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Rob J.H. van Bree, Catherine Bolman, Aart N. Mudde, Maartje M. van Stralen, Denise A. Peels, Hein de Vries, and Lilian Lechner

tested simultaneously using a cross-lagged panel design (see Figure  1 ). The time frames in such a design are the same for both mediation effects. Although the hypotheses have a strong common sense character, systematically testing them to sort out whether they can be confirmed or not contributes

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Raphael Frank, Insa Nixdorf, and Jürgen Beckmann

Findings on burnout and depression in athletes highlight their potential severity. Although both constructs are discussed in similar, stress-based concepts, it is unclear how they relate to each other. To address this issue, we conducted a crosssectional multiple linear regression analysis (MLR; N = 194) and a longitudinal analysis of a three-wave cross-lagged panel (CLP; n = 92) in German junior elite athletes. MLR showed that depression and burnout were both associated with chronic stress. Stress was a significant better predictor for both burnout and depression than each was for the other. CLP analysis on the constructs of burnout and depression revealed support for cross-paths in both directions. Thus, burnout and depression might cause each other to some degree, with no distinct direction of this link. However, as both syndromes do not fully explain each other, interchanging both terms and syndromes should be avoided. Preferably, future research might consider the transfer of knowledge between both syndromes to draw founded conclusions.

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Esmie P. Smith, Andrew P. Hill, and Howard K. Hall

Approach To test the three theoretical models cross-lagged panel analysis was used. Cross-lagged effects compare the relationship between variable X (e.g., perfectionism) at time 1 and variable Y at time 2 (e.g., burnout) in the presence of the relationship between variable Y (e.g., burnout) at time

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Ian David Boardley, Doris Matosic, and Mark William Bruner

three time points, collecting data from children (i.e., MD) and teachers (i.e., children’s aggression) at the start of fourth, fifth, and sixth grades. Cross-lagged panel analysis demonstrated weak positive cross-lagged effects of MD on aggression between Time 1 and Time 2 and Time 2 and Time 3. In

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Miguel A. López-Gajardo, Inmaculada González-Ponce, Tomás García-Calvo, Edgar Enrich-Alturo, and Francisco M. Leo

analyses were carried out using structural equation modeling to test longitudinal mediating pathways. 5 Four cross-lagged panel models (CLPM) were used to estimate time-specific direct and indirect effects ( Cole & Maxwell, 2003 ; Figure  1 ). The CLPM is composed of two parts: (a) an autoregressive part

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Lennart Raudsepp and Kristi Vink

results of the cross-lagged panel analysis are displayed in Table  3 . First, the basic model (model 1) with autoregressive paths and within-wave residual correlations fit the data well. All the autoregressive paths were significant and stable over time, indicating the individual differences remained

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Markus Gerber, Simon Best, Fabienne Meerstetter, Sandrine Isoard-Gautheur, Henrik Gustafsson, Renzo Bianchi, Daniel J. Madigan, Flora Colledge, Sebastian Ludyga, Edith Holsboer-Trachsler, and Serge Brand

 = .01–.058 (small), η 2  = .059–.137 (medium), and η 2  ≥ .138 (large). Finally, to gain insights into the reciprocal associations between burnout and insomnia symptoms, we tested cross-lagged panel models using the structural equation approach. As recommended by Anderson and Gerbing ( 1988 ), we

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Cézane Priscila Reuter, Caroline Brand, João Francisco de Castro Silveira, Letícia de Borba Schneiders, Jane Dagmar Pollo Renner, Letícia Borfe, and Ryan Donald Burns

scores for each observed variable were examined using paired t tests with effect sizes computed using Cohen delta ( d ). The primary analysis consisted of a cross-lagged panel model using Full Information Maximum Likelihood, which uses all cases within an analysis regardless of any missing data. Cross

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Oliver W.A. Wilson, Scott Graupensperger, M. Blair Evans, and Melissa Bopp

FVC, a longitudinal structural equation modeling technique called random intercept cross-lagged panel modeling (RI-CLPM) 46 was used. RI-CLPM is a multilevel approach that treats the 3 waves of data as nested within participants and controls for time-invariant trait-like differences

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Jeffrey Sallen, Christian Andrä, Sebastian Ludyga, Manuel Mücke, and Christian Herrmann

requirements (eg, homoscedasticity, linearity, normality of residuals) were identified. Four saturated pathway models in a cross-lagged panel design were analyzed. These models contained only manifest variables (Figures  1 and 2 ). Models A1 and A2 focused on the relationship between VPA and MC-OM. In models