Explain Individual Differences
**********

Lesson
================

.. article-info::
    :avatar: dnl_plastic.png
    :avatar-link: https://www.decisionneurosciencelab.com/
    :author: Elijah Galvan
    :date: September 1, 2023
    :read-time: 7 min read
    :class-container: sd-p-2 sd-outline-muted sd-rounded-1

Goal During this Stage
---------------

We want to try to characterize *why* some :bdg-success:`Subjects` make different :bdg-danger:`Decisions` than others. 
Thus, we are going to use demographic factors and/or dispositional psychological factors to try to predict these differences.

How to Achieve this Goal
------------

.. dropdown:: Common Dispositional Psychological Factors to Consider

    .. Note:: 

        I'm not an expert on the psychometrics of any of these psychological factors: I've just provided these to give you something to start with and think about in terms of how you want to predict behavior.

    **Personality** - Big Five Personality Inventory or HEXACO Personality Inventory

    **Morality** - Moral Foundations Questionnaire

    **Individualism/Collectivism** - Auckland Individualism-Collectivism Scale (AICS)

    **Dark Triad** - Short Dark Triad (SD3)

    **Social Dominance** - Social Dominance Orienation Scale

    **Emotional Intelligence** - Rotterdam Emotional Intelligence Scale

    **Approach/Avoidance Tendencies** - BIS/BAS Scale


Our answer to this question depends entirely on what we want to focus on: this table should help you determine what you may want to use. 
We're not going to bother using conceptual examples, all implemented examples are shown below.

.. table::
   :widths: auto

   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+
   |                                         | 1 :bdg-success:`Free Parameter` | 2+ :bdg-success:`Free Parameters`  | Cluster or Bin                     |
   +=========================================+=================================+====================================+====================================+
   | 1 Binary Variable                       | 2 Sample t-test (Ex. 1)         | Cluster Strength Analysis (Ex. 2)  | Chi-Square (Ex. 3)                 |
   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+
   | 1 Categorical Variable with 3+ Levels   | One-Way ANOVA (Ex. 4)           | Cluster Strength Analysis (Ex. 5)  | Chi-Square (Ex. 6)                 |
   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+
   | 1 Continuous Variable                   | Correlation (Ex. 7)             | Matrix Correlation (Ex. 8)         | Logistic Regression (Ex. 9)        |
   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+
   | 2+ Categorical Variables                | Multiple Regression (Ex. 10)    | Cluster Strength Analysis (Ex. 11) | Logistic Regression (Ex. 12)       |
   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+
   | Any 2+ Variables                        | Multiple Regression (Ex. 13)    | Matrix Correlation (Ex. 14)        | Logistic Regression (Ex. 15)       |
   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+
   | Any 2+ Continuous Variables             | Matrix Correlation (Ex. 16)     | Matrix Correlation (Ex. 17)        | Cluster Strength Analysis (Ex. 18) |
   +-----------------------------------------+---------------------------------+------------------------------------+------------------------------------+

.. dropdown:: Miscellaneous Examples

    Example 19: Using Weighted Averages of :bdg-success:`Free Parameters` over :bdg-primary:`Conditions` as Predictors of Preference-Relevant Atittudes

Examples
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