Type of Credit: Required
Credit(s)
Number of Students
This course introduces undergraduates to the fundamental terminology, concepts, interpretation, and communication of the descriptive and inferential statistics most commonly encountered in scientific research.
Learning statistics is like learning an everyday language that is widely used in the scientific community. For example, many research uses surveys, public opinion polls, censuses, and other quantitative data sources to document, describe, and explain a wide range of social phenomena. To join the conversations being conducted in this realm of research, you must be literate in the vocabulary of research, data analysis, and scientific thinking. Knowledge of statistics will enable you to understand professional research literature, communicate with experienced social scientists, conduct quantitative research, and help you access the growing body of social science knowledge. In addition, the course will help students to learn how to use data offered by international data archive projects such as International Social Survey Program or Asian Barometer to explore important social or political issues in the region.
能力項目說明
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This course is a required foundational course for all International College of Innovation students. It introduces students to the essential statistics tools and shows how they are used to analyze quantitative data. A fundamental understanding of these tools is a critical foundation for scientific research. The course covers descriptive statistics, inference from samples, hypothesis testing, and various basic statistical analyses. The course will also introduce R, a free statistical computing and graphics software.
Upon successful completion of this course, you will be able to complete the following tasks: 1. Explain basic concepts of social statistics (e.g., population vs. sample, sampling distribution). 2. Summarize numeric data by computing descriptive statistics (e.g., mean, variance) and creating tables and graphs. For each procedure, you will learn a hand calculation method (using calculators) and a computer method (using software such as R). 3. Compute various inferential statistics (e.g., t-score) using hand calculation and computer methods. 4. Apply probability theory to test research hypotheses. 5. Explain the differences among various statistical techniques and identify an appropriate method for a given set of variables and research questions. |
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
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Week |
Topic |
Content and Reading Assignment |
Teaching Activities and Homework |
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1 |
Course Introduction |
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Lecture |
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2 |
Statistics and Social Research |
Ch. 1 |
Lecture; In-class Discussion; Homework |
|
3 |
Basic Descriptive Statistics I |
Ch. 2 |
Lecture; In-class Discussion; Homework |
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4 |
Descriptive Statistics: Measures of Central Tendency |
Ch. 3 |
Lecture; In-class Discussion; Homework |
|
5 |
National Day |
Holiday |
|
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6 |
Descriptive Statistics: Measures of Dispersion |
Ch. 4 |
Lecture; In-class Discussion; Homework |
|
7 |
Introduction to R: The 1st Lesson |
|
Lecture; In-class Discussion |
|
8 |
The Normal Curve |
Ch. 5 |
Lecture; In-class Discussion; Homework |
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9 |
Introduction to Inferential Statistics: Sampling and the Sampling Distribution |
Ch. 6 |
Lecture; In-class Discussion; Homework |
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10 |
R: The 2nd Lesson |
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|
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11 |
The 1st Quiz and Review |
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Review |
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12 |
Estimation Process |
Ch. 7 |
Lecture; In-class Discussion; Homework |
|
13 |
Hypothesis Testing I: The One-Sample Case |
Ch. 8 |
Lecture; In-class Discussion; Homework |
|
14 |
Hypothesis Testing I: The One-Sample Case (continues) |
Ch. 8 |
Lecture; In-class Discussion; Homework |
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15 |
Hypothesis Testing II: The Two-Sample Case |
Ch. 9 |
Lecture; In-class Discussion; Homework |
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16 |
R project |
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Group project activity |
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17 |
R project |
|
Group project activity |
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18 |
Final Exam |
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Attendance: Class attendance is required. Statistics learning requires you to gradually but constantly build your knowledge and skills. It is tough to catch up once you get behind. You are also expected to contribute to the class by asking questions, participating in class discussions, and working with each other on in-class exercises. Therefore, your attendance is essential for making these contributions.
Reading Assignments: You are expected to read the assigned chapters before each session (see the class schedule above). To complete reading assignments successfully, you need to understand what is in each chapter. Therefore, in addition to highlighting the text and taking notes, I suggest you write down any specific questions. It would help if you planned to spend 3 hours before and 3 hours after the weekly class. You may find some chapters challenging to follow. Don't worry if this happens. It is crucial to finish reading the assigned chapter before each session to get a general idea about it and go back to it after class to ensure you understand the materials better.
Homework Assignments: Learning by doing is very important for your understanding of statistics. You will be given homework exercises at the end of most sessions. Homework questions for each chapter will be uploaded to Moodle. You will have at least a week to complete each assignment. If you start working on your homework early, you will have a chance to ask questions in the next class session before submitting your assignments. I will collect assignments at the beginning of the scheduled class sessions. You will lose points if you turn in your homework late (anytime after the class session starts and before 4:00 pm on the next day). You should upload your assignments to Moodle in MS Word format.
Group Project: There will be a two-week group project at the end of the semester (Week 16 & Week 18). You'll need to form project groups. Each group will have a maximum of 5 students per group and should consist of members from at least two countries. Each group will design a questionnaire to survey fellow students in the first week. The data collected will then be analyzed in the 2nd week with R.
Honor Code: Please help each other, by all means, to exchange notes for missed class sessions, study for exams, etc. However, the assignments (homework or quizzes) you turn in should result from your hard work. You should not copy your peer's work. In short, No Plagiarism or Cheating. Any violation, if confirmed after a third-party investigation, will result in a "zero" grade for that particular assignment or an "F" for the course, at my discretion.
Grading Homework Assignments: 55% Group Project: 15% Tests (quizzes including final): 20% Attendance: 10%
A+: 90-99 A: 85-89 A-: 80-84 B+: 77-79 B: 73-76 B-: 70-72 C+: 67-69 C: 63-66 C-: 60-62 D: 50-59
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Healey, J. F., 2021. Statistics: A Tool for Social Research and Data Analysis. New York: Cengage Learning. 11th edition. https://www.tsanghai.com.tw/book_detail.php?c=156&no=4403#p=1
Navarro, Danielle. Learning Statistics with R. https://learningstatisticswithr.com/