We study how consumer preferences affect the transmission of microeconomic price shocks to consumer price index (CPI) inflation. These preferences give rise to complementarities and substitutions between goods, generating demand-driven cross-price dependencies that either amplify or mitigate the impact of price shocks. Our results demonstrate that while both effects are present, positive spillovers due to complementarities dominate. The magnitude of these cross-price effects is significant, demonstrating their importance in shaping CPI inflation dynamics. Most importantly, demand-driven price linkages decisively shape the impact of producer prices on CPI inflation. These findings underscore the need to take into account demand-driven price dependencies when assessing the impact of price shocks on CPI inflation, rather than relying solely on supply-related ones.

Several studies have used a network analysis to recognize the dynamics and determinants of psychotic-like experiences (PLEs) in community samples. Their synthesis has not been provided so far. A systematic review of studies using a network analysis to assess the dynamics of PLEs in community samples was performed. Altogether, 27 studies were included. The overall percentage ranks of centrality metrics for PLEs were 23.5% for strength (20 studies), 26.0% for betweenness (5 studies), 29.7% for closeness (6 studies), 26.9% for expected influence (7 studies), and 29.1% for bridge expected influence (3 studies). Included studies covered three topics: phenomenology of PLEs and associated symptom domains (14 studies), exposure to stress and PLEs (7 studies), and PLEs with respect to suicide-related outcomes (6 studies). Several other symptom domains were directly connected to PLEs. A total of 6 studies investigated PLEs with respect to childhood trauma (CT) history. These studies demonstrated that PLEs are directly connected to CT history (4 studies) or a cumulative measure of environmental exposures (1 study). Moreover, CT was found to moderate the association of PLEs with other symptom domains (1 study). Two studies that revealed direct connections of CT with PLEs also found potential mediating effects of cognitive biases and general psychopathology. PLEs were also directly connected to suicide-related outcomes across all studies included within this topic. The findings imply that PLEs are transdiagnostic phenomena that do not represent the most central domain of psychopathology in community samples. Their occurrence might be associated with CT and suicide risk.

In this study, network analysis was conducted using an exploratory approach on the variables of self-efficacy, academic resilience (AR), cognitive test anxiety and academic achievement (ACH), which are frequently examined in educational research. Data were collected from a total of 828 Turkish secondary school adolescents (51.9% female), using three different self-reported scales for self-efficacy, AR and cognitive test anxiety, as well as an ACH scale. The data were analyzed using regularized partial correlation network analysis (EBICglasso). The results show that academic self-efficacy (ASE) stands out among the variables of the study and that there is a positive relationship between ASE and all other variables except cognitive test anxiety. Besides, increasing students’ ASE and AR levels plays a notable role in increasing their ACH levels. By providing new evidence on the relationships among these variables, this study offers insights that may inspire educational policy interventions.

We present a series of network analyses aiming to uncover the symptom constellations of depression, anxiety and somatization among 2,796 adult primary health care attendees in Goa, India, a low- and middle-income country (LMIC). Depression and anxiety are the leading neuropsychiatric causes of disability. Yet, the diagnostic boundaries and the characteristics of their dynamically intertwined symptom constellations remain obscure, particularly in non-Western settings. Regularized partial correlation networks were estimated and the diagnostic boundaries were explored using community detection analysis. The global and local connectivity of network structures of public versus private healthcare settings and treatment responders versus nonresponders were compared with a permutation test. Overall, depressed mood, panic, fatigue, concentration problems and somatic symptoms were the most central. Leveraging the longitudinal nature of the data, our analyses revealed baseline networks did not differ across treatment responders and nonresponders. The results did not support distinct illness subclusters of the CMDs. For public healthcare settings, panic was the most central symptom, whereas in private, fatigue was the most central. Findings highlight varying mechanism of illness development across socioeconomic backgrounds, with potential implications for case identification and treatment. This is the first study directly comparing the symptom constellations of two socioeconomically different groups in an LMIC.

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper, we propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the structure of the covariance or correlation matrix. Psychometric networks or other correlation-based models (e.g., factor models) can be subsequently estimated from the resultant splits. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.

How social networks influence human behavior has been an interesting topic in applied research. Existing methods often utilized scale-level behavioral data (e.g., total number of positive responses) to estimate the influence of a social network on human behavior. This study proposes a novel approach to studying social influence that utilizes item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents’ social network data and item-level behavior measures into a single space we call ‘interaction map’. The interaction map visualizes the association between the latent homophily among respondents and their item-level behaviors, revealing differential social influence effects across item-level behaviors. We also measure overall social influence by assessing the impact of the interaction map. We evaluate the properties of the proposed approach via extensive simulation studies and demonstrate the proposed approach with a real data in the context of studying how students’ friendship network influences their participation in school activities.

This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data—the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.

Objective: Patients with cognitive disorders such as Alzheimer’s disease (AD) and mild cognitive impairment (MCI) frequently exhibit depressive symptoms. Depressive symptoms can be evaluated with various measures and questionnaires. Geriatric Depression Scale (GDS) is a scale that can be used to measure symptoms in geriatric age. Many questionnaires usually sum up symptom scales. However, core symptoms of depression in these patients and connections between these symptoms have not been fully explored yet. Thus, the objectives of this study were: 1) to determine core symptoms of two cognitive disorders, Alzheimer’s disease and mild cognitive impairment; and 2) to investigate the network structure of depressive symptomatology in individuals with cognitive impairment in comparison with those with Alzheimer’s disease.

Methods: This study encompassed 5,354 patients with cognitive impairments such as Alzheimer’s disease [n = 1,889] and mild cognitive impairment [n = 3,464]. The Geriatric Depression Scale, a self-administered questionnaire, was employed to assess depressive symptomatology. Using exploratory graph analysis (EGA), a network analysis was conducted and the network structure was evaluated through regularized partial correlation models. To determine the centrality of depressive symptoms within each cohort, network parameters such as strength, betweenness, and closeness were examined. Additionally, to explore differences in the network structure between Alzheimer’s disease and mild cognitive impairment groups, a network comparison test was performed.

Results: In the analysis of centrality indices, “worthlessness’’ was identified as the most central symptom in the Geriatric Depression Scale among patients with Alzheimer’s disease, whereas “emptiness’’ was found to be the most central symptom in patients with mild cognitive impairment. Despite these differences in central symptoms, the comparative analysis showed no statistical difference in the overall network structure between Alzheimer’s disease and mild cognitive impairment groups.

Conclusion: Findings of this study could contribute to a better understanding of the manifestation of depressive symptoms in patients with cognitive impairment. These results are expected to aid in identifying and prioritizing core symptoms in these patients. Further research should be conducted to explore potential interventions tailored to these core symptoms in patients with Alzheimer’s disease and mild cognitive impairment. Finding out core symptoms in those groups might have clinical importance in that appropriate treatment for neuropsychiatric symptoms in patients with cognitive impairment could help preclude progression tofurtherimpairment.

Anxiety and depression are highly prevalent and comorbid problems in childhood, which deserve greater understanding for effective prevention and treatment. The main aim of the present study was to explore the comorbidity between anxiety and depression symptoms using a novel and valuable approach to study comorbidity, such as network analysis. Specifically, the connectivity between symptoms and possible relevant symptoms was examined through comorbidity estimation and shortest pathway networks, as well as bridge symptoms. This study comprised 281 Spanish-speaking children aged 8–12 years (45.2% girls), whose anxiety and depression symptoms were assessed through specific brief parent-report measures. Analyses revealed that in the comorbidity network, the most central symptoms were related to depression (“No good anymore,” “Could never be as good,” “Hated self,” “Did everything wrong,” “Nobody loved him/her”) or anxiety (“Suddenly feels really scared”). Furthermore, it was found that the most central bridge symptoms, whose activation would play a key role in the activation of other domain symptoms, were anxiety symptoms such as “Trouble going to school” and “Suddenly feels really scared” and depression symptoms, such as “Could never be as good” and “Hated self.” Additionally, the shortest path network suggested the existence of different possible pathways of connection between anxiety and depression symptoms. Overall, these findings help to understand the complexity of the anxiety-depression comorbidity. It suggests the existence of central and bridge symptoms that complement previous studies, which may be potential targets for interventions to prevent and treat childhood anxiety and depression.

Ideal points of MPs in the UK House of Commons (HoC) are characteristically difficult to ascertain due to tight party discipline and strategic voting by opposition members. This research note generates left/right ideal point estimates for 591 British MPs sitting in the HoC as of 22/08/2022, ascertained through their social media followership. Specifically, estimates are derived by conducting correspondence analysis (CA) on MP Twitter (X) follower networks, which are subsequently validated against an expert survey, confirming that these estimates have a high degree of between-party (R2 = 0.93) and within-party (Con: r = 0.84; Lab: r = 0.81) accuracy. The informative value of these estimates is then demonstrated by predicting candidate endorsement in the September 2022 Conservative leadership contest, confirming that an MP's ideal point was a statistically significant predictor of candidate endorsement, with Liz Truss drawing support primarily from the further right of the party.