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predictive models

Member Training: Analyzing Likert Scale Data

by TAF Support  1 Comment

Is it really ok to treat Likert items as continuous? And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately. [Read more…] about Member Training: Analyzing Likert Scale Data

Tagged With: Correlation, data transformations, Kendall's tau-b, kruskal-wallis, Likert Scale, mann-whitney u test, Ordinal Logistic Regression, predictive models, Somer's D, Spearman correlation

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Member Training: A Predictive Modeling Primer: Regression and Beyond

by guest contributer 

Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too! [Read more…] about Member Training: A Predictive Modeling Primer: Regression and Beyond

Tagged With: bagging, boosting, Bootstrap, cross-validation, decision trees, discriminant analysis, K-Nearest Neighbors, lasso, linear regression, logistic regression, predictive models, random forests, Regression, Resampling Techniques, ridge regression, shrinkage methods, subset selection, supervised learning, tree-based methods

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Descriptives Before Model Building

by Jeff Meyer  Leave a Comment

Stage 2One approach to model building is to use all predictors that make theoretical sense in the first model. For example, a first model for determining birth weight could include mother’s age, education, marital status, race, weight gain during pregnancy and gestation period.

The main effects of this model show that a mother’s education level and marital status are insignificant.
[Read more…] about Descriptives Before Model Building

Tagged With: Model Building, predictive models, significant

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Using Predicted Means to Understand Our Models

by Jeff Meyer 

The expression “can’t see the forest for the trees” often comes to mind when reviewing a statistical analysis. We get so involved in reporting “statistically significant” and p-values that we fail to explore the grand picture of our results.

It’s understandable that this can happen.  We have a hypothesis to test. We go through a multi-step process to create the best model fit possible. Too often the next and last step is to report which predictors are statistically significant and include their effect sizes.

[Read more…] about Using Predicted Means to Understand Our Models

Tagged With: best practices, predictive models, predictor variable

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Differences in Model Building Between Explanatory and Predictive Models

by Jeff Meyer  8 Comments

by Jeff Meyer, MPA, MBAStage 2

Suppose you are asked to create a model that will predict who will drop out of a program your organization offers. You decide to use a binary logistic regression because your outcome has two values: “0” for not dropping out and “1” for dropping out.

Most of us were trained in building models for the purpose of understanding and explaining the relationships between an outcome and a set of predictors. But model building works differently for purely predictive models. Where do we go from here? [Read more…] about Differences in Model Building Between Explanatory and Predictive Models

Tagged With: explanatory models, Model Building, overfitting, predictive models, predictors, significance testing, Training Data, validation data

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