Ellen L Hamaker
Biographic Data
| ID | 265545 |
|---|---|
| NAME | Ellen L Hamaker |
| GIVEN NAMES | Ellen L |
| FAMILY NAME | Hamaker |
| SIGNATURE | HAMAKER E L |
| AFFILIATIONS | Utrecht University |
| VERIFIED | No |
| TOTAL WORKS | 32 |
| TOTAL CITATIONS | 18 |
| AUTHOR COUNT | 32 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2005 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
The within-between dispute in cross-lagged panel research and how to move forward.
because it includes the chronic between-person differences when estimating prospective effects. The goal of the current paper is to consider this within-between dispute in its broader context and to examine various directions in which this discussion needs expansion. To this end, three different perspectives are adopted: that of the study design, patterns in empirical data, and the nature of our research questions. It will be argued that to move …
Correlated Residuals in Lagged-Effects Models: What They (Do Not) Represent in the Case of a Continuous-Time Process
The appeal of lagged-effects models, like the first-order vector autoregressive (VAR(1)) model, is the interpretation of the lagged coefficients in terms of predictive-and possibly causal-relationships between variables over time. While the focus in VAR(1) applications has traditionally been on the strength and sign of the lagged relationships, there has been a growing interest in the residual relationships (i.e., the correlations between the inn…
How to Model Ambulatory Assessments Measured at Different Frequencies: An N = 1 Approach
Ambulatory assessment has gained widespread popularity among researchers who study the dynamics of everyday experiences and behaviors, such as sleep patterns or emotional states. In this paper, we focus on the challenge that arises when we want to investigate the dynamic relations between variables measured at different frequencies. As a running example, we use a sleep quality variable measured once every morning and a momentary experience variab…
Dynamics Between Asynchronously Measured Variables: A Multilevel Approach to Momentary Affect and Morning Sleep Reports
The reciprocal relations between sleep and affect have been a common focus in psychological research. Researchers studying affective processes often collect data multiple times a day over several days. Subjective sleep quality, on the other hand, is generally measured once at the beginning of the day. This difference in measurement frequency creates a challenge when analyzing these data, because standard dynamic models are not equipped for this. …
Causal Effects of Time-Varying Exposures: A Comparison of Structural Equation Modeling and Marginal Structural Models in Cross-Lagged Panel Research
The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of c…
From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar
This special issue is a collection of papers inspired by Dr. Molenaar's work and innovations - a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar's creative breadth, the papers address a wide variety of topics - sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual vari…
Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models
low mean, and low variability. In this paper, we investigated whether-and to what extent-the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals' autoregressive parameters, while also varying the number of time points…
The Curious Case of the Cross-Sectional Correlation
has raised concerns about the meaning and utility of this measure, especially when the interest is in discovering general laws that apply to (all) individuals. Through using Cattell's databox and adopting a multilevel perspective, this paper provides a closer look at the cross-sectional correlation, with the goal to better understand its meaning when ergodicity is absent. An analytical expression is presented that shows the cross-sectional correl…
Joint Effects in Cross-Lagged Panel Research Using Structural Nested Mean Models
A popular approach among psychological researchers for investigating causal relationships from panel data is cross-lagged panel modeling within the structural equation modeling (SEM) framework. However, SEM models are critiqued in the causal inference literature for relying on unnecessarily many parametric assumptions, increasing the risk of model misspecification and bias. Instead, the use of structural nested mean models (SNMMs) with G-estimati…
How to measure and model personality traits in everyday life: A qualitative analysis of 300 big five personality items
Personality trait items imply momentary patterns of experiences in daily life. • ILD offers many measurement and modeling options for personality patterns. • A qualitative analysis uncovered pattern characteristics of 300 personality items. • Personality item implied patterns vary in content, timescale and event-relation. • Means of personality states poorly account for most of these patterns. Personality traits are often described with reference…
Causal inference on human behaviour
Three Extensions of the Random Intercept Cross-Lagged Panel Model
The random intercept cross-lagged panel model (RI-CLPM) is rapidly gaining popularity in psychology and related fields as a structural equation modeling (SEM) approach to longitudinal data. It decomposes observed scores into within-unit dynamics and stable, between-unit differences. This paper discusses three extensions of the RI-CLPM that researchers may be interested in, but are unsure of how to accomplish: (a) including stable, person-level ch…
The fixed versus random effects debate and how it relates to centering in multilevel modeling.
In many disciplines researchers use longitudinal panel data to investigate the potentially causal relationship between 2 variables. However, the conventions and concerns vary widely across disciplines. Here we focus on 2 concerns, that is: (a) the concern about random effects versus fixed effects, which is central in the (micro)econometrics/sociology literature; and (b) the concern about grand mean versus group (or person) mean centering, which i…
From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
This 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 rela…
A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus.
Version 8. The goal is to provide readers with a basic conceptual understanding of common models, template code, and result interpretation. We provide short descriptions of some advanced issues, but our main priority is to supply readers with a solid knowledge base so that the more advanced literature on the topic is more readily digestible to a larger group of researchers. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
A unified framework of longitudinal models to examine reciprocal relations.
Inferring reciprocal effects or causality between variables is a central aim of behavioral and psychological research. To address reciprocal effects, a variety of longitudinal models that include cross-lagged relations have been proposed in different contexts and disciplines. However, the relations between these cross-lagged models have not been systematically discussed in the literature. This lack of insight makes it difficult for researchers to…
At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the Cogito Study
With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use co…
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to cha…
No Time Like the Present: Discovering the Hidden Dynamics in Intensive Longitudinal Data
There has been a strong increase in the number of studies based on intensive longitudinal data, such as those obtained with experience sampling and daily diaries. These data contain a wealth of information regarding the dynamics of processes as they unfold within individuals over time. In this article, we discuss how combining intensive longitudinal data with either time-series analysis, which consists of modeling the temporal dependencies in the…
On the Use of Mixed Markov Models for Intensive Longitudinal Data
Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov mod…
Using a Few Snapshots to Distinguish Mountains from Waves: Weak Factorial Invariance in the Context of Trait-State Research
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 s…
How to compare cross-lagged associations in a multilevel autoregressive model.
By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach fo…
A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models
Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices-the Inverse-Wishart distr…
A critique of the cross-lagged panel model.
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 …
A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance
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 …
The integrated trait–state model
Causal inference on human behaviour
Conditions for the Equivalence of the Autoregressive Latent Trajectory Model and a Latent Growth Curve Model With Autoregressive Disturbances
Curran and Bollen combined two models for longitudinal panel data: the latent growth curve model and the autoregressive model. In their model, the autoregressive relationships are modeled between the observed variables. This is a different model than a latent growth curve model with autoregressive relationships between the disturbances. However, when the autoregressive parameter is invariant over time and lies between-1 and 1, it can be shown tha…
Joint Effects in Cross-Lagged Panel Research Using Structural Nested Mean Models
A popular approach among psychological researchers for investigating causal relationships from panel data is cross-lagged panel modeling within the structural equation modeling (SEM) framework. However, SEM models are critiqued in the causal inference literature for relying on unnecessarily many parametric assumptions, increasing the risk of model misspecification and bias. Instead, the use of structural nested mean models (SNMMs) with G-estimati…
Statistical Modeling of the Individual: Rationale and Application of Multivariate Stationary Time Series Analysis
Results obtained with interindividual techniques in a representative sample of a population are not necessarily generalizable to the individual members of this population. In this article the specific condition is presented that must be satisfied to generalize from the interindividual level to the intraindividual level. A way to investigate whether this condition is satisfied is by means of multivariate time series analysis. More generally, time …
Conditions for the Equivalence of the Autoregressive Latent Trajectory Model and a Latent Growth Curve Model With Autoregressive Disturbances
Curran and Bollen combined two models for longitudinal panel data: the latent growth curve model and the autoregressive model. In their model, the autoregressive relationships are modeled between the observed variables. This is a different model than a latent growth curve model with autoregressive relationships between the disturbances. However, when the autoregressive parameter is invariant over time and lies between-1 and 1, it can be shown tha…
The integrated trait–state model
Using Innovative Outliers to Detect Discrete Shifts in Dynamics in Group-Based State-Space Models
Outliers are typically regarded as data anomalies that should be discarded. However, dynamic or "innovative" outliers can be appropriately utilized to capture unusual but substantively meaningful shifts in a system's dynamics. We extend De Jong and Penzer's 1998 approach for representing outliers in single-subject state-space models to a group-based framework. The group-based model enables model predictions concerning the group as a whole while i…
Investigating inter-individual differences in short-term intra-individual variability.
Intra-individual variability over a short period of time may contain important information about how individuals differ from each other. In this article we begin by discussing diverse indicators for quantifying intra-individual variability and indicate their advantages and disadvantages. Then we propose an alternative method that models inter-individual differences in intra-individual variability by separately considering both the amplitude of fl…
A Meta-View of Multivariate Statistical Inference Methods in European Psychology Journals
We investigated the extent and nature of multivariate statistical inferential procedures used in eight European psychology journals covering a range of content (i.e., clinical, social, health, personality, organizational, developmental, educational, and cognitive). Multivariate methods included those found in popular texts that focused on prediction, group difference, and advanced modeling: multiple regression, logistic regression, analysis of co…
A critique of the cross-lagged panel model.
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 …
A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance
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 …
Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model
"Modeling Nonstationary Emotion Dynamics in Dyads Using a Semiparametric Time-Varying Vector Autoregressive Model." Multivariate Behavioral Research, 50(6), pp. 730–731
How to compare cross-lagged associations in a multilevel autoregressive model.
By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach fo…
A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models
Multilevel autoregressive models are especially suited for modeling between-person differences in within-person processes. Fitting these models with Bayesian techniques requires the specification of prior distributions for all parameters. Often it is desirable to specify prior distributions that have negligible effects on the resulting parameter estimates. However, the conjugate prior distribution for covariance matrices-the Inverse-Wishart distr…
No Time Like the Present: Discovering the Hidden Dynamics in Intensive Longitudinal Data
There has been a strong increase in the number of studies based on intensive longitudinal data, such as those obtained with experience sampling and daily diaries. These data contain a wealth of information regarding the dynamics of processes as they unfold within individuals over time. In this article, we discuss how combining intensive longitudinal data with either time-series analysis, which consists of modeling the temporal dependencies in the…
On the Use of Mixed Markov Models for Intensive Longitudinal Data
Markov modeling presents an attractive analytical framework for researchers who are interested in state-switching processes occurring within a person, dyad, family, group, or other system over time. Markov modeling is flexible and can be used with various types of data to study observed or latent state-switching processes, and can include subject-specific random effects to account for heterogeneity. We focus on the application of mixed Markov mod…
Using a Few Snapshots to Distinguish Mountains from Waves: Weak Factorial Invariance in the Context of Trait-State Research
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 s…
At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the Cogito Study
With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use co…
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to cha…
A unified framework of longitudinal models to examine reciprocal relations.
Inferring reciprocal effects or causality between variables is a central aim of behavioral and psychological research. To address reciprocal effects, a variety of longitudinal models that include cross-lagged relations have been proposed in different contexts and disciplines. However, the relations between these cross-lagged models have not been systematically discussed in the literature. This lack of insight makes it difficult for researchers to…
The fixed versus random effects debate and how it relates to centering in multilevel modeling.
In many disciplines researchers use longitudinal panel data to investigate the potentially causal relationship between 2 variables. However, the conventions and concerns vary widely across disciplines. Here we focus on 2 concerns, that is: (a) the concern about random effects versus fixed effects, which is central in the (micro)econometrics/sociology literature; and (b) the concern about grand mean versus group (or person) mean centering, which i…
From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
This 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 rela…
A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus.
Version 8. The goal is to provide readers with a basic conceptual understanding of common models, template code, and result interpretation. We provide short descriptions of some advanced issues, but our main priority is to supply readers with a solid knowledge base so that the more advanced literature on the topic is more readily digestible to a larger group of researchers. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Three Extensions of the Random Intercept Cross-Lagged Panel Model
The random intercept cross-lagged panel model (RI-CLPM) is rapidly gaining popularity in psychology and related fields as a structural equation modeling (SEM) approach to longitudinal data. It decomposes observed scores into within-unit dynamics and stable, between-unit differences. This paper discusses three extensions of the RI-CLPM that researchers may be interested in, but are unsure of how to accomplish: (a) including stable, person-level ch…
Causal Effects of Time-Varying Exposures: A Comparison of Structural Equation Modeling and Marginal Structural Models in Cross-Lagged Panel Research
The use of structural equation models for causal inference from panel data is critiqued in the causal inference literature for unnecessarily relying on a large number of parametric assumptions, and alternative methods originating from the potential outcomes framework have been recommended, such as inverse probability weighting (IPW) estimation of marginal structural models (MSMs). To better understand this criticism, we describe three phases of c…
From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar
This special issue is a collection of papers inspired by Dr. Molenaar's work and innovations - a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar's creative breadth, the papers address a wide variety of topics - sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual vari…
Skewness and Staging: Does the Floor Effect Induce Bias in Multilevel AR(1) Models
low mean, and low variability. In this paper, we investigated whether-and to what extent-the floor effect leads to erroneous conclusions by means of a simulation study. We describe three dynamic models which have meaningful substantive interpretations and can produce floor-effect data. We simulate multilevel data from these models, varying skewness independent of individuals' autoregressive parameters, while also varying the number of time points…
The Curious Case of the Cross-Sectional Correlation
has raised concerns about the meaning and utility of this measure, especially when the interest is in discovering general laws that apply to (all) individuals. Through using Cattell's databox and adopting a multilevel perspective, this paper provides a closer look at the cross-sectional correlation, with the goal to better understand its meaning when ergodicity is absent. An analytical expression is presented that shows the cross-sectional correl…
Econometrics (25 works) · Mathematics (25 works) · Computer Science (23 works) · Mental Health Research Topics (23 works) · Psychology (21 works) · Statistics (21 works) · Structural equation modeling (11 works) · Autoregressive model (10 works) · Multilevel model (9 works) · Data mining (8 works)