SAMPLING TECHNIQUES AND METHODS ‘CHAR’ FMCB SEMINAR PRESENTER: DR KAYODE. A. ONAWOLA 03/07/2013

SUBJECT SELECTION: Composition of study population ( e.g. mothers of under –five in a specified location) - Depends on the objectives of the study - location of the study in terms of the general characteristics and those of specific relevance - will the study cover the whole population or just a sample? - if a sample, then consider: - sample size and - sampling technique

Decisions on sampling: 1. Decide first whether or not to draw a sample if so, should it be - non-probability sample - Probability sample - consider - objectives of the study - Extent of representativeness of population - Factors e.g. cost, time & personnel 2. Calculate the sample size required - use formula - seek assistance of a statistician - estimates of proportion to be tested - degree of accuracy sought - magnitudes of differences you wish to test - confidence level - population size from which sample will be drawn

Decision on sampling contn’d: 3. Basic principles: a) Large sample size will yield more accurate result but is more costly b) Probability sample will provide quantitative data more representative of a large population c) If proposed analysis will study subgroups of your sample, then expand your sample size accordingly i.e. at least 50 case in the smallest subgroup (to study a group of contraceptive acceptors, sample size 400is needed, to extend to acceptors of particular methods, the sub-sample size will be too small). d) Sampling is done - when whole population study is not feasible - for efficient utilization of resources (time, money & manpower) - for better accuracy of analysis of collected data * however these are estimate with sampling error * sampling not ideal for rare events ( insufficient cases for adequate representation of population

SAMPLING TECHNIQUES Non-probability sampling 1. Accidental types (haphazard and convenienence) - selection is accidental - subject happens to be available - limited use, in exploratory surveys by investigators - employed for selection of focus group discussion (FGD) 2. Judgmental types (purposive and quota) - subject selection based on investigators believe or presumption that subject is typical of population -also used in FGD Probability sampling Known probability (chance) for selection Representative of the population Generalization of the finding possible

SIMPLE RANDOM SAMPLING - Every member has equal chance of selection into the sample - Only chance determines - More suitable for homogenous population - May be difficult if sample frame is large i.e. large-scale survey - minority subgroups may not be present in the sample in sufficient numbers - select sample after you decide the sample size(n) to be selected from the population size(N) balloting, casting a die, tossing of coin & drawing a lot Use of random numbers i.e. computer generated or table of numbers(approx equal probability)

STEPS IN SELECTION Assign numbers to each member of the population from 1-N (codes) the code depend on the number of digits in N i.e. if N=225 then the codes will be from (3-digits) Select by random method the starting column and row move in predetermined manner to select codes from 225 and below until the sample size n is attained ignore numbers greater than 225 and duplicated numbers SYSTEMATIC SAMPLING Randomly select the first subject and others are systematically selected through a predetermined sample interval No periodicity in sampling population or frame Sampling ratio or fraction is the number of units in the sample ÷ number of units in the sample frame and expressed as 1 in 3 or 1 in 5 etc Requires a list and numbers

STEPS IN SELECTION Assign numbers randomly Suppose N =50 and n =8, the codes will be from Cal sample interval (K), the nearest whole number to N/n (50/8) = 6.25 ~ 6 Select at random the starting point (x) and then select the k th subject i.e. x + k, x+2k, x +3k etc till sample size is attained Other methods not requiring listing:  Select every third (3 rd ) patient admitted to hospital or client that visits a service center  other forms of selection i.e. identification numbers that end with predetermined or randomly selected digits

STRATIFIED SAMPLING For heterogeneous population with certain characteristics i.e. age, sex, occupation Stratification done prior to sample selection i.e. stratification by sex and class level before selection For each stratum, samples are drown by any random method i.e. simple random or systematic random E.G. selection of students in a secondary school, stratification was by junior and senior classes and by sex  population in junior class = 400, males = 60% (240)  population in senior class = 300, males = 75% (225)  study population = 700  sample of 140 students to be drawn, sampling ratio = 140/700 = 1 in 5  using equal sampling ratio, sample size for junior class = 400 x 140/700 = 80, sample size for senior class = 300 x 140/700 = 60  selection of 80 students in junior class: Male = 0.6 x 80 = 48 Female = 80 – 48 = 32  selection of 60 students in senior class: male = 0.75 x 60 =45 Females = 60 – 45 = 15  Altogether there will be 93 Males and 47 Females in the sample

CLUSTER SAMPLING Population is divided into clusters of homogeneous units i.e. families, classes of a school or villages the sampling units are the clusters, the sampling frame is the list of these clusters and not individual subjects simple random sampling of the clusters are selected and all units in the cluster are examined. Cuts down on resources and the distance to be covered sampling error usually higher adapted for assessment of immunization e.g. random selection of 210 children by selecting 7 children in each of 30 clusters Steps in selection: Identification of geographical areas in the study location Identification of study population (age group(s) of interest Random selection of 30 clusters from study location Random selection of starting point (household) within each cluster and selection of 7 individuals from each cluster (7 x 30 = 210 subjects)

MULTISTAGE Used in large scale surveys Selection is done in stages until final sampling units are arrived at random sampling of first stage is selected from either towns, villages, or schools the second stage could be compounds, or houses or households random sampling of this second stage units is then selected, and studied. This is a two stage sampling Cuts down cost but sampling error is higher SNOWBALL SAMPLING Data is collected from small group of people with special characteristics who will then assist to identify others data is then collected from these new set of people who will help to identify others done till required sample size is attained also known as network or chain referral sampling Used for difficult to reach population or rare conditions i.e. leprosy and drug addicts

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