Maxfield 8e PPT Ch08 Sampling PDF

Chapter 8: Sampling © 2018 Cengage Learning. All Rights Reserved. 1  Learning Objectives Understand how the logic of probability sampling makes it possible to represent large populations with small subsets of those populations Recognize that the...

Chapter 8: Sampling © 2018 Cengage Learning. All Rights Reserved. 1  Learning Objectives Understand how the logic of probability sampling makes it possible to represent large populations with small subsets of those populations Recognize that the chief criterion of a sample’s quality is the degree to which it is representative of the population from which it was selected Summarize the chief principle of probability sampling: every member of the population has a known, nonzero probability of being selected into the sample Describe how probability sampling methods make it possible to select samples that will be quite representative Understand how our ability to estimate population parameters with sample statistics is rooted in the sampling distribution and probability theory Recognize how simple random sampling is logically the most fundamental technique in probability sampling © 2018 Cengage Learning. All Rights Reserved. 2  Learning Objectives, cont. Recognize how simple random sampling is logically the most fundamental technique in probability sampling Distinguish the variety of probability sampling designs that can be used and combined to suit different populations and research purposes: systematic sampling, stratified sampling (proportionate and disproportionate), and multistage cluster sampling Understand the basic features of the National Crime Victimization Survey and the British Crime Survey, two national crime surveys based on multistage cluster samples Recognize how nonprobability sampling methods are less statistically representative than probability sampling methods, and be able to offer appropriate examples for nonprobability sampling applications Distinguish the variety of nonprobability sampling types, including purposive sampling, quota sampling, and snowball sampling. Describe examples of each © 2018 Cengage Learning. All Rights Reserved. 3  Introduction Sampling: The process of selecting observations Often not possible to collect information from all persons or other units you wish to study Often not necessary to collect data from everyone out there Allows researcher to make a small subset of observations and then generalize to the rest of the population © 2018 Cengage Learning. All Rights Reserved. 4  The Logic of Probability Sampling Enables us to generalize findings from observing cases to a larger unobserved population Representative: Each member of the population has a known and equal chance of being selected into the sample Since we are not completely homogeneous, our sample must reflect—and be representative of— the variations that exist among us © 2018 Cengage Learning. All Rights Reserved. 5 Conscious and Unconscious Sampling Bias What is the proportion of FAU students who have been to an FAU football game? Be conscious of bias: When sample is not fully representative of the larger population from which it was selected Equal Probability of Selection Method (EPSEM) A sample is representative if its aggregate characteristics closely match the population’s aggregate characteristics; basis of probability sampling © 2018 Cengage Learning. All Rights Reserved. 6  Sampling Distribution Sample Element: Who or what are we studying (student) Population: Whole group (college freshmen) Population Parameter: The value for a given variable in a population Sample Statistic: The summary description of a given variable in the sample; we use sample statistics to make estimates or inferences of population parameters © 2018 Cengage Learning. All Rights Reserved. 7  Sampling Distribution, cont. Purpose of sampling: To select a set of elements from a population in such a way that descriptions of those elements (sample statistics) accurately portray the parameters of the total population from which the elements are selected The key to this process is random selection Sampling Distribution: The range of sample statistics we will obtain if we select many samples © 2018 Cengage Learning. All Rights Reserved. 8  Sampling Distribution, slide 3 Sampling Frame: list of elements in our population By increasing the number of samples selected and interviewed, increase the range of estimates provided by the sampling operation © 2018 Cengage Learning. All Rights Reserved. 9  Estimating Sampling Error If many independent random samples are selected from a population, then the sample statistics provided by those samples will be distributed around population parameter in a known way Probability theory gives us a formula for estimating how closely the sample statistics are clustered around the true value Standard Error: A measure of sampling error Tells us how sample statistics will be dispersed or clustered around a population parameter © 2018 Cengage Learning. All Rights Reserved. 10  Confidence Levels and Intervals Two key components of sampling error We express the accuracy of our sample statistics in terms of a level of confidence that the statistics fall within a specified interval from the parameter The logic of confidence levels and confidence intervals also provides the basis for determining the appropriate sample size for a study © 2018 Cengage Learning. All Rights Reserved. 11  Discussion Question 1 What if someone told you that they were 100% confident in an interpretation of their survey results? How might you reply? © 2018 Cengage Learning. All Rights Reserved. 12  Sampling Distribution Summary Random selection permits the researcher to link findings from a sample to the body of probability theory so as to estimate the accuracy of those findings All statements of accuracy in sampling must specify both a confidence level and a confidence interval The researcher must report that he or she is x percent confident that the population parameter is between two specific values © 2018 Cengage Learning. All Rights Reserved. 13  Populations & Sampling Frames Different types of probability sampling designs can be used alone or in combination for different research purposes Key feature of all probability sampling designs: the relationship between populations and sampling frames Sampling frame: The quasi-list of elements from which a probability sample is selected © 2018 Cengage Learning. All Rights Reserved. 14  Simple Random Sampling Each element in a sampling frame is assigned a number, choices are then made through random number generation as to which elements will be included in your sample Forms the basis of probability theory and the statistical tools we use to estimate population parameters, standard error, and confidence intervals © 2018 Cengage Learning. All Rights Reserved. 15  Systematic Sampling Systematic Sampling: Elements in the total list are chosen (systematically) for inclusion in the sample List of 10,000 elements, we want a sample of 1,000, select every tenth element Choose first element randomly Danger: “Periodicity" A periodic arrangement of elements in the list can make systematic sampling unwise © 2018 Cengage Learning. All Rights Reserved. 16  Stratified Sampling Stratified sampling: Ensures that appropriate numbers are drawn from homogeneous subsets of that population Method for obtaining a greater degree of representativeness—decreasing the probable sampling error Disproportionate stratified sampling: Way of obtaining a sufficient number of rare cases by selecting a disproportionate number To purposively produce samples that are not representative of a population on some variable © 2018 Cengage Learning. All Rights Reserved. 17  Multistage Cluster Sampling Compile a stratified group (cluster), sample it, then subsample that set... May be used when it is either impossible or impractical to compile an exhaustive list of the elements that compose the target population (Ex.: All law enforcement officers in the US) Involves the repetition of two basic steps: Listing Sampling © 2018 Cengage Learning. All Rights Reserved. 18  National Crime Victimization Survey Seeks to represent the nationwide population of persons 12+ living in households (≈ 42K units, 74K occupants in 2004) First defined are primary sampling units (PSUs) Largest are automatically included, smaller ones are stratified by size, population density, reported crimes, and other variables into about 150 strata Census enumeration districts are selected (CED) Clusters of four housing units from each CED are selected © 2018 Cengage Learning. All Rights Reserved. 19  British Crime Survey First stage: 289 Parliamentary constituencies, stratified by geographic area and population density Two sample points were selected, which were divided into four segments with equal #’s of delivery addresses One of these four segments was selected at random, then disproportionate sampling was conducted to obtain a greater number of inner-city respondents Household residents aged 16+ were listed, and one was randomly selected by interviewers (n=37,213 in 2004) © 2018 Cengage Learning. All Rights Reserved. 20  Discussion Question 2 What if you could administer a project such as the NCVS or the British Crime Survey? Which would you choose? © 2018 Cengage Learning. All Rights Reserved. 21  Nonprobability Sampling There are situations when it is impossible to select a probability sample Nonprobability sampling can be used Nonprobability sample is sampling in which the probability that an element will be included in the sample is not known Cannot generalize to larger population © 2018 Cengage Learning. All Rights Reserved. 22  Nonprobability Sampling, cont. Purposive sampling: Selecting a sample on the basis of your judgment and the purpose of the study Quota sampling: Units are selected so that total sample has the same distribution of characteristics as are assumed to exist in the population being studied Reliance on available subjects Snowball sampling: You interview some individuals, and then ask them to identify others who will participate in the study, who ask others, etc., etc. © 2018 Cengage Learning. All Rights Reserved. 23  Discussion Question 3 What if someone asked you to explain the strengths and weaknesses of snowball sampling? How would you respond? © 2018 Cengage Learning. All Rights Reserved. 24