PS1-SP12O - Statistics in Psychology 2

Course specification
Type of study	Bachelor academic studies
Study programme	Psychology
Course title	Statistics in Psychology 2
Acronym	Status	Semester	Number of classes	ECTS
PS1-SP12O	mandatory	2	2L + 3E	6.0
Lecturers
Lecturer	prof. dr Siniša Lakić
Lecturer/Associate (practicals)	Milana Damjenić, ma
Prerequisite				Form of prerequisites
Statistics in Psychology 1.				In order to take the exam, students need to have passed the course Statistics in Psychology 1.
Learning objectives
The aims of the course are that students acquire a sense of purpose, theoretical background, scope, and limitations of formal quantitative reasoning in psychology based on data collected through scientific research, as well as to acquire logical, conceptual, and technical knowledge on a range of approaches to statistical inference (null hypothesis testing, confidence intervals, meta-analysis, Bayesian statistics).
Learning outcomes
After successfully completing this course, students will be expected: To explain basic tenets of probability theory, mechanisms of its application in inferential statistics, as well as actual dilemmas related to different approaches in statistical inference; To use R GUIs to test basic hypothesis of prevalence, difference, and association in psychological research, while acknowledging the theoretical background of the testing procedures; To list the assumptions for using basic statistical tests and assess whether they have been met in a statistical software; To select adequate statistical tests for data analysis by recognizing their advantages and limitations, and to advocate for employing multiple statistical perspectives; To competently interpret the results of basic statistical tests and report them in a written form, by conforming to conventions of scientific publishing; To critically evaluate the results of basic statistical tests provided by other authors.
Content
Basic concepts of probability theory. Statistical hypotheses. Testing prevalence hypothesis with a single dimensional or categorical variable using P-values and null hypothesis significance testing (NHST). The importance, advantages and limitations of P-values and NHST. Type 1 and Type 2 errors. Misconceptions about P-values. Determinants of P-values. Questionable research practices related to calculations and reporting of P-values. P-value alternatives. Replications. Standardized effect sizes, advantages and limitations. Statistical power. Confidence intervals. Meta-analytical approach to statistical inference: pondering, models, graphical representation, moderating variables, advantages and limitations. Bayesian approach to statistical inference: a priori and posterior probabilities, Bayes factor, credible intervals, advantages and limitations. Reproducibility crisis in quantitative psychology. Pre-registration. The importance of documenting statistical analysis, sharing data and code. Analyzing basic categorical research designs: univariate and bivariate analysis. Analyzing correlation and regression designs: partial and semi-partial correlations, multiple linear regression analysis. Analyzing basic factorial research designs (between subjects, within subjects, mixed-designs). Typology and treatment of missing data.
Teaching Methods
Lectures, practicals using statistical software, project teamwork, participating in research.
Evaluation and grading
Mid-term exam (15 points) evaluates the knowledge acquisition in the middle of a semester. Class activities (5 points) are evaluated based on individual tasks during the semester, and participation in in-person and online discussions. Written report (25 points) done by a team of 3 to 5 students, related to statistical analysis of provided data from a psychological study, is evaluated against the defined criteria. Practical exam (15 points) is conducted individually by testing student's knowledge of using a statistical software. Theoretical exam (40 points) evaluates mastery of the complete course content.