Comm106, Week 1 nots
Comm106, Week 1 nots Comm106
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This 3 page Class Notes was uploaded by Erica Evans on Friday January 8, 2016. The Class Notes belongs to Comm106 at Stanford University taught by Jennifer Pan in Fall 2016. Since its upload, it has received 94 views. For similar materials see Communication Research Methods in Communication Studies at Stanford University.
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Date Created: 01/08/16
Comm106 Class 2 1/08/2015 Announcements: Bring laptops Wednesday and Friday next week so we can install R Make sure you are getting the Canvas notifications Review: Theory: A conjecture about the explanation of some phenomena of interest. Aka: an idea that explains a thing! Parts of a theory: 1. A thing that needs to be explained 2. The thing that does the explaining Requirements: -‐ Observable implications -‐ Falsifiable -‐ Practical way to test it -‐ As general as possible -‐ As parsimonious as possible Qualitative vs. Quantitative Data: Data: What we use to test out theories; we must collect and analyze data Quantitative: -‐ Numerical measures of things -‐ Involves statistical tests using variables -‐ Easy to replicate -‐ Cons: sometimes people want to do quantitative research projects, just for the sake of being quantitative, not because the subject is a particularly good thing to study -‐ Hard to test totally new theories, or things that have not been studied before Qualitative: -‐ Deep description, subtleties of meaning and interpretation -‐ Carried out through case studies, interviews -‐ Good when data is hard to get -‐ Cons: Hard to select which cases to study Both are good and can be used together! Sometimes you need the intuitive understanding that comes from qualitative research before you can understand massive amounts of data. Concepts and definitions: Concepts: ideas from the real world that we are theorizing about: -‐ Being conservative -‐Virtual reality -‐Attention span -‐Etc. Social scientists look at the relationship between concepts; find independent and dependent variable relationships Vague concepts: Ex: there are a lot of ways to define “conservative” So you must come up with a conceptual definition. Elements to a definition: -‐ How does a concept vary between subjects (Different groups, Different people, Different countries)? -‐ Who or what does the concept apply to (Group? Individual? Country? Company?) -‐ How do you measure that characteristic? Conceptual definition: Example: Conservatism is defined as the extent to which voters exhibit the characteristic of opposing abortion. Template: The concept of _________ is defined as the extent to which ________ exhibit the characteristic of _________. You must have definitions to do research. Analysis: What is the unit of analysis? You have to be careful about this. If you are trying to figure out if the majority of Stanford students like the meal plan, you can ask individuals, or you can ask each residence hall if the majority of their residents like the meal plan. But this is not the same thing! Measurement: Explicitly describe how the measure will take place: Ex: how do you measure happiness? The concept of happiness is defined as the extent to which individuals exhibit the characteristic of being satisfied with their lives. Operational definition (how you do the testing): Ask them to rate how satisfied they are with their life on a scale of 1 to 10. Measurement error: people may overestimate or underestimate their level of happiness… maybe based on their culture or personality. Systematic Measurement Error: (We really have to pay attention to this!) It means there is something wrong with your measurement. Ex: Americans always report that they are happy based on social expectations, even if they are not. If there is no systematic error, then it is called a valid measure. Random Measurement Error: (Assume that this exists most of the time) If there is no random error, then this is a reliable measure. Just because you have reliability, does not mean the measure is valid. Ex: The LSAT is reliable, but might not be a valid measure of someone’s ability to be a good lawyer. Assessing reliability: -‐ Measure now and measure again – test repeatedly -‐ Internal consistency: check that different parts of the measure agree with one another and give the same answers Assessing validity: (We are much more interested in seeing if a measure is Valid!) -‐ Face validity: something can mean different things to different people: like “sense of humor.” Variable Operational definition + reliability and validity requirements = variable Nominal level Separate subjects into categories But you cannot order categories: like hair color, gender, continent etc. Ordinal-‐level Here, you can order the levels, but they are not quantifiable: like always, sometimes, rarely, never. Interval-‐level Continuous measurement; lets you measure the exact difference: like time, distance.
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