Experimental Design, Data Analysis and Presentation
|Kod przedmiotu:||WB.INS-2||Kod Erasmus / ISCED:||(brak danych) / (0511) Biologia|
|Nazwa przedmiotu:||Experimental Design, Data Analysis and Presentation|
|Jednostka:||Instytut Nauk o Środowisku|
Kursy w języku angielskim dla studentów Erasmus+ (Wydział Biologii i Nauk o Ziemi)
|Punkty ECTS i inne:||9.00|
Zajęcia w cyklu "Semestr zimowy 2017/2018" (w trakcie)
|Okres:||2017-10-01 - 2018-01-28||
zobacz plan zajęć
Ćwiczenia, 80 godzin więcej informacji
Konwersatorium, 30 godzin więcej informacji
|Prowadzący grup:||Marcin Czarnołęski, Paweł Koteja, Joanna Rutkowska|
|Lista studentów:||(nie masz dostępu)|
(tylko po angielsku) Knowledge:
- Demonstrate understanding of the basics of methodology of science, specifically the concepts of research program, paradigm, hypothesis, falsification, and the scheme of empirical hypothesis testing.
- Can recognise limitations of scientific methodology such as limitations to generality of inferences and to inferences concerning causal mechanisms, and hypothetical status of scientific theories.
- Understand theoretical framework for statistical methods applied to biological sciences, specifically methods based on General Linear Model and the Least Squares estimation such as regression and correlation analyses and analyses of variance and covariance
- Distinguish between types of factors in experimental/quasi-experimental designs (manipulative vs. classification, fixed vs. random) and types of experimental structures (factorial vs. hierarchical).
- Are able to make a description of complete research project in a form that is required for a grant proposal for main research-funding agency (e.g., Polish National Science Centre or US National Science Foundation, depending on the students’ preferences).
- Can design proper multi-factor experiment or a quasi-experimental scheme of field observations allowing legitimate tests for a given research problem and a set of hypotheses.
- Can describe statistical model in the form of Linear Model and indicate proper ways of testing hypotheses concerning effects included in the model for a given complex experimental or quasi-experimental design including both factorial and hierarchical structures and both fixed and random factors.
- Can effectively use a spreadsheet computer programs e.g., Excel in order to prepare well-organized database, and a package of statistical software such as Statistica, SAS, R or other of comparable capacity to perform statistical analyses for the above mentioned models.
- Evaluate data and results using critical thinking skills.
- Can present results of an empirical research in a form of well organised, clearly argumentative essays/reports/oral presentations that are supported by strong evidence and assisted by multimedia tools.
- Effectively collaborate with other students in designing experiments, analysing results, and preparing written reports and oral presentations.
- Accept the need of compliance with the methodological requirements in designing research plans and interpreting results of empirical studies.
- Accept the importance of quality of research results presentation for effective scientific communication.
(tylko po angielsku) Knowledge of statistical methods at basic level, such as required for Bachelor’s degree (level I) for biology programme curriculum;
Ability to effectively use personal computer with Windows OS and basic office software (e.g., Microsoft Office, Open Office)
|Forma i warunki zaliczenia:||
(tylko po angielsku) Conditions of passing practical classes and getting admission to the final exam:
- accepted reports from individual work;
- accepted group work (research project, report, and presentation);
- >40% points in tests performed during practical classes.
- the final score for practical classes is passed – not passed.
Conditions of passing the final exam:
- >40% points from each of the two parts of the exam
Calculation of final score and scale of final grade:
- weights for calculating the final score:
- individual homework : 15%
- group work: 15%
- score from quizzes: 20%
- score from final exam: 50%
- scale of final grade (5 to 2 scale and corresponding A – F scale):
- < 60%: 2.0 (Fail)
- 60 - 66.9%: 3.0 (E)
- 67 - 74.9%: 3.5 (D)
- 75 - 81.9%: 4.0 (C)
- 82 - 89.9%: 4.5 (B)
- ≥90%: 5.0 (A)
|Metody sprawdzania i kryteria oceny efektów kształcenia uzyskanych przez studentów:||
(tylko po angielsku) - Evaluation of projects, reports, and presentations: passed – not passed (the projects and reports must meet the required quality level);
- Evaluation performed by students (each student will review proposal of another student), as a part of their training in critical thinking;
- Short tests performed every week during practical classes (checking theoretical knowledge);
- Practical exam consisting of two parts:
1) on paper: to design a plan of an experiment or a scheme of field-data collection, which allows to solve a given research problem within a given logistical limitations, and to propose an adequate statistical model for the design;
2) on computer: to perform a complete data analysis for a given problem and set of empirical results.
(tylko po angielsku) - Conversational lectures: 3hrs x 10 weeks;
- Practical training with computers: 3hrs x 10 weeks;
- Practical training of theoretical problems: 4hrs x 5 weeks;
- Practice of presentations of projects and reports: 3h x 10 weeks;
- Individual work;
- Individual work in small groups (2 - 4 students);
- Individual or small group consultations (instructors will be available for the students at least 1 hour/week).
|Bilans punktów ECTS:||
(tylko po angielsku) - Participation in conversational lectures: 30 hrs;
- Participation in practical classes: 80 hrs;
- Individual and team work during the semester including:
- preparation for tests performed during practical classes: 15 hrs;
- practice of the usage of database and statistical software: 15 hrs;
- analysis of various experimental designs: 15 hrs;
- preparation of research project and its presentation, preparation of a review of another student's project: 20 hrs;
- analysis of data from a virtual experiment, preparation of a written report and multimedia presentation of the results, and a review of another student's report: 20 hrs
- Individual consultations: 2 hrs;
- Preparation for the final exam: 30 hrs
TOTAL: 227 hrs
- Elements of the methodology of science: research program, paradigm, hypothesis, falsification, the scheme of empirical hypothesis testing, limitations of scientific methodology;
- Elements of the sociology of science: the exchange of scientific information, main mechanisms of financing scientific research, evaluation of achievements of scholars and research projects;
- Efficient presentation of research proposals and results: principles of communication and information perception, characteristics of different forms of graphical presentation of data, a typical grant application (such as submitted to National Science Centre), a report from an empirical research published in scientific journal, and oral presentation assisted with multimedia tools;
- Repertory of the basics of statistical methods;
- Advanced methods of statistical analysis of experimental data: theoretical basis of the least-squares estimation, analysis of regression and correlation, analysis of variance and covariance, and the General Linear Model; fixed, random and mixed models of ANOVA; factorial, hierarchical, repeated measures, and combined designs; multiple comparisons (a priori and a posteriori tests);
Practical classes and individual work:
- Practice of application of Excel and statistical packages software (e.g., Statistica, SAS, R);
- Work on projects defined by instructor:
- analysis of methodological errors in case studies;
- defining proper statistical model for complex experimental designs, identifying type of the factors and model structure, and proper error terms for hypotheses testing;
- planning the scheme of an experiment or field data collection for a given research problem and logistical limitations;
- Work on students' own projects (these can be real projects planned for MSc theses or "virtual" projects invented for the purpose of the course):
- individual presentation of preliminary proposals and analysis of their scientific value and methodological correctness;
- team preparation and presentation of a complete research proposal and grant application;
- critical reviewing the research proposal;
- performing complete statistical analysis for virtual results "obtained" in the project (generated by the instructor);
- preparation of a written report from the virtual project, in a form required for manuscripts submitted to scientific journals;
- oral presentation of results from the virtual project, in a form suitable for a presentation at a typical conference.
- critical review of the written and oral presentations of research report.
Required Texts and readings
- G. Quinn and M. Keough: Experimental design and data analysis for biologists. Cambridge U. Press (2002).
- Manuals and handbooks of statistical software packages (SAS, Statistica, R).
- J. Comfort: Effective presentations: student’s book. Oxford University Press (1996).
- Forms and instructions of grant applications.
- Instructions for authors in leading ecological and evolutionary biology journals.
Handbooks of statistical methods for students who need a repertory of basics of statistical analyses, e.g.:
- R. Sokal and J. F. Rohlf: Biometry. Freeman (1989 or a newer edition).
- A. Łomnicki: Wprowadzenie do statystyki dla przyrodników. PWN (1999 or a newer edition)
- G.A. Ferguson i Y. Takane: Analiza statystyczna w psychologii i pedagogice. PWN (1997).
Alternative handbooks and guides of scientific writing, e.g.:
- J. Weiner: Technika pisania i prezentowania przyrodniczych prac naukowych: przewodnik praktyczny. PWN (1998)
Właścicielem praw autorskich jest Uniwersytet Jagielloński w Krakowie.