Satisfaction Survey for practice with SPSS, 2019.doc

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Satisfaction survey to practice with SPSS, 2019 – Master program

Name: ______________________________________________ID: ___________

SATISFACTION Dear customer

Thank you for coming and giving us the opportunity of serving you. We know that continued customer satisfaction is the key to success. With that in mind, we invite your comments and suggestions regarding our CLEANLINESS, SERVICE AND EXHIBITS.

We’re proud to serve you and we really appreciate your constructive comments. We look forward to serving you again in the very near future.

SECTION A: BACKGROUND INFORMATION

Please answer by ticking (√) the appropriate box which you consider the right answer Gender

1Female 2Male

Marital status:

1Single 2Married 3Divorce

Age: _______(years)

Highest level of education attained

1Primary 2Secondary 3University 4Others

SECTION B: SATISFACTION

Please tick (√) the number that corresponds to your level of agreement. Choose one among these four statements: Excellent = 4, Good = 3, Fair = 2, Po

or = 1

# CLEANLINESS Excellent Good Fair Poor

1 Grounds 4 3 2 1

2 Galleries 4 3 2 1

3 Rest Rooms 4 3 2 1

4 Exhibits 4 3 2 1

SERVICE

5 Attitude 4 3 2 1

6 Courtesy 4 3 2 1

EXHIBITS

7 Appearance 4 3 2 1

8 Information 4 3 2 1

9 Interest 4 3 2 1

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Data from survey

# Gender MaritalStatus Age EducationLevel Grounds Galleries Rooms Exhibits AttitudeRest Cour-tesy Appea-rance Informa-tion Inte-rest

1 1 1 18 3 3 2 3 2 3 3 3 4

2 1 2 34 3 3 2 3 4 4 4 3 4 3

3 1 2 49 2 4 3 4 3 3 4 3 4 4

4 1 2 50 1 4 4 3 3 3 4 4 4 3

5 2 1 25 3 1 3 2 1 3 3 3 1 3

6 2 3 4 4 3 4 2 1 1 2

7 2 1 28 3 3 3 3 3 2 3 3 2 1

8 2 2 30 2 3 4 3 3 2 3 3 1 4

9 1 2 56 4 3 3 3 3 4 3 3 4 4

10 1 2 46 3 4 4 4 4 3 3 3 4 3

11 1 2 40 3 2 2 3 3 3 4 4 4 3

12 1 1 25 1 3 3 3 2 3 3 1 3 3

13 2 3 60 3 4 3 3 4 3 4 3 3 3

14 2 1 28 3 2 4 3 3 4 3 2 2 2

15 2 32 2 4 2 4 3 3 3 3 2 1

16 1 1 20 1 2 3 2 3 2 3 3 4 3

17 1 3 55 3 4 4 4 3 3 4 4 4 4

18 2 2 40 2 3 3 3 4 2 2 3 3 1

19 1 3 59 4 4 4 4 4 4 3 3 3 3

20 2 2 61 4 3 3 4 3 3 2 3 4

21 2 2 59 4 4 3 3 4 4 2 3 3 3

Questions:

1. Enter the data and Input the variable information. Check if they’re missing values and treat them (code 99), and also check the appropriate measure for each variable.

2. Check if the questionnaire is reliable with Cronbach’s Alpha

3. Create appropriate graph for Gender, Age, Marital Status and Highest level of education.

4. Transform the variable age into 4 intervals and a cut point for those less than or equal to 30 (30 or less, 31 – 40, 41 – 51, 52 or more), after that you create a frequency distribution table

5. Create a frequency distribution table for general information (demographic) with percentages.

6. Create a frequency distribution table for specific information (satisfaction) with percentages and give its report by interest groups (Cleanliness, Service and Exhibits).

7. Create a frequency distribution table for specific information with mean and standard deviation, and give its report by interest groups (Cleanliness, Service and Exhibits).

8. Obtain the frequencies for males and females separately (Split File) about groups of interest (Cleanliness, Service and Exhibits).

9. Obtain the frequencies for Level of Education separately (Split File) about groups of interest (Cleanliness, Service and Exhibits).

10. Create a confidence interval at 95% for the Age variable and total score of satisfaction.

11. Is there a significant difference respect to the gender of the customers regarding the general satisfaction?

12. Is there a significant difference with respect to client age (make a difference when clients are young "30 years or less" compared to adults) with respect to overall satisfaction?

13. Is there a significant difference with regard to the Level of education of the customers with respect to the general satisfaction?

14. Is there a significant difference with regard to the marital status of the customers with respect to the general satisfaction?

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Q1. Enter the data and fill in the information of the variables. Check if they’re missing values and treat them (code 99), and also check the appropriate measure for each variable.

Note: SPSS has two types of missing values. In short,

 system missing values are values that are completely absent from the data and

 user missing values are values that are present in the data but must be excluded from calculations.

Steps in SPSS: Transform < Recode into same variables < Select all the variables and then pass them to the right box < Old and New variables < In the box on the left side, select "System-or user-missing” then in the box on the right side in "Value" enter the code 99 < Add < Continue < OK (see the image below)

In the database (data view) the software will replace all the empty cells with the code 99

To verify the appropriate measure for each variable: In the "Measure" column, choose between "scale, ordinal

or nominal" according to the type of variable you are analyzing. (For example: gender and marital status are

nominal; level of education is ordinal, and age is scale)

Q2.

Check if the questionnaire is reliable

Reliability of research instruments is the extent to which results are consistent over time and are an accurate

representation of the population under study (Joppe, 2000).

We have 9 questions in this questionnaire that measure customer satisfaction. When analyzing the data, you

want to make sure that these questions (Q1 to Q9) reliably measure the same latent variable (that is, customer

satisfaction). To test the internal consistency, you can run Cronbach's alpha test using the reliability command

in SPSS, as follows:

1. From the top menu, click Analyze>Scale>Reliability Analysis.

2. Transfer variables Q1 through Q9 into the Items, and leave the model set as Alpha.

3. In the dialog box, click Statistics.

4. In the box description, select Item, Scale, and Scale if item deleted. In the inter-item box, select

Correlation.

5. Click Continue and then OK to generate the output.

To interpret the output, you can follow the rule of George and Mallery (2003):

Notes:

Cronbach's alpha reliability coefficient normally ranges between 0 and 1.

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Cronbach's alpha coefficient increases either as the number of items (variables) increases, or as the average

inter-item correlations increase (i.e., when the number of items is held constant).

Steps:

Output from SPSS

Reliability Statistics

Cronbach's Alpha N of Items

.702 9

Item-Total Statistics Scale Mean if Item

Deleted Scale Variance ifItem Deleted Total CorrelationCorrected Item- Cronbach's Alpha ifItem Deleted

Grounds (Q1) 24.67 11.176 .572 .632

Galleries(Q2) 24.67 14.353 .132 .717

Rest Rooms(Q3) 24.67 12.824 .531 .655

Exhibits(Q4) 24.67 12.706 .399 .672

Attitude(Q5) 24.78 13.712 .246 .699

Courtesy(Q6) 24.61 13.428 .361 .680

Appearance(Q7) 24.83 13.559 .303 .689

Information(Q8) 24.78 10.771 .530 .640

Interest(Q9) 25.00 12.235 .341 .687

This table can really help you to decide whether any items need to be removed. There are two columns of

interest here: First, the Corrected Item - Total Correlation column tells you how much each item correlates

with the overall questionnaire score. Correlations less than .30 (r < .30) indicate that the element can not

belong to the scale. Questions 2 and 5 have a problematic aspect considering this criterion.

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Q3.

Create appropriate graph for Age, Marital Status and Highest level of education.

Graph < Chart Builder < Column “Gallery” choose the graph according the type of the variable < Drag the

icon for the simple bar chart to the "canvas" which is the large area above the Gallery.

The Chart Builder displays a preview of the chart on the canvas, and then you add variables by dragging them

from the Variables list, which is located to the left of the canvas.

Finally

: Once your graph is produced you can edit it further like any piece of SPSS Output by double-clicking

on it.

Q4. Transform the variable age into 4 intervals and a cut point for those less than or equal to 30 (30 or less, 31 – 40, 41 – 51, 52 or more), after that you create a frequency distribution table.

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Then click “Make Labels” <Ok <OK. So you will see the new variable created in “Data View”

Q5. Create a frequency distribution table for general information (demographic) with percentages.

Analyze < Tables < Custom tables < Next select the variables and drag and drop them to the position "Rows". It is important that the level of measurement of all variables that you analyze is set correctly, because the default settings for the table will be based on that.

Next we click the button “Summary Statistics” to select the statistics we want to display in our table. We can add statistics to the table and we can remove some. In this example we have chosen to select ‘Table N %’.

Output

Frequency %

Gender of customers

Female 11 55.0%

Male 9 45.0%

Marital status of customers

Single 6 28.6%

Married 11 52.4%

Divorce 4 19.0%

Highest level of education attained

Primary 3 14.3%

Secondary 4 19.0%

University 9 42.9%

Others 5 23.8%

Age of customers

<= 30 7 35.0%

31 - 40 4 20.0%

41 - 51 3 15.0%

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Q6. Create a frequency distribution table for specific information (satisfaction) with percentages and give its report by interest groups (Cleanliness, Service and Exhibits).

Next, we click on the "Summary statistics" button to select the statistics we want to show in our table. We can add statistics to the table and we can eliminate some. In this example we have chosen to select Percentage.

Output

Poor Fair Good Excellent

Count Table N % Count Table N % Count Table N % Count Table N % Cleanliness

Grounds 1 4.8% 3 14.3% 8 38.1% 9 42.9%

Galleries 0 0.0% 4 20.0% 10 50.0% 6 30.0%

Rest room 0 0.0% 2 9.5% 13 61.9% 6 28.6%

Exhibits 1 5.0% 2 10.0% 10 50.0% 7 35.0%

Service

Attitude 0 0.0% 4 20.0% 11 55.0% 5 25.0%

Courtesy 0 0.0% 3 15.0% 11 55.0% 6 30.0%

Exhibits

Appearance 2 9.5% 2 9.5% 14 66.7% 3 14.3%

Information 3 14.3% 3 14.3% 7 33.3% 8 38.1%

Interest 3 14.3% 2 9.5% 10 47.6% 6 28.6%

Q7. Create a frequency distribution table for specific information with mean and standard deviation, and give its report by interest groups (Cleanliness, Service and Exhibits).

The first thing you should do is change the "Measure" column from the "ordinal" to "scale" value.

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Mean Deviation Minimum MaximumStandard Cleanliness

Grounds 3.2 0.9 1 4

Galleries 3.1 0.7 2 4

Rest Rooms 3.2 0.6 2 4

Exhibits 3.2 0.8 1 4

Service

Attitude 3.1 0.7 2 4

Courtesy 3.2 0.7 2 4

Exhibits

Appearance 2.9 0.8 1 4

Information 3 1.1 1 4

Interest 2.9 1 1 4

Q8. Obtain the frequencies for males and females separately (Split File)

Split File: To split the data in a way that separates the output for each group: Data > Split File. Select the option Organize output by groups. Double-click the variable “Gender to move it to the Groups Based on: < OK.

After, request the table as in the previous example, so the software offers the output by gender Customer's gender = Female

Mean Standard Deviation Minimum Maximum Cleanliness

Grounds 3.3 0.8 2 4

Service

Exhibits

Information 3.7 0.5 3 4

Interest 3.4 0.5 3 4

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Mean Standard Deviation Minimum Maximum Cleanliness

Grounds 3.0 1.0 1 4

Service

Exhibits 2.1 0.9 1 3

Information 2.6 1.1 1 4

Interest 3.4 0.5 3 4

Q9. Obtain the frequencies for Level of Education separately (Split File) about groups of interest (Cleanliness,

Service and Exhibits).

(Do the same as in the previous example)

Q10. Create a 95% confidence interval for the variable 'total satisfaction'

Before, we need to calculate the total score of satisfaction and total for each sub variable ‘Cleaning, Service

and Exhibits.’

Steps to create ‘total satisfaction’

Go to TRANSFORM < Compute Variable < write into the “Target Variable” Total_satisfaction < Function

group: Select ALL < Functions and special variables: Select SUM < then follow the steps in the figure below:

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Q11. Is there a significant difference respect to the gender of the customers regarding the total satisfaction and

each sub variable ‘SERVICE, CLEANING AND EXHIBITIONS’?

Before to do this, we must go to Data < Split file and say to the software ‘Analyze all cases, do not create

groups’ as the following figure:

Then perform the hypothesis tests with these scores:

Hypothesis for the total satisfaction and subvariables

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Output from SPSS: Interpret each value

Group Statistics

Gender of customers N Mean

Std. Deviation

Std. Error Mean

Total_SATISFACTION Female_Male 11₉ 29.27_24.33 3.74_4.06 1.13_1.35

Total_CLEANLINESS Female_Male 11₉ 12.73_11.78 2.37_2.22 0.71_0.74

Total_SERVICE Female_Male 11₉ 6.36_5.33 1.36_1.58 0.41_0.53

Total_EXHIBITS Female 11 10.18 1.33 0.40

Male 9 7.22 1.72 0.57

Independent Samples Test

Levene's Test for Equality of

Variances t-test for Equality of Means

F Sig. t df

Sig. (2-tailed)

Total_satisfaction Equal variances assumed _.021 _.885 _2.826 ₁₈ _.011

Equal variances not

assumed _2.802 _16.579 _.012

Total_CLEANLINESS Equal variances assumed _.682 _.420 _.916 ₁₈ _.372

Equal variances not

assumed _.922 _17.612 _.369

Total_SERVICE Equal variances assumed _.221 _.644 _1.566 ₁₈ _.135

Equal variances not

assumed _1.542 _15.956 _.143

Total_EXHIBITS Equal variances assumed _1.098 _.309 _4.353 ₁₈ _.000

Equal variances not

assumed _4.239 _14.900 _.001

Note: To interpret it is necessary to follow the instructions that you have in our Tutorial of SPSS vs 23, from

page 23 to 29.

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Output

Group Statistics

Age of customers N Mean Std. Deviation Std. Error Mean

Total SATISFACTION >= 30 14 29.21 3.17 0.85

< 30 6 23.33 1.86 0.76

Total_CLEANLINESS >= 30 14 13.29 1.86 0.50

< 30 6 10.33 1.86 0.76

Total_SERVICE >= 30 14 6.43 1.02 0.27

< 30 6 5.33 1.37 0.56

Total_EXHIBITS >= 30 14 9.50 1.70 0.45

< 30 6 7.67 1.86 0.76

Independent Samples Test

Levene's Test for

Equality of Variances t-test for Equality of Means

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

Total SATISFACTION Equal variances

assumed 2.980 .101 4.208 18

.001

Equal variances not

assumed 5.170 15.763 .000

Total_CLEANLINESS Equal variances

assumed .030 .865 3.255 18

.004

Equal variances not

assumed 3.252 9.510 .009

Total_SERVICE Equal variances

assumed .399 .536 1.996 18 .061

Equal variances not

assumed 1.765 7.491 .118

Total_EXHIBITS Equal variances

assumed .205 .656 2.153 18

.045

Equal variances not

assumed 2.071 8.773 .069

Q13. Is there a significant difference with regard to the Level of education of the customers with respect to the

total satisfaction and subvariables SERVICE, CLEANING AND EXHIBITIONS?

Hypothesis for the cleaning variable

Steps in SPSS to request an ANOVA, because the variable Level of Education has 4 levels or groups.

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Output from SPSS: Interpret each value

Descriptives

Total SATISFACTION

N Mean

Std.

Deviation Std. Error

95% Confidence Interval for Mean

Minimum Maximum Lower

Bound

Upper Bound

Primary 3 27.00 4.36 2.52 16.17 37.83 24.00 32.00

Secondary 4 26.75 3.59 1.80 21.03 32.47 24.00 32.00

University 9 27.22 4.71 1.57 23.60 30.84 20.00 34.00

Others 5 26.60 5.94 2.66 19.22 33.98 17.00 32.00

Total 21 26.95 4.46 0.97 24.92 28.98 17.00 34.00

Test of Homogeneity of Variances

Total SATISFACTION

Levene

Statistic df1 df2 Sig.

.536 3 17 .664

ANOVA

Total SATISFACTION

Sum of

Squares df

Mean

Square F Sig.

Between

Groups 1.447 3.000 0.482 0.021 0.996

Within

Groups 395.506 17.000 23.265

Total 396.952 20.000

Note: To interpret it is necessary to follow the instructions that you have in our Tutorial of SPSS vs 23, from

page 33 to 37.

Q14. Is there a significant difference with regard to the Marital status of the customers with respect to the

satisfaction of the SERVICE, CLEANING AND EXHIBITIONS?

(Do the same as in the previous example)