Kurzus nemzetközi vendég- és részidős hallgatóknak

Kar

Pedagógiai és Pszichológiai Kar

Szervezet

PPK Pszichológiai Intézet

Kód

PSZM21-MO-KOGN-102

Cím

Bases of Data Analyses in the Cognitive Field

Tervezett félév

Mindkét

ECTS

4

Nyelv

en

Oktatás célja

Aim of the course: The course reviews some common analysis methods applied in cognitive research: general data analysis methods, related mathematical and statistical methods. The class reviews some commonly used software types. Mostly, we discuss aspects that are relevant in cognitive research. Several debated and misunderstood aspects will also be discussed. Learning outcome, competences knowledge: Understanding the theoretical and conceptual background for data manipulation and analysis Practical implementations for data analyses techniques attitude: Trying to understand the relation between seemingly independent concepts. Actively exploring how seemingly simple techniques could be used in innovative ways skills: Ability to use appropriate data analyses methods in real life research programs autonomy/ responsibility Students are able to apply the acquired knowledge on their own, in accordance with the ethical guidelines of psychology, but only for purposes corresponding to their level of competence.

Tantárgy tartalma

Content of the course Topic of the course Which type of software to choose Analyzing behavioral data Diffusion model analysis Hypothesis tests The reasoning behind the tests and the main consequences Bayesian and frequentist solutions Automatic data analysis Bases of data manipulation Spreadsheet Reliability in cognitive areas Descriptives Describing a single variable Describing the relation of two variables, fitting functions Statistical simulations Monte Carlo and bootstrapping methods Statistical analysis with computer programming Learning activities, learning methods Hands-on analyses Data analyses homework

Számonkérés és értékelés

Evaluation of outcomes Learning requirements, mode of evaluation and criteria of evaluation: Data analysis related practical task. The students are offered a series of types of tasks (e.g., reanalyzing former data with new methods) and they can choose between the tasks depending on which task fits their learning goals the most. Mode of evaluation:  practical evaluating the submitted project work Criteria of evaluation: Approriateness of the solution provided for the data analysis task

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