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135 results on '"Hamaker, E.L."'

1. Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Author

Ryan, O., Hamaker, E.L., Leerstoel Hamaker, and Methodology and statistics for the behavioural and social sciences

Subjects
intensive longitudinal data, Psychometrics, Computer science, Network structure, Machine learning, computer.software_genre, 01 natural sciences, experience sampling methodology, 010104 statistics & probability, continuous-time modeling, 0504 sociology, 0101 mathematics, Representation (mathematics), General Psychology, business.industry, Applied Mathematics, 05 social sciences, Direct effects, 050401 social sciences methods, centrality, dynamical network analysis, Artificial intelligence, Centrality, business, computer, Network approach, Dependency (project management), Network analysis
Abstract

Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

Published
2022
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2. Psychopathological networks: Theory, methods and practice

Author

Bringmann, L.F., Albers, C.J., Bockting, C.L.H., Borsboom, D., Ceulemans, E., Cramer, A.O.J., Epskamp, S., Eronen, M.I., Hamaker, E.L., Kuppens, P., Lutz, W., McNally, R.J., Molenaar, P., Tio, P., Völkle, M.C., Wichers, M.C., Bringmann, L.F., Albers, C.J., Bockting, C.L.H., Borsboom, D., Ceulemans, E., Cramer, A.O.J., Epskamp, S., Eronen, M.I., Hamaker, E.L., Kuppens, P., Lutz, W., McNally, R.J., Molenaar, P., Tio, P., Völkle, M.C., and Wichers, M.C.

Abstract

Item does not contain fulltext, In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Published
2022

5. The baby and the bathwater: On the need for substantive-methodological synergy in organizational research

Author

Hofmans, J., Morin, A.J.S., Breitsohl, H., Ceulemans, E., Chénard-Poirier, L.A., Driver, C.C., Fernet, C., Gane, M., Gillet, N., González-Romá, V., Grimm, K.J., Hamaker, E.L., Hau, K.-T., Houle, S.A., Howard, J.L., Kline, R.B., Kuijpers, E., Leysen, T., Litalien, D., Mäkikangas, A., Marsh, H.W., McLarnon, M.J.W., Meyer, J.P., Navarro, J., Olivier, E., O'Neill, T.A., Pekrun, T.A., Salmela-Aro, K., Solinger, O.N., Sonnetag, S., Tay, L., Tóth-Király, I., Vallerand, R.J., Vandenberghe, C., Rossenberg, Y.G.T. van, Vantilborgh, T., Vergauwe, J., Vullinghs, J.T., Wang, M., Wen, Z., Wille, B., Hofmans, J., Morin, A.J.S., Breitsohl, H., Ceulemans, E., Chénard-Poirier, L.A., Driver, C.C., Fernet, C., Gane, M., Gillet, N., González-Romá, V., Grimm, K.J., Hamaker, E.L., Hau, K.-T., Houle, S.A., Howard, J.L., Kline, R.B., Kuijpers, E., Leysen, T., Litalien, D., Mäkikangas, A., Marsh, H.W., McLarnon, M.J.W., Meyer, J.P., Navarro, J., Olivier, E., O'Neill, T.A., Pekrun, T.A., Salmela-Aro, K., Solinger, O.N., Sonnetag, S., Tay, L., Tóth-Király, I., Vallerand, R.J., Vandenberghe, C., Rossenberg, Y.G.T. van, Vantilborgh, T., Vergauwe, J., Vullinghs, J.T., Wang, M., Wen, Z., and Wille, B.

Abstract

Contains fulltext : 242630.pdf (Publisher’s version ) (Closed access)

Published
2021

6. From Data to Causes II: Comparing Approaches to Panel Data Analysis

Author

Zyphur, M.J., Voelkle, M.C., Tay, L., Allison, P.D., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., Diener, E., Zyphur, M.J., Voelkle, M.C., Tay, L., Allison, P.D., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., and Diener, E.

Abstract

This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.

Published
2020

7. From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

Author

Zyphur, M.J., Allison, P.D., Tay, L., Voelkle, M.C., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., Diener, E., Zyphur, M.J., Allison, P.D., Tay, L., Voelkle, M.C., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., and Diener, E.

Abstract

his is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference. All authors: Michael J. Zyphur, Paul D. Allison, Louis Tay, Manuel C. Voelkle, Kristopher J. Preacher, Zhen Zhang, Ellen L. Hamaker, Ali Shamsollahi, Dean C. Pierides, Peter Koval, Ed Diener

Published
2020

8. From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)

Author

Leerstoel Hamaker, Methodology and statistics for the behavioural and social sciences, Zyphur, M.J., Allison, P.D., Tay, L., Voelkle, M.C., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., Diener, E., Leerstoel Hamaker, Methodology and statistics for the behavioural and social sciences, Zyphur, M.J., Allison, P.D., Tay, L., Voelkle, M.C., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., and Diener, E.

Published
2020

9. From Data to Causes II: Comparing Approaches to Panel Data Analysis

Author

Leerstoel Hamaker, Methodology and statistics for the behavioural and social sciences, Zyphur, M.J., Voelkle, M.C., Tay, L., Allison, P.D., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., Diener, E., Leerstoel Hamaker, Methodology and statistics for the behavioural and social sciences, Zyphur, M.J., Voelkle, M.C., Tay, L., Allison, P.D., Preacher, K.J., Zhang, Z., Hamaker, E.L., Shamsollahi, A., Pierides, D.C., Koval, P., and Diener, E.

Published
2020

11. Using a few snapshots to distinguish mountains from waves

Author

Hamaker, E.L., Schuurman, N.K., Zijlmans, Eva, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Department of Methodology and Statistics, Leerstoel Hoijtink, and Methodology and statistics for the behavioural and social sciences

Subjects
Statistics and Probability, Male, Personality Tests, 050103 clinical psychology, Empirical data, Time Factors, Adolescent, longitudinal data analysis, trait-state distinction, Experimental and Cognitive Psychology, Context (language use), 0504 sociology, Arts and Humanities (miscellaneous), Econometrics, Humans, 0501 psychology and cognitive sciences, Longitudinal Studies, multilevel modeling, Mathematics, Factor analysis, Factorial invariance, Aged, 80 and over, Analysis of Variance, Models, Statistical, Depression, 05 social sciences, Multilevel model, 050401 social sciences methods, General Medicine, Variance (accounting), State (functional analysis), Cross-Sectional Studies, Data Interpretation, Statistical, Trait, Multilevel Analysis, Regression Analysis, Female, Factor Analysis, Statistical, within-person versus between-person, Algorithms, Personality
Abstract

In this article, we show that the underlying dimensions obtained when factor analyzing cross-sectional data actually form a mix of within-person state dimensions and between-person trait dimensions. We propose a factor analytical model that distinguishes between four independent sources of variance: common trait, unique trait, common state, and unique state. We show that by testing whether there is weak factorial invariance across the trait and state factor structures, we can tackle the fundamental question first raised by Cattell; that is, are within-person state dimensions qualitatively the same as between-person trait dimensions? Furthermore, we discuss how this model is related to other trait-state factor models, and we illustrate its use with two empirical data sets. We end by discussing the implications for cross-sectional factor analysis and suggest potential future developments.

Published
2017
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13. Modeling Affect Dynamics

Author

Hamaker, E.L., Ceulemans, Eva, Grasman, R.P.P.P., Tuerlinckx, Francis, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, FMG, and Psychologische Methodenleer (Psychologie, FMG)

Subjects
Multivariate statistics, affective dynamics, intensive longitudinal data, Social Psychology, Dichotomy, Descriptive statistics, Process (engineering), Univariate, within-person, Experimental and Cognitive Psychology, Nonlinear system, Arts and Humanities (miscellaneous), Discrete time and continuous time, Frequency domain, Econometrics, Psychology
Abstract

The current article aims to provide an up-to-date synopsis of available techniques to study affect dynamics using intensive longitudinal data (ILD). We do so by introducing the following eight dichotomies that help elucidate what kind of data one has, what process aspects are of interest, and what research questions are being considered: (1) single- versus multiple-person data; (2) univariate versus multivariate models; (3) stationary versus nonstationary models; (4) linear versus nonlinear models; (5) discrete time versus continuous time models; (6) discrete versus continuous variables; (7) time versus frequency domain; and (8) modeling the process versus computing descriptives. In addition, we discuss what we believe to be the most urging future challenges regarding the modeling of affect dynamics.

Published
2015
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15. A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance

Author

Jongerling, J., Laurenceau, Jean-Phillippe, Hamaker, E.L., Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, and Research Methods and Techniques

Subjects
Statistics and Probability, Bayes estimator, Likelihood Functions, Models, Statistical, Computer science, Individuality, Experimental and Cognitive Psychology, Bayes Theorem, General Medicine, Variance (accounting), Random effects model, Residual, Algebraic formula for the variance, Affect, Arts and Humanities (miscellaneous), Autoregressive model, Statistics, Trait, Multilevel Analysis, Humans, Regression Analysis, Computer Simulation, Female, Sensitivity (control systems), Behavioral Research
Abstract

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

Published
2015
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16. Dynamic structural equation models

Author

Asparouhov, T., Hamaker, E.L., Muthen, Bengt, Asparouhov, T., Hamaker, E.L., and Muthen, Bengt

Abstract

This article presents dynamic structural equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the structural equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine time-series modeling with structural equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.

Published
2018

17. Dynamic structural equation models

Author

Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Asparouhov, T., Hamaker, E.L., Muthen, Bengt, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Asparouhov, T., Hamaker, E.L., and Muthen, Bengt

Published
2018

18. Dynamic structural equation models

Author

Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Asparouhov, T., Hamaker, E.L., Muthen, Bengt, Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Asparouhov, T., Hamaker, E.L., and Muthen, Bengt

Published
2018

19. Dynamic latent class analysis

Author

Asparouhov, T., Hamaker, E.L., Muthén, B., Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, and Methodology and statistics for the behavioural and social sciences

Subjects
Sociology and Political Science, Probabilistic latent semantic analysis, Markov chain, Latent growth modeling, 05 social sciences, 050401 social sciences methods, General Decision Sciences, 050109 social psychology, Markov model, Latent class model, 0504 sociology, Modeling and Simulation, Econometrics, Applied mathematics, 0501 psychology and cognitive sciences, Local independence, Latent variable model, General Economics, Econometrics and Finance, Class variable, Mathematics
Abstract

This article describes the general time-intensive longitudinal latent class modeling framework implemented in Mplus. For each individual a latent class variable is measured at each time point and the latent class changes across time follow a Markov process (i.e., a hidden or latent Markov model), with subject-specific transition probabilities that are estimated as random effects. Such a model for single-subject data has been referred to as the regime-switching state-space model. The latent class variable can be measured by continuous or categorical indicators, under the local independence condition, or more generally by a class-specific structural equation model or a dynamic structural equation model. We discuss the Bayesian estimation based on Markov chain Monto Carlo, which allows modeling with arbitrary long time series data and many random effects. The modeling framework is illustrated with several simulation studies.

Published
2017

20. Modeling BAS Dysregulation in Bipolar Disorder

Author

Hamaker, E.L., Grasman, R.P.P.P., Kamphuis, J.H., FMG, Psychologische Methodenleer (Psychologie, FMG), Brain and Cognition, and Klinische Psychologie (Psychologie, FMG)

Abstract

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with bipolar disorder. By formulating a number of alternative models that capture different kinds of theoretically predicted dysregulation, and by comparing these in both bipolar patients and controls, we aim to illustrate the heuristic potential this method of analysis has for clinical psychology. We argue that, not only can time series analysis elucidate specific maladaptive dynamics associated with psychopathology, it may also be clinically applied in symptom monitoring and the evaluation of therapeutic interventions.

Published
2016

21. Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model

Author

Bringmann, Laura F., Ferrer, Emilio, Hamaker, E.L., Borsboom, Denny, Tuerlinckx, Francis, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, FMG, Psychologische Methodenleer (Psychologie, FMG), Leerstoel Hoijtink, and Methodology and statistics for the behavioural and social sciences

Subjects
Statistics and Probability, Arts and Humanities (miscellaneous), Autoregressive model, Computer science, Dynamics (music), Speech recognition, Econometrics, Experimental and Cognitive Psychology, General Medicine, Interpersonal context, STAR model
Abstract

Emotion dynamics often arise in an interpersonal context, such as a romantic relationship. As time passes, emotion dynamics are prone to change, leading to nonstationarity (e.g., time-lagged relati...

Published
2016

22. Using a few snapshots to distinguish mountains from waves: Weak factorial invariance in the context of trait-state research

Author

Hamaker, E.L., Schuurman, N.K., Zijlmans, Eva, Hamaker, E.L., Schuurman, N.K., and Zijlmans, Eva

Abstract

In this article, we show that the underlying dimensions obtained when factor analyzing cross-sectional data actually form a mix of within-person state dimensions and between-person trait dimensions. We propose a factor analytical model that distinguishes between four independent sources of variance: common trait, unique trait, common state, and unique state. We show that by testing whether there is weak factorial invariance across the trait and state factor structures, we can tackle the fundamental question first raised by Cattell; that is, are within-person state dimensions qualitatively the same as between-person trait dimensions? Furthermore, we discuss how this model is related to other trait-state factor models, and we illustrate its use with two empirical data sets. We end by discussing the implications for cross-sectional factor analysis and suggest potential future developments.

Published
2017

24. Dynamic latent class analysis

Author

Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Asparouhov, T., Hamaker, E.L., Muthén, B., Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Asparouhov, T., Hamaker, E.L., and Muthén, B.

Published
2017

25. Dynamic latent class analysis

Author

Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Asparouhov, T., Hamaker, E.L., Muthén, B., Methodology and statistics for the behavioural and social sciences, Leerstoel Hoijtink, Asparouhov, T., Hamaker, E.L., and Muthén, B.

Published
2017

26. Incorporating measurement error in n=1 psychological autoregressive modeling

Author

Schuurman, N.K., Houtveen, J.H., Hamaker, E.L., Leerstoel Hoijtink, and Methodology and statistics for the behavioural and social sciences

Subjects
idiographic, n = 1, timeseries analysis, autoregressive modeling, measurement error, Bayesian modeling
Abstract

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.

Published
2015

30. A critique of the cross-lagged panel model

Author

Hamaker, E.L., Kuiper, R.M., Grasman, R.P.P.P., Hamaker, E.L., Kuiper, R.M., and Grasman, R.P.P.P.

Abstract

The cross-lagged panel model (CLPM) is believed by many to overcome the problems associated with the use of cross-lagged correlations as a way to study causal influences in longitudinal panel data. The current article, however, shows that if stability of constructs is to some extent of a trait-like, time-invariant nature, the autoregressive relationships of the CLPM fail to adequately account for this. As a result, the lagged parameters that are obtained with the CLPM do not represent the actual within-person relationships over time, and this may lead to erroneous conclusions regarding the presence, predominance, and sign of causal influences. In this article we present an alternative model that separates the within-person process from stable between-person differences through the inclusion of random intercepts, and we discuss how this model is related to existing structural equation models that include cross-lagged relationships. We derive the analytical relationship between the cross-lagged parameters from the CLPM and the alternative model, and use simulations to demonstrate the spurious results that may arise when using the CLPM to analyze data that include stable, trait-like individual differences. We also present a modeling strategy to avoid this pitfall and illustrate this using an empirical data set. The implications for both existing and future cross-lagged panel research are discussed.

Published
2015

32. Modeling affect dynamics : State-of-the-art and future challenges

Author

Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Hamaker, E.L., Ceulemans, Eva, Grasman, R.P.P.P., Tuerlinckx, Francis, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Hamaker, E.L., Ceulemans, Eva, Grasman, R.P.P.P., and Tuerlinckx, Francis

Published
2015

34. A critique of the cross-lagged panel model

Author

Leerstoel Heijden, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Hamaker, E.L., Kuiper, R.M., Grasman, R.P.P.P., Leerstoel Heijden, Leerstoel Hoijtink, Methodology and statistics for the behavioural and social sciences, Hamaker, E.L., Kuiper, R.M., and Grasman, R.P.P.P.

Published
2015

36. Equivalence and differences between structural equation modeling and state-space modeling techniques

Author

Chow, Sy-Miin, Ho, Moon-ho R., Hamaker, E.L., and Dolan, C.V.

Subjects
Sociale Wetenschappen
Abstract

State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and numerical simulations, with a focus on their use in representing intraindividual dynamics and interindividual differences. To demonstrate the respective strengths and weaknesses of the 2 approaches in representing these 2 aspects, we simulated data under (a) a cross-sectional common factor model, (b) a latent difference score model with random effects in intercept and slope, and (c) a bivariate dynamic factor analysis model with auto- and cross-regression parameters. Possible ways in which SEM and state-space modeling can be utilized as complementary tools in representing human developmental and other related processes are discussed.

Published
2010

41. The integrated trait-state model

Author

Hamaker, E.L., Nesselroade, J.R., Molenaar, P.C.M., Methodology and statistics for the behavioural and social sciences, and Afd methoden en statistieken

Published
2007

42. How to inspect fruit

Author

Hamaker, E.L., Methodology and statistics for the behavioural and social sciences, and Afd methoden en statistieken

Published
2007

46. Investigating spousal influence using moment-to-moment affect data from marital conflict

Author

Methoden en Statistiek, Methodology and statistics for the behavioural and social sciences, Afd methoden en statistieken, Madhyastha, T.M., Hamaker, E.L., Gottman, J.M., Methoden en Statistiek, Methodology and statistics for the behavioural and social sciences, Afd methoden en statistieken, Madhyastha, T.M., Hamaker, E.L., and Gottman, J.M.

Published
2011

47. On the trajectories of the predetermined ALT model: What are we really modeling?

Author

Methoden en Statistiek, Methodology and statistics for the behavioural and social sciences, Afd methoden en statistieken, Jongerling, J., Hamaker, E.L., Methoden en Statistiek, Methodology and statistics for the behavioural and social sciences, Afd methoden en statistieken, Jongerling, J., and Hamaker, E.L.

Published
2011

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