STATISTICS FOR LANGUAGE SCIENCES

This is an integrative course of the second-level degree course in Language Sciences. It contributes to the development of in-depth knowledge of quantitative methods and their application in the field of Language Sciences, as well as to the strengthening of the ability to analyse language data in a methodologically correct manner with reference to the objectives and the theoretical framework of reference.

- General outcome
The student will be introduced to the basic elements of statistics as a tool for describing and understanding reality expressed in number form. The relation between the information enclosed in numbers and the words employed to explain it and make it comprehensible will be explored. The student will be guided on the path, going from the basic definitions and concepts behind the need for knowledge, through data collection and analysis, to the reading and understanding of information expressed in statistical form. The course, therefore, aims to provide the tools allowing the student to proceed autonomously in the use of statistics, both to understand and correctly interpret results provided by others and to produce new results when conducting empirical research.

- Knowledge and understanding
The student will acquire in-depth knowledge concerning the main concepts and foundations of statistics, research methods and data collection, statistical indicators and measures, and, more in general, the tools to understand the numerical synthesis of reality. At the same time, the ability to understand better ways to formulate research questions and to carefully define the phenomenon under investigation so as to be able to use the quantitative statistical instruments that are available appropriately will be developed.

- Applying knowledge and comprehension
The student will be able to: reflect on, and take decisions about the main methodological issues relative to statistical data; know the main sources and modes of data search; read and understand statistical data extracting from it correct information and avoiding errors of interpretation; translate the areas of interest and the knowledge objectives into research questions and hypotheses; translate simple concepts into simple indicators and complex concepts into sets of indicators; plan the different stages of statistical data collection; identify the different modes of data collection in the light of objectives; design a simple questionnaire; carry out descriptive quantitative analysis; use the main inferential statistical instruments; write up a report to present the results properly.

- Making judgements
The student will develop the ability to elaborate complex information through the scientific approaches of the discipline; to discuss and reflect on the statistical methodology and on its results; to make autonomous and informed judgements and evaluations about the validity and feasibility of research proposals and research results (instruments, modes of analysis, and produced indicators).

- Communication skills
The student will master the basic specialized lexis of statistics to communicate clearly with specialists and non-specialists and to discuss methods and results of analyses with clarity and precision.

- Lifelong learning
The student will acquire the learning skills and strategies that will allow them to further their knowledge and competence, or further explore the main thematic areas of the discipline autonomously.

Basic mathematical and computer skills

1. Introduction to statistics
2. Experimental design
3. Data visualization
4. Descriptive statistics
5. Probability and distributions
6. Estimation and confidence intervals
7. Hypothesis testing
8. Comparing two populations
9. Correlation and simple linear regression

Agresti, A., Franklin, C. A., Klingenberg, B. (2017). Statistics: The art of science of learning from data. Pearson, 4th ed.
Spiegelhalter, D. (2019). The art of statistics: Learning from data. Penguin UK.

Additional suggested reading and materials are available on the Moodle platform.

The exam consists of a multiple-choice test, some exercises, and some open questions.
The exam will last 120 minutes.
- part one: ten (10) multiple choice questions;
- part two: two (2) practical exercises, e.g., a statistical calculation;
- part tree: one (1) open question.

The multiple-choice questions and the exercises allow for the assessment of the acquisition of the basic concepts; the open questions assess critical thought, critical analysis, and scientific writing. More in detail:
- Knowledge and comprehension: assessed through multiple-choice questions
- Applying knowledge and comprehension: assessed through multiple choice questions and practical exercises
- Making judgements: assessed through specific multiple-choice questions
- Communicative skills: assessed through the open questions
- Lifelong learning: assessed through the open questions

The open questions assess critical thought as well as the mastery of the content.

written

A. 18–20/30
Awarded when the student answers about half of the multiple-choice questions correctly and solves the exercises, obtaining about half of the available points. Shows sufficient but patchy knowledge and limited ability to apply statistical techniques. Open question: basic/essential level.

B. 21–26/30
Awarded when the student answers more than half of the multiple-choice questions correctly and solves the exercises above half of the available points. The grade is assigned in proportion to the number of correct answers (approximately 60%–75%), without serious errors. Shows generally adequate knowledge, not always in depth, with generally correct applications. Open question: adequate, with some imprecisions.

C. 27–30/30
Awarded when the student solves almost all exercises correctly and answers almost all multiple-choice questions correctly. Demonstrates good command of the topics and fully appropriate application of statistical techniques. Open question: good/excellent, clear and well argued.

D. 30/30 with honours (cum laude)
Awarded in the presence of complete knowledge and applied understanding across the syllabus: all multiple-choice questions correct and all exercises solved accurately. Open question: excellent in accuracy, critical depth, and clarity of exposition.

The course combines lecture-style presentations and hands-on lab sessions; students are required to bring a laptop to every class meeting.