The Experts below are selected from a list of 459 Experts worldwide ranked by ideXlab platform
Alan S. Rigby - One of the best experts on this subject based on the ideXlab platform.
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Statistical methods in epidemiology. VII. An overview of the χ 2 test for 2×2 contingency table analysis.
Disability and rehabilitation, 2001Co-Authors: Alan S. RigbyAbstract:Purpose: The odds ratio is an appropriate method of analysis for data in 2 2 2 contingency tables. However, other methods of analysis exist. One such method is based on the h 2 test of goodness-of-fit. Key players in the development of statistical theory include Pearson, Fisher and Yates. Method: Data are presented in the form of 2 2 2 contingency tables and a method of analysis based on the h 2 test is introduced. There are many variations of the basic test statistic, one of which is the h 2 test with Yates' Continuity Correction. The usefulness (or not) of Yates' Continuity Correction is discussed. Problems of interpretation when the method is applied to k 2 m tables are highlighted. Results: Some properties of the h 2 the test are illustrated by taking examples from the author's teaching experiences. Conclusion: Journal editors should be encouraged to give both observed and expected cell frequencies so that better information comes out of the h 2 test statistic.
Taehyuk Kwon - One of the best experts on this subject based on the ideXlab platform.
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the eccentric properties of the chi squared test with Yates Continuity Correction in extremely unbalanced 2 2 contingency table
The Korean Journal of Applied Statistics, 2010Co-Authors: Seungho Kang, Taehyuk KwonAbstract:Yates' Continuity Correction of the chi-squared test for testing the homogeneity of two binomial proportions in contingency tables is developed to lower the value of the test statistic slightly. The effect of Continuity Correction is expected to decrease as the sample size increases. However, in extremely unbalanced contingency tables, we find some cases where the effect of Continuity Correction is eccentric and is larger than expected. In such cases, we conclude that the chi-squared test with Continuity Correction should not be employed as a test statistic in both asymptotic tests and exact tests.
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The Eccentric Properties of the Chi-Squared Test with Yates' Continuity Correction in Extremely Unbalanced 2 2
2010Co-Authors: Seungho Kang, Taehyuk KwonAbstract:Abstract Yates’ Continuity Correction of the chi-squared test for testing the homogeneity of two binomial proportionsin 2 2 contingency tables is developed to lower the value of the test statistic slightly. The effect of continu-ity Correction is expected to decrease as the sample size increases. However, in extremely unbalanced 2 2contingency tables, we find some cases where the effect of Continuity Correction is eccentric and is largerthan expected. In such cases, we conclude that the chi-squared test with Continuity Correction should notbe employed as a test statistic in both asymptotic tests and exact tests. Keywords: Exact test, type I error, homogeneity, binomial distribution. 1. Introduction Testing the homogeneity of two binomial proportions is one of the most fundamental problems inmodern statistics and studies of extremely unbalanced 2 × 2 contingency tables are scant. We oftenencounter extremely unbalanced 2 × 2 contingency tables in many application fields when we wouldlike to compare the proportions of interest between a very large population and a small population.The chi-squared test with Yates’ Continuity Correction seems to still be popular and is introduced inmany books (for example, Rosner, 2000; Indrayan, 2008). Therefore, it is important to understandthe characteristics of the chi-squared test with Yates’ Continuity Correction. This paper showsthat the chi-squared test with Yates’ Continuity Correction has unexpected eccentric properties inextremely unbalanced 2
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The Eccentric Properties of the Chi-Squared Test with Yates ' Continuity Correction in Extremely Unbalanced 2 2 Contingency Tables
2010Co-Authors: Seungho Kang, Taehyuk KwonAbstract:Yates ’ Continuity Correction of the chi-squared test for testing the homogeneity of two binomial proportions in 22 contingency tables is developed to lower the value of the test statistic slightly. The effect of continu-ity Correction is expected to decrease as the sample size increases. However, in extremely unbalanced 2 2 contingency tables, we find some cases where the effect of Continuity Correction is eccentric and is larger than expected. In such cases, we conclude that the chi-squared test with Continuity Correction should not be employed as a test statistic in both asymptotic tests and exact tests
Seungho Kang - One of the best experts on this subject based on the ideXlab platform.
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the eccentric properties of the chi squared test with Yates Continuity Correction in extremely unbalanced 2 2 contingency table
The Korean Journal of Applied Statistics, 2010Co-Authors: Seungho Kang, Taehyuk KwonAbstract:Yates' Continuity Correction of the chi-squared test for testing the homogeneity of two binomial proportions in contingency tables is developed to lower the value of the test statistic slightly. The effect of Continuity Correction is expected to decrease as the sample size increases. However, in extremely unbalanced contingency tables, we find some cases where the effect of Continuity Correction is eccentric and is larger than expected. In such cases, we conclude that the chi-squared test with Continuity Correction should not be employed as a test statistic in both asymptotic tests and exact tests.
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The Eccentric Properties of the Chi-Squared Test with Yates' Continuity Correction in Extremely Unbalanced 2 2
2010Co-Authors: Seungho Kang, Taehyuk KwonAbstract:Abstract Yates’ Continuity Correction of the chi-squared test for testing the homogeneity of two binomial proportionsin 2 2 contingency tables is developed to lower the value of the test statistic slightly. The effect of continu-ity Correction is expected to decrease as the sample size increases. However, in extremely unbalanced 2 2contingency tables, we find some cases where the effect of Continuity Correction is eccentric and is largerthan expected. In such cases, we conclude that the chi-squared test with Continuity Correction should notbe employed as a test statistic in both asymptotic tests and exact tests. Keywords: Exact test, type I error, homogeneity, binomial distribution. 1. Introduction Testing the homogeneity of two binomial proportions is one of the most fundamental problems inmodern statistics and studies of extremely unbalanced 2 × 2 contingency tables are scant. We oftenencounter extremely unbalanced 2 × 2 contingency tables in many application fields when we wouldlike to compare the proportions of interest between a very large population and a small population.The chi-squared test with Yates’ Continuity Correction seems to still be popular and is introduced inmany books (for example, Rosner, 2000; Indrayan, 2008). Therefore, it is important to understandthe characteristics of the chi-squared test with Yates’ Continuity Correction. This paper showsthat the chi-squared test with Yates’ Continuity Correction has unexpected eccentric properties inextremely unbalanced 2
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The Eccentric Properties of the Chi-Squared Test with Yates ' Continuity Correction in Extremely Unbalanced 2 2 Contingency Tables
2010Co-Authors: Seungho Kang, Taehyuk KwonAbstract:Yates ’ Continuity Correction of the chi-squared test for testing the homogeneity of two binomial proportions in 22 contingency tables is developed to lower the value of the test statistic slightly. The effect of continu-ity Correction is expected to decrease as the sample size increases. However, in extremely unbalanced 2 2 contingency tables, we find some cases where the effect of Continuity Correction is eccentric and is larger than expected. In such cases, we conclude that the chi-squared test with Continuity Correction should not be employed as a test statistic in both asymptotic tests and exact tests
Jill J Francis - One of the best experts on this subject based on the ideXlab platform.
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Changing behaviour, 'more or less': do implementation and de-implementation interventions include different behaviour change techniques?
Implementation science : IS, 2021Co-Authors: Andrea M Patey, Jeremy M. Grimshaw, Jill J FrancisAbstract:BACKGROUND Decreasing ineffective or harmful healthcare practices (de-implementation) may require different approaches than those used to promote uptake of effective practices (implementation). Few psychological theories differentiate between processes involved in decreasing, versus increasing, behaviour. However, it is unknown whether implementation and de-implementation interventions already use different approaches. We used the behaviour change technique (BCT) taxonomy (version 1) (which includes 93 BCTs organised into 12 groupings) to investigate whether implementation and de-implementation interventions for clinician behaviour change use different BCTs. METHODS Intervention descriptions in 181 articles from three systematic reviews in the Cochrane Library were coded for (a) implementation versus de-implementation and (b) intervention content (BCTs) using the BCT taxonomy (v1). BCT frequencies were calculated and compared using Pearson's chi-squared (χ2), Yates' Continuity Correction and Fisher's exact test, where appropriate. Identified BCTs were ranked according to frequency and rankings for de-implementation versus implementation interventions were compared and described. RESULTS Twenty-nine and 25 BCTs were identified in implementation and de-implementation interventions respectively. Feedback on behaviour was identified more frequently in implementation than de-implementation (Χ2(2, n=178) = 15.693, p = .000057). Three BCTs were identified more frequently in de-implementation than implementation: Behaviour substitution (Χ2(2, n=178) = 14.561, p = .0001; Yates' Continuity Correction); Monitoring of behaviour by others without feedback (Χ2(2, n=178) = 16.187, p = .000057; Yates' Continuity Correction); and Restructuring social environment (p = .000273; Fisher's 2-sided exact test). CONCLUSIONS There were some significant differences between BCTs reported in implementation and de-implementation interventions suggesting that researchers may have implicit theories about different BCTs required for de-implementation and implementation. These findings do not imply that the BCTs identified as targeting implementation or de-implementation are effective, rather simply that they were more frequently used. These findings require replication for a wider range of clinical behaviours. The continued accumulation of additional knowledge and evidence into whether implementation and de-implementation is different will serve to better inform researchers and, subsequently, improve methods for intervention design.
Andrea M Patey - One of the best experts on this subject based on the ideXlab platform.
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Changing behaviour, 'more or less': do implementation and de-implementation interventions include different behaviour change techniques?
Implementation science : IS, 2021Co-Authors: Andrea M Patey, Jeremy M. Grimshaw, Jill J FrancisAbstract:BACKGROUND Decreasing ineffective or harmful healthcare practices (de-implementation) may require different approaches than those used to promote uptake of effective practices (implementation). Few psychological theories differentiate between processes involved in decreasing, versus increasing, behaviour. However, it is unknown whether implementation and de-implementation interventions already use different approaches. We used the behaviour change technique (BCT) taxonomy (version 1) (which includes 93 BCTs organised into 12 groupings) to investigate whether implementation and de-implementation interventions for clinician behaviour change use different BCTs. METHODS Intervention descriptions in 181 articles from three systematic reviews in the Cochrane Library were coded for (a) implementation versus de-implementation and (b) intervention content (BCTs) using the BCT taxonomy (v1). BCT frequencies were calculated and compared using Pearson's chi-squared (χ2), Yates' Continuity Correction and Fisher's exact test, where appropriate. Identified BCTs were ranked according to frequency and rankings for de-implementation versus implementation interventions were compared and described. RESULTS Twenty-nine and 25 BCTs were identified in implementation and de-implementation interventions respectively. Feedback on behaviour was identified more frequently in implementation than de-implementation (Χ2(2, n=178) = 15.693, p = .000057). Three BCTs were identified more frequently in de-implementation than implementation: Behaviour substitution (Χ2(2, n=178) = 14.561, p = .0001; Yates' Continuity Correction); Monitoring of behaviour by others without feedback (Χ2(2, n=178) = 16.187, p = .000057; Yates' Continuity Correction); and Restructuring social environment (p = .000273; Fisher's 2-sided exact test). CONCLUSIONS There were some significant differences between BCTs reported in implementation and de-implementation interventions suggesting that researchers may have implicit theories about different BCTs required for de-implementation and implementation. These findings do not imply that the BCTs identified as targeting implementation or de-implementation are effective, rather simply that they were more frequently used. These findings require replication for a wider range of clinical behaviours. The continued accumulation of additional knowledge and evidence into whether implementation and de-implementation is different will serve to better inform researchers and, subsequently, improve methods for intervention design.
