Psychometric network analysis offers a granular understanding of problem gambling (PG) by modeling individual symptoms within an interconnected system. Identifying central symptoms can help explain symptom co-occurrence and core symptoms relevant for escalating severity.

We estimated undirected cross-sectional and directed cross-lagged network models based on four representative Norwegian population surveys (2013, 2015, 2019, and 2022; age range 16–74) and assessed network stability, accuracy, and generalizability. Cross-sectional samples ranged from n = 3,232 to 6,034 (47–50% women) and cross-lagged samples included n = 3,043 (2013–2015, 50% women) and n = 2,749 (2019–2022, 47% women). PG symptoms were measured with the Problem Gambling Severity Index alongside self-reported gambling expenditure.

Within undirected networks, financial problems showed the strongest strength centrality and these networks showed strong generalizability, including the 2019 and 2022 surveys per Network Comparison Test (p = .65), edge presence (φ = .79), and edge weight (r = .85) correlations. Within directed networks, borrowing money had the strongest association with symptoms at follow-up (out-strength), while guilt at follow-up was most strongly predicted by other symptoms (in-strength). Generalizability between the 2013–2015 and 2019–2022 networks was inconclusive, with low correlation in edge presence (φ = .08) and high correlation in edge weights (r = .74).

Financial problems and borrowing money emerged as the most central PG symptoms in undirected and directed networks, respectively. Following further investigation, these symptoms may act as informative targets for screening, early intervention, and harm-reduction policy.

In clinical research, populations are often selected on the sum-score of diagnostic criteria such as symptoms. Estimating statistical models where a subset of the data is selected based on a function of the analyzed variables introduces Berkson's bias, which presents a potential threat to the validity of findings in the clinical literature. The aim of the present paper is to investigate the effect of Berkson's bias on the performance of the two most commonly used psychological network models: the Gaussian Graphical Model (GGM) for continuous and ordinal data, and the Ising Model for binary data.

In two simulation studies, we test how well the two models recover a true network structure when estimation is based on a subset of the data typically seen in clinical studies. The network is based on a dataset of 2807 patients diagnosed with major depression, and nodes in the network are items from the Hamilton Rating Scale for Depression (HRSD). The simulation studies test different scenarios by varying (1) sample size and (2) the cut-off value of the sum-score which governs the selection of participants.

The results of both studies indicate that higher cut-off values are associated with worse recovery of the network structure. As expected from the Berkson's bias literature, selection reduced recovery rates by inducing negative connections between the items.

Our findings provide evidence that Berkson's bias is a considerable and underappreciated problem in the clinical network literature. Furthermore, we discuss potential solutions to circumvent Berkson's bias and their pitfalls.