Categorical Data: Contingency Tables

2004 ◽  
pp. 208-252 ◽  
Keyword(s):  
Categorical Data ◽  
Contingency Tables

scholarly journals A Profile of the Organic Produce Consumer

2018 ◽  
Vol 66 (4) ◽  
pp. 1043-1052
Author(s):  
Martina Zámková ◽  
Martin Prokop ◽  
Radek Stolín
Keyword(s):  
Data Analysis ◽  
Food Analysis ◽  
Categorical Data ◽  
Dairy Products ◽  
Fruits And Vegetables ◽  
Contingency Tables ◽  
Organic Food ◽  
Factors Influencing ◽  
Organic Produce ◽  
Milk And Dairy Products

Our paper explores the factors influencing the consumers who buy organic food. Analysis of these factors enabled us to sort the consumers into groups based on their gender, age, education, and other identifiers. Further research then revealed more detailed shopping preferences of each one of those groups. The findings generated recommendations for producers and organic produce vendors on the best way to provide target marketing for different groups of consumers and therefore increase their sales of organic produce and food made from organic produce. Considering the use of categorical data, contingency tables and correspondence maps served as the best representation and processing tools. Data analysis showed that organic produce is most frequently purchased by respondents in the age of 45+ years, who also tend to spend more money for this range of products. At the same time, these would be the respondents, who struggle the most when recognizing organic produce and who have often never seen any advertisement for it. The respondents aged 25 years and less tend to purchase organic produce least frequently; they also often do not care about the origin of organic produce. Almost the same applies to families with multiple children. However, young respondents often grow their own organic produce. There is still a not insignificant percentage of consumers, who consider organic produce to be expensive and who do not believe in their qualities. As it turns out, when it comes to organic produce the respondents purchase most frequently fruits and vegetables, milk and dairy products.

scholarly journals A Framework to Interpret Nonstandard Log-Linear Models

2016 ◽  
Vol 36 (2) ◽  
Author(s):  
Patrick Mair
Keyword(s):  
Generalized Linear Models ◽  
Categorical Data ◽  
Linear Models ◽  
Contingency Tables ◽  
Design Matrix ◽  
Nested Models ◽  
Testing Strategies ◽  
The Family ◽  
Parsimonious Models ◽  
Log Linear

The formulation of log-linear models within the framework of Generalized Linear Models offers new possibilities in modeling categorical data. The resulting models are not restricted to the analysis of contingency tables in terms of ordinary hierarchical interactions. Such models are considered as the family of nonstandard log-linear models. The problem that can arise is an ambiguous interpretation of parameters. In the current paperthis problem is solved by looking at the effects coded in the design matrix and determining the numerical contribution of single effects. Based on these results, stepwise approaches are proposed in order to achieve parsimonious models. In addition, some testing strategies are presented to test such (eventually non-nested) models against each other. As a result, a whole interpretation framework is elaborated to examine nonstandard log-linear models in depth.

scholarly journals A deterministic approach to contingency tables

2019 ◽  
Author(s):  
Giorgio Gambirasio
Keyword(s):  
Set Theory ◽  
Categorical Data ◽  
Contingency Tables ◽  
Random Variable ◽  
Deterministic Approach ◽  
Chi Square ◽  
Positive Correlation ◽  
Chi Square Test

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.

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