Download
(use StuffIt Expander to extract the files) TURNER Version 1.0
now
For detailed information about system requirements see below.
Download
(use StuffIt Expander to extract the files) TURNER Version 1.0
now
For detailed information about system requirements see below.
TURNER was devised by Antony Unwin and has
been implemented and further developed by Stephan
Lauer. It is written in C++. TURNER follows the Macintosh
conventions and is consistent with other Mac packages. It is one of a
number of software development projects in the department of
Computer-oriented Statistics and Data Analysis at the University of
Augsburg.
Interactive contingency tables are new tools for flexibly investigating categorical data. The main operations possible in interactive tables are:
Interactivity not only means that the user can interact with the
data, but also that the results from the changes made by the user can
be seen instantaneously. Therefore, interactive contingency tables
not only offer the possibilities of comparing some static views of
different aspects of the data, they even allow to draw conclusions
from the manner things are changing.
The basic tool for presenting categorical data in TURNER is a
two-dimensional contingency table. This is obvious for data in two
dimensions, for data in more than two dimensions - stored in
so-called supercubes - this means that two-way contingency tables
will be displayed either showing the marginal distribution in a
projection or the conditional distribution in a slice. In the
literature slices are sometimes called partial tables and projections
marginal tables. The following figure shows slices and projections of
a four-dimensional data set.
Slices can exhibit quite different information than projections, it is therefore necessary to analyze all two-dimensional tables drawn from a multi-way contingency table.
Often in the analysis of survey data a regrouping of the pre-given categories might be very effectful. Instead of forcing the user to redefine groups in the edit mode, TURNER allows pooling categories by a single mouse click.
In the same manner it is possible to eliminate rows of a contingency table.
Loglinear models are often used as a part of research studies
where variables fall into different categories, for example in
predictor and control variables, or in global and individual
characteristics. In these cases an effective tool is needed to
build-up multi-way contingency tables for a selected portion of
variables. TURNER allows to select those variables that are not of
interest for the next analysis stage and then to calculate a
multi-way contingency table for all other variables with the data
collapsed over the selected variables. In the previous example, the
variable 'Preference' can be seen as the response variable, the other
three as explanatory variables. In an early step of the analysis it
seems natural to look at the dependencies of the design variables
ignoring the variable 'Preference'.
The order relation between observed and expected cell entries can be easily sketched from their colour. A blue colour indicates that the expected value is smaller than the observed one, a red colour indicates that the expected cell entry is greater or equal than the observed one.
In the same manner cells with expected values smaller than five are highlighted by brightness, light printing says that the expected value is greater than five, dark printing means that it is smaller than five.
A first indication that there exist associations between variables
can be drawn from the categories‘relative frequencies. TURNER
allows to display row or column percentages as well as total
percentages by simply moving the mouse in the upper left corner of a
table display.
The Power Divergence Window allows to show expected values for the
current loglinear model calculated by the iterative proportional
fitting algorithm and to show summands of the power divergence
statistic.
By simply double-clicking a cell its entry can be changed to see
what happens if. Such a construction of a hypothetical table allows
to study the effect of structural and random zeros in the data set.
Moreover, the effect of cells with small expected numbers can be
studied in detail. 
The display at the bottom shows how Pearson‘s chi-squared statistics change accordingly. In TURNER all manipulated values are coloured in orange.
The power-divergence family of statistics links the traditional test statistics, like Pearsons chi-squared, the likelihood-ratio-statistik or the Freeman-Tukey statistic, by a single real-valued parameter. It was introduced in Cressie and Read (1984) and consolidated and updated in Cressie and Read (1988). The power-divergence statistic is defined as
The standard test statistic for contingency tables is
Pearson‘s chi-squared test. Usually, conclusions about its
significance are drawn from the p-value. The main deficiency of the
chi-squared test is, however, its dependence from the sample size N.
Therefore, TURNER calculates the so-called N-value, that is the total
number of observations needed for a table to be significant with same
cell proportions. The N-value is especially advantegeous in comparing
tables.
The definition and testing of models for discrete multivariate data has been the subject of many statistical researchers in the last quarter century. A very widespread and powerful method is loglinear modeling. Loglinear models share many features with linear model methods for continuous variables. They describe association patterns among categorical variables. With the loglinear approach, cell counts in a contingency table are modelled in correspondence to the associations between variables. The linear predictors for the models have structure analogous to ANOVA models.
As many other software TURNER also is restricted to hierarchical
loglinear models. Hierarchical models means that whenever the model
contains higher-order effects it also incorporates all lower-order
effects composed from the variables.By standard a loglinear model in
TURNER includes all main effects and whenever a higher-order effect
is selected the model is automatically made hierarchical.
