Psy140A Syllabus, Summer 2015
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PSY140A
Statistical Analysis Software (SAS) Applications
Department of Psychology
Brandeis University
Summer 2015
Dr. Xiaodong Liu
Lecture: M. Tu. & Th. 11:00 – 1:20, Goldfarb Library, room 230
Instrcutor: Xiaodong Liu, xliu0806@brandeis.edu, Brown 106, 781-736-3244 (O).
Office hours: by appointment.
Course Description
Think about learning some new skills? Understanding SAS may be an addition to your
existing skills bank. SAS is widely used in both academic and industrial fields for data
management, data report, and data analyses. Try to search "SAS" in any online job search
website (vs. some other statistical tool) to see what you will get. In Psyc140A, we will
introduce the platform of SAS and focus on the application of SAS in data management
and data analyses (descriptive statistics, ANOVA, and regression analysis will be
covered).
By using examples (data) from different disciplines, students in this course will have a
hands-on experience using SAS for data input/import, data management & manipulation,
data report, descriptive statistics, graphics, and inferential statistics. Students will have
ample opportunities to practice programming in SAS and interpreting/making sense of
the output/statistics from SAS. Students may use this course to fulfill the University
quantitative reasoning requirement and school of social science distribution requirement.
This course may also be helpful for those who are interested in taking SAS Base
Programming for SAS 9 certification exam.
In this course, students with interest will also be encouraged to search online archived
data (second-hand data) and use SAS to analyze the archived data to address research
questions of their interest.
Experiential learning components are an integral part of this course. Built upon their
previous experience in or understanding of statistical analyses or data-driven related work
(either through an introductory statistic course, research methods course, research
projects), students will begin to involve in data manipulation and analyses from the
beginning of the course through both in-class practice and assignments.
Learning Objectives and Expected Skill Development
Students who successfully complete this course will be able to set up and use SAS for a
variety of tasks. Specifically, students will be able to:
1) Set up/input a SAS dataset;
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2) Read/import external data (e.g., from excel, SPSS format) into SAS and export a SAS
dataset;
3) Manipulate/Manage data in SAS;
4) Program in SAS;
5) Generate user friendly output (table, report, and graph, both descriptively and
inferentially);
6) Implement and understand GLM analyses (including ANOVA and Linear Regression)
in SAS;
7) (optional) Identify and analyze archived data (online);
Prerequisites
No prior SAS experience is required. Some introductory statistics experience (e.g.,
Psyc51a or an equivalent course) will be helpful.
Class Format
Class meetings will consist of lectures, demonstration, and programming practices.
Texts (recommended):
[online versions of the books are available through Brandeis library].
Delwiche, L. D. & Slaughter, S. J. (2012). The little sas book: a primer, fifth edition.
Cary, N.C. SAS Institute.
Cody, R. (2011). Sas statistics by example. Cary, N.C. SAS Institute.
SAS Institute (2011). SAS certification prep guide: base programming for SAS 9, third
edition. Cary, N.C. SAS Institute.
Course requirements and assessment:
Students enrolled in this course will be expected to: (a) attend all classes; (b) complete
the five assignments by the designated deadline. The assignments are mainly SAS
programming and a short memo of interpreting the statistical output; and (c) complete a
final take-home project. Students will be evaluated on their performance on class
participation (10%), assignments (60%), and the final take-home project (30%).
Guideline for letter grade:
95+
90-95
85-90
80-85
75-80
A
AB+
B
B-
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Academic integrity
Academic integrity is central to the mission of educational excellence at Brandeis
University. Each student is expected to turn in work completed independently, except
when assignments specifically authorize collaborative effort. It is not acceptable to use
the words or ideas of another person – be it a world-class philosopher or your lab partner
– without proper acknowledgement of that source. This means that you must use author
citations, endnotes, and, where appropriate, quotation marks to indicate the source of any
phrases, sentences, paragraphs, or ideas found in published volumes, on the internet, or
created by another student.
Violations of University policies on academic integrity, described in Section Three of
Rights and Responsibilities, may result in failure in the course or on the assignment, or in
suspension or dismissal from the University. If you are in doubt about the instructions
for any assignment in this course, it is your responsibility to ask for clarification.
Special needs
Students with a documented disability on record at Brandeis University and wish to have
a reasonable accommodation made should let the instructor know immediately.
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Course outline (topics and related reading) (subject to change):
Class session
Topics
Class 01 (6/1)
Introduction to SAS Windows Environment
Class 02
Getting Started Using SAS
Getting Data into SAS (1)
Getting Data into SAS (2)
Creating Permanent SAS Data Sets, SAS libraries,
Verifying data
Summarizing data: descriptive statistics (continuous
variables): Creating Formats and Labels, Proc Print,
Proc Means, Proc Univariate
Working with Data in SAS (I): Selecting variables
and observations in SAS (Conditional processing),
Performing Iterative Processing in SAS (array)
Working with Data in SAS (II): Working with Dates
in SAS, Using SAS functions
Class 03
Class 04 (6/8)
Class 05
Class 06
Recommended
Readings
C (Ch. 1)
D&S (Ch. 1)
D&S (Ch.s 1 & 2)
D&S (Ch. 2)
C (Ch.2)
D&S (Ch.4.1, 4.5& 4.10; Ch. 9.1-9.3)
D&S (Ch.3)
D&S (Ch. 3)
Class 08
Descriptive Statistics: Categorical variables,
Bivariate Association
Modifying Data in SAS (I)
C (Ch.s 3 & 4)
D&S (Ch.9.6-9.9)
D&S (Ch.6)
Class 09
Class 10 (6/22)
Modifying Data in SAS (II)
Summarizing data: graphically (and numerically)
D&S (Ch.6)
D&S (Ch.s 4 & 8)
Class 11
Inferential statistics: t-test & ANOVA
C (Ch.s 5-7)
Class 12
SAS ODS (Output Delivery System): enhancing
output
Inferential statistics: Categorical data analysis
D&S (Ch.5)
Class 07 (6/15)
Class 13 (6/29)
Class 14
last day of instruction
Inferential statistics: Regression analysis
Class 15 (7/2)
no class, final project (take-time)
Final project due (electronic version) by 1:30pm, F., 7/3
C (Ch. 10)
C (Ch.s 8 & 9)