SAMPLING TECHNIQUES CHAPTER 2 Dr. BALAMURUGAN MUTHURAMAN DATA COLLECTION It is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes. The data collection component of research is common to all fields of study including physical and social sciences, humanities, business, etc. While methods vary by discipline, the emphasis on ensuring accurate and honest collection remains the same. The goal for all data collection is to capture quality evidence that then translates to rich data analysis and allows the building of a convincing and credible answer to questions that have been posed TYPES OF DATA PRIMARY DATA - Collected by the researcher himself SECONDARY DATA - Collected by others to be "re-used" by the researcher METHOD OF PRIMARY DATA Observation method Indirect Oral Interviews Questionnaire method Schedule Method From Local Agents Survey Method SECONDARY DATA Data collected from a source that has already been published in any form is called as secondary data. The review of literature in any research is based on secondary data. Mostly from books, journals and periodicals Data Collection Flow SAMPLING Get information about large populations Less costs Less field time More accuracy i.e. Can Do A Better Job of Data Collection When its impossible to study the whole population Target Population The population to be studied/ to which the investigator wants to generalize his results Sampling Unit smallest unit from which sample can be selected Sampling frame List of all the sampling units from which sample is drawn Sampling scheme Method of selecting sampling units from sampling frame TYPES OF SAMPLING PROBABILITY SAMPLING All elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated. NON PROBABILITY SAMPLING In contrast, population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher's personal judgment that they are representative. The consequence is that an unknown portion of the population is excluded (e.g., those who did not volunteer). One of the most common types of non probability sample is called a convenience sample SIMPLE RANDOM SAMPLE Get a list or sampling frame This is the hard part! It must not systematically exclude anyone. Generate random numbers Select one person per random numbers SYSTEMATIC RANDOM SAMPLE Select a random number, which will be known as k Get a list of people, or observe a flow of people (e.g., pedestrians on a corner) Select every kth person Careful that there is no systematic rhythm to the flow or list of people. If every 4 th person on the list is, say, rich or senior or some other consistent pattern, avoid this method STRATIFIED RANDOM SAMPLE Separate your population into groups or strata Do either a simple random sample or systematic random sample from there Note you must know easily what the strata are before attempting this If your sampling frame is sorted by, say, school district, then youre able to use this method MULTI-STAGE CLUSTER SAMPLE Get a list of clusters, e.g., branches of a company Randomly sample clusters from that list Have a list of, say, 10 branches Randomly sample people within those branches This method is complex and expensive CONVENIENCE SAMPLE Find some people that are easy to find SNOWBALL SAMPLE Find a few people that are relevant to your topic. Ask them to refer you to more of them QUOTA SAMPLE Determine what the population looks like in terms of specific qualities. Create quotas based on those qualities. Select people for each quota