Simultaneous Multiple Comparison Procedures for Categorical Data

1986 ◽  
Vol 20 (3) ◽  
pp. 350-359 ◽  
Author(s):  
Wayne Hall ◽  
Kevin D. Bird
Keyword(s):  
Analysis Of Variance ◽  
Categorical Data ◽  
Multiple Comparisons ◽  
Multiple Comparison ◽  
Multiple Comparison Procedures

Methods are outlined for performing simultaneous multiple comparisons between groups when the dependent variable is one in which subjects are assigned to one of two or more categories. These methods provide tests which are analogous to Scheffe- and Bonferroni-adjusted tests of contrasts in the analysis of variance. Examples are provided of each of these procedures.

scholarly journals Multiple Comparison Procedures for the Differences of Proportion Parameters in Over-Reported Multiple-Sample Binomial Data

Stats ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 56-67
Author(s):  
Dewi Rahardja
Keyword(s):  
Real Data ◽  
Multiple Comparisons ◽  
Multiple Comparison ◽  
Omnibus Test ◽  
Binomial Data ◽  
Sequential Tests ◽  
Multiple Sample ◽  
Multiple Comparison Procedure ◽  
Multiple Comparison Procedures ◽  
Equality Test

In sequential tests, typically a (pairwise) multiple comparison procedure (MCP) is performed after an omnibus test (an overall equality test). In general, when an omnibus test (e.g., overall equality of multiple proportions test) is rejected, then we further conduct a (pairwise) multiple comparisons or MCPs to determine which (e.g., proportions) pairs the significant differences came from. In this article, via likelihood-based approaches, we acquire three confidence intervals (CIs) for comparing each pairwise proportion difference in the presence of over-reported binomial data. Our closed-form algorithm is easy to implement. As a result, for multiple-sample proportions differences, we can easily apply MCP adjustment methods (e.g., Bonferroni, Šidák, and Dunn) to address the multiplicity issue, unlike previous literatures. We illustrate our procedures to a real data example.

Comparing treatment means correctly and appropriately

1985 ◽  
Vol 15 (6) ◽  
pp. 1142-1148 ◽  
Author(s):  
Carl W. Mize ◽  
Richard C. Schultz
Keyword(s):  
Analysis Of Variance ◽  
Multiple Comparison ◽  
Response Models ◽  
Major Treatment ◽  
The Mean ◽  
The Difference ◽  
Set Up ◽  
Multiple Comparison Procedures ◽  
Range Test ◽  
Selection Of

Many researchers set up an experiment, make measurements, do an analysis of variance, calculate the mean response for each treatment, and then try to decide if the treatment means are significantly different and why. Duncan's multiple-range test is frequently used to test the difference among treatment means. It is, however, only one of a number of techniques that can be used to examine treatment means. Some researchers are unaware of the different techniques and that the interpretation of the results of an experiment can be strongly influenced by the technique used. In fact, using an inappropriate technique can lead to making incorrect recommendations and to completely missing major treatment effects. Selection of the appropriate technique to use for a particular experiment depends upon the nature of the treatments and the objectives of the research. This paper discusses four techniques (ranking treatment means, multiple comparison procedures, fitting response models, and using contrasts to make planned comparisons) that can be used to examine treatment means and presents examples of each one.

scholarly journals Analyzing one-way experiments: a piece of cake of a pain in the neck?

2009 ◽  
Vol 66 (4) ◽  
pp. 556-562 ◽  
Author(s):  
Marcin Kozak
Keyword(s):  
Data Analysis ◽  
Statistical Method ◽  
Analysis Of Variance ◽  
Classical Method ◽  
Environmental Studies ◽  
Multiple Comparison ◽  
Careful Attention ◽  
Factorial Experiments ◽  
Correct Method ◽  
Multiple Comparison Procedures

Statistics may be intricate. In practical data analysis many researchers stick to the most common methods, not even trying to find out whether these methods are appropriate for their data and whether other methods might be more useful. In this paper I attempt to show that when analyzing even simple one-way factorial experiments, a lot of issues need to be considered. A classical method to analyze such data is the analysis of variance, quite likely the most often used statistical method in agricultural, biological, ecological and environmental studies. I suspect this is why this method is quite often applied inappropriately: since the method is that common, it does not require too much consideration-this is how some may think. An incorrect analysis may provide false interpretation and conclusions, so one should pay careful attention to which approach to use in the analysis. I do not mean that one should apply difficult or complex statistics; I rather mean that one should apply a correct method that offers what one needs. So, various problems concerned with the analysis of variance and other approaches to analyze such data are discussed in the paper, including checking within-group normality and homocedasticity, analyzing experiments when any of these assumptions is violated, outliers presence, multiple comparison procedures, and other issues.

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