• Now create a hypothesis linking your IV with each of your DVs. (2) List that in your submission.
• (3) Offer and list a brief theory sketch as to why you think your two variables should be
related as specified in your hypothesis. What would make you expect the hypothesis to be confirmed more often than not?
You will now begin your foray into data analysis with SPSS (4).
• Using the instructions from the Manual or ‘Trial Run Instructions’
o Open the ANES2016 data set (4a)
• Either by writing out syntax, or using poll-down menus (see Manual)
o WEIGHT the data set using PW2016_FULL (see manual, pp. 14-16) (4b)
—————————————————————————————————————— Weights are often used to adjust certain sampling problems that we find in our data. The ANES researchers use weights to make sure that the sample they draw looks like the data found in the broader CENSUS enumeration on the following: education level, income, gender (again, this is only treated as a binary here). For example, look at th
e ‘Trial Run’ results. ‘Women’ comprised 52% of the Census data enumeration, but (unweighted valid %) a slightly higher 53.1% in the ANES original sample. To match the ANES sample with the sample, women’s responses have to count a bit less, men’s a bit more. Perhaps a better example would be a series of surveys called the EUROBAROMETER. In order to get a large enough sample from all countries within the EU, the sample size from each country is set at about 1000. Now, that’s fine when we compare countries with each other, but what happens when we want to look at attitudes of everyone within the EU? The 1000 or so from, say, Denmark, winds up counting the same as the 1000 from, say, Germany, even though Germany’s population is about 16 times larger. Before one can look at the attitudes of the entire EU, the responses of Danes have to be weighted down, Germans up until the weighted sample sizes are proportionate to the actual population sizes.
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