scholarly journals Analysing Sensitive Data from Dynamically-Generated Overlapping Contingency Tables

2020 ◽  
Vol 36 (2) ◽  
pp. 275-296
Author(s):  
Joshua J. Bon ◽  
Bernard Baffour ◽  
Melanie Spallek ◽  
Michele Haynes
Keyword(s):  
Linear Models ◽  
Contingency Tables ◽  
Sensitive Data ◽  
Data Set ◽  
Administrative Records ◽  
Cell Counts ◽  
Wide Range ◽  
Australian Census ◽  
Australian Bureau Of Statistics ◽  
Log Linear

AbstractContingency tables provide a convenient format to publish summary data from confidential survey and administrative records that capture a wide range of social and economic information. By their nature, contingency tables enable aggregation of potentially sensitive data, limiting disclosure of identifying information. Furthermore, censoring or perturbation can be used to desensitise low cell counts when they arise. However, access to detailed cross-classified tables for research is often restricted by data custodians when too many censored or perturbed cells are required to preserve privacy. In this article, we describe a framework for selecting and combining log-linear models when accessible data is restricted to overlapping marginal contingency tables. The approach is demonstrated through application to housing transition data from the Australian Census Longitudinal Data set provided by the Australian Bureau of Statistics.

scholarly journals The Application of Log-Linear Model to Selected Poison Patients

2020 ◽  
pp. 1-7
Author(s):  
Fatin N.S.A. ◽  
Norlida M.N. ◽  
Siti Z.M.J.
Keyword(s):  
Linear Model ◽  
Linear Models ◽  
Demographic Data ◽  
Contingency Tables ◽  
Categorical Variables ◽  
State Variables ◽  
Exposure Route ◽  
Higher Dimensional ◽  
Route Of Exposure ◽  
Log Linear

Log-linear model is a technique used to analyze the cross-classification categorical data or the contingency table. It is used to obtain the parsimony models that describe the interaction between the categorical variables in contingency tables. Log-linear models are commonly used in evaluating higher dimensional contingency tables that involves more than two categorical variables. This study focuses on analyzing data of poisoned patients from 2012 to 2014 using log-linear model. There are two model analyzed; model for demographic data of patients and model of poisoning information. For the first model, the variables involved are gender, age, race and state. Variables for the second model are circumstance of exposure, type of exposure, location of exposure, route of exposure and types of poison. Both log-linear models are developed to investigate the association between variables in the model. As a result of this study, the best model for demographic data and poisoning information are the model with three-ways interaction. For the best model of demographic data, there is an association between gender, age and race, race, gender and state as well as age, race and state. Meanwhile, the best model for poisoning information reveals that there is relationship between circumstance of exposure, route of exposure and type of poison, location of exposure, route of exposure and type of poison, circumstance of exposure, type of exposure and route of exposure, circumstance of exposure, location of exposure and route of exposure, circumstance of exposure, type of exposure and type of poison and also type of exposure, location of exposure and type of poison. Keywords: log-linear; demographic; gender; age; race; state; circumstance of exposure; type of exposure; location of exposure; route of exposure; types of poison

Log Linear Models and Tests of Independence for Contingency Tables

Biometrics ◽  
1972 ◽  
Vol 28 (1) ◽  
pp. 137 ◽  
Author(s):  
James E. Grizzle ◽  
O. Dale Williams
Keyword(s):  
Linear Models ◽  
Contingency Tables ◽  
Log Linear ◽  
Tests Of Independence

scholarly journals On Estimating the Linear-by-Linear Parameter for Ordinal Log-Linear Models: A Computational Study

2012 ◽  
Vol 2012 ◽  
pp. 1-12
Author(s):  
Eric J. Beh ◽  
Thomas B. Farver
Keyword(s):  
Linear Models ◽  
Computational Study ◽  
Contingency Tables ◽  
Linear Parameter ◽  
Iterative Techniques ◽  
Ordinal Variables ◽  
Linear Association ◽  
Iterative Procedures ◽  
Log Linear

Estimating linear-by-linear association has long been an important topic in the analysis of contingency tables. For ordinal variables, log-linear models may be used to detect the strength and magnitude of the association between such variables, and iterative procedures are traditionally used. Recently, studies have shown, by way of example, three non-iterative techniques can be used to quickly and accurately estimate the parameter. This paper provides a computational study of these procedures, and the results show that they are extremely accurate when compared with estimates obtained using Newton’s unidimensional method.

The impact of corruption on sport demand

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carlo Amenta ◽  
Paolo Di Betta
Keyword(s):  
Design Methodology ◽  
Linear Models ◽  
Negative Externalities ◽  
Data Set ◽  
Content Type ◽  
Long Run ◽  
Log Linear ◽  
Systemic Corruption ◽  
The Impact

PurposeThe article presents an empirical analysis that evaluates the effects of a systemic corruption scandal on the demand in the short and the long run. In 2006, the Calciopoli scandal uncovered the match rigging in the Italian soccer first division. The exemplary sportive sanction of relegating the primary culprit to the second division imposed further negative externalities on the other clubs. Should we prefer the sportive sanction on the team or the monetary fines for the club?Design/methodology/approachWe estimated two log-linear models of the demand side (stadium attendance) using a fixed effect estimator, on two panel data set made of all the Italian soccer clubs in the first and second division (Serie A and Serie B) for the seasons 2004/2005 to 2009/2010, considering the relegation of the Juventus as the event which impacted the demand for soccer.FindingsRelegating Juventus to Serie B caused an immediate decrease of 18.4% in the attendance for all the teams, both in Serie A and in Serie B, for the three seasons considered, and 1% decrease when all the seasons are considered to measure the fallout of the scandal on the fans' disaffection.Originality/valueThe effect of corruption in sport on demand is an important issue, and there are few studies already published. As for sports economics and management, our results are of interest for sport-governing bodies – as a case study that can help in designing a more effective sanctioning system to prevent corruption episodes.

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