Dissertation/Work Project/Internship Plan

Code:

2MK09

Acronym:

PDTPE

Keywords
Classification	Keyword
OFICIAL	Marketing

Instance: 2019/2020 - 1S

Active?	Yes
Responsible unit:	Management
Course/CS Responsible:	Master in Marketing

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MARKT	28	Bologna Official Syllabus	2	-	7,5	56	202,5

Teaching language

Suitable for English-speaking students

Objectives

The key objective of this course is to provide students with the skills and methodological tools necessary for the development of their dissertation projects, company project or internship. It is expectedthat students are able to identify opportunities for research, designi research goals and develop an appropriate methodological plan  fot those goals. For this, students should be able to:

1) identify a research topic;

2) identify the relevant literature and critically review it;

3) identify research opportunities and the objective / research question to explore;

4) identify and develop an appropriate methodological approach to the objective / research question identified, including tools for data collection and data analysis;

5) prepare a schedule of prospective activities;

6) present and discuss their dissertation project;

Learning outcomes and competences

The essential learning outcome is the development of the Msc the thesis project,, company project or company internship that students must develop during the 4th semester of course.

 

More generally, it is expected that students should be able to, departing from the identification of a problem for academic or business nature, identify and use the empirical data and the theoretical and methodological tools appropriate to their analysis, develop and implement a plan activities, present the results of the analysis in a systematic and grounded and (eventually) propose action plans.

Working method

Presencial

Program

LESSON 1 (1st part): stages of the research process (MARIA DO PILAR GONZÁLEZ and SOFIA CRUZ)

i. Research question;

ii. Exploratory readings;

iii. Theoretical framework: theories and concepts;

iv. Model analysis and research hypotheses construction;

v. Methodology;

vi. Collecting and treating the data;

vii. Conclusions.

Bibliography:

- Hart, C. (2008), Doing your Masters Dissertation: Realizing your Potential as a Social Scientist,

London: SAGE.

LESSON 1 (2nd part): scientific writing (MARIA DO PILAR GONZÁLEZ and SOFIA CRUZ)

i. Rules for presentation of dissertations and reports;

ii. How to structure a scientific work;

iii. Academic integrity.

Bibliography:

- Hart, C. (2008), Doing your Masters Dissertation: Realizing your Potential as a Social Scientist,

London: SAGE.

- Saunders, M., P. Lewis & A. Thornhill (2002), Research Methods for Business Students, 3rd ed.,

Edinburgh: Prentice-Hall.

LESSON 2: bibliometrics (tutorial support: AURORA TEIXEIRA)

i. Key concepts (bibliometrics, scienciometrics, meta-analysis);

ii. Bibliometrics as a tool for literature review;

iii. Data, sources, variables & methods;

iv. Practical application of bibliometrics using Scopus Sci Verse and Web of Science

bibliographic databases.

Bibliography:

- Castro e Silva, M. & A. A. C. Teixeira (2012), "Methods of assessing the evolution of science: a

review ", European Journal of Scientific Research, Vol. 68(4): 616-635.

- Cronin, B. & C. R. Sugimoto (eds.) (2014), Beyond Bibliometrics: Harnessing Multidimensional

Indicators of Scholarly Impact, London: MIT Press.

- Gingras, Y. (2016), Bibliometrics and Research Evaluation: Uses and Abuses, Cambridge: MIT Press.

- Osareh, F. (1996), Bibliometrics, Citation Analysis and Co-Citation Analysis: a review of analysis, in

Libri., vol.46: 149-158.

LESSON 3: sampling and surveys (tutorial support: MARIA EDUARDA SILVA)

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i. Data collection: introduction;

ii. Steps of research process;

iii. Data collection;

iv. Sampling (first concepts):

a. Probabilistic methods: systematic sampling; stratified sampling; cluster sampling;

multistage sampling;

b. Non probabilistic methods: convenience sampling; snowball sampling; quota sampling.

v. Sample survey/questionnaires:

a. Sources of information;

b. How to make a questionnaire;

c. Rules;

d. Assessing questionnaires;

e. Measurement scales.

vi. General overview of some Statistical Databases available in FEP.

Bibliography:

- Cohen L., L. Manion & K. Morrison (2005), Research Methods in Education, 5th ed., Taylor & Francis.

- Norland-Tilburg, E. V. (1990), “Controlling error in evaluation instruments”, Journal of Extension,

28(2) (http://www.joe.org/joe/1990summer/tt2.html).

- Trochim, W. M. K. (2006), The Research Methods Knowledge Base, 2nd ed.

(http://www.socialresearchmethods.net/kb/).

LESSONS 4A, 5A and 6A: qualitative methods (tutorial support: MARIA DA CONCEIÇÃO RAMOS e RAQUEL MENESES)

i. Qualitative research;

ii. Theoretical frameworks: narrative inquiry, phenomenology, ethnography, generic

qualitative inquiry, action-research, grounded theory;

iii. Qualitative designs;

iv. Data collection: observation, interview, focus-group, life stories, netnography, visual

methods;

v. Data analysis: content analysis (using or not NVivo11);

vi. Advantages and disadvantages of qualitative research;

vii. Ethical considerations.

Bibliography:

- Patton, M. (2014), Qualitative Research and Evaluation Methods, 4th ed., London: Sage Publications.

LESSON 4B: quantitative methods – parametric and non parametrical statistical tests (tutorial

support: ALEXANDRA RAMOS)

i. Introduction;

ii. Types of data;

iii. Descriptive statistics;

iv. Distributions, inference and hypothesis testing (assumptions and applications);

v. Parametrical tests:

a. T-Student test for 1 population;

b. T-Student test for 2 independent populations;

c. T-Student test for 2 paired samples;

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d. ANOVA.

vi. Non parametrical tests:

a. Normality tests;

b. Wilcoxon test for 1 population;

c. Mann-Whitney test for 2 independent populations;

d. Wilcoxon test for 2 paired populations;

e. Kruskall-Wallis test for k independent populations.

vii. Correlations;

viii. Chi-Square independence test.

Bibliography:

- Conover, W. J. (1999), Practical Nonparametric Statistics, 3rd ed., New York: John Wiley & Sons.

- Keller, G. (2008), Statistics for Management and Economics, Thomson.

LESSON 5B: quantitative methods – multivariate data analysis (tutorial support: MARIA PAULA

BRITO)

i. Data matrix and variables;

ii. Types of methods;

iii. Multiple regression;

iv. MANOVA;

v. Discriminant analysis;

vi. Factorial analysis;

vii. Principal component analysis;

viii. Cluster analysis:

a. Hierarchical method;

b. K-means.

Bibliography:

- Everitt, B. S. & G. Dunn (2013), Applied Multivariate Data Analysis, 2nd ed., Wiley.

- Hair Jr., J. F., W. C. Black, B. J. Babin & R. E. Anderson (2010), Multivariate Data Analysis – A Global

Perspective, 7th ed., Pearson Prentice Hall.

- Keller, G. (2008), Statistics for Management and Economics, Thomson.

- Sharma, S. (1996), Applied Multivariate Techniques, Wiley.

LESSON 6Bi: quantitative methods – social network analysis, opinion mining, sentiment analysis

(tutorial support: JOÃO GAMA)

i. Social network analysis. Social networks: interactions between actors. Professional social

networks, e.g. LinkedIn. Social network analysis:

a. The role of actors in the network: centralities and influence;

b. Communities and interactions;

c. Information diffusion in social networks.

ii. Sentiment analysis:

a. Opinions in the digital age: social media, blogs, tweets, etc.;

b. Text mining: identifying entities, and polarity.

Bibliography:

- Liu, B. (2012), Sentiment Analysis and Opinion Mining, Human Language Technologies.

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- Zafarani, R., M. Ali Abbasi & H. Liu (2014), Social Media Mining, Cambridge University Press.

LESSON 6Bii: quantitative methods – econometric methods (tutorial support: MARIA MARGARIDA

MELLO)

i. Object and method of Econometrics;

ii. The linear regression model;

iii. Qualitative dependent variable models;

iv. Time series models.

Bibliography:

- Asteriou, D. & S. G. Hall (2015), Applied Econometrics, 3rd ed., Palgrave.

- Griffiths, W. E., R. C. Hill & G. C. Lim (2012), Using EViews for Principles of Econometrics, 4th ed.,

John Wiley & Sons.

- Gujarati, D. (2014), Econometrics by Example, 2nd ed., Palgrave.

- Gujarati, D. N. & D. C. Porter (2009), Basic Econometrics, 5th ed., McGraw-Hill.

- Heij, C., P. De Boer, P. H. Franses, T. Kloek & H. K. Van Dijk (2004), Econometric Methods with

Applications in Business and Economics, Oxford University Press.

- Hill, R.C., W. E. Griffiths & G. C. Lim (2012), Principles of Econometrics, 4th ed., John Wiley & Sons.

Mandatory literature

Cooper Donald R.; Business research methods. ISBN: 007-126333-0
Hair Jr Joseph F; Essentials of business research methods. ISBN: 0-471-27136-5
Hart, Chris; Doing my Masters Dissertation, age Essential Study Skills, 2008

Teaching methods and learning activities

Lectures dedicated to the presentation of concepts and reearch tools.

 

· Sessions dedicated to the presentation and discussion of the project themes and the final project.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Apresentação/discussão de um trabalho científico	30,00
Trabalho escrito	70,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Elaboração de projeto	6,00
Total:	6,00

Eligibility for exams

Students must attend the classes

Calculation formula of final grade

30% presentation of final project.
70% final project report.

Deliverables are due til 23h59m of set dates. Delayed deliverables will result in a 1/20 points reduction of each deliverable grade per day.

There is no exame.

Special assessment (TE, DA, ...)

Not available.

Classification improvement

There is no grade improvement.