Statistics one and a half. Quantitative sociology in practice. 3500-FAKL-STAT15
How to deal with statistical hypothesis verification when we have small samples?
How to study the nonlinear, multidimensional world with linear multiple regression?
What does statistics have to do with Guinness beer?
Where can I find all this in SPSS?
Understanding the principles of conducting correct statistical data analysis is extremely important when starting social research - it helps to correctly interpret the results of analyses and protects against duplicating erroneous patterns.
The classes are addressed to people who would like to learn new, interesting methods of statistical analysis and thus expand their knowledge of statistical methods in sociology beyond the level of the current mandatory first-year statistics course. At the same time, it is good preparation for further study of statistical methods useful in social research, in particular for the Statistics 2 course. The knowledge and skills offered in the course are an essential supplement to the workshop of a sociologist-empiricist.
The central concept used in the course will be multiple regression and its various useful variants, which allow for the analysis of interactions between variables and the consideration of nonlinear relations between them.
The issues of statistical inference will refer to the least squares method and the maximum likelihood method of parameter estimation. The verification of statistical hypotheses will include new issues concerning both large and small random samples.
Theoretical classes will be supplemented by workshops in the computer lab.
Type of course
Mode
Prerequisites (description)
Course coordinators
Learning outcomes
Has basic knowledge of the criteria of inference correctness
Knows basic methods and techniques of social research and can choose appropriate methods to solve basic research problems
Can select proper research methods and techniques to conduct an analysis of a particular problem
Can interpret simple social phenomena using basic statistical methods
Can use the basic functions of a chosen computer program for data analysis
Assessment criteria
a) Methods of verifying learning outcomes: Activity in classes. Passing tests. Doing homework (on the Kampus platform). Written final colloquium.
b) Components of the final assessment and their weight: homework 20%, written final colloquium 80%
c) Permissible number of excused absences: 2
d) Grading scale: 5! : 95%; 5 : above 90%; 4+ : from 85%; 4 : from 75%; 3+ : from 65%; 3 : from 50%; 2 : less than 50%
e) Conditions for admission to second term:
Second term applies only to the written final assessment and is also possible for people who received a positive grade in the first attempt. In the event of not getting an admission to take the written final colloquium, it is not possible to take the second term of written final colloquium.
f) Rules for the second term: the same as in the first term (applies to the written colloquium).
Bibliography
Lissowski G., Haman J., Jasiński M. 2008 or later, Podstawy statystyki dla socjologów. WN Scholar
- and materials on selected issues prepared by the teacher
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: