シラバス参照

開講年度(Academic Year) 2020 
科目コード/科目名
(Course Code / Course Title)
自動登録/Module4 
テーマ/サブタイトル等
(Theme / Subtitle)
Survey Methods and Quantitative Analysis 
担当者 (Instructor) フォウラー,R(FOWLER RANDY) 
時間割 (Class Schedule) 秋学期他 (Fall Others)     オンライン
単位 (Credit) 2単位(2 Credits) 
科目ナンバリング
(Course Number)
MIB5011 
使用言語
(Language)
英語
(English) 
備考 (Notes)  
テキスト用コード (Text Code) KN004 



授業の
目標
Course
Objectives
Students will master the fundamentals of survey techniques and statistical analysis in the business context, as well as understand the uses and limitations of survey research. 
 
授業の
内容
Course
Contents
Lectures will cover important topics on survey techniques and statistical analysis, including matching research design with ontology, designing questions and survey format, sampling and data collection, hypothesis testing, ANOVA, and regression analysis. Challenges such as survey error and nonresponse bias will also be discussed. Students will also have the opportunity to become acquainted with statistical analysis tools by trying them in class under instructor supervision. All students will apply what they have learned by undertaking a survey research project, writing up the results to submit as a final report, and presenting their progress in slide presentations at the midterm (discussing research design) and at the conclusion of the course (discussing the entire project including analysis and results).  
 
授業計画
Course
Schedule
1. Overview and Introduction  
2. Ontology, Purposes of Survey Research, Sampling 
3. Sampling Error; Nonresponse Bias 
4. Survey Questions  
5. Validity; Survey Design 
6. Survey Interviewing; Preparing Data for Analysis; Ethical Issues; Reporting Survey Research 
7. Midterm presentations of Research and Survey Design 
8. Midterm presentations of Research and Survey Design (II) 
9. Statistical Analysis 
10. Statistical Analysis (II) 
11. Regression Analysis 
12. Regression Analysis (II)  
13. Conclusion & Review  
14. Final Research Presentations 
授業時間外
(予習・復習
等)の学習
Study
Required
Outside
of Class
Students should review material before class, and come prepared to work on computer-based quantitative data analysis. 
成績評価
方法・基準
Evaluation
種類(Kind) 割合(%) 基準(Criteria)
平常点(In-class Points) 100  %
最終レポート(Final Report)(40%) 、Midterm Research Presentation(30%) 、Final Research Presentation(30%)
テキスト
Textbooks
None
参考文献
Readings
その他
(HP等)
Others
(e.g. HP)
注意事項
Notice
 


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