CHAPTER 12 DETERMINING THE SAMPLE PLAN Important Topics of This Chapter Differences between population and sample. Sampling frame and frame error. Developing sampling plan. Basic sampling methods. Strength and Weaknesses of Basic Sampling techniques. Choosing Probability Vs. non-probability sampling. Definitions of Important Terms Population or Universe The total group of people from whom information is needed. Census Data obtained from or about every member of the population of interest. Sample A subset of the population of interest Sampling Error: Selection error Sampling size Sample Frame and Frame Error: Sample vs. Census Types of Study Conditions Favoring the Use of Sample Census 1. Budget Small Large 2. Time available Short Long 3. Population size Large Small 4. Variance in the characteristic Small Large 5. Cost of sampling errors Low High 6. Cost of nonsampling errors High Low 7. Nature of measurement Destructive Nondestructive 8. Attention to individual cases Yes No The Sampling Design Process Define the Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process Steps in Developing a Sampling Plan Step 1: Defining the Population: Bases for defining the population of interest include: Geography Demographics Use Awareness Step 2: Choosing a Sampling Frame Sampling frame List of population elements from which to select units to be sampled. Steps in Developing a Sampling Plan (cont.) Step 3: Selecting the Sampling Technique(s): Probability samples: Non-probability samples: Samples in which every element of the population has a known, nonzero probability of selection. Include the selection of specific elements from the population in a nonrandom manner. Sampling error: The difference between the sample value and the true value of the population mean. Steps in Developing a Sampling Plan (cont.) Advantages of probability samples Disadvantages of probability samples - The researcher can be sure of obtaining information from a representative cross section of the population of interest. - They are more expensive than non-probability samples of the sample size in most cases. The rules for selection increase interviewing costs and professional time must be spent in developing the sample design. - Sampling error can be computed. - The survey results are projectable to the total population. - Probability samples take more time to design and execute than nonprobability samples. Steps in Developing a Sampling Plan (cont.) Advantages of nonprobability samples - Non-probability samples cost less than probability samples. This characteristic of non-probability samples may have considerable appeal in those situations where accuracy is not of critical importance. -Non-probability samples ordinarily can be conducted more quickly than probability samples. Disadvantages of nonprobability samples - Sampling error cannot be computed. - The researcher does not know the degree to which the sample is representative of the population from which it was drawn. - The results of non-probability samples cannot and should not be projected to the total population. Steps in Developing a Sampling Plan (cont.) Step 4: Determine the Sample Size: Once the sampling method has been chosen, the next step is to determine the appropriate sample size. Developing Operational Procedures: Involves determining whether a probability or nonprobability sample is being used. Steps in Developing a Sampling Plan (cont.) Step 5: Execute the Sampling Process: The final step in the sampling process involves execution of the operational sampling plan discussed in the previous steps. It is important that this step include adequate checking to make sure that specified procedures are adhered to. Classification of Sampling Techniques Sampling Techniques Non-probability Sampling Techniques Convenience Sampling Judgment Samples Simple random Sampling Systematic Sampling Probability Sampling Techniques Quota Sampling Stratified Sampling Snowball Sampling Cluster Sampling Probability Sampling Methods Simple Random Sampling Is considered to be the purest form of probability sampling. A probability sample is a sample in which every element of the population has a known and equal probability of being selected into the sample. Probability of Selection = Sample Size Population Size Procedures for Drawing Probability Samples Simple Random Sampling 1. Select a suitable sampling frame 2. Each element is assigned a number from 1 to N (pop. size) 3. Generate n (sample size) different random numbers between 1 and N 4. The numbers generated denote the elements that should be included in the sample Probability Sampling Methods (cont.) Systematic Sampling Probability sampling in which the entire population is numbered, and elements are drawn using a skip interval. Population Size Skip Interval = Sample Size Systematic Sampling 1. Select a suitable sampling frame 2. Each element is assigned a number from 1 to N (pop. size) 3. Determine the sample interval i:i=N/n. If i is a fraction, round to the nearest integer 4. Select a random number, r, between 1 and i, as explained in simple random sampling 5. The elements with the following numbers will comprise the systematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i Probability Sampling Methods (cont.) Stratified Samples Stratified samples are probability samples that are distinguished by the following procedural steps: First, the original or parent population is divided into two or more mutually exclusive and exhaustive subsets (e.g., male and female). Second, simple random samples of elements from the two or more subsets are chosen independently from each other. Stratified Sampling 1. Select a suitable frame 2. Select the stratification variable(s) and the number of strata, H 3. Divide the entire population into H strata. Based on the classification variable, each element of the population is assigned to one of the H strata 4. In each stratum, number the elements from 1 to Nh (the pop. size of stratum h) 5. Determine the sample size of each stratum, nh, based on proportionate or disproportionate stratified sampling, where H h=1 nh = n 6. In each stratum select a simple random sample of size nh Probability Sampling Methods (cont.) Cluster Samples In the case of cluster samples, the sampling units are selected in groups. There are two basic steps in cluster sampling: First, the population of interest is divided into mutually exclusive and exhaustive subsets. Second, a random sample of the subsets is selected. Cluster Sampling 1. Assign a number from 1 to N to each element in the population 2. Divide the population in C clusters of which c will be included in the sample 3. Calculate the sampling interval i, i=N/c (round to nearest integer) 4. Select a random number r between 1 and i, as explained in simple random sampling 5. Identify elements with the following numbers: r,r+i,r+2i,... r+(c-1)i 6. Select the clusters that contain the identified elements 7. Select sampling units within each selected cluster based on SRS or systematic sampling 8. Remove clusters exceeding sampling interval i. Calculate new population size N*, number of clusters to be selected C*= C-1, and new sampling interval i*. Types of Cluster Sampling Cluster Sampling One-Step Approach Two-Step Approach Simple Cluster Sampling Multistage Approach Probability Proportionate to Size Sampling Non-probability Sampling Methods Convenience Samples Non-probability samples used primarily because they are easy to collect. Judgment Samples Non-probability samples in which the selection criteria are based on personal judgment that the element is representative of the population under study. Non-probability Sampling Methods (cont.) Quota Samples Non-probability samples in which population subgroups are classified on the basis of researcher judgment. Snowball Samples Non-probability samples in which selection of additional respondents is based on referrals from the initial respondents. Strengths and Weaknesses of Basic Sampling Techniques Technique Strengths Weaknesses Nonprobability Sampling Convenience sampling Least expensive, least time-consuming, most convenient Low cost, convenient, not time-consuming Sample can be controlled for certain characteristics Can estimate rare characteristics Selection bias, sample not representative, not recommended for descriptive or causal research Does not allow generalization, subjective Selection bias, no assurance of representativeness Time-consuming Easily understood, results projectable Difficult to construct sampling frame, expensive, lower precision, no assurance of representativeness. Can decrease representativeness Judgmental sampling Quota sampling Snowball sampling Probability sampling Simple random sampling (SRS) Systematic sampling Stratified sampling Cluster sampling Can increase representativeness, Easier to implement than SRS, sampling frame not necessary Include all important subpopulations, precision Easy to implement, cost effective Difficult to select relevant stratification variables, not feasible to stratify on many variables, expensive Imprecise, difficult to compute and interpret results Table 11.4 Choosing Non-probability vs. Probability Sampling Factors Conditions Favoring the Use of Nonprobability Probability sampling sampling Nature of research Exploratory Conclusive Relative magnitude of sampling and nonsampling errors Nonsampling errors are larger Sampling errors are larger Variability in the population Homogeneous (low) Heterogeneous (high) Statistical considerations Unfavorable Favorable Operational considerations Favorable Unfavorable