Basic Statistics
Course content
This course builds up on the general knowledge and familiarity gained with quantitative methods and working with data in R students gained in Elementary Sociological Methods I to enable them to become competent consumers of current quantitative sociological work and skilled producers of basic statistical analyses. The course progression starts with a broad overview of statistics and its applications, covers basic descriptive measures and visualizations, then introduces ordinary least squares regression as the workhorse of contemporary quantitative sociology, and closes with the fundamentals of how to go from describing relationships to assessing whether they represent reliable patterns or simple chance.
Compulsory course on the 2nd semester BSc in sociology
Knowledge:
Account for the logic and use of:
- levels of measurement and scale quality
- univariate measures of categorical and continuous variables
- z-standardization
- frequency and cross-tables
- basic measures of relations such as correlation
- ordinary least squares (OLS) regression
- statistical controls in regression analysis
- fundamentals of sampling theory
- fundamentals of inferential statistics, including hypothesis
testing through t-tests
Students learn to account for these topics. They learn to
explain the logic behind the use of statistical moments,
statistical test theory and statistical control in cross-reference
tables in social science research. They also learn to reflect on
the potential and limitations of statistical generalisation,
statistical control and the use of statistical moments and
measurements of relations.
Skills:
- calculate and report descriptive statistical measures for categorical and continuous variables
- produce and report on frequency, cross-, and summary descriptive tables
- implement basic variable transformations such as standardization or binarizing variables
- calculate and report basic measures of relations such as correlations
- conduct and report the result of bivariate and multiple OLS regression with categorical and continuous predictors
- formulate, conduct, and report the results of statistical hypothesis tests e.g., for group means and relative comparisons
- critically evaluate results of basic statistical analyses in
relation to a given problem in a way that demonstrates an
understanding of quantitative data and methodology, including its
potential and limitations.
Competences:
- acquire familiarity with advanced quantitative methods such as causal inference and data mining
- convert their knowledge and skills in quantitative analyses into reports or studies involving competent use of basic descriptive and inferential statistical analyses.
Lectures and Exercises
De Veaux, Richard D., Paul F. Velleman, and David. E. Bock. 2021. Stats: Data and Models. 5th, Globa ed. Harlow, UK: Pearson Education Limited.
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam date.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- Free written take-home essays are assignments for which students define and formulate a problem within the parameters of the course and based on an individual exam syllabus. The free written take-home essay must be no longer than 10 pages. For group assignments, an extra 5 pages is added per additional student. Further details for this exam form can be found in the Curriculum and in the General Guide to Examinations at KUnet.
- Examination prerequisites
-
Students need to hand in 10 quizzes throughout the course to be eligible for the exam.
- Aid
- All aids allowed
The Department of Sociology prohibits the use of generative AI software and large language models (AI/LLMs), such as ChatGPT, for generating novel and creative content in written exams. However, students may use AI/LLMs to enhance the presentation of their own original work, such as text editing, argument validation, or improving statistical programming code. Students must disclose in an appendix if and how AI/LLMs were used; this appendix will not count toward the page limit of the exam. This policy is in place to ensure that students’ written exams accurately reflect their own knowledge and understanding of the material. All students are required to include an AI declaration in their exam submissions regardless of whether they have used generative AI software or not. This declaration should be placed as the last page of the exam submission. Please note that the AI statement is not included in the calculation of the overall length of your assignment. The template for the AI statement can be found in the Digital Exam system and on the Study Pages on KUnet under “Written exam”. Exams that do not declare if and how AI/LLMs were used will be administratively rejected and counted as one exam attempt.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
-
Exam information:
The examination date can be found in the exam schedule here
The exact time and place will be available in Digital Exam from the middle of the semester.
- Re-exam
-
Reexam info:
The reexamination date/period can be found in the reexam schedule here
Same as the ordinary exam.
Note! This is a mandatory course, and it is therefore only possible to take the exam during the spring, as the course is not offered in the fall
Criteria for exam assessment
Please see the learning outcome
- Category
- Hours
- Lectures
- 28
- Preparation
- 125
- Exercises
- 28
- Exam
- 25
- English
- 206
Kursusinformation
- Language
- English
- Course number
- ASOB16107U
- ECTS
- 7,5 ECTS
- Programme level
- Bachelor
- Duration
-
1 semester
- Placement
- Spring
- Capacity
- Approx. 100 students
- Studyboard
- Department of Sociology, Study Council
Contracting department
- Department of Sociology
Contracting faculty
- Faculty of Social Sciences
Course Coordinators
- Mengni Chen (4-736b696e4679756934717b346a71)
- Jesper Fels Birkelund (6-776d813f3e414b6c7780787974397680396f76)
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Kursusinformation for indskrevne studerende