references.bib

@article{Baron1986,
abstract = {In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.},
author = {Baron, Reuben M. and Kenny, David A.},
issn = {01252208},
journal = {Journal of Personality and Social Psychology},
number = {6},
pages = {1173--1182},
title = {{The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations}},
volume = {51},
year = {1986}
}
@article{Berry2017,
author = {Berry, Daniel and Willoughby, Michael T.},
doi = {10.1111/cdev.12660},
issn = {00093920},
journal = {Child Development},
number = {4},
pages = {1186--1206},
title = {{On the Practical Interpretability of Cross-Lagged Panel Models: Rethinking a Developmental Workhorse}},
url = {http://doi.wiley.com/10.1111/cdev.12660},
volume = {88},
year = {2017}
}
@article{Bollen2004,
abstract = {Although there are a variety of statistical methods available for the analysis of longitudinal panel data, two approaches are of particular historical importance: the autoregressive (simplex) model and the latent trajectory (curve) model. These two approaches have been portrayed as competing methodologies such that one approach is superior to the other. We argue that the autoregressive and trajectory models are special cases of a more encompassing model that we call the autoregressive latent trajectory (ALT) model. In this paper we detail the underlying statistical theory and mathematical identification of this model, and demonstrate the ALT model using two empirical data sets. The first reanalyzes a simulated repeated measures data set that was previously used to argue against the autoregressive model, and we illustrate how the ALT model can recover the true latent curve model. Second, we apply the ALT model to real family income data on N=3912 adults over a seven year period and find evidence for both autoregressive and latent trajectory processes. Extensions and limitations are discussed.},
author = {Bollen, Kenneth A. and Curran, Patrick J.},
doi = {10.1177/0049124103260222},
issn = {0049-1241},
journal = {Sociological Methods & Research},
number = {3},
pages = {336--383},
title = {{Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions}},
url = {http://journals.sagepub.com/doi/10.1177/0049124103260222},
volume = {32},
year = {2004}
}
@article{Curran2014,
author = {Curran, Patrick J. and Howard, Andrea L. and Bainter, Sierra A. and Lane, Stephanie T. and McGinley, James S.},
doi = {10.1037/a0035297},
issn = {1939-2117},
journal = {Journal of Consulting and Clinical Psychology},
number = {5},
pages = {879--894},
title = {{The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals.}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/a0035297},
volume = {82},
year = {2014}
}
@article{Deboeck2016,
abstract = {Mediation is one concept that has shaped numerous theories. The list of problems associated with mediation models, however, has been growing. Mediation models based on cross-sectional data can produce unexpected estimates, so much so that making longitudinal or causal inferences is inadvisable. Even longitudinal mediation models have faults, as parameter estimates produced by these models are specific to the lag between observations, leading to much debate over appropriate lag selection. Using continuous time models (CTMs) rather than commonly employed discrete time models, one can estimate lag-independent parameters. We demonstrate methodology that allows for continuous time mediation analyses, with attention to concepts such as indirect and direct effects, partial mediation, the effect of lag, and the lags at which relations become maximal. A simulation compares common longitudinal mediation methods with CTMs. Reanalysis of a published covariance matrix demonstrates that CTMs can be fit to data used in longitudinal mediation studies.},
author = {Deboeck, Pascal R. and Preacher, Kristopher J.},
doi = {10.1080/10705511.2014.973960},
issn = {1070-5511},
journal = {Structural Equation Modeling: A Multidisciplinary Journal},
keywords = {Continuous time models,Cross-lagged panel model,Exact discrete model,Longitudinal mediation,Mediation},
number = {1},
pages = {61--75},
title = {{No Need to be Discrete: A Method for Continuous Time Mediation Analysis}},
url = {http://www.tandfonline.com/doi/full/10.1080/10705511.2014.973960},
volume = {23},
year = {2016}
}
@article{Dormann2015,
abstract = {Cross-lagged regression coefficients are frequently used to test hypotheses in panel designs. However, these coefficients have particular properties making them difficult to interpret. In particular, cross-lagged regression coefficients may vary, depending on the respective time lags between different sets of measurement occasions. This article introduces the concept of an optimal time lag. Further, it is demonstrated that optimal time lags in panel studies are related to the stabilities of the variables investigated, and that in unidirectional systems, they may be unrelated to the size of possible true effects. The results presented also suggest that optimal time lags for panel designs are usually quite short. Implications are (a) that interpreting cross-lagged regression coefficients requires taking the time lag between measurement occasions into account, and (b) that in much research, far shorter time lags than those frequently found in the literature are justifiable, and we call for more "shortitudinal" studies in the future.},
author = {Dormann, Christian and Griffin, Mark A.},
doi = {10.1037/met0000041},
issn = {1939-1463},
journal = {Psychological Methods},
keywords = {Continuous time modeling,Cross-lagged effects,Longitudinal research,Optimal time lag,Shortitudinal study},
number = {4},
pages = {489--505},
title = {{Optimal time lags in panel studies}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/met0000041},
volume = {20},
year = {2015}
}
@article{Gollob1987,
abstract = {Although it takes time for a cause to exert an effect, causal models often fail to allow adequately for time lags. In particular, causal models that contain cross-sectional relations (i.e., relations between values of 2 variables at the same time) are unsatisfactory because they omit the values of variables at prior times, they omit effects that variables can have on themselves, and they fail to specify the length of the causal interval that is being studied. These omissions can produce severe biases in estimates of the size of causal effects. Longitudinal models also can fail to take account of time lags properly, and this too can lead to severely biased estimates. The discussion illustrates the biases that can occur in both cross-sectional and longitudinal models, introduces the latent longitudinal approach to causal modeling, and shows how latent longitudinal models can be used to reduce bias by taking account of time lags even when data are available for only 1 point in time.},
author = {Gollob, Harry F. and Reichardt, Charles S.},
doi = {10.1111/j.1467-8624.1987.tb03492.x},
issn = {0009-3920},
journal = {Child Development},
month = {feb},
number = {1},
pages = {80--92},
pmid = {3816351},
title = {{Taking account of time lags in causal models}},
url = {http://doi.wiley.com/10.1111/j.1467-8624.1987.tb03492.x},
volume = {58},
year = {1987}
}
@article{Hamaker2005,
abstract = {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 that these models are algebraically equivalent. This result can be shown to generalize to the multivariate case. When the autoregressive parameters in the autoregressive latent trajectory model vary over time, the equivalence between the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances no longer holds. However, a latent growth curve model with time-varying autoregressive parameters for the disturbances could be considered an interesting alternative to the autoregressive latent trajectory model with time-varying autoregressive parameters.},
author = {Hamaker, Ellen L.},
doi = {10.1177/0049124104270220},
file = {:Users/jeroenmulder/SURFdrive/Research/Causality - Trends/literature/Hamaker2005.pdf:pdf},
isbn = {0049124104270},
issn = {00491241},
journal = {Sociological Methods and Research},
keywords = {Autoregression,Autoregressive latent trajectory model,Latent growth curve model,Structural equation modeling},
number = {3},
pages = {404--416},
title = {{Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances}},
volume = {33},
year = {2005}
}
@article{Hamaker2015,
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, timeinvariant 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.},
author = {Hamaker, Ellen L. and Kuiper, Rebecca M. and Grasman, Raoul P. P. P.},
doi = {10.1037/a0038889},
file = {:Users/jeroenmulder/SURFdrive/Research/Teacher corner - RICLPM/literature/Hamaker2015.pdf:pdf},
issn = {1939-1463},
journal = {Psychological Methods},
keywords = {Cross-lagged panel,Longitudinal model,Reciprocal effects,Trait-state models,Within-person dynamics},
number = {1},
pages = {102--116},
title = {{A critique of the cross-lagged panel model}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/a0038889},
volume = {20},
year = {2015}
}
@article{Hamaker2020,
author = {Hamaker, Ellen L. and Mulder, Jeroen D. and van IJzendoorn, Marinus H.},
doi = {10.1016/j.dcn.2020.100867},
issn = {18789293},
journal = {Developmental Cognitive Neuroscience},
month = {dec},
title = {{Description, prediction and causation: Methodological challenges of studying child and adolescent development}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1878929320301171},
volume = {46},
year = {2020}
}
@misc{Hernan2018,
abstract = {Causal inference is a core task of science. However, authors and editors often refrain from explicitly acknowledging the causal goal of research projects; they refer to causal effect estimates as associational estimates. This commentary argues that using the term “causal” is necessary to improve the quality of observational research. Specifically, being explicit about the causal objective of a study reduces ambiguity in the scientific question, errors in the data analysis, and excesses in the interpretation of the results.},
author = {Hern{\'{a}}n, Miguel A.},
booktitle = {American Journal of Public Health},
doi = {10.2105/AJPH.2018.304337},
issn = {15410048},
title = {{The C-word: Scientific euphemisms do not improve causal inference from observational data}},
year = {2018}
}
@article{Keijsers2016,
abstract = {This article aims to provide a critical analysis of how much we know about the effectiveness of parental monitoring in preventing adolescent delinquency. First, it describes the historical developments in parental monitoring research. Second, it explains why it is uncertain whether causal inferences can be drawn from contemporary research findings on the link of parenting and adolescent problem behaviors. Third, it is empirically demonstrated, using Random-Intercept Cross-Lagged Models, how distinguishing between-person and within-person associations may alter or strengthen conclusions regarding the links of parental monitoring and adolescent disclosure with adolescent delinquency. Previously detected correlations between parental monitoring and adolescent delinquency were not present at the within-family level. However, there were significant associations between within-person fluctuations in disclosure and delinquency. Together, these models provide stronger evidence for a potential causal link between disclosure and delinquency, but also suggest that previously detected linkages of parental monitoring and delinquency can be explained by stable between-person differences rather than causal processes operating within families.},
author = {Keijsers, Loes},
doi = {10.1177/0165025415592515},
issn = {0165-0254},
journal = {International Journal of Behavioral Development},
keywords = {Parental monitoring,adolescent disclosure,cross-lagged model,longitudinal,parental control,parental solicitation,within-person},
number = {3},
pages = {271--281},
title = {{Parental monitoring and adolescent problem behaviors}},
url = {http://journals.sagepub.com/doi/10.1177/0165025415592515},
volume = {40},
year = {2016}
}
@article{Kenny1995,
abstract = {Although researchers in clinical psychology routinely gather data in which many individuals respond at multiple times, there is not a standard way to analyze such data. A new approach for the analysis of such data is described. It is proposed that a person's current standing on a variable is caused by 3 sources of variance: a term that does not change (trait), a term that changes (state), and a random term (error). It is shown how structural equation modeling can be used to estimate such a model. An extended example is presented in which the correlations between variables are quite different at the trait, state, and error levels. {\textcopyright} 1995 American Psychological Association.},
author = {Kenny, David A. and Zautra, Alex},
doi = {10.1037/0022-006X.63.1.52},
issn = {1939-2117},
journal = {Journal of Consulting and Clinical Psychology},
number = {1},
pages = {52--59},
title = {{The trait-state-error model for multiwave data}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0022-006X.63.1.52},
volume = {63},
year = {1995}
}
@article{Liker1985,
abstract = {The method of first differences as an approach to modeling change is described and it is compared to more conventional two-wave panel models. Substantial advantages are found to the first-difference approach, especially if there are unmeasured, unchanging predictor variables in the model. It is also argued that there are substantial problems in the interpretation of results from the conventional two-wave models. Some of the analytic results are illustrated with a number of applications to the area of stressful life events. {\textcopyright} 1985.},
author = {Liker, Jeffrey K. and Augustyniak, Sue and Duncan, Greg J.},
doi = {10.1016/0049-089X(85)90013-4},
issn = {0049089X},
journal = {Social Science Research},
number = {1},
pages = {80--101},
title = {{Panel data and models of change: A comparison of first difference and conventional two-wave models}},
volume = {14},
year = {1985}
}
@article{Maxwell2007,
abstract = {Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. The authors considered the possibility that longitudinal mediation might occur under either of two different models of change: (a) an autoregressive model or (b) a random effects model. For both models, the authors demonstrated that cross-sectional approaches to mediation typically generate substantially biased estimates of longitudinal parameters even under the ideal conditions when mediation is complete. In longitudinal models where variable M completely mediates the effect of X on Y, cross-sectional estimates of the direct effect of X on Y, the indirect effect of X on Y through M, and the proportion of the total effect mediated by M are often highly misleading. (PsycINFO Database Record (c) 2010 APA, all rights reserved). (from the journal abstract)},
author = {Maxwell, Scott E. and Cole, David A.},
doi = {10.1037/1082-989X.12.1.23},
file = {:Users/jeroenmulder/SURFdrive/Education/S20 Summer School/literature/Maxwell2007.pdf:pdf},
issn = {1082989X},
journal = {Psychological Methods},
keywords = {Bias,Cross-sectional designs,Direct effect,Indirect effect,Longitudinal designs,Mediation},
number = {1},
pages = {23--44},
title = {{Bias in cross-sectional analyses of longitudinal mediation}},
volume = {12},
year = {2007}
}
@article{Ormel2002,
abstract = {A strong association between functional disability and depressive symptoms in older people has frequently been reported. Some studies attribute this association to the disabling effects of depression, others to the depressogenic effects of physical health-related disability. The authors examined the reciprocal effects between depressive symptoms and functional disability and their temporal character in a community-based cohort of 753 older people with physical limitations who were assessed at yearly intervals. They compared structural equation models that differed in terms of direction and speed of effects between patient-reported disability in instrumental and basic activities of daily living (IADL/ADLs) and depressive symptoms. The association between disability and depression could be separated into three components: (a) a strong contemporaneous effect of change in disability on depressive symptoms, (b) a weaker 1-year lagged effect of change in depressive symptoms on disability (probably indirect through physical health), and (c) a weak correlation between the trait (or stable) components of depression and disability. IADL/ADL disability and depressive symptoms are thus mutually reinforcing over time. Compensatory forces like effective treatment and age-related adaptation may protect elders against this potential downward trend. To improve quality of life in elderly adults, treatment should target disability when it is new and depression when it is persistent.},
author = {Ormel, Johan and Rijsdijk, Fr{\"{u}}hling V. and Sullivan, Mark and van Sonderen, E. and Kempen, G. I. J. M.},
doi = {10.1093/geronb/57.4.P338},
issn = {1079-5014},
journal = {The Journals of Gerontology Series B: Psychological Sciences and Social Sciences},
number = {4},
pages = {338--347},
pmid = {12084784},
title = {{Temporal and reciprocal relationship between IADL/ADL disability and depressive symptoms in late life}},
url = {https://academic.oup.com/psychsocgerontology/article-lookup/doi/10.1093/geronb/57.4.P338},
volume = {57},
year = {2002}
}
@article{Ousey2011,
abstract = {This study revisits a familiar question regarding the relationship between victimization and offending. Using longitudinal data on middle- and high-school students, the study examines competing arguments regarding the relationship between victimization and offending embedded within the "dynamic causal" and "population heterogeneity" perspectives. The analysis begins with models that estimate the longitudinal relationship between victimization and offending without accounting for the influence of time-stable individual heterogeneity. Next, the victimization-offending relationship is reconsidered after the effects of time-stable sources of heterogeneity, and time-varying covariates are controlled. While the initial results without controls for population heterogeneity are in line with much prior research and indicate a positive link between victimization and offending, results from models that control for time-stable individual differences suggest something new: a negative, reciprocal relationship between victimization and offending. These latter results are most consistent with the notion that the oft-reported victimization-offending link is driven by a combination of dynamic causal and population heterogeneity factors. Implications of these findings for theory and future research focusing on the victimizationoffending nexus are discussed. {\textcopyright} 2010 Springer Science+Business Media, LLC.},
author = {Ousey, Graham C. and Wilcox, Pamela and Fisher, Bonnie S.},
doi = {10.1007/s10940-010-9099-1},
issn = {0748-4518},
journal = {Journal of Quantitative Criminology},
keywords = {Dynamic causal effects,Latent variable models,Offending,Population heterogeneity,Reciprocal effects,Victimization},
number = {1},
pages = {53--84},
title = {{Something old, something new: Revisiting competing hypotheses of the victimization-offending relationship among adolescents}},
url = {http://link.springer.com/10.1007/s10940-010-9099-1},
volume = {27},
year = {2011}
}
@article{Humphreys1965,
abstract = {In a choice among assured, familiar outcomes of behavior, impulsiveness is the choice of less rewarding over more rewarding alternatives. Discussions of impulsiveness in the literature of economics, sociology, social psychology, dynamic psychology and psychiatry, behavioral psychology, and "behavior therapy" are reviewed. 'Impulsiveness seems to be best accounted for by the hyberbolic curves that have been found to describe the decline in effectiveness of rewards as the rewards are delayed from the time of choice. Such curves predict a reliable change of choice between some alternative rewards as a function of time. This change of choice provides a rationale for the known kinds of impulse control and relates them to several hitherto perplexing phenomena: behavioral rigidity, time-out from positive reinforcement, willpower, self-reward, compulsive traits, projection, boredom, and the capacity of punishing stimuli to attract attention.},
author = {Rogosa, David},
doi = {10.1037/0033-2909.88.2.245},
file = {:Users/jeroenmulder/SURFdrive/Education/S20 Summer School/literature/Rogosa1980.pdf:pdf},
issn = {1939-1455},
journal = {Psychological Bulletin},
number = {2},
pages = {245--258},
pmid = {13894690},
title = {{A critique of cross-lagged correlation.}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0033-2909.88.2.245},
volume = {88},
year = {1980}
}
@article{Soenens2008,
author = {Soenens, B. and Luyckx, K. and Vansteenkiste, M. and Duriez, B. and Goossens, L.},
doi = {10.1353/mpq.0.0005},
issn = {1535-0266},
journal = {Merrill-Palmer Quarterly},
number = {4},
pages = {411--444},
title = {{Clarifying the link between parental psychological control and adolescents' depressive symptoms: Reciprocal versus unidirectional models}},
url = {http://muse.jhu.edu/content/crossref/journals/merrill-palmer_quarterly/v054/54.4.soenens.html},
volume = {54},
year = {2008}
}
@article{Spinhoven2014,
author = {Spinhoven, P. and Penelo, E. and de Rooij, M. and Penninx, B. W. and Ormel, J.},
doi = {10.1017/S0033291713000822},
issn = {0033-2917},
journal = {Psychological Medicine},
number = {2},
pages = {337--348},
title = {{Reciprocal effects of stable and temporary components of neuroticism and affective disorders: results of a longitudinal cohort study}},
url = {https://www.cambridge.org/core/product/identifier/S0033291713000822/type/journal_article},
volume = {44},
year = {2014}
}
@article{TePoel2016,
author = {te Poel, Fam and Baumgartner, Susanne E. and Hartmann, Tilo and Tanis, Martin},
doi = {10.1016/j.janxdis.2016.07.009},
issn = {08876185},
journal = {Journal of Anxiety Disorders},
pages = {32--40},
title = {{The curious case of cyberchondria: A longitudinal study on the reciprocal relationship between health anxiety and online health information seeking}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0887618516301864},
volume = {43},
year = {2016}
}