Research Methods in Data Collection

Focus on exploring phenomena and understanding underlying motivations.
Data is collected through:
- Interviews (structured, semi-structured, unstructured)
- Focus groups
- Observations
- Open-ended surveys
Data is often text-based and analyzed thematically.

Aim to quantify data and generalize results from a sample to a population.
Data is collected through:
- Surveys with closed-ended questions
- Experiments (controlled studies)
- Secondary data analysis (existing datasets)
Analysis typically involves statistical methods.

Probability Sampling: Each member of the population has a known chance of being selected.
- Simple random sampling
- Stratified sampling
- Cluster sampling
Non-Probability Sampling: Not every individual has a chance of selection.
- Convenience sampling
- Purposive sampling
- Snowball sampling

Involves processing collected data to extract meaningful insights.
Qualitative data analysis methods include:
- Thematic analysis
- Grounded theory
- Content analysis
Quantitative data analysis often involves:
- Descriptive statistics (means, medians, modes)
- Inferential statistics (hypothesis testing, regression analysis)

Ensures data integrity and accuracy.
Techniques include:
- Triangulation (using multiple methods or data sources)
- Member checking (validating findings with participants)
- Peer debriefing (discussing findings with colleagues)
- Consistency checks (comparing data sets for discrepancies)

Nominal: Categories without a specific order (e.g., gender, color).
Ordinal: Categories with a meaningful order but no fixed interval (e.g., ranking).
Interval: Numeric scales with equal intervals but no true zero (e.g., temperature).
Ratio: Numeric scales with equal intervals and a true zero point (e.g., weight, height).

Surveys: Structured questionnaires distributed to participants.
Interviews: Direct conversations to collect detailed information.
Focus Groups: Group discussions targeting specific topics to gather diverse perspectives.
Observations: Systematic viewing of subjects in their natural environments.
Experiments: Controlled settings where variables are manipulated to study effects.

Focus on exploring human experiences and motivations.
Data collection methods include:
- Interviews, which can be structured, semi-structured, or unstructured.
- Focus groups for generating discussions among participants.
- Observations to gather information in natural settings.
- Open-ended surveys to capture detailed feedback.
Data tends to be text-based and is analyzed thematically to identify patterns and insights.

Aim to collect numerical data to quantify variables and generalize findings.
Data collection methods include:
- Surveys using closed-ended questions for structured responses.
- Controlled experiments to study the effects of manipulated variables.
- Secondary data analysis utilizing existing data sets.
Statistical methods are employed for data analysis, focusing on quantification and comparison.

Probability Sampling: Ensures every population member has a known selection chance.
- Methods include:
  - Simple random sampling for unbiased selection.
  - Stratified sampling to ensure representation across key subgroups.
  - Cluster sampling for efficiency in large population studies.
Non-Probability Sampling: Does not guarantee a known chance of selection.
- Methods include:
  - Convenience sampling for easy access to participants.
  - Purposive sampling targeting specific groups.
  - Snowball sampling using referrals for hard-to-reach populations.

Process of interpreting collected data to derive insights.
Qualitative data analysis techniques include:
- Thematic analysis to identify central themes.
- Grounded theory for generating theories from data.
- Content analysis for analyzing communication content.
Quantitative analysis typically involves:
- Descriptive statistics that summarize data.
- Inferential statistics for hypothesis testing and predictive analysis.

Ensures the reliability and accuracy of data collected.
Validation techniques include:
- Triangulation, using multiple methods or sources to verify findings.
- Member checking, where participants validate the results.
- Peer debriefing, involving discussions with colleagues for feedback.
- Consistency checks to identify discrepancies between data sets.

Nominal: Categorical data without order (e.g., gender, color).
Ordinal: Categorical data with a meaningful order but no specific interval (e.g., rankings).
Interval: Numeric data with equal intervals but no true zero (e.g., temperature).
Ratio: Numeric data with equal intervals and a true zero, allowing for a complete scale (e.g., weight, height).

Surveys: Structured questionnaires designed to gather specific information.
Interviews: Direct, in-depth conversations to capture rich, qualitative data.
Focus Groups: Group discussions steering towards specific topics for varied perspectives.
Observations: Systematic examination of behaviors in natural environments.
Experiments: Controlled research settings to manipulate variables and study their effects.