A Software Package for Contingency Table Construction and Analysis (original) (raw)
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Microsoft Excel add-in for the statistical analysis of contingency tables
International journal for innovation education and research, 2014
This paper introduces “Contingency table analysis”, a freely available menu-driven add-in program for Microsoft EXCEL, written in Visual Basic for Applications (VBA), for basic univariate and bivariate statistical analyses of contingency tables. The program provides modules for the statistical analysis of proportions, 2 × 2 tables, stratified 2 × 2 tables, and R × C tables. We compare the results of the analyses performed using our software with those obtained by commercially available statistical software. The comparison shows that our software performs equally well. The use of the add-in facilitates the convenient prosecution of basic statistical analyses on contingency tables from within EXCEL, sparing us the additional cost, or the inconvenience of alternating between multiple platforms, often incurred in using a commercial statistical package.
A Unified Approach for the Multivariate Analysis of Contingency Tables
Open Journal of Statistics, 2015
We present a unified approach to describing and linking several methods for representing categorical data in a contingency table. These methods include: correspondence analysis, Hellinger distance analysis, the log-ratio alternative, which is appropriate for compositional data, and the non-symmetrical correspondence analysis. We also present two solutions working with cummulative frequencies.
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We propose and illustrate a new graphical method to perform diagnostic analyses in two-way contingency tables. In this method, one observation is added or removed from each cell at a time, whilst the other cells are held constant, and the change in a test statistic of interest is graphically represented. The method provides a very simple way of determining how robust our model is (and hence our conclusions) to small changes introduced to the data. We illustrate via four examples, three of them from real-world applications, how this method works.
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We consider the problem of testing for independence against the consistent superiority of one treatment over another when the response variable is binary and is compared across two treatments in each of several strata. Specifically, we consider the randomized clinical trial setting. A number of issues arise in this context. First, should tables be combined if there are small or zero margins? Second, should one assume a common odds ratio across strata? Third, if the odds ratios differ across strata, then how does the standard test (based on a common odds ratio) perform? Fourth, are there other analyzes that are more appropriate for handling a situation in which the odds ratios may differ across strata? In addressing these issues we find that the frequently used Cochran-Mantel-Haenszel test may have a poor power profile, despite being optimal when the odds ratios are common. We develop novel tests that are analogous to the Smirnov, modified Smirnov, convex hull, and adaptive tests that have been proposed for ordered categorical data.
Advances in Methodology and Statistics, 2011
Spearman and Pearson correlation coefficient, Gamma coefficient, Kendall's tau-b, Kendall's tau-c, and Somers' d are the most commonly used measures of association for doubly ordered contingency tables. So far there has been no study expressing a priority on those measures of association. The aim of this study is to compare those measures of association for several types and different sample sizes of generated squared doubly ordered contingency tables and determine which measures of association are more efficient. It is found that both the sample sizes and the dimension of the doubly ordered contingency tables play a significant role on the effect of those measures of association.
A deterministic approach to contingency tables
2019
A tool has been developed to evaluate correlation between variables in 2x2 contingency tables of categorical data. The work is based on elementary Set Theory and does not make use of probabilistic and random variable concepts. This evaluator distinguishes between a negative and a positive correlation and may be an useful complement to chi-square test.
The LMS for testing independence in two-way contingency tables
Biometrical Letters
Summary In the statistical literature there are proposed many test measures to determine the independence of two qualitative variables in contingency tables, in particular in two-way contingency tables larger than 2×2. For statistical analysis, three of the so-called “chi-squared tests”—the T3 test, BP test and |χ| test—were selected. These tests were compared with a logarithmic minimum test, which is the author’s proposal. Critical values for the tests were determined with the Monte Carlo method. To compare the tests, an appropriate measure of untruthfulness of H0 was used and the power of the tests was calculated.
Journal of Physics: Conference Series
In this paper, we confined our attention to compare two methods to obtain a graphical depiction of the association (dependency) between three categorical variables. We shall first describe how to recode a three-way contingency table by discussing the Burt matrix form of the data. This method is known as multiple correspondence analysis (MCA). Another method is to preserve a three-way contingency table form using Tucker3, it's known as a three-way correspondence analysis (CA3). As a case study, we pay attention to analyze the association between race and gender in occupation field that may have contributes to differences in employment opportunity and the continuing increases in women's educational attainment. The results show that CA3 is more simple in computation and provide the graphical depiction of three-way association simultaneously, while MCA's plot can't. Consider to the cumulative inertia on the two-dimensional plot, the percentage inertia of CA3's plot is better than MCA's plot.
An Exploratory Graphical Method for Identifying Associations in r x c Contingency Tables
Journal of Modern Applied Statistical Methods, 2014
On finding a significant association between rows and columns of an r x c contingency table, the next step is to study the nature of the association in more detail. The use of a scree plot to visualize the largest contributions to Χ 2 among all cells in the table in order to determine the nature of the association in more detail is proposed.
Visualising contingency table data
A geometric object, a simplex, is useful for picturing the joint, conditional and marginal distributions within a contingency table. The joint distribution is rep- resented using weights on all vertices of the simplex, a conditional distribution by weights on vertices of a face of the simplex, and a marginal distribution by weights on the faces containing the conditional distributions. All detailed discussion is based on the simplest case, that of a two-by-two contingency table, for which all distributions are seen in a tetrahedron.