Custom Contrast Testing: Current Trends and a New Approach

2018 ◽  
Vol 93 (5) ◽  
pp. 223-244 ◽  
Author(s):  
Ryan D. Guggenmos ◽  
M. David Piercey ◽  
Christopher P. Agoglia
Keyword(s):  
Type I Error ◽  
Type I ◽  
Visual Evaluation ◽  
New Approach ◽  
Accounting Research ◽  
Elevated Type ◽  
Current Trends ◽  
Comprehensive Picture ◽  
Contrast Analysis ◽  
First Time

ABSTRACT Contrast analysis has become prevalent in experimental accounting research since Buckless and Ravenscroft (1990) introduced it to the accounting literature over 25 years ago. Since its initial introduction, the scope of contrast testing has expanded, yet guidance as to the most appropriate methods of specifying, conducting, interpreting, and exhibiting these tests has not. We survey the use of contrast analysis in the recent literature and propose a three-part testing approach that provides a more comprehensive picture of contrast results. Our approach considers three pieces of complementary evidence: the visual evaluation of fit, traditional significance testing, and quantitative evaluation of the contrast variance residual. Our measure of the contrast variance residual, q2, is proposed for the first time in this work. After proposing our approach, we walk through six common contrast testing scenarios where current practices may fall short and our approach may guide researchers. We extend Buckless and Ravenscroft (1990) and contribute to the accounting research methods literature by documenting current contrast analysis practices that result in elevated Type I error and by proposing a potential solution to mitigate these concerns.

scholarly journals A test of inflated zeros for Poisson regression models

2017 ◽  
Vol 28 (4) ◽  
pp. 1157-1169 ◽  
Author(s):  
Hua He ◽  
Hui Zhang ◽  
Peng Ye ◽  
Wan Tang
Keyword(s):  
Poisson Regression ◽  
Regression Models ◽  
Type I Error ◽  
Large Body ◽  
Poisson Model ◽  
Type I ◽  
New Approach ◽  
Poisson Models ◽  
Zero Inflated Poisson Models ◽  
Vuong Test

Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.

scholarly journals Case Report: Aicardi-Goutières Syndrome Caused by Novel TREX1 Variants

2021 ◽  
Vol 9 ◽  
Author(s):  
De Wu ◽  
Liwei Fang ◽  
Ting Huang ◽  
Songcheng Ying
Keyword(s):  
White Matter ◽  
Type I Interferons ◽  
Enzyme Linked Immunosorbent Assay ◽  
Type I ◽  
White Matter Volume ◽  
Elevated Type ◽  
Chinese Girl ◽  
The Face ◽  
Exonuclease 1 ◽  
First Time

TREX1 (three prime repair exonuclease 1) gene encodes DNA 3′ end repair exonuclease that plays an important role in DNA repair. Mutations in TREX1 gene have been identified as the cause of a rare autoimmune neurological disease, Aicardi-Goutières syndrome (AGS). Here, we report an AGS case of a 6-month-old Chinese girl with novel TREX1 variants. The patient had mild rashes on the face and legs, increased muscle tensions in the limbs, and positive cervical correction reflex. Cranial magnetic resonance imaging showed that there were patches of slightly longer T1 and T2 signals in the bilateral cerebral hemisphere and brainstem white matter, mainly in the frontotemporal lobe, together with decreased white matter volume, enlarged ventricles, and widened sulcus fissure. Total exon sequencing showed that the TREX1 gene of the child had mutations of c.137_138insC and c.292_293insA, which had not been reported before. In addition, elevated type I interferons were detected by using enzyme-linked immunosorbent assay in the patient's serum. Together, our study demonstrated that novel TREX1 variants (c.137_138insC and c.292_293insA) cause AGS for the first time.

scholarly journals A New Approach to Testing Mediation of the Microbiome using the LDM

2021 ◽  
Author(s):  
Ye Yue ◽  
Yi-Juan Hu
Keyword(s):  
Relative Abundance ◽  
Type I Error ◽  
R Package ◽  
New Method ◽  
Type I ◽  
Inverse Regression ◽  
Global Test ◽  
New Approach ◽  
Mediation Analyses ◽  
Mediating Role

Background: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null (no exposure-microbe association, no microbe-outcome association given the exposure, or neither), most existing methods for the global test such as MedTest and MODIMA treat the microbes as if they are all under the same type of null. Methods: We propose a new approach based on inverse regression that regresses the (possibly transformed) relative abundance of each taxon on the exposure and the exposure-adjusted outcome to assess the exposure-taxon and taxon-outcome associations simultaneously. Then the association p-values are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method is implemented in the LDM and enjoys all the features of the LDM, i.e., allowing an arbitrary number of taxa to be tested, supporting continuous, discrete, or multivariate exposures and outcomes as well as adjustment of confounding covariates, accommodating clustered data, and offering analysis at the relative abundance or presence-absence scale. We refer to this new method as LDM-med. Results: Using extensive simulations, we showed that LDM-med always controlled the type I error of the global test and had compelling power over existing methods; LDM-med always preserved the FDR of testing individual taxa and had much better sensitivity than alternative approaches. In contrast, MedTest and MODIMA had severely inflated type I error when different taxa were under different types of null. The flexibility of LDM-med for a variety of mediation analyses is illustrated by the application to a murine microbiome dataset. Availability and Implementation: Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

An Analysis of Statistical Power in Behavioral Accounting Research

2001 ◽  
Vol 13 (1) ◽  
pp. 63-84 ◽  
Author(s):  
Susan C. Borkowski ◽  
Mary Jeanne Welsh ◽  
Qinke (Michael) Zhang
Keyword(s):  
Statistical Power ◽  
Type I Error ◽  
Significance Testing ◽  
Type I ◽  
Type Ii ◽  
Behavioral Accounting ◽  
Significance Tests ◽  
Accounting Research ◽  
Sufficient Power ◽  
Type Ii Errors

Attention to statistical power and effect size can improve the design and the reporting of behavioral accounting research. Three accounting journals representative of current empirical behavioral accounting research are analyzed for their power (1−β), or control of Type II errors (β), and compared to research in other disciplines. Given this study's findings, additional attention should be directed to adequacy of sample sizes and study design to ensure sufficient power when Type I error is controlled at α = .05 as a baseline. We do not suggest replacing traditional significance testing, but rather augmenting it with the reporting of β to complement and interpret the relevance of a reported α in any given study. In addition, the presentation of results in alternative formats, such as those suggested in this study, will enhance the current reporting of significance tests. In turn, this will allow the reader a richer understanding of, and an increased trust in, a study's results and implications.

Improved Type I Error Control and Reduced Estimation Bias for DIF Detection Using SIBTEST

1998 ◽  
Vol 23 (4) ◽  
pp. 291-322 ◽  
Author(s):  
Hai Jiang ◽  
William Stout
Keyword(s):  
Linear Regression ◽  
Error Control ◽  
Type I Error ◽  
Piecewise Linear ◽  
Classical Test Theory ◽  
Test Theory ◽  
Estimation Bias ◽  
Type I ◽  
Error Performance ◽  
New Approach

One emphasis in the development and evaluation of SIBTEST has been the control of Type I error (false flagging of non-differential item functioning [DIF] items) inflation and estimation bias. SIBTEST has performed well in comparative simulation studies of Type I error and estimation bias relative to other procedures such as the Mantel-Haenszel and Logistic Regression. Nevertheless it has for a minority of cases that might occur in applications displayed sizable Type I error inflation and estimation bias. A vital part of SIBTEST is the regression correction, which adjusts for the Type I error-inflating and estimation-biasing influence of group target ability differences by using the linear regression of true on observed matching subtest scores from Classical Test Theory. In this paper, we propose a new regression correction, using essentially a two-segment piecewise linear regression of the true on observed matching subtest scores. A realistic simulation study of the new approach shows that when there is a clear group ability distributional difference, the new approach displays improved SIBTEST Type I error performance; when there is no group ability distributional difference, its Type I error rate is comparable to the current SIBTEST. We have also conducted a power study which indicates that the new approach has on average similar power as the current SIBTEST. We concluded that the new version of SIBTEST, although not perfectly robust, seems appropriately robust against sizable Type I error inflation, while retaining other desirable features of the current version.

scholarly journals Permutation tests for hypothesis testing with animal social data: problems and potential solutions

2020 ◽  
Author(s):  
Damien R. Farine ◽  
Gerald G. Carter
Keyword(s):  
Type I Error ◽  
Permutation Test ◽  
Permutation Tests ◽  
Error Rates ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Type I Error Rates ◽  
Social Network Data ◽  
Elevated Type

ABSTRACTGenerating insights about a null hypothesis requires not only a good dataset, but also statistical tests that are reliable and actually address the null hypothesis of interest. Recent studies have found that permutation tests, which are widely used to test hypotheses when working with animal social network data, can suffer from high rates of type I error (false positives) and type II error (false negatives).Here, we first outline why pre-network and node permutation tests have elevated type I and II error rates. We then propose a new procedure, the double permutation test, that addresses some of the limitations of existing approaches by combining pre-network and node permutations.We conduct a range of simulations, allowing us to estimate error rates under different scenarios, including errors caused by confounding effects of social or non-social structure in the raw data.We show that double permutation tests avoid elevated type I errors, while remaining sufficiently sensitive to avoid elevated type II errors. By contrast, the existing solutions we tested, including node permutations, pre-network permutations, and regression models with control variables, all exhibit elevated errors under at least one set of simulated conditions. Type I error rates from double permutation remain close to 5% in the same scenarios where type I error rates from pre-network permutation tests exceed 30%.The double permutation test provides a potential solution to issues arising from elevated type I and type II error rates when testing hypotheses with social network data. We also discuss other approaches, including restricted node permutations, testing multiple null hypotheses, and splitting large datasets to generate replicated networks, that can strengthen our ability to make robust inferences. Finally, we highlight ways that uncertainty can be explicitly considered during the analysis using permutation-based or Bayesian methods.

There's No Place Like Home: The Influence of Home-State Going-Concern Reporting Rates on Going-Concern Opinion Propensity and Accuracy

2015 ◽  
Vol 35 (2) ◽  
pp. 23-51 ◽  
Author(s):  
Allen D. Blay ◽  
James R. Moon ◽  
Jeffrey S. Paterson
Keyword(s):  
Audit Quality ◽  
Type I Error ◽  
State Level ◽  
Error Rates ◽  
Data Availability ◽  
Type I ◽  
Going Concern ◽  
Going Concern Opinion ◽  
Financial Characteristics ◽  
First Time

SUMMARY Prior research has had success identifying client financial characteristics that influence auditors' going-concern reporting decisions. In contrast, relatively little research has addressed whether auditors' circumstances and surroundings influence their propensities to issue modified opinions. We investigate whether auditors' decisions to issue GC opinions are affected by the rate of GC opinions being given in their proximate area. Controlling for factors that prior research associates with going-concern opinions and state-level economics, we find that non-Big 4 auditors located in states with relatively high first-time going-concern rates in the prior year are up to 6 percent more likely to issue first-time going-concern opinions. The results from our state-based GC measure casts doubt that this increased propensity is explained by economic factors and suggests that psychological factors may explain this behavior among auditors. Interestingly, this higher propensity increases auditors' Type I error rates without decreasing their Type II error rates, further suggesting economics alone do not explain these results. Such evidence challenges the generally accepted notion that a higher propensity to issue a going-concern opinion always reflects higher audit quality. JEL Classifications: M41; M42. Data Availability: All data are available from public sources.

scholarly journals COEFFICIENT OF VARIATION: A NEW APPROACH FOR THE STUDY IN MAIZE EXPERIMENTS

2020 ◽  
Vol 38 (2) ◽  
pp. 185
Author(s):  
Maicon NARDINO ◽  
Juliana Machado PEREIRA ◽  
Vinícius Torres MARQUES ◽  
Fabiano Costa D'AVILA ◽  
Francisco​ Dias FRANCO ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Treatment Effect ◽  
Coefficient Of Variation ◽  
Type I Error ◽  
Type I ◽  
New Approach ◽  
Power Of The Test ◽  
High Treatment ◽  
Treatment Number

The magnitude of the variation coefficient (CV) is insufficient to validate the quality of the experiment, regardless of the number of treatments, repetitions and effect of treatments. The objective was to develop a new approach to the study of coefficient of variation, as well as evaluations of these nuances with applicability in new scientific research. The study was conducted via computer simulation. The replicates (r) ranged from 2, 3, 4, 5, 10 to 20. The treatment number (t) ranged from t 5, 10, 15, 20, 25 and 30. In each of these combined scenarios we have the variation of 25 different CVs, ranging from 1, 3, 5, 7, ..., 49 to 51 %. It was imposed the variation of 11-1 treatment effects 0, 240, 480, 720, ..., 2000, 2400 kg ha-1, totaling 9,900.00 scenarios. The type I error is statistically invariant in the scenarios studied. With high treatment effect the CV has no implications on the power of the test (1-β). The results obtained in this research reveal that experiments with a high percentage of CV are sufficient to obtain high probabilities of the power of the F test, which do not compromise the complementary analyzes.

Haplotype-based analysis of selective sweeps in sheep

Genome ◽  
2014 ◽  
Vol 57 (8) ◽  
pp. 433-437 ◽  
Author(s):  
James W. Kijas
Keyword(s):  
Type I Error ◽  
Domestic Animal ◽  
Population History ◽  
Phenotypic Traits ◽  
Type I ◽  
Selective Sweeps ◽  
Elevated Type ◽  
Snp Data ◽  
Simulation Based ◽  
Genomic Regions

Domestic animals represent an extremely useful model for linking genotypic and phenotypic variation. One approach involves identifying allele frequency differences between populations, using FST, to detect selective sweeps. While simple to calculate, FST may generate false positives due to aspects of population history. This prompted the development of hapFLK, a metric that measures haplotype differentiation while accounting for the genetic relationship between populations. The focus of this paper was to apply hapFLK in sheep with available SNP50 genotypes. The hapFLK approach identified a known selective sweep on chromosome 10 with high precision. Further, five regions were identified centered on genes with strong evidence for positive selection (COL1A2, NCAPG, LCORL, and RXFP2). Estimation of global FST revealed many more genomic regions, providing empirical data in support of published simulation-based results concerning elevated type I error associated with FST when it is being used to characterize sweep regions. The findings, while conducted using sheep SNP data, are likely to be applicable across those domestic animal species that have undergone artificial selection for desirable phenotypic traits.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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