MMX9120 Quantitative Research Methods (6.0 ECTS)

 

Professor Petri Nokelainen Tampere University

petri.nokelainen@tuni.fi

Professional Growth and Learning research group

 

Course homepage

https://homepages.tuni.fi/petri.nokelainen/mmx9120

Zoom link

https://zoom.us/j/8819432540

 

Schedule

 

Session

Topic

Date and room

Date and room

1.

Issues on study design

Thu 09.02.2023 2.15-5.30 PM

Fri 10.02.2023 2.15-5.30 PM

 

 

SOC-407

SOC-407

2.

Statistical methods

Wed 22.02.2023 2.15-5.30 PM

Thu 23.02.2023 2.15-5.30 PM

 

 

14:15-15:30 SOC-210

15:30-17:30 SOC-412

SOC-408

3.

Structural equation modeling

Thu 09.03.2023 2.15-5.30 PM

Fri 10.03.2023 2.15-5.30 PM

 

 

SOC-412

SOC-409

4.

Bayesian modeling

Thu 23.03.2023 2.15-5.30 PM

Fri 24.03.2023 2.15-5.30 PM

 

 

SOC-408

SOC-409

 

Course topics

 

Session 1: Issues on study design

https://homepages.tuni.fi/petri.nokelainen/mmx9120/1_design

-       Cross-sectional and longitudinal designs

 

Session 2: Statistical methods (with R jamovi)

https://homepages.tuni.fi/petri.nokelainen/mmx9120/2_basic_statistics

-       Correlational analysis: r

-       Comparing group means: t-test, analysis of variance (ANOVA)

-       Predicting dependent variable values: linear regression

-       Investigation of change over time: paired t-test, repeated measures ANOVA, linear growth modeling

 

Session 3: Structural equation modeling (with R lavaan)

https://homepages.tuni.fi/petri.nokelainen/mmx9120/3_sem

-       Observed path analysis, latent confirmatory factor analysis, latent path analysis

 

Session 4: Bayesian modeling (with R brms)

https://homepages.tuni.fi/petri.nokelainen/mmx9120/4_bayes

-       Basic understanding of conducting Bayesian regression analysis (incl. understanding of the role of priors)

 

 

Goal

The main goal of this course is to give a post graduate level introduction to quantitative research designs and methods, structural equation modeling and Bayesian modeling and show with selected computer exercises how to apply theoretical knowledge into practice.

 

Outcomes

After the course, participants will have knowledge and understanding of basic concepts of quantitative research, improved skills of planning and implementing quantitative research designs, basic understanding of structural equation modeling and Bayesian modeling.

 

Assessment

The course includes distance learning assignments (scientific essays and computer exercises, see "Course topics" for more information), that are returned to the lecturer in one document (merge all assignments into word or pdf) via email (petri.nokelainen@tuni.fi) by Monday 10.04.2023.

Each session contains 1-3 assignments of which participants select one (1) assignment to complete. As there are four sessions, the total number of essays or computer exercises to be completed for this course is four.

The course is evaluated on a scale from 0 (reject) to 5 (outstanding).

Computer exercises during the course hours (sessions 2-4) can be completed as group work (e.g., two students in a group), but each student is expected to complete the home assignments on his/her own (and return an individual collection of home assignments to the lecturer complied into a single file by 10.04.2023).

 

Software

The software for the course is downloadable through the following links:


R statistical computing environment https://www.r-project.org
R Studio graphical interface https://www.rstudio.com/products/rstudio/download
R Jamovi SPSS like interface https://www.jamovi.org/download.html
R JAGS https://sourceforge.net/projects/mcmc-jags/files/latest/download