Data analysis and statistics – 2022 Fall

Date: Friday 14-17, biweekly on even weeks. The first class is on 16 September. The last class is on 2 December, so we may have it in person.

Venue: Izu 422

Topics

The course reviews some common analysis methods applied in cognitive research: General analysis methods, and mathematical and statistical analyses. Mainly, the aspects that are relevant to cognitive research are discussed. We also highlight several debated and misunderstood aspects.


  • Diffusion model analysis (slides)

  • Reliability (slides)

    • (For this talk, make sure to understand correlation and one-sample t-test)

  • Software solutions

    • Which type of software to choose (slides)

    • Spreadsheet (slides)

    • CogStat (slides)

      • (For this talk, make sure to understand the basic descriptive statistics, hypothesis tests, interval estimation, and standardized effect size.)

  • Hypothesis tests

    • The reasoning behind the tests and the main consequences (slides)

    • Bayesian analysis (slides - coming soon)

  • Descriptives

  • Statistical analysis with computer programming (optional topic)

    • Statistics in Matlab

    • Statistics in R

    • Statistics in LibreOffice Calc with macros and with formulas

Requirements

The next two requirements should be fulfilled. (Second, optional topics should be approved, the deadline is 14 October extended deadline is 11 November)

  • Exam with practical tasks. At the end of the term time.

  • Choose one of the topics below. The deadline is the date of the last lecture. Update: the deadline for the written report/material is still 9 December, but the date of the presentation is 2 December.

    • Create a new or extend an existing Wikipedia article in English or in Hungarian on statistics or data analysis topics (e.g., statisztika téma) (for Hungarian speakers, this might help: leírás). You can translate the article from another language, if the source language includes high-quality material. Minimum length is 6000 characters (with spaces) for new material or for extensions, and 8000 characters for translations.

    • CogStat (choose one from the list below)

    • Analyze your previous results insome non-traditional way.

      • Contact me about the chosen topic and methods.

    • Implementing extra statistical or data analysis functions for LibreOffice. (This could be implemented either as a Basic script or a spreadsheet template.)

    • If you have any further ideas that are related to the topics of the course and which could be useful for you and/or for others, contact me about the idea.

  • Micro oral report about the chosen task. In the last lecture.

      • In a maximum of 5 minutes, summarize the most unexpected details of the work you've done. Slides are optional.

Alternative requirements

If you did not have any statistical courses before, you may have this course as an intro course with some advanced topics.

There'll be an exam in the last class. Mostly, you'll get data files, and specific questions (e.g., is there a gender difference in the GSLK test (the GSLK is totally made up)), and the reply should be given in APA style. There will be a few questions regarding the original topics of the class too.

As a preparation, you should read about specific topics at home, and we will discuss the chapters in the class.

The www.learningstatisticswithcogstat.com book is recommended, but any other books with similar topics can be used (see the Recommended materials below).

Chapters to be discussed:

  • October 28: Chapters 1-6

  • November 11: Chapters 7-10

  • November 25: Chapters 11-14

For the exam: data file, submission form.

Recommended materials

Software packages we may use:

  • Diffusion Model Visualizer

  • LibreOffice

  • CogStat

  • Books in Hungarian:

  • Krajcsi, A. (2008). Kísérletvezérlés és adatelemzés a kognitív tudományban. Szeged: Szegedi Egyetemi Kiadó.

  • Janacsek, K., & Krajcsi, A. (2009). Statisztika a pszichológiai kutatásban.

Books in English: