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Lecture 7

SOC350H5 Lecture 7: Lecture 7

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University of Toronto Mississauga
David Pettinicchio

Lecture 7  Final project – asks about intermediating relationship  Dummy variable – categorical variable coded as 0/1  How to code those with multiple variables Recall level of measurement  Gender 0 0/1  Categorical data that is not dichotomous  How to interpret them substantively  OLS have dep variable that is interval  When it comes to categorical data as indep – can do a lot  Categorical variable that have category of 0 as reference – dummy  Interpret attributes of variable  0- reference (comparing to 0) Categorical variable  Race/relig – not quantitative but can include them in quant analysis  How do you interpret effects of those with multiple categories – not just comparing male to female  Understand that you are recoding variable into 0/1 category and omitting referent category Dummy variable  Attributes of 0 and 1  Interpret the category that is 1 in reference to what is called 0  There is no rule that this is 0 and this is 1 (do you wanna say males compared to females or females compared to male) – different  Do you want to make white referent category that you compare other races to  Comparison – want to be intuitive – how do you want to interpret matters  Recode variable so you make referent variable 0  By default the software recognizes 0 as referent  Its important to be consistent but when you deal with multiple categories – its important to be consistent  If you return to data, you wont remember otherwise Basic – eg Gender  Male 0/female 1  Comparing female to male  When you interpret the findings you have to interpret it based on referent category  Output from SPSS  Gender/fathers and mothers occupational prestige score  The way you interpret it:  Compared to being male, being female increases respondent’s occupational prestige score score by .606 Multiple categories  A lot of variables are not just 0 and 1  Variable has multiple attributes  Look at race  Being black or other impacts occupational prestige in comparison to white  Pick and recode variable that you want referent – make 0  Recode the rest as different variable  SPSS knows when you make everything 0 – it knows that referent category was white  Everything has to be in the model at same time though  When you recode race – all categories have to be in model at the same time  No longer have “race” in model  Referent is white – in comparison to white, being black decreases occupational prestige score by 4.168 The nature of relationship  The idea of what relationship look like between variables is a theoretical question  SPSS doesn’t tell you  You have to know that  Do I ha
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