Sample Size and the Detection of Correlation--A Signal Detection Account: Comment on Kareev (2000) and Juslin and Olsson (2005).

2005 ◽  
Vol 112 (1) ◽  
pp. 268-279 ◽  
Author(s):  
Richard B. Anderson ◽  
Michael E. Doherty ◽  
Neil D. Berg ◽  
Jeff C. Friedrich
Keyword(s):  
Signal Detection ◽  
Sample Size

scholarly journals Sample size and the detection of means: A signal detection account

2007 ◽  
Vol 35 (1) ◽  
pp. 50-58 ◽  
Author(s):  
Richard B. Anderson ◽  
Michael E. Doherty
Keyword(s):  
Signal Detection ◽  
Sample Size

How can we be so blind?: Investigating interactions among instructions, perception, and navigation

2017 ◽  
Author(s):  
Rafael Antonio Garcia ◽  
W. Jake Jacobs
Keyword(s):  
Individual Differences ◽  
Signal Detection ◽  
Sample Size ◽  
Virtual Space ◽  
Alternative Methods ◽  
Large Sample Size ◽  
Pilot Studies ◽  
As If

Prior research suggests that instructions modify place learning and navigation behaviors in a virtual space. Two pilot studies indicated that under certain instructional sets, participants behaved as if they were navigating in a room without distal cues—even though their ability to identify icons present in the room appears unaffected. In this study, we did a follow-up replicating those findings. Additionally, we attempted to measure the quality of cognitive map formation using two alternative methods (signal detection and rank ordering methods). The results of these methods were mixed; however, the large sample size, and appropriate controls used in this study solidify the interpretations of the two earlier pilot studies. Even with the vast individual differences in instruction adherence, participants given instructions that do not match the environmental contingencies (incongruent) behaved as if they were navigating in a room without distal cues.

scholarly journals Insights into Criteria for Statistical Significance from Signal Detection Analysis

2019 ◽  
Vol 3 ◽  
Author(s):  
Jessica K. Witt
Keyword(s):  
Signal Detection ◽  
Sample Size ◽  
Effect Size ◽  
Statistical Significance ◽  
Bayes Factors ◽  
False Alarms ◽  
P Values ◽  
Questionable Research Practices ◽  
Detection Analysis ◽  
Signal Detection Analysis

What is best criterion for determining statistical significance? In psychology, the criterion has been p < .05. This criterion has been criticized since its inception, and the criticisms have been rejuvenated with recent failures to replicate studies published in top psychology journals. Several replacement criteria have been suggested including reducing the alpha level to .005 or switching to other types of criteria such as Bayes factors or effect sizes. Here, various decision criteria for statistical significance were evaluated using signal detection analysis on the outcomes of simulated data. The signal detection measure of area under the curve (AUC) is a measure of discriminability with a value of 1 indicating perfect discriminability and 0.5 indicating chance performance. Applied to criteria for statistical significance, it provides an estimate of the decision criterion’s performance in discriminating real effects from null effects. AUCs were high (M = .96, median = .97) for p values, suggesting merit in using p values to discriminate significant effects. AUCs can be used to assess methodological questions such as how much improvement will be gained with increased sample size, how much discriminability will be lost with questionable research practices, and whether it is better to run a single high-powered study or a study plus a replication at lower powers. AUCs were also used to compare performance across p values, Bayes factors, and effect size (Cohen’s d). AUCs were equivalent for p values and Bayes factors and were slightly higher for effect size. Signal detection analysis provides separate measures of discriminability and bias. With respect to bias, the specific thresholds that produced maximally-optimal utility depended on sample size, although this dependency was particularly notable for p values and less so for Bayes factors. The application of signal detection theory to the issue of statistical significance highlights the need to focus on both false alarms and misses, rather than false alarms alone.

So, Whatever Happened to Signal Detection Theory?

1995 ◽  
Vol 40 (10) ◽  
pp. 972-972
Author(s):  
Jerome R. Busemeyer
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory

Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions.

2001 ◽  
Vol 6 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Michaela Kiernan ◽  
Helena C. Kraemer ◽  
Marilyn A. Winkleby ◽  
Abby C. King ◽  
C. Barr Taylor
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Signal Detection ◽  
Tailored Interventions
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