Comm106 Final Study Guide
Comm106 Final Study Guide Comm106
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This 3 page Study Guide was uploaded by Erica Evans on Monday March 14, 2016. The Study Guide belongs to Comm106 at Stanford University taught by Jennifer Pan in Fall 2016. Since its upload, it has received 88 views. For similar materials see Communication Research Methods in Communication Studies at Stanford University.
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Date Created: 03/14/16
Comm106 Study Guide Final Terms and Concepts to know Unitnonresponse When some people don t want to participate in a survey or experiment Item nonresponse When someone s taking your survey but they don t want to answer particular questions Misreporting When someone puts down a false answer because they feel some kind of pressure Social desirability bias When someone put down the answer to a question that they think is most socially acceptable rather than the truth Like saying they voted or will vote because they want to seem like a good citizen Observer effect people act differently when they know they are being observed People try to be their best selves Or people being surveyed pick up on cues about what the experimenter is looking for Hypothesis a testable statement about the empirical relationship between an independent variable and a dependent variable Elements of a good hypothesis 1 Comparing states a relationship between two variables dependent and independent 2 Specific 3 Direction makes an assertion about what will change if we adjust one value Cross Tabulation shows the distribution across the values of the dependent variable the difference between the independent variables Used for Nominal dependent variable Ordinal dependent variable Comparison of Means Use when there would be too many values if we did cross tabulation For example interval dependent variable Positive Relationship both variables increase together Negative Relationship As the value of one variable increases the other decreases Linear Relationship When the points roughly make a straight line Null Hypothesis the skeptical assumption that your hypothesis is false The Alternative hypothesis says that there is in fact a relationship between what we are observing Type 1 error When the null is correct and we reject it Type 2 error When the null should be rejected and we do not reject it Pvalue Tells us what is statistically significant Statistical Significance The traditional threshold is 5 Given that the null hypothesis is true we will reject it with a probability of 5 How to analyze a graph Pattern What is the shape Curvilinear Linear No pattern Direction negative or positive Strength how close are the points to each other Does it look consistent 0 Are there exceptions Correlation Coefficient how closely does the data follow a straightline trend and how closely does the data cluster to that line Denoted by r value from 1 to 1 Correlation formula X 1241 average 2 sd 141 Y 1343 average 3 sd163 Zscore numberaveragesd Multiply the corresponding 2 scores to get 4 numbers then add them all together divide by n1 which is 41 in this case R 87O17403 87 Linear Regression Drawing a linear line to best fit the data so we can estimate how much y changes for every x on average Residual the difference between the actual value and the predicted value OLS estimation ordinary least squares estimation 9 a technique used to draw the line of regression where you minimize the total squared vertical distances from the actual data points in the sample Qualitative Research Used when testing a theory requires studying tracing causal processes in depth rather than number crunching May be necessary if the population size is just really small Comparative CrossCase Study When you compare Y and X in a small number of cases This is close to regression analysis in spirit but is not numerical Process Tracing the causal mechanism is the set of intervening steps thought to take us from X to Y You can analyze the case in detail to see if these intervening steps are actually there Most Similar Design Find at least two cases that are different on the outcome different Y values but cases that are similar on most X s Conduct a case study and see what X s are different You can hypothesize that that factor might be causing an effect Most Different Design Test the relationship between 1 X and 1 Y Find two cases that are similar on outcome Y and differ on many different X s except one X which is the same Hypothesis Testing There has to be a difference on only one X and on Y You already know what X you have in mind that will explain the difference when you are hypothesis testing Exploration When it all seems the same but there is a difference in Y you explore various factors to see what might be causing the change
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