Preparing a questionnaire (Understanding data types)

In doing quantitative research, questionnaire remain the most appropriate mean to collect primary data. Saunders et al (2007) define a questionnaire as a form which contains a specific set of questions which relates closely to the objectives of a research. A questionnaire might seem easy to define or to construct but involves a lengthy process because of different types of data which can be found among a set of questions.

McClelland (1995) says that data types must be considered prior to the designing of a questionnaire and students must know about these data as specific analysis must be done through the use of Excel or Spss.

Pagano(2006) mentions that there are four types of data and these are :

1) Nominal Variables (also known as categorical variables).

2)) Ordinal Variables

3) Interval Level Variables

4) Ratio Level Variable (Also known as Scale Variable in Spss).

With the use of the questionnaire below, we will now define exactly the abovementioned Variables and explain how we can use those in SPSS.

Health Club Satisfaction Questionnaire

CUSTOMER SATISFACTION QUESTIONNAIRE

This questionnaire has been constructed through intense consultations with the client and the marketing research group to gain information that can and will be analysed to provide knowledge concerning the attitudes and behavioural patterns of both users and non-users of the Health Club.

The information collected will be confidential and of interest to both the Health Club Marketing Unit and the management of the Club.

Section A - About Yourself

1) What age bracket do you fall into?

Under 20

20 - 24

25 - 29

30 - 34

35+

2) How many people are in your immediate family _______

3) What is your gender?

Male

Female

4) Do you attend the club regularly?

Yes

No

5) To which area of the city do you belong?

North

South

East

West

Other (Please specify) _________

6) Identify option for how often do you visit the Club?

At least once a day

2 - 3- times a week

Once a week

Once a fortnight

Once a month

Other

7) If the Club was not associated with the City would you still use it?

Yes

No

8) Are you aware food is served until 7.00pm?

Yes No

9) At what time do you most frequently visit the Club?

(Please tick one box only)

Morning 8.30 - 11.30

Lunch 11.30 - 1.30

Afternoon 1.30 - 4.30

Evening 4.30 - 7.30

Night 7.30 - Close

10) Rank the main reasons for you using the Club? (5 is main reason, 1 is lowest priority reason) (Please select one rank for each choice only)

Advice

Keep Fit

Socialising

Special Events (Organised by Club)

Special Occasion (Organised by acquaintances)

11) How satisfied are you with the following factors: (Please tick one box for every factor)

Very Satisfied Neutral Dissatisfied Very

Satisfied Dissatisfied

Speed of service

Quality of service

Cleanliness

Value for money

Equipment availability

Decor

Staff Courtesy

Events organised

by Club

Thank you

Nominal Variable

Nominal Variable can be defined as data which are non-numeric in nature. The word “nominal” has a Latin origin which means name. Nominal Variables can be assigned a code in the form of a number but which can only be used as a label. Nominal Data can be counted but cannot be measured. For instance, from the above questionnaire, question 3 will be collecting Nominal Data. What is your Gender: Male or Female? The amount of male and female can be counted but none can be analyzed or measured.

Say for example there is a sample of 13 people who participated in a survey. Compile the questionnaire and enter data into SPSS.

Process: Proceed to Variable Page

Rename Variable0001 to Gender

The type of data should stay Numeric

Then Proceed to the decimal Column and put the decimals to 0

Proceed to the Values and then enter Values 1=Male 2=Female

Proceed to the Measure Column and choose Nominal

The Variable SPREADSHEET should look like this

The Data view spreadsheet should look as such

Scenario: Analyze the amount of male to female and then compute the data with the use of a pie chart. Nominal data can mainly be represented through Bar Charts or Pie Charts

Click on analyze in the data view tab and then on frequencies and then click on gender in the small tab and then on pie charts and change frequencies to percentage and then click on continue and then ok.

The result will appear in the output page.

Then analyze accordingly

How to use Nominal Variables

Nominal Variables can be graphed and understood but can also be used in terms of correlation. For example, from the above questionnaire, you want to see if there is a correlation between female gym users and morning gym usage. Your research has found that women tend to go to the gym in the morning and men in the evening because of different work schedule and activities and your findings need to find whether there is such a correlation.

H1 states that most women go to the gym in the morning and men in the evening

H2 states that most women do not go to the gym in the morning and men in the evening

Proceed to the Data View column

Click on analyze and then on correlation and then on bavariate and then select gender and attendance time

The output page on SPSS will be as such

Thus, it has been found out that H1 is correct and that more women go to the gym in the morning and men in the evening.

So, proceed to your data view page and look for gender and then attendance time

Ordinal Variables

Ordinal Variables can be defined as a set of data which can be ranked or which can be out in order. Ordinal Variables can be ordered and counted but cannot be measured. For example, say you want to rank how satisfied the customers of the club are with the quality of service at the club. Question 11, part 2 in the above questionnaire. 1 is very dissatisfied and 5 which is very satisfied.

Input the data in SPSS

Proceed to Variable View tab in SPSS and input the information

Enter the data the same way you entered data for gender and then proceed to Values and enter values 1: Very Dissatisfied to 5: Very Satisfied and in the measure column, post ordinal

The Date View Page should look like this.

The output page should look like the above.

So out of 13 people, 6 gym members were satisfied with service quality , 4 were neutral and one was satisfied and one was dissatisfied and one was very dissatisfied.

How can we use ordinal Variable?

Say for example, you want to check the relationship between the age of the gym members and the city in which they attend the gym.

The Chi Square test shows that most gym members are between the age of 30-34 and are located in the south city.

Interval Data

Interval Data can be defined as those data which value are equally split between each interval. The most common example is temperature in degrees Fahrenheit. The difference for example between the temperature of 3 and 4 will be the same as the difference between the temperature of 31 and 32.

Ratios between numbers on the scale are not meaningful, so operations such as multiplication and division cannot be carried out directly. But ratios of differences can be expressed; for example, one difference can be twice another. Interval Data cannot be found in SPSS.

Ratio Data

Ratio Data is interval data with a natural zero point. For example, time is ratio since 0 times is meaningful. For instance, question 2 from the above questionnaire can be a ratio data. Say for instance, there are 0 people in the immediate family, the data is still meaningful. It all depends on what the author wants to find.

So from question 1 to 11:

Question 1: Nominal Data

Question 2 : Ratio Date

Question 3 : Nominal

Question 4 : Nominal

Question 5 is Nominal.

Question 6 is ordinal.

Question 7 is nominal

Question 8 is Nominal

Question 9 is Nominal

Question 10 is Nominal

Question 11 is Ordinal