Overview

GOLDMineR is a generalized regression program for predicting a dichotomous, ordinal, or grouped continuous outcome variables with specialized, interactive graphics. GOLDMineR has the following unique features:

• General Ordinal Logit Model
• Patented Graphics
• Fast Step-wise Inclusion Algorithm
• Gains Chart Output

See how GOLDMineR compares with other regression programs. Learn More

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Capabilities

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.

GOLDMineR® is an acronym for Graphical Ordinal Logit Displays based on Monotonic Regression. With it, 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
• Include both categorical and continuous predictors into the model
• Build regression models for dependent variables containing ordered outcome categories (where the ordering can be prespecified or determined by the model)

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

Tutorials

All sample datasets and sample .arr files used the tutorials below are downloaded to your computer when you install the demo version of GOLDMineR.

Tutorial 1: Beginning a GOLDMineR® Analysis download PDF          + overview - overview In this tutorial we use sample dataset #4 and show how to specify a model and obtain an effects plot. You will: open a previously saved data file select the dependent and predictor variable experiment with different scaling types generate plots and tables use various display options

Tutorial 2: Assessing the Effects of a Reminder Call download PDF          + overview - overview In this tutorial, we estimate the effects of 1) the dollar amount mailed out with the survey (X1: PAYMENT - $1, $2, or $10) and 2) a reminder call (X2: CALL - - Yes or No) on the return rate of a mail survey. This tutorial illustrates the use of the goodness of fit statistic to examine the validity of the scaling and other assumptions made by a model obtaining various tables and regression plots, and the poor prediction and biased parameter estimates that can result from model misspecification (i.e., use of improper scores).

Tutorial 3: Analysis of a Qualitative Predictor Variable download PDF          + overview - overview Sometimes cases on the analysis file are randomly assigned to a ‘hold-out’ sample and not used in the development of the segmentation tree. Instead, such cases are reserved for the purpose of ‘validating’ the tree. In this tutorial we utilize the data file holdout.sav to illustrate the use of SI-CHAID in this way. In particular, from each dependent category (‘paid respondents’, ‘unpaid respondents’ and ‘nonresponders’) we randomly assigned each case in the ‘subscrib.sav’ file to one of two equally likely groups by generating the variable SAMPLE (1=test, 2 = holdout).

Advanced Tutorial: Detailed example with qualitative predictor download PDF          + overview - overview In Chapter 8 hypothetical mail survey data were used to demonstrate the importance of assessing the model fit to test the validity of the assumptions made by a model. In the current example we will analyze real data from the Mail Survey Experiment (Magidson, 1994b) where the actual dollar payments tested were $1 (the control), $2, $3 and $4, the experiment being designed so that there is no correlation between X1:PAYMENT and X2:CALL. We will again use the model fit statistic to choose between two alternative models, one that treats PAYMENT as a quantitative predictor with equidistant category scores (Model A) and one that treats PAYMENT as a qualitative predictor (Model B).

Advanced Tutorial: Stouffer's American Soldier Data link to tutorial          + overview - overview This note presents an analysis of a famous data example in GOLDMineR® and shows unique insights into the effects present in the data. This note also shows how you can use an odds framework to understand effects in the data. In his 1972 article "A Modified Multiple Regression Approach to the Analysis of Dichotomous Variables," Leo Goodman presents a version of data presented by Stouffer et al. in their study "The American Soldier." Stouffer's study was published in 1949, and was based upon surveying American soldiers in World War II. The data should be understood in the context of the segregated American military and society of the time. The data have been analyzed many times, and Goodman's approach is both very fruitful for understanding what is going on in the data, as well as superior to other approaches used in the past. Goodman's analysis is replicable in SPSS Genlog or in GOLDMineR® .

Advanced Tutorial: GOLDMineR® in biomedical research link to tutorial          + overview - overview In his book Categorical Data Analysis, Alan Agresti discusses a 40-case dataset that relates respondents’ mental impairment to two explanatory variables. Agresti employs the cumulative logit model, while we use the adjacent category logit model to get insight into the data.