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sampling plan

Learning Objectives

  1. Understand the concept and procedure of sampling.

  2. List the primary types of sampling techniques: Non-probability vs Probability.

  3. Determine the sample size required for research.

Sampling Procedure Steps

  1. Define the Target Population.

  2. Identify the Sampling Frame.

  3. Select a Sampling Procedure.

  4. Determine the Sample Size.

  5. Select the Sample Elements.

  6. Collect the Data from the Designated Elements.

Target Population Definition

  • Definition: A population (N) includes all cases that meet specified criteria for membership.

  • Example: Households in Sacramento, CA with one or more children under 18.

Population Parameters

Parameter vs. Statistics

  • Parameter: Characteristic of the entire population, determinable through a census.

  • Statistics: Measure calculated from sample data to estimate population parameters.

Identifying the Sampling Frame

  • Sampling Frame: List of population elements from which sample (n) is drawn.

  • Common sampling frames include:

    • Customer databases

    • Telephone directories

    • Lists from data compilers.

Choosing a Sampling Procedure

Types of Samples

  • Nonprobability Samples:

    • Convenience Sample

    • Quota Sample

    • Judgment Sample

  • Probability Samples:

    • Simple Random Sample

    • Stratified Sample

    • Systematic Sample

Nonprobability Sampling Definition

  • Nonprobability Sample: Depends on researcher’s judgment; some individuals may have no chance of selection.

  • Cannot estimate sampling error.

  • Techniques include:

    1. Convenience

    2. Quota

    3. Judgment (e.g., Snowball)

  • Probability Sample: Each element has a known chance of selection; sampling error can be assessed.

Convenience Sampling Example

  • Example of Convenience Sampling:

    • Target population: W.P. Carey School of Business students.

    • Sampling Location: BAC building first floor hallway, collecting data from students between 9:00 am and 11:00 am.

Quota Sampling Explanation

  • Quota Sample: Ensures proportions of characteristics match those in the target population.

  • Example Scenario: Investigating undergraduate students’ attitudes towards a new technology fee based on class and gender distribution.

Class and Gender Quota Sample Idea

  • Details on how to set up quotas for the sample based on class and gender:

    • 30 Freshmen, 15 Freshman Females, etc.

Simple Random Sampling Process

  • Simple Random Sample: Each population element has an equal probability of selection.

  • Steps to execute:

    1. Select sampling frame.

    2. Assign numbers to elements.

    3. Generate random numbers for sample selection.

Simple Random Sampling Example Tables

  • Example tables illustrating how to set up sampling selection for simple random sampling.

In-class Assignment Instructions

  • Implementation of simple random sampling using Excel functions.

Stratified Sampling Explanation

  • Stratified Sample: Population is divided into subsets; simple random samples are taken from each.

  • Example case: Feedback on exam preferences from male and female students in a marketing research class.

Stratified Sampling Setup Tables

  • Layout sample tables for stratified sampling by gender and class.

In-class Assignment for Stratified Sample

  • Instructions for carrying out stratified sampling in a class setting using Excel functions.

Nonprobability vs. Probability Sample

Nonprobability Sample

  • Cannot estimate sampling error; results not generalizable.

Probability Sample

  • Allows statistical assessment of sampling error; results generalizable to the population.

Sample Size Determination

Key Factors Affecting Sample Size

  1. Amount of Diversity/Variation.

  2. Degree of Precision.

  3. Degree of Confidence.

  • Formula used for sample size determination in simple random sampling: [ n = \frac{z^2 \cdot \sigma^2}{D^2} ]

Exercises for Sample Size Calculation

  • Exercises illustrating the calculations for determining sample sizes based on parameters such as standard deviation, error range, and confidence level.

Exercise 1 Example Calculation

  • Given parameters for soda consumption survey, resulting sample size determined to be 97.

Exercise 2 Example Calculation

  • Surveys on average age at ASU football games, determining required sample sizes for different confidence levels and error ranges.

Page 41: Final Sampling Procedure Steps

  • Confirmation of steps for drawing a sample again highlighted.