Download presentation

Published byJayson Dunfield Modified over 4 years ago

1
**1. Nominal Measures of Association 2. Ordinal Measure s of Association**

2
**ASSOCIATION Association**

The strength of relationship between 2 variables Knowing how much variables are related may enable you to predict the value of 1 variable when you know the value of another As with test statistics, the proper measure of association depends on how variables are measured

3
**Significance vs. Association**

Association = strength of relationship Test statistics = how different findings are from null They do capture the strength of a relationship t = number of standard errors that separate means Chi-Square = how different our findings are from what is expected under null If null is no relationship, then higher Chi-square values indicate stronger relationships. HOWEVER --- test statistics are also influenced by other stuff (e.g., sample size)

4
**MEASURES OF ASSOCIATION FOR NOMINAL-LEVEL VARIABLES**

“Chi-Square Based” Measures 2 indicates how different our findings are from what is expected under null 2 also gets larger with higher sample size (more confidence in larger samples) To get a “pure” measure of strength, you have to remove influence of N Phi Cramer's V

5
**PHI Phi (Φ) = 2 √ N Formula standardizes 2 value by sample size**

Measure ranges from 0 (no relationship) to values considerably >1 (Exception: for a 2x2 bivariate table, upper limit of Φ= 1)

6
**PHI Example: 2 x 2 table LIMITATION OF Φ: 2=5.28**

Lack of clear upper limit makes Φ an undesirable measure of association

7
CRAMER’S V Cramer’s V = 2 √ (N)(Minimum of r-1, c-1) Unlike Φ, Cramer’s V will always have an upper limit of 1, regardless of # of cells in table For 2x2 table, Φ & Cramer’s V will have the same value Cramer’s V ranges from 0 (no relationship) to +1 (perfect relationship)

8
**2-BASED MEASURES OF ASSOCIATION**

Sample problem 1: The chi square for a 5 x 3 bivariate table examining the relationship between area of Duluth one lives in & type of movie preference is 8.42, significant at .05 (N=100). Calculate & interpret Cramer’s V. ANSWER: (Minimum of r-1, c-1) = 3-1 = 2 Cramer’s V = .21 Interpretation: There is a relatively weak association between area of the city lived in and movie preference. Cogdon Lincoln Park Observation Hill Lakeside Chester Park Comedy Drama Action

9
**2-BASED MEASURES OF ASSOCIATION**

Sample problem 2: The chi square for a 4 x 4 bivariate table examining the relationship between type of vehicle driven & political affiliation is 12.32, sig. at .05 (N=300). Calculate & interpret Cramer’s V. ANSWER: (Minimum of r-1, c-1) = 4 -1 = 3 Cramer’s V = .12 Interpretation: There is a very weak association between type of vehicle driven & political affiliation. Compact Truck SUV Luxury

10
**SUMMARY: 2 -BASED MEASURES OF ASSOCIATION**

Limitation of Φ & Cramer’s V: No direct or meaningful interpretation for values between 0-1 Both measure relative strength (e.g., .80 is stronger association than .40), but have no substantive meaning; hard to interpret “Rules of Thumb” for what is a weak, moderate, or strong relationship vary across disciplines AGAIN, THESE ARE FOR NOMINAL-LEVEL VARIABLES ONLY.

11
LAMBDA (λ) PRE (Proportional Reduction in Error) is the logic that underlies the definition & computation of lambda Tells us the reduction in error we gain by using the IV to predict the DV Range 0-1 (i.e., “proportional” reduction) E1 – Attempt to predict the category into which each case will fall on DV or “Y” while ignoring IV or “X” E2 – Predict the category of each case on Y while taking X into account The stronger the association between the variables the greater the reduction in errors

12
**LAMBDA: EXAMPLE 1 Risk Classification Re-arrested Low Medium High**

Does risk classification in prison affect the likelihood of being rearrested after release? (2=43.7) Risk Classification Re-arrested Low Medium High Total Yes 25 20 75 120 No 50 15 85 40 90 205

13
**LAMBDA: EXAMPLE Find E1 (# of errors made when ignoring X)**

E1 = N – (largest row total) = = 85 Risk Classification Re-arrested Low Medium High Total Yes 25 20 75 120 No 50 15 85 40 90 205

14
**LAMBDA: EXAMPLE Find E2 (# of errors made when accounting for X)**

E2 = (each column’s total – largest N in column) = (75-50) + (40-20) + (90-75) = = 60 Risk Classification Re-arrested Low Medium High Total Yes 25 20 75 120 No 50 15 85 40 90 205

15
**CALCULATING LAMBDA: EXAMPLE**

Calculate Lambda λ = E1 – E2 = = 25 = 0.294 E Interpretation – when multiplied by 100, λ indicates the % reduction in error achieved by using X to predict Y, rather than predicting Y “blind” (without X) 0.294 x 100 = 29.4% - “Knowledge of risk classification in prison improves our ability to predict rearrest by 29%.”

16
LAMBDA: EXAMPLE 2 What is the strength of the relationship between citizens’ race and attitude toward police? (obtained chi square is > (2[critical]) Calculate & interpret lambda to answer this question Attitude towards police Race Totals Black White Other Positive 40 150 35 225 Negative 80 95 55 230 120 245 90 455

17
**LAMBDA: EXAMPLE 2 E1 = N – (largest row total) 455 – 230 = 225**

E2 = (each column’s total – largest N in column) (120 – 80) + (245 – 150) + (90 – 55) = = 170 λ = E1 – E2 = = = 0.244 E INTERPRETATION: x 100 = 24.4% - “Knowledge of an individual’s race improves our ability to predict attitude towards police by 24%” Attitude towards police Race Totals Black White Other Positive 40 150 35 225 Negative 80 95 55 230 120 245 90 455

18
SPSS EXAMPLE IS THERE A SIGNIFICANT RELATIONSHIP B/T GENDER & VOTING BEHAVIOR? If so, what is the strength of association between these variables? ANSWER TO Q1: “YES”

19
**SPSS EXAMPLE ANSWER TO QUESTION 2:**

By either measure, the association between these variables appears to be weak

20
**2 LIMITATIONS OF LAMBDA 1. Asymmetric**

Value of the statistic will vary depending on which variable is taken as independent 2. Misleading when one of the row totals is much larger than the other(s) For this reason, when row totals are extremely uneven, use a chi square-based measure instead

21
**ORDINAL MEASURE OF ASSOCIATION**

GAMMA For examining STRENGTH & DIRECTION of “collapsed” ordinal variables (<6 categories) Like Lambda, a PRE-based measure Range is -1.0 to +1.0

22
**GAMMA Logic: Applying PRE to PAIRS of individuals Prejudice**

Lower Class Middle Class Upper Class Low Kenny Tim Kim Middle Joey Deb Ross High Randy Eric Barb

23
**GAMMA CONSIDER KENNY-DEB PAIR**

In the language of Gamma, this is a “same” pair direction of difference on 1 variable is the same as direction on the other If you focused on the Kenny-Eric pair, you would come to the same conclusion Prejudice Lower Class Middle Class Upper Class Low Kenny Tim Kim Middle Joey Deb Ross High Randy Eric Barb

24
**GAMMA NOW LOOK AT THE TIM-JOEY PAIR**

In the language of Gamma, this is a “different” pair direction of difference on one variable is opposite of the difference on the other Prejudice Lower Class Middle Class Upper Class Low Kenny Tim Kim Middle Joey Deb Ross High Randy Eric Barb

25
**GAMMA Logic: Applying PRE to PAIRS of individuals Formula:**

same – different same + different

26
**GAMMA Prejudice Lower Class Middle Class Upper Class Low Kenny Tim Kim**

If you were to account for all the pairs in this table, you would find that there were 9 “same” & 9 “different” pairs Applying the Gamma formula, we would get: 9 – 9 = = 0.0 Prejudice Lower Class Middle Class Upper Class Low Kenny Tim Kim Middle Joey Deb Ross High Randy Eric Barb Meaning: knowing how 2 people differ on social class would not improve your guesses as to how they differ on prejudice. In other words, knowing social class doesn’t provide you with any info on a person’s level of prejudice.

27
**GAMMA 3-case example Applying the Gamma formula, we would get:**

3 – 0 = = 1.00 Prejudice Lower Class Middle Class Upper Class Low Kenny Middle Deb High Barb

28
**Gamma: Example 1 Examining the relationship between:**

FEHELP (“Wife should help husband’s career first”) & FEFAM (“Better for man to work, women to tend home”) Both variables are ordinal, coded 1 (strongly agree) to 4 (strongly disagree)

29
Gamma: Example 1 Based on the info in this table, does there seem to be a relationship between these factors? Does there seem to be a positive or negative relationship between them? Does this appear to be a strong or weak relationship?

30
GAMMA Do we reject the null hypothesis of independence between these 2 variables? Yes, the Pearson chi square p value (.000) is < alpha (.05) It’s worthwhile to look at gamma. Interpretation: There is a strong positive relationship between these factors. Knowing someone’s view on a wife’s “first priority” improves our ability to predict whether they agree that women should tend home by 75.5%.

31
USING GSS DATA Construct a contingency table using two ordinal level variables Are the two variables significantly related? How strong is the relationship? What direction is the relationship?

Similar presentations

OK

Overview Chi-square showed us how to determine whether two (nominal or ordinal) variables are statistically significantly related to each other. But statistical.

Overview Chi-square showed us how to determine whether two (nominal or ordinal) variables are statistically significantly related to each other. But statistical.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google