scholarly journals Evaluating the Impact of Multidimensionality on Type I and Type II Error Rates Using the Q-Index Item Fit Statistic for the Rasch Model

2021 ◽  
Author(s):  
Samantha Estrada
Keyword(s):  
Rasch Model ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Fit Indices ◽  
Fit Statistics ◽  
Item Fit ◽  
Q Index ◽  
The Rasch Model

To understand the role of fit statistics in Rasch measurement is simple: applied researchers can only benefit from the desirable properties of the Rasch model when the data fit the model. The purpose of the current study was to assess the Q-Index robustness (Ostini and Nering, 2006), and its performance was compared to the current popular fit statistics known as MSQ Infit, MSQ Outfit, and standardized Infit and Outfit (ZSTDs) under varying conditions of test length, sample size, item difficulty (normal and uniform), and dimensionality utilizing a Monte Carlo simulation. The Type I and Type II error rates are also examined across fit indices. This study provides applied researchers guidelines the robustness and appropriateness of the use of the Q-Index, which is an alternative to the currently available item fit statistics. The Q-Index was slightly more sensitive to the levels of multidimensionality set in the study while MSQ Infit, Outfit, and standardized Infit and Outfit (ZSTDs) failed to identify the multidimensional conditions. The Type I error rate of the Q-Index was lower than the rest of the fit indices; however, the Type II error rate was higher than the anticipated β=.20 across all fit indices.

Type I and type II error rates for quantitative trait loci (QTL) mapping studies using recombinant inbred mouse strains

1996 ◽  
Vol 26 (2) ◽  
pp. 149-160 ◽  
Author(s):  
J. K. Belknap ◽  
S. R. Mitchell ◽  
L. A. O'Toole ◽  
M. L. Helms ◽  
J. C. Crabbe
Keyword(s):  
Quantitative Trait ◽  
Recombinant Inbred ◽  
Inbred Mouse ◽  
Error Rates ◽  
Mouse Strains ◽  
Type I ◽  
Inbred Mouse Strains ◽  
Type Ii ◽  
Type Ii Error ◽  
Recombinant Inbred Mouse

Predictive value of phase II clinical trials in pancreatic cancer: Rethinking the road to progress.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4036-4036 ◽  
Author(s):  
Daniel M. Halperin ◽  
J. Jack Lee ◽  
James C. Yao
Keyword(s):  
Clinical Trials ◽  
Phase Ii ◽  
Predictive Value ◽  
Error Rates ◽  
Phase Iii ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Phase Ii Trials ◽  
Fda Approval

4036 Background: Few new therapies for pancreatic adenocarcinoma (PC) have been approved by the Food and Drug Administration (FDA) or recommended by the National Comprehensive Cancer Network (NCCN), reflecting frequent failures in phase III trials. We hypothesize that the high failure rate in large trials is due to a low predictive value for “positive” phase II studies. Methods: Given a median time from initiation of clinical trials to FDA approval of 6.3 years, we conducted a systematic search of the clinicaltrials.gov database for phase II interventional trials of antineoplastic therapy in PC initiated from 1999-2004. We reviewed drug labels and NCCN guidelines for FDA approval and guideline recommendations. Results: We identified 70 phase II trials that met our inclusion criteria. Forty-five evaluated compounds without preexisting FDA approval, 23 evaluated drugs approved in other diseases, and 2 evaluated cellular therapies. With a median follow-up of 12.5 years, none of these drugs gained FDA approval in PC. Four trials, all combining chemotherapy with radiation, eventually resulted in NCCN recommendations. Forty-two of the trials have been published. Of 16 studies providing pre-specified type I error rates, these rates were ≥0.1 in 8 studies, 0.05 in 6 studies and <0.025 in 2 studies. Of 21 studies specifying type II error rates, 7 used >0.1, 10 used 0.1, and 4 used <0.1. Published studies reported a median enrollment of 47 subjects. Fourteen trials reported utilizing a randomized design. Conclusions: The low rate of phase II trials resulting in eventual regulatory approval of therapies for PC reflects the challenge of conquering a tough disease as well as deficiencies in the statistical designs. New strategies are necessary to quantify and improve odds of success in drug development. Statistical parameters of individual or coupled phase II trials should be tailored to achieve the desired predictive value prior to initiating pivotal phase III studies. Positive predictive value of a phase II study assuming a 1%, 2%, or 5% prior probability of success and 10% type II error rate. [Table: see text]

The High Cost of Complexity in Experimental Design and Data Analysis: Type I and Type II Error Rates in Multiway ANOVA

2002 ◽  
Vol 28 (4) ◽  
pp. 515-530 ◽  
Author(s):  
Rachel A. Smith ◽  
Timothy R. Levine ◽  
Kenneth A. Lachlan ◽  
Thomas A. Fediuk
Keyword(s):  
Data Analysis ◽  
Experimental Design ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error

A New and Simpler Approximation for ANOVA Under Variance Heterogeneity

1994 ◽  
Vol 19 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Ralph A. Alexander ◽  
Diane M. Govern
Keyword(s):  
Type I Error ◽  
Order Approximation ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Tail Probabilities ◽  
Type I Error Rates ◽  
Heterogeneity Of Variance ◽  
The Face

A new approximation is proposed for testing the equality of k independent means in the face of heterogeneity of variance. Monte Carlo simulations show that the new procedure has Type I error rates that are very nearly nominal and Type II error rates that are quite close to those produced by James’s (1951) second-order approximation. In addition, it is computationally the simplest approximation yet to appear, and it is easily applied to Scheffé (1959) -type multiple contrasts and to the calculation of approximate tail probabilities.

Presentation of Type I and Type II Error Rates to Non-statisticians

2004 ◽  
Vol 26 (2) ◽  
pp. 46-48
Author(s):  
Joe Hauptman
Keyword(s):  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error

scholarly journals Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality

2020 ◽  
Author(s):  
Riko Kelter
Keyword(s):  
Null Hypothesis ◽  
Error Control ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Preliminary Assessment ◽  
Type I Error Rates ◽  
Practical Research

Abstract Testing for differences between two groups is among the most frequently carried out statistical methods in empirical research. The traditional frequentist approach is to make use of null hypothesis significance tests which use p values to reject a null hypothesis. Recently, a lot of research has emerged which proposes Bayesian versions of the most common parametric and nonparametric frequentist two-sample tests. These proposals include Student’s two-sample t-test and its nonparametric counterpart, the Mann–Whitney U test. In this paper, the underlying assumptions, models and their implications for practical research of recently proposed Bayesian two-sample tests are explored and contrasted with the frequentist solutions. An extensive simulation study is provided, the results of which demonstrate that the proposed Bayesian tests achieve better type I error control at slightly increased type II error rates. These results are important, because balancing the type I and II errors is a crucial goal in a variety of research, and shifting towards the Bayesian two-sample tests while simultaneously increasing the sample size yields smaller type I error rates. What is more, the results highlight that the differences in type II error rates between frequentist and Bayesian two-sample tests depend on the magnitude of the underlying effect.

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