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Ryerson University
MKT 400
Mark Speed

MKT500 – Chapter 9 Sampling Basic Sampling Concepts • Population = entire group under study • Sample = subset of the population • Census = accounting of everyone • Sampling error = error due to: – Method of sample selection – Size of the sample • Sample frame = master list of population Determining Sample Size • Accuracy of the sample = +/-x% • In theory, compared to a census • Sample will be x% above or below actual • Larger the sample, greater accuracy • After 1000, gains become marginal • Calculating Sample Size • Variability: how much respondents agree – Calculate p = estimated %age in the population – Then, q = 100% - p – Variability: p Times q. • Level of confidence: z – Usually = 95%; z = 1.96 • Desired accuracy: e – Specify acceptable level of sample error = +/-x% 1 Sample Error, 95% Confidence Sample Size with XL - Data Analyst • Load XL Data Analyst file • On menu, choose “Calculate, Sample Size” • Sample Size with XL • Results show various options, sensitivity Probability Sampling Methods • Random sample means all have a chance – Anyone in population could be selected • Probability sampling methods get random sample • Essential to have representative results • Four probability sampling techniques • Simple random sampling – Guarantees each member has identical chance – Can use random numbers technique – Table of random numbers provides this – Like a lottery • Systematic sampling – Use a list of members of population – Choose random starting point – Develop skip interval to cover an entire list – More efficient, but still random • Cluster sampling – Defined population broken into clusters – Each must be similar to the others – Select a few, complete census on them • Probability Sampling Methods [cont’d] • Stratified sampling
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