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Lecture 1

SOC350H5 Lecture 1: Lecture 1

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University of Toronto Mississauga
David Pettinicchio

Lecture 1  Pick any research question – answer the question with data we have  CGSS data  SOC222 with objective of final project – think about what research project you want to do  Labs – help think about what you’re interested in, expose you to data and do stuff with data (step by step)  Extend knowledge of soc222 in a different/application way  Address problems with data – learn  How to do your own project  Data in analytical way  Data for lab and data for project different  3 data set for project under lab  Topics in tutorial follow lecture Exploring Quantitative Research in Sociology What is Quantitative Data  Eg of what is the kind of idea behind what we will be doing  Do same logic as published research and what they do with regression  In quant method – use stat data  Answer a research question  Important differences  We are interested in measuring data – operationalized  Study crime – what do you mean by that? What kind? What does it mean?  Various ways you can measure it  Think about crime and equality – variables that can measure that  The real world is not same as textbook – messy  Settle for measures that can best measure what you want to do – not everything is that neat in real world  Real world of doing quant analysis  Generally qualitative people do their own research and generate their own info  Don’t rely on producer of data – question them Why do we want to quantify?  Make inferences to broad array of population – qual cant do that  We can use inferential stats to make predictions  Describe data  Not suppose to be data mining  Question shouldn’t be driven by nature of data – why are you interested in this  Why does x lead to y  Not because its in data – interesting because its things people think about  Correlation – doesn’t mean anything  Have to have a theoretical reason for your question  How do I match my research question to what I have available Research question  Are you seeking to make general claims, uncover broad patterns, satisfy evidence  Should be driven by what you are trying to accomplish  Population and crime  Show graphically relationships Refresher on regression  Bivariate regression  Y= a + bx 1  B – slope/ regression coefficient  A – y intercept  X – independent  Y – dependent  ^ on top of y = predicted value of y  This model allows us to plug in value of x that is not on data to predict for potential y  We always regress for dependent variable (outcome) – not the independent  You can also figure out if they are statistically sig – generalizable  Distance between case and prediction line (data point and line) – Error  Least sq reg reduces total amount on error (distance between case and line)  Lambda and gamma – based on least sq  Regression – prediction model  Say a lot about big questions with relatively small samples in comparisons (large patterns) Life expectancy and wealth question  Graph shows line of fit – line for ordinarily sq reg  R squared – measure of the strength of relationship  Our independent variable is explaining 40% of change  That single variable (wealth) explains 40 percent of variation in life expectancy – good
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