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Regular version of the site
Master 2019/2020

Qualitative and Quantitative Research Methods in Psychology

Area of studies: Psychology
Delivered by: School of Psychology
When: 1 year, 1-3 module
Mode of studies: offline
Instructors: Tadamasa Sawada
Master’s programme: Cognitive Sciences and Technologies: From Neuron to Cognition
Language: English
ECTS credits: 8

Course Syllabus


This course reviews general techniques and methodologies for a research project in cognitive science: literature review, hypothesis making, experimental design, data analysis, statistics, and inference. Students learn these techniques and methodologies with theories behind them and have some practices of them in the course. The course is designed to provide the knowledge and competencies necessary to plan and conduct their research projects in cognitive science.
Learning Objectives

Learning Objectives

  • 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
Expected Learning Outcomes

Expected Learning Outcomes

  • know ethics in cognitive science;
  • know how to do scientific discussion
  • understand the ongoing discussion about replication crisis and publication bias in science
  • 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
Course Contents

Course Contents

  • Cognitive science as a field of science
    How 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 project
    A 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.
  • Sampling and data collection
    Sample 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 plans
    Experimentation. 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 visualization
    Row 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-test
    Conventional 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.
  • Statistical methods 2: Likelihood analysis, Bayesian statistics
    Curve fitting, MCMC, Bayes factor
  • Meta-analysis on behavioral studies
    Data collection, Forrest plot, Funnel plot, Publication-bias.
Assessment Elements

Assessment Elements

  • non-blocking home assignments
    8 home assignments; Its weight in grading is 0.3. The 1st-retake of a home assignment is evaluated in the same way as the original assignment. The 2nd-retake of the assignment comes with 20% of penalty on its score. Namely, its score is first computed in the same way as the original assignment and then the computed score is multiplied by 0.8.
  • non-blocking Writing tests
    2 writing tests around the end of the 1st and 2nd modules; Its weight in grading is 0.3. The 1st-retake of a test is evaluated in the same way as the original test. The 2nd-retake of the test comes with 20% of penalty on its score. Namely, its score is first computed in the same way as the original test and then the computed score is multiplied by 0.8.
  • non-blocking Final writing test
    The test (3rd module of 2019-2020) has been already performed in a period 10 days before the exam week in accordance with course program. No further exam required. Экзамен проводится в письменной форме (тест по материалам курса). Экзамен проводится на платформе Zoom (https://www.zoom.us/). Компьютер студента должен удовлетворять требованиям: наличие рабочей камеры и микрофона, поддержка Zoom. Для участия в экзамене студент обязан: поставить на аватар свою фотографию, явиться на экзамен согласно точному расписанию, при ответе включить камеру и микрофон. Во время экзамена студентам запрещено: выключать камеру, пользоваться конспектами и подсказками. Кратковременным нарушением связи во время экзамена считается нарушение связи менее минуты. Долговременным нарушением связи во время экзамена считается нарушение длиной в минуту и более. При долговременном нарушении связи студент не может продолжить участие в экзамене.
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * home assignments + 0.4 * Final writing test + 0.3 * Writing tests


Recommended Core Bibliography

  • Dekking F. M. et al. A Modern Introduction to Probability and Statistics: Understanding why and how. – Springer Science & Business Media, 2005. – 488 pp.
  • 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

Recommended Additional Bibliography

  • 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