List of modes of study:

* Participation in classroom work

* Exercise(s)

* Assignment 

Learning outcomes: After the course, the students are familiar with different ways of conducting interviews, have a thorough understanding of the contextual nature and key features of interaction in interviews, and are able to critically evaluate studies based on interview data.

Teacher in charge: Matti Hyvärinen

Lecturers: Lotta Junnilainen, Mari Korpela,  Pirjo Nikander, Ilkka Pietilä, Eeva Puumala, Markku Sippola, Rebecca Lund

Course description:

Interviewing is one of the classic means of data collection. Recently, it is often framed more in terms of an interactional encounter instead of simple data gathering or collection.  Consequently, the emphasis is moving from questioning towards listening and encouraging. This course consists of four days of contact teaching, lectures from researchers with extensive expertise of various forms of interviews as well as workshops, in which the students have the chance to introduce their own research projects. The course covers key elements pertinent to data generation through interviewing as well as expert lectures on one-on-one interviews, focus groups, and interviews in narrative and ethnographic research. The workshops, based on student presentations on their ongoing doctoral thesis, provide a joint discussion platform to develop key questions, open up questions on various steps of the interview process, and to raise special questions linked with different data generation traditions. The discussions provide students the opportunity to share joint problems, and to receive feedback and new ideas from fellow students and lecturers on their research designs and analytic procedure.

Teaching: Contact teaching covers 22 hours (16 hours of lectures and 6 hours of workshops).

Pre-assignment: Before the course, enrolled students are expected to write a one page description of their own doctoral research, introducing research topics and questions, data utilized, and possible analytic methods in use. The pre-assignment should also include each participant’s prior ideas and potential concerns regarding generating interview data. The students will reconsider these ideas and concerns in their reflection papers that are prepared after the contact teaching period. Detailed instructions will be made available to enrolled students through Moodle.

Course reading: tba

Number of students: 25 students, of which 10 will have an opportunity to introduce their own research project to other participants. Selection will be made on the basis of pre-assignments. Enrolment period 1.8.-15.8.2018.

Study credits: Students introducing their own research in the workshops will receive 5 ECTS and other students 3 ECTS. For completing the course all students are expected to conduct the pre-assignment, actively participate in contact teaching, and prepare a reflection paper (3-5 pages) focusing on how the course has improved their skills to plan and conduct interviews as well as increased their understanding of data generation and analysis of interviews. Students’ presentations in workshops will consist of 10-15 minutes’ talks and a written summary (1 page) of the project. In addition, each presenter is also supposed to act as a discussant for another student’s presentation.

Preliminary schedule:

Thu, Sep 6

Room C6 (Main building)

10 :15 - 12 Matti Hyvärinen : «Introduction « Interviewing or asking questions ? »

12 :30 – 14 Markku Sippola : « Recruitment of interviewees: Problems and biases.”

14 :15 – 16 WORKSHOP : participants’ papers

Wed Sep 12

Room A3 (Main building)

10 :15 - 12 Pirjo Nikander : «Key (and difficult) questions to anyone planning to use and analyse interview data”

12:30  Mari Korpela: ”Interviewing in ethnographic research”

14 :15 – 16  WORKSHOP : participants’ papers

Thu Sep 13

Room C6 (Main building)

10 :15 – 12  Lotta Junnilainen : "Interviewing in ethnographic research on urban inequality”

12 :30 -14  Ilkka Pietilä : «Focus groups »

14 :15 – 16  WORKSHOP : participants’ papers

Fri Sep 14

Room C6 (Main building)

10:15 – 12 Eeva Puumala: “Transcultural interviews”

12:30 – 14 Rebecca Lund: “Interviewing in institutional ethnography”

This course explores methods for the analysis of longitudinal data and latent variable methods for linear models, generalized linear models, and nonlinear models.  Focusing on applications, this course explores: the analysis of repeated measures ANOVA, multivariate approaches, random-effects regression, covariance-pattern models, generalized-estimating equations and generalizations, latent variable methods including finite mixture modelling, and likelihood methods.  Students will develop expertise using the SAS and R computer packages, although no previous programming experience will be assumed.  Grading is based on homework and computer assignments and a project, as well as several exams.

Course begins with a lecture on Monday 17th of September at 2 pm.

There are three exams during the course.

Recommended preceding studies:

Basic courses of statistics and Regression analysis

Course Contents

Matrix basic operations, random number generation, cross-validation, Jackknife, Bootstrap, use of the R program

Modes of Study and Registration

Independent work and exercises, mainly for CBDA-students.

Please see the Moodle page for details and instructions.

Lectures of the course MTTTA14 Tilastotieteen matriisilaskenta ja laskennalliset menetelmät are held only in Finnish. The lectures are based on study material which is available in English for independent study.

In the workshop students can get support for their studies in Statistics.

The idea is that students work independently or in groups with the problems that have arisen. The workshop has a teacher and you can ask for the advice from him. However, the idea is that the teacher will not solve the problems for you. The workshop does not provide any compensation of the solved exercises.

Support in using statistical software (e.g. SPSS and R) is also available in workshop.

Period I: especially for students taking MTTTA14 Matrices for Statistics and Computational Methods.

Period II: especially for students taking MTTTA2 Matemaattisen tilastotieteen perusteet.

Period III: especially for students taking MTTTA4 Statistical Inference 1.

Read about the recommended prior knowledge from the course website.

Enroll on the course TIETA6 Data Structures in NettiOpsu

Self studying, weekly excercises, practical work, and exam.

Modes of study

- Lectures
- Exercises (independent work)
- Exam

This course contains no contact teaching.

General description

Knowledge about statistical methods and data analysis is of great importance in almost any field of research. In this course, general concepts of statistics will be provided so that students can be able to independently carry out a small scale empirical research with the statistical software SPSS or R. After the course, students should be familiar with the basic concepts of statistics, ranging from descriptive statistics, basic inference (estimation, confidence intervals and hypothesis testing), linear models (analysis of variance, simple and multiple linear regression), non-parametric tests and logistic regression.

Space is limited in this course due to computer room capacity. Priority will be granted for the first enrolments, based on the proportions: 60% PhD students and 40% for BSc and MSc students.

Please note that this course cannot be included inside the minimum 120 ECTS of Master's Degree Programme in CBDA (basic level course).

MTTTP1 Tilastotieteen johdantokurssi lectured in period I, II or III-IV is recommended for Finnish students.

This course is organized as a web course with an introductory lecture.

If needed, priority is given to the students in CBDA

Visualization of quantitative data when reporting and publishing findings

Course description:

It is commonly said that “a picture is worth a thousand words”. The same is true when reporting findings of an analysis of quantitative data. A proper visualization of the results might make the difference between the success and failure in telling a story or in publishing one’s findings. This course gives, first, a brief introduction to the R software. Second, the course focuses on visualization of quantitative data which is of utmost importance when reporting and publishing findings. Examples and applications will be done for multivariate, temporal, spatial and text data. Examples used during the course will be based on the R software, and preliminary knowledge of this software is required.

Goals: The course:

• Introduces participants to the R environment
• Introduces the participants to data exploration and data visualization
• Provides a recent variety of techniques and strategies to visualize quantitative data

Place: Computer classroom Ml 50 Linna building

Programme

11.1.2019

09.15-12.00   Introduction to R

12.00-13.00   Lunch break

13.00-16.00   Introduction to data exploration and data visualization: types of data and of databases; online databases; Visualization of multivariate data

18.1.2019

09.15-12.00   Visualization of temporal data and of spatial data; text visualization

12.00-13.00   lunch break

13.00-16.00 Real time big data applications

PLEASE NOTE: Attendance to BOTH days is required for the completion of the course.

Teacher: Paulo Canas Rodrigues

Pre-assignment: Please write a short (one A4) text stating:

1)    Your name & disciplinary background

2)    State your own motivation for participating on this course and what do you expect to learn.

DEADLINE for the pre-assignments to be announced.

In addition, participants will write a mini-assignment after the second meeting with a two weeks’ deadline.

Enrolment via NettiOpsu. Maximum number of students is 24. Selection method is draw. Students should check the selection result via NettiOpsu after the enrolment period.

Modes of study

- Lectures
- Exercises (independent work)
- Exam

This course is organized as a web course.

This course will give a detailed overview of statistical models for modern regression and classification with emphasis on applications. A number of examples and case studies will be examined. This course will cover a range of models from linear regression through various classes of more flexible models including fully nonparametric regression models. We will consider both regression and classification problems. Methods such as splines, additive models, multivariate adaptive regression splines (MARS), neural networks, classification and regression trees (CART), linear and flexible discriminant analysis, generalized additive models, nearest- neighbor rules and learning vector quantization will be discussed.

Recommended preceding studies:

Basic courses of statistics and Regression analysis.

Please note

Students who have completed course MTTA2 Ei-parametrinen regressio can not get full credits of this course because some of the contents overlap.

Learning outcomes:
After the course, students can apply both nonparametric statistical methods and basic parametric tests. Students can select and use an appropriate statistical methods for data analysing.

General description

This course is meant for students, who have pre-knowledge on the basics of statistics (eg. course Introduction to Statistics).

Course topics include: Understanding notations of statistics, different measurement scales, basic knowledge of statistics.
Nonparametric statistical methods, basic parametric statistical methods, and regression and correlations between parameters.
Presenting your data and results of statistical tests.

Course completion and assessment:
Completing the course requires active participation in lectures and approved project work. Lecture and project work materials will be delivered via Moodle.

Timetable

Mon 4.3. lecture 9-11 in Linna K108

Mon 11.3. lecture 9-11 in Pinni A3111

Mon 18.3. lecture 9-11 in Pinni A3111

Mon 25.3. lecture 9-11 in Pinni A3111 + computer classroom 11-12 (Pinni B1084)

Mon 1.4. lecture 9-11 in Pinni A3111 + computer classroom 11-12 (Pinni B1084)

Mon 8.4. lecture 9-11 in Pinni A3111 + computer classroom 11-12 (Pinni B1084)

Mon 15.4. lecture 9-11 in Pinni A3111 + computer classroom 11-12 (Pinni B1084)

Assessment scale: Pass - Fail

Enrollment: In Nettiopsu. The amount of participants will be limited according to available computer classroom places.

Litareture and readings:
- Lecture notes.
- Vimala Veeraraghavan, Suhas Shetgovekar: Textbook of Parametric and Nonparametric Statistics. SAGE Publications Ltd.
- Martin Bland: An Introduction to Medical Statistics. Oxford Medical Publications. 3rd edition.

The course is organized as a web course with an introductory lecture.

Learning outcomes
After completing the course, the participants
- know the phases of the process of knowledge discovery (data prepocessing, data mining and postprocessing)
- know basic data mining tasks and methods
- are aware of possibilities of utilising data mining in different research fields

Description

In data mining, large quantities of data are explored and analysed by automatic and semi-automatic means to discover novel, interesting information. Data mining is an interdisciplinary field combining e.g. methods from computer sciences and statistics. It has wide, diverse application areas from education, social, business and administrative sciences to medical and life sciences.

Course contents
-    Lectures 10 h
-    Hands-on exercises with data mining tools 10 h
-    Reading research articles related to applications of data mining methods in participant’s own field and writing a short report
-    Giving a presentation on applications of data mining in participant’s own field (presentation session 3 h)

Teachers: Kati Iltanen, Martti Juhola, Henry Joutsijoki

Target group

The course is intended for post-graduate students who are interested in data mining. No computer sciences or statistics background is required.

Enrolment: At the maximum 15 students, minimum 10 students. Selection method is draw.

Teaching:

Lectures:

4.4. at 10-12 Pinni A2089 (Juhola)

11.4.  at 10-12 Pinni A2089 (Juhola)

24.4. at 10-12 Pinni A2088 (Iltanen)

3.5. at 10-12 Pinni A2089 (Joutsijoki)

10.5. at 10-12 Pinni A2089 (Joutsijoki)

Practices:

4.4. at 12-14 Pinni B1084 (Joutsijoki)

11.4. at 12-14 Virta computer classroom 53 (Joutsijoki)

24.4. at 12-14 Pinni B1084 (Iltanen)

3.5. at 12-14 Pinni B1084 (Joutsijoki)

10.5. at 12-14 Pinni B1084 (Joutsijoki)

Presentation session

17.5. at 10-13 Pinni A2089

Evaluation: Pass/fail

Content

It is quite common that we do not get all the information we want for  our statistical analysis. For example, in medical research a person can  refuse to provide certain information they feel sensitive, such as  weight, substance abuse, sexual orientation etc. Particularly, missing  data in longitudinal studies is more the rule than an exception. Missing  data in statistical analysis causes all sorts of problems. For example,  the desired statistical method cannot be directly applied; loss of  information or the results obtained can be biased if the analysis is not  done properly accomplished. The course introduces various missing data  mechanisms and their effects on statistical analysis. In addition, it  presents and evaluates some of the commonly used methods for statistical  analysis with missing data. Also advanced methods are presented, as well as special methods for the analysis of  longitudinal data including likelihood-based methods and multiple imputation.

Modes of Study

Course work, exam.

This course is an advanced study version of MTTA2 Statistical Analysis with Missing Data, and the student can only complete one of the two versions.