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Chapter 7

221 Chapter7 ( very detailed )

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Department
Sociology
Course
SOC221H5
Professor
Lingqin Feng
Semester
Fall

Description
Chapter7: Qualitative and Quantitative sampling Introduction - Quantitative style researchers’ primary goal is to get a representative sample or a small collection of units or cases from a much larger collection or population  They send to use sampling based on theories of probability from mathematics( called probability sampling)  2 motives for using probability or random sampling : Saving time and cost , accuracy  Census: Attempt to count everyone in the target population - Qualitative researchers focus on how the sample or small collection of cases, unites or activities illuminates  Sampling by collect cases, events or actions that clarify and deepen understanding  Concern to find cases that will enhance what the researchers learn about the process of social life in a specific context  Second type of sample: non probability sampling Non-Probability Sampling - Used by qualitative researchers - Rarely determine the sample size in advance and have limited knowledge about the larger group or population from which the sample is taken - Selects cases gradually with special content of a case determining whether it is chosen - Some techniques are….  Haphazard, Accidental or convenience sampling: researcher collects anyone that they happen to come across. Is not recommended. Can get a sample that misrepresents the population. They are cheap and quick but the systematic errors that easily occur make them worse than no sample at all. Ex... Person on the street interviews.  Quota sampling: the researcher identifies relevant categories of people ( male  Or female, above 30 or under etc.). Then decide how many in each category and thus the number of people in various categories of the sample is fixed. Researcher can ensure that some difference are in the samples , getting a more better representation of the population  Purposive sampling: expert uses judgment in selecting cases with specific purpose in mind. Researcher never knows whether the cases selected represent the population- used in exploratory research or in field research. Appropriate in 3 situations… 1) Uses it to select unique cases that are especially informative 2) To select member of a difficult to reach, socialized population. 3) Researcher wants to identify particular type of cases for in depth investigation. Less to generalize to a larger population than it is to gain a deeper understanding of types. o A special case of purposive sampling is known as deviant case sampling in which researcher seeks cases that differ from the dominant pattern or that differ from the predominant characteristics of other cases. Goal is to locate collection of unusual cases that are not representative of the whole. Deviant cases selected inn hopes of learning about the social life by considering cases that fall inside the general pattern  Snowball sampling: aka network, chain referral or reputational sampling. Is method for identifying and sampling selecting cases in a network. Based on an analogy to a snowball. Begins small but then becomes larger as it rolled on wet snow and pick up additional snow. Similarly this begins with few people or cases and spreads out on the basis of links to the initial cases. Used to sample a network. Represent network by a sociogram (a diagram if circles connected with lines. ( shows direct and indirect connections between the two)( . The circles represent each person or case and the line represent friendship or other linkages. Also use it in combination with purposive sampling  Sequential sampling: researcher continues together cases until the amount of new information of diversity of cases is filled. Requires researcher to continually evaluate all the collected cases. Related to concept of theoretical sampling which is tied to grounded theory. Researchers who use grounded theory employ theoretical sampling which means they continue to collect data until no new information emerges which is referred to as theoretical saturation Probability Sampling -Population, elements, and sampling frames  Element: unit of analysis or case in a population- can be person, group, etc…  Population: the large pool is the population which is important in sampling. Refers to the unit being samples, locating and boundaries of population.  Target population: the specific poo; of cases that they want to study.  Sampling ratio: the ration of the size of the sample of the sample to the size of the target population is the ration. Ex… Population is 50 000 people and a sample of 150 is drawn. Thus sampling ration is 150/50 000+0.003 or 0.3%.  Population is an abstract concept- cannot truly freeze the population to measure it. It’s always changing thus researcher needs to estimate the population. Must be an operational definition. Does this by developing a specific list that closely approximates all the elements in the population. This list is a sampling frame.  Sampling frame: A list of cases in a population or the best approximation of it. Ex… Want to see population of Canada thus looks at everyone’s driving license (sampling frame). But it may include some of those out the target population and omit those inside of it thus its almost always inaccurate.  Any characteristic of a population (ex… % of city residents who smoke, height of women over 21) is a population parameter. The true characteristic of the population. Determined when all elements in a population are measured. Never absolute for large populations, must estimate on basis of samples by using information from the sample called statistics to estimate the population parameter. Why random? - Probability theory relies on random process - Random refers to a process that generates a mathematically random result. The selection process operates in random method and researcher can calculate the probability of outcomes. - Each elements have an equal chance of being selected - Researchers must identify specific elements to include in the sample - Most likely to yield results that truly represents the population. - Can measure sampling error: How much a sample deviates from being representative of the population. Related to idea called margin of error. - Margin of error: estimates of amount of error that exist in a surveys results - Random sample: Based on sophisticated mathematics. Uses mathematical random process so that each sampling element in the population has an equal chance of being selected. Types of probability samples -Simple random:  Researcher develops accurate sampling frame, selects elements from the sampling frame according to a mathematically random procedure then locates the exact element that was selected for inclusion in the sample.  After numbering all elements in a sampling frame, a researcher uses a list of random numbers to
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