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Chien-Chih Chou, Kuan-Chou Chen, Mei-Yao Huang, Hsin-Yu Tu and Chung-Ju Huang

significant interaction effects that were identified, whereas multiple comparisons were used to follow-up on any significant main effects. To control for experiment-wise inflation of α, additional Bonferroni adjustments were made. Small to moderate ESs were observed for each of the EF domains of interest (0

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Matti Hyvärinen, Sarianna Sipilä, Janne Kulmala, Harto Hakonen, Tuija H. Tammelin, Urho M. Kujala, Vuokko Kovanen and Eija K. Laakkonen

–Watson statistic was used to test the independence of observations, and the variance inflation factors were studied to test the multicollinearity. Furthermore, P–P plots and histograms were plotted to check that the residuals were approximately normally distributed, and scatterplots were used to test the

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Mindy Patterson, Wanyi Wang and Alexis Ortiz

females, respectively. The residuals in the dependent variable were approximately normally distributed. Multicollinearity assumptions using tolerance and variance inflation factor (VIF) were also tested. In the present study, tolerance < 0.10 or VIF > 10 was considered multicollinearity. The following

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Grace Yan, Dustin Steller, Nicholas M. Watanabe and Nels Popp

regression (Full Regression) used all the variables described in Function 1. Last, in order to account for the potential for multicollinearity in the data, variance-inflation factors (VIF) were calculated after estimating the full model. We found that the month variables all had high levels of collinearity

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Maria-Christina Kosteli, Jennifer Cumming and Sarah E. Williams

low to moderate (all < .07). Moreover, none of the variables has a condition index of above 30 and a variance inflation factor of above 3. Table 1 Means, Standard Deviations, Alpha Coefficients and Correlations Matrix 1 2 3 4 5 6 α 1. Self-regulatory imagery 1 .94 2. Self-efficacy .29 ** 1 .83 3

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Ross Armstrong, Christopher Michael Brogden and Matt Greig

investigated by calculating the variance inflation factor and tolerance statistic which indicated that multicollinearity was within acceptable levels. 33 – 35 The physiological responses of heart rate and RPE of pooled dancers and hypermobile versus nonhypermobile dancers during the DAFT were analyzed using a

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Kari Roethlisberger, Vista Beasley, Jeffrey Martin, Brigid Byrd, Krista Munroe-Chandler and Irene Muir

analysis; sport enjoyment was the outcome variable for the second regression analysis. Before running the regression analyses we checked for multicollinearity. Multicollinearity was not evident in the regression equations as tolerance was > .10 for all variables and the Variance Inflation Factor (VIF) was

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Kyungyeol (Anthony) Kim, Kevin K. Byon and Paul M. Pedersen

constructs ( Podsakoff, MacKenzie, & Podsakoff, 2012 ). Our survey experiment and time-lag data helped lessen such inflation, strengthening the robustness of the research findings ( Podsakoff et al., 2012 ). Another interesting contribution of this article pertains to the findings of the moderating effects

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Brendan Dwyer, Joris Drayer and Stephen L. Shapiro

also provided sufficient power. The dependent variable was binary (DFS/no DFS), and the continuous indicators in the model were linear with the logit form of the dependent variable. Potential multicollinearity issues within the model were also examined by investigating variance inflation factors and

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Georgios Nalbantis, Marcel Fahrner and Tim Pawlowski

against the unrestricted model’s coefficients. The results of the suest-based Hausman test suggest that the assumption has not been violated. Finally, all variance inflation factors for linear predictor variables are well below 3, not indicating any problem of multicollinearity in our data. Results The