統計方法 Statistics in Language Studies
統計方法 Statistics in Language Studies
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課程基本資訊
| 項目 | 內容 |
| 課程名稱 | 統計方法 |
| 開課學期 | 110-1 |
| 授課對象 |
語言學研究所 神經生物與認知科學學程 |
| 授課教師 | 馮怡蓁 |
| 課號 | LING 7008 |
| 課程辨識碼 | 142 M0220 |
| 班次 | |
| 學分 | 3 |
| 全/半年 | 半年 |
| 必/選修 | 選修 |
| 上課時間 | 星期二 4,5(11:20~13:10)星期五 4,5(11:20~13:10) |
| 上課地點 | 樂學館305 |
| 備註 | 本課程以英語授課。密集課程。上課時間：11:30-13:20。 |
課程大綱
| 項目 | 內容 |
| 課程概述 | 待補 |
| 課程目標 | This course intends to provide an overview of statistics used in linguistic experiments. It is designed for students who have never taken college-level statistics before. Students who wish to attend higher level statistics courses should take statistics in the psychology department instead. Topics covered include research ethics, problem identification and hypothesis formation, variable determination, hypothesis testing (z-test, t-test, correlation, regression, ANOVA, chi-square, and some nonparametric tests), external validity, and conventional formats in research reports. |
| 課程要求 | Grading Your final grade for this course will be based on the total number of points you accumulate during the semester. There will be NO curving. You will be evaluated according to the following criteria: Participation 10% Quiz 10% Exam 1 15% Exam 2 20% Exam 3 25% Project 20% Participation As you might have already noticed, there are a lot of materials that need to be covered in class. To help you attain the designated knowledge level more smoothly and efficiently, the class will be conducted in a blended format, with a combination of pre-recorded lectures and real-time discussion and hands-on sessions. It is thus absolutely necessary for you to participate in all designated activities, both inside and outside class. This includes finishing your reading assignments BEFORE class, viewing pre-recorded lectures PRIOR to real-time sessions, and actively participating in real-time sessions. This way, we will have more time to zoom in on the sections that are particularly problematic. My plan is NOT to go over every detail in the textbooks, as that would not be an efficient way of using our limited class time and would largely reduce the amount of time available for things that we could only do in class. A successful class only comes naturally from a joint effort between you and me, each doing our parts, so please help me help you by coming to class prepared, and I will likewise do the same. If you have any questions/comments/suggestions, please do not hesitate to help me make this course better by giving me your input in class and also outside the classroom. I would really appreciate it if you could all actively participate in classroom activities and discussions. Lab The lab is designed to reinforce the material covered in the lecture portion. Through labwork, you will begin to apply the statistical methods you learn in lectures. You will also be introduced to the computer program R. Lab is intended to give you the opportunity to bring up any questions about the material covered in readings and lectures. Exams There are three noncumulative exams in total. No make-up exams can be given except in the case of death in the family or dire documented medical emergencies. Any other reasons for a make-up exam would only be possible if prior consent from the instructor is obtained. Please view exams only as a way to help me assess how well we have been doing. Course project You are required to turn in a set of statistical analyses on your research project. The project does not have to be fully completed, but the data have to be authentic. You are required to apply what we have covered in class and select appropriate analyses for your data. You will be given a 20-min presentation time slot on 1/7 for an oral report. During this time, you are to defend the statistical analyses you choose and highlight whatever significant findings you have found in your study. You can use whatever media you please but the style of presentation should be formal. You will be graded on both your presentation skills and audience responses, in addition to the content. The written report is due on 1/12, Thursday, at 5:00p. 10% of the grades will be deducted for each late day. It should be no more than a 10-paged, double-spaced, polished report incorporating the comments you have gathered in the presentation. The report should follow a formal format, and should include appropriate graphs, statistical details, and adequate interpretations. A note on collaboration Students often find it helpful to form study groups with classmates to discuss the readings and exercises each week. The course instructor encourages such cooperative study habits. However, each student must work independently in writing up the project report. |
| 參考書目 | 待補 |
| 指定閱讀 | Textbook 1. Rumsey, D. 2011. Statistics for Dummies. 2nd Edition. Wiley Publishing. 2. Rumsey, D. 2009. Statistics II for Dummies. Wiley Publishing. 3. Pagano, R. R. 2013. Understanding Statistics in the Behavioral Sciences. Wadsworth Publishing. 10th edition. 4. https://statistics.laerd.com/statistical-guides/repeated-measures-anova-statistical-guide.php 5. Winter, B. (2013). Linear models and linear mixed effects models in R with linguistic applications. arXiv:1308.5499. |
課程進度
| 週次 | 日期 | 單元主題 |
| 第1週 | 9/21,9/24 | Tuesday: Friday: 01 Introduction; scientific method; jargons (1:1-4, 21) / HW: Download R |
| 第2週 | 9/28,10/01 | Tuesday: 02 Descriptive statistics (1:5) / 03 Graphing techniques (1:6-7) Friday: 04 Random sampling and probability (3:8) |
| 第3週 | 10/05,10/08 | Tuesday: 05 Probability distribution (1:8-10) Friday: 06 Research design (1-16, 1-17) / 07 Sampling distribution (1-11) |
| 第4週 | 10/12,10/15 | Tuesday: 08 Margin of error (1-12, 1-13) Friday: Exam 1 |
| 第5週 | 10/19,10/22 | Tuesday: 09 Hypothesis testing (1-14, 1-15, 2-3) Friday: 10 One-way ANOVA (3-15) |
| 第6週 | 10/26,10/29 | Tuesday: 11 Multiple comparisons (3-15) Friday: 12 Repeated measures (4) |
| 第7週 | 11/02,11/05 | Tuesday: 13 Two-way ANOVA (2-12) Friday: Exam 2 |
| 第8週 | 11/09,11/12 | Tuesday: 14 Correlation & simple regression (1-18, 2-4) Friday: 15 Multiple regression & model selection (2-5, 2-6, 4) |
| 第9週 | 11/16,11/19 | Tuesday: 16 Nonlinear & Logistic regression (2-7, 2-8) Friday: 17 Chi-square (1-19, 2-13, 2-14, 2-15) |
| 第10週 | 11/23,11/26 | Tuesday: 18 Nonparametric tests (3-17) Friday: 18 Nonparametric tests (3-17) / Wrap-up |
| 第11週 | 11/30,12/03 | Tuesday: CP1: Project discussion Friday: Exam 3 |
| 第12週 | 12/07,12/10 | Tuesday: Project Friday: Project |
| 第13週 | 12/14,12/17 | Tuesday: Project Friday: Project |
| 第14週 | 12/21,12/24 | Tuesday: Project Friday: Project |
| 第15週 | 12/28,12/31 | Tuesday: Project Friday: Project |
| 第16週 | 1/04,1/07 | Tuesday: Project Friday: CP2: Presentation |
| 第17週 | 1/11,1/14 | Tuesday: Project Friday: CP3: Final report |
| 第18週 | 1/18,1/21 |
成績評量方式
| 編號 | 項目 | 百分比 | 說明 |
面談時間
| 編號 | 星期 | 開始時間 | 結束時間 |
| 備註 | 無 |