PSYC 1004B – Introduction to Quantitative Methods in
Psychology
Department of Psychology
The University of Hong Kong
Lectures:
Thursdays
10:30 – 12:20, CPD2.58
Instructor:
Office:
Email:
Office hours:
Dr Dorita Chang
JCT658
changd@hku.hk
By appointment (BA)
Tutor/Coordinator:
Office:
Email:
Office hours:
Tutorial(s):
Mr Tommy Li
JCT618
dstea@hku.hk
Friday 1:30pm or (BA)
Thu 09:30 pm; 12:30 pm; 1:30 pm
Tutor:
Office:
Email:
Office hours:
Tutorial(s):
Mr Tommy Cheung
JCT712
tckl@connect.hku.hk
By appointment (BA)
Tue 11:30 am; 12:30 pm
Ms Ng Ka Wai
JCT618
kawaing@hku.hk
Tuesday 1:30pm or (BA)
Wed 12:30 pm; 1:30 pm
Course Description
This course is designed to provide students with the basic background in data analysis for
psychology and social science research. Basic descriptive statistics will first be introduced.
The logic of statistical inference and scientific explanation, and the merits and limitations
of quantitative approaches to the study of psychological phenomena will then be discussed.
Finally, basic inferential statistics and fundamentals of hypothesis testing will be
introduced. By the end of the course, you should have the knowledge and ability to
critically evaluate behavioural science research from a methodological and statistical
perspective, to have the fundamental skills required for conducting empirical research on
your own, and to be able to communicate scientific results accurately and concisely.
Learning Objectives
- To critically evaluate behavioural science research from a methodological and
statistical perspective.
- To understand basic research designs and their limitations.
- To select and execute statistical analyses for basic research questions.
- To be able to use specialized software (SPSS) in order to explore and analyse data.
- To be able to interpret, and report results of basic statistical analyses.
- To synthesize material in concise, written form.
Textbook
Caldwell, S. (2012). Statistics Unplugged (4th Edition). Wadsworth, Cengage.
For those not familiar with using SPSS, a useful reference text is listed below:
Kirkpatrick, L.A., Feeney, B. C. (2015). A Simple Guide to IBM SPSS for
Version 22.0. Cengage Learning.
Computer Resources
Lecture slides will be made available on Moodle. We will use SPSS for statistical analyses.
Assessment
Participation/Tutorials
Participation
SPSS Exercises (x2)
6%
14%
Homework
Homework Assignments
Final Integrated Assignment/Report
20%
10%
Mid-term quiz
Final quiz
15%
35%
Quizzes
There will be one mid-term quiz (Mar 24) and one comprehensive final quiz (Apr 28).
The quiz materials MUST be returned. Leaving the testing room with quiz materials will
be viewed as academic dishonesty. No make-up quizzes will be permitted. In the case of a
student missing a quiz due to medical reason (with a valid medical proof), his/her
performance in the missed quiz will be predicted based on his/her performance in the other
components of the course at the end of the semester.
Academic Dishonesty
Academic dishonesty will not be tolerated. Any student who engages in any form of
academic dishonesty (e.g., cheating on exams, plagiarism, interfering with grading) will
receive a grade of F in this course and will be reported to the Office of Student Conduct &
Ethical Development for further disciplinary action. There will be no exceptions. If you are
not sure what constitutes the academic offense of plagiarism, consult your Lecturer or
Tutor. You may also consult the relevant HKU webpage on plagiarism at
http://www.rss.hku.hk/plagiarism.
Plagiarism
A hardcopy and a softcopy are required for all written assignments. The softcopy will be
checked for plagiarism against a database of articles, books, webpages, and essays
submitted by students at HKU and other universities. No credit will be given for an
assignment that contains plagiarized materials. Further penalties will also be applied. These
penalties include a zero mark for participation in course tutorials and a zero mark for the
course. Plagiarism will also be reported to your Faculty for consideration of possible
disciplinary action.
Assignment Submissions
No late assignments will be accepted, unless a valid medical proof (medical certificate) is
presented. Assignments are due at the start of the lecture on the day of the deadline. Each
assignment submission should be accompanied by a title page with the course code,
instructor’s name, your name, UID, and tutorial session written clearly.
WK
1
DATE
Jan 21
CONTENTS
Introduction
READINGS
TUTORIAL/EXERCISE
ASSIGNMENT DUE
2
Jan 28
Basic descriptive statistics (measures of central
tendency and variability)
Chapter 2
3
Feb 4
Chapters 3, 4
1. Intro to SPSS; Describing
and Visualizing Data
4
Feb 11
Distributions (visualizing freqs, percentile ranks)
Z-scores and the normal distribution
No class (Lunar new year)
5
Feb 18
Correlation and the simple linear regression
Chapter 12
2. Score standardization and the
normal distribution
6
Feb 25
Sampling and the central limit theorem
The confidence interval
Chapters 5, 6
3. Correlation and Regression
7
Mar 3
Chapter 9
4. SPSS Exercise 1
Assignment 2
8
Mar 10
Inferential statistics basic concepts (the null and
alternative hypotheses; Type I and II errors)
No class (Reading Week)
9
Mar 17
10
Mar 24
11
Mar 31
Hypothesis testing: t test, two related and two
independent samples
Chapter 8
5. Hypothesis Testing I
Assignment 3
12
Apr 7
Chapter 10
6. Hypothesis Testing II
13
Apr 14
Hypothesis testing: single-variable, multi-level
designs. The basic ANOVA and multiple
comparisons.
Power
(revisit Power and
Effect, Chapter 9)
7. Hypothesis Testing III
14
15
Apr 21
Apr 28
Review
Final Quiz (10:30 – 12:20; Venue: CPD 2.58 & CPD LG.07)
16
May 5
Final Assignment Due
Assignment 1
Inferential statistics: concepts reviewed
Chapter 7
Hypothesis testing: t test, single sample
Review
Mid-term Quiz (10:30 – 12:20; Venue: CPD 2.58 & CPD 2.16)
Assignment 4
8. SPSS Exercise 2
Note: The schedule, readings, and assignments are subject to change. Any changes will be announced in class.
Final Assignment