SMC Core Curriculum Course Proposal Form
Fall 2014
Electronically submit this course form and attachments to the Chair of the CCC by
October 1. Please submit a separate proposal for each desired learning goal.
1. Name of Proposer: Caitlin Powell
2. Email address: cap9@stmarys-ca.edu
3. Department/Program of Proposer: Psychology
4. Name of Department/Program housing the course: Psychology
5. Name(s) of Program Director/Department Chair housing the course: Paul
Zarnoth
6. Course Acronym, Number and Title: PSYCH 105, Advanced Psychological
Statistics
7. Proposal is for All Sections of the course: X
Proposal is for instructor’s section(s) (Engaging the World only): _____
8. Course Prerequisites (if any): PSYCH 003, PSYCH 103
9. Unit Value of Course: 1 credit
10. Mark with an X the Learning Goal for which the course is being proposed.
(Please submit a separate proposal for each desired goal.)
Pathways to Knowledge (at most one)
Artistic Understanding – Artistic Analysis only: ____
Artistic Understanding – Creative Practice only: ____
Artistic Understanding – Both Artistic Analysis and Creative Practice: ____
Mathematical Understanding: X
Scientific Understanding: ____
Social, Historical, Cultural Understanding: ____
Christian Foundations: ____
Theological Explorations: ____
Engaging the World (as appropriate, generally zero to two)
American Diversity: ____
Common Good: ____
Community Engagement: ____
Global Perspectives: ____
11. Expected Attachments:
a) Syllabus: Current course syllabus, expected to contain a course description
and learning outcomes. The course’s learning outcomes must include
coverage of the Learning Outcomes associated with the Core Curriculum
Learning Goal for which the course is being proposed.
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b) Teaching and Learning: A narrative that explains how the course will guide
students toward achieving each Learning Outcome and how coursework
(e.g., papers, exams, videotaped presentations) will be used to measure
student achievement of each Learning Outcome. Please address the outcomes
directly and one by one.
Additional Guidelines:
a) While courses and individual sections within courses may vary, the Core should
provide relatively consistent experiences. Thus our expectation is that each
section of a Pathway to Knowledge course will satisfy all the corresponding
learning outcomes. We also expect that the features of any syllabus submitted
are sufficiently generic to faithfully represent all sections of the course. The CCC
relies on department chairs and program directors to oversee a reasonable
degree of uniformity in how its courses address the learning outcomes.
b) We encourage departments and programs to develop courses so that an
Engaging the World goal can apply to all sections (in which case we will expect a
representative syllabus). We also welcome proposals from individual
instructors.
c) Any course approved for the Core must provide data for the assessment of Core
Curriculum learning goals at an institutional level. Via this proposal a
chair/program director agrees to oversee the submission of the student work
necessary for the assessment of the learning goals, and that his/her instructors
of Core courses will participate in assessment exercises, if asked. Similarly, if the
proposal is from an instructor, that individual agrees to oversee submission of
work from appropriate sections of their course.
d) (Legal and Logistical Workshop) Each instructor of a Community Engagement
(CE) course must participate in a workshop each year before the course is
taught. This workshop will outline the logistical and pedagogical support the
College will be providing to CE courses, as well as provide updates on any legal
or regulatory requirements of community-involved courses. At the workshops,
instructors will be provided information about legal and logistical paperwork
that must be completed by stated deadlines in order to ensure that each CE
course / experience can run safely and effectively.
e) (Pedagogical Workshops) Each instructor of a Community Engagement (CE)
course is required to have training in the pedagogy of Community Engagement.
Faculty who have studied the pedagogy of CE or taught such courses in the past
are invited to submit those experiences to the Community Engagement Working
Group as evidence of qualification. For faculty without previous CE experience
or training, CILSA provides regular workshops each semester that will assist the
instructor in integrating the community engagement learning outcomes into
their course. (These workshops are also available for faculty who wish a
refresher.)
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f) Faculty interested in determining whether a more permanent Engaging the
World designation is appropriate for their course may apply for a one-year
“provisional” designation. Please contact the chair of the CCC and/or chair of the
Working Group for details.
Coverage of the learning outcomes for Mathematical Understanding for PSYCH
105
Overview: PSYCH 105 is designed for senior Psychology majors interested in pursuing
graduate-level work in Experimental and Clinical areas of research. The course expands
beyond the descriptive and introduction to inferential statistical concepts covered in
PSYCH 003 and 103 by exploring how to analyze non-traditional data using complex
techniques. Furthermore, this class will focus more specifically on statistical analysis of
data rather than the process of collecting data and designing research. One section of this
course would be offered every other year, and the number of students would be capped at
23. As the prerequisites of the course are PSYCH 003 and 103, it is anticipated that the
majority of the students taking the course would be Psychology majors, but anyone who
has the prerequisites would qualify. (Please note: in the following description, there may
be some differences in jargon and terminology that are field specific. If you require
further information or specificity, please let me know.)
Learning outcomes: The learning outcomes for Mathematical understanding are:
“Students will
1. Apply abstract and logical reasoning to identify patterns and solve mathematical
problems; and
2. Communicate mathematical ideas and concepts accurately and clearly using
mathematical symbols, language, and formulas”
Learning Outcome 1: The first learning outcome for mathematical
understanding will be fulfilled by the following things students are expected to be able to
do:
2.) Read research articles using these analysis strategies and understand them,
3.) Apply abstract and logical reasoning to make appropriate analysis choices,
4.) Apply logical reasoning to solve mathematical problems using SPSS and hand
calculations, and identify the pattern of results.
To expand on this further, students will be expected to identify appropriate tests given the
constraints of the data they are working with; when presented with a nested design, nonlinear data, missing data, or small N datasets, it is expected that students will be able to
identify the necessary adjustments that must be made, and identify the assumptions,
strengths, and weaknesses of the analysis strategies used, as well as demonstrate basic
understanding of patterns of results that use these adjustments.
In addition, students will be learning about hierarchical regression analysis, bootstrap
mediational analysis, and advanced contrast coding techniques, and will be expected to
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both demonstrate knowledge about when it is appropriate to use these tests, as well as
solve mathematical problems using those analysis strategies. Students will be solve
problems both using statistical software to analyze existing datasets, and by applying
formulas to raw data (“hand calculations”).
Assessment of Learning Outcome 1: This outcome will be assessed specifically
through several components of the class: 1.) an oral report project, where students must
present a research article that uses one of the methodologies discussed in class and
explain the researchers’ choices and the pattern of results, 2.) exams and practice
problems, where students will solve computational problems, describe the patterns of
results, and make choices about appropriate analysis strategies, 3.) students’ final project,
where they must take an existing dataset, identify an appropriate analysis strategy,
implement it using SPSS, and explain the pattern of results.
Learning Outcome 2: The second learning outcome for mathematical
understanding will be fulfilled by the following:
1.) Demonstrate working knowledge of the underlying theories and formulas behind these
statistical tests, both by using mathematical symbols and language, as well as by
describing these principles in English
Students will learn about several statistical tests (i.e. contrast coding, hierarchical
regression analysis) and concepts (nested data, non-linear data) multiple ways, including
learning about the underlying formulas associated with these tests and concepts. Students
will be expected to solve mathematical problems using these formulas, identify the
appropriate symbols associated with these formulas, modify the formulas as appropriate,
as well as explain and describe the components of the various formulas they will be
implementing. For example, if learning about regression analyses that involve non-linear
modeling, students would be asked to create formulas to reflect the shape of data
(logarithmic, cubic, or quadratic), and use these modified formulas to solve for predicted
values. Likewise, when learning about nested models, students would determine sources
of variance by breaking down existing variance formulas to reflect the nested nature of
the variables.
Assessment of Learning Outcome 2: This outcome will be assessed through
exams and practice problems, which will test their ability to solve computational
problems using formulas, and correctly identify and describe the various components of
the formulas in question.
Training and Expertise: I have my PhD in Experimental Psychology. As part of my
graduate training, I took graduate-level coursework in Multiple Regression,
Psychometrics, Structural Equation Modeling, and Latent Growth Curve Modeling. I also
took part in a seminar in Multilevel Modeling, and fMRI data analysis. In addition, I was
the teaching assistant for the graduate Multiple Regression and Psychometric courses for
two semesters each. The course may also potentially be occasionally taught by Dr. Paul
Zarnoth, who received a minor in Quantitative Psychology in his graduate training, and
took courses in unidiminsional and multidimensional scaling, and advanced statistical
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analyses, or by Dr. Emily Hause who has a PhD in I/O psychology and has taken
graduate level courses in ANOVA, Multiple Regression, Structural Equation Modeling,
and Item Response Theory.
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