research

M.Sc. Research Methods Statistics

Overview of Research Methods

1. Introduction to Research Methods
  • Focus on the differences between qualitative and quantitative research.
  • Importance of qualitative methods in health informatics.
2. Key Qualitative Methods
a. Content Analysis
  • Role: Systematic examination of communication artifacts.
  • Technique: Involves coding the data and identifying themes within the content.
b. Grounded Theory
  • Role: Aims to generate a theory grounded in empirical data.
  • Technique:
    • Open Sampling: Selection of participants randomly.
    • Rich Qualitative Data: Data is comprehensive and informative.
    • Open Coding: Identifying themes and concepts in data; initial classification without preconceptions.
    • Stop and Memo: Continuous reflection and note-taking throughout the data analysis.
    • Constant Comparison: Analyzing data from interviews, observations, literature reviews, ensuring the theory evolves from data.
    • Axial and Selective Coding: Subsequently refine codes to develop and deepen understanding.
    • Theoretical Saturation: Continues data collection until no new insights are gained.
    • Glasser and Strauss—Parting of Ways: Diverging views on grounded theory practice.

Research Designs in Health Informatics

3. Empirical Design Types
  • Cross-sectional Design: Observations at a single point in time.
  • Longitudinal Design: Observations over an extended period to track changes.
  • Case Study: In-depth analysis of a single entity or event.
  • Comparative Design: Examining differences across groups or conditions.
  • Experiments: Controlled tests to assess causal relationships.
4. The Positivist Paradigm
  • Typically quantitative and hypothesis-driven.
a. Key Concepts
  • Hypothesis: A states that A causes B.
  • Dependent Variable: The outcome impacted by the independent variable.
  • Independent Variable: The variable manipulated to observe its effect.
b. Control Mechanisms
  • Objective: Ensure only the independent variable influences the dependent variable.
  • Techniques include:
    • Eliminating confounding factors.
    • Holding factors constant.
    • Random selection of subjects.
    • Using control groups to illustrate design.
    • Blind research to minimize bias.

Importance of Statistics in Research

5. Statistical Foundations
a. Variability and Probability
  • Variability: Analyzes differences in data representation like means or histograms.
  • Statistical Probability: Provides measures of how likely results are due to chance, with lesser probability yielding higher confidence in results.
    • Levels of Significance:
      • P = 0.01 (1% chance).
      • P = 0.05 (5% chance).
      • Acceptable levels vary by context.
6. Practical Application of Statistics
a. Data Analysis Example
  • Condition 1 Scores: [10, 5, 6, 3, 4, 4, 4, 2, 5, 5].
  • Condition 2 Scores: [6, 1, 3, 4, 8, 7, 5, 6, 7, 5].
  • Totals:
    • Condition 1 Total: 64
    • Condition 2 Total: 40
    • Mean for Condition 1: 6.4
    • Mean for Condition 2: 3.6

Experimental Statistics

7. Overview of Statistics Types
a. Distinction Between Statistic Types
  • Inferential Statistics: Used to infer properties about a population from a sample.
  • Descriptive Statistics: A tool for summarizing and describing data.

Parametric vs Non-parametric Techniques

8. Definitions
a. Parametric Techniques
  • Applicable under specific conditions:
    • Data on interval scales.
    • Normal distribution of data.
    • Homogeneity of variance (similar variability among groups).
b. Non-parametric Techniques
  • Used when one or more of the parametric assumptions do not hold.

Selecting Statistical Tests

9. Test Selection Criteria
a. Type of Test
  • Two-tailed vs One-tailed tests.
b. Considerations in Experimental Design
  • Types of subjects (between/within).
  • Aim of the experiment (A causes B).
  • Number of conditions being tested.
10. Decision Trees for Statistical Tests
a. Non-parametric Decision Tree
  • Choose based on:
    • Type of analysis required (i.e., Difference between conditions).
    • Specific techniques for dependent variables.

Non-parametric Tests

11. Specific Techniques
a. Mann-Whitney Test
  • When: Used for two independent conditions.
  • Analysis Method: Ranks scores and examines total for calculations.
  • Formula:
    • $U = n1n2 + (n1(n1 + 1)/2) - T1$, where $T1$ is the sum of ranks for one group.
12. Wilcoxon Test
  • When: Within subject analysis for two dependent conditions.
  • Mechanism includes:
    • Compare ranks to analyze differences (Negative ranks, Positive ranks).
    • Summation of negative/positive ranks for test statistic.
13. Friedman Test
  • When: Within subjects for more than two conditions.
  • Mechanism: Rank within rows; compare sums to check for non-random differences.
14. Kruskal-Wallis Test
  • When: Between subjects for more than two conditions.
  • Mechanism: Rank totalizes and checks for variability in ranks across conditions.

Correlation Analysis

15. Spearman Correlation Coefficient
  • Examines relationships between variables without asserting causation:
    • Formula:
      R=1−6∑d2n(n2−1)R = 1 - \frac{6 \sum{d^2}}{n(n^2 - 1)}
    • Important to rank the data for comparison.
16. Chi-square Test
  • Application: Used to assess categorical data.
  • Mechanism: Compares observed with expected frequencies.
    • Formula:
      χ2=∑(O−E)2E\chi^2 = \sum \frac{(O - E)^2}{E}
      where $O$ is observed frequency and $E$ is expected frequency.
  • Requires a minimum of 5 expected observations per category for reliability.
17. Outro: Fundamentals of Statistics
  • Goals include providing an introduction to statistics, applying decision trees for test selection, and focusing on non-parametric, inferential statistics.