Presentation is loading. Please wait.

Presentation is loading. Please wait.

9: Examining Relationships in Quantitative Research ESSENTIALS OF MARKETING RESEARCH Hair/Wolfinbarger/Ortinau/Bush.

Similar presentations


Presentation on theme: "9: Examining Relationships in Quantitative Research ESSENTIALS OF MARKETING RESEARCH Hair/Wolfinbarger/Ortinau/Bush."— Presentation transcript:

1 9: Examining Relationships in Quantitative Research ESSENTIALS OF MARKETING RESEARCH Hair/Wolfinbarger/Ortinau/Bush

2 13-2 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Relationships between Variables  Is there a relationship between the two variables we are interested in?  How strong is the relationship?  How can that relationship be best described?

3 13-3 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Describing Relationships Between Variables PresenceDirection Strength of association Type

4 13-4 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Covariation and Variable Relationships  Covariation is amount of change in one variable that is consistently related to the change in another variable  A scatter diagram graphically plots the relative position of two variables using a horizontal and a vertical axis to represent the variable values

5 13-5 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 13.1 Scatter Diagram Illustrates No Relationship

6 13-6 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 13.2 Positive Relationship between X and Y

7 13-7 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 13.3 Negative Relationship between X and Y

8 13-8 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 16.4 Curvilinear Relationship between X and Y

9 13-9 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Correlation Analysis  Pearson Correlation Coefficient–statistical measure of the strength of a linear relationship between two metric variables  Varies between – 1.00 and  The higher the correlation coefficient–the stronger the level of association  Correlation coefficient can be either positive or negative

10 13-10 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 13.5 Strength of Correlation Coefficients

11 13-11 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 13.6 SPSS Pearson Correlation Example

12 13-12 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 What is Regression Analysis?  A method for arriving at more detailed answers (predictions) than can be provided by the correlation coefficient  Assumptions  Variables are measured on interval or ratio scales  Variables come fro a normal population  Error terms are normally and independently distributed

13 13-13 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit 13.9 Straight Line Relationship in Regression

14 13-14 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 y=a + bX + e i y=the dependent variable a=the intercept b=the slope X=the independent variable used to predict y e i =the error for the prediction Formula for a Straight Line

15 13-15 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit Fitting the Regression Line Using the “Least Squares” Procedure

16 13-16 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Ordinary Least Squares (OLS) OLS is a statistical procedure that estimates regression equation coefficients which produce the lowest sum of squared differences between the actual and predicted values of the dependent variable

17 13-17 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Exhibit SPSS Results for Bivariate Regression

18 13-18 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 SPSS Results say...  Percieved reasonableness of prices is positively related to overall customer satisfaction  Th relationship is positive  But weak!  Prices and satisfaction is associated, but there are other factors as well!!

19 13-19 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008 Multiple Regression Analysis Multiple regression analysis is a statistical technique which analyzes the linear relationship between a dependent variable and multiple independent variables by estimating coefficients for the equation for a straight line

20 13-20 Copyright © 2008 by the McGraw-Hill Companies, Inc. All rights reserved. Hair/Wolfinbarger/Ortinau/Bush, Essentials of Marketing Research 1e © McGraw-Hill/Irwin2008  Assess the statistical significance of the overall regression model using the F statistic and its associated probability  Evaluate the obtained r2 to see how large it is  Examine the individual regression coefficient and their t-test statistic to see which are statistically significant  Look at the beta coefficient to assess relative influence Evaluating a Regression Analysis


Download ppt "9: Examining Relationships in Quantitative Research ESSENTIALS OF MARKETING RESEARCH Hair/Wolfinbarger/Ortinau/Bush."

Similar presentations


Ads by Google