Parents’ posttraumatic stress after burns in their school-aged child: A prospective study.

Egberts, Marthe R.; Schoot, Rens van de; Geenen, Rinie; Loey, Nancy E. Van

doi:10.1037/hea0000448

Cited by 25 publications

(32 citation statements)

References 41 publications

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“…To answer this question, we provide the pcc.step2step3 with 1 Figure 5). The new data by Egberts et al (2017) scored in the extreme 1.1% of the predicted data with respect to the replication hypothesis. Hence, we reject the replication of H 0 by Egberts et al (2017).…”

Section: The Prior Predictive P-valuementioning

confidence: 85%

“…The linear slope of intrusion is regressed on anger and parent gender (i.e., β anger*s , β genderP*s ). Egberts, Schoot, Geenen, and Van Loey (2017)…”

Section: An Example Of a Multilevel Longitudinal Growth Curve Modelmentioning

confidence: 99%

“…With the prior predictive p-value we can check the agreement with H 0 in Egberts et al (2017). If we separately analyze Egberts et al (2017).…”

Section: The Prior Predictive P-valuementioning

confidence: 99%

“…With the prior predictive p-value we can check the agreement with H 0 in Egberts et al (2017). If we separately analyze Egberts et al (2017). We obtain the following results: s = −0.70, q = 0.03, β genderP = 5.59, β guilt = 0.96, β anger = 0.64, β threat = 2.15, β genderP*s = −0.15, β anger*s = 0.00.…”

Section: The Prior Predictive P-valuementioning

confidence: 99%

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How to Test Replication for Structural Equation Models

Zondervan‐Zwijnenburg¹

2019

Preprint

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This paper introduces the prior predictive p-value as a manner to test replication in structural equation models. Using the replication of a piecewise latent growth model as a running example, the study explains the steps of the prior predictive p-value and illustrates them with R-code. The R-code included in the paper and the Supplementary R-script guides the reader through each analysis step. All steps to compute the prior predictive p-value are also incorporated in the Replication R-package. Finally, the study demonstrates how the replication of a more advanced structural equation model - a multilevel latent growth curve model - can be tested.

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Section: The Prior Predictive P-valuementioning

confidence: 85%

“…The linear slope of intrusion is regressed on anger and parent gender (i.e., β anger*s , β genderP*s ). Egberts, Schoot, Geenen, and Van Loey (2017)…”

Section: An Example Of a Multilevel Longitudinal Growth Curve Modelmentioning

confidence: 99%

“…With the prior predictive p-value we can check the agreement with H 0 in Egberts et al (2017). If we separately analyze Egberts et al (2017).…”

Section: The Prior Predictive P-valuementioning

confidence: 99%

Section: The Prior Predictive P-valuementioning

confidence: 99%

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How to Test Replication for Structural Equation Models

Zondervan‐Zwijnenburg¹

2019

Preprint

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show abstract

“…Given the complexity of the LGM model relative to the sample size, prior information was incorporated to facilitate the estimation of the model (i.e., step 1 of the WAMBS checklist). In addition to careful consideration of the plausible parameter space (see Chapter 3 by Van de Schoot, Veen, Smeets, Winter, and Depaoli), we used previous results to inform the priors in our current model (Egberts, Van de Schoot, Geenen, & Van Loey, 2017).…”

Section: Specifying and Understanding Priorsmentioning

confidence: 99%

Small Sample Size Solutions

Schoot¹,

Miočević²

2020

Self Cite

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Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

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Gender differences in the experienced emotional intensity of experimentally induced memories of negative scenes

Staugaard

Berntsen

2020

Psychological Research

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It is well documented that women have an increased risk of emotional disorders, such as anxiety and depression. Such disorders are typically characterized by intrusive memories and rumination of past events, but findings are mixed as to whether women have enhanced access to memories of emotional events. Some studies have found that women, compared with men, report more frequent and more intense memories of emotionally stressful events, whereas other studies have failed to replicate this effect. These conflicting findings may reflect the use of different memory sampling techniques (e.g., retrospective versus experimental data) and limited control for factors associated with both gender and emotional memory. The purpose of the present study was to investigate gender differences in memory for emotionally negative events, using three different sampling methods, while at the same time controlling for parameters that might co-vary with gender. Consistent with some previous studies, we found that women and men did not differ in their frequencies of emotionally negative involuntary memories. However, women rated their memories as more intense and arousing than men did, and women also reported higher increases in state anxiety after retrieval.Female gender accounted for unique variance in the emotional intensity and subjective arousal associated with negative memories, when controlling for other theoretically derived variables. The findings provide evidence that female gender is associated with a stronger emotional response to memories of negative events, but not that women remember such events more frequently than men do.

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Parents’ posttraumatic stress after burns in their school-aged child: A prospective study.

Cited by 25 publications

References 41 publications

How to Test Replication for Structural Equation Models

How to Test Replication for Structural Equation Models

Small Sample Size Solutions

Gender differences in the experienced emotional intensity of experimentally induced memories of negative scenes

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