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# Correlations - PowerPoint PPT Presentation

Correlations. 11/5/2013. BSS Career Fair. Wednesday 11/6/2013- Mabee A & B 12:30-2:30P. Readings. Chapter 8 Correlation and Linear Regression (Pollock) (pp. 182-187 ) Chapter 8 Correlation and Regression (Pollock Workbook). Homework Due 11/7. Chapter 7 Pollock Workbook Question 1

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### Correlations

11/5/2013

• Wednesday 11/6/2013- Mabee A & B

• 12:30-2:30P

• Chapter 8 Correlation and Linear Regression (Pollock) (pp. 182-187)

• Chapter 8 Correlation and Regression (Pollock Workbook)

• Chapter 7 Pollock Workbook

• Question 1

• A, B, C, D, E, F

• Question 2

• A, B, C, D

• Question 3 (use the dataset from the homework page)

• A, B, C, D

• Question 5

• A, B, C D, E

• When

• Wednesday10-12

• Thursday 8-12

• And by appointment

• Students will be able to interpret and explain empirical data.

• Students will achieve competency in conducting statistical data analysis using the SPSS software program.

• To determine whether a relationship exists between two variablesand did not arise by chance. (Statistical Significance)

• To measure the strength of the relationship between an independent and a dependent variable? (association)

Nominal

Ordinal

Strength

Significance

Direction!

• Strength

• Significance

How to Control for a Variable?

• the relationship between two variables may be spurious, weak or even too strong

• "controlling" for a third variable is a method of removing or separating the effects of another variable.

• This gets at the underlying relationship

• Is there an antecedent variable at play?

• Is the observation different for different groups of people

• H1: People with children will have different views on legalization than others of the same ideology

• Cross-tabs

• Input Row Variable

• Input Column Variable

• To control for a variable place it in the area that says Layer 1 of 1.

• DV- homosex2

• IV- partyid3

• Control- race 2

### Finally Correlations

You have been waiting to use this

• Any relationship between two variables

• Correlation does not mean causation

• Variable A influences variable B

• Variable B influences variable A

• It is a coincidence

• Some other variable (C) influences both A and B

Note the lower case r

• Pearson’s Product Movement (Pearson’s r)

• A way of measuring the goodness of fit between two continuous variables

• Variables must be continuous.

• You cannot use ordinal or nominal variables here

• Small samples >30 can give you odd results

• Measure from -1 to 0 to 1.

• -1 means a perfect negative relationship

• 0 is the absence of any relationship

• +1 is a perfect positive relationship

• Like Somers’ D, Pearson's "r" scores tell us

• Direction

• Strength of Association

• Statistical significance of the measure

• Squaring the (r) value provides a measure of how much better we can do in predicting the value of the d.vby knowing the independent variable.

• We call this a r2(r-square) value.

• Significance Levels: We use the .05 level

• *=significant at .05

• **= significant at.01

• No Stars= No Significance

• Relationship strengths of r-square values

• .000 to .10 = none-

• .11-.20 weak-moderate

• .20-.35 moderate

• .35-.50 moderate- strong

• .50 there is a strong relationship

• We Square the correlation value .733

• This gives us a value of .537 (r-square)

• From this we can say 53.7% (PRE) of the variation in the dependent variable can be explained by the independent variable

• We cannot, however, say that being Baptist increases the syphilis rate.

• Violent Crime Rate, Teen Unemployment Rate, Roadway congestion, Heart Disease

• Coal consumption , Adequate Sanitation, Child Mortality, Child Immunization

• Analyze

• Correlate

• Bivariate

• You can include multiple variables

• We can think of this line as a prediction line.

• The closer the dots to the line, the stronger the relationship, the further the dots the weaker the line.

• If all the data points are right on the regression line, then there is a perfect linear relationship between the two variables.

• This only graphs a correlation...... this means that it does not mean causality nor should it be used for testing!

• Graphs

• Legacy Dialogs

• Scatter/Dot...

Select simple

Choose Define

• put your variable in the Label Cases by area

• Click on Options, and this will open up a window

• Click on display chart with case labels and continue

• Click OK

Including a fit Line with your Scatterplot

Do not use scatterplots for testing! There are better measures, especially if you have more than 1 iv. (your paper should not include any scatterplots)