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SOSC 3993 (5)
Lecture 6

# SOSC 3993 Lecture 6 Sampling.docx

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School
York University
Department
Social Science
Course
SOSC 3993
Professor
Tracy Supruniuk
Semester
Summer

Description
SOSC 3993 May 30, 2013 Lecture 6- Sampling  Why would researchers take a sample? o Because its hard to take an entire population o Less time consuming o Cheaper and convenient o Selecting a sample for a quantitative sample  Why would you select a sample? o 2 types of samples 1. Random sampling - able to generalize  Probability sampling 2. Convenience sampling- less generalizable  Non- probability sampling  Non – probability sampling o Causes bias in sampling  Ex: an interview in your family won’t be able to generalize to all of Torontonians. o Volunteer people, some people volunteer for specific things though. o Not a waste of time, the information is important either way  Ex: study people at York with anxiety not generalizable but useful o Interested in getting an in depth information o Elements  samples; units of analysis o Population-  entire set of study elements ( what you want to study )  Ex: sample population of Torontonians o Study population ( what you can study)  Ex: study people who commit white collar crimes, everyone` in Canada gives info, except Alberta. o Finer distinction o Describe where you get your info o Sampling frame  List of all the possible elements from which your sample can be taken.  Applies more to random sampling o Population parameters  Ex: people in Toronto, but you have no time to study all of them  You take sample of Toronto, which generally represents the entire population of Toronto  Characteristics of your population  Ex: 1500 torontonians to see if who would they vote for, and hope that they would represent what the general population would vote for.  It’s a fact about the population  Ex: sample and find out what the majority of the age groups are. o Sampling error  Degree of error given to a sample in a specific time  Reflects the difference of what you fin din your sample and the population parameters.  More random, smaller sampling error  Ex: 50 % women 50 % men, random sampling possible you can get all men but highly unlikely
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