Open accessJournal Article

Sample size recommendations for continuous-time models: compensating shorter time series with larger numbers of persons and vice versa

04 Mar 2021-Structural Equation Modeling (Routledge)-Vol. 28, Iss: 2, pp 229-236
Abstract: Autoregressive modeling has traditionally been concerned with time-series data from one unit (N = 1). For short time series (T < 50), estimation performance problems are well studied and documented...

Topics: Autoregressive model (55%), , Series (mathematics) (50%)
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Blair T. Crewther1, Martin Hecht2, Neill Potts, Liam P. Kilduff3  +3 moreInstitutions (4)
Abstract: In sport, testosterone has been positioned as a substrate for motivation with both directional and time dependencies. However, evidence is scarce when considering the complexities of competitive sport and no work has explicitly modeled these dependencies. To address these gaps, we investigated the bidirectional and time-dependent interrelationships between testosterone and training motivation in an elite rugby environment. Thirty-six male athletes were monitored across training weeks before and after eight international rugby matches. Pre-breakfast measures of salivary testosterone and training motivation (1–10 rating) were taken on training, competition, and recovery days (up to 40 tests). Using a continuous-time (CT) model, within-person estimates of autoregressive effects (persistence) and cross-lagged effects (relationships) were derived. A stronger, more persistent temporal association was identified for testosterone than for motivation. Cross-lagged effects verified that training motivation was positively related to testosterone at latter time points (p

Topics: Testosterone (patch) (51%)

4 Citations

Open accessJournal Article
Martin Hecht1, Steffen Zitzmann2Institutions (2)
Abstract: Cross-lagged panel models have been commonly applied to investigate the dynamic interplay of variables. In such discrete-time models, the size of the cross-lagged effects depends on the length of t...

1 Citations

Journal Article
Lixian Cui1, Qian Sun1, Fanyi Yu2, Jingwei Xu2  +1 moreInstitutions (2)
Abstract: Current understandings of emotional concordance are still limited though it has been conceptualized and examined in various ways. We argue that emotional concordance could be better understood within individuals across real time in specific measurement contexts. The current study examined emotional dynamic within-person concordance within physiological subsystems and between physiological and expressive subsystems. We also explored the moderating roles of between-person factors on the within-person concordance and discordance. We found strong concordance within sympathetic indicators (PEP and CO), and between sympathetic and parasympathetic indicators (PEP and RSA), almost across all laboratory tasks. Evidence for concordance was generally weak between physiology and facial expression and have mostly been found between sympathetic indicator (PEP) and facial expressions. Participant socioeconomic status (SES) and sexual orientation seemed to moderate the emotional concordance. We discussed our findings across the various laboratory tasks in the current study.

Topics: Concordance (56%), Facial expression (51%)

1 Citations

Open accessJournal Article
Charles C. Driver1Institutions (1)
Abstract: Continuous-time models generally imply a stochastic differential equation for latent processes, coupled to a measurement model. Various computational issues can arise, and there are different estim...

Topics: , State space (61%),

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Open accessJournal Article
01 Jan 2014-MSOR connections
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

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229,202 Citations

Open accessJournal Article
Thomas J. DiCiccio1, Bradley Efron2Institutions (2)
Abstract: This article surveys bootstrap methods for producing good approximate confidence intervals. The goal is to improve by an order of magnitude upon the accuracy of the standard intervals $\hat{\theta} \pm z^{(\alpha)} \hat{\sigma}$, in a way that allows routine application even to very complicated problems. Both theory and examples are used to show how this is done. The first seven sections provide a heuristic overview of four bootstrap confidence interval procedures: $BC_a$, bootstrap-t , ABC and calibration. Sections 8 and 9 describe the theory behind these methods, and their close connection with the likelihood-based confidence interval theory developed by Barndorff-Nielsen, Cox and Reid and others.

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1,655 Citations

Journal Article
Abstract: A common question asked by researchers is, "What sample size do I need for my study?" Over the years, several rules of thumb have been proposed. In reality there is no rule of thumb that applies to all situations. The sample size needed for a study depends on many factors, including the size of the model, distribution of the variables, amount of missing data, reliability of the variables, and strength of the relations among the variables. The purpose of this article is to demonstrate how substantive researchers can use a Monte Carlo study to decide on sample size and determine power. Two models are used as examples, a confirmatory factor analysis (CFA) model and a growth model. The analyses are carried out using the Mplus program (Muthen& Muthen 1998).

Topics: , Rule of thumb (51%), Missing data (50%)

1,455 Citations

Journal Article
James R. Carpenter1, John F. BithellInstitutions (1)
Abstract: Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example.

Topics: Resampling (54%), Confidence interval (51%)

1,259 Citations

Open accessJournal ArticleDOI: 10.1037/A0038889
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.

Topics: Panel data (51%),

956 Citations

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