山口大学教務システム - 2101000204-Advanced Data Analysis

開講年度	開講学部等
2026	大学院人間社会科学研究科（修士課程）
開講学期	曜日時限	授業形態	AL（アクティブ・ラーニング）ポイント
前期	水1～2	講義	10.0
時間割番号	科目名[英文名]			使用言語	単位数
2101000204	Advanced Data Analysis[Advanced Data Analysis]			英語	2
担当教員（責任）[ローマ字表記]					メディア授業
西尾建[NISHIO Tatsuru]					ー
担当教員[ローマ字表記]
西尾建　[NISHIO Tatsuru]
特定科目区分		対象学生		対象年次
ディプロマ・ポリシーに関わる項目			カリキュラムマップ(授業科目とDPとの対応関係はこちらから閲覧できます)

授業の目的と概要

This course introduces core concepts and analytical frameworks in quantitative data analytics, with a particular focus on survey research design and empirical data analysis. Teaching will be conducted through a combination of lectures and computer-based workshops held at the Media and Information Technology Center. In the lectures, students will learn principles of questionnaire design, measurement and scale construction, data preparation, and methodological considerations in quantitative research.

In the computer workshops, students will design and implement their own surveys, collect and manage data, and conduct statistical analyses using SPSS. Particular emphasis is placed on the practical application of factor analysis to identify underlying constructs in survey data and regression analysis to examine relationships among variables and test research hypotheses. Through hands-on exercises, students will develop skills in selecting appropriate analytical techniques, interpreting statistical results, and presenting findings in an academic and professional context.

The course assumes no prior knowledge of SPSS and is designed for graduate students seeking to acquire both methodological rigor and practical competence in quantitative survey research.

授業の到達目標

The objective of this course is to develop the skills necessary to design and conduct survey-based research, perform multivariate analyses, and effectively present and report empirical findings at the level required for master’s and doctoral theses. Through lectures and hands-on data analysis, students will acquire practical competencies in questionnaire design, measurement and scale construction, data preparation, and the application of appropriate multivariate analytical methods.

In addition, the course aims to enhance students’ ability to interpret statistical results, link analytical outcomes to research questions and theoretical frameworks, and communicate findings in a clear and academically rigorous manner. By the end of the course, students are expected to be able to produce well-structured academic reports that meet the standards of graduate-level research and contribute to the development of their master’s or doctoral thesis projects.

授業計画

【全体】

This course provides an introduction to quantitative methods of data analysis, focusing on applications in social science and applied research. The course begins with descriptive statistics and basic data presentation using tables and graphs, followed by fundamental probability concepts such as the normal distribution, sampling distributions, and the central limit theorem.

Inferential statistical techniques are then introduced, including independent t-tests, correlation analysis, and analysis of variance (ANOVA), with an emphasis on hypothesis testing and interpretation of results. The course also covers simple and multiple regression analysis, including the assessment of multicollinearity using Variance Inflation Factors (VIF).

In the later part of the course, students learn multivariate methods commonly used in survey research, including Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Depending on students’ progress in multivariate analysis, Structural Equation Modelling (SEM) is introduced as an advanced topic to provide an overview of its conceptual framework and research applications. Throughout the course, students apply these methods using SPSS and develop skills in interpreting and reporting analytical results in an academic context.

	項目	内容	授業時間外学習	備考

第1回	Introduction	Guidance	Preparation and review of lecture contents. Estimated study time: approximately 4 hours

第2回

Basic Analysis

Research Methodology