PS1-SP11O - Statistics in Psychology 1

Course specification
Type of study	Bachelor academic studies
Study programme	Psychology
Course title	Statistics in Psychology 1
Acronym	Status	Semester	Number of classes	ECTS
PS1-SP11O	mandatory	1	2L + 3E	6.0
Lecturers
Lecturer	prof. dr Siniša Lakić
Lecturer/Associate (practicals)	Milana Damjenić Mihaldžić, ma
Prerequisite				Form of prerequisites
None.
Learning objectives
The aims of the course are that students acquire a sense of purpose, scope, and limitations of using quantitative data in psychology collected through scientific research and that they gain logical, conceptual, and technical knowledge which will enable them to competently conduct, interpret, and report descriptive statistical analysis (tabular and graphical presentation of univariate distributions, as well as presentation of associations and differences).
Learning outcomes
After successfully completing this course, you will be expected: - to argue for using statistics in psychological science and define the limits of its scope; - to design a simple database in the spreadsheet software, enter data collected online or via paper-and-pencil methods, validate entered data, and prepare the dataset for statistical analysis; - to use statistical software (R programming language derivatives: JASP and Jamovi) and within it to present results from a sample at hand by using various graphical and numerical methods (descriptive statistics: measures of central tendency, measures of variability, indexes of inner location, correlation/association coefficients) while taking into account their theoretical background; - to select appropriate techniques of descriptive statistics in the realistic context, by acknowledging advantages and limitations thereof; - to adequately interpret the results of descriptive statistics analysis by using expert language, but also to be able to express the interpretation using lay language.
Content
Introduction to statistics in psychology. The objectives: description, exploration, prediction, inference. Advantages and limitations of using statistics in psychology. Population and sample. Necessary mathematical operations. Defining basic concepts (measurement, variable, score, the object of measurement, scales, instruments, the levels of measurement, parameter, statistic/estimate). Statistical representation of scientific models. Creating codebooks and inputting data into spreadsheet software. Introducing statistical software (R language and its GUIs JASP and Jamovi). Loading and saving datasets. Checking data validity. Editing and transforming data. Tabular and graphical representation of categorical variables. Treating rarely populated categories. Measures of central tendency and variability. Graphical representation of single dimensional variables. Indexes of inner location. Identifying and treating outliers. Mathematical distributions in statistics. Normal distribution and estimating deviations from a normal distribution. Surface under normal distribution. z and T scores and their applications. Central limit theorem. Numerical and graphical representation of associations between dimensional variables. Covariance. Linear regression. Nonlinear association. Numerical and graphical representation of associations between dimensional and categorical variables. Numerical and graphical representation of associations between categorical variables. Exploratory data analysis.
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.