scholarly journals Biased parameter estimates and inflated type I error rates in analysis of covariance (and analysis of partial variance) arising from unreliability: Alternatives and remedial strategies.

2010 ◽  
Vol 119 (2) ◽  
pp. 307-319 ◽  
Author(s):  
Richard E. Zinbarg ◽  
Satoru Suzuki ◽  
Amanda A. Uliaszek ◽  
Alison R. Lewis
Keyword(s):  
Type I Error ◽  
Error Rates ◽  
Analysis Of Covariance ◽  
Parameter Estimates ◽  
Type I ◽  
Type I Error Rates ◽  
Inflated Type ◽  
Remedial Strategies

scholarly journals Another Warning about Median Reaction Time --- Version of 11 Feb 2020

2020 ◽  
Author(s):  
Jeff Miller
Keyword(s):  
Type I Error ◽  
Reaction Times ◽  
Error Rates ◽  
Type I ◽  
Experimental Conditions ◽  
Type I Error Rates ◽  
Correction Technique ◽  
Inflated Type ◽  
The Mean ◽  
Rt Distributions

Contrary to the warning of Miller (1988), Rousselet and Wilcox (2020) argued that it is better to summarize each participant’s single-trial reaction times (RTs) in a given condition with the median than with the mean when comparing the central tendencies of RT distributions across experimental conditions. They acknowledged that median RTs can produce inflated Type I error rates when conditions differ in the number of trials tested, consistent with Miller’s warning, but they showed that the bias responsible for this error rate inflation could be eliminated with a bootstrap bias correction technique. The present simulations extend their analysis by examining the power of bias-corrected medians to detect true experimental effects and by comparing this power with the power of analyses using means and regular medians. Unfortunately, although bias-corrected medians solve the problem of inflated Type I error rates, their power is lower than that of means or regular medians in many realistic situations. In addition, even when conditions do not differ in the number of trials tested, the power of tests (e.g., t-tests) is generally lower using medians rather than means as the summary measures. Thus, the present simulations demonstrate that summary means will often provide the most powerful test for differences between conditions, and they show what aspects of the RT distributions determine the size of the power advantage for means.

scholarly journals Generalized additive models and inflated type I error rates of smoother significance tests

2011 ◽  
Vol 55 (1) ◽  
pp. 366-374 ◽  
Author(s):  
Robin L. Young ◽  
Janice Weinberg ◽  
Verónica Vieira ◽  
Al Ozonoff ◽  
Thomas F. Webster
Keyword(s):  
Type I Error ◽  
Generalized Additive Models ◽  
Error Rates ◽  
Additive Models ◽  
Type I ◽  
Significance Tests ◽  
Type I Error Rates ◽  
Inflated Type

scholarly journals Bias, Type I Error Rates, and Statistical Power of a Latent Mediation Model in the Presence of Violations of Invariance

2017 ◽  
Vol 78 (3) ◽  
pp. 460-481 ◽  
Author(s):  
Margarita Olivera-Aguilar ◽  
Samuel H. Rikoon ◽  
Oscar Gonzalez ◽  
Yasemin Kisbu-Sakarya ◽  
David P. MacKinnon
Keyword(s):  
Measurement Invariance ◽  
Statistical Power ◽  
Type I Error ◽  
Error Rates ◽  
Parameter Estimates ◽  
Type I ◽  
Mediation Model ◽  
Type I Error Rates ◽  
Mediated Effects ◽  
The Impact

When testing a statistical mediation model, it is assumed that factorial measurement invariance holds for the mediating construct across levels of the independent variable X. The consequences of failing to address the violations of measurement invariance in mediation models are largely unknown. The purpose of the present study was to systematically examine the impact of mediator noninvariance on the Type I error rates, statistical power, and relative bias in parameter estimates of the mediated effect in the single mediator model. The results of a large simulation study indicated that, in general, the mediated effect was robust to violations of invariance in loadings. In contrast, most conditions with violations of intercept invariance exhibited severely positively biased mediated effects, Type I error rates above acceptable levels, and statistical power larger than in the invariant conditions. The implications of these results are discussed and recommendations are offered.

Using the Graded Response Model to Control Spurious Interactions in Moderated Multiple Regression

2012 ◽  
Vol 36 (2) ◽  
pp. 122-146 ◽  
Author(s):  
Brendan J. Morse ◽  
George A. Johanson ◽  
Rodger W. Griffeth
Keyword(s):  
Latent Variable ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Response Model ◽  
Graded Response Model ◽  
Simulation Research ◽  
Type I Error Rates ◽  
Graded Response ◽  
Inflated Type

Recent simulation research has demonstrated that using simple raw score to operationalize a latent construct can result in inflated Type I error rates for the interaction term of a moderated statistical model when the interaction (or lack thereof) is proposed at the latent variable level. Rescaling the scores using an appropriate item response theory (IRT) model can mitigate this effect under similar conditions. However, this work has thus far been limited to dichotomous data. The purpose of this study was to extend this investigation to multicategory (polytomous) data using the graded response model (GRM). Consistent with previous studies, inflated Type I error rates were observed under some conditions when polytomous number-correct scores were used, and were mitigated when the data were rescaled with the GRM. These results support the proposition that IRT-derived scores are more robust to spurious interaction effects in moderated statistical models than simple raw scores under certain conditions.

scholarly journals Another Warning about Median Reaction Time --- Version of 11 Feb 2020

2020 ◽  
Author(s):  
Jeff Miller
Keyword(s):  
Reaction Time ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Median Reaction Time ◽  
Type I Error Rates ◽  
Powerful Test ◽  
Inflated Type ◽  
Rt Distributions

The present simulations examine the power of bias-corrected medians to detect true experimental effects on reaction time and compare this power with the power of analyses using means and regular medians. Unfortunately, although bias-corrected medians solve the problem of inflated Type I error rates, their power is lower than that of means or regular medians in many realistic situations. The simulations demonstrate that means will often provide the most powerful test for condition differences, and they show what aspects of the RT distributions should be checked to determine whether means or medians will provide greater power.

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