Management Research Methods

Syllabus

Thematic definition of lectures: 1. Applied and basic research in business management, its characteristics and starting points. What types of problems require managerial research: identification of problems or opportunities in the company, subsequent analysis and assessment of problems and opportunities, evaluation of possible solutions, assessment of past procedures and decisions, comparison of the situation in the company with the environment. 2. Research process and research cycle. Creating a research project. Sources of information in professional and scientific literature. Obtaining and searching for relevant literary sources, assessing the quality of literary sources. Description of the process of transformation of raw data into information and subsequently into knowledge. Ethics in research. 3. Solving specific business problems using specific research designs. Basic classification of research designs. Research plan. Comparison of research designs from a methodological point of view (quality of output, limits and possible biases), but also time, labor and cost. Introduction to the probability of occurrence of phenomena and statistical significance. 4. Measurement in research. Variables and their types: nominal, ordinal, cardinal (interval, ratio). Data coding and data insertion. Indexes and summary indicators. Variables and their position in measuring tools. Theoretical basis of the analysis. Creating assumptions, setting hypotheses and testing them. Introduction to the process of creating hypotheses and their verification. 5. Description and descriptive analysis of quantitative data. Central tendency indicators. Variability indicators. Normality of data distribution. Gaussian curve. Presentation of results in graphic form. Frequently chart types used for specific results. Confidence interval. 6. Comparing groups. Parametric tests. Application conditions. When to choose parametric and when non-parametric tests to compare groups. Options for verifying the normality of data distribution. Robustness of parametric tests, susceptibility to distortion. 7. Comparing groups. Nonparametric tests. Application conditions. Verification of normality of data distribution. Robustness of nonparametric tests. 8. Measurement of relationships between variables (Pearson's r, Kendall's tau). Regression analysis compared to correlation. 9. Simple and multiple regression analysis. Creation of regression models and assessment of the quality of the regression model. Coefficient of determination. 10. Forecasting trends. Quantitative, based on time series as well as on regression models. Qualitative techniques as possible development scenarios. Creative techniques based on existing data and expected trends. 11. Data collection and character of data in quantitative and qualitative research and their quality. Primary data and secondary data, their advantages and limits. Secondary data sources. Questionnaire questions and practical application (open, forced answers, scales). 12. Measurement quality and data quality. Reliability, validity, objectivity, sensitivity of scientific measurement and specific measurement tools. 13. Summary interpretation and reporting results, creation of research reports. Ethics in managerial research. Making recommendations for decision making. Thematic definition of exercises: 1. Introduction of open-source statistical software: JAM, JASP, and PSPP. Individual installation for different operating systems. Working with files, saving, format, file type. Saving results, data, graphs, and outputs to MS Word, Excel. 2. Data entry and file preparation for analysis. Data import and their format, subsequent storage and data management. Copy, search, aggregate, divide, weigh, sort and organize data. Encoding and recoding variables. Analysis outputs and their format. 3. Probability of occurrence of phenomena and statistical significance. Its level, the most used types and their interpretation. Mistakes in researching and drawing conclusions and ways to avoid them. First order error α, second order error β. Effect size and two calculation methods. Intervals for results, their interpretation and practical applicability. 4. Examination and testing of data distribution, frequency analysis, use of histograms. Normality of data distribution. Graphical representation of normal, platykurtic, leptokurtic data distribution. Contingency and frequency tables, Chi square distribution. 5. Description and basic analysis of quantitative data. Calculations and interpretations of indicators. Indicators of central tendency (mean, mode, median). Variability indicators (variance and standard deviation). Relationship to data distribution and relationships between them. 6. Parametric tests. Two - sample t - test of independent groups. Paired t - test. One - sample t - test. Analysis of variance (ANOVA). Leven's test, normality of data distribution. 7. Nonparametric tests. Mann - Whitney test, Wilcoxon test. Normality of data distribution and its testing using the Shapiro – Wilk test, use of the Leven test. 8. Measuring relationships between variables. Correlation (Pearson's r). Kendall's tau B and C. Calculation of mutual relations using: Pearson correlation coefficient and Kendall's Tau. Chi-square test. Calculation and interpretation of odds ratio. 9. Simple linear regression analysis and multiple linear regression analysis. Difference from correlation. Procedure for creating and verifying regression models. Interpretation of regression analysis results. 10. Trend calculation. Quantitative, based on time series as well as on regression models. 11. Practical procedures of data collection and the nature of data in quantitative and qualitative research and their quality. Methods used in electronic data collection. Advantages and limits. 12. Reliability analysis of measuring tools Comparison of Cronbach's alpha and McDonald's omega methods for the whole questionnaire and for individual items. 13. Practical practice of summary interpretation and reporting of results, creation of research reports, presentation of research results. Making recommendations for decision making.