SODC007 Quantitative and Qualitative Research Methods 2

Faculty of Education
Autumn 2024
Extent and Intensity
0/0/0. 6 credit(s). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium).
Teacher(s)
doc. PhDr. Jiří Němec, Ph.D. (lecturer)
Guaranteed by
doc. PhDr. Jiří Němec, Ph.D.
Department of Social Education – Faculty of Education
Contact Person: Ing. et Ing. Jana Šajkovicsová
Supplier department: Department of Social Education – Faculty of Education
Prerequisites
SODC006 Research Methods 2
The course is designed as a mandatory in the PhD study programme of Social Education. The course builds on SPC006 Quantitative and Qualitative Methods of Education Research 1 and expands specific topics in the context of the implementation of doctoral students' dissertations.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.

The capacity limit for the course is 5 student(s).
Current registration and enrolment status: enrolled: 1/5, only registered: 0/5
fields of study / plans the course is directly associated with
Course objectives
The course objective is to improve doctoral students‘ skills to draft both a qualitative and quantitative research design in the field of Social education and practise the types of analysis and interpretation of qualitative and quantitative data. PhD students will apply the acquired skills in the development of their dissertation thesis.
Learning outcomes
The PhD students will be able to: - in the discussion on the topic of the dissertation to demonstrate a deep orientation in the methods of data collection, analysis and interpretation, including the so-called multidimensional data; - demonstrate the ability to analyze data on their own matrix of data from dissertation research; - test hypotheses through appropriately selected statistical analyzes; - give examples of application of selected statistical procedures in the environment of pedagogical research, which can be connected with the topic of the dissertation; - interpret qualitative research data; - design an evaluation type of research for the topic of the dissertation.
Syllabus
  • Introduction to statistical procedures and processing of quantitative data. 1. Descriptive theories (we describe a certain phenomenon in a qualitative manner). Explanation theories (we explain the given phenomenon). Prediction theories (we predict the given phenomenon). Research phase of working with data – collecting them, analyzing and interpreting. 2. Data sets and sample data sets: statistical set, criteria for set membership, basic data set (universum), sample data set, set size, representativeness, methods of random sampling, stratified random sampling, multistage cluster sampling, systematic random sampling, voluntary response sampling, convenience sampling, quota sampling. 3. Basic statistical attributes: qualities of variables (independent and dependent), types of variables (qualitative, quantitative, alternative, continuous, discrete). 4. Variables characteristics, definition of fundamental levels of measuring data (nominal, ordinal, interval, ratio). 5. Types of distribution and their characteristics: measures of location and their use with different types of data: the mean, the median, the mode; measures of variance: variance spanning, variance and standard deviation; measures of skewness and kurtosis. 6. Testing hypotheses, principle, testing criteria, probability calculation, level of significance. Demonstrate using a particular example. 7. Statistical processing and interpretation of data. Basic knowledge of Statistica software (SPSS). Basic terms – descriptive and inductive statistics, measure of central tendency, average, modus, standard deviation, variance, measure of skewness and kurtosis, histogram. The skill of interpretation based on the knowledge of descriptive statistics is required. 8. Statistical induction – statistical inference, tests of normal distribution, testing hypotheses, chi-square distribution, t-tests, corelation analysis, regression analysis, variance analysis. 9. Multilevel statistical methods – PCA analysis, factor analysis, cluster analysis, discriminant analysis. 10. Introduction to Statistica software – description of the main windows in the programme, using input data, data checking, creating tables and their use, data display, graphs of input data, graphs of data blocks, outputs, worksheets and protocols, editing graphs.
  • Introduction to qualitative data 11. Qualitative methods and educational phenomena. Qualitative methods and theories and their social conceptions. Field research. The conception of category and its coding, dimension. Qualitative research paradigm model. Relations between categories, contextual conditions of observation. Inductive and deductive approach. Theoretical background of story conceptualization. Characteristics of theory. Analytic view of phenomena and processes. Interaction effects and matrixes. Experience versus formal theory (a starting point for a dialogue and observation). Method of recording and diagrams, processes of phenomena (data) classification. Rules of science and empirical grounding of qualitative research. 12. Theoretical framework of qualitative research. Conception of hermeneutics, phenomenology school, Frankfurt school, the impact of cognitive and humanistic psychology. Combining qualitative methods, triangulation. Qualitative observation, interrogation, interview, etc. Structure of a written report about qualitative research. Principles of qualitative research, research problem, sources of research problems, formulation of research questions. Theoretical sensitivity, its definition and sources (relation between creativity and science), working with literature, term definitions.
Literature
    required literature
  • SILVERMAN, D. Jako robiť kvalitatívny výskum. Bratislava : IKAR, 2005.
  • KERLINGER, F. N. Základy výzkumu chování : pedagogický a psychologický výzkum : Foundations of behavioral research (Orig.). Vyd. 1. Praha : Academia, 1972.
  • HENDL, J. Přehled statistických metod zpracování dat. Praha : Portál, 2004.
  • MAŇÁK, J.; ŠVEC, V.; ŠVEC, Š. Slovník pedagogické metodologie. Brno : Paido, 2005.
    recommended literature
  • MAREŠ, Petr, Ladislav RABUŠIC and Petr SOUKUP. Analýza sociálněvědních dat (nejen) v SPSS (Data analysis in social sciences (using SPSS)). 1. vyd. Brno: Masarykova univerzita, 2015, 508 pp. ISBN 978-80-210-6362-4. Projekty Nakladatelství Munipress info
  • MELOUN, Milan, Jiří MILITKÝ and Martin HILL. Počítačová analýza vícerozměrných dat v příkladech. Vyd. 1. Praha: Academia, 2005, 449 s. ISBN 8020013350. info
Teaching methods
discussion, self-study, research implementation, feedback evaluation.
Assessment methods
Examination: The evaluation will include: ability to critically reflect on the chosen research procedure (advantages, possible limitations, distortions, etc.), creativity to design your own research (especially the originality of the procedure), ability to explain the research problem, skills to gather and analyse date in the natural environment of social reality, elaborate theoretical grounding, ability to lead a discussion.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
Teacher's information
http://moodlinka.ped.muni.cz/mod/resource/view.php?id=8832
Examination requirements: submission of own data (data matrix) from the implemented research (pre-research within the dissertation) and a demonstration of the analysis (qualitative and quantitative).
The course is also listed under the following terms Autumn 2023, Spring 2024, Spring 2025.
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