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Course Description
The educational statistics course provides students with knowledge and skills in order to complete final assignments in the form of theses, especially those that use a quantitative research approach along with methods for collecting data, processing or analyzing it and drawing conclusions based on the data collection and analysis carried out.
Program Objectives (PO)
- Mahasiswa mampu mengembangkan pemikiran logis, kritis, sistematis, dan kreatif dalam mengolah, menganalisis, dan menginterpretasikan data statistik untuk mendukung penelitian sejarah maupun ilmu sosial.
- Mahasiswa mampu merancang, melaksanakan, dan menilai penerapan metode statistik dalam proses pembelajaran sejarah, baik untuk analisis hasil belajar siswa maupun evaluasi efektivitas strategi pembelajaran.
- Mahasiswa mampu memecahkan kompleksitas permasalahan pembelajaran sejarah dengan memanfaatkan pendekatan interdisipliner dan multidisipliner melalui penerapan analisis statistik berbasis riset.
Aktifitas Pembelajaran
-
Pertemuan 1 Menganalisis perbedaan konsep statistik deskriptif dan inferensial -
Pertemuan 2 Menganalisis perbedaan skala pengukuran data -
Pertemuan 3 Mendesain grafik yang sesuai dengan jenis data -
Pertemuan 4 Menghitung mean, median, modus dari dataset sejarah -
Pertemuan 5 Menganalisis perbedaan nilai varians dan simpangan baku -
Pertemuan 6 Menghitung kuartil, desil, persentil -
Pertemuan 7 Menganalisis distribusi frekuensi data -
Pertemuan 8 Ujian Tengah Semester -
Pertemuan 9 Menganalisis peluang kejadian sederhana dan majemuk -
Pertemuan 10 Menganalisis perbedaan distribusi normal, binomial, poisson -
Pertemuan 11 Menghitung estimasi titik dan interval -
Pertemuan 12 Menghitung uji t, ANOVA, regresi sederhana -
Pertemuan 13 Menghitung uji t, ANOVA, regresi sederhana -
Pertemuan 14 Menghitung uji Chi-Square, Mann-Whitney -
Pertemuan 15 Mengolah data sejarah menggunakan software statistik -
Pertemuan 16 Ujian Akhir Semester
References
- Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python (2nd ed.). O’Reilly.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer.
- Huntington-Klein, N. (2021). The effect: An introduction to research design and causality. Chapman & Hall/CRC.
- Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC.
- Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press. Cambridge University Press & Assessment
- Cunningham, S. (2021). Causal inference: The mixtape. Yale University Press.
- Downey, A. B. (2021). Think Bayes: Bayesian statistics in Python (2nd ed.). O’Reilly.
- McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (2nd ed.). Chapman & Hall/CRC.
- Çetinkaya-Rundel, M., & Hardin, J. (2024). Introduction to modern statistics (2nd ed.). OpenIntro.
Lecturer
Difficult Things About Education.
$75$10
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