Table 1.
BIAT procedure.
Figure 1.
Schematics of the same single response trial of one block of the IAT on the left, and the BIAT on the right.
In the IAT, the correct response is the left key because Awful belongs to the Category Bad. In the BIAT, the correct response is the left key because Awful does not belong to the categories Democrats or Good.
Table 2.
A step-by-step guide for calculating the recommended D score from Table 8.
Table 3.
Comparison of retaining or removing 1 st four trials of each block (Study 1), candidate data transformations (Study 2), and bad versus good focal blocks (Study 6) across evaluation criteria for political attitudes.
Figure 2.
Latency operating characteristics, showing variation in mean standardized values of the five candidate BIAT scoring algorithms across deciles of the sample's distribution of average speed of responding for the political BIAT.
For this plot, the algorithms were computed after deleting 4 warm-up trials from each response block and also deleting latencies greater than 10,000 ms. There were 202 or 203 respondents in each decile. Most noticeable in the graph is the inferior performance (smaller effect sizes) for the reciprocal measure, and strongest performance for the D measure. Also noticeable is that the D measure was smallest for the slowest subjects, whereas the log and latency measures were largest for the slowest subjects.
Figure 3.
Latency operating characteristics, showing variation in mean standardized values of the five candidate BIAT scoring algorithms across deciles of the sample's distribution of average speed of responding for the political BIAT.
Pretreatment of the data involved removing 4 warm-up trials per block, latencies slower than 10s, and latencies faster than 400 ms. There were 202 or 203 respondents in each decile. The most noticeable effects visible in the graph are improvement in performance of the reciprocal measure relative to its poor showing in Fig. 2, and the contrast between the relative stability across speed variations for four of the measures and the increasing magnitude of the (untransformed) latency-difference measure as responding went from fast (left of graph) to slow.
Table 4.
Comparison of fast and slow latency treatments across evaluation criteria for politics good focal response blocks (Study 3).
Table 5.
Comparing effects of removing versus retaining error trials for good focal blocks on evaluation criteria (Study 4).
Table 6.
Effects of applying task exclusion criteria on evaluation criteria for politics (Study 5).
Figure 4.
Effects of seven criteria for excluding respondents as a function of their proportion of fast responses (latency <300 ms) on correlations with self-reported preference between Democrats and Republicans for five BIAT data transformations (Study 5).
Higher correlations indicate better performance. The furthest left datapoint indicates no exclusion of participants; the furthest right datapoint indicates exclusion of all participants that had even a single fast response. Sample size (n) on the x-axis indicates the number of participants retained with that exclusion criterion.
Table 7.
Analyses of D measure based on First 40 Trials vs. Second 40 Trials.
Table 8.
Recommended scoring practice for BIAT using procedure described in Table 1.