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Regular version of the site
Master 2019/2020

Qualitative and Quantitative Research Methods in Psychology

Type: Compulsory course (Applied Social Psychology)
Area of studies: Psychology
Delivered by: School of Psychology
When: 1 year, 1-3 module
Mode of studies: Full time
Master’s programme: Applied Social Psychology
Language: English
ECTS credits: 8

Course Syllabus


The course reviews the principal steps taken during a psychological research study and aims to provide students with the knowledge and competencies necessary to plan and conduct research projects of their own leading to M.Sc. dissertation and future scientific publications.
Learning Objectives

Learning Objectives

  • learn about the principal steps of a research project in Psychology, as well as the choices that each step involves and the different possibilities that exist;
  • learn about the possibilities and limitations of quantitative, qualitative, and mixed-methods approaches in application to different research questions;
  • learn to formulate research questions and develop them into testable hypotheses;
  • explore the possibilities of data collection and different approaches to sampling, learn to choose an appropriate sampling approach for their research question;
  • learn about the different paradigms of measurement in psychology and ways to apply the essential psychometric criteria to evaluate the quality of a quantitative measurement approach;
  • study the common experimental, quasi-experimental, and non-experimental plans and learn to evaluate research plans, discover and prevent the associated threats to data validity;
  • practice in preparing their quantitative data for analysis, evaluating data quality, working with missing data;
  • learn about the possibilities and limitations of conventional statistical hypothesis testing approaches and criteria, as well as some contemporary multivariate statistical methods;
  • learn to choose and apply in practice a set of appropriate statistical tests for their research questions
Expected Learning Outcomes

Expected Learning Outcomes

  • Know the problem of ‘objectivity’ and the evolution of research paradigms in psychology
  • Know homothetic, hermeneutic, and idiographic approaches to research and explanation.
  • Perform literature reviews
  • Be able to formulate good hypotheses
  • Know ethical guidelines for psychological research involving human participants or animals and for scientific publications.
  • Know the principles of sampling
  • Know data collection methods
  • Know basic theories of measurement
  • Know how to assess reliability and validity
  • Plan different experimental designs
  • Know non-experimental (correlational) designs
  • Know specific non-experimental designs
  • Could prepare data for analysis
  • Is able to handle missing data.
  • Apply criteria for nominal data (cross-tables), parametric sample comparisons (Student t, ANOVA), nonparametric sample conparisons (Mann-Whitney, Wilcoxon, Kruskal-Wallis), inter-rater agreement (reliability, Cohen’s kappa), correlations (Guilford’s phi, pont-biserial, Spearman, Pearson).
  • Write up the results in APA style and visualize different types of data.
  • Apply general linear model to the data
  • Use advanced modelling approaches
  • Use principal components analysis and factor analysis
  • Use quantitative, qualitative, and mixed-methods approaches
  • Use mixed-methods approaches, such as repertoire grids, ultimate concerns technique
Course Contents

Course Contents

  • 1. Human being as a challenge: Research paradigms in psychology
    Scientific method and the criteria of science in psychology. The complexity of human beings: humans as evolving biological, social, and cultural beings. A systemic multilevel perspective on human behavior. Holism and reductionism in psychology. Complexity of research methods as a function of degrees of freedom of the reality studied. Psychology and the problem of free will. The problem of ‘objectivity’ and the evolution of research paradigms in psychology. Positivist and alternative (postpositivist) paradigms: philosophical assumptions and consequences for methodology. Nomothetic, hermeneutic, and idiographic approaches to research and explanation. Qualitative and quantitative methods.
  • 2. Planning your research: Theories, hypotheses, and potential pitfalls
    The stages of scientific research process and types of research studies. Where do research questions come from? Levels of scientific theories and the place of theory in psychological research. Formulating good hypotheses. Operationalizing your research question: seven methodological steps. Doing literature reviews: choose keywords, find material, structure it, write it up. How to find out quickly what’s happening in a field of research: three practical ways to do it. Types of research publications: which ones can we trust? Five questions to assess the quality of a literature review. Research ethics. Academic integrity and its violations. Plagiarism and ways to avoid it. Ethical guidelines for psychological research involving human participants or animals. Ethical guidelines for scientific publications.
  • 3. Getting your data: Sources and samples
    Sources of psychological data: behavior, physiological processes, activity products, self-reports, peer reports, biographical and archival data. A review of data collection methods: observation, interviews, focus groups, surveys, objective physiological measurements, using archival data. Sample as an indicant of general population: representativeness and sample bias. Law of large numbers and the importance of sample size. Random variables and distributions. A review of descriptive statistics. Normal distribution as an ideal: properties of normal and standard normal distributions. Standard error (of the mean) as a function of sample size. Sampling. The advantages and limitations of systematic approaches (random sample, systematic random sample, stratified sample, cluster sample, multi-stage strategies) and opportunistic approaches (snowball sample, convenience sample, self-selecting sample, theoretical sampling). Volunteer bias. Internet samples: limitations and possibilities. Developing an online study the easy way (using readymade interface) and the hard way (from scratch): technical challenges vs. research possibilities.
  • 4. Psychological measurement: Psychophysics and Psychometrics
    Subjective measurements in psychology and related sciences: psychophysics and psychometrics. The notion of scale and Stevens’ classification (nominal, ordinal, interval, and ratio scales). A review of psychophysical methods. Threshold detection: method of adjustment, method of limits, method of constant stimuli, adaptive method. Signal detection theory. Representational theory of measurement as a basis for psychophysics and its critique: ‘operational’ and ‘classical’ approaches to measurement. Psychometrics. Thurstone, Guttman, Likert scales. Varieties of rating scales and associated biases. Measurement result as a random variable. Random error. Inverse relationship of reliability and “standard error” (random error) in classical test theory (CTT). Assessing reliability: Cronbach’s alpha and other methods. Assumptions and limitations of classical test theory. The concept of systematic error (bias). The idea and advantages of Item Response Theory. Measurement validity: the notions of construct validity, operational validity, convergent and discriminant (divergent) validity, structural validity, criterial validity, predictive validity, face validity, expert validity. Ways to establish validity of a measure; multitrait-multimethod approach; nomological network. Formulating items to reduce random error. Varieties of systematic error (biases) in selfreports and ways to prevent them. Norms, standard scales, and conversion formulae. Steps to develop a psychometric instrument.
  • 5. Research designs 1: Experiments
    Causal and non-causal hypotheses. Necessary conditions for causal inference. The logic of experimentation. Variables: independent, dependent, and extraneous (confounding) variables; typical examples. Typical experimenter and respondent biases and ways to control them (double blind method, deception, hidden experiment, post-experimental control). Validity of experiments: ideal experiment as a validity reference point. Classification of experiments by goal, by setting, and by relation to practice. Experimental designs and factors that jeopardize internal and external validity. Pre-experimental designs vs. true experimental designs. Between-groups designs and within-groups designs. Experimental control in between-group designs: controlling group non-equivalence (randomization, 4 matching, etc.). Experimental control in within-group designs: controlling time / position effects (randomization, counterbalancing, etc.). Factorial experiments. Mixed plans: time-group interactions. Fixed and random factors. Theoretically predicted factors, factors as covariates (reducing error variance), factors to control for contextual effects. Intentional confounding: Latin squares. Statistical approaches to analyze experimental data
  • 6. Research designs 2: Quasi-experimental and non-experimental designs
    Quasi-experimental designs: manipulation without complete control. Typical plans and examples. Small-N designs: using idiographic approach in experimental settings. Non-experimental (correlational) designs. Correlations: the place of correlational analysis in a correlational study. Cross-sectional (between-groups) designs, longitudinal (within-group) designs, and mixed (multiple cohort longitudinal) designs. Ex post facto designs. Specific non-experimental designs. Twin studies: shared genes and shared environment as independent variables; heritability coefficients. Cross-cultural studies: culture as independent variable; the problems of equivalence and sources of bias.
  • 7. Quantitative methods 1: Testing statistical hypotheses
    A review of statistical hypothesis testing. The null-hypothesis testing debate. Effect sizes (r, Cohen’s d, R-squared), their relationships and interpretation. Effect size and statistical significance: confidence intervals. Meta-analysis: principles, steps, and examples. Statistical power and its determinants, performing power analyses in GPower. Preparing your data for analysis. Checking data quality. Exploratory data analysis. Analyzing distributions: criteria of a normal distribution. Distribution problems and ways to cope with them. Dealing with outliers. Data transformations. Handling missing data. MCAR, MAR, NMAR conditions. Traditional approaches (listwise, pairwise, mean substitution, single imputation) and robust approaches (model-based full-information maximum likelihood, data-based expectation maximization, Bayesian multiple imputation).
  • 8. Quantitative methods 2: Comparing samples and looking for pairwise associations
    A summary review of elementary statistical criteria and their assumptions. Criteria for nominal data (cross-tables), parametric sample comparisons (Student t, ANOVA), nonparametric sample conparisons (Mann-Whitney, Wilcoxon, Kruskal-Wallis), inter-rater agreement (reliability, Cohen’s kappa), correlations (Guilford’s phi, pont-biserial, Spearman, Pearson). The relationship between linear regression and Pearson product-moment correlation coefficients. Coefficient of determination. Comparing effect sizes in parametric and nonparametric tests. Recent developments in exploring associations: distance correlation and maximal information coefficient. Writing up your results in APA style: the general structure of a quantitative research report. Presenting your data in the form of text, tables, and figures: useful suggestions. Visualizing different types of data.
  • 9. Quantitative methods 3: General Linear Model
    Models of associations of 3 variables. Multiple regression: purpose, assumptions and limitations, steps, presenting results. Dummy coding and effect coding. Simultaneous and sequential (hierarchical) linear regression. General linear model as a general framework for ANOVA and regression. ANCOVA: purpose, assumptions and limitations, steps, interpreting results, presenting results. MANOVA: purpose, assumptions and limitations, steps, interpreting results, presenting results. Using (M)AN(C)OVAs to analyze repeated-measures experimental data. Nesting. Testing for simple moderation using GLM/ANOVA and hierarchical linear regression. Mediation: criteria and ways to establish. Complex hypotheses (moderated mediation and mediated moderation). Path analysis. Regression and causality
  • 10. Quantitative methods 4: Multivariate exploratory and confirmatory methods
    Establishing dimensions. Principal components analysis and factor analysis: assumptions & limitations, requirements, caveats, steps, and data interpretation. Criteria for choice of the number of factors: Kaiser’s criterion, scree plot, parallel analysis, minimum average partial. Canonical correlation analysis, multidimensional scaling: aims and possibilities. Exploratory factor analysis tools for dichotomous and ordinal data. Classification. Hierarchical cluster analysis: algorithms, metrics, challenges & limitations. K-means classification. Person-oriented approach: analyzing individual patterns of change in longitudinal data. Latent profile analysis, latent class analysis, and latent transition analysis: general idea. The notion of discriminant analysis. Advanced modelling approaches. Structural equation modeling: aims and possibilities, limitations & caveats. Path models and latent variable models. Model specification, model fit assessment, nested models, modification indices. Applications of confirmatory factor analysis in psychology. Artifacts resulting from data aggregation. Intraclass correlation. Addressing hierarchically structured data using multilevel models: regression-based and latent variable-based approaches
  • 11. Qualitative Research
    Advantages of qualitative approach. Steps of a qualitative study: choosing material, selection principle, analysis approach. Extracting meaning at different levels: descriptive phenomenological analysis, interpretative phenomenological analysis, thematic analysis and qualitative content analysis, quantitative content analysis. Software for content analysis and thematic analysis. Critical discourse analysis. Procedures for establishing validity of qualitative data. Strengths and limitations of quantitative, qualitative, and mixed-methods approaches. Aims of mixedmethods approaches. Ways to unite the two paradigms. Examples of integration of qualitative and quantitative approaches: repertoire grids, ultimate concerns technique. The current directions and nearest perspectives of research methods in psychology.
  • 12. Mixed-Methods Research
    Strengths and limitations of quantitative, qualitative, and mixed-methods approaches. Aims of mixedmethods approaches. Ways to unite the two paradigms. Examples of integration of qualitative and quantitative approaches: repertoire grids, ultimate concerns technique. The current directions and nearest perspectives of research methods in psychology.
Assessment Elements

Assessment Elements

  • non-blocking S: grade for activity at the seminars
  • non-blocking H: average grade for home assignments
  • non-blocking T: average score on tests given at the end of each module
  • non-blocking Final Exam
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    CourseFinalScore = 0.6 * (0.5 * H + 0.3 * T + 0.2 * S) + 0.4 * FinalExamScore


Recommended Core Bibliography

  • Frost, N. (2011). Qualitative Research Methods in Psychology : Combining Core Approaches. Maidenhead: McGraw-Hill Education. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=382480
  • Giles, D. (2002). Advanced Research Methods in Psychology. Hove, East Sussex: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=548434
  • Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064

Recommended Additional Bibliography

  • Robins, R. W., Fraley, R. C., & Krueger, R. F. (2007). Handbook of Research Methods in Personality Psychology. New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=211290