|
|
JOHN CABOT UNIVERSITY
COURSE CODE: "PS 208"
COURSE NAME: "Introduction to Statistical Analyses of Psychological Data"
SEMESTER & YEAR:
Spring 2022
|
SYLLABUS
INSTRUCTOR:
Merel Keijsers
EMAIL: [email protected]
HOURS:
MW 11:30 AM 12:45 PM
TOTAL NO. OF CONTACT HOURS:
45
CREDITS:
3
PREREQUISITES:
Prerequisite: PS 210
OFFICE HOURS:
|
|
COURSE DESCRIPTION:
The course introduces students to the statistical methods commonly used in psychological research and provides the
necessary foundation in statistical reasoning to think critically about psychological findings reported in research articles and
in the media. Students will learn how to use statistics in the context of research, what statistical test is appropriate given the
research design and the type of data collected, and why statistical tests are used to draw conclusion in research. They will also
learn how to write up their own statistical analyses in APA style. The course includes a laboratory component where students
will familiarize themselves with statistical software and will learn how to use it for managing and analyzing data. Sample
topics include: scales of measurements, measures of central tendency and variability, the logic of hypothesis testing
(including limitations and modern approaches), parametric and nonparametric tests, effect size, confidence intervals, power
and sample size.
Minimum passing grade for students enrolled for the BA in Psychological science: C-
|
SUMMARY OF COURSE CONTENT:
The course introduces students to the statistical models and tests commonly used in psychological research and provides the necessary foundation in statistical reasoning to think critically about psychological findings reported in research articles and in the media. Students will learn how to use statistics in the context of research, what statistical test is appropriate given the research design and the type of data collected, and why statistical tests are used to draw conclusion in research. They will also learn how to code for their own tests using R, and to write up their own statistical analyses in APA style.
The course includes a laboratory component where students will familiarize themselves with statistical software and will learn how to use it for managing and analyzing data
|
LEARNING OUTCOMES:
By the end of this course, students should be able to
- Identify and use appropriate statistical procedures and terminology.
- Be comfortable using statistical software to manage and analyze data.
- Understand how statistical methods are used to test hypotheses.
- Interpret quantitative data displayed in statistics, graphs, and tables, including statistical symbols in research reports.
- Accurately summarize and present statistical results in a meaningful manner both orally and in writing. Write using APA Style.
|
TEXTBOOK:
| Book Title | Author | Publisher | ISBN number | Library Call Number | Comments | Format | Local Bookstore | Online Purchase |
| Discovering statistics using R | Andy Field, Jeremy Miles, Zoe Field | SAGE publications | ISBN-13: 978-1446200469 | | | | | |
|
REQUIRED RESERVED READING:
RECOMMENDED RESERVED READING:
|
GRADING POLICY
-ASSESSMENT METHODS:
| Assignment | Guidelines | Weight |
| Midterm | Written exam that covers students' understanding of different tests and how they work | 20% |
| Final | Written exam that covers students' understanding of different tests and how they work | 20% |
| assignment 1 | Coding and writing-up assignment where students create their own t-test function | 12.5% |
| assignment 2 | Coding and writing up assignment where students perform and report on an ANOVA test | 12.5% |
| assignment 3 | Coding and writing up assignment where students perform a categorical data analysis and reliability test | 12.5% |
| assignment 4 | Coding and writing up assignment where students have to create two versions of a graph (using the same data): one misleading version and one accurate | 12.5% |
| Group presentation | In groups of 3, students hold a short (20 minute) presentation on a topic like p-hacking, the replication crisis, frequentist vs Bayesian approaches to testing, etc. | 10% |
-ASSESSMENT CRITERIA:
AWork of this quality directly addresses the question or problem raised and provides a coherent argument displaying an extensive knowledge of relevant information or content. This type of work demonstrates the ability to critically evaluate concepts and theory and has an element of novelty and originality. There is clear evidence of a significant amount of reading beyond that required for the course.
BThis is highly competent level of performance and directly addresses the question or problem raised.There is a demonstration of some ability to critically evaluatetheory and concepts and relate them to practice. Discussions reflect the student’s own arguments and are not simply a repetition of standard lecture andreference material. The work does not suffer from any major errors or omissions and provides evidence of reading beyond the required assignments.
CThis is an acceptable level of performance and provides answers that are clear but limited, reflecting the information offered in the lectures and reference readings.
DThis level of performances demonstrates that the student lacks a coherent grasp of the material.Important information is omitted and irrelevant points included.In effect, the student has barely done enough to persuade the instructor that s/he should not fail.
FThis work fails to show any knowledge or understanding of the issues raised in the question. Most of the material in the answer is irrelevant.
-ATTENDANCE REQUIREMENTS:
ATTENDANCE REQUIREMENTS AND EXAMINATION POLICY
You cannot make-up a major exam (midterm or final) without the permission of the Dean’s Office. The Dean’s Office will grant such permission only when the absence was caused by a serious impediment, such as a documented illness, hospitalization or death in the immediate family (in which you must attend the funeral) or other situations of similar gravity. Absences due to other meaningful conflicts, such as job interviews, family celebrations, travel difficulties, student misunderstandings or personal convenience, will not be excused. Students who will be absent from a major exam must notify the Dean’s Office prior to that exam. Absences from class due to the observance of a religious holiday will normally be excused. Individual students who will have to miss class to observe a religious holiday should notify the instructor by the end of the Add/Drop period to make prior arrangements for making up any work that will be missed. The final exam period runs until ____________
|
|
|
ACADEMIC HONESTY
As stated in the university catalog, any student who commits an act of academic
dishonesty will receive a failing grade on the work in which the dishonesty occurred.
In addition, acts of academic dishonesty, irrespective of the weight of the assignment,
may result in the student receiving a failing grade in the course. Instances of
academic dishonesty will be reported to the Dean of Academic Affairs. A student
who is reported twice for academic dishonesty is subject to summary dismissal from
the University. In such a case, the Academic Council will then make a recommendation
to the President, who will make the final decision.
|
STUDENTS WITH LEARNING OR OTHER DISABILITIES
John Cabot University does not discriminate on the basis of disability or handicap.
Students with approved accommodations must inform their professors at the beginning
of the term. Please see the website for the complete policy.
|
|
|
SCHEDULE
|
|
|
Week
|
Topic
|
|
Readings
|
|
Week 1
|
Why stats + throwback to methods
|
|
Chapter 1
|
|
Week 2
|
Descriptive Statistics
|
|
Chapter 2
|
|
Week 3
|
Hypothesis testing
|
|
Chapter 1 + selected reading (will follow)
|
|
Week 4
|
Probability, z-statistic and t-test
[handout assignment 1]
|
|
Chapter 9
|
|
Week 5
|
GLM 1; an introduction
|
|
Chapter 10 (and 7?)
|
|
Week 6
|
GLM 2; one-way ANOVA
|
|
Chapter 10
|
|
|
|
|
Week 7
|
GLM 3; factorial ANOVA
[handout assignment 2]
|
|
Chapter 12
|
|
Week 8
|
Midterm; GLM 4 (ANCOVA, linear regression)
|
|
Chapter 11; Chapter 7
|
|
Week 9
|
Categorical data
|
|
Chapter 18
|
|
Week 10
|
Correlation, reliability, validity
[handout assignment 3]
|
|
Chapter 6
|
|
Week 11
|
Data visualisation
|
|
Chapter 4
|
|
Week 12
|
Some more hypothesis testing
[handout assignment 4]
|
|
Selected readings (will follow)
|
|
Week 13
|
Group presentations
|
|
|
|
Week 14
|
Review
|
|
|
|
|
|