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University of Guelph
Sociology and Anthropology
SOAN 2120
David Walters

Sampling - qualitative research uses nonrandom samples or nonprobability samples o this means they rarely determine the sample size in advance and have limited knowledge about the larger group from which the sample is taken Types of Nonprobability Sampling - Haphazard o Get any cases in the manner that is convenient o Cons: can produce ineffective, unrepresentative samples o Eg: television interviewers on the street – they conveniently go to whoever is around and interview them - Quota Sampling o Get a preset number of cases in each of several predetermined categories that will reflect the diversity of the population o Researcher identifies relevant categories of people (i.e. males or females), then decides how many to get in each category o Improvement over haphazard because it ensures some differences - Purposive o Get all possible cases that fit particular criteria using various methods o Expert uses judgment in selecting cases with a specific purpose in mind o Appropriate in three situations  Select unique cases that are informative  Select members of a difficult to reach population  When research wants to identify certain types of cases for in- depth investigation - Snowball o Get cases using referrals from one or a few cases, and then referrals from those cases, and so forth. o Method for identifying and sampling the cases in a network o It essentially “snowballs” - Deviant Case o Get cases that substantially differ from the dominant pattern (special type of purposive) - Sequential o Get cases until there is no additional information or new characteristics o A researcher gathers cases until the amount of new information or diversity of cases is filled Types of Probability Sampling - Simple Random o Researcher develops an accurate sampling frame, selects elements from the sampling frame according to the mathematically random procedure, then locates the exact element that was selected for the inclusion in the sample o Researcher uses a list of random numbers to decide which elements to select o Example on page 117 (red and white marbles) o Sampling distribution = the set of many random samples o Central limit theorem = tells us that as the number of different random samples in a sampling distribution increases toward infinity, the pattern of samples and the population parameter become more predictable - Systematic Sampling o Simple random sampling but with a shortcut for random selection o First step is to number each element in the sampling frame o Researcher calculates a sampling interval, and the interval becomes his or her quasi-random selection method - Stratified Sampling o Researcher first divides the population into sub-populations (strata) on the basis of supplementary information o Then the researcher draws a random sample from each subpopulation o In this type of sampling, the researcher controls the relative size of each stratum o It produces samples that are more representative of the population than simple random sampling if the stratum information is accurate - Cluster Sampling o Addresses two problems:  Researchers lack a good sampling frame for a dispersed population  The cost to reach a sampled element is very high o A cluster is a unit that contains final sampling elements itself o Researcher first samples clusters then draws a second sample from within the clusters selected in the first stage of sampling o Advantage? Create a good sampling frame o Researcher draws several samples in stages  Stage 1 is random sampling of big clusters  Stage 2 is random sampling of small clusters within the big bluster  Stage 3 is sampling of elements from the small clusters o Less expensive but also less accurate Survey Research Steps in the Process of Survey Research Step 1: - develop hypotheses - decide on type of survey - write survey questions - design layout Step 2: - plan how to record data Step 3: - decide on target population - get sampling frame - decide on sample size - select sample Step 4: - locate respondents - conduct interviews - record data Step 5: - enter data into computers - recheck all data - perform statistical analysis on data Step 6: - describe methods and findings in report - present findings Twelve Things to Avoid When Writing Survey Questions! 1. jargon, slang, and abbreviations 2. ambiguity, confusion, and vagueness 3. emotional language 4. prestige bias 5. double-barreled questions 6. do not confuse beliefs with reality 7. leading questions 8. asking questions that are beyond respondents’ capabilities 9. false premises 10. asking about intentions in the distant future 11. double negatives 12. overlapping or unbalanced response categories Types of Questions and Response Categories Threatening Questions - questions about sensitive issues - must be asked with great care - part of a larger issue of self-presentation and ego protection - from uneasy to easy… o masturbation o sex o drugs/being drunk o income o happiness o education Socially Desirable Questions - occurs when respondents destroy answers to make their reports conform to social norms Knowledge Questions - can be threatening because respondents do not want to appear ignorant - many people have inaccurate factual knowledge Skip or Contingency Questions - avoid asking questions that are irrelevant for a respondent - two-or more part question = contingency question Open versus Closed Questions - open-ended = any response, unstructured - closed-ended = gives the respondent fixed responses, structured Pros Cons Open-ended - unlimited answers - may give different - detailed answers degrees of detail in some - reveal respondent’s but not others logic/thinking - comparisons and analysis become difficult - coding becomes difficult Closed- - easier and quicker - respondents with no ended - easy to compare opinion can answer - easier to code and analyze - misinterpretation - respondent may get frustrated if their desired answer is not available Nonattitudes and the Middle Positions - “not sure”, “don’t know”, “no opinion” - issue of nonattitudes can be approached by
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