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MODULE 5 -STUDY POPULATION -SELECTING A POPULATION -SELECTING A SAMPLE Workshop on Research Methodology and Proposal Development, Conference Hall, National TB Control Programme Islamabad, 18-27 June, 2004. Population and Determinants.

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Module 5 study population selecting a population selecting a sample

MODULE 5

-STUDY POPULATION

-SELECTING A POPULATION

-SELECTING A SAMPLE

Workshop on Research Methodology and Proposal Development, Conference Hall,

National TB Control Programme

Islamabad, 18-27 June, 2004

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Population and determinants

Population and Determinants

                                     

  • Case with determinant

  • Case without determinant

    Non-Case with determinant

    non-case without determinant

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Comprehensive survey versus sample survey

COMPREHENSIVE SURVEY VERSUS SAMPLE SURVEY

  • Ideally, the study population could be surveyed in order to reach high degree of accuracy for study results, e.g census.

  • However, this is usually not feasible due to cost, time and effort. Therefore, a representative sample of the “target” population should be selected.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Comprehensive survey versus sample survey1

COMPREHENSIVE SURVEY VERSUS SAMPLE SURVEY

  • The sample should be selected in “such a way” that permits generalization of the results over the population from which the sample was selected. Therefore, the sample should be:

    • Representative of the target population

    • Unbiased

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sampling what do we need to consider

Sampling: what do we need to consider?

  • Define the target population

  • Define eligibility criteria of the study subjects

  • Sample size

  • Sampling technique

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Study population

STUDY POPULATION

  • Should be clearly defined otherwise cannot do the sampling

  • Could be: persons, villages, institutions, records.

  • The sampling frame: is a list of sampling units (study units) from which the sample will be selected. These SU are given serial numbers starting from No 1 and ending with a No equal to the target population size (or total No of sampling units in that population.

  • Depends on the study problem

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Study population and study units

STUDY POPULATION AND STUDY UNITS

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Representativeness in quantitative and qualitative research

Representativeness in quantitative and qualitative research

  • A representative sample has the main characteristics of the study population

  • Representativeness is necessary in quantitative research to avoid biased results. Eg sampling QoC for TB patients in rural areas will not give a true picture of the QoC for TB patients in the whole province/county/country.

  • In qualitative research, study units providing the richest possible information eg key informants chosen purposively and not at random.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Purposeful sampling methods in qualitative research

Purposeful sampling methods in qualitative research

  • Qualitative research methods are typically used when focusing on a limited number of informants whom we select strategically so that their in-depth information gives us more insight into an issue about which little is known.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


1 extreme case sampling

1. Extreme case sampling

Selection of extreme cases is a powerful and rapid strategy

  • Eg. Good and very poor compliers to treatment

  • Eg Selection of well nourished and poor nourished to study contributing factors to malnutrition

  • Eg systematic comparison of poorly and well functioning district TB centres will give insight into factors contributing to satisfactory functioning of district centres

  • Eg. Description of single, deviant cases (AIDS discovery)

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


2 maximum variation sampling

2. Maximum variation sampling

To obtain insight in a certain issue with all its variations. Selection rules are developed ie not by chance or personal preference.

  • Eg. How Stigma manifests itself in different groups. Samples should be taken from; males/females; rural/urban; rich/poor; illiterate/educated…

  • Eg to assess whether social distance influences stigma, samples from near and far relatives should be taken.

  • A fixed number per group could be drawn: QUOTA SAMPLING

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


3 homogenous sampling

3. Homogenous sampling

To have specific information about one particular group only which is more at risk that others

  • Eg. In country S, death registers indicate that suicide is more frequent among boys than girls. Therefore, conducting in-depth interviews with parents, close relatives, teachers and friends of a number of boys who committed suicide.

  • In FGD, homogenous groups are selected because participants discuss more freely when they are amongst people of same social class.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


4 typical case sampling

4. Typical case sampling

To describe some cases which are “typical” for the group one is interested.

  • Eg. Typical family in a rural village

  • Eg. Typical health problems of miners

  • They are illustrative and cannot be generalized on the whole group

  • Can be selected in cooperation with the key informants who know the study population well.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


5 critical case sampling

5. Critical case sampling

Critical cases are those who “can make a difference” with respect to an intervention you want to introduce

  • Eg. Before propagating a weaning food, there is a need to know if it is affordable to all mothers. Therefore, some low-income mothers are interviewed as “test cases”. If they manage to use, then it is affordable to the whole community.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


6 snowball or chain sampling

6.Snowball or chain sampling

Suitable for locating key informants or critical cases. You start with one or two information rich key informants and ask them if they know persons who know a lot about the study topic. If a particular person is recommended by 2-3 key informants then he will be a valuable key informant.

  • Eg. In an exploratory study on coping behaviour of AIDS orphans, it seemed that child-headed household managed by girls survived better than those managed by boys. The interviewers interviewed more girls and boys heading households.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Random sampling strategies for quantitative research

Random sampling strategies for quantitative research

  • If the aim is to measure the distribution of a variable in the population or to test a hypothesis. Results from the sample will be generalized on the population, hence random sampling (representativeness)

  • Involves random selection procedures to ensure that each unit of the sample is chosen on the basis of chance. All units of the study population should have an equal chance of being selected in the sample.

  • Requires listing the study units. This list is called the sampling frame.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


1 simple random sampling

1. Simple random sampling

Simplest form

  • Sampling frame construction, sample size estimation

  • Ideal bowel method (lottery)

  • Table of random number

  • Random number list of EpiInfo

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


2 systematic sampling

2. Systematic sampling

  • Individuals are chosen at regular intervals eg every 5th from the sampling frame

  • Eg to select 100 students from 1200, the sampling proportion would be (sample size/population)(100/1200=1/12)

  • Therefore, 1/12 is the sampling interval i.e every 12th student will be selected.

  • The first student is selected by simple random method.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


3 stratified sampling

3-Stratified sampling

  • To include small groups with specific characteristics relevant to the study objectives Eg. Urban/rural

  • Sampling frame is divided into STRATA

  • Sample size for each stratum is proportionate to the size of that stratum (method of proportionate allocation):

    • Stratum sample size= stratum size X sample size

      Population size

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


4 cluster sampling

4-Cluster sampling

  • If not possible to perform SRM due to the lack of sampling frame or logistic difficulties (eg people scattered over large geographic area)

  • Selection of groups of study units (CLUSTERS) instead of study units individually

  • Clusters are often geographic units eg districts, villages

  • Eg in a KAP study in rural area, a list of villages was made and some villages were randomly selected, then all study units in the selected villages were interviewed.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Cluster sampling using probability proportional to size method pps

Cluster sampling using probability proportional to size method (PPS)

Sampling interval= population size/no of clusters (30)

867000/30= 28900

9465

38365

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


5 multistage sampling

5. Multistage sampling

  • In very large populations sampling may be done in 2 or more stages. This is often the case in community-based studies in which people are interviewed from different villages, and these villages have to be selected from different areas.

  • Carried out in phases and usually involves more than one sampling method.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Module 5 study population selecting a population selecting a sample

  • Eg. In a study on the utilization of pit latrines in a district of 150 homesteads. The district is composed of 6 wards and each ward has 6-9 villages.

    • The following 4-stage sampling procedure could be performed:

    • Select 3 wards out of the 6 by SRM (1st stage)

    • Select 5 villages from each ward by SRM (2nd stage)

    • Select 10 households from each village (3rd stage) by the following method: centre of the village (main building/mosque); spin a bottle and choose the direction of the bottle neck; walk in selected direction and select every nth household till completing 10.

    • Decide whom to interview eg head of household.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Bias in sampling

Bias in sampling

  • Leads to distortion of study results

  • Causes:

    • Improper sampling

    • Non-response

    • Studying volunteers only

    • Sampling patients only

    • Missing cases of short duration

    • Seasonal bias

    • Tarmac bias (areas accessible by car versus inaccessible ones)

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in qualitative research

Sample size estimation in qualitative research

  • No fixed rule.

  • The size depends on WHAT you try to find out.

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research for single proportion

Sample size estimation in quantitative research: for single proportion

  • Input criteria: estimated proportion of the condition; margin of error; confidence interval

  • N=z2pq/d2

  • Or N=pq/SE2 (margin of error is +/- SE if a precision of 95% is required)

  • Epiinfo

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research

Sample size estimation in quantitative research:

for single mean:

  • Input criteria: estimated mean; confidence interval; SD; standard error

  • N=SD2/SE2

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research1

Sample size estimation in quantitative research:

for single rate:

  • Input criteria: estimated rate; confidence interval; standard error

  • N=rate/SE2

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research2

Sample size estimation in quantitative research:

for single proportion (survey)

  • Input criteria: estimated proportion of the condition; margin of error; confidence interval

  • N=z2pq/d2

  • Or N=pq/SE2 (margin of error is +/- SE if a precision of 95% is required)

  • Epiinfo

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research3

Sample size estimation in quantitative research:

Difference between two means (cross-sectional or experimental)

  • N=2SD2 X[Z1(1-alpha)+Z2(1-beta)]2

    [mean1-mean2]

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research4

Sample size estimation in quantitative research:

Difference between 2 proportions (cohort, cross-sectional, or experimental)

  • Input criteria: difference between the two groups; confidence interval

  • N=p1q1Xp2q2

    SE2

  • Epiinfo formula:

    [p1q1+p2q2] X[Z1(1-alpha)+Z2(1-beta)]2

    [p1-p2]2

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


Sample size estimation in quantitative research5

Sample size estimation in quantitative research:

  • For case-control: same formula as for comparison between proportions, expect that p1 and p2 refer to the frequency of exposure of cases or controls to the risk factors

Workshop on Research Methods and Proposal Development, NTP, Islamabad, Pakistan


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