| Description |
In this introductory online course for Bachelor students we will discuss the foundational basis of quantitative research methodologies applicable to empirical research in English linguistics. Students will be introduced to the notions of hypothesis testing, central tendency in data, experimental designs and basic statistical procedures. We will discuss basic steps in experimental designs, and look at statistical procedures for data analysis (t-tests and linear models) expressed in R programming language. By the end of this course students should have the ability to perform simple statistical analysis in R (t-tests) and discuss experimental research designs. This proseminar is recommended for students who are thinking of doing an applied project and/or write a BA or MA thesis in English linguistics.
Instructions:
The course is constructed as an online proseminar with all the materials located on Moodle. For each week participants will be presented with a video lecture (uploaded on Wednesdays), slides and a reading assignment. The reading assignment will
broadly cover the topics discussed in video lecture. It is strongly recommended that students read the reading assignments before the watching the lecture, as this will allow them to have a better understanding of topics discussed. The course is broadly split into three parts. Part 1 deals with the theoretical concepts of experimental designs and analysis. Part 2 provides introduction to R environment and shows how the theoretical tools learned in Part 1 can be applied for data analysis. Part 3 provides a summary of the course and discusses broader topics of interest in statistics and empirical research Evaluation of the course:
The course will be evaluated in a series of multiple-choice questionnaires that will cover the topics discussed in Parts 1 and 2 of the course.
Outline of the course and reading materials:
0. Introduction to the course
1. Empirical research designs
o Main reading: John W. Creswell, Research design: Qualitative, quantitative, and mixed methods approach. Chapter 1
2. Variables, Sampling and Hypothesis testing
o Main reading: Timothy Urdan, Statistics in Plain English, Chapter 1
3. Experimental designs
o Main reading: Timothy Urdan, Statistics in Plain English, Chapter 1 - 2
4. Measurements and Central Tendency
o Main reading: Timothy Urdan, Statistics in Plain English, Chapter 3 - 4
5. Statistics part. 1
o Main reading: Timothy Urdan, Statistics in Plain English, Chapter 7
6. Statistics part. 2
o Main reading: Timothy Urdan, Statistics in Plain English, Chapter 7
7. Introduction to R: basic operations with data
o Main reading: The Art of R Programming, CH 1 - 6 (selective reading)
8. Introduction to statistical tests in R: t-tests and linear models
o Main reading: The Art of R Programming, CH 1 - 6 (selective reading)
9. Summary of the course and advanced topics
o Advanced readings:
. Cohen, J. (1990) Things I have learned (so far)
.Henrich, J. et al., (2010) The weirdest people in the world?
. Kuzon, W. et al, (1996) The 7 deadly sins of statistical analysis
.Clark, H.H. (1973) Language as fixed effect fallacy |