Implementing ODA from Within Stata: A Priori Hypothesis, Three-Category Class Variable, Four-Level (Integer) Attribute

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis LLC & Linden Consulting Group LLC

This paper describes how to test a directional (confirmatory) hypothesis for a design relating a three-category class (“dependent”) variable and a four-level categorical ordinal attribute (“Likert-type independent variable”) vis-à-vis the new Stata package for implementing ODA.

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Implementing ODA from Within Stata: Directional Hypothesis, Multicategorical Class Variable, Ordinal Attribute

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis LLC & Linden Consulting Group LLC

This paper describes how to assess a confirmatory (directional) hypothesis for a design involving a multicategorical class (“dependent”) variable and an ordinal attribute (“independent variable”) using the new Stata package for implementing ODA.

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Implementing ODA from Within Stata: Directional Hypothesis, Multicategorical Class Variable and Attribute

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis LLC & Linden Consulting Group LLC

This paper demonstrates how to evaluate a confirmatory (directional) hypothesis for a design involving a multicategorical class (“dependent”) variable and a multicategorical attribute (“independent variable”) using the new Stata package for implementing ODA.

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Implementing ODA from Within Stata: Nondirectional, Multicategorical Class Variable, Multicategorical Attribute

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis LLC & Linden Consulting Group LLC

This paper describes how to evaluate an exploratory (nondirectional) hypothesis for a design involving a multicategorical class (“dependent”) variable and a multicategorical attribute (“independent variable”) using the new Stata package for implementing ODA.

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Implementing ODA from Within Stata: Confirmatory Hypothesis, Binary Class Variable, Continuous Attribute

Paul R. Yarnold & Ariel Linden

Optimal Data Analysis LLC & Linden Consulting Group LLC

This paper describes how a confirmatory (a priori, directional, one-tailed) hypothesis involving a binary (dichotomous) class variable and continuous (interval or ratio) attribute is evaluated via MegaODA software using the new Stata package implementing ODA analysis.

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