2025 (Current Year) Faculty Courses Liberal arts and basic science courses Humanities and social science courses
Statistics B
- Academic unit or major
- Humanities and social science courses
- Instructor(s)
- Shin Okubo
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Intensive
- Class
- -
- Course Code
- LAH.T201
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 19, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course examines descriptive statistics and inferential statistics, both are indispensable to the analysis of social survey data. Descriptive statistics summarize the characteristics of a sample, and inferential statistics generalize the characteristics of the population from a sample.
Students not only learn the basics of them, but apply them to analyze social survey data and discuss the results using practical approaches. This leads students to consider how to think about various issues in contemporary society (e.g. economic inequality, meritocracy, health, declining birthrate and aging population, time use, gender).
This course treats social gap and inequality as the main topic of analysis, using some large-scale social survey data.
Course description and aims
At the end of this course, students will be able to:
1) Understand the basic process of social surveys.
2) Learn basic methods of analysis of social survey data.
3) Adequately describe and explain social phenomena using analytic findings.
Keywords
social statistics, descriptive statistics, inferential statistics, probability, estimate, hypothesis test, analysis of variance (ANOVA), correlation, regression
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Using the handouts, the instructor will lecture on basic methods of statistics for social data analysis and practical analyses of survey data. After last class, the take-home test (including a review test and a short report) must be submitted by the due date.
This course is an intensive course. The schedule is as follows.
Thursday, 21nd Aug 2025: Class 1 (period 3-4), Class 2 (period 5-6), Class 3 (period 7-8)
Friday, 22rd Aug 2025: Class 4 (period 3-4), Class 5 (period 5-6), Class 6 (period 7-8)
Tuesday, 26th Aug 2025: Class 7 (period 3-4), Class 8 (period 5-6), Class 9 (period 7-8)
Wednesday, 27th Aug 2025: Class 10 (period 5-6), Class 11 (period 7-8)
Thursday, 28th Aug 2025: Class 12 (period 3-4), Class 13 (period 5-6), Class 14 (period 7-8)
Information on the lecture room will be provided as soon as decided.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction: The rudiments of social research methods, descriptive statistics and inferential statistics | Explain the function of social statistics, data collection method and the significance of descriptive statistics and inferential statistics. |
| Class 2 | Basis of inferential statistics [1]: Population and sample, probabitily and probabitily distribution, hypothesis test | Explain the relationship between inferential statistics and central limit theorem and the significance of random sampling. |
| Class 3 | Basis of inferential statistics [2]: Estimating the population mean and population proportion, the process of hypothesis testing | Explain point estimate, interval estimate and the process of hypothesis testing. |
| Class 4 | Basic analysis [1]: Crosstabulation and chi-square test, correlation coefficient and t-test | Explain chi-square test, scatter plots and correlation coefficient. |
| Class 5 | Basic analysis [2]: Mean difference test, significance testing with proportions (t-test, one-way analysis of variance) | Explain mean difference test, significance testing with proportions. |
| Class 6 | Basic analysis [3]: Consideration of Elaboration (multiple crosstabulation, partial correlation coefficient, two-way analysis of variance) | Explain analysis with a third variable. |
| Class 7 | Basis of regression analysis: OLS (ordinary least squares), Regression coefficient, coefficient of determination, BLUE (best linear unbiased estimator) | Explain regression coefficient, coefficient of determination and ordinary least squares. |
| Class 8 | Multiple regression analysis [1]: Partial regression coefficient, multicollinearity | Explain multiple regression analysis using quantitative variables as independent variables. |
| Class 9 | Multiple regression analysis [2]: Analysis of dummy variables, interaction, hierarchical multiple regression analysis | Explain multiple regression analysis using qualitative variables as independent variables. |
| Class 10 | Logistic regression analysis: Binomial logit, regression coefficient and odds ratio, maximum likelihood estimation method, multinomial logit, ordinal logit | Explain logistic regression analysis and points of caution in interpretation of the results. |
| Class 11 | Multilevel analysis: Random intercept model, random coefficient model | Explain the methods of analysis of nested data. |
| Class 12 | Panel data analysis: Fixed effect model, random effect model | Explain the methods of panel data analysis. |
| Class 13 | Basis of causal inference: Randomized controlled trial, difference-in-difference analysis, regression discontinuity design, propensity score analysis | Explain the basic concepts and methods of causal inference using social survey data. |
| Class 14 | Reading literature on social science | Read literature using social statistics and explain the relationship between results and discussion. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required. (Note: Handouts based on several reference books are distributed in PDF format.)
Reference books, course materials, etc.
Hoel, P. G., 1976, Elementary Statistics, 4th ed., John Wiley & Sons; ISBN-13: 978-0471016946
Kezuka, K., 2022, Introduction to Statistics for Social Sciences, Kodansha.(In Japanese); ISBN-13: 978-4065284506
Yamamoto, I., 2015, Econometrics for Empirical Analysis, Chuokeizaisha.(In Japanese); ISBN-13: 978-4502168116
Evaluation methods and criteria
Grades are based on class participation (20%) and a take-home test includes the following.
[1] Review test of the course content (40%)
[2] Short report on the results of specific analysis of different issues in contemporary society (40%)
Related courses
- LAH.T101 ： Statistics A
- LAH.T301 ： Statistics C
- LAH.S434 ： Essence of Humanities and Social Sciences38:Statistics
Prerequisites
Nothing in particular.
Other
This is an intensive course. If acquiring credits for this course is related to your own graduation, eligibility for undergraduate major affiliation or for independent research project for the Bachelor's Degree, please keep the following two points in mind and fully understand the risks before taking this course.
- Intensive courses are held in a short period of time, and since there are many classes per day, as a general rule, it is not possible to take excused absences.
- There is a possibility that grades will not be reported in time for graduation decisions.