Topic 1

Overview of Data Collection

Data collection is the systematic process of gathering information to answer research questions and test hypotheses. The quality of your research depends heavily on the quality of your data, making careful planning and execution of data collection essential for valid, reliable findings.

What is Data?

Quantitative Data

Numerical information that can be measured and analyzed statistically

Examples:
  • Test scores (85, 92, 78)
  • Age in years (25, 34, 41)
  • Income levels ($45,000)
  • Likert scale ratings (1-5)
  • Counts and frequencies
  • Physiological measurements

Collected via: Surveys, experiments, tests, physiological instruments

Qualitative Data

Non-numerical information describing qualities, characteristics, or meanings

Examples:
  • Interview transcripts
  • Open-ended responses
  • Field observation notes
  • Documents and artifacts
  • Video/audio recordings
  • Photographs and images

Collected via: Interviews, observations, documents, focus groups

Primary vs. Secondary Data

Primary Data

Definition: Original data collected firsthand by the researcher for the specific study

Characteristics:
  • Collected specifically for your research
  • You control the process
  • Tailored to your research questions
  • More time-consuming and expensive
  • Current and relevant
Methods:
  • Surveys and questionnaires
  • Interviews
  • Observations
  • Experiments
  • Focus groups

Secondary Data

Definition: Existing data collected by others for different purposes that you analyze for your study

Characteristics:
  • Already exists
  • Less control over quality
  • May not perfectly match your needs
  • Faster and cheaper to obtain
  • May be dated
Sources:
  • Government statistics
  • Published research datasets
  • Organizational records
  • Historical documents
  • Census data

Key Data Collection Methods

Surveys/Questionnaires

Structured instruments with predetermined questions administered to many respondents

Quantitative Efficient Scalable

Interviews

Directed conversations to explore perspectives, experiences, and meanings in depth

Qualitative In-depth Flexible

Observations

Systematic watching and recording of behaviors, events, or phenomena

Both Types Natural Setting Direct

Focus Groups

Moderated group discussions to explore shared perspectives and group dynamics

Qualitative Interactive Exploratory

Experiments

Controlled manipulation of variables to test cause-and-effect relationships

Quantitative Controlled Causal

Document Analysis

Systematic examination of existing documents, records, and artifacts

Both Types Unobtrusive Historical

Choosing Data Collection Methods

Consider These Factors:

1
Research Questions

What type of data do your questions require?

  • "How many?" → Surveys, quantitative
  • "How do people experience?" → Interviews, qualitative
  • "What happens when?" → Observations
  • "Does X cause Y?" → Experiments
2
Research Design

Match method to your overall approach

  • Experimental: Controlled measurements
  • Survey: Questionnaires
  • Phenomenology: In-depth interviews
  • Ethnography: Observations, field notes
  • Mixed methods: Multiple approaches
3
Available Resources

What can you realistically accomplish?

  • Time: Interviews take more time than surveys
  • Budget: Some methods cost more
  • Personnel: Do you need trained interviewers?
  • Technology: Online vs. paper-based
4
Population Characteristics

What methods will work with your participants?

  • Literacy levels: Written surveys may not work
  • Access: Can you reach them in person?
  • Sensitivity: Private topics may need interviews
  • Location: Geographic dispersion
5
Validity Needs

What level of rigor is required?

  • Exploratory: Flexible methods OK
  • Confirmatory: Standardized methods needed
  • Sensitive topics: Multiple methods for triangulation

Mixed Methods Approach

Consider combining multiple data collection methods:

  • Triangulation: Use multiple methods to verify findings
  • Complementarity: Use one method to elaborate on another
  • Sequential: Use qualitative first to develop survey items
  • Concurrent: Collect both types simultaneously

Example: Conduct interviews to understand experiences, then survey to measure prevalence of themes found.

Data Quality Considerations

Validity

Are you measuring what you intend to measure?

  • Clear, unambiguous questions
  • Appropriate for your constructs
  • Pilot testing before use

Reliability

Would you get consistent results if repeated?

  • Standardized procedures
  • Training for data collectors
  • Clear coding schemes

Accuracy

Are participants providing truthful information?

  • Confidentiality assurances
  • Non-leading questions
  • Cross-checking responses

Completeness

Are you getting all the data you need?

  • Minimize missing data
  • Follow-up on incomplete responses
  • Account for non-response
Topic 2

Surveys and Questionnaires

Surveys are the most widely used data collection method in social and behavioral research. They allow efficient collection of standardized data from large samples. This topic covers survey design principles, question types, administration modes, and common pitfalls to avoid.

What is a Survey?

A survey is a systematic method of collecting data from a sample using standardized questions. All respondents receive the same questions in the same format, enabling comparison and statistical analysis.

Survey vs. Questionnaire

Questionnaire

The instrument—the set of questions themselves

Survey

The process—the entire data collection method including questionnaire, sampling, and administration

Types of Survey Questions

Closed-Ended Questions

Fixed response options provided

1. Multiple Choice (Single Answer)

What is your highest level of education?

  • ○ High school or less
  • ○ Some college
  • ○ Bachelor's degree
  • ○ Master's degree
  • ○ Doctoral degree
2. Multiple Choice (Multiple Answers)

Which social media platforms do you use? (Select all that apply)

  • ☐ Facebook
  • ☐ Instagram
  • ☐ Twitter/X
  • ☐ TikTok
  • ☐ LinkedIn
  • ☐ None
3. Likert Scale

I am satisfied with my current job.

  • ○ Strongly Disagree
  • ○ Disagree
  • ○ Neutral
  • ○ Agree
  • ○ Strongly Agree
4. Rating Scale

How would you rate the quality of service? (1 = Poor, 10 = Excellent)

12345 678910
5. Yes/No (Dichotomous)

Have you ever traveled abroad?

  • ○ Yes
  • ○ No
6. Ranking

Rank the following factors by importance (1 = Most Important):

  • ___ Salary
  • ___ Work-life balance
  • ___ Career growth
  • ___ Job security
Advantages
  • Easy to analyze statistically
  • Quick for respondents
  • Standardized responses
  • Reduces ambiguity
Disadvantages
  • May miss important options
  • Forces choices that don't fit
  • No depth or explanation
  • May influence responses

Open-Ended Questions

Respondents provide their own answers in their own words

Short Answer

What is your occupation?

________________________
Extended Response

Please describe your experience with online learning during the pandemic.

Advantages
  • Rich, detailed responses
  • Discover unexpected themes
  • Respondent's own words
  • No researcher bias in options
Disadvantages
  • Time-consuming to analyze
  • Difficult to compare
  • More respondent effort
  • Lower response rates

Principles of Good Question Design

DO

  • Use simple, clear language that all respondents understand
  • Ask one thing at a time (avoid double-barreled questions)
  • Make questions specific rather than vague
  • Provide balanced response options
  • Include "Don't know" or "N/A" when appropriate
  • Use consistent scales throughout
  • Order questions logically
  • Start with easy, non-threatening questions
  • Put sensitive questions later
  • Pilot test before full administration

DON'T

  • Use jargon or technical terms without explanation
  • Ask leading questions that suggest an answer
  • Use double negatives
  • Make assumptions about respondents
  • Ask about things respondents don't know
  • Use loaded or emotional language
  • Make the survey too long
  • Require answers to optional questions
  • Use overlapping response categories
  • Ask hypothetical questions (unless necessary)

Common Question Problems (with Fixes)

Double-Barreled Question

Bad: "How satisfied are you with the salary and benefits at your company?"

Problem: Asks about two things—what if satisfied with one but not the other?

Better:

  • "How satisfied are you with the salary at your company?"
  • "How satisfied are you with the benefits at your company?"

Leading Question

Bad: "Don't you agree that the new policy is beneficial?"

Problem: Suggests the expected answer

Better: "What is your opinion of the new policy?"

Loaded Question

Bad: "How often do you waste time on social media?"

Problem: "Waste" is judgmental language

Better: "On average, how many hours per day do you spend on social media?"

Double Negative

Bad: "Do you agree or disagree: Students should not be prohibited from using phones?"

Problem: Confusing double negative

Better: "Do you agree or disagree: Students should be allowed to use phones in class?"

Vague Question

Bad: "Do you exercise regularly?"

Problem: "Regularly" means different things to different people

Better: "In a typical week, how many days do you exercise for at least 30 minutes?"

Overlapping Categories

Bad: "What is your age? □ 18-25 □ 25-35 □ 35-45"

Problem: Where does a 25-year-old check?

Better: "What is your age? □ 18-24 □ 25-34 □ 35-44"

Survey Administration Modes

Online Surveys

Web-based questionnaires sent via email or link

Platforms: Google Forms, SurveyMonkey, Qualtrics, Microsoft Forms

Pros: Cheap, fast, easy to distribute, automatic data entry

Cons: Low response rates, excludes non-internet users, no control over environment

Response rate: Typically 10-30%

Mail Surveys

Paper questionnaires mailed to respondents

Pros: Reaches those without internet, respondent can take time

Cons: Slow, expensive (printing, postage), data entry required

Response rate: Typically 20-40% (higher with follow-ups)

Telephone Surveys

Interviewer reads questions and records responses by phone

Pros: Higher response rate, can clarify questions, reaches diverse populations

Cons: Expensive (interviewer time), caller ID screening, limited question types

Response rate: Declining, now often 10-30%

In-Person Surveys

Face-to-face administration by trained interviewer

Pros: Highest response rates, can use visual aids, observe non-verbal cues

Cons: Very expensive, interviewer bias possible, time-consuming

Response rate: 50-80%

Survey Structure

1

Introduction

  • Study purpose and importance
  • Who is conducting it
  • Estimated time to complete
  • Confidentiality assurance
  • Voluntary participation note
  • Contact information for questions
2

Screening Questions (if needed)

  • Eligibility criteria
  • Skip logic for ineligible respondents
3

Main Questions

  • Start with easy, engaging questions
  • Group by topic (with clear section headings)
  • Move from general to specific
  • Place sensitive questions later
  • Use transitions between sections
4

Demographics

  • Usually at the end (can be sensitive)
  • Only what's needed for analysis
  • Age, gender, education, etc.
5

Closing

  • Thank the respondent
  • Space for additional comments
  • Instructions for submission
  • Information about results (if applicable)

Survey Length Guidelines

Online surveys: 5-10 minutes optimal (15 minutes maximum)

Paper surveys: 2-4 pages

Phone surveys: 10-15 minutes

In-person surveys: 30-45 minutes acceptable

Remember: Shorter surveys get higher response rates and better quality data!

Topic 3

Interviews

Interviews are purposeful conversations that allow researchers to explore participants' perspectives, experiences, and meanings in depth. Unlike surveys, interviews are flexible and can adapt to each participant, making them ideal for qualitative research seeking rich, detailed understanding.

Types of Interviews

Structured Interviews

Predetermined questions asked in exact order—like an oral questionnaire

Characteristics:

  • Fixed questions, fixed order
  • Little flexibility
  • Standardized across participants
  • Easier to compare and analyze

Best for:

  • When comparability is important
  • Large number of interviews
  • Mixed methods (qualitative component)
  • Multiple interviewers
High Structure

Semi-Structured Interviews

Prepared guide with key questions, but flexibility to explore

Characteristics:

  • Interview guide with main topics
  • Can probe and follow up
  • Order can be adapted
  • Balance of consistency and flexibility

Best for:

  • Most qualitative research
  • When key topics are known
  • Exploratory studies
  • Phenomenological research
Medium Structure

Unstructured Interviews

Informal, conversational approach with minimal predetermined questions

Characteristics:

  • Open-ended conversation
  • Participant leads direction
  • Maximum flexibility
  • Each interview unique

Best for:

  • Ethnographic research
  • Life history/narrative research
  • When little is known about topic
  • Deeply personal topics
Low Structure

Interview Formats

Individual Interviews

One-on-one conversation with single participant

  • Duration: 30-90 minutes typical
  • Private, confidential setting
  • Deep exploration possible
  • Good for sensitive topics

Focus Groups

Group discussion with 6-10 participants

  • Duration: 60-120 minutes
  • Moderator guides discussion
  • Group dynamics generate ideas
  • Good for exploring shared perspectives

Online Interviews

Video call or chat-based interviews

  • Zoom, Teams, Skype
  • Overcomes geographic barriers
  • Cost-effective
  • Some non-verbal cues lost

Phone Interviews

Voice-only interviews

  • Convenient, flexible scheduling
  • Anonymous for participant
  • No visual cues
  • Often shorter duration

Developing an Interview Guide

Interview Guide Components:

1. Opening
  • Thank participant
  • Explain purpose
  • Review consent
  • Ask permission to record
  • Warm-up question (easy, non-threatening)
2. Main Questions
  • Start with broad, open questions
  • Move to more specific topics
  • Include follow-up probes
  • Save sensitive questions for later
3. Closing
  • "Is there anything else you'd like to add?"
  • Thank participant
  • Explain next steps
  • Offer to share results

Sample Interview Guide

Topic: Experience of first-generation college students

Opening:

"Thank you for agreeing to participate. I'm interested in learning about your experience as a first-generation college student. There are no right or wrong answers—I want to hear your perspective."

Warm-up: "Can you tell me a bit about yourself and what you're studying?"

Main Questions:

  1. "Can you describe how you decided to go to college?"

    Probes: Who influenced you? What factors did you consider?

  2. "What was your transition to college like?"

    Probes: What challenges did you face? What helped?

  3. "How does being first-generation affect your college experience?"

    Probes: Academic life? Social life? Relationship with family?

  4. "What supports have been most helpful to you?"

  5. "What advice would you give to other first-generation students?"

Closing:

"Is there anything else about your experience that I haven't asked about?"

"Thank you so much for sharing your experiences with me."

Interview Skills and Techniques

Active Listening

  • Give full attention
  • Use verbal encouragers ("mm-hmm," "I see")
  • Maintain appropriate eye contact
  • Don't interrupt
  • Show you're engaged (nodding)

Probing

  • Elaboration: "Can you tell me more about that?"
  • Clarification: "What do you mean by...?"
  • Example: "Can you give me an example?"
  • Contrast: "How is that different from...?"
  • Reflection: "It sounds like you felt..."

Using Silence

  • Allow pauses for thinking
  • Don't rush to fill silence
  • Participants often add more
  • Count to 5 before probing

Staying Neutral

  • Don't express personal opinions
  • Avoid judgmental reactions
  • Accept all responses equally
  • Don't lead toward expected answers

Managing the Interview

  • Keep on topic without being rigid
  • Redirect when necessary
  • Monitor time
  • Handle emotional responses appropriately

Building Rapport

  • Warm, friendly demeanor
  • Show genuine interest
  • Use participant's language
  • Share appropriate self-disclosure

Recording and Transcription

Audio Recording

  • Most common method
  • Allows focus on conversation
  • Captures exact words
  • Requires transcription
  • Get explicit consent

Video Recording

  • Captures non-verbal cues
  • More intrusive for participant
  • Useful for focus groups
  • More complex setup

Note-Taking

  • Backup to recording
  • Capture observations
  • Note non-verbal behavior
  • Jot down follow-up questions

Transcription Options:

  • Verbatim: Every word, pause, "um," etc. (most detailed)
  • Intelligent verbatim: Exact words but cleaned up
  • Summary: Main points only (least detailed)

Transcription Tools:

  • Otter.ai, Rev.ai (AI-assisted)
  • Professional transcription services
  • Self-transcription (time-consuming but immersive)

Rule of thumb: 1 hour interview = 4-6 hours transcription (manual)

Common Interview Mistakes

  • Talking too much: Participant should do 80% of talking
  • Leading questions: "Don't you think that...?"
  • Closed questions: Asking yes/no when you want elaboration
  • Interrupting: Cutting off important points
  • Reading questions robotically: Losing natural flow
  • Not probing: Accepting surface-level answers
  • Being judgmental: Reacting to responses
Topic 4

Observations

Observation involves systematically watching and recording behavior, events, or phenomena as they occur naturally. Unlike surveys or interviews that rely on self-report, observations capture what people actually do rather than what they say they do, providing direct evidence of behavior in context.

Types of Observation

Participant Observation

Researcher actively participates in the setting while observing

Complete Participant

Fully immersed, identity as researcher hidden

Participant-as-Observer

Participates but role as researcher known

Observer-as-Participant

Primarily observes, minimal participation

Complete Observer

Observes without any participation

Advantages
  • Insider perspective
  • Rich, contextual data
  • Can access private settings
Disadvantages
  • Time-consuming
  • Risk of "going native"
  • Ethical concerns if covert

Non-Participant Observation

Researcher observes without becoming part of the setting

Methods:

  • Observation through one-way mirrors
  • Video recording public behavior
  • Observing from a distance
  • Reviewing recorded footage
Advantages
  • Less influence on behavior
  • More objective stance
  • Can observe many settings
Disadvantages
  • May miss context/meaning
  • Limited access
  • Surface-level understanding

Structured vs. Unstructured Observation

Structured Observation

Predetermined categories and coding scheme

Characteristics:
  • Specific behaviors defined in advance
  • Observation checklist or coding form
  • Counts, frequencies, durations
  • High inter-observer reliability possible
  • Quantitative data
Example Coding Scheme:

Classroom Behavior Observation

Behavior Frequency Duration
Hand raised ||| -
On-task behavior - 35 min
Off-task behavior - 10 min
Peer interaction |||| | -

Unstructured Observation

Open-ended recording of what is observed

Characteristics:
  • No predetermined categories
  • Rich narrative descriptions
  • Holistic view of setting
  • Themes emerge from data
  • Qualitative data
Field Notes Example:

"3:15 PM - Teacher asks question about photosynthesis. Most students look down at desks. Maria raises hand eagerly, almost bouncing in seat. Teacher calls on student in back row who wasn't raising hand—student seems surprised, gives hesitant answer. Maria looks disappointed, slumps in chair. Tension seems to build between two students in corner who are whispering..."

Observation Recording Methods

Field Notes

Written descriptions of observations

Descriptive Notes

Objective descriptions of what you see, hear, smell

  • Setting and physical environment
  • People present
  • Actions and behaviors
  • Conversations (verbatim when possible)
  • Sequence of events
Reflective Notes

Your thoughts, feelings, interpretations

  • Emerging insights
  • Questions that arise
  • Connections to theory
  • Methodological notes

Checklists

Pre-determined list of behaviors to mark as present/absent

Best for: Structured observation, specific behaviors

Rating Scales

Rate quality or intensity of observed behaviors

Example: "Student engagement: 1 (low) to 5 (high)"

Time Sampling

Record behavior at specific intervals

Example: Every 30 seconds, record whether behavior is occurring

Event Sampling

Record each time a specific behavior occurs

Example: Record every instance of aggressive behavior

Video/Audio Recording

Capture behavior for later analysis

Advantages: Permanent record, can review multiple times

Writing Good Field Notes

DO

  • Write soon: Record notes as soon as possible after observation
  • Be detailed: Include sensory details, exact quotes
  • Be concrete: Describe specific behaviors, not interpretations
  • Separate observation from interpretation: Keep them distinct
  • Include context: Time, place, people present
  • Note what didn't happen: Expected behaviors that were absent

DON'T

  • Use vague language: "The room was nice" (too general)
  • Rely on memory: Jot notes during observation
  • Only record what confirms expectations: Note surprises too
  • Skip the mundane: Routine can be meaningful

Field Notes: Poor vs. Good

Poor:

"Students were engaged. The teacher did a good job. Some kids were disruptive."

Problem: Vague, interpretive, no details

Good:

"10:30 AM - Teacher poses question about Civil War causes. 15 of 24 students have notebooks open. Maria leans forward, hand raised within 3 seconds. Two boys in back row (wearing matching jerseys) are passing notes, giggling quietly. Teacher makes eye contact with them, pauses mid-sentence. Boys stop, look at desks. Teacher continues without comment. [Reflection: Power of eye contact—no verbal reprimand needed. Wonder if this is typical classroom management strategy.]"

Challenges in Observation

Observer Effect (Reactivity)

Problem: People change behavior when they know they're being watched (Hawthorne effect)

Solutions:
  • Extended time in field (habituation)
  • Unobtrusive observation
  • Blend into setting
  • Use hidden cameras (with ethics approval)

Observer Bias

Problem: Researcher sees what they expect to see

Solutions:
  • Use multiple observers
  • Calculate inter-rater reliability
  • Structured coding scheme
  • Keep reflexive journal

Going Native

Problem: Losing objectivity by becoming too immersed

Solutions:
  • Regular debriefing with colleagues
  • Maintain researcher identity
  • Take breaks from field
  • Keep analytical memos

Access

Problem: Getting permission to observe

Solutions:
  • Build relationships with gatekeepers
  • Explain research clearly
  • Offer something in return
  • Start with public spaces

Ethical Considerations in Observation

  • Informed consent: Do participants know they're being observed?
  • Privacy: Is the setting public or private?
  • Confidentiality: Can individuals be identified?
  • Deception: Is covert observation justified?
  • Harm: Could observation cause distress?

General rule: Public behavior in public places can be observed without consent, but private settings require permission. When in doubt, consult your IRB/ethics board.

Topic 5

Secondary Data and Documents

Secondary data analysis uses existing data that was collected by others for different purposes. Document analysis examines written materials, records, and artifacts. These approaches are efficient, unobtrusive, and provide access to historical or large-scale data that would be impossible to collect firsthand.

Types of Secondary Data Sources

Government and Official Statistics

  • Census data
  • National health surveys
  • Economic indicators
  • Crime statistics
  • Education statistics
  • Labor force surveys

Where to find: Government websites, National Statistics Office, World Bank, OECD

Research Datasets

  • Published study datasets
  • Research repositories
  • Longitudinal studies
  • Panel studies

Where to find: ICPSR, UK Data Service, Dataverse, Kaggle, Open Science Framework

Organizational Records

  • Personnel records
  • Financial statements
  • Meeting minutes
  • Performance data
  • Customer records

Access: Usually requires organizational permission and data sharing agreement

Media and Publications

  • Newspaper articles
  • Magazines
  • News broadcasts
  • Advertisements
  • Social media content

Where to find: News archives, LexisNexis, ProQuest, Internet Archive

Historical Documents

  • Letters and diaries
  • Government archives
  • Historical photographs
  • Maps
  • Court records

Where to find: National archives, libraries, museums, digital humanities collections

Digital and Social Media Data

  • Twitter/X posts
  • Reddit discussions
  • Online reviews
  • Website content
  • App usage data

Access: APIs, web scraping, data services (check terms of service and ethics)

Advantages and Disadvantages of Secondary Data

Advantages

  • Cost-effective: No data collection expenses
  • Time-saving: Data already exists
  • Large samples: Access to national/international datasets
  • Longitudinal data: Track changes over time
  • Unobtrusive: No observer effect
  • Replication: Others can verify your analysis
  • Access to historical data: Study the past
  • High-quality data: Often professionally collected

Disadvantages

  • Not tailored: May not match your research questions exactly
  • Quality unknown: You didn't collect it—don't know procedures
  • Missing variables: Key variables may not exist
  • Definitions differ: Concepts measured differently than you'd prefer
  • Dated: Data may be old
  • Access restrictions: Some data not publicly available
  • Learning curve: Complex datasets require study

Document Analysis

Document analysis is the systematic procedure for reviewing or evaluating documents—both printed and electronic materials.

Types of Documents:

Personal Documents
  • Diaries and journals
  • Letters
  • Autobiographies
  • Photographs
Official Documents
  • Policy documents
  • Annual reports
  • Meeting minutes
  • Organizational charts
Public Records
  • Court records
  • Birth/death certificates
  • Property records
  • Voting records
Mass Media
  • News articles
  • TV programs
  • Films
  • Advertisements

Document Analysis Process:

1
Find and Select Documents

Identify relevant documents; determine inclusion criteria

2
Assess Authenticity

Is the document genuine? When and by whom was it created?

3
Assess Credibility

Is the information accurate? What was the author's purpose?

4
Understand Context

What circumstances produced this document?

5
Analyze Content

Code and analyze—may use content analysis or thematic analysis

Content Analysis

Content analysis is a systematic method for analyzing text, images, or other media content. It can be quantitative (counting) or qualitative (interpreting).

Quantitative Content Analysis

Systematic counting and categorization

  • Define categories in advance
  • Count frequencies
  • Statistical analysis possible

Example: Count mentions of climate change in news articles over 10 years to track media attention

Qualitative Content Analysis

Interpretation of meanings

  • Categories emerge from data
  • Focus on context and meaning
  • Latent content (underlying meaning)

Example: Analyze how climate change is framed in different news outlets

Content Analysis Steps:

  1. Define research question
  2. Select content sample (which documents, time period)
  3. Define unit of analysis (word, sentence, paragraph, article)
  4. Develop coding scheme (categories, definitions)
  5. Train coders and establish inter-rater reliability
  6. Code content
  7. Analyze and interpret

Evaluating Secondary Sources

Questions to Ask:

Who?
  • Who collected the data?
  • What was their purpose?
  • Are they credible?
What?
  • What data was collected?
  • How are variables defined?
  • What's missing?
When?
  • When was data collected?
  • Is it current enough?
  • What time period covered?
Where?
  • Geographic coverage?
  • Applicable to your population?
How?
  • How was data collected?
  • What sampling method?
  • Response rates?
Quality?
  • Validity and reliability?
  • Known limitations?
  • Documentation available?

Tips for Using Secondary Data

  • Read the documentation: Codebooks, user guides explain variables
  • Understand the sample: Who was included/excluded?
  • Check variable definitions: May differ from your conceptualization
  • Note limitations: Be transparent about data constraints
  • Cite properly: Give credit to original data collectors
  • Check for existing analyses: What have others found?

Ethical Considerations

  • Privacy: Even if data is public, consider participant privacy
  • Consent: Was consent given for your type of analysis?
  • Terms of use: Follow data provider's rules
  • Attribution: Properly cite data sources
  • Social media: Users may not expect research use
Summary

Module 06 Key Takeaways

What You've Learned

  • Data collection methods include surveys, interviews, observations, and document analysis—each with unique strengths
  • Good survey questions are clear, unbiased, and appropriate for respondents
  • Interviews range from structured to unstructured, requiring active listening and probing skills
  • Observation captures actual behavior but requires careful attention to observer effects and bias
  • Secondary data is efficient but requires critical evaluation of quality and fit

Next Steps

In Module 07: Measurement and Scales, you'll learn about validity and reliability of measurement instruments, types of scales (nominal, ordinal, interval, ratio), and how to develop or select appropriate measures for your research constructs.

Continue to Module 07
Practice

Data Collection Practice Exercises

Applied Data Collection Tasks

  1. Survey Design: Create a 10-question survey on a topic of your choice:
    • Include at least 3 question types
    • Write clear instructions
    • Review for common problems
    • Pilot test with 3 people and revise
  2. Interview Guide: Develop a semi-structured interview guide:
    • 5-7 main questions with probes
    • Opening and closing scripts
    • Practice interviewing a colleague
  3. Observation Exercise: Conduct a 30-minute observation:
    • Choose a public setting (café, park, classroom)
    • Take detailed field notes
    • Separate descriptions from interpretations
  4. Secondary Data: Find a publicly available dataset:
    • Read the documentation
    • Identify 3 research questions it could answer
    • List limitations for your use
  5. Method Selection: For your research question, justify which data collection method(s) would be most appropriate and explain why.
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