 MKTG 3342 Fall 2008 Professor Edward Fox

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MKTG 3342 Fall 2008 Professor Edward Fox
SAMPLING PROCEDURES MKTG 3342 Fall 2008 Professor Edward Fox

SAMPLING PROCEDURES Outline 1. INTRODUCTION: Sampling vs. Census
2. PROCEDURE FOR DRAWING SAMPLE 3. TYPES OF SAMPLING PLANS 4. NONPROBABILITY SAMPLES 5. PROBABILITY SAMPLES

1. SAMPLING VS. CENSUS ADVANTAGES OF SAMPLING Time Cost DISADVANTAGES Because only a sample has been drawn, there is associated uncertainty (error)

1. SAMPLING VS. CENSUS Target Population Sample

A. DEFINE THE TARGET POPULATION
2. PROCEDURE FOR DRAWING SAMPLE (slightly different from procedure in your book) A. DEFINE THE TARGET POPULATION the specification of people or cases on whom the research is to be conducted. B. IDENTIFY THE SAMPLING FRAME Listing of population elements from which sample is drawn. C. SELECT THE SAMPLING PLAN D. DETERMINE SAMPLE SIZE E. SELECT THE SAMPLING UNITS

3. TYPES OF SAMPLING PROCEDURES
SAMPLE DESIGN NONPROBABILITY SAMPLES PROBABILITY SAMPLES - CONVENIENCE - SIMPLE RANDOM - JUDGMENTAL - STRATIFIED - QUOTA PROPORTIONATE - SNOWBALL DISPROPORTIONATE - CLUSTER - SYSTEMATIC

PROBABILITY VS. NONPROBABILITY
PROBABILITY SAMPLING Every member of the population has a known, non-zero probability of being selected NON-PROBABILITY SAMPLING The probability of any particular member being chosen for the sample is unknown

NONPROBABILITY SAMPLING METHODS
CONVENIENCE SAMPLES Nonprobability samples used primarily because they are easy to collect JUDGMENT SAMPLES Nonprobability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study

NONPROBABILITY SAMPLING METHODS
QUOTA SAMPLES Nonprobability samples in which population subgroups are classified on the basis of researcher judgment SNOWBALL SAMPLES Nonprobability samples in which selection of additional respondents is based on referrals from the initial respondents

PROBABILITY SAMPLING METHODS
SIMPLE RANDOM SAMPLING A probability sample in which every element of the population has a known and equal probability of being selected into the sample Sample Size Probability of Selection = Population Size

PROBABILITY SAMPLING METHODS
STRATIFIED RANDOM SAMPLING INVOLVES THE FOLLOWING TWO-STEP PROCEDURE: I. The parent population is divided into mutually exclusive and collectively exhaustive subsets (strata) II. A simple random sample is chosen from each subset

REASONS FOR STRATIFIED SAMPLING
-- Investigate characteristics of interest by subgroup; stratification allows for adequate representation of different subgroups -- Increase precision (reduce sampling error) EXAMPLE Suppose I want to investigate if low-income users default more on credit card than high-income users. I want to ensure adequate representation of people with both high and low incomes, so I divide the population on the basis of income and take a random sample from the high-income group and the low-income group.

PROPORTIONATE VS. DISPROPORTIONATE STRATIFIED SAMPLING
PROPORTIONATE STRATIFIED SAMPLING: Take sample size in (same) proportion to size of the population in each subgroup or stratum; e.g., suppose there are 3,000 high-income users and 10,000 low-income users; then take maybe 30 (1%) high-income and l00 (1%) low-income users DISPROPORTIONATE STRATIFIED SAMPLING: Sample size not necessarily in proportion to population subgroup size; e.g. take 60 (2%) high-income consumers and 100 (1%) low-income users because I think there is substantial variation among high-income consumers

CLUSTER SAMPLING TWO-STEP PROCEDURE:
-- Population is divided into mutually exclusive and collectively exhaustive subsets -- A random sample of the subsets is selected -- In one-stage cluster sampling, all elements in the randomly selected subsets are included -- In two-stage cluster sampling, a sample is selected probabilistically from each randomly selected subset

MOTIVATION FOR CLUSTER SAMPLING
GENERALLY LOWER COST (but less accurate) For example, In the income / credit default case, suppose you divide people based on where they live (say, by zip code), then randomly select zip codes (say and 75212) and investigate either everyone in both zip codes or a random sample of people from both zip codes

DIFFERENCE BETWEEN STRATIFICATION AND CLUSTERING
The variable used for stratification must be related to research focus (income, in our example) The variable used for clustering must not be related to research focus (zip code, in our example)

PROBABILITY SAMPLING METHODS
SYSTEMATIC SAMPLING Probability sampling in which the entire population is numbered. The first number is drawn randomly. Subsequent elements are drawn using a skip interval. Population Size Skip Interval = Sample Size

PROBABILITY SAMPLING METHODS
Example of systematic sampling Suppose I want to pick 100 phone numbers to call from a telephone directory with 1000 pages. Use Population Size (1000) = 10 Skip Interval = Sample Size (100) First, draw a random number between 1 and 10 (say you get 7); then pick pages 7, 17, 27, …997 From each page you can pick a phone number (say on top right corner)

SUMMARY OF KEY POINTS (1 of 2)
The population is the total group of people in whose opinions one is interested A census involves collecting desired information from all the members of the population A sample is simply a subset of a population

SUMMARY OF KEY POINTS (2 of 2)
Probability sampling methods are selected in such a way that every element of the population has a known, nonzero probability of selection Nonprobability sampling methods include all methods that select specific elements from the population in a nonrandom manner Stratified probability sampling is generally the best method for selecting a sample, if time and budget permit

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