Qualitative vs Quantitative Data Collection

• Data collection methods fall into two main categories: qualitative and quantitative.

Qualitative Data Collection

• Focuses on understanding concepts, experiences, and meanings that cannot be objectively measured using mathematics.
• Aims to explore the 'why' and 'how' of a phenomenon.
• Provides in-depth insights. Data is descriptive, exploratory, based on knowledge, practices and understanding.

Quantitative Data Collection

• Focuses on numerical data and statistical analysis.
• Used to measure variables, identify patterns, and establish relationships between different factors.
• Answers "how many," "how much," or "to what extent" questions.
• Essential for objective and data-driven decision-making.
• Data is numerical, quantifiable and statistical.

Key Differences Between Qualitative and Quantitative Methods

• Data Type (Qualitative): Expressed in words and descriptions.
• Data Type (Quantitative): Expressed in numerical values and statistics.
• Focus (Qualitative): Focuses on understanding “why” and “how” of a phenomenon.
• Focus (Quantitative): Focuses on numerical data and statistical analysis that is used to measure variables.
• Data Collection Method (Qualitative): Involves in-depth interviews, focus groups, and participant observation.
• Data Collection Method (Quantitative): Includes surveys, experiments, and questionnaires.
• Outcome (Qualitative): Provides rich, detailed insights but is not easily measurable or generalizable.
• Outcome (Quantitative): Enables statistical comparisons but may lack depth in understanding experiences.

Effectiveness of Each Method in Research Contexts

• Understanding Personal Beliefs and Experiences: Qualitative methods are best because they allow people to express thoughts in their own words.
• Measuring Trends, Patterns, and General Population Characteristics: Quantitative methods are best, providing statistical evidence that can be generalized.
• Capturing the Lived Experiences of Marginalized Communities: Qualitative methods are best, giving voice to those often unheard and allowing deeper exploration of issues like discrimination and access to resources.
• Policy-Making and Large-Scale Decision-Making: Quantitative methods are best, providing concrete data to support evidence-based policies.
• Exploring New Topics Without Predefined Assumptions: Qualitative methods are best as open-ended methods allow unexpected insights to emerge.
• Evaluating the Impact of Social Programs or Interventions: Both qualitative and quantitative methods are beneficial; a mix of numerical data and personal stories provides a more complete understanding.

Bias in Qualitative and Quantitative Research

• Bias can influence both qualitative and quantitative research, affecting the accuracy and fairness of findings.

Bias in Qualitative Research

• Qualitative research focuses on subjective experiences, making it susceptible to biases related to interpretation, participant selection, and data collection methods.

Types of Bias in Qualitative Research

• Researcher Bias: Occurs when a researcher’s own beliefs, assumptions, or expectations influence data collection and analysis.
• Mitigation: Use multiple researchers to cross-check interpretations, conduct peer reviews, and keep a reflective journal to minimize personal bias.
• Selection Bias: Happens when participants are chosen in a way that does not represent the full diversity of the population.
• Mitigation: Use purposeful and diverse sampling strategies, ensure inclusion of marginalized groups, and clearly define selection criteria.
• Social Desirability Bias: Participants may give answers they think are socially acceptable rather than their true opinions.
• Mitigation: Assure anonymity and confidentiality, use indirect questioning techniques, and cross-check responses through multiple data sources.
• Hawthorne Effect: Participants alter their behavior because they know they are being observed.
• Mitigation: Use long-term observations, consider covert observation (if ethically appropriate), and ensure participants feel comfortable.

Bias in Quantitative Research

• Quantitative research deals with numerical data, but biases can still emerge in how data is collected, analyzed, or interpreted.

Types of Bias in Quantitative Research

• Sampling Bias: Occurs when the sample does not accurately represent the target population.
• Mitigation: Use random or stratified sampling, ensure adequate sample size, and adjust weighting in analysis to correct imbalances.
• Measurement Bias: Happens when the tools or methods used to collect data systematically favor certain outcomes.
• Mitigation: Pre-test surveys and questionnaires, use validated and standardized measurement tools, and ensure clear wording.
• Nonresponse Bias: Happens when people who choose not to participate in a study differ significantly from those who do respond.
• Mitigation: Increase follow-up efforts and incentives, analyze differences between respondents and nonrespondents, and use weighting techniques to correct for missing data.
• Reporting Bias: Results may be presented in a way that emphasizes significant findings while downplaying negative or neutral results.
• Mitigation: Follow transparent reporting guidelines, publish full datasets and methods to allow verification, and encourage independent replication of studies.