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Sociology (1,464)
SOC221H5 (75)
Chapter 7

Chapter 7: Qualitative and Quantitative Sampling.docx

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School
University of Toronto Mississauga
Department
Sociology
Course
SOC221H5
Professor
Jayne Baker
Semester
Winter

Description
Chapter 7: Qualitative and Quantitative Sampling Introduction  Quantitative researchers more concerned with sampling; primary goal to get a representative sample (smaller set of cases a researcher selects from large pool and generalizes to population) and tend to use sampling based on theories of probability (called probability sampling)  Using probability/random sampling has two motivations: 1. Save time and cost and 2. Accuracy  Census: an attempt to count everyone in a target population (takes place in Canada every 5 years)  Qualitative researchers focus on how the sample or small collection of cases illuminates key features of social life; purpose of sampling is to collect cases, events or actions that clarify and deepen understanding o Focus on finding cases that will enhance what the researchers learn about processes of social life in specific context and use nonprobability sampling Nonprobability Sampling  Non-random sample: type of sample in which the sampling elements are selected using something other than a mathematically random process  Rarely determine sample size in advance and have limited knowledge about large group/population from which sample is taken  Select cases gradually with specific context of case determining whether it is chosen Types of Nonprobability samples: Haphazard Get any cases in any manner that is convenient Quota Get a pre-set number of cases in each of several predetermined categories that will reflect diversity of population, using haphazard methods Purposive Get all possible cases that fit particular criteria, using various methods Snowball Get cases using referrals from one or few cases, and then referrals from those cases, and so on Sequential Get cases until there is no additional information/new characteristics (*often used with other sampling methods) Haphazard, Accidental, or Convenience Sampling  Haphazard sampling: a type of non-random sample in which the researcher selects anyone he happens to come across  Can produce ineffective, unrepresentative samples and not recommended  Cheap and quick but many systematic errors  I.e. person on the street interviews seen on TV Quota Sampling  Defn: type of non-random sample in which the researcher first identifies general categories into which cases or people will be selected, then he selects predetermined number of cases in each category  Researcher can ensure that some differences are in the sample (i.e. age)  Researchers use haphazard sampling once the quota samples fixes the categories and number of cases in each category Purposive Sampling  Defn: researcher uses wide range of methods to locate all possible cases of a highly specific and difficult-to-reach population  Used in situations in which expert uses judgment in selecting cases with specific purpose in mind  Researcher never knows whether the cases selected represent the population  Appropriate in three situations: 1. Researcher uses it to select unique cases that are especially informative 2. To select members of difficult-to-reach, specialized population; i.e. researcher wants to study prostitutes so he finds different ways to find as many to include in his study as possible (places where they solicit, social groups they interact with or police who work with prostitutes) 3. When a researcher wants to identify particular types of cases for in-depth investigation; purpose less to generalize to larger population than to gain deeper understanding of types  Deviant case sampling: type of non-random sample, especially used by qualitative researchers, in which a researcher selects unusual or nonconforming cases purposely as a way to provide greater insight into social processes or a setting o Seek cases that differ from dominant pattern or that differ from predominant characteristics of other cases o Goal is to locate collection of unusual, different, or peculiar cases that are not representative of the whole o I.e.. Researcher studying high school dropouts Snowball Sampling  Defn: type of non-random sample in which the researcher begins with one case, then, based on information about interrelationships form that case, identifies other cases, and then repeats the process again and again  Also called network, chain referral or reputational sampling  Method of identifying and sampling the cases in a network  Social researchers often interested in interconnected network of people or organizations  Crucial feature is that each person or unit connected with another through direct/indirect linkage  Sociogram: diagram or map that shows the network of social relationships, influence patterns or communication paths among group of people or units  Also use snowball sampling in combination with purposive sampling as in case of Albanese (2006) in qualitative study of women in Quebec whose children were in provincial childcare Sequential Sampling  Defn: type of non-random sample in which a researcher tries to find as many relevant cases as possible, until time, financial resources, or his energy are exhausted, and there is no new information or diversity from the cases  Information is gathered until marginal utility, or incremental benefit for additional cases, levels off or drops significantly  Theoretical sampling: an iterative sampling technique associated with the grounded theory approach in which the sample size is determined when the data reach theoretical saturation; continue to collect data until no new information emerges  Theoretical saturation: a term associated with grounded theory approach that refers to the point at which no new themes emerge from the data and sampling is considered complete Probability Sampling Populations, Elements, and Sampling Frames  Researcher draws sample from larger pool of cases, or elements  Sampling element: name for a case or single unit to be selected; unit of analysis in population o Can be a person, group or organization  Large pool is the population (name for large general group of many cases from which researcher draws sample and which is usually stated in theoretical terms); can also be called universe  Target population: name for large general group of many cases from which a sample is drawn and which is specified in very concrete terms; specific pool of cases that he wants to study  Sampling ratio: number of cases in the sample divided by the number of cases in the population or the sampling frame, or the proportion of the population in the sample; ratio of the size of the sample to the size of the target population  Population is an abstract concept, cant be frozen at any time to measure it accurately o Therefore, the researcher needs to estimate the population; researcher operationalizes a population by developing specific list that closely approximates all the elements in population o Sampling frame: list of cases in a population, or the best approximation of it (i.e. telephone directories, tax records) o Good sampling frame crucial to good sampling  Population parameter: characteristic of the entire population that is estimated from a sample; determined when all elements in population are measured o Never known with absolute accuracy for large populations o Statistic: numerical estimate of population parameter computer from a sample Why Random?  Probability relies on random processes  Random: refers to process that generates mathematically random result; selection process operates in truly random method and researcher can calculate probability of outcomes o Each element has equal probability of being selected  Sampling error: how much a sample deviates from being representative of the population; deviation between sample results and a population parameter due to random processes  Margin of error: estimate about the a
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