Table 1. Linear Mixed Model Regression for Dotloc Onset Error (N = 138).
Last Updated: 2019-04-08 19:53:54 code csv
index term B SE t df Pr(>|t|)
1 (Intercept) 8.8063 0.2212 39.8188 138.997270 0.0
2 osmsos 0.9109 0.3462 2.6314 136.000135 0.0095
3 trialTypepofa -0.0389 0.0204 -1.9025 27048.458219 0.0571
4 TrialNum 0.1578 0.054 2.9248 137.000056 0.004
index sigma logLik AIC BIC REMLcrit df.residual
1 1.681747 -53426.616458 106869.232915 106934.957081 106853.232915 27316

Linear Mixed Model Fit by REML (Laplace Approximation) ['lmer']. This table summarizes effects on onset error rate with trial number, operating system, and stimulus.

Statistical Analysis

Participants with 'Dotloc' or 'Stimulus' Onset Error median above 3SD (n = [17, 25, 49, 54, 59, 77, 80, 89, 112, 123, 138, 140, 150, 153, 180, 182, 185, 212, 221, 248, 250, 256, 262, 269, 292, 294, 298, 319, 999999, 111111, 156], 18.7%) were excluded from analysis (see methods). We employed linear mixed effects models with random intercepts and slopes using the lmer() function in the lme4 R package (R Core Team, 2013; Bates, Mächler, Bolker, & Walker, 2015). For our model, Operating System, Stimulus (IAPS, POFA), and Trial. were included as fixed effects. Random effects for Trial, and Participant. were included in the model to account for their respective variation in their slopes and intercepts. Dotloc Onset Error was used as the outcome measure.

For our analysis of Dotloc Onset Error, our results revealed a statistically significant effect of Operating System (t = 0.9109, SE = 0.3462, p = 0.0095), no statistically significant effect of Stimulus (t = -0.0389, SE = 0.0204, p = 0.0571), a statistically significant effect of Trial (t = 0.1578, SE = 0.054, p = 0.004).

Figure 1. Individual Trend Plot of the Difference Between Expected and True Onset Time for Dotloc Onset Error (nested by subject:trial, window=5).
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Each individual subjects individual trend is indicated here. Participants with 'Dotloc' or 'Stimulus' onset error rate 3 SD above the median are indicated here with a semi-opaque line. The graph has been clipped at y = 250 for displaying purposes.
Figure 2. Trend Plot of the Difference Between Expected and True Onset Time for Dotloc Onset Error (nested by subject:trial).
Last Updated: 2019-04-08 19:53:54
Data is either unbinned (c,d) or binned into 33 discrete evenly-sized groups (a,b). The model is still fit using the original data. No participants have been excluded for this analysis. The binned graph has been clipped at y = 1000 for displaying purposes.
Figure 3. Q-Q Plot (iaps, pofa).
Last Updated: 2019-04-08 19:53:54
The Normal Q-Q plot compares the standardized residuals against the theoretical quantiles from a standard normal distribution. If the model residuals are normally distributed, then the points on this graph will be plotted in a generally straight line.
Figure 4. Residuals vs Fitted Plot (iaps, pofa).
Last Updated: 2019-04-08 19:53:54
The Residuals vs Fitted plot is used to identify residual distribution. If residuals are following a visible trend on the graph, then the homogeneity assumption was violated.