Research Techniques: Qualitative, Quantitative, and Mixed-Method Approaches

I. Qualitative Research Techniques

Definition:
Qualitative research explores non-numerical phenomena (e.g., beliefs, experiences, social dynamics) through narrative and descriptive methods. Focuses on understanding why and how behaviors, attitudes, or patterns occur.

Key Characteristics:

• Subjective, contextual, and interpretive.

• Small sample sizes with purposeful sampling.

• Emphasizes depth over breadth.

• Often inductive (develops theories from data).

Common Approaches:

1. Ethnography:

   • Immersive study of cultures/communities (e.g., observing indigenous healthcare practices).

   • Example from text: Ethnographic studies on hygiene behaviors in rural Uganda.

2. Phenomenology:

   • Examines lived experiences (e.g., patient narratives of chronic illness).

   • Used in nursing research to explore patient satisfaction.

3. Grounded Theory:

   • Develops theories from systematically analyzed qualitative data (e.g., generating a model for community-led HIV prevention).

4. Case Study:

   • In-depth analysis of a single case (e.g., studying a hospital’s response to a cholera outbreak).

Data Collection Methods:

• Interviews:

  • Structured (fixed questions), semi-structured (guided but flexible), or unstructured (open-ended).

  • Example: Key informant interviews with nurses on workplace burnout.

• Focus Group Discussions (FGDs):

  • Group discussions to explore collective views (e.g., community perceptions of vaccination drives).

• Observation:

  • Participant/non-participant observation (e.g., documenting handwashing practices in clinics).

• Document Analysis:

  • Review of diaries, letters, or historical records (e.g., analyzing patient diaries for pain management trends).

Analysis Techniques:

• Thematic Analysis: Identifies recurring themes (e.g., coding interview transcripts for patterns in patient anxiety).

• Content Analysis: Systematically categorizes text/visual data (e.g., analyzing social media posts for health misinformation).

• Narrative Analysis: Focuses on storytelling structures (e.g., patient recovery journeys).

Strengths:

• Captures rich, nuanced insights.

• Flexible and adaptive to emerging data.

• Ideal for exploratory studies or marginalized voices.

Limitations:

• Time-intensive and resource-heavy.

• Subjectivity may affect reliability.

• Difficult to generalize findings.

II. Quantitative Research Techniques

Definition:
Quantitative research quantifies variables using numerical data and statistical analysis. Focuses on what, when, and how much to test hypotheses or establish causal relationships.

Key Characteristics:

• Objective, structured, and replicable.

• Large sample sizes with random/probability sampling.

• Deductive (tests existing theories).

Common Approaches:

1. Experimental Designs:

   • Manipulates variables to observe effects (e.g., RCTs testing drug efficacy).

   • Example: Testing painkiller effectiveness post-circumcision (p. 98).

2. Cross-Sectional Studies:

   • Snapshot data collection at one time point (e.g., surveys on malaria prevalence in Katanga).

3. Longitudinal Studies:

   • Tracks changes over time (e.g., cohort studies on HIV progression).

4. Correlational Research:

   • Examines relationships between variables (e.g., income vs. healthcare access).

Data Collection Methods:

• Surveys/Questionnaires:

  • Closed-ended questions (e.g., Likert scales, multiple-choice).

  • Example: Structured surveys on bedbug spread in Mulago hostels (p. 22).

• Structured Observations:

  • Checklists/rating scales (e.g., counting hand hygiene compliance in hospitals).

• Biometric Measurements:

  • Clinical data (e.g., viral load counts in HIV studies).

Analysis Techniques:

Descriptive Statistics:

• • Mean, median, mode, SD (e.g., calculating average patient wait times).

• Inferential Statistics:

  • t-tests, ANOVA (group comparisons).

  • Regression analysis (predicting outcomes).

  • Chi-square (testing associations).

• Statistical Software:

  • SPSS, STATA, or R for data modeling.

Strengths:

• Generalizable results.

• High reliability and objectivity.

• Efficient for testing hypotheses.

Limitations:

• May oversimplify complex phenomena.

• Requires large, representative samples.

• Limited context for “why” behind patterns.

III. Mixed-Method Approaches

Definition:
Combines qualitative and quantitative techniques to leverage strengths of both (e.g., using surveys and interviews).

Common Designs:

1. Explanatory Sequential:

   • Quantitative data first, followed by qualitative to explain results (e.g., survey on vaccine hesitancy + FGDs to explore reasons).

2. Exploratory Sequential:

   • Qualitative data informs quantitative tools (e.g., interviews identify key variables for a subsequent survey).

Example from Text:

• HIV Adherence Study (p. 127): Mixed-methods design combining viral load data (quantitative) with patient interviews (qualitative) to assess ART efficacy.

Strengths:

• Comprehensive insights.

• Triangulation enhances validity.

• Addresses both breadth and depth.

Challenges:

• Resource-intensive.

• Requires expertise in both methodologies.

IV. Ethical Considerations in Both Approaches

1. Informed Consent:

   Clear explanation of study purpose, risks, and rights (e.g., consent forms for HIV trials, ).

2. Confidentiality:

   • Anonymizing data (e.g., using codes instead of names in surveys).

3. Non-Maleficence:

   • Avoiding harm (e.g., ensuring vulnerable groups are not stigmatized).

4. Cultural Sensitivity:

   • Adapting tools for diverse populations (e.g., translating questionnaires into local languages).