SURVEY – 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 SERVICE, CLEANLINESS 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.
WE APPRECIATE YOUR OPINION ON THE FOLLOWING (please check boxes bellow)
Excellent = 4, Good = 3, Fair = 2, Poor = 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
DEMOGRAPHICS
Please answer by ticking (√) the appropriate box which you consider the right answer
Gender
1Female 2Male
Marital status:
1Single 2Married 3Divorce
Age: _______(years)
Highest level of education attained
1Primary 2Secondary 3University 4Others
Data from survey
# Gender Marital Status Age EducationLevel Grounds Galleries Rest
Rooms Exhibits Attitude Courtesy
Appearanc
e Information Intere
st
1 F Single 18 University 3 4 3 2 1 2 1 1 3
2 F Married 34 University 3 2 3 1 4 2 1 4 3
3 F Single 49 Secondary 4 3 4 2 3 1 2 2 3
4 Married 50 Primary 2 1 3 2 3 1 1 1 1
5 M Single 25 University 1 3 2 1 3 3 4 2 2
6 M Divorce Others 4 3 4 2 3 3 2
7 M Single 28 University 3 3 3 3 2 3 3 3 1
8 M Married 30 Secondary 3 4 3 3 2 3 3 3 4
9 F Married 56 Primary 3 2 3 3 2 1 3 4 3
10 M Divorce 46 University 4 2 1 4 3 1 3 2 3
11 F Single 40 University 2 2 3 2 3 2 3 2 3
12 F Married 25 Primary 2 3 3 2 3 1 4 2 4
13 M Married 60 University 2 3 3 2 3 2 2 1 2
14 M Single 28 University 2 4 3 3 4 3 2 2 2
15 M Married 32 Secondary 4 2 4 3 3 3 2 2 2
16 F Single 20 Primary 4 3 1 3 4 3 3 3 1
17 F Divorce 55 University 4 4 4 3 2 4 4
18 M Single 40 Secondary 3 3 3 4 2 2 3 3 1
19 F Divorce 59 Secondary 4 4 4 4 4 2 3 3 2
20 M Married 61 Secondary 3 3 1 2 2 3 3 4
21 M Divorce 59 Primary 4 3 3 4 4 2 3 2 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. Create appropriate graph for Age, Marital Status and Highest level of education. 3. Create a frequency distribution table for demographic information.
3.1. Transform the variable age into 5 intervals (25 or less, 26 – 34, 35 – 43, 44 – 52, 53 or more)
4. Create a frequency distribution table for specific information with percentages and give its report by interest groups (Cleanliness, Service and Exhibits).
5. Create a frequency distribution table for specific information with mean and standard deviation, and give its report by interest groups (Cleanliness, Service and Exhibits).
6. Obtain the frequencies for males and females separately (Split File) about groups of interest (Cleanliness, Service and Exhibits).
7. Obtain the frequencies for Level of Education separately (Split File) about groups of interest (Cleanliness, Service and Exhibits).
8. Create a confidence interval at 95% for the Age variable.
9. Is there a significant difference respect to the gender of the customers regarding the satisfaction of the SERVICE, CLEANING AND EXHIBITIONS?
10. Is there a significant difference respect to age (make a cut point for those under or equal to 30) of the customers with respect to the satisfaction of the SERVICE, CLEANING AND EXHIBITIONS?
11. Is there a significant difference with regard to the Level of education of the customers with respect to the satisfaction of the SERVICE, CLEANING AND EXHIBITIONS?
13. Check if assumptions are assumed for questions 9 to 12. 14. Check if the questionnaire is reliable
Solution
Q1. 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.
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.
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 missing” then in the box on the right side in "Value" enter the code 99 < Add < Continue < OK
In the database (data view) the software will replace all the empty cells with the code 99
Next In the "variable view", at the bottom, go to the column "Missing" and write in "Discrete Missing Value" the code 99, then copy and paste all the questions of this code.
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
Q2. 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.
Q3. Create a frequency distribution table for demographic information.
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. 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 Standard Deviation, Minimum and Maximum for the variable “age” for the categorical variable choose percentages.
Count Table N %
Gender of customers
Female 9 45.0%
Male 11 55.0%
Marital status of customers
Single 8 38.1%
Married 8 38.1%
Divorce 5 23.8%
Highest level of education attained
Primary 5 23.8%
Secondary 6 28.6%
University 9 42.9%
Others 1 4.8%
Total 21 100.0%
Transform < Visual Binning < Select the variable whose values will be grouped into intervals (bins) <Continue< Type into the “Binned Variable” the name of the variable that you want to appear (e.g. Age_categories) <Make cutpoints< then follow the steps in the picture below < then apply
Then click “Make Labels” <Ok <OK. Then you will see the new variable created in “Data View”
Q4. Create a frequency distribution table for specific information with percentages and give its report by interest groups (Cleanliness, Service and Exhibits).
Poor Fair Good Excellent
Count Table N % Count Table N % Count Table N % Count Table N %
Cleanliness
Grounds 2 9.5% 5 23.8% 7 33.3% 7 33.3%
Galleries 1 5.0% 5 25.0% 9 45.0% 5 25.0%
Rest room 2 9.5% 1 4.8% 13 61.9% 5 23.8%
Exhibits 3 15.0% 6 30.0% 7 35.0% 4 20.0%
Service
Attitude 1 5.0% 6 30.0% 8 40.0% 5 25.0%
Courtesy 5 23.8% 9 42.9% 6 28.6% 1 4.8%
Exhibits
Appearance 3 15.0% 4 20.0% 11 55.0% 2 10.0%
Information 3 15.0% 8 40.0% 7 35.0% 2 10.0%
Interest 4 19.0% 6 28.6% 7 33.3% 4 19.0%
Q5. Create a frequency distribution table for specific information with mean, standard deviation, minimum and maximum, and give its report by interest groups (Cleanliness, Service and Exhibits).
Mean
Standard
Deviation Minimum Maximum
Cleanliness
Grounds 2.90 1.00 1.00 4.00
Galleries 2.90 0.85 1.00 4.00
Rest room 3.00 0.84 1.00 4.00
Exhibits 2.60 0.99 1.00 4.00
Service
Attitude 2.85 0.88 1.00 4.00
Courtesy 2.14 0.85 1.00 4.00
Exhibits
Appearance 2.60 0.88 1.00 4.00
Information 2.40 0.88 1.00 4.00
Interest 2.52 1.03 1.00 4.00
Q6. Obtain the frequencies for males and females separately (Split File) about groups of interest (Cleanliness, Service and Exhibits).
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
Gender of customers = Female
Mean
Standard
Deviation Minimum Maximum
Grounds 3.4 0.7 2.0 4.0
Galleries 3.0 0.9 2.0 4.0
Rest room 3.1 0.9 1.0 4.0
Exhibits 2.4 0.9 1.0 4.0
Attitude 2.9 1.1 1.0 4.0
Courtesy 2.0 1.0 1.0 4.0
Appearance 2.5 1.1 1.0 4.0
Information 2.6 1.1 1.0 4.0
Gender of customers = Male
Mean
Standard
Deviation Minimum Maximum
Grounds 2.5 1.0 1.0 4.0
Galleries 3.0 0.7 2.0 4.0
Rest room 2.9 0.8 1.0 4.0
Exhibits 2.8 1.1 1.0 4.0
Attitude 2.8 0.8 2.0 4.0
Courtesy 2.4 0.7 1.0 3.0
Appearance 2.8 0.6 2.0 4.0
Information 2.4 0.7 1.0 3.0
Interest 2.4 1.0 1.0 4.0
Q7. 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)
Q8. Create a confidence interval at 95% for the Age variable.
Q9. Is there a significant difference respect to the gender of the customers regarding the satisfaction of the SERVICE, CLEANING AND EXHIBITIONS?
To do this, we go to Data< Split file
After, we need to calculate the total score of each one: Cleaning, Service and Exhibits.
Perform the same steps for Service and Exhibits, then you will see in the Data View the computed variables. Then perform the hypothesis tests with these scores:
Hypothesis for the cleaning variable
Output from SPSS: Interpret each value
Group Statistics
Gender of customers N Mean Std. Deviation Std. Error Mean
Evaluation for the cleaning variable
Female 9 12.0000 2.29129 .76376
Male 11 10.7273 2.53341 .76385
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) DifferenceMean DifferenceStd. Error Evaluation for
the cleaning variable
Equal variances assumed
.357 .558 1.166 18 .259 1.27273 1.09166
Equal variances not assumed
1.178 17.778 .254 1.27273 1.08019
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.
Q10. Is there a significant difference respect to age (make a cut point for those under or equal to 30) of the customers with respect to the satisfaction of the SERVICE, CLEANING AND EXHIBITIONS?
Q11. Is there a significant difference with regard to the Level of education of the customers with respect to the satisfaction of the 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.
Output from SPSS: Interpret each value
Descriptives Cleanning
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean
Minimum Maximum
Lower Bound Upper Bound
Primary 5 10.8000 2.16795 .96954 8.1081 13.4919 8.00 14.00
Secondary 6 12.1667 2.99444 1.22247 9.0242 15.3091 7.00 16.00
University 9 11.0000 2.34521 .78174 9.1973 12.8027 7.00 15.00
Others 1 8.0000 . . . . 8.00 8.00
Total 21 11.1429 2.49571 .54461 10.0068 12.2789 7.00 16.00
Test of Homogeneity of Variances Cleanning
Levene Statistic df1 df2 Sig.
.249a 2 17 .782
a. Groups with only one case are ignored in computing the test of homogeneity of variance for Cleanning.
ANOVA Cleanning
Sum of Squares df Mean Square F Sig.
Between Groups 16.938 3 5.646 .892 .465
Within Groups 107.633 17 6.331
Total 124.571 20
The same do this for Service and Exhibitions
Q12. 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)
Q13. Check if assumptions are assumed for questions 9 to 12. (Special assumptions for homogeneity of variances.)
Q14. 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, then Scale, and then 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):
… > 0.5 > 0.6 > 0.7 > 0.8 > 0.9
Unacceptable Poor Questionable Acceptable Good Excellent
Notes:
Cronbach's alpha reliability coefficient normally ranges between 0 and 1.
The closer the coefficient is to 1.0, the greater is the internal consistency of the items (variables) in the scale.
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).
Output from SPSS
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha Based on Standardized Items
N of Items
.415 .408 9
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 that r = .30 indicate that the item may not belong on the scale. Question 9, 3, 5 and 6 are looks problematic considering this criterion.
For the last column about Cronbach’s Alpha if Item Deleted. As the name suggests, this column gives you the Cronbach’s alpha score you would get if you removed each item from the questionnaire. Remember, our current score is α = .415. If this score goes down if we deleted an item, we want to keep it. But if this score goes up after the item is deleted, we might want to delete it as it would make our questionnaire more reliable. In this case, deleting Question 9 and 3 would increase our Cronbach’s alpha score to α = .546, so deletion should be considered.
