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Lecture 2

SOC100H5 Lecture 2: SOC100 Lecture 2 (Research Methodology)
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Department
Sociology
Course
SOC100H5
Professor
Jayne Baker
Semester
Winter

Description
SOC100 Lecture 2 (Research Methodology) Research cycle 1) formulating a research question - grows out of our existing knowledge - an observation may lead you to it 2) looking at existing literature (lit review) - what do we know what do we not know how are we inserting ourselves into that dialogue of that discipline 3) selecting an appropriate method - field research - official statistics - questionnaires - surveys - needs to be APPROPRIATE 4) collecting data - e,g you’re doing focus group - you’re collecting data by the words given to you by the focus group participants - data can be quantitative or qualitative - quantitative: in the form of numbers - qualitative data: non numerical, given by words 5) analyzing data - make sense of the data you’ve collected 6) report results - report your results to professional groups - they have annual meetings where people come together to present their work to give and receive feedback - all sociologists aim to have their work published as a journal or article - peer-reviewed: people who are also in the field, deemed experts look at the article and access whether or not it is good enough to be printed into the journal • when you’re actually doing research, the cycle is not as linear as this Terminology • Variable: “ a concept that can take on more than one value.” - examples: age, educational level • independent variable: “the presumed cause in a case-and-effect relationship.” • Dependent variable: “the presumed effect in a cause-and-effect relationship.” • XY - e.g you’re looking at work place harassment policies (cause) - effect: the rates of work place harassment - cause: divorce  effect: deviant of crime in children • correlation: data that suggests that these two things are linked, there is a relationship - e.g facebook usage goes up, gpa go down they’re linked but you cant say usage caused the gpa to go down • causation: definitively X causes Y, and not that you have evidence that X and Y are related • population: “the entire group about which the researcher wants to generalize” • sample: “the part of the population of interest that is selected for analysis.” - Sampling frame: list of all the elements in a population - E.g all of those with a drivers license - Probability is mathematically random everyone in the sampling frame has an equal likelihood to be selected - Probability sampling: tends to be quantitative - Non-probability sampling: not mathematically random, tends to be qualitative - E.g. looking at Canadian universities math programs so as a researcher we have to be more selective about who ends up in my sample (non- probability sampling) Methods • Experiments - method most associated with the natural sciences - test xY via an experiment - control group - experimental group: has exposure to the independent variable - looking for impact of the variable - pro: looking for causation(best method to establish causation) - con: lab setting is very different from how people actual
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