GOLDMineR® 2.5 is a generalized regression program for predicting a dichotomous, ordinal, or grouped continuous outcome variables with specialized, interactive graphics (see product description below for more details). An extensive manual (250 pages) including examples and tutorials is available here.
GOLDMineR® is licensed on a perpetual basis (does not expire). Discounts for multiple users are automatically applied to your order (2-4 licenses – 15%, 5-9 licenses – 20%, 10+ licenses – 25%).
GOLDMineR® is an acronym for Graphical Ordinal Logit Displays based on Monotonic Regression and is a generalized regression program for predicting a dichotomous, ordinal, or grouped continuous outcome variables with specialized, interactive graphics.
It 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:
4. Very Likely
3. Somewhat Likely
2. Somewhat Unlikely
1. Very Unlikely
1. Mild Improvement
2. Moderate Improvement
1. Less than eight years of schooling
2. Eight – 11 years of schooling
3. 12 years of schooling
2. 13 or more years of schooling
1. Less than $15,000
2. $15,000 – $30,000
3. $30,000 – $50,000
4. $50,000 and above
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.
To summarize, GOLDMineR® has the following unique features:
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.
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.
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 collinear 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.
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.
- 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)
GOLDMineR® is designed to run on Windows 10.
System Requirements: 16MB Drive Space, 512MB of RAM.
Input files: SPSS system files, delimited text files.