메뉴 건너뛰기




Volumn 25, Issue 3, 2018, Pages 359-388

Dynamic Structural Equation Models

Author keywords

Baysian methods; dynamic factor analysis; intensive longitudinal data; time series analysis

Indexed keywords

BAYESIAN NETWORKS; MARKOV PROCESSES; MONTE CARLO METHODS; STRUCTURAL ANALYSIS;

EID: 85039160037     PISSN: 10705511     EISSN: 15328007     Source Type: Journal    
DOI: 10.1080/10705511.2017.1406803     Document Type: Article
Times cited : (561)

References (31)
  • 1
    • 0032329779 scopus 로고    scopus 로고
    • A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis–Hastings algorithm
    • Arminger, G., & Muthen, B., (1998). A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the Metropolis–Hastings algorithm. Psychometrika, 63, 271–300. doi:10.1007/BF02294856
    • (1998) Psychometrika , vol.63 , pp. 271-300
    • Arminger, G.1    Muthen, B.2
  • 2
    • 84865304109 scopus 로고    scopus 로고
    • Bayesian analysis using Mplus: Technical implementation
    • Retrieved from
    • Asparouhov, T., & Muthén, B., (2010). Bayesian analysis using Mplus: Technical implementation (Technical report, Version 3). Retrieved from http://statmodel.com/download/Bayes3.pdf
    • (2010) Technical report, Version 3
    • Asparouhov, T.1    Muthén, B.2
  • 5
    • 33847083094 scopus 로고    scopus 로고
    • A comparison of Bayesian and likelihood-based methods for fitting multilevel models
    • Browne, W., & Draper, D., (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 1, 473–514. doi:10.1214/06-BA117
    • (2006) Bayesian Analysis , vol.1 , pp. 473-514
    • Browne, W.1    Draper, D.2
  • 6
    • 0000014224 scopus 로고
    • Linear smoothers and additive models
    • Buja, A., Hastie, T. J., & Tibshirani, R., (1989). Linear smoothers and additive models. Annals of Statistics, 17, 453–510. doi:10.1214/aos/1176347115
    • (1989) Annals of Statistics , vol.17 , pp. 453-510
    • Buja, A.1    Hastie, T.J.2    Tibshirani, R.3
  • 7
    • 70450277983 scopus 로고    scopus 로고
    • Deviance information criteria for missing data models
    • Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M., (2006). Deviance information criteria for missing data models. Bayesian Analysis, 1, 651–673. doi:10.1214/06-BA122
    • (2006) Bayesian Analysis , vol.1 , pp. 651-673
    • Celeux, G.1    Forbes, F.2    Robert, C.P.3    Titterington, D.M.4
  • 8
    • 21344474305 scopus 로고    scopus 로고
    • Accelerating Monte Carlo Markov Chain Convergence for Cumulative-Link Generalized Linear Models
    • Cowles, M. K., (1996). Accelerating Monte Carlo Markov Chain Convergence for Cumulative-Link Generalized Linear Models. Statistics and Computing, 6, 101–111.
    • (1996) Statistics and Computing , vol.6 , pp. 101-111
    • Cowles, M.K.1
  • 9
    • 84931416754 scopus 로고    scopus 로고
    • No need to be discrete: A method for continuous time mediation analysis
    • Deboeck, P., & Preacher, K., (2016). No need to be discrete: A method for continuous time mediation analysis. Structural Equation Modeling, 23, 61–75. doi:10.1080/10705511.2014.973960
    • (2016) Structural Equation Modeling , vol.23 , pp. 61-75
    • Deboeck, P.1    Preacher, K.2
  • 11
    • 0004296209 scopus 로고    scopus 로고
    • 7th ed., Upper Saddle River, NJ: Prentice Hall
    • Greene, W. H., (2014). Econometric analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
    • (2014) Econometric analysis
    • Greene, W.H.1
  • 12
    • 13444267778 scopus 로고    scopus 로고
    • Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances
    • Hamaker, E. L., (2005). Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances. Sociological Methods & Research, 33, 404–416. doi:10.1177/0049124104270220
    • (2005) Sociological Methods & Research , vol.33 , pp. 404-416
    • Hamaker, E.L.1
  • 13
    • 85044205381 scopus 로고    scopus 로고
    • At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study
    • Manuscript
    • Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B., (2017). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Manuscript submitted for publication.
    • (2017) submitted for publication
    • Hamaker, E.L.1    Asparouhov, T.2    Brose, A.3    Schmiedek, F.4    Muthén, B.5
  • 14
    • 84926631094 scopus 로고    scopus 로고
    • To center or not to center? Investigating inertia with a multilevel autoregressive model
    • Hamaker, E. L., & Grasman, R. P. P. P., (2015). To center or not to center? Investigating inertia with a multilevel autoregressive model. Frontiers in Psychology, 5, 1492. doi:10.3389/fpsyg.2014.01492
    • (2015) Frontiers in Psychology , vol.5 , pp. 1492
    • Hamaker, E.L.1    Grasman, R.P.P.P.2
  • 15
    • 57749113638 scopus 로고    scopus 로고
    • Analysis of affective instability in ecological momentary assessment: Indices using successive difference and group comparison via multilevel modeling
    • Jahng, S., Wood, P. K., & Trull, T. J., (2008). Analysis of affective instability in ecological momentary assessment: Indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13, 354–375. doi:10.1037/a0014173
    • (2008) Psychological Methods , vol.13 , pp. 354-375
    • Jahng, S.1    Wood, P.K.2    Trull, T.J.3
  • 16
    • 84931470688 scopus 로고    scopus 로고
    • A multilevel AR(1) model: Allowing for inter-individual differences in trait-scores, inertia, and innovation variance
    • Jongerling, J., Laurenceau, J. P., & Hamaker, E., (2015). A multilevel AR(1) model: Allowing for inter-individual differences in trait-scores, inertia, and innovation variance. Multivariate Behavioral Research, 50, 334–349. doi:10.1080/00273171.2014.1003772
    • (2015) Multivariate Behavioral Research , vol.50 , pp. 334-349
    • Jongerling, J.1    Laurenceau, J.P.2    Hamaker, E.3
  • 17
    • 85024429815 scopus 로고
    • A new approach to linear filtering and prediction problems
    • Kalman, R. E., (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45.
    • (1960) Journal of Basic Engineering , vol.82 , pp. 35-45
    • Kalman, R.E.1
  • 19
    • 48449087653 scopus 로고    scopus 로고
    • The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies
    • Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., & Muthén, B., (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203–229. doi:10.1037/a0012869
    • (2008) Psychological Methods , vol.13 , pp. 203-229
    • Lüdtke, O.1    Marsh, H.W.2    Robitzsch, A.3    Trautwein, U.4    Asparouhov, T.5    Muthén, B.6
  • 20
    • 0000312259 scopus 로고
    • A dynamic factor model for the analysis of multivariate time series
    • Molenaar, P. C. M., (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50, 181–202. doi:10.1007/BF02294246
    • (1985) Psychometrika , vol.50 , pp. 181-202
    • Molenaar, P.C.M.1
  • 21
    • 85013042307 scopus 로고    scopus 로고
    • Equivalent dynamic models
    • Molenaar, P. C. M., (2017). Equivalent dynamic models. Multivariate Behavioral Research, 52, 242–258. doi:10.1080/00273171.2016.1277681
    • (2017) Multivariate Behavioral Research , vol.52 , pp. 242-258
    • Molenaar, P.C.M.1
  • 22
  • 23
    • 83755185629 scopus 로고    scopus 로고
    • A hierarchical latent stochastic differential equation model for affective dynamics
    • Oravecz, Z., Tuerlinckx, F., & Vandekerckhove, J., (2011). A hierarchical latent stochastic differential equation model for affective dynamics. Psychological Methods, 16, 468–490. doi:10.1037/a0024375
    • (2011) Psychological Methods , vol.16 , pp. 468-490
    • Oravecz, Z.1    Tuerlinckx, F.2    Vandekerckhove, J.3
  • 25
    • 85057918727 scopus 로고    scopus 로고
    • Incorporating measurement error in n = 1 psychological autoregressive modeling
    • Schuurman, N., Houtveen, J., & Hamaker, E., (2015). Incorporating measurement error in n = 1 psychological autoregressive modeling. Frontiers in Psychology, 6, 1038. doi:10.3389/fpsyg.2015.01038
    • (2015) Frontiers in Psychology , vol.6 , pp. 1038
    • Schuurman, N.1    Houtveen, J.2    Hamaker, E.3
  • 26
    • 0036435040 scopus 로고    scopus 로고
    • Bayesian measures of model complexity and fit
    • Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. Series B. 64, 583–639
    • Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B. 64, 583–639.
    • (2002) Journal of the Royal Statistical Society
  • 27
    • 84919421716 scopus 로고    scopus 로고
    • The role of ambulatory assessment in psychological science
    • Trull, T., & Ebner-Priemer, U., (2014). The role of ambulatory assessment in psychological science. Current Directions in Psychological Science, 23, 466–470. doi:10.1177/0963721414550706
    • (2014) Current Directions in Psychological Science , vol.23 , pp. 466-470
    • Trull, T.1    Ebner-Priemer, U.2
  • 28
    • 21644476631 scopus 로고    scopus 로고
    • Conditional Akaike information for mixed-effects models
    • Vaida, F., & Blanchard, S., (2005). Conditional Akaike information for mixed-effects models. Biometrika, 92, 351–370. doi:10.1093/biomet/92.2.351
    • (2005) Biometrika , vol.92 , pp. 351-370
    • Vaida, F.1    Blanchard, S.2
  • 29
    • 84872514308 scopus 로고    scopus 로고
    • Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes
    • Voelkle, M. C., & Oud, J. H. L., (2013). Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes. British Journal of Mathematical and Statistical Psychology, 66, 103–126. doi:10.1111/j.2044-8317.2012.02043.x
    • (2013) British Journal of Mathematical and Statistical Psychology , vol.66 , pp. 103-126
    • Voelkle, M.C.1    Oud, J.H.L.2
  • 30
    • 47949098250 scopus 로고    scopus 로고
    • Comparisons of four methods for estimating a dynamic factor model
    • Zhang, Z., Hamaker, E., & Nesselroade, J., (2008). Comparisons of four methods for estimating a dynamic factor model. Structural Equation Modeling, 15, 377–402. doi:10.1080/10705510802154281
    • (2008) Structural Equation Modeling , vol.15 , pp. 377-402
    • Zhang, Z.1    Hamaker, E.2    Nesselroade, J.3
  • 31
    • 47949126160 scopus 로고    scopus 로고
    • Bayesian estimation of categorical dynamic factor models
    • Zhang, Z., & Nesselroade, J., (2007). Bayesian estimation of categorical dynamic factor models. Multivariate Behavioral Research, 42, 729–756. doi:10.1080/00273170701715998
    • (2007) Multivariate Behavioral Research , vol.42 , pp. 729-756
    • Zhang, Z.1    Nesselroade, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.