(in Polish) Big Data w psychologii społecznej

elementary

(in Polish) Zgodnie z programem studiów uchwalonym przez Senat UKSW:

https://monitor.uksw.edu.pl/docs/search

BDAS2_W16, BDAS2_U15, BDAS2_K03

This seminar aims to provide students with advanced knowledge of techniques for acquiring, processing, and analyzing psychological data, including Text Mining algorithms, cluster analysis, decision trees, and machine learning. Through active participation in discussions and practical application of these techniques, students develop analytical skills and critical thinking in psychological research. Additionally, the seminar allows for the exchange of views and reflection on the ethical aspects of psychological research in the digital age. Various techniques for analyzing data are also presented, including processing qualitative data, diagnosing personality disorders using artificial intelligence algorithms, and cluster analysis. The seminar also covers discovering patterns in numerical data, utilizing decision trees, behavior prediction, and social networks.

The aim of the seminar is to provide students with advanced knowledge of techniques for acquiring, processing, and analyzing psychological data, including Text Mining algorithms, cluster analysis, decision trees, and machine learning. Through active participation in discussions and practical application of the discussed techniques, students develop analytical, interpretive, and critical thinking skills in the context of conducting psychological research. Additionally, the seminar enables students to exchange views and reflect on the ethical and methodological aspects of psychological research in the digital era.

1. Acquiring Psychological Data from the Internet (Web Crawling) and Other Sources

- Introduction to acquiring psychological data from various online platforms and conducting independent research.

- Issues regarding confidentiality and ethics of psychological research in the digital era.

2. Processing Qualitative Data into Quantitative Data: Text Mining Algorithms

- Techniques for transforming qualitative data (e.g., from interviews, surveys, websites) into quantitative data using Text Mining algorithms.

- Conversion of unstructured data into structured data: methods and tools.

3-4. Analysis of Qualitative Data Using Text Mining Algorithms

- Determining word frequency, word clustering analysis, identifying relationships between words, and principal component analysis.

- Examples of applications in psychological research: thematic exploration and pattern detection.

5. Presentation of Results of Qualitative Data Analysis

- Techniques for presenting results of qualitative data analysis using algorithms: visualizations and interpretation.

6. Pattern Discovery in Numerical Data: Pattern Analysis Algorithms

- Issues of result generalizability, model complexity, and simplification in the context of pattern discovery in numerical data.

7. Diagnosis of Personality Disorders Using Artificial Intelligence Algorithms

- Utilization of AI algorithms for the diagnosis and classification of personality disorders based on psychological data.

8. Interpretation of Profiles of Research Participants: Cluster Analysis

- Interpretation of research results for multiple variables and profiling of research participants using cluster analysis.

9. Fitting Model Curve to Empirical Curves

- Techniques for determining the fit of theoretical curves to empirical profiles to identify characteristic clusters in the population.

10-11. Discovery of Rules in Psychological Research: Decision Trees

- Utilization of decision trees for discovering classification and regression rules in psychological research.

12-13. Prediction in Psychological Sciences: Machine Learning

- Utilization of artificial neural networks and support vector machines for behavior prediction and personality diagnosis.

- Examples of applications in graphological analysis context.

14. Network Analytics (Social Network Analysis) in Psychological Research

- Discovery and description of relationships in social groups using network analysis: utilizing graphs for social structure analysis.

15. Utilization of Kohonen Networks and Random Forests in Psychological Research

- Practical application of Kohonen networks and random forests in psychological data analysis: pattern detection and prediction.

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Bartczak, M., Bokus, B., Chronowska, R., Szymańska, A., & Ważyńska, A. (2017). The Dialogical Self’s Round Table: Who Sits at It and Where? Psychology of Language and Communication, 21(1), 84–108. Zajęcia 5

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Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022. (Zajęcia 3)

Elder, J., Hill, T., Miner, G., Nisbet, B., Delen, D., & Fast, A. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Application. Oxford: Elsevier. Zajęcia 1, 2, 3, 5

Grzesiuk, L., Jastrzębska, J., Rutkowska, M., & Szymańska, A. (2022). The Relationship Between the Manifestations of Mobbing and the Reactions of Mobbing Victims. Medycyna Pracy, 73(1), 1–12. Zajęcia 8

Groves, R. M., Fowler Jr, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology. John Wiley & Sons. (Zajęcia 1)

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media. (Zajęcia 4)

James, G., Hastie, T., Tibshirani, R., & Witten, D. (2013). An introduction to statistical learning. Springer Science & Business Media. (Zajęcia 4)

Kosinski, M., Matz, S. C., Gosling, S. D., Popov, V., & Stillwell, D. (2015). Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines.

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Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.

Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Burlington, MA: Academic Press (Elsevier). Zajęcia 7, 10, 11

Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. (Zajęcia 3)

Raschka, S., & Mirjalili, V. (2017). Python Machine Learning. Packt Publishing Ltd. (Zajęcia 5)

Szymańska, A. (2012). Parental Directiveness as a Predictor of Children’s Behavior at Kindergarten. Psychology of Language and Communication, 16(3), 1–24. Zajęcia 10, 11

Szymańska, A. (2017a). Wykorzystanie algorytmów Text Mining do analizy danych tekstowych w psychologii [Usage of text mining algorithms to analyze textual data in psychology]. Socjolingwistyka, 33, 99–116. Zajęcia 3, 4, 5

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Szymańska, A. (2018). Predicting model for aggressive directiveness in the light of Tadeusz Tomaszewski’s theory of action: structural and data mining approach. Psychology of Language and Communication, 22(1), 354–371. https://doi.org/10.2478/plc-2018-0016 Zajęcia 12, 13

Szymańska, A. (2019). The transfer of parental mistakes in the family of origin of mothers of pre-school children:54 A structural and artificial intelligence approach. Warszawa: Wydawnictwo Naukowe Uniwersytetu Kardynała Stefana Wyszyńskiego. Zajęcia 3, 4, 5, 8, 12, 13

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Szymańska, A., & Aranowska, E. (2019). Parental Stress in the Relationship with the Child and Personality Traits that Parents Shape in their Children. Early Child Development and Care. https://doi.org/10.1080/03004430.2019.1611569 Zajęcia 8

Szymańska, A., & Aranowska, E. (2022). Raising a child to live in society – Personality traits parents develop and prevent from developing in their preschool children. Studia z Teorii Wychowania, 41(4), 409–431. Zajęcia 5

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Subject learning outcomes of this course can be divided into three main areas: knowledge, competencies, and skills.

Knowledge:

1. Understanding various techniques for acquiring psychological data from the Internet and other sources.

2. Familiarity with algorithms and tools for processing qualitative data into quantitative data, including Text Mining techniques.

3. Knowledge of methods for analyzing qualitative data, such as determining word frequencies, cluster analysis, identifying relationships between words, and principal component analysis.

4. Awareness of ways to present the results of qualitative data analysis.

Competencies:

1. Ability to prepare and conduct presentations on selected topics related to psychological data and their analysis.

2. Ability to actively participate in moderated discussions, exchange views, and analyze different perspectives.

3. Ability to apply learned techniques and tools to analyze real-life cases in psychology.

Skills:

1. Ability to process qualitative data into quantitative data using Text Mining algorithms.

2. Ability to analyze qualitative data and present the results of analysis.

3. Ability to critically analyze and interpret the results of psychological research based on the techniques learned.

These subject learning outcomes are designed to equip students with the necessary knowledge, competencies, and skills to effectively conduct psychological data analysis and present their research findings.

The assessment of the classes is based on the student's prepared and delivered presentation during the classes and their participation in the discussions. The assessment criteria include:

1. Content of the presentation: The evaluation will consider the clarity and completeness of the presentation's content, as well as the depth of discussion on selected topics related to the class theme presented by the student.

2. Understanding of the material: The assessment will take into account the student's understanding of the material presented during the classes and their ability to convey this understanding in a manner accessible to other participants within the student's presentation.

3. Quality of the presentation: The assessment will consider the structure, clarity, and visual appeal of the presentation, as well as the student's ability to effectively utilize available tools.

4. Ability to answer questions: The assessment will consider the student's ability to provide accurate answers to questions and clarity in explaining concepts during the discussion following the student's presentation.

5. Active participation in the discussion: The assessment will consider active participation in the class discussion, including asking questions, providing insightful comments, and offering constructive opinions on the presented materials by the student.

6. Critical thinking: The assessment will consider the ability to critically analyze the presented materials and express personal views in accordance with the principles of scholarly discussion by the student.

7. Ethics and respect in discussion: The assessment will consider respect for the opinions of other participants and adherence to the principles of academic discussion ethics by the student.

8. Time and organization of the presentation: The assessment will consider effective time management and organization of the presentation in a way conducive to understanding and assimilating information by the participants, conducted by the student.