VŠTE:AN_MOP Research methods - Informace o předmětu

AN_MOP Research methods

Rozsah

0/4/0. 4 kr. Ukončení: z.

Vyučující

Robin Kunju Mol Raj, MBA, PhD. (cvičící)
prof. Ing. Marek Vochozka, MBA, Ph.D., dr. h.c. (cvičící)

Garance

prof. Ing. Marek Vochozka, MBA, Ph.D., dr. h.c.
Katedra financí podniku a ekonomie – Ústav znalectví a oceňování – Rektor – Vysoká škola technická a ekonomická v Českých Budějovicích
Dodavatelské pracoviště: Katedra financí podniku a ekonomie – Ústav znalectví a oceňování – Rektor – Vysoká škola technická a ekonomická v Českých Budějovicích

Rozvrh seminárních/paralelních skupin

AN_MOP/S01: Pá 16:30–18:00 H209, Pá 18:10–19:40 H209, R. Kunju Mol Raj

Předpoklady

OBOR(SLOGp)

The course follows on from the bachelor course in research methods (SA_TTM or an equivalent). Knowledge of the structure of an academic text, of the ISO 690 citation standard and of the basics of descriptive and inferential statistics including hypothesis testing in MS Excel is assumed. Students who lack this can acquire it from the Research Methods Study Text and Workbook available in the study materials of the course.

Omezení zápisu do předmětu

Předmět je nabízen i studentům mimo mateřské obory.

Mateřské obory/plány

• Podniková ekonomika - Ekonom výroby (program VŠTE, N_PE) (2)

Cíle předmětu opírající se o výstupy z učení

The aim of the course is to enable students to carry out research independently at the level of a follow-up master programme: to choose and defend a research design, apply advanced scientific methods to real data, interpret the results correctly, write them up as an academic text that stands up in the research community, and present and defend that text.

The core of the course is advanced research methods. The course follows on from the bachelor course Research Methods and takes it one level up. Where the bachelor course teaches the procedure and the basic methods, here students work with multiple regression and its assumptions, analysis of variance, time series analysis and forecasting, an introduction to neural networks and machine learning, systematic review governed by a search protocol, qualitative coding and triangulation, mixed design, and the field methods of business economics — modelling and simulation, scenario and expert methods. Students must defend their choice of method against the alternatives and demonstrate that its assumptions hold.

The course treats an academic text as a research report written in real time: over the semester students work on a single complex, unstructured, open-ended task and write about it continuously, recording the whole procedure so that it is verifiable and repeatable.

Part of the aim is responsible and transparent use of artificial-intelligence tools: students recognise the boundary between permissible support and substitution of their own work and declare such use properly.

Výstupy z učení

Upon successful completion of the course the student:

1. formulates a research problem, identifies a research gap and derives from it the aim of the work, the research questions and the hypotheses;
2. carries out a systematic review governed by a search protocol in Web of Science and Scopus, assesses source quality and recognises predatory journals;
3. builds a theoretical framework from the review and critically appraises the state of knowledge;
4. chooses a research design (quantitative, qualitative or mixed), defends it against the alternatives and names the threats to the validity of the conclusions;
5. assembles and cleans a data set, selects a sampling technique and justifies the sample size;
6. applies advanced qualitative methods — semi-structured and expert interviews, focus groups, content analysis with coding and categorisation, case study — and ensures triangulation and saturation;
7. applies advanced quantitative methods — hypothesis testing, analysis of variance, correlation analysis and multiple regression — and verifies the model assumptions including residual diagnostics and multicollinearity;
8. analyses a time series, decomposes it, estimates trend and seasonality, produces a forecast and states its limits;
9. explains the principle of neural networks and machine learning in economic research and judges when their use is and is not appropriate;
10. applies the field methods of business economics — financial analysis and credit-scoring models, comparison and benchmarking, modelling and simulation, scenario and expert methods;
11. writes the methodology both in bullet form and as continuous text so that the procedure can be repeated by another person;
12. presents and interprets results including tables and figures, confronts them with the state of knowledge and names the limitations of the research;
13. formulates the contribution of the results for a named addressee and writes it as a separate numbered chapter Contribution of the Results;
14. applies the rules of formal presentation and the ISO 690 citation standard and observes the principles of research ethics;
15. uses artificial-intelligence tools in line with the rules of the Institute and declares such use transparently;
16. solves a complex open-ended task, presents the results and defends the research in scholarly discussion.

Osnova

Seminars (the course has no lectures; 0/4/0). The seminar is run as the continuous writing of one academic text. Students work largely on their own and consult the text with the teacher as it grows; each part submitted is returned with comments. Every block has two parts: the method is explained and then immediately applied to the student own topic.

1. Science, research and the current discourse of empirical research. Open science and reproducibility. Research ethics, copyright and plagiarism. Artificial intelligence: the three-level model of admissibility and the AI use declaration. Topic selection, registration with the National Technical Library.
2. Typology of academic texts and academic journals. Systematic review: search protocol, keywords, Boolean operators, Web of Science and Scopus, citation and keyword analysis. Criteria of source quality, predatory journals.
3. From topic to research problem: the research gap, operationalisation, title, introduction and aim.
4. Review I — collecting and sorting sources according to the protocol.
5. Review II — synthesis of findings, theoretical framework, formulation of hypotheses and research questions.
6. Research design: quantitative, qualitative and mixed. Validity, reliability and threats to the validity of conclusions. Defending the choice of design against the alternatives.
7. Data and sampling: primary and secondary data, databases, sampling techniques, sample size, data cleaning and missing values.
8. Advanced qualitative methods: semi-structured and expert interviews, focus groups, content analysis, coding and categorisation, case study, triangulation and saturation.
9. Advanced quantitative methods I: hypothesis testing, analysis of variance, correlation analysis, simple and multiple regression, model assumptions, residual diagnostics and multicollinearity.
10. Advanced quantitative methods II: time series analysis, decomposition, trend and seasonality, forecasting and its limits. Introduction to neural networks and machine learning in economic research.
11. Methods in business economics: financial analysis and credit-scoring models, comparison and benchmarking, modelling and simulation, scenario methods, expert methods.
12. Results and discussion: data presentation, tables and figures, interpretation, confrontation with the literature, limitations. The chapter Contribution of the Results in three paragraphs.
13. Conclusion, abstract, finalisation of the text. The AI use declaration. Presentation and defence of the research, trial presentation.

Literatura

povinná literatura
• VOCHOZKA, M. and Z. ROWLAND, 2026. Research Methods: Study Text. České Budějovice: Institute of Technology and Business in České Budějovice. ISBN 978-80-7468-219-3.
• VOCHOZKA, M. and Z. ROWLAND, 2026. Research Methods: Workbook. České Budějovice: Institute of Technology and Business in České Budějovice. ISBN 978-80-7468-221-6.
• VOCHOZKA, M. and Z. ROWLAND, 2026. Methodology of Academic Writing at VŠTE. České Budějovice: Institute of Technology and Business in České Budějovice.
• VOCHOZKA, M. and Z. ROWLAND, 2026. Principles of the Use of Artificial Intelligence in Academic Work at VŠTE. České Budějovice: Institute of Technology and Business in České Budějovice.

doporučená literatura
• SAUNDERS, M. N. K., P. LEWIS and A. THORNHILL, 2023. Research Methods for Business Students. 9th ed. Harlow: Pearson. ISBN 978-1-292-40272-7.
• BELL, E., B. HARLEY and A. BRYMAN, 2022. Business Research Methods. 6th ed. Oxford: Oxford University Press. ISBN 978-0-19-886944-3.
• CRESWELL, J. W. and J. D. CRESWELL, 2023. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 6th ed. Thousand Oaks: SAGE. ISBN 978-1-07-181794-0.
• YIN, R. K., 2018. Case Study Research and Applications: Design and Methods. 6th ed. Thousand Oaks: SAGE. ISBN 978-1-5063-3616-9.
• HAIR, J. F., W. C. BLACK, B. J. BABIN and R. E. ANDERSON, 2019. Multivariate Data Analysis. 8th ed. Andover: Cengage Learning. ISBN 978-1-4737-5654-0.
• BHATTACHERJEE, A., 2012. Social Science Research: Principles, Methods, and Practices. 2nd ed. Tampa: University of South Florida. ISBN 978-1-4751-4612-7.
• ISO 690:2021. Information and documentation – Guidelines for information sources. Geneva: International Organization for Standardization.

Organizační formy výuky

seminář
tutoriál
konzultace

Komplexní výukové metody

frontální výuka
projektová výuka
brainstorming
kritické myšlení
samostatná práce – individuální nebo individualizovaná činnost
výuka podporovaná multimediálními technologiemi
e-learning

Studijní zátěž

Aktivita	Počet hodin za semestr
	Prezenční forma	Kombinovaná forma
Conclusions	3	3
Discussion	7	7
Hypotheses, Research questions	3	3
Literature review	12	12
Methodology	12	12
Presentation preparation, research paper defence	4	4
Příprava na seminář, cvičení, tutoriál		36
Research paper formatting	2	2
Results	6	6
Title and Introduction of research paper	3	3
Účast na semináři/cvičeních/tutoriálu/exkurzi	52	16
Celkem:	104	104

Metody hodnocení a jejich poměr

prezentace 20%
seminární práce 80%

Podmínky testu

The course is completed by a course credit. To pass, a student must obtain at least 70 of 100 points in total across both assessment components. No continuous or final test is used in this course.

Seminar paper — 80 points. The paper is assessed using the course assessment form: three binary gates and ten scored content criteria of 8 points each (introduction, aim, research questions, literature review, methodology, results, discussion, conclusion, citations, formal presentation). The binary gates are: the work states the aim and also fulfils it; the work cites at least 15 relevant sources; the work contains an introduction, a literature review, a methodology, results, a discussion and a conclusion. If any gate is marked NO, the work is graded F regardless of the points obtained.

At master level the Methodology criterion additionally assesses whether the method chosen is proportionate to the research question, whether the choice is defended against the alternatives and whether the assumptions of the method are shown to hold. Descriptive statistics without inference are not sufficient.

Presentation and defence of the seminar paper — 20 points. Assessed on clarity of the message, handling of data and graphics, keeping to time, and the ability to defend methodological decisions in scholarly discussion.

Overall classification (100–0 points): A 100–90, B 89.99–84, C 83.99–77, D 76.99–73, E 72.99–70, FX 69.99–30, F 29.99–0.

Vyučovací jazyk

Angličtina

Odkaz a informace učitele

http://ec.europa.eu/research/openvision/pdf/rise/adams_impact_of_o pen_science_methods.pdf (přístupný přehledový text v angličtině)

Study materials. The course uses the complete Research Methods set of materials in English: the Study Text (13 chapters), the Workbook, seminar worksheets including data files, the Methodology of Academic Writing at VŠTE, the Principles of the Use of Artificial Intelligence in Academic Work at VŠTE, thesis templates and model works. The materials are stored in the study materials of the course in the Information System. At master level the emphasis moves to chapters 6 to 11, that is to research design, qualitative and quantitative methods, and the field methods of business economics.

Link to the master thesis. The Methodology of Academic Writing, the templates and the model works apply to the master thesis as well. The seminar paper in this course is preparation for it: students are advised to choose a topic that can be developed into a master thesis. The supervisor and opponent assessment forms in the Information System remain unchanged; the Methodology only interprets them.

Citation style. The basic citation style is ISO 690, author-date (Harvard) referencing. APA 7 is an admissible alternative by agreement with the teacher, applied consistently. Only one citation style may be used in a single work; the chosen style is stated in the methodology chapter. Mixing styles is a reason for returning the work.

Artificial intelligence. Students are required to familiarise themselves with the Principles of the Use of Artificial Intelligence in Academic Work at VŠTE. The use of AI tools is declared in the AI use declaration, a compulsory part of the submitted text. Interpreting the results and formulating the conclusions always remain the student own work.

Software and information support. MS Excel including the Analysis ToolPak (US locale). Web of Science and Scopus (via the Institute VPN). The catalogue and electronic information resources of the Institute library. Registration with the National Technical Library is compulsory. The plagiarism detection tool in the Information System. Thesis templates in DOCX.

Attendance and continuous submission. Attendance in the course is defined in a separate internal standard of the Institute for all forms of study. Students submit their work in parts according to the seminar schedule: introduction including the aim and research questions in week 3, literature review in week 5, research design and methodology in week 7, data set and first results in week 10, discussion in week 12 and the final text in week 13. The final text is uploaded to the submission folder by the teacher once approved.

Students with an individual study plan. A student with an individual study plan who cannot take part in the continuous assessment completes it at a time agreed with the teacher. Such a student is required to contact the teacher immediately upon approval of the plan; otherwise the plan will not be taken into account. Questions may be addressed to the course guarantor at vochozka@mail.vstecb.cz.

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