Comm106 Week 6 notes
Comm106 Week 6 notes Comm106
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This 3 page Bundle was uploaded by Erica Evans on Monday February 15, 2016. The Bundle belongs to Comm106 at Stanford University taught by Jennifer Pan in Fall 2016. Since its upload, it has received 36 views. For similar materials see Communication Research Methods in Communication Studies at Stanford University.
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Date Created: 02/15/16
Comm106 2/10/2016 Causal explanations: Why are women paid less than men? • There are many different causal explanations, they could have many different steps • Break them down into intervening variables that you can test one by one • You can’t just prove “sexism” • You can test: does time off lead to lack of promotions? • You can test: do people have notions of appropriate gender behavior • You can test: do women negotiate with less aggression than men • Let’s say childcare responsibilities are the reason women earn less than men: • Separate the units on the dependent variable, and look at effects on independent variable • A hypothesis is a testable statement about the empirical relationship between an independent variable and a dependent variable. • State a relationship between two variables: dependent and independent. “Comparing” • Be “Specific” • Make an assertion about what will change if we adjust one value, the “direction” • 1) Comparing, 2) Specific, 3) Direction • In a comparison of [units of analysis] those having [one value on the independent variable] will be more likely to have [one value on the dependent variable] than will those having [a different value on the independent variable] • In comparison of women those have more childcare responsibilities will be more likely to have higher salaries than will those having less childcare responsibilities. • Nominal, dependent variable: cross-‐tabulation • Ordinal, dependent variable cross-‐tabulation • Interval, dependent variable: comparison of means Men shave their face more than women • Indpendent variable = gender (nominal) • Dependent variable = frequency of shaving (ordinal) – never, rarely, sometimes, often, always Cross Tabulation: • Make a table to show the value for each combination of variables • Females that shave: never, rarely, sometimes, often, always • Males that shave: never, rarely, sometimes, often, always • We can then compare horizontally • The cross tab shows the distribution across the values of the dependent variable, the difference between the independent variables Comparison of Means: • Per capita milk consumption in Kg/capita • Countries are nominal, and the average per capita milk consumption is continuous • There would be too many values if we did cross tabulation, so we just compare the means Graphing: • Put the independent variable on the x axis and independent variable on the y axis • Positive relationship = both variables increase together • As value of one variable increases, the other decreases = negative relationship • Relationship is linear if the points make roughly a straight line Comm106 2/12/2016 How can we confirm a hypothesis? • The Null hypothesis is the skeptical assumption • We write the null hypothesis as H(sub)0 • The alternative hypothesis – H(sub)A • Says there is a relationship between what we are observing • Either we reject the null hypothesis or we fail to reject H0 • Be careful about your language! If you can reject the null, you cannot say you know the alternative hypothesis is true! • What if we make mistakes? • When the null is correct and we reject it, it is type one • When the null should be rejected and we do not reject it, this is type 2 • Type 1 error: • Traditional threshold is 5% -‐-‐ Given that the null hypothesis is true, we will reject it with a probability of 5%. • The P-‐value tells us what is statistically significant Statistical Significance: • When people say something is statistically significant, that might not be large • Women are .0002% more likely than men to watch Fox News • Or it might not be substantively important: like women have a better sense of smell than men • A difference is not statistically significant
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