Quantitative Research

Definition

• Quantitative research: A method for the quantitative collection and analysis of data.
• Clearly defined steps that follow one after another.

Aim

• Gain knowledge about reality or check existing knowledge.
• Collect and analyze data to understand or test reality.

Data Collection

• Uses quantitative methods to gather numerically measurable data.
• Common methods: surveys, experiments.
• Example: A questionnaire asking about satisfaction with online teaching with predefined answer options.
• Data received is structured (e.g., rows for respondents, columns for questions).

Data Analysis

1. Descriptive Statistics

   • Describe the sample using statistical characteristics (mean, standard deviation, frequencies).
   • Use diagrams to visualize results.
   • Cannot make statements about the population.

2. Hypothesis Testing Study

   • Test hypotheses about the population based on collected data.
   • Methods: t-test, analysis of variance, correlation analysis.

3. Exploratory Study

   • Identify unknown patterns from data.
   • Method: cluster analysis.

• Mixed-methods: Combine descriptive, hypothesis testing, and exploratory approaches.

Literature Research and Theories

• Quantitative research tests statements about a small, complex section of reality.
• Develop simplified theories free of contradictions.
• Obtain theories through extensive literature search.
• Derive hypotheses from theories, formulate them for testing with data.
• Literature review is performed at the beginning of research activities.

Structured Research Process

1. Find a Research Topic
2. Literature Research
   • Often done in parallel with finding a research topic.
   • Determine the state of research and theoretical background.
   • Derive hypotheses from literature.
3. Research Design
   • Define how to answer the research problem.
   • Typically involves descriptive work and hypothesis testing.
4. Formulate Hypotheses
5. Define Variables (Operationalization)
   • Define how to measure variables in hypotheses.
   • Simple variables: directly measurable (age, gender).
   • Latent variables: require more complex measurement (environmental well-being, physical health).
6. Data Collection
   • Common methods: surveys, experiments.
7. Data Preparation
   • Clean data before analysis.
8. Data Analysis
9. Presentation of Results