Last bits of inferential statistics. Check in Proposal DRAFT due Tuesday Research in media assignment Tuesday Quiz Tuesday the 29 th –Will cover statistics.

Last bits of inferential statistics

Check in

• Proposal DRAFT due Tuesday

• Research in media assignment Tuesday

• Quiz Tuesday the 29th

– Will cover statistics material– Multiple choice

Conducting statistical tests

• Testing our hypotheses numerically

• Do the data support the null distribution?

• Do the data support the alternate distribution?

• Tests tell us if the difference in distributions is significantly different– Not just look different, but different enough so

we are pretty sure its not an accident

Some basic tests

• Remember there are many different types of distributions

• The normal distribution and others

• Tests are often named for their distributions

Tests—first look

• Chi-Squared– The X2 distribution

• T-test– The students T distribution

• Rho– The correlation coefficient R– Assumes a normal distribution or T

distribution

How to choose which test?

• Many tests may answer the same type of question

• Which test you use depends on your questions and variables

• Are your variables continuous or categorical?

• May also matter if independent variable is one type, and dependent variable is another type

What type of variable do I have

• Independent variable– The predictor– E.g. If I believe that gender predicts income,

then gender is the IV

• Dependent variable– The outcome– E.g. if I believe that anxiety predicts eating

habits, then eating habits is the DV

What type of variable do I have?

• Not all questions have an independent and dependent variable

• If I’m asking if two things are ASSOCIATED or CORRELATED there is no actual IV or DV

• A variable may be an IV in one question and a DV in another question, and vice versa

• It depends on my question

Continuous vs. categorical?

• Continuous– May actually be based on ordinal– When I have a large range of numerical

ratings– Height, weight– Symptoms, feelings

Continuous vs. categorical

• Categorical– May have levels– Has few categories– 1,2,3– Yes/no– Red/blue

Which test?

• If my question is– Are these two groups different?

• And my outcome is – Categorical

• I use a X2 test

Differences between 2 groups

• IV is 2 groups– E.g. men and women– Catholics and Protestants– People who have a peanut allergy and people

who don’t

• So I know that the IV is categorical

Differences between 2 groups

• If DV is also categorical

• Will also be a sort of “grouped” variable– Employed/unemployed– Married/not married– College degree/no college degree

Chi Squared Test

• If our predictor is categorical

• And our outcome is categorical

• Then we use a chi-squared test– A “2 x 2 table approach”

Example

• Does gender predict employment– Gender is categorical– Employment is categorical– This is a chi-squared test

• Tells us if there is a difference in the expected rate of occurrence, and the observed rate of occurrence

Example

• Assume gender is 50/50• If gender does NOT predict employment,

then we expect equal numbers of men and women to be employed

• If gender DOES predict employment we observe unequal numbers of men and women employed

• Chi square tests if observed and expected are significantly different

Which test?

• If my question is are these 2 groups different

• And my outcome is continuous

• I use a T-test

T-tests

• Also a test of difference between groups

• When the IV is categorical

• And the DV is continuous

• E.g. IV—men/women

• DV—height in inches

T-tests-examples

• IV—Dogs/Cats

• DV—weight in pounds and ounces

• IV—employed/unemployed

• DV—income in dollars

• IV—full time student/not full time

• DV—average hours of sleep per night

Which test?

• If my question is are these characteristics related or unrelated

• I am looking at the relation between two continuous variables

• I use a correlation test

Correlation

• Not a test of differences between groups

• A test of whether two continuous variables are related or unrelated

• The statistic is R

Examples

• Height and Weight

• Grade point average and hours of study

• Hours of sleep and scale measure of exhaustion