The Community, Lingnan University
Course Title
: Social Statistics
Course Code
Number of Credits / Semester
Number of Teaching Hours / Week
Pre-requisite
:
:
:
:
SSC 001
3
3
None
Lecturers:
Dr. LU Jiafang (Office: SO312; Email: lujf@ln.edu.hk; Tel: 26167153)
Mode of Tuition
2-hour lecture and 1-hour tutorial per week
Course Description
Statistical reasoning is vital for students majoring in social sciences to achieve critical
and rational reasoning. This course aims at helping them develop the ability in
statistical thinking and reasoning in the social and behavioral science perspective.
Fundamentals of statistics like data-type recognition, data variability and data shape
are introduced first to pave way for students to explore inferential statistical
applications such as hypothesis testing and the tests of significance. Meanwhile,
correlation analysis like simple regression is included as the final remark in this
course. This course equips students with a much more solid background in social
statistics, which is useful for their future pursuit of studies and helps them improve the
appreciation of their study in the bachelor degree’s level. They will also learn how
to use statistical software package like SPSS/PC+ to tackle statistics problems.
Learning Outcomes
Upon completion of this course, students should be able to:
1. demonstrate the ability to respond to an argument using statistical information;
2. critically evaluate various statistical studies presented in daily life; and
3. use statistical reasoning in real world issue analysis.
Measurement of Learning Outcomes
1. Take-home case exercises are used to assist students to evaluate statistical
issues related to social research;
2. Class discussion will be adopted to facilitate students to respond to an
argument using statistical information; and
3. Quizzes, Mid-term and final examinations are arranged to assess students’
ability to apply statistical reasoning.
The Community, Lingnan University
Indicative Contents
Introduction: Scientific reasoning in social sciences.
Data and Measurement: Level of Measurement and Forms of data.
Descriptive Statistics: Data central tendency, and data Dispersion.
Inferential Statistics: Probability, sampling, probability distributions, tests of
significance.
Correlation Analysis: Contingency tables and simple regression analysis
Textbook (Required)
Sirkin, R. M. Statistics for the Social Sciences. Third Edition. Sage, 2005.
Supplementary Readings
Schacht, S. P. and J. E. Aspelmeier. Social and Behavioral Statistics: A User-Friendly
Approach. Second Edition. Westview, 2005.
Healey, Joseph F. 2005. Statistics: A Tool for Social Research. 7th edition, Belmont,
Cal.: Wadsworth.
Teaching and Learning Methods
A series of lectures, exercises, and class discussion will be given to students with core
course contents delivered. After learning a new statistics concept, students are
encouraged to use the available statistical software packages to help in their analyses.
An additional 2 or 3 hours per week are expected for private/group study outside the
class.
Assessment Methods
Continuous Assessment (60%)
Exercises & Classroom Discussion: 20%
Quizzes: 20%
Mid Term 20%
Final Term Examinations (40%)
The Community, Lingnan University
APPENDIX
Lecture Schedule
Week
Date
Schedule
Readings
Week 2
September 10
Lecture
Chapter 1, 2
Week 3
September 17
Lecture
Chapter 4
Week 4
September 24
Quiz 1
Week 5
October 1
—
Week 6
October 8
Lecture
Chapter 5
Week 7
October 15
Lecture
Chapter 6
Week 8
October 22
Mid Term
Week 9
October 29
Lecture
Chapter 8
Week 10
November 5
Lecture
Chapter 7, 8
Week 11
November 12
QUIZ 2
Week 12
November 19
Lecture
Chapter 9
Week 13
November 26
Lecture
Chapter 10
Week 14
December 3
Computer Application
Guidelines for Tutorial
1. All students are required to attend tutorials. Absence in tutorial without
justifiable reasons will adversely affect one’s final score (one mark per absence);
2. Students should attend tutorials punctually. No attendance will be counted 10
minutes after the tutorial;
3. Before each tutorial, student is required to finish assigned exercises individually;
4. In the first 15 minutes of a tutorial, students work in groups and check their
solutions with each other until all of the group members get into agreement on a
group solution;
5. The instructor will randomly pick up a group member from each group to present
exercise solutions; the correctness of solutions and clearness of explanation will
be evaluated.