Statistics in scientific research 15-SWBN-11

Week 1: Why statistics?
Week 2: Installing and running R
Week 3: Installing and loading packages; working with files and data frames; functions
Week 4: Tables
Week 5: Graphs
Week 6: Mean, Median, Mode, Measures of variability
Week 7: Further descriptive statistics
Week 8: Data subsetting and transformation, sorting and merging
Week 9: Scripts, loops, and conditionals
Week 10: Probability and statistical inference
Week 11: Estimating from samples
Week 12: Hypothesis testing
Week 13: Comparing frequencies
Week 14: Comparing two means
Week 15: Comparing several means: ANOVA

Module learning aims

• familiarize participants with descriptive and inferential statistics • make them understand the philosophical and logical foundations of statistical inference • teach participants how to summarize, graph, and analyze various types of data, so they can conduct such analyses on their own • teach participants (with no programming or mathematical background assumed) how to use R hands-on, currently the leading open-source environment for statistical computing • give participants skills useful in analyzing their own data for their thesis work or any other project involving quantitative data • allow participants to read published literature critically

Major

English philology

Methods of teaching for learning outcomes achievement

(in Polish) wykład interaktywny, wykonywanie zadań, praca na platformie Moodle

Course module conducted remotely (e-learning)

This course is offered as a partial e-learning course, which means that in place of 15% of the class contact time you're given extra time to work outside of class. This concerns especially: - the practice in coding in R - studying the two textbooks and my R companion to Woods et. al. (see below)

Student workload (ECTS credits)

(in Polish) Godziny zajęć (wg planu studiów) z nauczycielem 30h Praca własna studenta# 30h (wykonywanie zadań, praca na platformie e-learningowej, przygotowanie do zadań zaliczeniowych, wykonanie projektu) SUMA GODZIN 60 SUMARYCZNA LICZBA PUNKTÓW ECTS DLA MODUŁU (PRZEDMIOTU) 2

Cycle of studies

2nd cycle

Year of studies (where relevant)

Year 1

Pre-requisites in terms of knowledge, skills and social competences

• you will be working on your own computer, so you will need a portable computer running Windows, Mac OS, or Linux (Android currently does not support R) • no special mathematical knowledge or programming experience is required, just very basic computer literacy • if you can, please install R (https://www.r-project.org) and RStudio (https://www.rstudio.com/home) prior to the first class and make sure you have an active internet connection (eduroam or your own)

Course coordinators

Term 2019/SL:

Robert Lew

Term 2022/SL:

Ewa Tomczak-Łukaszewska

Term 2020/SL:

Robert Lew

Term 2018/SL:

Robert Lew

Term 2023/SL:

Ewa Tomczak-Łukaszewska

Term 2021/SL:

Ewa Tomczak-Łukaszewska

Term 2024/SL:

Ewa Tomczak-Łukaszewska

Learning outcomes

• participants will have a grasp of the concepts of descriptive and inferential statistics
• they will understand the philosophical and logical foundations of statistical inference
• they will be able to summarize, graph, and analyze various types of data, so they can conduct such analyses on their own
• they will be able to use R hands-on
• they will be able to read published literature critically when it comes to data analysis methods

Assessment criteria

Your final grade will reflect the following: attendance, performance in class, assignments, and quizzes

Bibliography

Navarro, Daniel. 2015. Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.5). Adelaide, Australia: University of Adelaide. (http://ua.edu.au/ccs/teaching/lsr).
R Core Team. 2015. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. (https://www.R-project.org).
Woods, Anthony, Paul Fletcher and Arthur Hughes. 1986. Statistics in language studies. Cambridge: Cambridge University Press.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 15-SWBN-11 in USOSweb