Advanced Research
Code: 42230 ECTS Credits: 10| Degree | Type | Year | Semester |
|---|---|---|---|
| 4313148 Marketing | OB | 0 | 1 |
Contact
- Name:
- Giuseppe Lamberti
- Email:
- Desconegut
Teaching groups languages
You can check it through this link. To consult the language you will need to enter the CODE of the subject. Please note that this information is provisional until 30 November 2023.
Teachers
- Jordi Lopez Sintas
- Giuseppe Lamberti
Prerequisites
It is recommended that the students have a basic knowledge of market research and data analysis. It is also recommendable to follow the propedéutic course "Introduction to Multivariate analysis" organized by the Master's Degree.
Objectives and Contextualisation
Marketing decision making is increasingly complex and requires greater knowledge of consumer behavior, both to understand its behavior and to predict it (marketing strategy).
This module will deepen in the indispensable tools for a reliable and valid information collection as well as methods for its analysis to support a better decision-making in marketing based in data. We will look at both qualitative and quantitative methods and address the methodological advances resulting from new technologies.
All this will be framed in practical works. For data manipulation and analysis, an open, free and open access data analysis environment (R software) and different graphical interfaces (Rstudio, R cloud) will be used to analyze the data available for the problem posed. Therefore, this part of the module will be eminently practical and will be carried out in the computer room or with a personal laptop.
Competences
- Design and carry out market research.
- Develop communicative skills in oral presentations before critical audiences.
- Develop management and leadership skills.
- Draft clear, precise reports on commercial problems.
- Plan and conduct advertising campaigns.
- Work in interdisciplinary teams.
- Work with the data sources, methodologies and techniques of scientific research, and the IT tools of marketing.
Learning Outcomes
- Apply the different research methods.
- Correctly plan the different stages of a qualitative and quantitative market research process.
- Correctly use IT tools to analyse data.
- Design research in the field of advertising.
- Develop communicative skills in oral presentations before critical audiences.
- Develop management and leadership skills.
- Draft clear, precise reports on commercial problems.
- Identify the characteristics of the database in order to analyse the data.
- Identify the pathologies or errors that affect market research.
- Know the research techniques that are commonly applied to advertising research.
- Manage the resources needed for the development of a research process.
- Recognise and identify the different research methodologies.
- Work in interdisciplinary teams.
Content
BLOCK I: QUALITATIVE RESEARCH
1. Comparison between quantitative and qualitative research
BLOCK II: QUANTITATIVE RESEARCH
Part C: Quantitative methods of research in marketing - I (2.5 ECTS)
1) Models for building perception and preference maps: Analysis of the main ACP components
• Introduction to the methodology and main applications
• Computing the components
• Definition of component numbers, circle of correlations and interpretation
• Interpretation of the graphics of the components
2) Models for segmenting markets: Cluster Analysis
• Introduction to the methodology and main applications
• Hierarchical Clustering and K-means
• Main methods of proximity calculation
• Definition of groups
• Interpretation of results
3) Models per construir mapes de percepcions i de preferencies:Analysis of correspondences
• Introduction tothe methodology and main applications
• Profiles columns and row, distance from the square Chi.
•Factor calculations.
• Graphic representation.
• Interpretation of results.
Part D: Quantitative methods of research in marketing - II (2.5 ECTS)
1) Models for classifying clients: Analysis of the Discriminating
• Introduction to the methodology and main applications
• Linear and quadratic discriminant function
• Table of confusion
• Graphic representation
• Predictive use of discriminant analysis
2) Review linear regression, logistic regression, multinomial regression
• Review of linear regression
• Introduction to logistic regression: main applications
• Calculation of the coefficients
• Interpretation of results
• Model validation: waste analysis
• Introduction to multinomial regression: the main applications
• Calculation of the coefficients
• Interpretation of results
• Model validation: analysis of residuals
3) Models models: structural equations
• Introduction to the methodology and main applications
• Definition of Latent variables and manifest variables
• Estimation methods
• Validation of the model
• Interpretation of coefficients and graphic representations
Methodology
The teaching methodology and assessment proposed in the guide may undergo some modification depending on the restrictions on attendance imposed by the health authorities.
A set of different methodologies will be used: lectures, essays, projects, discussion of practical cases and exercises.
15 minutes will be reserved to answer the student satisfaction surveys
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lectures, case discussion and presentation of work | 75 | 3 | 1, 7, 10, 5, 6, 4, 11, 8, 9, 2, 12, 13, 3 |
| Type: Supervised | |||
| Tutorials and follow-up of the work to be carried out and the cases to be prepared | 50 | 2 | 1, 7, 10, 5, 6, 4, 11, 8, 9, 2, 12, 13, 3 |
| Type: Autonomous | |||
| Readings, preparation of cases and practices, study and elaboration of schemes | 100 | 4 | 1, 7, 10, 5, 6, 4, 11, 8, 9, 2, 12, 13, 3 |
Assessment
This subject/modul does not offer the opcion for a comprehensive evaluation
General assessment rules
This module is structured in different parts that are in charge of different lecturers. The final grade of the module consists in the average of the marks of each subject or part that make up the module.
It is considered that the module has been approved if:
1) the mark of each part of the module is greater than or equal to 5 (on a scale of 0 to 10) and
2) the final grade of the module is greater than or equal to 5 (on a scale of 0 to 10)
If the module is not approved, the coordination of the master program will offer the student the possibility of re-evaluating the parts that make up the module that has not been passed only if the mark is greater than 3.5, according to the assessment of lecturers of the modules and the coordination. If the student passes the re-evaluation, the maximum grade that will be obtained in the re-evaluated part will be 5. The re-assessment schedule will be made public along with the list of notes of the module.
The marks of each part of the module
The student will have a non-marked grade if he / she does not attend at least 80% of the in-person classes (a check will be carried out through a signature sheet or with the activities done in class to evaluate) or if he does not present at least 66.66% of the continuous evaluation activities. Each lecturer will specify in his/her guide the way in which the students will evaluate. If not specified in the guide, these evaluation rules will be delivered the first day of class in writing.
- Calendar of evaluation activities
The dates of the evaluation activities (midterm exams, exercises in the classroom, assignments, ...) will be announced well in advance during the semester.
The date of the final exam is scheduled in the assessment calendar of the Faculty.
"The dates of evaluation activities cannot be modified, unless thereisan exceptional and duly justified reason why an evaluation activity cannot be carried out. In this case, the degree coordinator will contact both the teaching staff and the affected student, and a new date will be scheduled within the same academic period to make up for the missed evaluation activity." Section 1 of Article 115. Calendar of evaluation activities (Academic Regulations UAB). Students of the Faculty of Economics and Business, who in accordance with the previous paragraph need to change an evaluation activity date must process the request by filling out an Application for exams' reschedule https://eformularis.uab.cat/group/deganat_feie/application-for-exams-reschedule
Grade revision process
After all grading activities have ended, students will be informed of the date and way in which the course grades will be published. Students will be also be informed of the procedure, place, date and time of grade revision following University regulations.
Retake Process
"To be eligible to participate in the retake process, it is required for students to have been previously been evaluated for at least two thirds of the total evaluation activities of the subject." Section 3 of Article 112 ter. The recovery (UAB Academic Regulations).Additionally, it is required that the student to have achieved an average grade of the subject between 3.5 and 4.9.
All students are required to perform the evaluation activities. If the student's grade is 5 or higher, the student passes the course and it cannot be subject to further evaluation. If the student grade is less than 3.5, the student will have to repeat the course the following year. Students who have obtaineda grade that is equal to or greater than 3.5 and less than 5 can take a second chance exam. The lecturers will decide the type of the second chance exam. When the second exam grade is greater than 5, the final grade will be a PASS with a maximum numerical grade of 5. When the second exam grade is less than 5, the final grade will be a FAIL with a numerical grade equal to the grade achieved in the course grade (not the second chance exam grade).
A student who does not perform any evaluative task is considered “not evaluable”, therefore, a student who performs a continuous assessment component can no longer be qualified with a "not evaluable".
The date of the retake exam will be posted in the calendar of evaluation activities of the Faculty. Students who take this exam and pass, will get a grade of 5 for the subject. If the student does not pass the retake, the grade will remain unchanged, and hence, student will fail the course.
Irregularities in evaluation activities
In spite of other disciplinary measures deemed appropriate, and in accordance with current academic regulations, "in the case that the student makes any irregularity that could lead to a significant variation in the grade of an evaluation activity, it will be graded with a 0, regardless of the disciplinary process that can be instructed. In case of various irregularities occur in the evaluation of the same subject, the final grade of this subject will be 0". Section 10 of Article 116. Results of the evaluation. (UAB Academic Regulations).
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Deliveries of individual or collective work (40%) | 40% | 20 | 0.8 | 1, 7, 10, 5, 6, 4, 11, 8, 9, 2, 12, 13, 3 |
| Individual assessment through individual examination or delivery (40%) | 40% | 3 | 0.12 | 1, 7, 10, 5, 6, 4, 11, 8, 9, 2, 12, 13, 3 |
| Participation in class discussions (20%) | 20% | 2 | 0.08 | 1, 7, 10, 5, 6, 4, 11, 8, 9, 2, 12, 13, 3 |
Bibliography
SECTION I: QUALITATIVE RESEARCH
Basic bibliography
Kuckartz, U., & Rädiker, S. (2019). Analyzing Qualitative Data with MAXQDA: Text, Audio, and Video. Springer International Publishing. https://doi.org/10.1007/978-3-030-15671-8
Rädiker, S., & Kuckartz, U. (2020). Analyzing Open-Ended Survey Questions with MAXQDA. MAXQDA Press. https://doi.org/10.36192/978-3-948768027
Rädiker, S., & Kuckartz, U. (2020). Focused Analysis of Qualitative Interviews with MAXQDA (1st ed.). MAXQDA Press. https://doi.org/10.36192/978-3-948768072.
Rädiker, Stefan, and Andre Morgenstern-Einenkel. Collaborative Data Analysis Using MAXQDA TeamCloud. 1st ed. DE: MAXQDA Press, 2022. https://doi.org/10.36192/978-3-948768140.
Gizzi, Michael C., and Stefan Rädiker, eds. The Practice of Qualitative Data Analysis. DE: MAXQDA Press, 2021. https://doi.org/10.36192/978-3-948768058.Kuckartz, A., & Sharp, M. (2011). Responsibility: A Key Category for Understanding the Discourse on the Financial Crisis—Analyzing the KWALON Data Set with MAXQDA 10. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 12.
Complementary bibliography
Becker, H. S. (1998). "Tricks of the Trade: How to Think About Your Research While You're Doing It". Chicago: The University of Chicago Press.
Blaikie, N. W. H. (2000). Designing social research: The logic of anticipation. Polity Press.
Bryman, A. (1988). Quantity and Quality in Social Research. London, Boston: Unwin Hyman.
Creswell, J. W.(1998). Qualitative Inquiry and Research Design: Choosing Among Five Traditions. Thousand Oaks (Calif.) [etc.]: Sage.
Denzin, N.K., and Y.S. Lincoln (1994), Handbook of Qualitative Research. CA: Sage.
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook (Third edition). SAGE Publications, Inc.
Patton, M. Q. (2002). Qualitative research & evaluation methods (3rd ed.). Thousand Oaks, CA: Sage.
Saldaña, J. (2009). The Coding Manual for Qualitative Researchers. SAGE.
Seale, C. (1999). The Quality of Qualitative Research. London: SAGE Publications.
Shank, G. D. (2002). Qualitative Research: A Personal Skills Approach. Upper Saddle River, NewJersey: Merrill Prentice Hall
Strauss, A. L. (1991). Basics of Qualitative Research: Grounded Theory Procedures and Technique (3rd. printing.). Newbury Park: SAGE.
Schedule: See campus virtual (digital learning platform)
BLOCK II: QUANTITATIVE RESEARCH
Hair, Joseph F, Rolph E Anderson, Ronald L Tatham, and William C Black. 2009. Multivariate Data Analysis with Readings. 7th ed. Upper Saddle River, NJ: Prentice Hall International Editions.
Modern Marketing Research: Concepts, Methods, and Cases, Feinberg, F.M. et al., Second Edition, published by Cengage Learning, 2012
Lilien, G.L. and Rangaswamy, A. 2004. Marketing Engineering: Computer-Assisted Marketing Analysis and Planning, Prentice Hall, Inc.
Chapman, N.C., and McDonnell, E., Feit. 2015. R for Marketing Research and Analytics, Springer-Verlag, Switzerland.
Software
Students have at their disposal the software:
-Google suite
-Zotero (bibliographic reference manager)
-R Language and Environment for Data Analysis (open source)
-Rstudio (an open source GUI for using R)
-Rstudio.cloud
-QualCoder (qualitative data analysis software based on Python, open source)
-RQDA (qualitative data analysis software based on R, open source)
-Dedoose (web-based qualitative data analysis software)
- MaxQDA and Nvivo