Calculating and graphing within-subject confidence intervals for ANOVA

Baguley, Thom

doi:10.3758/s13428-011-0123-7

Cited by 214 publications

(264 citation statements)

References 32 publications

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“…These are hard to analyze, grasp and interpret appropriately (Cohen, 1990). There is no perfect method for analyzing complex designs using estimation (Franz and Loftus, 2012;Baguley, 2012), and even NHST procedures like ANOVA that have been specifically developed for such designs are not without issues (Smith et al, 2002;Baguley, 2012;Kirby and Gerlanc, 2013, p.28;Rosnow andRosenthal, 1989, p.1281;Cumming, 2012, p.420). Faithfully communicating results from complex designs is simply hard, no matter which method is used.…”

Section: Before Analyzing Datamentioning

confidence: 99%

“…Similarly, do not use procedures that "adjust" or "correct" the length of confidence intervals unless there are good reasons to do so. Several such procedures have been described to facilitate visual inference or reinforce the equivalence with classical NHST procedures (Baguley, 2012;Tryon, 2001;Bender and Lange, 2001), but their downside is that they change the meaning of confidence intervals and increase the amount of contextual information required to interpret them. Finally, do not show standard errors (SEs) in your plots.…”

Section: Plotting Confidence Intervalsmentioning

confidence: 99%

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Fair Statistical Communication in HCI

Dragicevic

2016

Human–Computer Interaction Series

271

247

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Statistics are tools to help end users accomplish their task. In research, to be qualified as usable, statistical tools should help researchers advance scientific knowledge by supporting and promoting the effective communication of research findings. Yet areas such as human-computer interaction (HCI) have adopted toolsi.e., p-values and dichotomous testing procedures -that have proven to be poor at supporting these tasks. The abusive use of these procedures has been severely criticized in a range of disciplines for several decades, suggesting that tools should be blamed, not end users. This chapter explains in a non-technical manner why it would be beneficial for HCI to switch to an estimation approach, i.e., reporting informative charts with effect sizes and interval estimates, and offering nuanced interpretations of our results. Advice is offered on how to communicate our empirical results in a clear, accurate, and transparent way without using any tests or p-values.

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Section: Before Analyzing Datamentioning

confidence: 99%

Section: Plotting Confidence Intervalsmentioning

confidence: 99%

Fair Statistical Communication in HCI

Dragicevic

2016

Human–Computer Interaction Series

271

247

View full text Add to dashboard Cite

show abstract

“…Multilevel modeling allows both participants and stimuli to be simultaneously treated as random effects, thereby maximizing generalizability (Clark, 1973;Judd, Westfall, & Kenny, 2012). When the random effects are fully crossed (i.e., when all participants experience all stimuli), conventional analyses (including separate by-items or by-subjects analyses) can lead to massive Type 1 error inflation (Baguley, 2012;Clark, 1973;Judd et al, 2012). The most appropriate analysis therefore takes into account both sources of variability.…”

Section: Data Analysis and Multilevel Modelingmentioning

confidence: 99%

Concordant Cues in Faces and Voices

et al. 2016

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Information from faces and voices combines to provide multimodal signals about a person. Faces and voices may offer redundant, overlapping (backup signals), or complementary information (multiple messages). This article reports two experiments which investigated the extent to which faces and voices deliver concordant information about dimensions of fitness and quality. In Experiment 1, participants rated faces and voices on scales for masculinity/femininity, age, health, height, and weight. The results showed that people make similar judgments from faces and voices, with particularly strong correlations for masculinity/femininity, health, and height. If, as these results suggest, faces and voices constitute backup signals for various dimensions, it is hypothetically possible that people would be able to accurately match novel faces and voices for identity. However, previous investigations into novel face-voice matching offer contradictory results. In Experiment 2, participants saw a face and heard a voice and were required to decide whether the face and voice belonged to the same person. Matching accuracy was significantly above chance level, suggesting that judgments made independently from faces and voices are sufficiently similar that people can match the two. Both sets of results were analyzed using multilevel modeling and are interpreted as being consistent with the backup signal hypothesis.

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“…There was no significant interaction between false information and stimulus voice, F(1, 69) = .33, p = .57. Figure 1 shows the main effect of false information on target pitch ratings, including the two-tiered 95% confidence intervals (CIs) (Baguley, 2012a) for mean pitch ratings.…”

Section: Effect Of False Information On Ratings Of Pitchmentioning

confidence: 99%

Unfamiliar voice identification: Effect of post-event information on accuracy and voice ratings

2014

Journal of European Psychology Students

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This study addressed the effect of misleading post-event information (PEI) on voice ratings, identification accuracy, and confidence, as well as the link between verbal recall and accuracy. Participants listened to a dialogue between male and female targets, then read misleading information about voice pitch. Participants engaged in verbal recall, rated voices on a feature checklist, and made a lineup decision. Accuracy rates were low, especially on target-absent lineups. Confidence and accuracy were unrelated, but the number of facts recalled about the voice predicted later lineup accuracy. There was a main effect of misinformation on ratings of target voice pitch, but there was no effect on identification accuracy or confidence ratings. As voice lineup evidence from earwitnesses is used in courts, the findings have potential applied relevance.

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Calculating and graphing within-subject confidence intervals for ANOVA

Cited by 214 publications

References 32 publications

Fair Statistical Communication in HCI

Fair Statistical Communication in HCI

Concordant Cues in Faces and Voices

Unfamiliar voice identification: Effect of post-event information on accuracy and voice ratings

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