Data analysis and statistics – 2023 Fall

Date: Friday 14:00-17:00, biweekly on even weeks. The first class is on 15 September.

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.

• (You may be interested in the Third Best Practices in Data Analysis and Statistics Symposium in Pécs, Hungary.)

• 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.)

  • Statistics in Python (optional topic) (slides)

• Diffusion model analysis (slides)

• Reliability (slides)

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

• Hypothesis tests

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

  • Bayesian analysis (slides - coming soon)

• Descriptives

• • Describing a single variable (slides)

  • Describing the relation of two variables (slides)

    • Fitting functions

  • Comparing variables and groups (slides)

Requirements

The following three requirements should be fulfilled.

• Exam with practical tasks. At the end of the term time (Dec 8).

• Choose one of the topics below. The deadline of sending the project report is the date of the last lecture (Dec 8). (The topics should be approved; the deadline is 13 October.)

  • Create a new or extend an existing Wikipedia article in English or 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. The minimum length is 6000 characters (with spaces) for new material or extensions and 8000 characters for translations.

    • A few Hungarian articles from the last years: A statisztika helytelen használata, Feltáró faktorelemzés, Ellenőrző faktorelemzés, Mintavételi hiba, Lineáris diszkriminancia-analízis

    • Review related tasks: (0) Within a week after the topic choice deadline, add your topic to this list. Ask for permission, which I'll approve automatically. After each topic is added to the list, add your name as a reviewer for another topic. (1) After completing your article, you should send it to another student (reviewer) so they can add comments and suggestions. The deadline is 10 days before the final deadline (Nov 28). (2) Your peer will review the text and return it to you. The deadline is 5 days before the final deadline (Dec 3). (3) Finalize your article, then send me your final version, your peer's comments, and the version you sent to your peer.

    • (Only in Hungarian) Új szócikk készítése vagy kiegészítése a Statisztika a pszichológiai kutatásban weboldalra. Legkevesebb 5000 karakter szóközzel.

  • CogStat (choose one from the list below)

    • Localize it to a new language or improve and/or extend previous localizations.

    • Propose new procedures with appropriate justifications.

      • A few examples

      • Additional resources for further ideas

    • An empirical test of the efficiency of various statistical software types

  • Analyze your previous results in some non-traditional way.

    • Contact me about the chosen topic and methods.

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

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

• Micro-oral report about the chosen task. In the last lecture (Dec 8).

  • • In a maximum of 5 minutes, summarize the most unexpected details of your work. Slides are optional.

Exam link (intentionally not available at the moment)

Alternative requirements

If you did not have any statistical courses before (you don't have a psychology, mathematics, or computer science bachelor's degree), 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 report should be 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 class.

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

Chapters to be discussed:

• September 29: Chapters 1-6

• October 13: Chapters 7-10

• October 27: Chapters 11-14

Find some example tasks in the second sheet of the data file.

Statistics consultation (in Hungarian)

Exam link (intentionally not available at the moment)

Recommended materials

Books for general statistical materials

• Danielle J. Navarro / David R. Foxcroft / Thomas J. Faulkenberry / Róbert Fodor: Learning statistics with R / jamovi / JASP / CogStat

• Russell Poldrack: Statistical Thinking for the 21st Century

• Andy Field, (Jeremy Miles, Zoe Field): Discovering Statistics Using SPSS / R / SAS. Sage.

• More statistics and data analysis textbooks at the Open Textbook Library and LibreTexts