Continuous Time Structural Equation Modeling with R Package ctsem

We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem

Perceptions of Problem Behavior in Adolescents’ Families: Perceiver, Target, and Family Effects

Considerable research has focused on the reliability and validity of informant reports of family behavior, especially maternal reports of adolescent problem behavior. None of these studies, however, has based their orientation on a theoretical model of interpersonal perception. In this study we used the social relations model (SRM) to examine family members’ reports of each others’ externalizing and internalizing problem behavior. Two parents and two adolescents in 69 families rated each others’ behavior within a round-robin design. SRM analysis showed that within-family perceptions of externalizing and internalizing behaviors are consistently due to three sources of variance; perceiver, target, and family effects. A family/contextual effect on informant reports of problem behavior has not been previously reported

Makes Religion Happy or Makes Happiness Religious? An Analysis of a Three-Wave Panel Using and Comparing Discrete and Continuous-Time Techniques.

Item does not contain fulltextThe reciprocal effects of religiosity and life satisfaction are examined in a three-wave panel study of German former high school students at ages 30, 43 and 56. Religiosity is measured as church attendance and Christian belief such that three measures are followed up over three time points. Analyses by structural equation modelling in discrete time and continuous time are compared. According to both methods, church attendance has the strongest autoregression/auto-effect, followed by Christian worldview, and next by life satisfaction; furthermore, all cross-regressions/cross-effects are slightly negative. The answer to both questions in the title is therefore negative. In contrast to the cross-regressions in the discrete-time analysis, the continuous-time analysis reveals significance of all negative cross-effects and reverses the strength order of the cross-effects between the two dimensions of religiosity. Continuous-time analysis also enables to compute and display the complete autoregression and cross-regression functions as well as the development of means and variances of the three variables across continuous time

Where Have the Persons Gone?

Much effort has been made to develop models for longitudinal data analysis, but comparably less attention has been paid to the use of individual specific values on latent variables in longitudinal models. In a tutorial style, this article introduces the reader to four common approaches to obtain individual scores – individual mean score, Bartlett method, regression method, Kalman filter – and reviews criteria commonly used to evaluate their performance. By means of simulated data, we mimic realistic scenarios and investigate in how far analytic results on the asymptotic performance of individual scores translate into practical situations. We end this article with a discussion of the use and usefulness of individual scores.Peer Reviewe

The willingness to pay for in-house piped water in urban and rural Indonesia

This paper analyses household preferences for in-house pipedwater in urban and rural Indonesia via a hedonic price model, specified as a constrained autoregression-structural equation model (ASEM). ASEM reduces bias due to time-varying omitted variables and measurement errors. In addition, it provides a convenient way of testing and correcting for endogeneity. On the basis of the Indonesia Family Life Survey data set, we find that on average urban and rural households have the same willingness to pay for in-house piped water, that is, 34.24 per cent of their monthly house rent. For the 25 per cent urban and rural households with lowest expenditure, this percentage is equivalent to 9.41 per cent and 7.57 per cent of their monthly expenditure, respectively. The findings support a need for further investment in in-house piped water in both areas, particularly for the households with the lowest expenditure levels

Continuous Time Structural Equation Modeling with R

We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem

Reply to Steele & Ferrer: Modeling oscillation, approximately or exactly?

Item does not contain fulltextThis article addresses modeling oscillation in continuous time. It criticizes Steele and Ferrer's article "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011), particularly the approximate estimation procedure applied. This procedure is the latent version of the local linear approximation procedure based on Boker (2001) and Boker, Neale, and Rausch (2004). It furthermore presents two exact alternative estimation procedures, one using filter techniques and the other using structural equation modeling.9 p