The first purpose of the course is to provide students with a conceptual overview of the statistical and econometric methods of data processing and analysis with emphasis on the choice – depending on the available data – of the appropriate methods and the conditions and procedures for their application. The second purpose of the course is to contribute to the understanding of the processes allowing the investigation of the relationships between various variables (expressing temporal and / or spatial phenomena), since the “increasing pressure” observed in planning and making rational decisions requires the development of adequate theoretical and empirical models on which the above analyzes can be based. After a brief reminder of the essential concepts of descriptive statistics, the first theoretical part of the course aims at presenting and analyzing specific topics of exploratory statistical analysis while the second theoretical part focuses on the methods of inductive statistics through which it is possible to draw reliable conclusions and projections. Through the systematic analysis of representative examples, emphasis is given on the implementation of the methods as well as on the interpretation of results according to the possibilities and limitations of the relevant statistical methods. The examples concern real data to help students become familiar with multiple aspects of site design.
The course aims:
to proven knowledge and understanding about the design of scientific research, methodology and research methods. Developing the knowledge and understanding sub-research objects offers at the same time the opportunity to expand their scientific horizons.
To acquire knowledge about the use of different statistical techniques and methods.
to obtain, by systematically applying the relevant methods, the necessary capabilities to adapt to actual research processes especially as regards search, treatment and interpretation of reliable information. At the end of the course, students will be able to design and carry out, in real terms, a small-scale as well as a large-scale scientific research.
to develop their ability to critically analyze, evaluate and synthesize complex and multi-dimensional concepts
to implement autonomous work
to practice criticism and self-criticism
to promote the progress of the knowledge society and generating new research ideas
to promote free, creative and inductive thinking.
The course is organized according to the following sections:
Concepts and alternative statistical methods of data analysis. Collection and organization of primary and secondary data in databases. Simple descriptive statistical analysis.
Calculation and statistical analysis of indicators such as concentration / dispersion indices, divergence / participation indices, specialization and participation coefficients.
Exploratory Factor Analysis: (i) Principal Component Analysis, (ii) Principal Factor Analysis.
Reliability and consistency analysis (Cronbach’s a) with Likert-scale. Confirmatory Factor Analysis.
Data Classification – Typology: Purpose of Typology, Method Selection: (i) Hierarchical Cluster Analysis (ii) k-means clusters method.
Introduction to induction techniques: (i) useful distributions (Normal distribution, Student’s t, X2, Fisher, etc.), (ii) control procedures (assumptions, significance level, power, p-value), (iii) selection of statistical criteria depending on whether the variables are quantitative or categorical with two or more categories.
Applying with SPSS tests for the mean, variance, proportion, mean difference (ANOVA), proportion difference, correlation between categorical variables. Relationship tests between variables (correlation, One-way ANOVA, etc.).
The Linear Model and Curve Estimation: From Linear to Nonlinear.
9. Multiple regression analysis.
Violations of the most important linear model assumptions: controls and solutions. Multicollinearity, auto-correlation, Heteroskedastikotita, Model specification errors.
11. Spatial Autocorrelation: General and Local Spatial Correlation. General Spatial Autocorrelation Index (Moran I, Geary C), Local Spatial Autocorrelation Index (LISA).
12. Application of Spatial Econometrics Models: Spatial Autoregressive Model (SAR), Spatial Error Model (SEM) and Durbin Spatial Model (SDM).
Preparation for exams.
The evaluation process is structured as follows: 40% of the final grade comes from small exercises. Small exercises concern simple applications of the main statistical methods of data analysis. Excel, SPSS and GeoDaspace software will be used to prepare the work.
The remaining 60% of the final grade results from the preparation of the study (main work) and its presentation (oral) during the final exam period of the 2nd semester.
The Study (main work) is based on the use of combined methods and technical analysis in order to evaluate the obtained results on the basis of:
(a) statistical criteria (reliability of results) and,
(b) conceptual criteria:
to what extent the statistical results contribute to the interpretation of the phenomenon under consideration,
to what extent we can draw reliable conclusions based on our central research question and Hypotheses.
The above assessment process gives to the students the opportunity to deal with the course during the semester and thus to be in time prepared for the oral presentation of their work at the end of the semester.
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Anselin, L. (1999), Spatial Econometrics. Available from: https://csiss.ncgia.ucsb.edu/aboutus/ presentations/files/baltchap.pdf.
Anselin L. (1995), Local Indicators of Spatial Association (LISA), Geographical Analysis, 27: 93-115
Anselin L. (2003), GeoDa 0.9.3 User’s Guide, University of Illinois at Urbana-Champaign.
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Brown, J. D. (2009b), Statistics Corner. Questions and answers about language testing statistics: Choosing the right number of components or factors in PCA and EFA. Shiken: JALT Testing & Evaluation SIG Newsletter, 13(2), p.19-23.
Brown, J. D. (2009c), Statistics Corner. Questions and answers about language testing statistics: Choosing the right type of rotation in PCA and EFA. Shiken: JALT Testing & Evaluation SIG Newsletter, 13(3), 20 – 25
Davidson R. and Mackinnon J. G. (2004), Econometric Theory and Methods, Oxford University Press
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Florax R. G. J. M. and Nijkamp P. (2003), Misspecification in Linear Spatial Regression Models, Tinbergen Institute Discussion Papers, 03-081.
Fotheringham S., Brunsdon C. and Charlton M. (2000), Quantitative Geography: Perspectives on Modern Spatial Analysis, Sage, chapters 1-2 & 5.
Getis A. and Ord J. K. (1996), Local Spatial Statistics: An Overview in Longley P. and Batty M. (eds): Spatial Analysis: Modeling in a GIS Environment, Wiley, 261-277
Greene W. (2008), Econometric Analysis, Prentice
Gujarati D. N. (2003), Basic Econometrics, McGraw Hill
Haining R. (2003), Spatial Data Analysis: Theory and Practice, Cambridge University Press, chapters 0-2 & 5-7
Keller, G. (2010), Στατιστική για οικονομικά και διοίκηση επιχειρήσεων, Εκδόσεις Επίκεντρο, Θεσσαλονίκη
Le Gallo J. and Ertur C. (2003), Exploratory Spatial Data Analysis (ESDA) of the Distribution of Regional Capita GDP in Europe, 1980-1995, Papers in Regional Science, 82: 175-201.
Μαυρομάτης Γ. (1999), Στατιστικά Μοντέλα και Μέθοδοι Ανάλυσης δεδομένων. Θεσσαλονίκη: University Studio Press