education 795 class notes applied research logistic regression note set 10

Education 795 Class Notes

Applied Research

Logistic Regression

Note set 10

Today’s Agenda

Announcements (ours and yours)

Q/A

Applied Research

Logistic regression

Pure vs. Applied Research

Pure research‘Pure research is that type of research which is directed towards increase of knowledge in science… where the primary aim is a fuller understanding of the subject under study rather than the application thereof’ (NSF, 1959)

Applied research‘Research carried out for the purpose of solving practical problems’ (Pedhazur & Pedhazur, 1991)

What do You Think?

The pure researcher believes the applied scientists are not creative, that applied work attracts only mediocre men/women, and that applied research is like working from a cookbook.The applied researcher believes the pure scientist to be a snob, working in his/her ivory tower and afraid to put his/her findings to a real test… like Bacon’s spider, spinning webs out off his substance.

(Storer, 1966, p. 108)

Sociobehavioral Research and Policy Advocacy

Another endless debate over whether scientists should limit the presentation of their findings or also act as advocates for policies they presumably support…

Poem for the Day

Thou shalt not answer questionnairesOr quizzes upon World-Affairs,

Nor with complianceTake any test. Thou shalt not sitWith statisticians nor commit

A social science

(Auden, 1950, p. 69)

We Turn Now to Logistic Regression

When / why do we use logistic regression?

Theory behind logistic regression

Running logistic regression on SPSS

Interpreting logistic regression analysis

When and Why?

To test predictors when the outcome variable is a categorical dichotomy (yes/no, pass/fail, survive/die)

Used because having a categorical dichotomy as an outcome variable violates the assumption of linearity and homogeneity in normal regression

Dichotomous Outcomes

Aldrich & Nelson present two solutions to the violation of the linear regression assumptions for dichotomous outcomes

Linear Probability ModelsWeighted Least Squares (which we will not cover)

Nonlinear Probability ModelsLogit (Most commonly used)Probit (which we will not cover)

Nonlinear Probability Model

Log (P/(1-P)=b0+b1X1+…+bnXn

Let’s look at the left hand sideP=probability of success (defined by the researcher, e.g. pass, graduate, survive)1-P=probability of failure (1-probability of success)This ratio demands that the estimated coefficients remain positiveTaking the Logarithm of this ratio restricts the range to be from 0 to 1The left hand side is commonly referred to as the “logit” or the log(odds)

Odds Ratio

P/(1-P) is called an odds. Simple Example: Hat with 5 red chips and 10 green chips. You win if you pull a red chip.

Probability of winning is 1/3Probability of not winning is 2/3Odds of winning = 1/3 / 2/3 = ½ or 1:2

In other words, your odds of winning are 1 to 2 (there are 2 green chips for every 1 red

chip, in lay terms, you are less likely to win then to lose)

Odds Ratio

Let’s reverse the exampleSimple Example: Hat with 10 red chips and 5 green chips. You win if you pull a red chip.

Probability of winning is 2/3Probability of not winning is 1/3Odds of winning = 2/3 / 1/3 = 2 or 2:1

In other words, your odds of winning are 2 to 1 (there are 1 green chips for every 2 red

chip, in lay terms, you are more likely to win then to lose)

Morale of the Story

For Odds Ratios Less than 1, success is less likely

For Odds Ratios Greater than 1, success is more likely

Logistic Function

Continuous

Smooth S-shaped curve

Takes on values between 0<=p<=1

Increases monotonically

Symmetric around 0

Let’s take a look

The S-Shaped Curve

P(Y=1)

Solving for P

Single predictor

Multiple predictors

Assumptions of Nonlinear Model

Random sampleIndependent observationsX’s are independent (minimal collinearity among the predictors)

EstimationTechnique called maximum likelihood estimation is used in logit models

Interpretation Issues

Unlike the coefficients in a linear regression model, logistic regression results cannot be interpreted as the rate of change in the expected value the dependent variable, but the change in the probability of Y = 1 for any particular X

The rate of change in the probability of Y = 1 is dependent upon the value of X (and all other Xs, if there are any in the analysis)

Interpreting Coefficients

The output from SPSS will include the ’s (the estimated log odds) and the exp() the odds.

How do we interpret the odds?If exp()=1.6, then a one unit increase in X predicts a 60% increase in the odds of success.

If exp()=2.6 then a one unit increase in X predicts a 160% increase in the odds of success

If exp()=.7 then a one unit increase in X predicts a 30% decrease in the odds of success

A Simple Example

Many natural applications of logistic regression are from medicine

Understanding Coronary Heart Disease (CHD):

100 Cases

2 VariablesAge (measured in years)

Evidence of CHD (1 = Yes, 0 = No)

Sample Output

Sample Output 2

Output 3

•Wald is similar to t-statistic•Tests the null = 0

Exp(B) indicates the change inodds resulting from a unit changein the predictor:

Exp(B) > 1: as X Probability Exp(B) < 1: as X Probability

In Class Project

Run a logistic regression with one predictor.

Interpret the odds ratio

For Next Week

Read and DiscussThe Status of Women and Minorities Among Community College Faculty. Perna. L.W. (2003) Research in Higher Education 44(2) p. 204-240