Data Collection Methods

Choose and implement appropriate data collection methods for your research. Covers primary and secondary data sources with quality assurance, instrument development, and practical implementation guidance.

Usage

Describe your research question, target population, available resources, and timeline. The guide recommends collection methods, helps develop instruments, and establishes quality assurance procedures.

Parameters

• Method: Survey, Interview, Observation, Existing data, Sensor/device, or Mixed
• Data type: Quantitative, Qualitative, or Mixed
• Population: Accessible, Hard-to-reach, Online, or In-person
• Scale: Small (n<50), Medium (50-500), or Large (500+)

Examples

1. Mixed Methods Data Collection: Design a convergent study using online surveys (n=500) and follow-up interviews (n=20) — sampling frame, instrument alignment, timing, and integration strategy.

1. Observational Study Protocol: Create a structured observation protocol for classroom research — coding scheme development, inter-rater reliability training, recording procedures, and sampling schedule.

1. Secondary Data Analysis: Identify and evaluate existing datasets for a research question — data quality assessment, variable availability, access procedures, and citation requirements for public datasets.

1. Digital Trace Data: Collect and process web analytics, social media data, or app usage data — API access, data cleaning pipelines, ethical considerations, and representativeness limitations.

Guidelines

• Method selection is driven by research question, not convenience or familiarity
• Instrument reliability is established through pilot testing and psychometric analysis
• Validity evidence covers content, criterion, and construct validity for new measures
• Sampling methods are appropriate to the design: probability for generalization, purposive for depth
• Data quality checks are built into collection (range checks, skip logic, attention items)
• Missing data is anticipated with planned prevention and analysis strategies
• Training protocols ensure consistency when multiple collectors are involved
• Digital data collection addresses informed consent, data security, and platform compliance
• Data management plans specify storage, backup, format, and documentation standards
• Pilot studies test the full data collection process end-to-end before main study launch