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Lecture

COMM2001 October 21, 2013.docx
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Department
Communication Studies
Course
COMM 2002
Professor
Heather Pyman
Semester
Fall

Description
Univariate data analysis • Surveys and the construction of different types of questions • How to describe different distributions and that data that we are looking at o Now we are moving onto data analysis Getting Started • Think about data analysis at an early stage in the research process • Decisions about methods and sample size affect the kinds of analysis you can do • Level of measurement critical to analysis • Data analysis that is possible that is important to the research analysis • For us, what we need to take away from this is that all the data that we are looking at, each question is measured at a different type of measurement • Run on a different type of measurement that someone else collects for us • Variables can be constructed to varying levels of complexity Levels of measurement • Nominal o A set of distinct categories • Ordinal o Response categories with order to them • Interval o Reponses categories have order plus equal measurable distance between categories • Set of names with no numerical value to them • When we think about political parties for example, we will have a set of distinct categories • We have to assign a numerical value to each of these labels • SPSS does not read words, only looks at values that are assigned to those categories • Important thing to note that SPSS does not know whether there is a real value to the value of one, two, etc. • Could just as easily start numbering with no difference to information that SPSS is looking at -> Category and value has no meaning • Basis of what can and cannot be done that are measured at the nominal number • Nominal = name • If we have a variable that is just a list of things -> political parties, environmental groups, baseball names, etc. just a numerical value • Move from nominal measure to ordinal measures -> go to measurement of scientists • Order of categories from low to high or low to high, the points of the scale -> each point sounds like some more or some less • Think of agreements -> strongly agree, neither, disagree, etc. • Can also have options such as always, sometimes, often, never • Or not important, somewhat important, very important • Assign values to everything, still 1, 2, 3, 4 (not applicable) • No way to quantify the agreements, etc. They are not units, have to know when we have real meaning and when you don't • All we know with ordinal measures is whether the scale is low to high or high to low, cannot quantify it o There is order to it, but no real mathematical meaning o Final measure is called interval measure • Real mathematical meaning to the points on the stage • Look at people's ages, or at a thermometer, or a ruler, we look at measures • With this data, it is difficult to look at it, so we do collapsible data, put interval level into different variables with order to it -> ex. 18-19-20-21-22 etc. but can put into 18-24,25-34, 35-44 • Important to note that at the interval level with real meaning in the scale is that it is different Examples: • What is the temperature in this room? _______________Co • On a scale of hot to cold, how would you describe the temperature in this room? Is it… Hot Warm Cool Cold • Which of the following describes the temperature in the room? Stuffy Chilly Hot Pleasant • We know that scales go from high to low -> can have values of different numbers • When SPSS is doing the analysis, it assumes that you know what the data is • Different correlation coefficients, we know if the scales are from high to low or low to high Why do we care? • Advantages of Higher Level measurement o Precision o Ability to use better statistical measures • Disadvantages o Difficult to look at distributions o Asking questions about education or support • Ex. People who are more educated are more likely to support Harper • We would have the options • Look at how variabil
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