3.2 multiple regression step by step

8/20/2019 3.2 Multiple Regression Step by Step

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The Multiple Regression Step by Step

In order to get the results of simple and multiple regressions, you need to handle with the following

steps:

1. Compute Mean Scores

You need to compute the mean score of each factor in the independent constructs, as well

as the factor of the dependent constructs, which the relationship is going to test. However,the mean score procedures of logistic regression and multiple regression are quite the

same.

For an example is to predict the relationship between “Competence Upgrading ” and

“Technology Innovation”. So, “Technology Innovation” is dependent construct, which

consists of“ One

”factors and “Competence Upgrading ” is independent construct, which

consists of “Two ” factors as named and shown in the questionnaire design.

In order to compute the mean scores of each factor of dependent and independent

variables, you should pick up the final formal r esul ts of factor analysis stage . This

means that “how many items still remain after the factor analysis?”

Compute mean score instruction:

Click on Transform Compute Variable…(then you will get Figure 1)



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After you clicked on “Compute Variable”, you have to do some following steps:

Look at Figure 2, you have to type factor name (you can label any name) in “Target

Variable” is on the top-left panel.

In Numeric Expression panel, you can type “ Mean (? , ? ) “ and question mark “?”means that you have to insert items that you are going to compute after the factor

analysis. Figure 3 :



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Or you can scroll and find the “Statistical” key term at the right hand side and then

click on it. You will see “Mean” as shown in Figure 4. Then, you just double click on

“Mean” and you can insert items in “Numeric Expression” panel.

Then, you click “OK”. You can see the mean score of this factor on data view page as in

Figure 5.



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2. How to produce the results of multiple regressions…?

Analyze >> Regression >> Linear >> Select a factor of Dependent construct from the left panel to

“Dependent” >> Select all the factors of independent constructs from the left panel to

“Independent” (See Figure 6)

>> Click on “Statistics” >> Select “R squared change”, “Collinearity Diagnostics”, “Durbin-Watson”, and “Covariance Matrix” >> Click on “Continue.” (See Figure 7)

Figure 6

Figure 7



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For “Method”, you may need to select “Stepwise” >> Click on “OK” (See Figure 8)

3. How to check and select the results…?

a. Model Summary — you will see: R-square, Adjusted R-square, Durbin-Walson (D-W) and other

related values.b. ANOVA — you will see: F-value and significant level (sig.)

c. Coefficients — you will see: Standardized Coefficient (Beta —β), t-value, p-value, and VIF (VIFrange, supposed that you will have two independent variables) then you may need to write down

lowest and highest VIF value.

d. Excluded Variables

By following this procedure, there are some main criterions to treat the results of multiple regressions:

1. To delete or reduce factor while running multiple regression in order to fit the rules of thumb,

you do need to make sure and observe or compare with other factors, which have the lowest t-

value (actually, criterion of t-value > 1.96) and p-value criteria (p <0.05 or <0.001 or <0.10),then you can decide to delete that variable (factors). To delete variable, you must go back to theregression stage again and then move the variable (s) to the left panel and click on “OK.”

2. ***

p<0.001,** p<0.01,

* p <0.05,

+ p<0.1 with t-value (t >1.96) is significant level.

Fi ure 8



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OUTPUTS OF MULTIPLE R EGRESSION STEPS…



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Hypothesis – H9 : Competence upgrading positively influences technology innovation.

Table 2 : The results of the influence of Competence Upgrading on Technology Innovation

Independent VariablesCompetence Upgrading

Dependent Variable

Technology Innovation (TIMean)

Model-1 Model-2

Beta (β) Beta (β)

Exploration Competence (CUercF1) 0.481***

-

Exploitation Competence (CUetcF2) - 0.527***

R 2 0.231 0.278

Adj-R 2 0.227 0.274

F -value 59.561 76.150

P -value 0.000 0.000

D-W 1.731 1.684

VIF Range 1.000 1.000

t-value 7.718 8.726

Method Stepwise Stepwise

Note:***

p < 0.001,**

p<0.01,* p< 0.05,

+ p < 0.1

3.2 multiple regression step by step

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