营销调研Sampling.ppt

Four basic types of random sampling techniques: Simple Random Sampling Systematic Sampling Stratified Sampling Cluster or Multi-stage Sampling   Simple Random Sampling This is the ideal choice as it is a ‘perfect’ random method. Using this method, individuals are randomly selected from a list of the population and every single individual has an equal chance of selection. Simple Random Samples To select a sample at random, we first need to define where the sample will come from. The sampling frame is a list of individuals from which the sample is drawn. E.g., To select a random sample of students from a college, we might obtain a list of all registered full-time students. When defining sampling frame, must deal with details defining the population; are part-time students included? How about current study-abroad students? Once we have our sampling frame, the easiest way to choose an SRS is with random numbers.  Systematic Sampling Systematic sampling is a frequently used variant of simple random sampling. When performing systematic sampling, every kth element from the list is selected (this is referred to as the sample interval) from a randomly selected starting point. For example, if we have a listed population of 6000 members and wish to draw a sample of 200, we would select every 30th (6000 divided by 200) person from the list. In practice, we would randomly select a number between 1 and 30 to act as our starting point.  Stratified Sampling Stratified sampling is a variant on simple random Used when there are a number of distinct subgroups, within each of which it is required that there is full representation. A stratified sample is constructed by classifying the population in sub-populations (or strata), based on some well-known characteristics of the population, such as age, gender or socio-economic status. The selection of elements is then made separately from within each strata, usually by random or systematic sampling methods