1. Introduction to Sampling
FROM RESEARCH DESIGN TO DATA COLLECTION

2. 4. Sampling When designing a sample, what do you need to know?
THREE FACTORS:
Population of interest
Size (known/infinite)
Key characteristics
Geographic location
Demographics
Unit of interest
How can you access population of interest?
What biases occur through the type of access possible
Resources
ITERATIVE CYCLE DESIGNING SAMPLING GIVEN THREE FACTORS ABOVE

3. 4. Sampling Types of sampling: Purposive Random Stratified
Typically for qualitative methods or structured key informant interviews
Selecting specific members of the population to best identify key information (teachers and doctors to provide more information on schools/hospitals)
Random
Typically used for quantitative methods and structured/closed questionnaires
Random sampling allows for findings generalizable for the population of interest
Stratified
By geographical location
By demographic group (females/males)
By beneficiaries (types)
Clustered
Normally when limited access to areas or limited resources to access full areas
Enlarge clusters by location/group and (accounting for the design effect) use these clusters to generalize to whole population

4. 4. Sampling Check list for random sampling:
What is the unity of interest?
Household, family, individual
What sample size do we need for which area,
What level of geographical interest
do we need a buffer to account for non- response?
Can you access the population of interest?
Does each member of the population of interest have an equal chance of being selected?
What implicit bias is in the sampling methodology, how can you account for this?
Do you have sufficient budget to interview number of respondents?

5. 4. Sampling Examples of how to randomly access respondents:
Telephone lists
Who has working phones – types of respondants
Do telephone lists exist in country?
Who holds the telephone lists – are some population of interest excluded from telephone lists
Are these telephone lists easy to access, ensure sufficient time to solicit phone numbers
Instead can we generate random telephone numbers to identify participants?
Random GIS
To ensure no urban/rural bias – is their geographical breakdowns of population per area
If not, how can we approximate population density per area?
Satellite imagery of roads/shelters
Electricity usage?
Constantly ask the question: does my population of interest have equal chance of being randomly selected?

6. 4. Sampling Check list for purposive sampling:
For key informant interviews:
Do participants know the answers to your questions? Who would know the information your are seeking?
Ensure there is a diversity of respondents
Try to interview more than one per “type of respondent”
For group exercises:
Between 5 – 8 participants per groups
Think about power relations to encourage open discussions
Ensure participants are not too similar (from same family) to provoke more meaningful and diverse discussion
Good to develop a participant selection SOP/Screener to determine how participants will be selected

7. 4. Sampling

8. 6. Data collection Planning
DATA COLLECTION CHECKLIST:
Safe and large enough location for training:
Data collection timeframe:
How long will it take to travel between interviews
Will enumerators require stays in hotels etc to access remote locations
Organisational and reporting structure of teams
How will enumerators find locations of participants?
Is a call centre necessary, if so ensure the following:
Sufficient phones/credit
Quiet space for phone calls
ODK Tool for phone interviews
Clear sampling strategy (if applicable) for phone interviews

9. 12. Data analysis
So now you know the results in your sample, how will you know if these hold true in your population of interest?
Inference for continuous variables – values are numerical
E.g. household expenditure, household income, exact age of household head
T-test (for difference between TWO groups) – SPSS
ANOVA (for difference amongst THREE OR MORE groups) – SPSS
Inference for categorical variables – values are categorical
E.g. pit latrine; flush latrine; etc
E.g. 0-3 years old; 4-6 years old; etc
Proportions
Chi-square test – SPSS
If sample size of group you are reporting on is not sufficient, misleading to present the data unless caveated in text

10. 14. Reporting and Representing Data
Reporting census data – actual numbers OR proportions – depending on interest:
1. Overall population finding – e.g. 5,466 (52%) households in Za’atari had an acceptable food consumption score.
2. Disaggregated population finding – e.g. The number of households with an acceptable food consumption score varied from 544 households in District 8 to 2,444 households in District 2.
Reporting sample data – ALWAYS PROPORTIONS:
Overall population finding – e.g. 52% of refugee households in KRI have an acceptable food consumption score
Disaggregated population finding –
IF difference is statistically significant – e.g. ‘43% of households in Dohuk governorate had an acceptable food consumption score, compared to 49% in Sulaymanyiah and 54% in Erbil governorates’.
IF difference is NOT statistically significant – ‘No statistically significant difference was found when comparing governorates.’