Download presentation

Published byElle Tough Modified over 3 years ago

1
**Association Between Two Variables Measured at the Nominal Level**

Chapter 13 Association Between Two Variables Measured at the Nominal Level

2
**Nominal Level Measures of Association**

It is always useful to compute column percentages for bivariate tables. But, it is also useful to have a summary measure – a single number – to indicate the strength of the relationship. That’s what we’ll learn about in this chapter.

3
**Nominal Level Measures of Association**

For nominal level variables, there are two commonly used measures of association: Phi or Cramer’s V Lambda

4
Nominal Measures: Phi Phi is used for 2x2 tables. The formula for Phi:

5
**Nominal Measures: Cramer’s V**

Cramer’s V is used for tables larger than 2x2. Formula for Cramer’s V:

6
**SPSS: Phi and Kramer’s V**

SPSS has both instructions combined into one You need to know which one applies Phi if it’s a 2 x 2 table Kramer’s V for any other cross tabulation

7
**Strength of Phi or Kramer’s V**

Value Strength Between 0.0 and 0.10 Weak Between 0.10 and 0.30 Moderate Greater than 0.30 Strong

8
**Let’s ask SPSS to calculate a few chi square based measures**

Class and happiness Ager3 and grass Ager3 and attend Attend and grass Attend and happy

9
**Nominal Measures: Lambda**

Like Phi and Kramer’s V, Lambda is used to measure the strength of the relationship between nominal variables in bivariate tables. Unlike Phi, Lambda is a PRE(proportional reduction of error) measure and its value has a more direct interpretation. While Phi is only an index of strength, the value of Lambda tells us the improvement in predicting Y while taking X into account.

10
**Nominal Measures: Lambda**

Formula for Lambda:

11
Lambda as PRE measure E1 = errors made in predicting the dependent variable without knowing the independent variable = N – largest row total E2 = For each column, subtract the largest cell frequency from the col. total and add those values This will become more clear when we look at an example

12
**Association and Bivariate Tables**

To compute λ, we must first find E1 and E2: E1 = N – largest row total = 44 – 22 = 22 E2 = For each column, subtract the largest cell frequency from the col. total = (27 – 17) + (17 – 12) = = 15 Lambda = (E1-E2)/E1 = (22-15)/22 = 7/22 = .32 Low Author. High Author. Totals Low Efficiency 10 12 22 High Efficiency 17 5 27 44

13
**Nominal Measures: Lambda**

Lambda is a PRE measure. A Lambda of .32 means that knowing authoritarianism (X) increases our ability to predict efficiency (Y) by 32%.

14
**The Limitations of Lambda**

Lambda gives an indication of the strength of the relationship only. It does not give information about pattern. To analyze the pattern of the relationship, use the column %s in the bivariate table. When the mode is the same in each column of the independent variable, lambda will be zero even if a relationship exists. Thus we request both lambda and Kramer’s V/Phi.

15
**Calculate lambda for this example.**

Low Author. High Author. Totals Low Efficiency 2 (7%) 8 (47%) 10(23%) High Efficiency 25 (93%) 9 (53%) 34(77%) 27(100%) 17(100%) 44(100%)

Similar presentations

OK

Chapter 14 Association Between Variables Measured at the Ordinal Level.

Chapter 14 Association Between Variables Measured at the Ordinal Level.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To ensure the functioning of the site, we use **cookies**. We share information about your activities on the site with our partners and Google partners: social networks and companies engaged in advertising and web analytics. For more information, see the Privacy Policy and Google Privacy & Terms.
Your consent to our cookies if you continue to use this website.

Ads by Google

Ppt on biological pest control Ppt on cartesian product wiki Ppt on negotiation skills Ppt on power diode rectifier Ppt on mohandas karamchand gandhi Ppt on the art of war online Ppt on leverages on Ppt on mind reading computer download Git anatomy and physiology ppt on cells Ppt on depth first search algorithms