SAMPLING • Census -- the entire population – most useful is the population ("n") is small – or the cost of making an error are high • Sample -- contacting a portion of the population (e.g., 10% or 25%) – best with a very large population (n) – easiest with a homogeneous population 7/17/2016 Marketing Research 1 Sampling -- graphically Population 7/17/2016 Marketing Research Sample 2 The sampling process 1. Determine the target population who are the people you want information on? age, gender, product use 2. Determine the sampling frame how will you get the names, phone numbers or addresses? – existing lists, phone book, random digit dialing 7/17/2016 Marketing Research 3 The sampling process 3. Select a sampling procedure: A. Probability (random) sample – equal chance of being included in the sample – random number table, even-odd, etc. B. Stratified – equalizing "important" variables • year in school, geographic area, product use, etc. 7/17/2016 Marketing Research 4 The sampling process C. Nonprobability sampling – convenience sample • people in my classes – "snowball" sample • friends of friends – "quota" sampling • 50 women, 50 men – mall intercepts • Park Place Mall 7/17/2016 Marketing Research 5 Generalization • You can only generalize to the population from which you sampled – U of L students not college students • geographic, different majors, different jobs, etc. – College students not Canadian population • younger, poorer, etc. – Canadians not people everywhere • less traditional, more affluent, etc. 7/17/2016 Marketing Research 6 Instead of this (good)… Population 7/17/2016 Marketing Research Sample 7 …this (bad)… Sample Population 7/17/2016 Marketing Research 8 …or this (VERY bad)… Sample Population 7/17/2016 Marketing Research 9 Drawing inferences from samples • Population estimates – % who smoke, buy your product, etc • 25% of sample • what % of population? – very dangerous with a non-representative sample or with low response rates 7/17/2016 Marketing Research 10 Drawing inferences from samples • Relationships – e.g., exposure to ads and liking for the product – relationships (qualitatively different) – some danger with a non-representative sample • less problematic in experiments – (other factors are controlled) 7/17/2016 Marketing Research 11 Sampling messages • To be able to draw inferences about a sample of messages – (e.g., "funny" ads vs. "informational" ads) • you should also sample from the universe of those messages. 7/17/2016 Marketing Research 12 Instead of this (bad)…. Messages 1 7/17/2016 Marketing Research 13 …this (good) Messages 7/17/2016 Sample Marketing Research 14 The End 7/17/2016 Marketing Research 15