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Categorical predictors SERT genotype and training level, as well as within subject measures "stimuli status" (ie. High- vs. low- status image pool) and "face vs. scrambled" were included in the analysis of the amount of time spent viewing each picture.
Correspondingly, instrument validity was evaluated by estimating the distribution of residuals for item and subject measures.
These computed baseline and result levels from each subject and biomarker were used to compute additional subject measures, such as change from baseline.
Lastly, no methods for statistical corrections for multiple within subject measures used in the preparation of the raw EEG data were reported.
This was done for the sake of completeness, although this measure of model performance should be interpreted with caution because (within subject) measures of accuracy, such as this, do not reflect predictive validity at between subject level.
Study design: IFHK, YL, MLMH, CN, RHHE, RWP, VAS; Subject measures: IFHK, YL; Analysis and interpretation of data: IFHK, YL, MLMH, CN, VAS; Manuscript preparation: IFHK, YL, CN, RHHE, RWP, VAS; Statistical analysis: IFHK, YL, VAS.
Similar(54)
Time is the subject, measured and manipulated, felt and observed, by 20th-century composers from John Cage ("4'33" ") to Gyorgy Ligeti ("Symphonic Poem" for multiple metronomes).
Each subject measured the time spent using the tools to carry out DE and AE steps.
A 4×2×2 repeated measures ANOVA was performed on movement time using GROUP (YAC, OAC, Mild PD, Advanced PD) as the between subjects measure and PHASE (advance, withdrawal) and MUSIC (no music, music) as the within subject measure.
A 4×2×2 repeated measures ANOVA was performed on movement time using GROUP (YAC, OAC, Mild PD, Advanced PD) as the between subjects measure and PHASE (advance, withdrawal) and CONDITION (vision, occlusion) as the within subject measure.
represents time-independent i th covariate for j th subject measured at the baseline.
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