Digital quantitive and qualitative data analysis

Course Information
TitleΑνάλυση Ψηφιακών ποσοτικών και ποιοτικών δεδομένων / Digital quantitive and qualitative data analysis
SchoolPrimary Education
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter/Spring
CoordinatorChristos Tourtouras
Course ID600015100

Class Information
Academic Year2021 – 2022
Class PeriodSpring
Faculty Instructors
Class ID
Course Type 2011-2015
Knowledge Deepening / Consolidation
Mode of Delivery
  • Face to face
Language of Instruction
  • Greek (Instruction, Examination)
Required Courses
  • ΔΠΔΜ303 Research Methods in Education
General Prerequisites
Students should have already been familiar with the basic concepts and stages of the research methodology, as well as with the most common techniques and tools on quantitative and qualitative data collection.
Learning Outcomes
Students who attend the course should be able to know and use the basic statistical criteria of quantitative data analysis, as well as should be familiar with a qualitative data analysis software.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
In this course we will clarify basic statistical concepts (e.g. types of variables, research population, sample, measurement scales, reliability and validity, etc.) which are necessary for further involvement with SPSS. Afterwards, the students will have a whole practice on the implementations provided by the above statistical package (e.g. creating a virtual database, selecting variables, encoding and transforming data, controlling sample randomness etc.). In addition, there will be a detailed reference to the possibilities of Descriptive Statistics provided by the program (e.g. measures of dispersion, central tendency, asymmetry and homogeneity, absolute, relative and cumulative frequencies, crosstabs and multiple responses analyses etc.). It will also be analyzed and learned how to be counted the basic premise of normality of the distribution of the variable values. In addition, the students will be able to conduct testing hypothesis and clarify some other relative definitions (e.g. zero and alternative hypothesis, one-way/two-way testing hypothesis, standard errors, statistically significant differences in means or relations between variables, intervals of confidence). Subsequently, the students will be exercised in the most common statistical criteria of Inferential Statistics (t-test, x2, one-way/two-way ANOVA, MANOVA, Linear Bivariate/Partial Correlation, Simple/Multiple Linear Regression, Factor Analysis, Mann-Whitney, Kruskal-Wallis, Wilcoxon etc.). Finally, the trainees will face with the features of a qualitative data analysis package (e.g. ATLAS or N-Vivo).
Descriptive Statistics, Inferential Statistics, Quantitative Data, Qualitative Data, SPSS, ATLAS.
Educational Material Types
  • Slide presentations
  • Multimedia
  • Interactive excersises
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Course Organization
Laboratory Work602
Reading Assigment401.3
Written assigments602
Student Assessment
In order to get familiarized with the scientific research, the students will take part in exercises. The implementation of these exercises will help both the students and the teachers to form the final evaluation. In addition, the students will be divided in two groups and required to plan and carry out a nine-stages survey, analyzing the data and presenting and discussing the results in a final text. The evaluation will simultaneously be formative and final. For the convenience of students, suitable bibliography and instructions will also be offered.
Student Assessment methods
  • Written Assignment (Summative)
  • Oral Exams (Formative)
  • Performance / Staging (Summative)
  • Labortatory Assignment (Formative)
Additional bibliography for study
Ζαφειρόπουλος, Κ. & Μυλωνάς, Ν. (2017). "Στατιστική με SPSS. Περιέχει Θεωρία Πιθανοτήτων". Αθήνα: Τζιόλα. Bryman, Al. (2017). "Μέθοδοι Κοινωνικής Έρευνας". Αθήνα: Gutenberg. Τσιώλης, Γ. (2014). "Μέθοδοι και τεχνικές ανάλυσης στην ποιοτική κοινωνική έρευνα". Αθήνα: Κριτική ΑΕ. Δαφέρμος, Β. (2013). "Παραγοντική Ανάλυση. Διερευνητική με SPSS και επιβεβαιωτική με το LISREL και το AMOS". Θεσσαλονίκη: Ζήτη. Δαφέρμος, Β. (2011). "Κοινωνική Στατιστική & Μεθοδολογία Έρευνας με το SPSS". Θεσσαλονίκη: Ζήτη. Κατσής, Αθ., Σιδερίδης, Γ., & Εμβαλωτής, Αν. (2010). "Στατιστικές μέθοδοι στις Κοινωνικές Επιστήμες". Αθήνα: Τόπος. Χάλκος, Εμ. Γ. (2007). "Στατιστική. Θεωρία, Εφαρμογές & Χρήση Στατιστικών Προγραμμάτων σε Η/Υ". Αθήνα: Τυπωθήτω-Γ. Δαρδανός. Νόβα-Καλτσούνη, Χρ. (2006). "Μεθοδολογία εμπειρικής έρευνας στις Κοινωνικές Επιστήμες. Ανάλυση Δεδομένων με τη χρήση του SPSS 13". Αθήνα: Gutenberg. Ιωσηφίδης, Θ. & Σπυριδάκης, Μ. (2006) (Επιμ.). "Ποιοτική κοινωνική έρευνα. Μεθοδολογικές προσεγγίσεις και ανάλυση δεδομένων". Αθήνα: Κριτική ΑΕ. Ιωσηφίδης, Θ. (2003). "Ανάλυση Ποιοτικών Δεδομένων στις Κοινωνικές Επιστήμες". Αθήνα: Κριτική ΑΕ.
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