Below are the results of a Multiple Regression Analysis (MRA) that was conducted

Question

Below are the results of a Multiple Regression Analysis (MRA) that was conducted to determine which of the following variables could be used to predict the number of cancelled therapy sessions among youth in a residential treatment center: age, sex, having earned off–campus privileges, treatment group, the number of serious behavioral incidents (SBI), and the quality of the therapist–client relationship (Quality). Overall, the model explained 30.3% of the variance in the number of cancelled therapy sessions.

   (Points for each sub-item are provided below; 20 points total)

COEFFICIENTS

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

(Constant)

4.379

.882

4.967

.000

Age

–.001

.071

–.001

–.013

.990

Sexa

–1.328

.292

–.369

–4.547

.000

Privilegesb

.432

.413

.120

1.045

.298

Groupc

–.549

.359

–.153

–1.527

.130

SBI

.070

.034

.238

2.075

.040

Quality

–.202

.071

–.268

–2.838

.005

a Sex (0=Male, 1=Female)

b Earned Off–Campus Privileges (0=No, 1=Yes)

c Treatment Group (0=Routine Treatment, 1=New Treatment)

c.   Identify which variables did not significantly predict the number of cancelled therapy sessions among youth in a residential treatment center.   

   (2 points)  

d.   Identify which variables significantly predict the number of cancelled therapy sessions among youth in a residential treatment center. Which variable was the strongest predictor?

   (2 points)  

e.   For each of the variables that significantly predict the number of cancelled therapy sessions among youth in a residential treatment center, specify and interpret the direction of its relationship with the outcome variable.

I dont understand how I determine these answers.

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

(Constant)

4.379

.882

4.967

.000

Age

–.001

.071

–.001

–.013

.990

Sexa

–1.328

.292

–.369

–4.547

.000

Privilegesb

.432

.413

.120

1.045

.298

Groupc

–.549

.359

–.153

–1.527

.130

SBI

.070

.034

.238

2.075

.040

Quality

–.202

.071

–.268

–2.838

.005

Explanation / Answer

c) To identify variables did not significantly predict the number of cancelled therapy sessions among youth in a residential treatment center, we need to look at the p value. Generally a cut of 0.05 is taken. So if p value is less than 0.05, we can say that the variable is statistically significant. If p value is greater than 0.05, we can say the variable is not statistically significant.

From the coefficients table given, we can see that variables Age, Privelege, Group are not significant as their sig value is greater than 0.05.

d) With the same logic as above, we can see that variables Sex, SBI and Quality are significant variables as their sig value is less than 0.05. The variable having the lowest p value will be the strongest predictor. Here we can see that p value for Sex is 0 upto three decimal places, which indicates it is the strongest predictor.

e) Interpretation is as follows:

Sex - On an average the number of cancelled therapy sessions decrease by a factor of 1.328 if attendant is females as compared to males.

SBI - For every 1 unit icrease in SBI, the number of cancelled therapy sessions increase by a factor of 0.070.

Quality - For every 1 unit icrease in Quality, the number of cancelled therapy sessions decrease by a factor of 0.202.

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