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Høst 2018
FSK-3006 Model theory and data processing methods - 10 stp
The course is administrated by
Norges fiskerihøgskole
Type of course
This cource is obligatory in the Master's programme in International Fisheries Management (IFM). The course can also be taken as a singular course.
Course overlap
STA-3300 Applied Statistics 2 3 stp
Course contents
Course contents
The course covers basic statistical theory and the quantitative and qualitative analysis of biological and economic information as well as methods used in social sciences related to fisheries management. Students are drilled in data retrieval, treatment, analysis and presentation, using simple and widely available software.
The course covers basic statistical theory and the quantitative and qualitative analysis of biological and economic information as well as methods used in social sciences related to fisheries management. Students are drilled in data retrieval, treatment, analysis and presentation, using simple and widely available software.
Application deadline
Applicants from Nordic countries: 1 June for the autumn semester and 1 December for the spring semester. Exchange students and Fulbright students: 1 October for the spring semester and 15 April for the autumn semester.
Admission requirements
Application code: 9371
Recommended entrance requirements are BIO-3556 Fishery Biology and Harvest Technology or similar background, and basic familiarity with quantitative methods.
Recommended entrance requirements are BIO-3556 Fishery Biology and Harvest Technology or similar background, and basic familiarity with quantitative methods.
Objective of the course
Objective of the course
Intended learning outcomes:
Knowledge:
- apply basic quantitative methods to process, analyze, test hypotheses and visualize structured and unstructured data (e.g. generalized linear models)
- apply basic qualitative methods to process, analyze, test hypotheses and visualize structured and unstructured data (e.g. content analysis, conceptual frameworks)
- explain results of the data processing and data analysis methods that were used
- use simple models to represent systems and make forecasts
- familiarize with both academic and business-like research and development environments
Skills:
- summarize and present qualitative and quantitative data
- perform statistical analyses using different software
- practice different oral and written communication skills
General competences:
- communicate findings of analyses to support decision-making
Relevance for study program:
The course provides a practical background for scientific analysis and reporting in a multidisciplinary environment.
Language of instruction
The language of instruction and all syllabus material is English.
Teaching methods
Teaching methods
Lectures, seminars, workshops, computer labs, peer-teaching, simulations, flipped-classroom.
The learning outcomes are effectively achieved through active student participation. Students are expected to prepare before every session.
Lectures, seminars, workshops, computer labs, peer-teaching, simulations, flipped-classroom.
The learning outcomes are effectively achieved through active student participation. Students are expected to prepare before every session.
Assessment
The course has portfolio assessment.
Three reports together count 50% and essay (home exam) counts 50% of final grade.
The grading scale is A-F, where A-E is passed, and F is failed.
Submission of reports and essay: electronically.
Portefolio
- Written report on quantitative methods assignment
- Written report on qualitative methods assignment
- Written report on modelling assignment
- Final essay (home exam) on given topic (two weeks)
Work requirements: - Oral presentation on given topic (quantitative method, qualitative method or modelling)
- Oral presentation of the essay (on given topic).
A re-sit exam will be arranged in the next semester.
Recommended reading/syllabus
Mandatory reading/syllabus
- Creswell, John W.: Qualitative inquiry and research design. 2007 SAGE Publications. Chapter 8 (to page 166).
- Cumming, Geoff; Fidler, Fiona; Vaux, David L.: Error bars in experimental biology.2007 The Journal of cell biology, 177(1):7-11.
- Franzblau, L. E. and Chung, K. C.: Graphs, tables and figures in scientific publications: the good, the bad, and how not to be the latter. 2012. The Journal of Hand Surgery37 (3): 591 - 596.
- Johnson, Burke R.; Christensen, Larry: Chapter 2. Quantitative, Qualitative, and Mixed Research, (Available on CANVAS).
- Kawulich, Barbara B.: Data analysis techniques in qualitative research. 2004 Journal of Research in Education. 14(1):96-113.
- Lofland, John; Snow, David; Anderson, Leon; Lofland, Lyn H.: Analyzing social settings. 2006 Wadsworth, Cengage Learning, Chapter 9 III - VI.
- Miles, Matthew; Huberman, Michael: Qualitative data analysis. 1994 Sage Publications. Chapter 2A and Chapter 4.
- Shahbaba, B. (2012) Biostatistics with R : An Introduction to Statistics Through Biological Data. Springer.
- Santos, J.: FI¿H IT 1.0 - Student Manual: A Training System for Aquatic Resource Managers. Septentrio Educational. 2015(3). DOI: http://dx.doi.org/10.7557/se.2015.3.
- Silverman, David (editor): Qualitative research. 2011 SAGE Publications. Chapter 15.
- Shields, Linda; Twycross, Alison: The difference between quantitative and qualitative research. 2003 Paediatric nursing, 15(9):24.
- Verdinelli, Susana; Scagnoli, Norma I.: Data display in qualitative research. 2013 International Journal of Qualitative Methods. 12(1):359-81.
- Zuur, A. F., IenoE. N., & Smith, G. M.: Analyzing Ecological Data 2007. Springer. Chapters 2, 4, 5 (pages 49 - 73) & 6 (pages 88 - 96).
- Zuur, A. F., Ieno, E. N., & Elphick, C. S. : A protocol for data exploration to avoid common statistical problems. 2010 Methods in Ecology and Evolution, 1(1), 3-14.