Objectives and competences
• Students explain key issues related to statistical methods in social sciences that include descriptive and bivariate statistical analysis, parametrical and non-parametrical hypothesis testing, and dealing with biases of empirical research.
• Students distinguish between different complex statistical methods of multivariate analysis which are applied in the field of the empirical sociological research.
• Students independently analysed data with the advance multivariate statistical methods in a software tool (e.g. SPSS, STATA).
• Students design and interpret graphical presentations of the findings, obtained with the advance statistical data analysis.
• Students critically evaluate research quality and ethical aspects in planning, measuring, and interpreting of the multivariate research.
Content (Syllabus outline)
This course is designed to expose students to diverse advanced research methodologies commonly used by social scientists, particularly in the field of sociology. Key methodological and analytical issues of the empirical sociological research will be discussed in detail.
The course will include an in-depth analysis of the basic statistical concepts and descriptive statistics such as research population and sample, type of variables, composite variables, types of frequency distribution, coefficients, indexes, and weights, measures of mean and variability values, confidence intervals, compare means, testing hypothesis, and biases in quantitative empirical research.
Extended and in-depth will be knowledge of bivariate statistics from the aspects understanding, using, and interpretation of parametric calculation of inferential statistics such as chi-square, t-test for dependent and independent samples, ANOVA, Pearson’s correlation, linear regression analysis, and non-parametric calculation: crosstabs and contingency coefficient, Mann-Whitney U test, Wilcoxon test, Kruskal-Wallis test, and Spearman’s rang correlation.
Complex and frequently used statistical methods of multivariate analysis, such as principal component analysis, factor analysis, multiple regression analysis, and cluster analysis (hierarchical clustering with dendrogram) will be introduced. Each analysis will be explained from the aspects of possible use in the sociological research, objectives of statistical analysis, model calculation and model estimation. Approaches to graphical presentations of findings based on statistical data analysis (different plots of relations between variables) will be presented.
Particular emphasis will be dedicated to the use of software packages for the multivariate data analysis (SPSS, STATA), interpretation of its findings, and research quality assessment, in particular of validity (principal component analysis, and factor analysis), reliability (coefficient Cronbach alpha), as well as ethical aspects of statistical data collection and analysis.
Learning and teaching methods
Higher education lectures; Group research projects/ assignments; Demonstration; Written assignments; Didactical use of informational and communication technology.
Intended learning outcomes - knowledge and understanding
On completion of this course, the student will:
• select a suitable bivariate or multivariate statistical test for data analysis based on the research objectives, recognition of variables types, estimation of frequency data distribution, and other tests parameters,
• distinguish between most frequently used methods of multivariate analysis, such as principal component analysis, factor analysis, multiple regression analysis, and cluster analysis,
• distinguish between different graphical presentations of statistical data analysis and their interpretation,
• cross-linking of theoretical-methodological concepts with empirical evidence,
• recognise and critically assess the research quality of advance statistical methods from the aspect of its scientific validity and reliability.
Intended learning outcomes - transferable/key skills and other attributes
On completion of this course, the student will:
• use software programs for multivariate statistical analysis independently,
• write a report on research findings with multivariate statistical analysis.
Readings
• Grimm, L. G.& Yarnold, P. R. (ur.) (2010). Reading and Understanding Multivariate Statistics (14th ed.). Washington: American Psychological Association.
• Pituch, K. A., & Stevens, J. P. (2016). Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM's SPSS (6th ed.). New York: Routledge: Taylor & Francis Group.
• Ferligoj, A., & Kogovšek, T. (2002). Multivariatna analiza: primeri. Ljubljana: Univerza v Ljubljani, Fakulteta za družbene vede.
• Čagran, B., & Bratina, T. (2017). Metodologija pedagoškega raziskovanja: skripta zbranega študijskega gradiva. Maribor: Univerza v Mariboru, Pedagoška fakulteta.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson
Prerequisits
Prerequisits for acceding the course:
None.
Conditions for prerequisits:
Each of the prescribed commitments must be assessed with a passing grade.
Passing grade of seminar work is required for taking the written exam.
