Scientific research in management (in English)

  • Credits: 10
  • Ending: Examination
  • Range: 16sP
  • Semester: winter
  • Year: 1
  • Faculty of Business Management

Teachers

Included in study programs

Business Economics and Management

Business Economics and Management

Teaching results

Knowledge:
• The student will gain new and expand existing knowledge of available scholarly databases, how to register in them, and consequently how to work with literature searches in both paid and publicly available databases.
• About the process and procedures for transforming theoretical constructs/concepts/definitions into empirical measurement tools with specifically defined expected relationships, leading to the establishment of goals and subsequent design to achieve the goals.
• All commonly used scientific designs, their various subcategories, and the statistical methods used to implement them will be discussed. Specifically: exploratory research design (exploring data, identifying latent variables), descriptive research design (relationships between variables, comparing groups, verifying latent variables), explanatory research design (identifying effects of variables)
• On the application of specific statistical methods used for specific research designs.
• About the system and organization of writing research articles, custom dissertation.
Competence:
• In working systematically, deeply and comprehensively with the scientific literature. How to identify potentially promising lines of research from the latest scientific literature, based on a deep and thoughtful argument supported by the latest literature findings.
• Work with theoretical constructs/concepts/definitions used in the domain. The student will learn to relate specific measurement tools (questionnaires, inventories, tasks, stimulus materials, formulas, equations, etc.) to theoretical constructs and will be able to systematically argue why one particular construct was chosen from among several options. At the same time, he/she will be able to assess their time, cost and practical advantages and limitations.
• He/she will be able to set realistic and achievable goals framed and described in the most up-to-date scientific terms and based on current scientific and theoretical constructs. In relation to the objectives, he/she is able to establish hypotheses.
• Based on the objectives, the student will be competent to choose the most appropriate research design that can achieve the stated objectives.
• Practically implement and successfully complete primary data collection or secondary data collection according to the established research design.
• Prepare data in the format and structure required by the statistical opensource software JAMOVI and then, based on extensive knowledge of specific statistical methods/tests/analyses, independently analyze the data collected, test the stated hypotheses and thereby fulfill the individual objectives as well as the main goal of the research/final paper.
• To present the empirical results in a discussion in the context of the theoretical basis and background. On this basis, to make a unique and original own scientific contribution that extends current scientific knowledge in a specific specific field/domain.
• To elaborate the theoretical part, objectives, methodology and results in the form of a scientific article that fulfils all the prerequisites necessary for publication in a high impact factor, international, peer-reviewed, peer-reviewed, peer-reviewed, peer-reviewed, peer-reviewed, peer-reviewed, peer-reviewed, peer-reviewed, peer-reviewed journal.
• Design an organized detailed publication plan in the categories of literature study, objectives, design, collection, analysis, and writing based on realistic estimates.
Skill:
• Navigate and work with scholarly article databases. Skill in search based on various techniques, criteria, databases
• Successfully master the process of identifying literature that is relevant to the problem at hand.
• Relationship between the methodology used and the possibilities of subsequent analysis and the outcome of the analysis. When research is needed and when we can rely on existing information.
• The process of identifying and correctly stating the problem. Determining the analysis, identifying variables, setting research objectives, questions and then hypotheses.
• In the procedures for testing hypotheses.
• Use a wide range of even difficult statistical tests to test hypotheses and achieve the stated objectives.
• In summarising their own results and interpreting them in the context of existing studies and theoretical frameworks.

Indicative content

1. Dissertation as a scientific qualification thesis, its distinction from professional qualification theses (bachelor's thesis, diploma thesis).
2. Work with literature and systematic creation of the theoretical base necessary in the dissertation and in scientific articles.
3. Identification of a specific scientific research problem.
4. Principles of using and defining the constructs/concepts/definitions that form the abstract and theoretical framework of the selected problem and working with these constructs.
5. Exploratory research design and statistical methods used in this type of design I.
6. Exploratory research design and statistical methods used in this type of design II.
7. Descriptive research design I.
8. Exploratory research design.

Support literature

1. FIELD, Andy. Discovering statistics using IBM SPSS statistics. Sage, 2013.
2. PERVEZ Ghauri - GRØNHAUG Kjell - STRANGE Roger. Research methods in business studies. Cambridge University Press, 2020.
3. EVANS, Jonathan. How to Be a Researcher: A strategic guide for academic success. Routledge: 2015 ISBN: 978-1138917316
4. BOWERMAN, Bruce. Business Statistics in Practice: Using Data, Modeling, and Analytics. McGraw-Hill Higher Education: 2016.
5. JOHNSON, Andrew - SUMPTER, John. How to be a Better Scientist. Routledge: 2018. ISBN: 978-1138731295
6. ROBERTS, Carol. - Hyatt Laura M. The dissertation journey: A practical and comprehensive guide to planning, writing, and defending your dissertation. Corwin Press. 3 vydanie: 2018. ISBN: 978-1506373317
7. MOORE, David et al. The practice of statistics for business and economics. WH Freeman: 2016.
8. ZIKMUND, William - CARR Jon - GRIFFIN Mitch. Business Research Methods. Cengage Learning, 2013.
9. SAUNDERS, Mark - LEWIS Philip - THORNHILL Adrian. Research Methods for Business Students (4th edn.) (2011).
10. GREENER, Sue. Business research methods. BookBoon, 2008.
11. THARENOU, Phyllis - DONOHUE Ross - COOPER Brian. Management research methods. Cambridge University Press, 2007.
12. FIELD, Andy - HOLE Graham. How to design and report experiments. Sage, 2002.
13. ADAMS, John - HAFIZ TA Khan - RAESIDE Robert. Research methods for business and social science students. SAGE Publications India, 2014.
Scientific Papers:
1. ALBERS, Casper – LAKENS, Daniël. "When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias." Journal of experimental social psychology 74, 2018, s. 187-195.
2. ALTMAN, Douglas G - BLAND J. Martin. "Statistics notes: the normal distribution." Bmj 310, no. 6975, 1995, s. 298.
3. BISHARA, Anthony J. – HITTNER, James B.. "Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches." Psychological methods 17, no. 3, 2012,s 399.
4. BROWN, J. "Choosing the right number of components or factors in PCA and EFA." JALT Testing & Evaluation SIG Newsletter 13, no. 2, 2009.
5. BURMEISTER, Elizabeth, - AITKEN Leanne. "Sample size: How many is enough?." Australian Critical Care 25, no. 4, 2012, s. 271-274.
6. COHEN, Patricia - COHEN Jacob - AIKEN Leona S. - WEST Stephen G.. "The problem of units and the circumstance for POMP." Multivariate behavioral research 34, no. 3, 1999, s. 315-346.
7. COSTELLO, Anna B. - OSBORNE Jason. "Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis." Practical assessment, research, and evaluation 10, no. 1, 2005, s 7.
8. INTHOUT, Joanna - IOANNIDIS John - BORM George F.. "The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method." BMC medical research methodology 14, no. 1, 2014, s 1-12.
9. KOLDE, Raivo - LAUR Sven - ADLER Priit - VILO Jaak. "Robust rank aggregation for gene list integration and meta-analysis." Bioinformatics 28, no. 4, 2012, s. 573-580.
10. LENTH, Russell V. "Some practical guidelines for effective sample size determination." The American Statistician 55, no. 3, 2001, s. 187-193.
11. LIN, Shili. "Rank aggregation methods." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 5, 2010, s. 555-570.
12. LUMLEY, Thomas - DIEHR, Paula - EMERSON Scott - CHEN Lu. "The importance of the normality assumption in large public health data sets." Annual review of public health 23, no. 1, 2002, s. 151-169.
13. MACCALLUM, Robert C. - WIDAMAN Keith F - ZHANG Shaobo - HONG Sehee. "Sample size in factor analysis." Psychological methods 4, no. 1 (1999): 84.
14. MOELLER, Julia. "A word on standardization in longitudinal studies: don't." Frontiers in psychology 6, 2015, s. 1389.
15. MORDKOFF, J. Toby. "The assumption (s) of normality." Dostupno na: goo. gl/g7MCwK (Pristupljeno 27.05. 2017.), 2016.
16. OSBORNE, Jason W. "What is rotating in exploratory factor analysis?." Practical Assessment, Research, and Evaluation 20, no. 1, 2015, s 2.
17. QURESHI, M. E. - HARRISON Steve R. - WEGENER M. K.. "Validation of multicriteria analysis models." Agricultural Systems 62, no. 2, 1999, s. 105-116.
18. SCHMITT, Thomas A. - SASS and Daniel. "Rotation criteria and hypothesis testing for exploratory factor analysis: Implications for factor pattern loadings and interfactor correlations." Educational and Psychological Measurement 71, no. 1, 2011, s. 95-113.
19. SEIDE, Svenja E. - RÖVER Christian - FRIEDE Tim. "Likelihood-based random-effects meta-analysis with few studies: empirical and simulation studies." BMC medical research methodology 19, no. 1, 2019, s. 1-14.
20. NG, Marie - LIN Jingjing. "Testing for mediation effects under non-normality and heteroscedasticity: a comparison of classic and modern methods." International Journal of Quantitative Research in Education 3, no. 1-2, 2016, s. 24-40.
21. SUGASAWA, Shonosuke -NOMA Hisashi. "A unified method for improved inference in random effects meta-analysis." Biostatistics 22, no. 1, 2021, s. 114-130.
22. XIA, Yan - YANG Yanyun. "RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods." Behavior research methods 51, no. 1, 2019, s. 409-428.

Syllabus

1. Dissertation as a scientific qualification thesis, its distinction from professional qualification theses (bachelor's thesis, diploma thesis). Scientific research at doctoral studies. The importance and status of scientific publishing in the process of doctoral studies and in scientific research. Dissertation project as a scientific project and characteristics of scientific research projects. The overall process of scientific research and its 11 individual steps. Time, cost and labour requirements in scientific research. 2. Working with the literature and the systematic development of the theoretical base necessary in a dissertation and in scientific articles. The process of building and composing a scientific argument. A system for establishing the theoretical literature base necessary to generate one's own unique scholarly contribution in a given area of research. Scientific databases such as Scopus, Science direct, Web of science and others (Elsevier, Sage, etc.). Registration options, working environment, advanced search system, settings and customization. Google Scholar public database and working with it. 3. Identifying a specific scientific research problem, which can be framed as: a) a problem for which scientific research is absent or insufficient b) a problem for which different groups of researchers come to different conclusions. Anchoring the problem as a unifying research theme that links theory, objectives, methods, discussion to results and conclusions. Making the argument. 4. Principles of using and defining the constructs/concepts/definitions that form the abstract and theoretical framework of the selected problem and working with these constructs. The process of establishing expected relationships between constructs as a starting point for setting research objectives and methodology. The process of transforming theoretical constructs/concepts/definitions into the form of concrete practical measurement tools (questionnaires, inventories, tasks, stimulus materials, formulas, equations, etc.) designed to measure these constructs. The benefits and limitations of different measurement tools for one particular construct in terms of time, cost, and practicality. Formation of realistic and achievable goals, based on theoretical literature review, determination of relationships between constructs/concepts, selection of appropriate measurement tools. 5. Exploratory research design and statistical methods used in this type of design I. Expand knowledge and skills in descriptive statistics (measures of central tendency, location, and variability), graphical display of data (histograms, box plots (bar plots)), methods and tests used to measure the distribution of data and to measure homoskedasticity. Identification of latent (hidden) variables using exploratory factor analysis. Comparison of exploratory factor analysis methods versus principal components analysis. Conditions of application and limitations of these methods. 6. Exploratory research design and statistical methods used in this type of design II Frequency analysis, contingency tables, chi-square test. Cluster/cluster analysis. Hierarchical and non-hierarchical clustering methods. 7. Descriptive research design I: Examination of relationships between variables by type of variable: nominal (Cramer's V, Phi,), ordinal/cardinal (Gamma, Kendall's tau B, Mantel-Haenszel test,/ Pearson's r ). Statistical methods used to measure relationships for nominal, ordinal and cardinal variables. Descriptive research design II: Comparison of 2 independent groups. Parametric (Student's t -test, Welch's test) and non-parametric methods of comparing groups. Mann - Whitney test, Wilcoxon test Comparison of three or more independent groups. Parametric (ANOVA) and non-parametric methods of comparing groups. Kruskal Wallis test. Descriptive research design III: Validation of latent variables. Confirmatory factor analysis 8. Explanatory research design. Exploring the influence of one/multiple variables on another variable, identifying predictors/determinants and relationships between them. Regression models and moderation, mediation and mixed models. Exploring the influence of one/multiple variables on another variable, identifying predictors/determinants and the relationships between them. Structural models.

Requirements to complete the course

40 % elaboration of a project related to the methodology of own dissertation, 60 % presentation and defence of the project

Student workload

260 h - participation in consultations 16 h, preparation for consultations 32 h, processing of continuous assignments 16 h, processing of scientific state 90 h, preparation for examination 106 h)

Language whose command is required to complete the course

English

Date of approval: 11.03.2024

Date of the latest change: 14.05.2022