Data Analysis in Management

Syllabus

Thematic definition of lectures: 1. The importance of data and analysis for the creation of information and subsequent knowledge as a basis for quality decision-making and for the success of the company. Creation of information from data and subsequent knowledge from information and modern management based on data analysis. Transforming ideas into a research problem. 2. Scientific theory and applicable practical significance of theory. Research categorization: exploratory research, descriptive research, causal research. Summaries of research outputs as a systematic review study and meta-analysis. Experimental, quasi-experimental and correlation design of research, design of case studies. Action - based research. Research plan. Experimental design. 3. Population, sample, probability, sampling, probability distribution. Basic types of sampling and the differences between them: random sampling, deliberate sampling, snowball sampling, quota, proportional, and stratified sampling. Data distribution. Central limit theorem. Selection error, systematic error. Probability of occurrence of phenomena and its distribution. 4. Creating new specialized questionnaires and other measuring tools according to scientific principles and using existing ones. Asking questions. Interview principles and possible biases. Group interviews and focus groups. In-depth interview. Telephone interview. Internet surveys. Qualitative research. Content analysis. Observation. Free associations. Determination of sample size. Pilot research. Construct, criterion, predictive, content, ecological validity. Objectivity at collection and objectivity of the measuring tool. 5. Presentation of practical recommendations and skills needed to search for data in scientific databases and professional databases. Internal and external data. Theoretical anchoring of the problem. 6. Assumptions and hypotheses created from them. Principles and logic in verifying the null hypothesis. Practical recommendations for creating and formulating hypotheses, common mistakes. Significance level in case of hypotheses. 7. Regression analysis: hierarchical and logistical, conditions for application, practical usage. Validation of regression analysis. Comparison of regression models. 8. Structural equation modeling (SEM). Explanation of the method. 9. Mediation as an extension of regression analysis. Explanation of basic principles of mediation. The difference between mediation and moderation. Graphic representation of mediation models. Direct, indirect and overall effect. Presentation and graphical interpretation of results. 10. Moderation as an extension of regression analysis. Choice of mediation or moderation according to theoretical and logical expectations and assumptions. Creation of independent moderation models and their combinations with mediation models. Model evaluation and model interpretation. 11. Reduction of the number of variables using exploratory factor analysis. Exploratory factor analysis (EFA). Factor identification process, factor loading, scree plot. 12. Confirmatory factor analysis (CFA). Verification of existing measuring instruments using confirmatory factor analysis. 13. The importance of systematic studies and meta-analyzes as carriers of knowledge. Comparison of these types of studies. Advantages of meta-analysis over systematic study. Data preparation for meta-analysis. Thematic definition of exercises: 1. Presentation of the installation of current versions of open source software used for statistical / data analysis (JAMOVI, JASP and PSPP). Description and descriptive analysis of quantitative data. PivotTables, chi square distribution. Graphic display. 2. Data distribution. Shapiro-Wilk test, Kolgomorov Smirn test, graphical presentation of data distribution. Bar graphs with confidence intervals, definition of the whole data set and determination of the examined sample. Investigation of phenomena in homogeneous and small data files and vice versa in large and heterogeneous data files. Suitable methods and their limits 3. Calculation of effect size by Cohen's d, Person's r for parametric and nonparametric tests. Interpretation of effect size. Mutual conversion. 4. Probability of phenomena and its distribution. Practical calculation. Statistical versus material significance. Error α, error β. 5. Measurement of relationships between variables according to the type of variable (variations of relationships between types of variables: nominal with nominal, ordinal, interval, etc.) and according to the normality of data distribution. Relationships measured by the following methods (Cramer V, Lambda, Phi, Gamma, Eta, Spearman's rho). 6. Regression analysis and conditions for the application of regression analysis (multicollinearity, homoskedasticity, etc.) and overall validation of regression models. Durbin-Watson test, Cook distances, residual graphs. Calculation of multicollinearity. 7. Hierarchical regression analysis creation of hierarchical regression models. The process of creating models and subsequent comparison of models according to specific parameters. Logistic regression analysis for: a) two outputs, b) n outputs, c) with ordinal output. 8. Structural modeling. Explanation of principles, creation of structural models, evaluation and comparison of models according to the its parameters. Design and preparation of a structural model. 9. Mediation as an extension of regression analysis and its calculations. Graphic representation of mediation models. Direct, indirect and total effect, calculation and reporting. Presentation of results and graphical interpretation of results. 10. Moderation as an extension of regression analysis and its calculations. Choice of mediation or moderation according to theoretical and logical expectations and assumptions. Creation of combined mediation and moderation models. Model evaluation and model interpretation. 11. Exploratory factor analysis (EFA). Selection of method parameters based on theoretical assumptions. Selection of a suitable method of factor extraction. Possibilities of selection of rotation factors (Varimax, Quartimax, Promax, Oblimin, Simplimax) Bartett's sphericity test, KMO test, scree plot. 12. Confirmatory factor analysis (CFA). Difference between EFA and CFA. Model parameters and their interpretation as: chi square test, comparative fit index (CFI), Tucker Lewis Index (TLI), Root mean square error of approximation (RMSEA), Akaike's Information Criterion (AIC). 13. Meta-analysis. Meta-analysis calculation and its interpretation. Meta-analysis based on correlation coefficients, means (differences in means) effect sizes. Presenting the results of meta-analysis.