error affect scores from psychological assessments and using that information to improve those assessments. Although generalizability theory designs have traditionally been analyzed using analyses of variance (ANOVA) procedures, the same analyses can be replicated and extended using structural equation models. We collected multi-occasion data from inventories measuring numerous dimensions of personality, self-concept, and socially desirable responding to compare variance components, generalizability coefficients, dependability coefficients, and proportions of universe score and measurement error variance using structural equation modeling versus ANOVA techniques. We further applied structural equation modeling techniques to continuous latent response variable metrics and derived Monte Carlo-based confidence intervals for those indices on both observed score and continuous latent response variable metrics. Results for observed scores estimated using structural equation modeling and ANOVA procedures seldom varied. Differences in reliability between raw score and continuous latent response variable metrics were much greater for scales with dichotomous responses, thereby highlighting the value of doing analyses on both metrics to evaluate gains that might be achieved by increasing response options. We provide detailed guidelines for applying the demonstrated techniques using structural equation modeling and ANOVA-based statistical software.

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

Confirmatory Factor Analyses in Psychological Test Adaptation and Development

New Mechanistic and Therapeutic Insights into Purified Phospholipase A2 from Pseudechis Australis Snake Venom - a Novel Anticancer Agent for the Treatment of Precursor B-Cell Acute Lymphoblastic Leukaemia

Introduction Precursor B-cell acute lymphoblastic leukaemia (pre-B ALL) is the most common childhood malignancy and the leading cause of cancer-related death in children and young adults. Standard treatment options for pre-B ALL typically involve multiple regimens of DNA-damaging chemotherapy agents, which despite their high curative rates, are often tolerated poorly by patients. Previously our group has purified, and characterised a novel anti-leukaemia compound, termed PA-PLA2 from Pseudechis australis snake venom. Comprised of three phospholipase A2 (PLA2) enzymes, PA-PLA2 was shown to induce necrotic cell death in pre-B ALL cells, with RNA-sequencing analysis revealing enrichment of PI3K/Akt signalling as well as inflammation and immune response genes. Importantly, PA-PLA2 has also been shown to be non-cytotoxic towards normal stem cells from donor peripheral blood and bone marrow, raising the prospect of its use as a novel and selective treatment. In this study, we further explore the mechanism of action and therapeutic potential of PA-PLA2 in relevant models of pre-B ALL. Methods PA-PLA2 was purified and characterised previously using, reverse-phase high-performance liquid chromatography and mass spectrometry (Boncan et al., 2021). Reh and SD-1 pre-B ALL in vitro cell lines, were used to study PA-PLA2's anti-leukaemia action. Protein immunoblotting was used to validate previously performed RNA-sequencing, proteomics and phosphoproteomics analysis. Densitometry analysis of immunoblots was completed using ImageJ software. To assess the therapeutic potential of PA-PLA2 in combination with standard of care therapies, we performed a drug screen, comprised of a custom panel of 48 standard of care and clinical agent compounds. CellTox™ Green cytotoxicity assay was used as a readout of cell death, with effective drug combinations validated using CellTiter® Glo viability assay, and combination indices (CI) calculated using CompuSyn software. Results Time course protein immunoblotting analyses of PA-PLA2-sensitive cell lines, Reh and SD-1 treated with IC50 doses of PA-PLA2 (Reh; 1µg/mL, SD-1; 25µg/mL) revealed activation of pRIPK1Ser166/RIPK1 in both cell lines after one hour treatment (p<0.0001 and p<0.01 respectively). Activation was accompanied by cleavage of RIPK1 after 24 hours in Reh cells only (p<0.05). One hour treatment with PA-PLA2 was also found to activate pAktSer473/Akt(pan) and pAktThr308/Akt(pan) signalling in Reh cells (p<0.001 and p<0.05 respectively). In contrast, a transient and latent response of Akt activation was observed in SD-1 cells, with pAktSer473/Akt(pan) levels initially decreasing after one hour (p<0.05), before increasing again and resulting in activation at 24 hours (p<0.01). Activation of pAktThr308/Akt(pan) was only observed after 24 hours treatment also (p<0.05). Examining the signalling axes driving PA-PLA2's mechanism of action, phosphoproteomics showed a reduction in pc-MycSer348 expression in both cell lines (p<0.05). Immunoblotting analysis confirmed the c-Myc modifying role of PA-PLA2, with c-Myc total expression found to be elevated in SD-1 cells after 8 hours treatment (p<0.001). To evaluate the therapeutic benefit of including PA-PLA2 in standard of care regimens, we performed a drug screen that identified combinations of PA-PLA2 with Cytarabine, Idarubicin-HCl or Vorinostat (SAHA) as being synergistic in Reh cells. SAHA displayed the best synergism profile, with CI values of < 1 being obtained for all combinations of PA-PLA2 (0.1, 0.5, 1, 2.5 and 5µg/mL) with SAHA (1,10 and 20µM). Importantly, proteomics analysis revealed increased expression of HDAC2 in Reh cells (p<0.05), implicating HDAC2 as a possible effector of synergism. Conclusion This study has identified RIPK1 and Akt as effectors of the mechanism of action of PA-PLA2. PA-PLA2 treatment had a modifying role on the expression of the multifunctional transcription factor c-Myc. Importantly PA-PLA2 was shown to act synergistically with Cytarabine as well as SAHA and Idarubicin-HCl, with HDAC2 implicated as a possible effector of synergism with SAHA. Overall, this study has further elucidated the mechanism of action of PA-PLA2, with the identified synergy of PA-PLA2 with standard of care agents possibly yielding a novel, treatment option for patients with pre-B ALL.

Ordinal state-trait regression for intensive longitudinal data.

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).

Analgesic, Hypoglycemic and Antidiarrheal Activities Screening of Canna indica L.

The rhizome of Canna indica L. (family: Cannaceae) was extracted in methanol and two doses- 200 and 400 mg/kg body weight of the extract were employed to reveal central and peripheral analgesic, hypoglycemic and antidiarrheal properties in vivo. According to tail immersion technique, both doses significantly rose the time of latent response for 90 minutes compared to control mice group in assessing central analgesia. Acetic acid-induced writhing process demonstrated 37.29% and 64.41% inhibition of mice movement at both doses, respectively which indicated promising and statistically significant peripheral analgesic effect. On the other hand, hypoglycemic and antidiarrheal activities maintained dose and time dependent manner. Moreover, the extract of rhizome displayed statistically significant antidiarrheal activity at 400 mg/kg b.w. dose. Bangladesh Pharmaceutical Journal 25(2): 137-142, 2022 (July)

Unfolding Judicial Ideology: A Data-Generating Priors Approach with an Application to the Brazilian Supreme Court

Abstract Spatial models, in the form of latent response theories (ideal point estimation), have been widely used to study the voting behavior of judges in collegial courts. In some specific institutional contexts, building coherent testable hypotheses with conventional methodology is challenging. We set up a non-parametric method to identify the number and nature of the latent ideological traits allegedly orienting judicial voting behavior in the absence of prior information regarding the nature of their preferences. We draw information from explorative cluster analysis conducted on votes cast by judges in the decisions of the court to construct priors in the context of Item Response Theory. We concentrate on the Brazilian Supreme Court in the period 2009–2018. We primarily find that votes express a split which groups judges into two distinct clusters. On one side, we find judges appointed further back in time and with longer tenure on the bench; on the other side, we observe judges more recently appointed and with shorter experience. Judges are likely to respond to the presidential appointer and to elements related to their origin, university education, and career background (aside from the guidance of their own experience). Our study provides an original empirical approach that is not limited to the Brazilian Supreme Court, but is suitable to investigate judicial voting behavior when the nature of potential ideological drivers is debatable, controversial, or unknown.

Feature screening with latent responses.

A novel feature screening method is proposed to examine the correlation between latent responses and potential predictors in ultrahigh-dimensional data analysis. First, a confirmatory factor analysis (CFA) model is used to characterize latent responses through multiple observed variables. The expectation-maximization algorithm is employed to estimate the parameters in the CFA model. Second, R-Vector (RV) correlation is used to measure the dependence between the multivariate latent responses and covariates of interest. Third, a feature screening procedure is proposed on the basis of an unbiased estimator of the RV coefficient. The sure screening property of the proposed screening procedure is established under certain mild conditions. Monte Carlo simulations are conducted to assess the finite-sample performance of the feature screening procedure. The proposed method is applied to an investigation of the relationship between psychological well-being and the humangenome.

How to derive expected values of structural equation model parameters when treating discrete data as continuous

ABSTRACT This tutorial presents an analytical derivation of univariate and bivariate moments of numerically weighted ordinal variables, implied by their latent responses’ covariance matrix and thresholds. Fitting a SEM to those moments yields population-level SEM parameters when discrete data are treated as continuous, which is less computationally intensive than Monte Carlo simulation to calculate transformation (discretization) error. A real-data example demonstrates how this method could help inform researchers how best to treat their discrete data, and a simulation replication demonstrates the potential of this method to add value to a Monte Carlo study comparing estimators that make different assumptions about discrete data.

A psychometric modeling approach to fuzzy rating data

Modeling fuzziness and imprecision in human rating data is a crucial problem in many research areas, including applied statistics, behavioral, social, and health sciences. Because of the interplay between cognitive, affective, and contextual factors, the process of answering survey questions is a complex task, which can barely be captured by standard (crisp) rating responses. Fuzzy rating scales have progressively been adopted to overcome some of the limitations of standard rating scales, including their inability to disentangle decision uncertainty from individual responses. The aim of this article is to provide a novel fuzzy scaling procedure which uses Item Response Theory trees (IRTrees) as a psychometric model for the stage-wise latent response process. In so doing, fuzziness of rating data is modeled using the overall rater's pattern of responses instead of being computed using a single-item based approach. This offers a consistent system for interpreting fuzziness in terms of individual-based decision uncertainty. A simulation study and two empirical applications are adopted to assess the characteristics of the proposed model and provide converging results about its effectiveness in modeling fuzziness and imprecision in rating data.

Fashion involvement, opinion-seeking and product variety as stimulators for fashion e-commerce: an investigated model based on S-O-R model

PurposeThis study's aim was to investigate the stimulators of fashion e-consumers within e-commerce environments. The study proposed a framework utilizing stimulus–organism–response (S-O-R) theory to suggest that fashion involvement and opinion-seeking would act as sociopsychological stimuli, while product variety as an objective stimulus. Perceived quality is proposed as an organism, moderated by perceived price. Consumer buying behavior within e-commerce environments presents the framework's response. The study looked at variables with deeper insights into Malaysian fashion consumers.Design/methodology/approachA quantitative method was used to assess the significance of relationships within the proposed model. Partial least squares structural equation modeling technique was implemented to assess the framework's relationships with a sample size of 374.FindingsResults indicate that fashion involvement is significantly associated as a sociopsychological stimulus, with product variety being an objective stimulus for Malaysian fashion e-consumers. Perceived quality is significantly represented as an organism through the framework, and buying behavior is the latent response. Price would significantly moderate the relationship between fashion involvement and quality. Opinion-seeking was found not to be a significant stimulus for Malaysian e-consumers.Originality/valueContribution of this study goes to the existing literature by providing a deeper understanding of Malaysian e-consumer behavior by applying S-O-R theory. Malaysian fashion e-consumerism was suggested to be influenced by product involvement, quality, price, opinion-seeking and product range offered; therefore, a proposed framework was demonstrated and tested.

On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. In an illustrative simulation study, it is shown that the Mislevy-Wu model provides unbiased model parameters. Moreover, the simulation replicates the finding from various simulation studies from the literature that scoring missing item responses as wrong provides biased estimates if the latent ignorability assumption holds in the data-generating model. However, if missing item responses are generated such that they can only be generated from incorrect item responses, applying an item response model that relies on latent ignorability results in biased estimates. The Mislevy-Wu model guarantees unbiased parameter estimates if the more general Mislevy-Wu model holds in the data-generating model. In addition, this article uses the PISA 2018 mathematics dataset as a case study to investigate the consequences of different missing data treatments on country means and country standard deviations. Obtained country means and country standard deviations can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, in the discussion section, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data

Careless and insufficient effort responding (C/IER) can pose a major threat to data quality and, as such, to validity of inferences drawn from questionnaire data. A rich body of methods aiming at its detection has been developed. Most of these methods can detect only specific types of C/IER patterns. However, typically different types of C/IER patterns occur within one data set and need to be accounted for. We present a model-based approach for detecting manifold manifestations of C/IER at once. This is achieved by leveraging response time (RT) information available from computer-administered questionnaires and integrating theoretical considerations on C/IER with recent psychometric modeling approaches. The approach a) takes the specifics of attentive response behavior on questionnaires into account by incorporating the distance–difficulty hypothesis, b) allows for attentiveness to vary on the screen-by-respondent level, c) allows for respondents with different trait and speed levels to differ in their attentiveness, and d) at once deals with various response patterns arising from C/IER. The approach makes use of item-level RTs. An adapted version for aggregated RTs is presented that supports screening for C/IER behavior on the respondent level. Parameter recovery is investigated in a simulation study. The approach is illustrated in an empirical example, comparing different RT measures and contrasting the proposed model-based procedure against indicator-based multiple-hurdle approaches.

A Latent Class Model for Multivariate Binary Data Subject to Missingness

When researchers are interested in measuring social phenomena that cannot be measured using a single variable, the appropriate statistical tool to be used is a latent variable model. A number of manifest variables is used to define the latent phenomenon. The manifest variables may be incomplete due to different forms of non-response that may or may not be random. In such cases, especially when the missingness is nonignorable, it is inevitable to include a missingness mechanism in the model to obtain valid estimates for parameters. In social surveys, categorical items can be considered the most common type of variable. We thus propose a latent class model where two categorical latent variables are defined; one represents the latent phenomenon of interest, and another represents a respondent’s propensity to respond to survey items. All manifest items are considered to be categorical. The proposed model incorporates a missingness mechanism that accounts for forms of missingness that may not be random by allowing the latent response propensity class to depend on the latent phenomenon under consideration, given a set of covariates. The Expectation-Maximization (EM) algorithm is used for estimating the proposed model. The proposed model is used to analyze data from 2014 Egyptian Demographic and Health Survey (EDHS14). Missing data is artificially created in order to study results under the three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).

A Bayesian nonparametric approach to dynamic item-response modeling: An application to the GUSTO cohort study.

Statistical analysis of questionnaire data is often performed employing techniques from item‐response theory. In this framework, it is possible to differentiate respondent profiles and characterize the questions (items) included in the questionnaire via interpretable parameters. These models are often crosssectional and aim at evaluating the performance of the respondents. The motivating application of this work is the analysis of psychometric questionnaires taken by a group of mothers at different time points and by their children at one later time point. The data are available through the GUSTO cohort study. To this end, we propose a Bayesian semiparametric model and extend the current literature by: (i) introducing temporal dependence among questionnaires taken at different time points; (ii) jointly modeling the responses to questionnaires taken from different, but related, groups of subjects (in our case mothers and children), introducing a further dependency structure and therefore sharing of information; (iii) allowing clustering of subjects based on their latent response profile. The proposed model is able to identify three main groups of mother/child pairs characterized by their response profiles. Furthermore, we report an interesting maternal reporting bias effect strongly affecting the clustering structure of the mother/child dyads.