ΔΠΠ512: Research Methods

Module Purpose and Objectives 

The purpose of this module is to provide the student with the knowledge and tools necessary to be able to deepen and successfully engage with environmental research in all its phases. An overview of the role of research in promoting knowledge is emphasized, with emphasis on the specificities of environmental research using examples and case studies. The goals of the module are to:

- Introduce student to Research Methodology
- Demonstrate the particularities of environmental research
- Combine theory with practice to answer research questions
- Provides equipment for the use of the most commonly used statistical methods

By successfully completing this course the student should be able to:

• Comprehend the necessity for a spatial approach in environmental problems
• Use relevant software and ways of analysis and interpretation of geospatial data
• Produce and manage geospatial data
• Design and implement the necessary stages for geospatial analysis in environmental research.

Module Content

Weekly Content

1. Research Methodology Theory: definitions, types of scientific research, stages of research, operational plan in scientific research, funding and research ethics

2. Quantitative and Qualitative Research Methods: main characteristics of qualitative and quantitative research, principal methods in qualitative and quantitative research, questionnaires and interviews

3. Data sources and data collection: main data sources, primary vs secondary data, data quality, phases of literature reviews

4. Experimental Design: experiments, dependent and independent variables, response, direct comparisons, repetition, sampling, random sample selection, block design, comparative design, experimental approaches

5. Field Study: historical evolution of field studies, Field study topics, observation skills, Field study types, Field study design, Field study implementation, Real World examples

6. Visualization – Presentation of Research Results: Construction and Interpretation of visual representation, data representation models, frequent errors in data presentation

7. Scientific Writing: Main forms of scientific writing, similarities and differences, writing skills, dissertation aim and structure, topic selection and research proposal writing, main parts of scientific paper, peer review journals

8. Descriptive Statistics: mean, median, mode, standard deviation, variance, data distributions, z-value, research and null hypothesis, statistical significance, accepting or not the null hypothesis, type I and II errors

9. Linear Regression: statistical model components and model construction, interpretation and evaluation of linear regression models

10. Analysis of Variance (ANOVA): ANOVA with one and two factors, results interpretation, evaluation of results

11. Non-parametric statistics: comparing parametric and non-parametric test, presentation and results interpretation using tables, evaluation of the most common non-parametric tests (X², Kruskal- Wallis, Friedman test)

12. Time series analysis: time series datasets, main ways of data processing, autocorrelation function, General Linear Model (GLM), Autoregression model (AR), Moving average models, Autoregressive moving average models, Autoregressive integrated moving average models (ARIMA).

13. Exploratory Data Analysis: Construction of data diagrams, evaluation of data relationships, identification of mathematical relationship, Data trace plots, Probability plots, Scatter plots, Lag plots

Hands on laboratory exercises with the use of SPSS (parametric and non-parametric tests) will take place.