Sampling Fundamentals 1 Sampling Fundamentals Population Sample Census

Sampling Fundamentals 1

Sampling Fundamentals • Population • Sample • Census • Parameter • Statistic

The One and Only Goal in Sampling!! Select a sample that is as representative as possible. So that an accurate inference about the population can be made – goal of marketing research

Sampling Fundamentals • When Is Census Appropriate? • When Is Sample Appropriate?

Error in Sampling • Total Error • Sampling Error • Non-sampling Error (dealt with in chapter 4)

Sampling Process: Identify Population • Question: For a toy store in RH • Question: For a small bookstore in RH specializing in romance novels

Sampling Process: Determine sampling frame • List and contact information of population members used to obtain the sample from • Example – to address a population of all advertising agencies in the US, the sampling frame would be the Standard Directory of Advertising Agencies • Availability of lists is limited, lists may be obsolete and incomplete

Problems with sampling frames • Subset problem – The sampling frame is smaller than the population • Superset problem – Sampling frame is larger than the population • Intersection problem – A combination of the subset and superset problem

Problems with sampling frames

Sampling Process: Sampling Procedure Probability Sampling Nonprobability Sampling

Sampling Procedure -Simple Random Sampl Probability Sampling -Systematic Sampling -Stratified Sampling -Cluster Sampling Procedures Here’s the difference! Non-Probability Sampling -Convenience Sampling -Judgmental Sampling -Snowball Sampling -Quota Sampling Probability Sampling: Each subject has the same non-zero probability of getting into the sample!

Probability Sampling Techniques Simple Random Sampling • Each population member has equal, non-zero probability of being selected • Equivalent to choosing with replacement

Probability Sampling Techniques • Accuracy – cost trade off • Sampling Efficiency = Accuracy/Cost – Sampling efficiency can be increased by either reducing the cost, increasing the accuracy or doing both – This has led to modifying simple random sampling procedures

Probability Sampling Techniques Stratified Sampling • The chosen sample is forced to contain units from each of the segments or strata of the population • Sometimes groups (strata) are naturally present in the population • Between-group differences on the variable of interest are high and within-group differences are low • Then it makes better sense to do simple random sampling within each group and vary within-group sample size according to – Variation on variable of interest – Cost of generating the sample – Size of group in population • Increases accuracy at a faster rate than cost

Stratified Sampling – what strata are naturally present

Directly Proportionate Stratified Sampling Consumer type Group size 10 Percent directly proportional stratified sample size Brand-loyal 400 40 Variety-seeking 200 20 Total 600 60

Inversely Proportional Stratified Sampling • 600 consumers in the population: • 200 are heavy drinkers • 400 are light drinkers. • If heavy drinkers opinions are valued more and a sample size of 60 is desired, a 10 percent inversely proportional stratified sampling is employed. Selection probabilities are computed as follows: Denominator 600/200 + 600/400 = 3 + 1. 5 = 4. 5 Heavy Drinkers proportion and sample size 3/ 4. 5 = 0. 667; 0. 667 * 60 = 40 Light drinkers proportion and sample size 1. 5 / 4. 5 = 0. 333; 0. 333 * 60 = 20

Probability Sampling Techniques Cluster Sampling • Involves dividing population into clusters • Random sample of clusters is selected and all members of a cluster are interviewed • Advantages – Decreases cost at a faster rate than accuracy – Effective when sub-groups representative of the population can be identified

Cluster Sampling • Math knowledge of all middle school children in the US • Attitudes to cell phones amongst all college students in the US • Knowledge of credit amongst all freshman college students in the US

A Comparison of Stratified and Cluster Sampling Stratified sampling Cluster sampling Homogeneity within group Homogeneity between groups Heterogeneity within groups All groups are included Random selection of groups Random sampling in each group Census within the group Sampling efficiency improved by increasing accuracy at a faster rate than cost Sampling efficiency improved by decreasing cost at a faster rate than accuracy.

Probability Sampling Techniques • Systematic Sampling – Systematically spreads the sample through the entire list of population members – E. g. every tenth person in a phone book – Bias can be introduced when the members in the list are ordered according to some logic. E. g. listing women members first in a list at a dance club. – If the list is randomly ordered then systematic sampling results closely approximate simple random sampling – If the list is cyclically ordered then systematic sampling efficiency is lower than that of simple random sampling

Non-Probability Sampling • Benefits – Driven by convenience – Costs may be less • Common Uses – Exploratory research – Pre-testing questionnaires – Surveying homogeneous populations – Operational ease required

Non-Probability Sampling Techniques • Judgmental • Snowball • Convenience • Quota