# 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.

Education

Compulsory course on the 2nd semester BSc in sociology

This course is not open to credit- and Exchange students

Learning outcome

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.

Competencies

• 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.

Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Portfolio, -
Type of assessment details
Individual.
A portfolio assignment is defined as a series of short assignments during the course  that address one or more set questions and feedback is offered during the course. All of the assignments are submitted together for assessment at the end of the course. The portfolio assignments must be no longer than 10 pages in total.
Further details for this exam form can be found in the Curriculum and in the General Guide to Examinations at KUnet.
Exam registration requirements

Students need to hand in 10 quizzes throughout the course to be eligible for the exam.

Aid
All aids allowed

Policy on the Use of Generative AI Software and Large Language Models in Exams

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 if and how AI/LLMs have been used in an appendix, which 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.

Marking scale
Censorship form
No external censorship
Exam period

Exchange students and Danish full degree guest students please see the homepage of Sociology;
www.sociology.ku.dk under Education --> Exams

Re-exam

If the re-exam is taken during the ordinary exam period: see ordinary exam form

If the re-exam is taken during the re-exam period:

Written take-home essay with NEW formulated questions

Individual

Abovementioned applies to course registrations in Spring 2023 and onward.

If you have been registered for the course before Spring 2023, please write to the study administration: soc-studieadm@soc.ku.dk.

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

• 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
Schedulegroup
See Timetable
Capacity
Vejl. 120 personer
Studyboard
Department of Sociology, Study Council
##### Contracting department
• Department of Sociology
##### Contracting faculty
• Faculty of Social Sciences
##### Course Coordinators
• Mengni Chen   (4-7169676c44777367326f7932686f)
• Jesper Fels Birkelund   (6-6f657937363943646f7870716c316e7831676e)
Saved on the 28-02-2024

### Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students