Qualitative and Quantitative Research Methods in Psychology
- learn ethics in cognitive science;
- learn skills for literature search and for reviewing them
- learn critical thinking skills and how to do scientific discussion
- learn how to formulate research questions and develop them into testable hypotheses
- learn how to plan and design experiments for testing the hypotheses
- learn possibilities and limitations of experiments
- learn how to prepare data for analysis and how to analyze the data
- learn possibilities and limitations of analysis and statistical methods
- know ethics in cognitive science;
- know how to do scientific discussion
- know how to develop their skills and knowledge by themselves
- have critical thinking skills
- know theories behind the statistical analysis
- know statistical analysis in Cognitive science
- understand the ongoing discussion about replication crisis and publication bias in science
- Cognitive science as a field of scienceHow cognitive science can be science. Practical/theoretical limitations in cognitive science. Properties of data obtained from human beings. Reductionism in science and unavoidable holism in cognitive science. Statistics as a descriptive method. Research ethics.
- Starting a research projectA process determining research topic and formulating a project. Literature review: choosing/changing its keywords, checking back earlier studies, checking recent relevant studies using article search engine. Finding interest and formulating question and hypothesis. Types of questions, theories, and scientific explanation. Research ethics: human participants and animal subjects, research misconduct (e.g. plagiarism, forgery), questionable research methods.
- Basic probabilityProbability and probability density, Probability distribution function, Cumulative distribution function, Law of large numbers, Conditional probability, Bayes theorem
- Sampling and data collectionSample as an indicant of general population: representativeness and sample bias. Types of data: behavioral, physiological, neuroscientific, and archival. Methods of collecting behavioral data: adjustment method, multiple forced choice, and reaction-time. Psychophysics.
- Experiments and quasi-experimental and non-experimental plansExperimentation. Types of variables. Experimenter and respondent biases. Betweengroups designs and within-groups designs. Fixed and random factors. Intentional confounding: Latin squares. Quasi-experimental plans (manipulation without complete control) and non-experimental (correlational) studies. Cross-sectional (between-groups) designs, longitudinal (withingroup) designs, and mixed (multiple cohort longitudinal) designs. Ex post facto designs. Specific non-experimental plans: twin studies, cross-cultural studies.
- Data organization and visualizationRow data and its process using software (e.g. Excel, Matlab, R). Graph plotting. Organizing data for further analysis.
- Statistical methods 1: t-test, regression, repeated-measure ANOVA, ANCOVA, Chi-square-testConventional statistical methods: t-test, regression, repeated-measure ANOVA, ANCOVA, Chi-square-test. The null-hypothesis. Effect size and statistical power. Analyzing distributions: normality test. Power analysis: conventional methods and Monte-Carlo method.
- Meta-analysis on behavioral studiesData collection, Forrest plot, Funnel plot, Publication-bias.
- Statistical methods 2: Likelihood analysis, Bayesian statisticsCurve fitting, MCMC, Bayes factor
- Home assignments8 home assignments; Its weight in grading is 0.25.
- Writing tests (the 1st module)A writing test around the end of the 1st module; Its weight in grading is 0.25.
- Writing tests (the 2nd module)A writing test around the end of the 2nd module; Its weight in grading is 0.25.
- Writing test (final)A writing test around the end of the 3rd module; Its weight in grading is 0.25.
- Interim assessment (3 module)0.25 * Writing tests (the 2nd module) + 0.25 * Home assignments + 0.25 * Writing test (final) + 0.25 * Writing tests (the 1st module)
- Dekking F. M. et al. A Modern Introduction to Probability and Statistics: Understanding why and how. – Springer Science & Business Media, 2005. – 488 pp.
- Eric Goh Ming Hui. (2019). Learn R for Applied Statistics : With Data Visualizations, Regressions, and Statistics. Apress.
- Kruschke, J. K. . V. (DE-588)143634666, (DE-627)662785142, (DE-576)346169313, aut. (2015). Doing Bayesian data analysis a tutorial with R, JAGS, and Stan John K. Kruschke, Dept. of Psychological and Brain Sciences, Indiana University, Bloomington. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.415512638
- Lars-Göran Johansson. (2015). Philosophy of Science for Scientists (Vol. 1st ed. 2016). Springer.
- Weiner, I. B., Velicer, W. F., & Schinka, J. A. (2013). Handbook of Psychology, Research Methods in Psychology (Vol. 2nd ed). Chichester: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=497780
- Schwarzer, G., Carpenter, J. R., & Rücker, G. (2015). Meta-Analysis with R. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1079134