copyright 2008 by pearson education, inc. upper saddle river, new jersey 07458 all rights reserved....

Copyright © 2008 by Pearson Education, Inc.Upper Saddle River, New Jersey 07458All rights reserved.

John W. Creswell Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research, third edition

Chapter 12

Correlational Designs



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



12.3

When to Use Correlational Designs

To examine the relationship between two or more variablesTo 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



12.4

Types of Correlational Designs: Explanatory Design

Correlate two or more variablesCollect data at one point in timeAnalyze all participants as a single groupObtain at least two scores for each individual in the group—one for each variableReport the correlation statisticInterpretation based on statistical test results indicate that the changes in one variable are reflected in changes in the other



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 studyCriterion variable: The outcome being predicted“Prediction” usually used in the titlePredictor variables usually measured at one point in time; the criterion variable measured at a later point in timePurpose is to forecast future performance



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)



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 relationship1 – perfect correlation or relationshipCorrelation values can also be negative indicating an inverse relationship



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)



12.9

Displays of Scores in a ScatterplotHours ofInternet useper week

Depression (scores from 15–45)

+

Depression scoresY=D.V.

50

40

30

20

10 M

M +

-

-

Hours of Internet UseX=I.V.

5 10 15 20

29.39.7Mean Score4818Jamal172Maxine306Jose207Angela4415Todd255Rosa209Bill185Patricia4113Chad3017Laura



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



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 .

copyright 2008 by pearson education, inc. upper saddle river, new jersey 07458 all rights reserved....

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