Survey and Sampling

Assistant Professor: Ahmed S. Ishtiaque

ULAB

What is a Survey?

• A systematic method of collecting information from a sample of people from a population about a set of questions for the purposes of describing some attributes of the population

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Features of a Survey

• Information is collected from a sample of population

• By asking questions using a standardized questionnaire

• Produce statistics (quantitative or numerical description about some aspects of the study population)

• Generalizable to the whole population

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Types of Survey (timeline based)• Cross sectional

– collect information on outcome of interest and population variables

– at one particular time

• Panel/ Cohort – repeated administration of a questionnaire to a

“panel‟ of households/ group of people sharing common experience/ characteristics

– variables are measured on the same units over time– can add extra module to answer a new RQ

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Types of Survey (content based)

• Descriptive Survey: Description of a population, certain behavior, life style, disease prevalence etc.

• Analytical Survey: Hypothesis driven charaterized by identifying association or linakage between/amongst the variables.

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1. Establish the goal of the project - What do you want to know

2. Select the sample - Whom will you interview

3. Choose interview methodology - How will you interview

4. Specify variables of interest – What information do you want

5. Create questionnaire (i.e. instrument) - What will you ask

Steps of survey

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6. Pre-test questionnaire – Are your questions clear, understandable and follow logical patterns

7. Conduct interviews - Ask the questions

8. Data editing and entry – Check for consistency

9. Data analysis – Answer research questions

Steps of survey (contd..)

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• Think of whether your survey aims at collecting information at the individual or at the household level

• Set up inclusion and exclusion criteria for sample

Sample Determination (Whom will you interview)

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Choose interview methodology – How will you interview?

Self-administered questionnaire

Face-to-face interviews

Preserves confidentiality – limits risk of providing expected answers

Interferes with confidentiality – higher risk of providing expected answers

Requires extremely simple & well-structured questions

Allows interviewer to work with interviewee on more complex questions

High probability of low response rate/missing values

Probable introduction of bias depending on how questions are phrased

Hybrid interview strategies: Phone, computer-assisted & email

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» Written Survey

• Oral Survey

– Electronic Survey

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Choose interview methodology (How will you interview)

Self-administered questionnaire or face-to-face interview?

Choice depends on:• Study question• Study setting

• Structure of questionnaire• Resources available

• Research team preference

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Specify variables of interest (What information do you want)

• Always write down list of variables of interest before drafting the questionnaire

• Check for balance and coherence between variables

• Develop a question or a set of questions for each variable in the list

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Specify variables of interest (contd..)(What information do you want )

Variables Questions

Age What is your age?

Profession What is your job?

Marital status What is your marital status?

This strategyalso facilitates

division ofquestionsin sections

Strengths of a Survey

• Useful in describing characteristics of a large population

• Very large samples are feasible, making results statistically significant

• Standardized questions make measurement more precise by enforcing uniform definitions upon the participants.

• High reliability is easy to obtain14

Weaknesses of a Survey

• Not a good method for research on sensitive topics

• Require the initial study design (the tool and administration of the tool) to remain unchanged throughout the data collection.

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Errors in Survey

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Characteristics of Population

Sample of population members who

answer questions

Answers respondent give

Issue:How well answers

measure characteristics to

be described?

Issue:How closely sample responding mirrors

population?

Related to:Sampling

Related to:Questionnaire design & administration

Why Sampling? • Often difficult or impossible to study total

population• Studying a part may provide dependable

information• Important to select a part or subgroup of the

population in a way that the information obtained is generalizable to the total population

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Sampling

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• This part or subgroup is called sample and the process of selecting the sample is sampling

• Why sample?– Resources (time, money) and workload– Gives results with known accuracy that can be

calculated mathematically

What Sampling?Population

Sample

Using data to say something (make an inference) with confidence, about a whole (population) based

on the study of a only a few (sample).

Sampling Frame

Sampling Process

What you want to talk

about

What you actually

observe in the data

Inference

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The Sampling Process

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process

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Population• Sometimes referred as ‘Universe’

– The entirety. – All the members/elements within a specific

category. – Size and characteristics depend on type of

study

• To define a population – ask yourself: – What am I studying?– To whom or what does my study result apply

to? (related to generalization)21

Sample

Study population: the population that is actually listed in your sampling frame

Sampling frame: listing of study population from which you'll draw your sample

Sample: a part of the whole Sampling: process of selection of the

required number of sampling units from a defined population

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An example

• What percentage of Women (20-55) in Dhaka Metropolitan city in Bangladesh were diagnosed with uterine cancer in 2000?–Study Population?–Sampling Frame?

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• Study Population?• Women (20-55) in Metropolitan Dhaka in

2000 who do not have a history of hysterectomy.

• Sampling Frame?• List of all women (20-55) in Metropolitan

Dhaka in 2000 who do not have a history of hysterectomy.

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Classification of Sampling Techniques

Sampling Techniques

NonprobabilitySampling Techniques

ProbabilitySampling Techniques

ConvenienceSampling

PurposiveSampling

QuotaSampling

SnowballSampling

SystematicSampling

StratifiedSampling

ClusterSampling

Multi-stageSampling

Simple RandomSampling 25

Probability Sampling

• A sampling method that gives each unit in the population a known, non-zero equal chance of being selected is called a probability sampling method

• No unit receives preferences over the other• No units is left out intentionally

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Type of Probability Sampling

Simple random sampling Systematic sampling Stratified sampling Cluster sampling Multi-stage sampling

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Simple Random Sampling

Each group member has the same probability of being selected

Example: Selecting balls from a basket Can be done by using sampling frame and

random table Best applicable to homogeneous population Example ?

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Simple Random Sampling• The most basic sampling design.• If you can make a complete list of your target

population then you can use simple random sampling

• The idea is to assign a number to each of the units in a population and then use a random number generator of some sort to choose the respondents for the analysis.

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Systematic Sampling An estimate is made of expected total number of

units in the study population Divide this number by required sample size The resulting number is the sampling interval (n) Every nth unit is selected till the total sample size is

drawn

Example ?

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Stratified Sampling

When a population is heterogeneous Study population is first divided into homogeneous

groups or classes called strata The choice of stratification variables depends on

the variables that matter for responses.

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Stratified Sampling In most public health research, natural candidates

are race, income, education, gender, location, etc. Simple random sampling is performed in each

strata Allocation of samples among the strata can be

proportional to the size of the strata

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Stratified Sampling• Proportionate: when sampling units in the

strata are selected proportional to their representation in the source population

• Disproportionate: deliberately increasing the size of sampling units selected from a particular strata so they represent a disproportionate figure in the sample compared to the source population

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Cluster Sampling Groups of individuals are sampling units rather

than individuals Population is first divided into groups or

clusters A part of these clusters are then selected using

simple random sampling or systematic sampling

Saves money and time

Example ?

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Multistage Sampling When the total population is large and diverse Sample selection is carried out in several

stages Different sampling units at different stages or

levels Units are selected using simple random

sampling or systematic sampling

Example ?

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Non-probability Sampling

• The sampling units are selected as convenient to the researcher

• Has a greater chance of giving biased results

• Example ?

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Non-probability Sampling: types

• Convenience sampling: A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access.

• Purposive sampling: Sampling technique that rely on the judgment of the researcher when it comes to selecting the units that are to be studied. 

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Type of Non-probability Sampling

• Snowball sampling: Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach.

• Quota sampling: the researcher decides in advance on certain key characteristics which s/he will use to stratify the sample.

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Selecting a sampling method

• Population to be studied– Size/geographical distribution– Heterogeneity with respect to variable

• Availability of list of sampling units• Level of precision required• Resources available

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Differences between two types of sampling

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Probability sampling Non-probability sampling

Always for quantitative research method

Usually for qualitative research method

Sampling units have known probability of being selected

No such thing

Involves statistical analysis Does not involve statistics.

Results can be generalized Results are not intended to be generalized

Sample size calculation is involved

Sample size calculation is not involved

Sampling Error

• Samples may be different from population – Choosing sample frame– Process of selecting sample– Failure to collect answers from everyone

(non-response)

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An example:Bangladesh Maternal Mortality and Health Care

Survey (BMMS) 20102nd nationally representative survey to:• provide national estimates of the maternal mortality

ratio (MMR) in Bangladesh

• identify causes of maternal deaths among adult women;

• understand antenatal, delivery and post-natal care

Sample Size: around 175,000 households

Sampling method: multi-stage cluster sampling

Used a three stage sampling procedure • First stage: wards in urban and unions in rural

were used as the primary sampling units (PSUs)

• Second stage: selected two mohallas in each ward and two mouzas in each union

• Third stage: Each selected mohalla and mouza was segmented into clusters and one of these was selected from each selected mohalla and mouza

Unions

Segment

Household

Mouzas

Rural Areas

Wards

Segment

Household

Mohallas

Urban Areas

Sample Sizes Selected

Domains Clusters HouseholdsUrban 654 42510

Other Urban 488 31720Rural 1566 101790Total 2708 176020

Response Rates: Households

Urban Rural Total0

25

50

75

100 98.6 98.9 98.898.2 98.6 98.4

2001 2010

Response Rates Women

Urban Rural Total0

25

50

75

100 96.6 97.3 97.296.9 97.7 97.3

2001 2010