 | | GOLDMineR® 2.0: A Unique Regression Program
Understand your data like never before with GOLDMineR® , unrivaled modeling software for dichotomous, ordered categorical, and grouped continuous outcome variables.
Applications Features
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GOLDMineR 2.0 User's Guide - Download the GOLDMineR manual.(PDF, 2.26 MB)
NEW! "Mining for Gold gets easier and a lot more fun!" - Review of GOLDMineR in Marketing Research Magazine, Spring 2004 issue. By Ken Deal. (PDF)
GOLDMineR® 's patented graphical interface features intuitive, easy-to-use graphs and charts that make results easy to interpret:
GOLDMineR® is an acronym for Graphical Ordinal Logit Displays based on Monotonic Regression. With it, you can better understand data by:
- moving beyond linear regression
which is not appropriate for categorical dependent variables
- including both categorical and
continuous predictors into the model
- gaining insight into your data and
model using innovative patented graphical displays
- building regression models for dependent variables containing ordered outcome categories (where the ordering can be prespecified or determined by the model)
How GOLDMineR® compares to other regression programs:
GoldMineR has the following unique features:
General Ordinal Logit Model
Suppose your dependent variable is a rating, such as a likelihood
to buy: very likely, somewhat likely, somewhat unlikely, very unlikely. What is
the relative distance between these categories? Uniform scaling (3,2,1,0) may
give very different results than scores such as (3,1,0.5,0), which suggest
greater difference between "very likely" and the other levels. GOLDMineR®
estimates "optimal" dependent variable scores simultaneously with the estimation
of the regression coefficients.
This model is an extension of Leo Goodman's RC association
model.
Patented
Graphics
Select effects coding (or other coding) for your
nominal predictors with a simple mouse click AFTER you estimated the model (no
need to re-estimate). See a unique graphical
display which interprets the effects for you. See how the log-odds ratio
compares each category with the average category (reference) under effects
coding. Then click on any category in the graph to instantly transform to dummy
coding, where the selected category becomes the new reference.
Fast step-wise inclusion algorithm
Select the most important predictors to include in model. If you
have many correlated predictors, GOLDMineR® 's Search feature will leave out the
ones that are colinear with those included in the model. Search ranks all
predictors according to their unique p-value. You can then enter the most
significant, one step at a time, reviewing the results of the model whenever you
want. Click "search all" to run Search automatically until all significant
predictors have been entered.
Gains Chart Output
In addition to the standard regression type output,
an interactive gains chart summary is available, which summarizes the
performance of the model in deciles or other quartile groupings.
Look who's using GOLDMineR® (Applications)
GOLDMineR® gives you a regression framework for working with various types of data. Use GOLDMineR® across a wide range of applications including:
"A major breakthrough! Monotonic regression may soon become the regression standard."
Keith Potts,
V.P. Direct Marketing,
Banknorth Group, Inc.
Use The Best Approach for Ordered Categorical Outcomes
GOLDMineR® delivers the right regression framework to create better models. For example, while linear regression is fundamental in research, its measurement level requirements are not always met. Linear regression is not appropriate if a dependent variable is dichotomous or consists of ordered categories. For lack of good software alternatives, researchers often use it anyway for ordinal outcome variables, such as:
| Rating scales ("Likelihood of
purchase") |
Medical treatment
outcomes |
| 4. Very Likely
|
-1. Worse |
| 3. Somewhat Likely |
0. Stationary |
| 2. Somewhat Unlikely |
1. Mild Improvement |
| 1. Very Unlikely |
2. Moderate Improvement |
|
3. Well |
| Discretized variables, such as educational attainment |
Partially ordered variables,
such as grouped income |
| 1. Less than eight years of schooling
|
1. Less than $15,000 |
| 2. Eight - 11 years of schooling |
2. $15,000 - $30,000 |
| 3. 12 years of schooling |
3. $30,000 - $50,000 |
| 2. 13 or more years of schooling |
4. $50,000 and above |
|
5. Unknown |
Access flexible tools to enhance understanding
GOLDMineR® gives you innovative, patented graphical displays so you can easily interpret your effect estimates and visually assess your model. Output features both regression and loglinear statistics, including model summary R-square and goodness of fit chi-square statistics. A table window displays observed or expected counts, probabilities, odds or odds ratios. And an advanced search feature delivers automatic predictor variable selection, so you can easily select important predictors from your database. Plus, you can easily import data into GOLDMineR® , since it reads a variety of file types, including the SPSS® system file format.
With GOLDMineR® , you can:
- Use innovative graphical displays
and charts to better visualize and assess you model
- Use the table window to access
various counts, odds and residual tables
- Use the search facility for
automated predictor variable selection
- Update your results immediately for
changes in contrast coding of qualitative variables
- Model and predict dichotomous and
ordered categorical dependent variables
- Generate code for scoring external
files
- View GOLDMineR® 's logit model
graphically vs. a competing linear model applied to the same
data
- Explore and assess competing models
through visual displays and statistical measures of fit
- Use different scoring systems for
your categorical variables
- Assess the relative impact of individual predictors on the response variable
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