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MERLOT II


    

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Inferential Stats (PSY302)

by Ji Son
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Description:

Inferential Statistics for Psychology majors taught at CSULA.

Prerequisites:

Descriptive statistics course (e.g., PSY202).

Learning Outcomes:
Pedagogy Approach:

This course is aimed at helping students gain both concrete skills and conceptual knowledge.


Conceptual knowledge is commonly lacking in many US math classrooms. Many students have previously experienced math (and statistics) as a jumble of arbitrary equations and problem solving strategies.  This course focuses on why a particular equation stands for a particular concept and how concepts fit together.

 
This leap--from math as "calculating answers" to math as a system of concepts--tends to be challenging for many students. Because of this, students will grapple with similar concepts in different ways over and over again in multiple class assignments. This will not be an easy process. It will take you a long time and many mistakes to build understanding. The purpose is to LEARN FROM EACH ATTEMPT, to fix your mistakes, and ultimately gain more conceptual knowledge (and a better grade!). That is the essence of expertise-building!  Here is a recent article further emphasizing the importance of learning from previous mistakes (
http://www.wired.com/wiredscience/2011/10/why-do-some-people-learn-faster-2/).

Students may be frustrated at these conceptual methods of learning at first.  However, it is important for students to remember learning is incremental! Every take, every question, every mistake adds to learning.

Assessment Methodology:

One of the ways this course focuses on concepts is through statistics theater and essay questions... with these performances and essays, students must learn how to understand and articulate reasons and concepts rather than plugging in numbers into formulas. Students are also asked to engaged in meta-cognitive reflections on their learning (e.g., Do I understand the purpose of measuring distance with variability? Do I know what types of situations should be analyzed using correlation?)

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