Background
Even though investigating predictors of intervention success (e.g Cognitive Training, CT) is gaining more and more interest in the light of an individualized medicine, results on specific predictors of intervention success in the overall field are mixed and inconsistent due to different and sometimes inappropriate statistical methods used. Therefore, the present paper gives a guidance on the appropriate use of multiple regression analyses to identify predictors of CT and similar non-pharmacological interventions.
Methods
We simulated data based on a predefined true model and ran a series of different analyses to evaluate their performance in retrieving the true model coefficients. The true model consisted of a 2 (between: experimental vs. control group) × 2 (within: pre- vs. post-treatment) design with two continuous predictors, one of which predicted the success in the intervention group and the other did not. In analyzing the data, we considered four commonly used dependent variables (post-test score, absolute change score, relative change score, residual score), five regression models, eight sample sizes, and four levels of reliability.
Results
Our results indicated that a regression model including the investigated predictor, Group (experimental vs. control), pre-test score, and the interaction between the investigated predictor and the Group as predictors, and the absolute change score as the dependent variable seemed most convenient for the given experimental design. Although the pre-test score should be included as a predictor in the regression model for reasons of statistical power, its coefficient should not be interpreted because even if there is no true relationship, a negative and statistically significant regression coefficient commonly emerges.
Conclusion
Employing simulation methods, theoretical reasoning, and mathematical derivations, we were able to derive recommendations regarding the analysis of data in one of the most prevalent experimental designs in research on CT and external predictors of CT success. These insights can contribute to the application of considered data analyses in future studies and facilitate cumulative knowledge gain.
Previous studies have demonstrated that highly narcissistic individuals perceive themselves as grandiose and devaluate and sometimes overvalue others. These results are mainly based on behavioural data, but we still know little about the neural correlates underlying, such as perceptional processes. To this end, we investigated event-related potential components (ERP) of visual face processing (P1 and N170) and their variations with narcissism. Participants (N = 59) completed the Narcissistic Admiration and Rivalry Questionnaire and were shown pictures of their own face, a celebrity’s face, and a stranger’s face. Variations of P1 and N170 with Admiration and Rivalry were analysed using multilevel models. Results revealed moderating effects of both narcissism dimensions on the ERP components of interest. Participants with either high Admiration or low Rivalry scores showed a lower P1 amplitude when viewing their own face compared with when viewing a celebrity’s face. Moreover, the Self-Stranger difference in the N170 component (higher N170 amplitude in the Self condition) was larger for higher Rivalry scores. The findings showed, for the first time, variations of both narcissism dimensions with ERPs of early face processing. We related these effects to processes of attentional selection, an expectancy-driven perception, and the mobilisation of defensive systems. The results demonstrated that by linking self-report instruments to P1 and N170, and possibly to other ERP components, we might better understand self- and other-perception in narcissism.
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