Review: What’s a T Test for?

Comparing the means of the two groups Research Questions:

• Is group A’s mean different from group B’s mean? (2 tailed)

• Is group A’s mean greater than group B’s mean? (1 tailed)

Example: Are people willing to pay more for GREEBN vs. YELLOW chocolate?

Group A Group B

GREEN chocolate YELLOW chocolate

Willing to pay $3.2 Willing to pay $2.9

Review: What are inferential statistics?

T test is an inferential statistic We will discuss two more: chi square

and regression What are inferential statistics?

Choosing the right test for your research

Research Question Inferential Statistics

Compare means of 2 numeric variables

T test

Relate 2 numeric variables Pearson Correlation r

Relate 2 categorical variables Pearson Chi Square

Use 1+ IVs to explain 1 numeric DV

Regression

Chi Square & RegressionMBA724 Research

Learning Objectives Understand Pearson Chi Square

• Definition/Purpose• Mathematical concepts• Assumptions• Reporting chi square results

Understand regression• Definition/Purpose• Mathematical concepts• Assumptions• Assessing model fit• Reading SPSS outputs• Reporting regression results

Pearson Chi Square Purpose – See if there’s a relationship

between 2 categorical variables Example of categorical variables:

Giant Eagle store – Market District? (yes/no) Has child play area (yes/no) Gender (male/female) Commit fraud (yes/no)

Example Research Questions:• Are Giant Eagle’s Market District stores more likely

than other GE stores to have a child play area?• Are men more likely than women to commit fraud?

Basic Ideas

EXPECTED VALUES FOR NULL HYPOTHESIS: NO DIFFERENCE BETWEEN MEN/WOMEN

DATA YOU HAVE COLLECTED

Fraud No Fraud

Men (20) 2 (20%) 10

Women (30)

3 (20%) 15

Fraud No Fraud

Men 19 (95%) 1

Women 9 (30%) 21

Question: Do your data differ significantly from what’s expected for “no difference between men/women?”

Are men more likely than women to commit fraud?

Reading the Chi Square results

Count is the actual data/observations

Expected Count is the theoretical expected

values (table on left on last slide)

20.576 is the chi square value

1 is the degree of freedom

The test is significant (p

< .001)

Reporting Chi Square Result

There was a significant association between gender and fraud commitment X2(1, N=50)=20.576, p <.001. Based on the contingency table, men appear to have a greater likelihood of committing fraud than women.

Chi Square Assumptions Independence – Each case

contributes to only one of the cells in the contingency table

Each cell should be expected to have a value of at least 5

Each variable is normally distributed

Summary What’s the purpose of Chi Square? What kind of research question is it

designed to answer?