Objectives and competences
• Students explain key issues related to methods of data analyses in social sciences that include univariate and bivariate statistical analysis, parametrical and non-parametrical hypothesis testing, qualitative content analysis, 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 and qualitative content analysis using a software tools (SPSS, QDA Miner).
• Students design and interpret graphical presentations of the findings, obtained with the advanced statistical and qualitative analyses of social science data.
• Students critically evaluate research quality and ethical considerations in planning, measuring, and interpreting of the social science 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 a systematic overview and in-depth study of the fundamental statistical and qualitative methods of social science data analysis. Particular emphasis will be given to a consideration of a new developing methodologies and software tools for data analysis.
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, discriminant analysis, multivariate analysis of variance and cluster analysis (hierarchical clustering with dendrogram, non-hierarchical clustering) 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 qualitative content analysis of social science data (e.g. interview, focus group, text analysis) with the use of software packages (QDA Miner, NVivo, Atlas.ti, MAXQDA), interpretation of its findings, and research quality assessment, in particular of validity, reliability, objectivity and ethical considerations of social science data collection and analysis.
Learning and teaching methods
Higher education lectures; Demonstration; Written assignments; Didactical use of informational and communication technology.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
On completion of this course, the student will:
• select a suitable univariate, 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,
• properly conducting process of coding and merging codes into categories and themes according to the qualitative content analysis,
• distinguish between most frequently used methods of multivariate analysis, such as principal component analysis, factor analysis, multiple regression analysis, discriminant analysis, multivariate analysis of variance and cluster analysis,
• distinguish between different graphical presentations of statistical and qualitative data analysis and their interpretation,
• cross-linking of theoretical-methodological concepts with empirical evidence,
• recognise and critically evaluate the research quality of analyses of social science data from the aspect of its scientific validity, reliability objectivity, and ethical considerations.
Intended learning outcomes - transferable/key skills and other attributes
Transferable/Key Skills and other attributes:
On completion of this course, the student will:
• use software programs of SPSS and QDA Miner for analysis of social science data independently,
• write a report on research findings with statistical and qualitative analyses of social science data.
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.
• Adu, P. (2019). A Step-by-Step Guide to Qualitative Data Coding. London: Routledge.
• 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.
Additional information on implementation and assessment Research Project 1 - 20%
Research Project 2 - 20%
Research Project 3 - 20%
Final Seminar Paper - 20%
Final Exam - 20%
