Ordinal data are extremely common in psychological research, with variables often assessed using Likert-type scales that take on only a few values. At the same time, researchers are increasingly fitting network models to ordinal item-level data. Yet very little work has evaluated how network estimation techniques perform when data are ordinal. We use a Monte Carlo simulation to evaluate and compare the performance of three estimation methods applied to either Pearson or polychoric correlations: EBIC graphical lasso with regularized edge estimates ("EBIC"), BIC model selection with partial correlation edge estimates ("BIC"), and multiple regression with p-value-based edge selection and partial correlation edge estimates ("MR"). We vary the number and distribution of thresholds, distribution of the underlying continuous data, sample size, model size, and network density, and we evaluate results in terms of model structure (sensitivity and false positive rate) and edge weight bias.Our results show that the effect of treating the data as ordinal versus continuous depends primarily on the number of levels in the data, and that estimation performance was affected by the sample size, the shape of the underlying distribution, and the symmetry of underlying thresholds. Furthermore, which estimation method is recommended depends on the research goals: MR methods tended to maximize sensitivity of edge detection, BIC approaches minimized false positives, and either one of these produced accurate edge weight estimates in sufficiently large samples. We identify some particularly difficult combinations of conditions for which no method produces stable results.

Continuous-time (CT) models are a flexible approach for modeling longitudinal data of psychological constructs. When using CT models, a researcher can assume one underlying continuous function for the phenomenon of interest. In principle, these models overcome some limitations of discrete-time (DT) models and allow researchers to compare findings across measures collected using different time intervals, such as daily, weekly, or monthly intervals. Theoretically, the parameters for equivalent models can be rescaled into a common time interval that allows for comparisons across individuals and studies, irrespective of the time interval used for sampling. In this study, we carry out a Monte Carlo simulation to examine the capability of CT autoregressive (CT-AR) models to recover the true dynamics of a process when the sampling interval is different from the time scale of the true generating process. We use two generating time intervals (daily or weekly) with varying strengths of the AR parameter and assess its recovery when sampled at different intervals (daily, weekly, or monthly). Our findings indicate that sampling at a faster time interval than the generating dynamics can mostly recover the generating AR effects. Sampling at a slower time interval requires stronger generating AR effects for satisfactory recovery, otherwise the estimation results show high bias and poor coverage. Based on our findings, we recommend researchers use sampling intervals guided by theory about the variable under study, and whenever possible, sample as frequently as possible.