Articles with "type error" as a keyword



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ComPaSS‐GWAS: A method to reduce type I error in genome‐wide association studies when replication data are not available

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Published in 2019 at "Genetic Epidemiology"

DOI: 10.1002/gepi.22168

Abstract: Results from association studies are traditionally corroborated by replicating the findings in an independent data set. Although replication studies may be comparable for the main trait or phenotype of interest, it is unlikely that secondary… read more here.

Keywords: type error; association; gwas; replication ... See more keywords
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Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions.

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Published in 2020 at "Statistics in medicine"

DOI: 10.1002/sim.8505

Abstract: The statistical practice of modeling interaction with two linear main effects and a product term is ubiquitous in the statistical and epidemiological literature. Most data modelers are aware that the misspecification of main effects can… read more here.

Keywords: type error; interaction; misspecification main; main effect ... See more keywords
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Type I Error Rates and Parameter Bias in Multivariate Behavioral Genetic Models

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Published in 2019 at "Behavior Genetics"

DOI: 10.1007/s10519-018-9942-y

Abstract: For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more… read more here.

Keywords: error; type error; error rates; parameter bias ... See more keywords
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Type I Error Probability Spending for Post-Market Drug and Vaccine Safety Surveillance With Poisson Data

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Published in 2018 at "Methodology and Computing in Applied Probability"

DOI: 10.1007/s11009-017-9586-z

Abstract: Statistical sequential hypothesis testing is meant to analyze cumulative data accruing in time. The methods can be divided in two types, group and continuous sequential approaches, and a question that arises is if one approach… read more here.

Keywords: type; type error; error probability; poisson ... See more keywords
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Systematic identification and communication of type errors*

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Published in 2018 at "Journal of Functional Programming"

DOI: 10.1017/s095679681700020x

Abstract: Abstract When type inference fails, it is often difficult to pinpoint the cause of the type error among many potential candidates. Generating informative messages to remove the type error is another difficult task due to… read more here.

Keywords: cause; systematic identification; error; type error ... See more keywords
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Permutation Randomization Methods for Testing Measurement Equivalence and Detecting Differential Item Functioning in Multiple-Group Confirmatory Factor Analysis

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Published in 2018 at "Psychological Methods"

DOI: 10.1037/met0000152

Abstract: Abstract In multigroup factor analysis, different levels of measurement invariance are accepted as tenable when researchers observe a nonsignificant (&Dgr;)&khgr;2 test after imposing certain equality constraints across groups. Large samples yield high power to detect… read more here.

Keywords: type error; factor analysis; invariance; permutation ... See more keywords
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Reconsideration of the type I error rate for psychological science in the era of replication.

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Published in 2022 at "Psychological methods"

DOI: 10.1037/met0000490

Abstract: Careful consideration of the tradeoff between Type I and Type II error rates when designing experiments is critical for maximizing statistical decision accuracy. Typically, Type I error rates (e.g., .05) are significantly lower than Type… read more here.

Keywords: error; error rate; replication; type error ... See more keywords
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Comparison of Type I error rates and statistical power of different propensity score methods

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Published in 2018 at "Journal of Statistical Computation and Simulation"

DOI: 10.1080/00949655.2017.1406937

Abstract: ABSTRACT Propensity score analysis (PSA) is a technique to correct for potential confounding in observational studies. Covariate adjustment, matching, stratification, and inverse weighting are the four most commonly used methods involving propensity scores. The main… read more here.

Keywords: type error; psa; propensity; power ... See more keywords
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A comparison of the type I error rates of three assessment methods for indirect effects

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Published in 2019 at "Journal of Statistical Computation and Simulation"

DOI: 10.1080/00949655.2019.1577858

Abstract: ABSTRACT Mediation analysis is a popular statistical analysis verifying the relation between an independent variable and a dependent variable through a mediator. There are three traditional tests to assess indirect effects: the Baron and Kenny… read more here.

Keywords: type error; comparison type; error rates; effect ... See more keywords
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Impact of unbalanced DIF item proportions on group-specific DIF identification

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Published in 2018 at "Communications in Statistics - Theory and Methods"

DOI: 10.1080/03610926.2018.1481968

Abstract: Abstract Differences in type I error and power rates for majority and minority groups are investigated when differential item functioning (DIF) contamination in a test is unbalanced. Typically, type I error and power rates are… read more here.

Keywords: type error; unbalanced dif; dif contamination; error power ... See more keywords
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Applying a Multiple Comparison Control to IRT Item-fit Testing

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Published in 2020 at "Applied Measurement in Education"

DOI: 10.1080/08957347.2020.1789138

Abstract: ABSTRACT We used simulation techniques to assess the item-level and familywise Type I error control and power of an IRT item-fit statistic, the S-X2 . Previous research indicated that the S-X2 has good Type I… read more here.

Keywords: familywise type; type error; control; item fit ... See more keywords