final

Group Members(SEC 2)I. Falil Mohiuddin Gaalib – 0930083

II. Tasnima Zaman – 0910390III. Sheikh Mohammad Mustafa – 1020056IV. Tanzia Farzeen – 0930251

RELIABILITY /VARIABLES=Att Cmn Ip Tr Cs Sq Ri /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=DESCRIPTIVE CORR

/SUMMARY=TOTAL.

Reliability

[DataSet1] C:\Users\Gaalib\Documents\PROJECT.sav

Scale: ALL VARIABLES

Case Processing Summary

N %

Cases Valid 240 100.0

Excludeda 0 .0

Total 240 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.918 .924 7

Item Statistics

Mean Std. Deviation N

Attitude 3.6431 .70126 240

Communication 3.1969 .63751 240

Image of the products 3.7062 .60131 240

Trust 3.3708 .69629 240

Customer Satisfaction 3.3479 .79538 240

Service Quality 3.4167 .64288 240

Repurchase Intention 3.2083 1.07554 240

Inter-Item Correlation Matrix

Attitude Communication

Image of the

products Trust

Customer

Satisfaction Service Quality

Repurchase

Intention

Attitude 1.000 .468 .652 .614 .686 .646 .676

Communication .468 1.000 .448 .651 .644 .577 .522

Image of the products .652 .448 1.000 .594 .624 .607 .567

Trust .614 .651 .594 1.000 .809 .670 .697

Customer Satisfaction .686 .644 .624 .809 1.000 .740 .789

Service Quality .646 .577 .607 .670 .740 1.000 .669

Repurchase Intention .676 .522 .567 .697 .789 .669 1.000

Item-Total Statistics

Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha

if Item Deleted

Attitude 20.2469 14.086 .748 .595 .906

Communication 20.6931 14.926 .646 .477 .915

Image of the products 20.1837 14.963 .686 .511 .912

Trust 20.5191 13.816 .814 .700 .899

Customer Satisfaction 20.5420 12.863 .880 .792 .891

Service Quality 20.4733 14.295 .783 .619 .903

Repurchase Intention 20.6816 11.569 .791 .668 .911

Reliability test:

Process:

Analyze Scale Reliability Analysis (Alpha Model)

Descriptive For

1. Select Item.

2. Scale if item deleted

Inter item Correlation Continue Ok

Interpretation of the analysis:

The number we are most interested in is Cronbach’s alpha value which is .918. The closer to that value is then it considered as more reliable. If the value is above .7 considered as adequate, anything above .8 is

considered as optimal, so the closer it gets to 1 is better. If we want to improve the reliability we should look at inter item correlation matrix. Correlations between variables should be high. Appropriate is .3 to .4 .

In item total statistics we should look at the last column which states that “Cronbach’s alpha if deleted” that means if we delete any of the variable what will be Cjronbac’s alpha value in our case we don’t need to

delete anything.

CORRELATIONS /VARIABLES=Cs Sq Ri /PRINT=TWOTAIL NOSIG

/MISSING=PAIRWISE.

Correlations


Correlations

Customer

Satisfaction Service Quality

Repurchase

Intention

Customer Satisfaction Pearson Correlation 1 .740** .789**

Sig. (2-tailed) .000 .000

N 240 240 240

Service Quality Pearson Correlation .740** 1 .669**

Sig. (2-tailed) .000 .000

N 240 240 240

Repurchase Intention Pearson Correlation .789** .669** 1

Sig. (2-tailed) .000 .000

N 240 240 240

**. Correlation is significant at the 0.01 level (2-tailed).

For the ease of our interpretation we choose three variables but it can be done by using all the variables.

Process:

Analyze Correlate Bivariate

After choosing Bivariate we need to choose our variables, to select variables click on the variable name in the left hand column so it is highlighted and then click the arrow in between the two columns to move the

variable to the right column. After that SPSS automatically selects the Pearson Correlation statistic. Once both variables have been moved over we can continue our process by clicking ok.


In our analysis we have seen that the Sig. value is .000 which is less than .05 we can say that there is a significant correlation between the frequencies of customer satisfaction, service quality and repurchase

intention. The values of the Pearson Correlation range from -1 to +1 with negative numbers representing a negative correlation as one variable increases, the other variable decreases and positive numbers

representing a positive correlation as one variable increases, the other also increases. The closer the value is to -1 or +1, the stronger the association is between the variables.

So in our analysis a positive relationship between the frequencies of three variables ( Customer Satisfaction, Service Quality and Repurchase Intention). Pearson Correlation values are .789, .740, .669 on the basis

of these values we can say that there are stronger association between the variables.

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Ri

/METHOD=ENTER Sq Cs.

Regression


Variables Entered/Removedb

Model Variables Entered

Variables

Removed Method

1 Customer

Satisfaction,

Service Qualitya

. Enter

a. All requested variables entered.

b. Dependent Variable: Repurchase Intention

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .799a .639 .636 .64906

a. Predictors: (Constant), Customer Satisfaction, Service Quality

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 176.629 2 88.314 209.633 .000a

Residual 99.843 237 .421

Total 276.472 239

a. Predictors: (Constant), Customer Satisfaction, Service Quality

b. Dependent Variable: Repurchase Intention

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) -.810 .228 -3.558 .000

Service Quality .315 .097 .188 3.245 .001

Customer Satisfaction .879 .079 .650 11.192 .000

a. Dependent Variable: Repurchase Intention

For the ease of interpretation we used three variables for this analysis.

Process:

Analyze Regression Linear. After that we have to select one dependent variable and independent variable though the number of independent variable is not fixed. After choosing variables click ok to continue

the operation.


Regression analysis gives us four tables, but among all of them Model Summary table is most important because from that model summary table we can understand that by what percentage our dependent variable

is dependent on our selected independent variables. In our analysis we can see that R Square is .639 that means Service Quality and Customer Satisfaction has 63% impact on Repurchase intention.

If we look at ANOVA table we can see that Sig .000 that means there is a significant correlation between three variables.

T-TEST GROUPS=Profession(1 2) /MISSING=ANALYSIS /VARIABLES=Cs Sq Ri

/CRITERIA=CI(.9500).

T-Test


Group Statistics

1=Student , 2=Service Holder N Mean Std. Deviation Std. Error Mean

Customer Satisfaction St 120 3.4562 .76742 .07006

Sh 120 3.2396 .81116 .07405

Service Quality St 120 3.4333 .71740 .06549

Sh 120 3.4000 .56111 .05122

Repurchase Intention St 120 3.3806 1.10706 .10106

Sh 120 3.0361 1.01877 .09300

Independent Samples Test

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) Mean Difference

Std. Error

Difference

95% Confidence Interval of the

Difference

Lower Upper

Customer Satisfaction Equal variances assumed 1.936 .165 2.126 238 .035 .21667 .10194 .01585 .41748

Equal variances not assumed 2.126 237.272 .035 .21667 .10194 .01585 .41748

Service Quality Equal variances assumed 3.949 .048 .401 238 .689 .03333 .08314 -.13045 .19712

Equal variances not assumed .401 224.948 .689 .03333 .08314 -.13050 .19717

Repurchase Intention Equal variances assumed .184 .669 2.508 238 .013 .34444 .13734 .07389 .61500

Equal variances not assumed 2.508 236.375 .013 .34444 .13734 .07388 .61501

For the ease of interpretation we used three variables

Process:

Analyze Compare means Independent Samples t-test. Then select grouping variables, in our case profession, then select test variables and then click ok.


This analysis gives us two tables which are group statistics and independent samples test. In group statistics table we can see that number of participants, Mean, Std. Deviation and Std. Error Mean. Then if we go

down to independent samples test then we have to look for t-test quality of means and in that section we look for three specific columns which are t, df (degree of freedom) and Sig (significance value). In

significant value is less than .05 we can say that groups significantly differ. So in our analysis we can see that in terms of customer satisfaction groups significantly differs from each other. In terms of service

quality they doesn’t differ from each other, and finally in terms of repurchase intention groups do differ significantly from each other.