Sampling Techniques LEARNING OBJECTIVES : After studying this module, participants will be able to : 1. Identify and define the population to be studied. 2. Identify and describe common methods of sampling. 3. Discuss problems of bias that should be avoided when selecting a sample. 4. Use random number tables and 5. Use random allocation (randomization) in experimental studies / clinical trials.

Sampling Research Problem Study Population Study Units the selection of a number of study units from a defined study population

Examples ProblemStudy populationStudy unit Malnutrition related to weaning in District X High drop-out rates in primary schools in District Y Inappropriate record- keeping for records on hypertensive patients registered in hospital Z All children 6-24 months of age in District X All primary schools in District Y All records on hypertensive patients in hospital Z One child between 6 and 24 months in District X One primary school in District Y One record on a hypertensive patient registered in hospital Z

Main Principle Validity of Research Results/Conclusions Research Methodology: -Study Design -Data Collection -Sampling etc Representative

Sampling Techniques I. Non Probability Sampling : 1. Convenience sampling 2. Quota sampling 3. Judgement/purposive sampling 4. Snowball sampling

II. Probability Sampling 1. Simple random sampling 2. Systematic sampling 3. Stratified sampling 4. Cluster sampling 5. Multistage sampling

CONVENIENCE SAMPLING is a method in which for convenience sake the study units that happen to be available at the time of data collection are selected in the sample. QUOTA SAMPLING is a method that ensures that a certain number of sample units from different categories with specific characteristics appear in the sample so that all these characteristics are represented.

Simple Random Sampling This is the simplest form of probability sampling. To select a simple random sample you need to : Make a numbered list of all units in the population from which you want to draw a sample. Decide on the size of the sample (this will be discussed later). Select the required number of sampling units, using a “lottery” method or a table of random numbers.

Systematic Sampling In a SYSTEMATIC SAMPLING individuals are chosen at regular intervals (for example every fifth) from the sampling frame. Ideally we randomly select a number to tell us where to start selecting individuals from the list.

Stratified Sampling the sample includes representative groups of study units with specific characteristics (for example, residents from urban and rural areas, or different age groups), the sampling frame must be divided into groups, or STRATA, according to these characteristics. Random or systematic samples of a predetermined size will then have to be obtained from each group (stratum).

Cluster Sampling The selection of groups of study units (clusters) instead of the selection of study units individually.

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

BIAS IN SAMPLING Bias in sampling is a systematic error in sampling procedures that leads to a distortion in the results of the study. There are several possible sources of bias in sampling. The best known source of bias is nonresponse.

There are several ways to deal with the problem and reduce the possibility of bias : - Data collection tools have to be pretested. - If nonresponse is due to absence of the subjects, follow-up of nonrespondents may be considered, - If nonresponse is due to refusal to cooperate, an extra, separate study of nonrespondents may be considered to discover to what extent they differ from respondents. - Another strategy is to include additional people in the sample, so that nonrespondents who were absent during data collection can be replaced.

Random number tables : How to use random number tables 1. First, decide how large a number you need. Next, count if it a one, two, or larger digit number. For example, if your sampling frame consists of 500 units, you must use three digits. 2. Decide beforehand whether you are going to go across the page to the right, down the page, across the page to the left L, or up the page.

How to use random number tables : 3. Without looking at the table, and using a pencil, pen, stick, or even your finger, pin-point a number. 4. If this number is within the range you need, take it. If not, continue to the next number in the direction you chose before-hand, (across, up or down the page), until you find a number that is within the range you need.

Random Number (example) : Coloumn … … etc

CLASS EXERCISES Indicate and discuss an appropriate sampling procedure for use in the following situations (also define : study population; study unit and sampling frame). 1.To estimate the prevalence of malnutrition of children under-five years of age in sub district X. If the population divided into 5 sub villages (each village is heterogenous), all of children in the village are registered. 2. To estimate the regional distribution of patients who attended a big teaching hospital in a 12-month period. 3. To select 25 % of patients for interview among those attending a physician’s clinic in a single day. 4. To estimate the proportion of family planning acceptors based on type of contraception in West-Java. 5. How to make random allocation, if in the experimental study we have 2 treatments and 30 patients. (use : simple randomization and random permuted blocks). 6. The same with no 5. but we have 3 treatments.

Random allocation Random allocation : only in experimental study. Rationale : 1. Avoid selection bias in treatment assignment 2. Distribute patients variables among treatment groups by chance alone 3. Equal prognostic composition  valid comparison 4. Permit use of statistical tests.

Random allocation The basic types of randomization : A. Fixed randomization 1. Simple randomization 2. Random permuted Blocks 3. Stratified randomization B. Adaptive randomization 1. Number adaptive 2. Baseline adaptive 3. Outcome adaptive C. Others