1. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition Chapter 12 Correlational Designs

2. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.2 What Is Correlational Research? In correlational research designs, investigators use the correlation statistical test to describe and measure the degree of association (or relationship) between two or more variables or sets of scores Statistic that expresses linear relationships is the product-moment correlation (Pearson R) coefficient

3. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.3 When to Use Correlational Designs To examine the relationship between two or more variables To predict an outcome: –Look at how the variables co-vary together –Use one variable to predict the score on another variable – be careful not to assume causality

4. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.4 Types of Correlational Designs: Explanatory Design Correlate two or more variables Collect data at one point in time Analyze all participants as a single group Obtain at least two scores for each individual in the group—one for each variable Report the correlation statistic Interpretation based on statistical test results indicate that the changes in one variable are reflected in changes in the other

5. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.5 Types of Correlational Designs: Prediction Designs Predictor variable: A variable that is used to make a forecast about an outcome in the correlational study Criterion variable: The outcome being predicted “Prediction” usually used in the title Predictor variables usually measured at one point in time; the criterion variable measured at a later point in time Purpose is to forecast future performance

6. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.6 Characteristics of Correlational Designs Displays of scores (scatterplots and matrices) Associations between scores (direction, form, and strength) Multiple variable analysis (partial correlations and multiple regression)

7. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.7 Associations Between Two Scores Direction (positive or negative) Form (linear or nonlinear) Degree and strength (size of coefficient) Correlation values range from: 0 - non correlation or relationship 1 – perfect correlation or relationship Correlation values can also be negative indicating an inverse relationship

8. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.8 Association Between Two Scores: Linear and Nonlinear Patterns A. Positive Linear (r = +.75) B. Negative Linear (r = -.68) C.No Correlation (r =.00)

9. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.9 Displays of Scores in a Scatterplot Hours of Internet use per week Depression (scores from 15–45) + Depression scores Y=D.V. 50 40 30 20 10 M M + - - Hours of Internet Use X=I.V. 510 1520 29.39.7Mean Score 4818Jamal 172Maxine 306Jose 207Angela 4415Todd 255Rosa 20 9 Bill 18 5 Patricia 41 13 Chad 3017Laura

10. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.10 Displays of Scores in a Correlation Matrix 1.School satisfaction 2. Extra-curricular activities 3. Friendship 4. Self-esteem 5. Pride in school 6. Self-awareness 1 2 3 4 5 6 - - - - - - -.33 **.24 -.03 -.15.65 **.24 * -.09 -.02.49**.16.29** -.02.39**.03.22 *p <.05 **p <.01

11. Copyright © 2008 by Pearson Education, Inc. Upper Saddle River, New Jersey 07458 All rights reserved. John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition 12.11 Association Between Two Scores: Degree and Strength of Association.20–.35: When correlations range from.20 to.35, there is only a slight relationship..35–.65: When correlations are above.35, they are useful for limited prediction..66–.85: When correlations fall into this range, good prediction can result from one variable to the other. Coefficients in this range would be considered very good..86 and above: Correlations in this range are typically achieved for studies of construct validity or test-retest reliability.