Sampling Design, Questionnaire Design & Data ib

Sampling Design, Questionnaire Design & Data

Indraneel BhowmikTripura University

1@ICFAI University, 22nd Jan 2015

Data

Types• Quantitative- numerical• Qualitative- non numerical

Collected by • Individual researchers, • Development planners, • Donor agencies, • Any other institutions

2ICFAI University, 22nd Jan 2015

Data Collection......... is any process of preparing and collecting

data

The purpose of data collection is to-• obtain information • keep on record, • make decisions about important issues,

or• pass information on to others

Data are primarily collected to provide information regarding a

specific topic improve the decision-making by

focusing on objective information

I think the problem is... becomes... The data indicate the problem is


http://en.wikipedia.org/wiki/Data

However

Simply collecting data is not enough- relevant/ specific data is needed to tell what is occurring

The key issue is not:

How do we collect data?

ButBut:

How do we obtain useful data?

Need-

a well-defined Data Collection process


ISSUES TO CONSIDER

• What is your research question?• What is your target population?• What do you know about this

population? • Do you have a sample frame?• What shape is it in?• Do you have an existing

Questionnaire/ Schedule?• By when do you need your

data?• How much money do you have?


Determinants of the Mode of Data Collection…..

Population+

Characteristics Of The Sample+

Types of Questions+

Question Topic+

Response Rate+

Cost ₹+

Time


Sampling Sampling or Census → to be decided by

the researcher Choice → governed by the discipline

Social anthropologists- Social anthropologists- complete enumeration.

Data from each unit of the population- “participant-observation”.

Economists prefer the sampling Advantages Advantages of sampling method

less less workloadworkload better better control over data collection control over data collection

process process reduction reduction of non-sampling errorsof non-sampling errors

Disadvantage of sampling method Sampling errors

However EasierEasier to control sampling errors as

compared to non-sampling errors. Sampling errors are scientificallyscientifically

controllable.

Thus, it is preferablepreferable to work with a sample

rather than going in for complete

enumeration. 7ICFAI University, 22nd Jan 2015

Drawing a Sample1. Define the target population

keeping in view the objectives of the study and then draw a sample

2. The sample could be a ‘probability probability samplesample’ or a ‘non-probabilitynon-probability’ or ‘purposive samplepurposive sample’

3. Probability sample - each member of the population has a known non-zero probability of being selected.– Examples: random sampling,

systematic sampling, stratified random sampling etc.

1. Non-probability or purposive sample - members are selected from the population in some non-random manner– Examples: Convenience sampling,

judgment sampling, quota sampling etc.


Probability Sample – Types• Random sampling - purest form

– Each member of the population has an equalequal chance

• Systematic sampling -– With the sample size being determined, every every

nnthth record is selected from the population– Simpler than random sampling technique – FrequentlyFrequently used to select a specified number

of records from a computer file• Cluster Random Sampling-

– Divide Population into clusters– Randomly sample Clusters– Measure all units within sampled clusters

• Stratified Random sampling – Most Common– Divide population into non-overlapping

groups (Strata)- N1+N2+ ….+Ni = N; – Yardsticks- landholdings, asset holdings,

incomes, caste, religion, occupation etc.– Draw sample with probabilities proportional

to size of each stratum (PPS sampling)– Reduces sampling error - each stratum is

homogeneous Operational problems

Information necessary for stratification may not be available at the beginning of the work.


Non- Probability Sample• Convenience (Accidental or Haphazard)

sampling – – Often during preliminarypreliminary research efforts

to get a gross estimate of the results– Selected because they are convenientconvenient

(Man on the street)• Judgment (Purposive) sampling –

– Extension of convenience sampling– Selected on judgment, to serve the

purpose – Ex: draw the entire sample from one

"representative" village, even though the population includes a cluster of villages

• Expert Sampling– Assembling persons with known

experience and expertise (sub-case of purposive)

• Quota sampling – – Non-probability equivalent of stratified

sampling– IdentifiesIdentifies the stratums and their

proportions; then convenience or judgment sampling is used to select the required number from each stratum

• Snowball Sampling- – Identify somebody meeting the criteria,

use him/her for identifying others10ICFAI University, 22nd Jan 2015

Choosing -Random or Purposive• Sociologists and social anthropologists prefer

purposive sampling– May Work for studying a typical social/economic

phenomenon– but it does not help to obtain reliable estimates

• Statisticians prefer random sampling as it is free from any purposiveness or bias.

• Economists choose between purposive & random depending upon the nature of the problem in hand

• Rudra (1989) – – Purposive for studying a social phenomenon more

intensively, subsequently random for testing the hypotheses for generalisation

– method of “randomising the population rather than randomising the sample”

– Ex: to draw a sample of AWs -not necessary to prepare complete village listing, just walk randomly across village & find some labourers working or gossiping

– meeting them there you could fix your sample• Olsen (1993) –

– method of “creative augmentation” when some sensitive information is needed

– find out some households/persons with the characteristic you are looking for

– utilise them as your informants to augment the list 11ICFAI University, 22nd Jan 2015

Sample Size Misconception among some researchers that

larger the sample size, it is better Efficiency of a sample scheme

does not depend on sample size alone depends both on the size of sample (n) and

population variance (σ2). For higher value of σ2 , higher should be n.

• Formula for calculation of variance for distribution of sample mean [V(m)]– V(m) = (σ2)/n

or, n= (σ2)/ V(m)

when , σ2 = 0; n=1 is enough

• To know the population variance (σ2)- – Preliminary work and pilot surveys can help.

Reading past literature and/or discussions with knowledgeable persons - very useful

Israel (1992, 2009, 2013)- provides a table on the desirable sample size with respect to the population http://edis.ifas.ufl.edu/pd006


BASIC SURVEY DESIGNS

• Cross-Sectional Surveys: Data are collected at one point in time from a sample selected to represent a larger population.

• Longitudinal Surveys = Trend, Cohort, and Panel – Trend: Surveys of sample

population at different points in time

– Cohort: Study of same population each time data are collected, although samples studied may be different

– Panel: Collection of data at various time points with the same sample of respondents.


Questionnaire and schedule are not the same……………………

• Questionnaire can be sent via mail via mail but schedule is done only personally

• Questionnaire is cheapercheaper method than schedule (for schedule you have to move everywhere)

• Questionnaire can be returned without answering all the questions but, in schedule, enumerator ensures the filling ensures the filling all the questions

• Questionnaire can be filled by anyone but schedule is always filled by enumerator

• Respondent should be literate & co-operative in Questionnaire but schedule can be used for illiterates

• Risk of incomplete & wrong information is more in Questionnaire


Questionnaire Design

One of the most critical stages of survey research designs

Using common sense and good grammar is a necessary condition but not a satisfactory one…..

It is erroneous to assume that people will understand the questions….

They may• not simply know about the topic, • be unawareunaware of the issue, • lack any interest, • confuseconfuse it with something else, or• simply refuse to answer


So, how do we go.........1.1. RelevantRelevant- no unnecessary information

2.2. AccuracyAccuracy- information is reliable &valid

3. Avoid Ambiguity, Confusion, & Vagueness- use simple, understandable, unbiased, unambiguous words; e.g- marital status

4.4. No Double Barreled No Double Barreled Query- each question should be about one and only one issue

5. Avoid Avoid LeadingLeading Questions- e.g.,- ‘You, don’t smoke, do you?

6. Avoid Avoid LoadedLoaded Questions- e.g., Spouse beating

7. Do not over Tax over Tax Respondent's Memory

8. Arrange Questions in Proper Sequence- Start with easier things and then build up (funnel technique)

9. Use Use Filter Filter Question, if Needed

10.10. Avoid Avoid OverlappingOverlapping response category16ICFAI University, 22nd Jan 2015

Layout of the questionnaire

• Two Two issues- – Physical layout of the questionnaire and – Format of questions and responses.

• Number each question and put identifying information

• Do not cramp questions or create confusing appearance

• Make a cover sheet cover sheet for admin purposes

• Time and Date is a must• Identity and observation of the

interviewer• Instructions for filling up should be in

the questionnaire, but in a different style to enable differentiation

• More important More important for mail, web questionnaires- appearance act as the persuader

• Add a cover letter for mail surveys; end with a thanks 17ICFAI University, 22nd Jan 2015

Pilot Survey is a must because.....• On a small scale, carried out prior to

the main survey• To gain information to improve the

efficiency of the main survey • Test a questionnaire• Ascertain the time taken by field

procedure • Determine the most effective size of

sampling unit• Save financial resources because if

errors are found in the questionnaire or interview early on, there would be a lesser chance of unreliable results

• Also called “Exploratory survey” in special circumstance when little is known about the material or domain under inquiry.

The main objective - determine

whether conducting a large-scale

survey is worth the effort18ICFAI University, 22nd Jan 2015

http://explorable.com/definition-of-reliability.html

Post Script• Random & Purposive Sampling are not

rivals, rather they are complementary– Multi-stage Sampling

• Physical appearance of Questionnaire has to be attractive but such case is not necessary with schedule

• Data collection has to be fruitful• A survey - as good as the questions it

asks• Success of Questionnaire depends on

its design but in case of Schedule it depends on honesty & competency of Enumerator

RememberRemember- The respondents are doing you a

- The respondents are doing you a

favourfavour, so don’t act , so don’t act over-smartover-smart and pretend

and pretend

that you know a lotthat you know a lot


Thanks

Comments & Criticism welcome to

[email protected]


Sampling Design, Questionnaire Design & Data ib

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