scholarly journals Handling initial conditions and endogenous covariates in dynamic/transition models for binary data with unobserved heterogeneity

2013 ◽  
Vol 63 (2) ◽  
pp. 211-237 ◽  
Author(s):  
Anders Skrondal ◽  
Sophia Rabe-Hesketh
Keyword(s):  
Binary Data ◽  
Initial Conditions ◽  
Unobserved Heterogeneity ◽  
Dynamic Transition ◽  
Transition Models ◽  
Endogenous Covariates

Estimation of dynamic models of recurrent events with censored data

2020 ◽  
Author(s):  
Sanghyeok Lee ◽  
Tue Gørgens
Keyword(s):  
Monte Carlo ◽  
Censored Data ◽  
Recurrent Events ◽  
Dynamic Models ◽  
Initial Conditions ◽  
Unobserved Heterogeneity ◽  
Likelihood Estimation ◽  
Quadrature Rule ◽  
Maximum Simulated Likelihood ◽  
Simulated Likelihood

Summary In this paper, we consider estimation of dynamic models of recurrent events (event histories) in continuous time using censored data. We develop maximum simulated likelihood estimators where missing data are integrated out using Monte Carlo and importance sampling methods. We allow for random effects and integrate out this unobserved heterogeneity using a quadrature rule. In Monte Carlo experiments, we find that maximum simulated likelihood estimation is practically feasible and performs better than both listwise deletion and auxiliary modelling of initial conditions. In an empirical application, we study ischaemic heart disease events for male Maoris in New Zealand.

scholarly journals Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models?

2020 ◽  
Author(s):  
Henrik Kenneth Andersen
Keyword(s):  
Structural Equation ◽  
Initial Conditions ◽  
Unobserved Heterogeneity ◽  
Real Data ◽  
Equation Modeling ◽  
Panel Models ◽  
Latent Trajectory ◽  
Random Intercept ◽  
Residual Level ◽  
Latent Curve

Panel models in structural equation modeling that combine static and dynamic components for investigating reciprocal relations while controlling for time-invariant unobserved heterogeneity are becoming increasingly popular. Recently, the Latent Curve Model with Structured Residuals and the Random-Intercept Cross-Lagged Panel Model were suggested as ‘residual-level’ versions of the more traditional Autoregressive Latent Trajectory and Dynamic Panel Models, respectively. Their main benefit is that they allow for a more straightforward interpretation of the trajectory factors. It is not widely known, however, that the residual-level models place potentially strong assumptions on the initial conditions, i.e., the process that was occurring before the observation period began. If the process under investigation is nonstationary (e.g., growing exponentially), has not been going on for long enough to reach equilibrium, or has been ‘knocked’ out of equilibrium, potentially due to an intervention, then the residual-level models are not appropriate. This is shown analytically and with demonstrations using real data. A simple amendment is suggested to relax the assumptions on the initial conditions while retaining the models’ beneficial aspects.

scholarly journals The role of collective action for the emergence and consolidation of democracy

2020 ◽  
Vol 16 (6) ◽  
pp. 831-862
Author(s):  
Paolo Li Donni ◽  
Maria Marino
Keyword(s):  
Collective Action ◽  
Initial Conditions ◽  
Unobserved Heterogeneity ◽  
Economic Factors ◽  
Time Varying ◽  
Dynamic Nature ◽  
Structural Dynamic ◽  
Dynamic Framework ◽  
Structural Dynamic Model

AbstractThe role of citizens' collective action for the emergence and consolidation of democracy is generally analysed within bottom-up theories. However, top-down theories show that elites might impede or promote both democracy and collective action through a set of strategies which are often unobserved and vary over time. Democratic persistence and change require then to be assessed in a dynamic framework which considers both citizens and elites' strategies. For such reason, on a large sample of countries in the period 1971–2014, we jointly estimate the probability of collective action and democracy using a Structural Dynamic Model. This allows us to account for the dynamic nature of the two political phenomena under investigation by controlling for their persistence, for initial conditions and time-varying unobserved heterogeneity. We find that collective action matters for the emergence of democracy but not for its consolidation which seems to be related to more structural economic factors.

University Dropout

2008 ◽  
Vol 29 (3) ◽  
pp. 134-147 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Nicolas Sander
Keyword(s):  
Grade Point Average ◽  
Structural Equation ◽  
Unobserved Heterogeneity ◽  
School Grade ◽  
Grade Point ◽  
High School Grade ◽  
Hands On ◽  
University Dropout ◽  
The Individual ◽  
A Prospective Study

University dropout is a politically and economically important factor. While a number of studies address this issue cross-sectionally by analyzing different cohorts, or retrospectively via questionnaires, few of them are truly longitudinal and focus on the individual as the unit of interest. In contrast to these studies, an individual differences perspective is adopted in the present paper. For this purpose, a hands-on introduction to a recently proposed structural equation (SEM) approach to discrete-time survival analysis is provided ( Muthén & Masyn, 2005 ). In a next step, a prospective study with N = 1096 students, observed across four semesters, is introduced. As expected, average university grade proved to be an important predictor of future dropout, while high-school grade-point average (GPA) yielded no incremental predictive validity but was completely mediated by university grade. Accounting for unobserved heterogeneity, three latent classes could be identified with differential predictor-criterion relations, suggesting the need to pay closer attention to the composition of the student population.

The Beck Hopelessness Scale

2016 ◽  
Vol 32 (2) ◽  
pp. 111-118 ◽  
Author(s):  
Marianna Szabó ◽  
Veronika Mészáros ◽  
Judit Sallay ◽  
Gyöngyi Ajtay ◽  
Viktor Boross ◽  
...  
Keyword(s):  
Binary Data ◽  
Factor Model ◽  
Clinical Sample ◽  
Analytical Techniques ◽  
Cross Cultural ◽  
Cultural Validity ◽  
Beck Hopelessness Scale ◽  
Method Effects ◽  
Hungarian Population ◽  
Components Analysis

Abstract. The aim of the present study was to examine the construct and cross-cultural validity of the Beck Hopelessness Scale (BHS; Beck, Weissman, Lester, & Trexler, 1974 ). Beck et al. applied exploratory Principal Components Analysis and argued that the scale measured three specific components (affective, motivational, and cognitive). Subsequent studies identified one, two, three, or more factors, highlighting a lack of clarity regarding the scale’s construct validity. In a large clinical sample, we tested the original three-factor model and explored alternative models using both confirmatory and exploratory factor analytical techniques appropriate for analyzing binary data. In doing so, we investigated whether method variance needs to be taken into account in understanding the structure of the BHS. Our findings supported a bifactor model that explicitly included method effects. We concluded that the BHS measures a single underlying construct of hopelessness, and that an incorporation of method effects consolidates previous findings where positively and negatively worded items loaded on separate factors. Our study further contributes to establishing the cross-cultural validity of this instrument by showing that BHS scores differentiate between depressed, anxious, and nonclinical groups in a Hungarian population.

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