Basic Analysis

(CIB pdf formfields Demoversion)

BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS

ASSOCIATE PROFESSOR DR ERNEST CYRIL DE RUN
DR LO MAY CHIUN
HERIYADI KUSNARYADI

BASIC ANALYSIS: 1


PREAMBLE

This book was originally written as notes for my students of EBQ2053
Research Methodology at Universiti Malaysia Sarawak. Nevertheless, as we
looked through it and with the various courses and seminars that we have
given, we began to realize that what was being said was universal for all
researchers, either those just starting out at 2nd year university of seasoned
well published researchers. We all need to know the basics. Nevertheless, at
the same time, even seasoned researchers tend to forget some methods that
they do not always use. Therefore the idea for this book, as a handout for
students yet at the same time a quick guide and reference for the seasoned
researcher. Please note that we are using SPSS v15 and AMOS v4.

May it be of help to all who strive to better themselves.

This book is dedicated to or my darling wife, Doren, and my dearest son,
Walter.

Associate Professor Dr Ernest Cyril de Run
16 November 2007

BASIC ANALYSIS: 2


1. What is SPSS?

SPSS refers to computer software named Statistical Program for Social
Sciences and it comes in various versions and adds on. It is software and not
a method of analysis. Therefore please do not state that you are using SPSS
to analysis whatever in your research paper. You may state that you use this
statistical package in order to run a certain analysis such as ANOVA or any
other method.

SPSS is statistical and data management software that is widely used. This is
partly because it is simple to use, user friendly, and does not require coding
as by SAS. You may use code in Syntax, but that’s another story. In most
cases, you can just copy and paste code from SPSS output into Syntax thus
not requiring you to write your own code.

The output that is presented by SPSS is also simple and easy to understand,
making it widely copied and not properly presented for academia purposes.

See output example in Example of Output.

It also allows for the use of graphs that makes presentations much clearer.

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1.1 How to Open the Program

There are at least three ways to open the SPSS program on your computer.

They are:

1. From your Desktop, select Start, All Programs, SPSS for Windows, SPSS
15.0 for Windows.

A window will appear asking you what you would like to do, with a few
choices. We normally just click on Cancel. Then we will open an existing file
that we want or start keying in data.

2. Open an existing file by clicking on it, and SPSS will start.

An output document will appear too. We would normally close the document;
some people prefer to keep it open to look at the records.

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3. From Desktop, if there is a shortcut, click on it.

A window will appear asking you what you would like to do, with a few
choices. We normally just cancel it and then open an existing file that we want
or start keying in data.

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1.2. An Overview of the Program

Now that you have a SPSS program open, let’s look into its components.

Let’s start from the bottom upwards to the top.

At the very bottom, you will see a note, “SPSS Processor is Ready”. This is a
neat feature that tells you what you already know. And what more, when you
work on a slow computer, it will tell you that it is processing.

Next, you will notice the words Data View and Variable View. Data View
shows you the Data (numbers or words, depending on what mode your
computer is on) and Variable view show you the inner working or the meaning
of that words or numbers.

We will look into Variable View later on. What we are looking at now is known
as Data View.

Data View

Next you will notice this empty box (where soon your data will be placed in). It
is cordoned by a series of numbers (rows) and ‘var’ in the column. This will
change once you keyed in the appropriate terms in the Variable view later.

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The next line shows the various buttons that you may use. Most importantly,
the SAVE button. Please do use this regularly. Others refer to “go to” buttons
or “Insert” buttons. The “Value Label” (also known as “toe tag” icon) will
determine whether your screen shows numeric values or their labels as
dictated in the Values section in the Variable View.

The top line. The all-important line. Few things to know.
1. File, Save. This is extremely important in SPSS, as in all computer
programs. Please do remember to save continuously while working on SPSS.
You may also use CTRL S.

2. File, New, (your choice of Data, Syntax, Output, Draft Output, Script).
This is used when you wish to open anew file while you are working on a
different file. If you choose Data, a new Data file (similar to what you are
seeing now, will open) and the same goes for the others.

3. File, Open, (your choice of Data, Syntax, Output, Draft Output, Script).
This is used when you wish to open an existing file while you are working on a
different file. If you choose Data, an existing Data file (similar to what you are
seeing now, will be called on in a new window that you will have to choose to
open) and the same goes for the others.

4. Edit, Options, Draft Viewer – make sure that the Display Command in
Log is ticked. This will then display the codes for all the commands that you
use, which you can later incorporate into your Syntax.

The others will be discussed as and when we use them.

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Variable View

Let’s look into Variable View. Click on it.

Let’s start from the bottom upwards to the top.

At the very bottom, you will see a note, “SPSS Processor is Ready”. And you
will notice that the top is also the same. The only difference is the middle. The
rows here refer to the coding that you will use in the columns in the Data
View.

Let’s look at each column in the Variable View.

1. Name. Refers to the name of the column in the Data View. Normally
this will coincide with the questions in your questionnaire so that it will be
easier to track down once you run an analysis. The name must be unique,
start with a letter, and up to 8 characters. Use short terms, as this will make
life easier when running analysis later on. Plus you can place a longer
explanation in the “Label.” Click on the appropriate box, and type in the name.
SPSS is kind of tricky here, especially if you want to use the hyphen, as SPSS
thinks its a minus sign. You can use underscore. Also, don’t have space
between terms.

2. Type. This refers to the type of data that you will be typing in. There are
8 types (Numeric, Comma, Dot, Scientific notation, Date, Dollar, Custom

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Currency, String), but the most common are Numeric and String. Numeric
refers to the use of numbers and String refers to the use of alphabets or
alphabets and numbers. There are implications to this choice. If you choose
String, data there cannot be analyzed by numeric operations (i.e. Means).
Even if your data is in Ringgit Malaysia, we would still suggest that you use
Numeric instead of Dollar.

3. Width. This refers to the number of characters that SPSS will allow to
be placed in the column in Data View. This includes dot, commas, spacing,
and everything that is typed in.

4. Decimals. This refers to the number of decimals that SPSS will display.
Interestingly, SPSS will calculate more decimals than you need to know, but
will show only the decimals that you need to be shown.

5. Label. As noted earlier, this is a place where you may type in text that
explains the column. The maximum space is for 255 characters but we do
suggest that you be brief as this will appear in your analysis and would make
your tables look ugly.

6. Values. This is where you assign meaning to the numbers that you are
using. Clicking up the Values box (where the three dots are), will open
another window that allows you to key in the appropriate meanings for each
value. In the Values Label dialogue box, you can click in the Value field the
appropriate number, then click in the Value Label field to type in what that
number represents. Always click on Add after that, otherwise its not kept in
the list. You can also change or remove values by clicking the appropriate
box.

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7. Missing. You may inform SPSS that certain data should be treated as
missing by using certain numerical code. This can be done by filling in the
Discrete missing values with values of your choice. You may also just leave
the field blank, where SPSS will display – that is known as SYSTEM
MISSING data.

8. Columns. Refers to how wide a column should be.

9. Align. You can also align your data accordingly. You may choose to
align left, right, or center.

10. Measure. This is important as you decide what type of data that you
have. As the saying goes, rubbish in, rubbish out. SPSS does not differentiate
between interval and ratio, so these two are placed together as scale. The
other two forms of data measurement remains, which are ordinal and nominal.

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2. How to Key in Interview-based Data

Many have told me that SPSS can’t handle cases where there are open-
ended questions in a questionnaire or when there is a transcript of an
interview. Such data requires NuDist or other similar computer programs to
analyze it. However we beg to differ, as there is more than one way to skin a
cat.

Planning for Interview Key In

Refer to the attached Interview Transcripts.

We would normally open an individual file for each research question of the
interview. In this case there are two research questions, why do they join and
why do they stay on in a Multi Level Marketing Company. You would also
notice that there are some demographics and ancillary questions that would
be nice to have in a data form to help analyze the data that is found.

Therefore we would first and foremost arrange the data in the interview
according to how we would want to key it in SPSS. The first section would be
the demographics and ancillary data and the second section would be the
relevant research questions.

Key in Interview Data

Open SPSS.

Create variables in the Variable view that can represent the demographics.
We see the possibility for Gender, Age, Marital Status, Race, Education level,
Member of which MLM COMPANY, Member since, and Level in the company.

For the research question, the way we would do it is to note the answers
given by the fifteen respondents. We would code it accordingly, or even get a
second coder or third person involved if there is disagreement as to how to
code it. Refer to Research Methodology books on how to do coding. Once
this is done, We are ready to enter the data into SPSS. In many cases, we do
this on the fly, which is to code and key in immediately.

Key in Interview Data - Example

Let’s do Interview 1.

For demographics, we would key in gender in the Name first, and type in the
label, Gender. For Values, we would key in 1 for Male and 2 for Female. If you
notice, after you typed in gender in name, all other variables automatically
appear. After doing so, when you open Data View, you can see the first
column named “gender.’

BASIC ANALYSIS: 12


Do this for all the other variables.

For age, we normally leave Values blank, as we would key in the actual age
first and at a later stage transform this into a scaled dataset.

See SPSS file Interview Demographics.

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For the first research question, why do they join MLM companies, we would
look at the answer given to the question, code it, and key it in as a Yes/No.

See SPSS file Interview RQ1 for the Variable and Data View.

BASIC ANALYSIS: 14


Then what we would do is to delete all the coding and answers given to the
first research question (Go to Variable View, highlight the relevant rows, click

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Delete). This will leave me with the demographic file. We will then save it as
another file and proceed to key in the answers / codes to the second research
question.

See SPSS file Interview RQ2 for the Variable and Data View.

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Assignment

Now try by yourself to key in the remaining interviews.

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3. How to key in Questionnaire based Data and to Transform.

The process to key in for a Questionnaire based data is also similar. Except
that in most cases here one will be working with Scale instead of Nominal
data.

The most important thing here is to plan everything from the perspective of
keying into SPSS so that when the data comes, you can immediately post the
data into SPSS. This means questionnaire design must take into account he
limitations of SPSS and the requirements of the method of analysis.

The demographics section will pretty well be the same as the earlier
discussion. The only difference will be the data coding for the questionnaires
and perhaps the positioning of the data in the SPSS.

Key in Questionnaire Data Example

See the example questionnaire in the file Example Questionnaire SR &
Loyalty.
Then refer to the SPSS file where data has already been keyed in, Example
Questionnaire 1.

Look at how the coding in the SPSS file mirrors the questionnaire. In this
case, the data for age had already been coded into groups by the researcher.
Others are just keyed in as per what the respondents have answered.

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Please take note that as you key in the data from the questionnaire, write the
relevant corresponding row number on to the questionnaire. This is important
for later stages of checking data.

Checking for Mistakes

Once completed keying in all the data, check if there were any mistakes in
what was keyed in. How to do so? Two ways. The first is to select Analyze,
Descriptive Statistics, Frequencies. A dialogue box would appear. Select all
the variables and transfer it to the Variables box. Then click OK.

Once you have done so, the output would appear. Check if there are any
missing data or numbers that should not be in the dataset. As an example, if
you used a Likert Scale with 5 anchors then you shouldn’t have any other
numbers aside from 1, 2, 3, 4, and 5. So if you find number 11, or 22, or 6,
there must have been a mistake in keying in the data.

The second method is to select Analyze, Descriptive Statistics, Descriptives.
A dialogue box would appear. Select all the variables and transfer it to the
Variables box. Then click OK.

Once you have done so, the output would appear. Check if there are any
numbers that do not represent the Minimum and Maximum in the dataset. As
an example, if you used a Likert Scale with 5 anchors then you shouldn’t have
any other numbers aside from 1, 2, 3, 4, and 5. So if you find number 11, or
22, or 6, there must have been a mistake in keying in the data. Check also if
any of the Means are extraordinary large or small.

Correcting Mistakes

If you found something wrong, what do you do?

Firstly, determine why was it wrong? Was it because the wrong number was
keyed in or that the data was missing data or for any other reason.

Secondly, identify where is the data wrongly keyed in? In which column?

Thirdly, look up the relevant column in the Data View. Click on it.

Fourth, press CRTL F simultaneously and a Find Data dialogue window will
appear. Key in the relevant number or item that was wrong to find out which
row it is in.

Fifth. Once the row is identified, go back to your bundles of questionnaire that
has been marked by row number and search for the questionnaire that
represents the row that contains the wrong key in. Type in the right number.

Recode

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Let’s now look into Recode.

Lets assume the researcher wants to recode the Educational Level data of
respondents from the current 7 values (1 = SPM, 2 = STPM, 3 = Matriculation,
4 = Diploma, 5 = Undergrad, 6 = Degree, 7 = Master) into only 3 values, that
is those with an educational level up to school level, those with pre-university,
and those with an University education.

The first thing to do is to look into the data itself, as to whether there is
sufficient numbers to do such a recode. Running a frequency does this. This
will be explained later.

After running a frequency and noting that there are sufficient data, then you
may proceed to Recode. Click on Transform. You will notice that there are two
types of Recode instructions. They are:
1. Recode into Same Variable, and
2. Recode into Different Variable.

The choice is yours depending on what you intend to do. We would nearly
always recode into a different variable, as we prefer to leave our initial data
intact so that we may return to it at a later stage.

So, click on Transform, Recode into Different Variable, and a dialogue box will
appear.

Find the variable that we wish to recode, Education Level, and click on it.
Transfer it to the Input Variable -> Output Variable box.

Once you have done so, the name will be recorded and the box will be
renamed Numeric Variable -> Output Variable.

If you notice on the Output variable box, there is Name and Label, which
corresponds to the new name and label that you wish for this variable. Type in

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for name, newedu and for label type in, New Education Level. Click on
Change.

You will notice that the Numeric Variable -> Output Variable box now shows
the old and new name.

Now click on Old and New Values.

A new dialogue box will appear, Recode into Different Variable: Old and New
Values.

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There are two sections to this dialogue box. The first part refers to the old
data and the other part is to the new data that you wish to create. For the old
data, you are given options as how to categorize the data, from a stand alone
value, system missing values, range or all other values. For the new data you
are given 3 choices, to key in a new value, system missing, or copy the old
data.

We wanted to create 3 values, which are those with an educational level up to
school level, those with pre-university, and those with a University education.
In the old data, school level education refers to number 1 and 2. Since this is
within a range, key in number 1 to 2 in Range in the old data section and in
the new data section type in 1. Click on Add, otherwise it will not be added
into the new data.

Do the same thing for number 3 and 4 of the old data, which refers to pre-
university education. In the new data section type in 2. Click on Add.

For University level, you may still use range, or use range, value through
HIGHEST. In the new data section type in 3. Click on Add.

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Click on Continue, which will close the dialogue box and bring you back to the
Recode into Different Variable dialogue box.

Click OK.

SPSS will run the data and an Output table will appear with the code. You
may think about / consider saving the code to use it in Syntax later on.

Go back to the SPSS file and look in the Variable View.

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You will see a new row, with the name newedu and with the label, New
Education Level. You will also notice that there is no data in Values. Click on
the values box and key in the relevant new values.

When you click on the value box, the Value Label dialogue box will appear.
Remember that your new data was coded as 1 for educational level up to
school level, 2 for those with pre-university, and 3 for those with an University
education.

In the Value Label dialogue box, type in 1 for value and school level for value
label. Click on Add.

In the Value Label dialogue box, type in 2 for value and pre-university level for
value label. Click on Add.

In the Value Label dialogue box, type in 3 for value and university level for
value label. Click on Add.

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Click on OK.

Run frequency again to check if the data has been transformed properly.

Assignment

Now try by yourself to recode into a different variable for the following
situation.

1. Use the dataset Assignment 1. Recode into a different variable for the
current variable by the name City to two (2) values. The first are those from
West Malaysia and the second are those from East Malaysia.

2. Use the dataset Assignment 1. Recode into a different variable for the
current variable by the name Age to your own determination of values. This
must reflect the data.

Compute

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Compute refers to a method where SPSS runs a computation for you in order
to create a new variable.

Refer to the current dataset, Example newedu.

There is a section there with 11 statements on loyalty. See row 23 to 33 in the
Variable View and as shown here, Table 1.

Table 1. Loyalty Items by Rows
Row Name Label

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23 highprob There is a high probability that you will dine at this
restaurant again.
24 recomend You have recommended other people to patronize this
restaurant.
25 sayptive You will say positive thing to other people about the service
provided by this restaurant.
26 feedback You will give positive feedback to this restaurant.

27 trynew You will try the new food or drinks that are recommended
by this restaurant.
28 pricrise You will continue to dine at this restaurant even if the price
or service charge is increased somewhat.
29 prefer You have strong preference on this restaurant.

30 changed You will keep dining at this restaurant; regardless of
everything being changed somewhat.
31 firstcho This restaurant is the first choice in your mind when you
consider having dinner outside.
32 oneofcho Assume that you have only three choices when you are in
need of having dinner, this restaurant must be one of them.
33 regular You have regularly dined at this restaurant for a long
period of time.

Row 23 to Row 27 represents variables that make up Behavioral Loyalty.
Row 28 to Row 30 represents variables that make up for Attitudinal Loyalty.
Row 31 to Row 33 makes up for Cognition Loyalty. The average sum of all
rows creates a measurement for Loyalty.

Let’s say we wish to create a variable named Behavioral Loyalty. We know it
is the average sum of rows 23 to rows 27.

Click on Transform, Compute Variable.

A Compute Variable dialogue box will appear.

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Target Variable refers to the new variable name that we wish to create; in this
case let’s name it behloy.

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Numeric Expression refers to the mathematical formula that we intend to use
to create this new Target Variable. In this case it is the average of the sum of
rows 23 to 27.

The formula then is (highprob + recomend + sayptive + feedback + trynew) /
5.

This is placed in the numeric expression by typing in “ ( “ followed by clicking
on the appropriate variable and bringing it to the Numeric Expression (click on
the arrow). Do this for all the variables required and then place the “ ) ”. Then
place the divide sign ( / ) followed by the number to be divided by to obtain the
average.

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Click OK.

An output will appear. You may consider saving this output for future use.

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Open the data file and look in Variable View. You will find a new row with the
name behloy. There is no label and no values. You must input this. For label,
we suggest Behavioral Loyalty and for values, you can just copy from the
original loyalty dataset and paste. Copy by clicking on the right side of the
mouse when placed on the original values and then click on Copy on the left
side of the mouse. Then click on the new values box, click the right side of the
mouse to depict the dialogue box and click on paste on the left side of the
mouse.

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Open the Data View and have a look at the behloy data column. You will
notice that it is no longer a single number but one with two decimal points.
This is not correct for Likert scale, so it has to be changed. You may just
change it by clicking on the Decimals column in the Variable view and
reducing it to 0 decimals. However, we don’t prefer this as when you run a
frequency SPSS will still show the different decimal points.

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We prefer to open a Microsoft Excel file. Copy all the variables from SPSS
and paste it in the Excel file. Highlight all the numbers in the Excel file. Then
click on Format, Cells and a Format Cells dialogue box will appear. Select
Number and 0 decimal places. Click OK. All the numbers would change to a
single decimal. Copy this and paste it back onto SPSS.

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Select all the data in SPSS. Copy.

Paste the data in Microsoft Excel.

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Select Format, Cells. This is the Format Cells dialogue box. Change the
decimals to 0 and click OK.

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Copy the data and paste in back in SPSS.

Assignment

Now try by yourself to compute into a different variable for the following
situation.

1. Use the dataset Example behloy. Compute the various loyalty variables into
Attitude and Cognition Loyalty.

2. Use the dataset Example behloy. Compute the various loyalty variables into
Overall Loyalty.

See answer here in Example Loyalty.

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4. Syntax

SPSS is run on a program language that most of us will not even use or be
familiar with, Nevertheless, by knowing some simple tricks of the trade, it will
make life easier especially when running repetitive analysis. Syntax in SPSS
is the program language. We do not recommend that you learn it, but if you
wish to do so you may look in the Help topics in SPSS or in its manuals.
When you are running syntax, you can find out what are the commands,
subcommands, and keywords by pressing on F1. For me, and for most
researchers, it would be sufficient enough that you know how to create the
command language and how to run it again and again for your task.

By now you will realize that we have used most of the commands found in the
menu and dialogue boxes. This is because it is easy to use and easier to
understand. However, if you need to repeat your analysis, you can save the
command language in a ‘Syntax’ file so that you can run an analysis at a later
date or to repeat various analyses.

A syntax file is just a file that carries the SPSS language commands. You can
type or paste syntax into a syntax window that is already open.

You can open a new syntax window by choosing: File, New, Syntax.

To save a ‘syntax’ file, from the menus choose: File, Save.

To open a saved syntax file, from the menus choose: File, Open, Syntax.
Select a syntax file that you want from the dialogue box. If no syntax files are
displayed, make sure Syntax (*.sps) is selected in the Files of type drop-down
list. Click Open.

How to get the Commands

As discussed earlier, the normal ways are by reading the manuals and Help
section. We suggest some simpler ways.

Whenever you run an analysis, you will notice that there is a Paste button.
When you click on the paste button, a syntax file will open with the syntax for
the analysis that you intended to do.

Open the file Example Loyalty.

Choose Analyze, Descriptive Statistics, Descriptive. The Descriptives
dialogue box will appear. Choose the variables behloy, attloy, cogloy, and
allloy that were created earlier. If you click on OK, you will get an Output table.
Instead click on Paste.

You will see a Syntax window appear with the commands for the analysis that
you wanted to do.

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You can save the syntax file, as Example Syntax.

Another way to obtain syntax commands is by running the analysis.

You will obtain an output.

If you notice that at the top section of the output is the very same syntax
command as what you have saved earlier.

Create a new syntax file or open an existing syntax file.

Copy the syntax command in the output file and paste it in the syntax file.

Assignment

Now try by yourself to create your own syntax file.

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5. Output

We have been discussing quite a number of matters while looking at the
Output file, yet without discussing this rather important file. As you may have
noticed, every time that you do an analysis or any action in SPSS, an output
file will appear. You can close it or leave it on, depending on your personal
taste and need. We would normally close it as we prefer to have the new
syntax commands and without the clutter of past work. However, sometimes
the past work in itself is essential. Therefore the choice is yours.

In the case of an analysis, you will obtain an output.

See Example of Output.

You will notice that the output file is divided into two sections. One is more of
Headings and the other is the exact output itself. There will be the SPSS
commands syntax, and the various tables relevant to the analysis carried out.

From the output, you can copy whatever data that is relevant to your study
and paste it onto other programs such as MSWord. This is what most
students do. Please don’t do this, as it indicates a lack of analysis on your
part.

This is how students normally present such findings.

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Gender

Frequen Valid Cumulativ
cy Percent Percent e Percent
Valid Male 105 42.2 42.2 42.2
Femal
144 57.8 57.8 100.0
e
Total 249 100.0 100.0

Race

Valid Malay 57 22.9 22.9 22.9
Chines
148 59.4 59.4 82.3
e
Iban 15 6.0 6.0 88.4
Others 29 11.6 11.6 100.0
Total 249 100.0 100.0

Age

Valid 15-2
173 69.5 69.5 69.5
4
25-3
63 25.3 25.3 94.8
4
35-4
13 5.2 5.2 100.0
4
Total 249 100.0 100.0

Education Level

Valid SPM 60 24.1 24.1 24.1
STPM 33 13.3 13.3 37.3
Matriculatio
6 2.4 2.4 39.8
n
Diploma 29 11.6 11.6 51.4
Undergradu
34 13.7 13.7 65.1
ate
Degree 83 33.3 33.3 98.4
Master 4 1.6 1.6 100.0
Total 249 100.0 100.0

BASIC ANALYSIS: 40


Gender

150

100
Frequency

50

0
Male Female
Gender

Age

200

150
Frequency

100

50

0
15-24 25-34 35-44
Age

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Race

150

100
Frequency

50

0
Malay Chinese Iban Others
Race

Education Level

100

80
Frequency

60

40

20

0
SPM STPM Matriculation Diploma Undergraduate Degree Master

Education Level

Again, please don’t do this.

This is common in most students’ presentation of SPSS findings from an
output. A direct cut and paste of the output file. Plus the graphs and the data
set are redundant. Students do this even when the output is for a regression
or a factor analysis. Please note, and we will discuss this, that there are
norms of presentation for various types of analysis.

BASIC ANALYSIS: 42


In this case, a better mode of presentation of the can be done by cut and
paste, but then to remodel the various tables into an acceptable Table for
presentation, such as follows:

Table 1: Respondents Profile
Variable Frequency Percent
Gender Male 105 42.2
Female 144 57.8
Race Malay 57 22.9
Chinese 148 59.4
Iban 15 6.0
Others 29 11.6
Age 15-24 173 69.5
25-34 63 25.3
35-44 13 5.2
Education SPM 60 24.1
Level STPM 33 13.3
Matriculation 6 2.4
Diploma 29 11.6
Undergraduate 34 13.7
Degree 83 33.3
Master 4 1.6

Aside from cut and paste, one can also export what was found in the output
file to MS Word. This is done by right clicking the mouse and selecting Export.

BASIC ANALYSIS: 43


In the Export format box, select Word/RTF file (*.doc).

Then in Export File, click on Browse and select where you wish to save the
exported document to.

Click OK.

BASIC ANALYSIS: 44


The document should appear as OUTPUT.DOC.

It is now a MS Word document and you can create tables from the file instead
of cut and paste over and over again.

Assignment

1. Now try by yourself to run the above frequency.

2. Then try to run the Export function.

BASIC ANALYSIS: 45


6. Some Assumptions

Normally Distributed

Nearly all of these analyses that are discussed here require that the data be
normally distributed. You can check this in a Q-Q Plot.

Click on Analyze, Descriptives, Q-Q Plots.

Select the variable that you require, in this case apology and age. Note that
your Test Distribution is Normal.

BASIC ANALYSIS: 46


Click on OK. The relevant output should appear. See Output QQ Plot.

Check on the Q-Q Plot that the data is normally distributed by noting if the
dots run on the line. These two look acceptable.

Variances are Equal

You can check whether the variances of all variables used are equal by noting
the Levene's Test for Equality of Variances. This will normally appear
whenever you run analysis that requires it.

If the Levene test is significant (the value in Sig. is less than 0.05) then this
indicates that the variance of the two samples are significantly different.

If the Levene test is not significant (the value in Sig. is more than 0.05) then
this indicates that the variance of the two samples are approximately equal.

BASIC ANALYSIS: 47


7. How to Analyze: Frequency


Lets say you want to know the frequency of your respondent’s gender.

Select Analyze, Descriptive Statistics, Frequencies. The Frequencies dialogue
box will appear.

Select Gender and transfer it to the Variables box.

Click on OK.

The Output file will appear. See earlier examples of an output file.

BASIC ANALYSIS: 48


Note

There are three buttons at the bottom of the dialogue box, Statistics, Charts,
and Format.

When you click on Statistics, a Statistics dialogue box will appear. It has four
mini boxes, Percentiles Values, Central Tendencies, Dispersion, and
Distribution.

Percentiles Values is used in cases where you want to know groupings by
quartiles or cut off points, such as in the event that you want to create a new
grouping as discussed in Recode. This will allow you to see what are the
grouping like.

When you click on Charts, it allows you to design your own chart. SPSS
provides you with a number of choices. Once you obtained the Output, you
may then copy it and use it in other programs such as MS Word.

BASIC ANALYSIS: 49


How to Present the Findings

Refer to Output discussion.

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a frequency
from the above dataset for education level.

2. Determine what are the Quartiles for this dataset. Create a pie chart for
the Quartiles.

3. Determine what is the cut point for three groups for education level.
Create a bar chart for the three groups.

BASIC ANALYSIS: 50


8. How to Analyze: Crosstabulation


Lets say you want to know the relationship between gender and its
relationship with the use of apology in service recovery.

Select Analyze, Descriptive Statistics, Crosstabs.

The Crosstabs dialogue box will appear.

You will notice that there are two boxes, one for rows and the other for
columns. This is how you data will be shown, so careful planning has to be
done in order to present your data nicely. In this case we would rather have
Gender in the column and the use of apology in service recovery in the rows.
Why? This is because it would make it easier to see as well as to present the
data later on.

BASIC ANALYSIS: 51


You will also notice that there are three buttons at the bottom of the dialogue
box, Statistics, Cells, and Format.

In Statistics, the normal thing we would do is to click on Chi-square and in
most cases even this is ignored.

In Cells, the main issue is whether to click on Percentages by row, column or
both. This will depend on what you intend to find out.

BASIC ANALYSIS: 52


If click on row, the output will be as such.

If click on column, the output will be as such.

BASIC ANALYSIS: 53


As you can see, therefore the presentation of the data and its interpretation
will differ. The researcher in line with his/her research question and
objectives must make a decision. In this case, we choose by column.

As for Format, we normally just let it be.

Click OK.

The Output file will appear. See Crosstab Output.

BASIC ANALYSIS: 54



Crosstab can be presented in a variety of ways, of which the easiest is to
make it into a Table as shown here for both frequency and percentage, or can
easily be shown for either one by itself.

BASIC ANALYSIS: 55


Table 1: Crosstabulation of Possibility of Apology by Gender
Apology Gender
Male Female

No chance Frequency 0 1
% .0% .7%
Very slight Frequency 0 1
possibility % .0% .7%
Slight Frequency 0 5
possibility % .0% 3.5%
Some Frequency 4 7
possibility % 3.8% 4.9%
Fair possibility Frequency 4 7
% 3.8% 4.9%
Fairly good Frequency 8 19
Good Frequency 18 24
Probable Frequency 11 10
% 10.5% 6.9%
Very probable Frequency 22 25
% 21.0% 17.4%
Almost sure Frequency 13 19
% 12.4% 13.2%
Certain, Frequency 25 26
practically % 23.8% 18.1%
certain

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a cross
tabulation from the above dataset for education level and apology.

2. Prepare a Table to depict your findings.

BASIC ANALYSIS: 56


9. How to Analyze: Means


Lets say you want to know the Means of the various measurements of loyalty
that you have used, from the variables to the summations.

Select Analyze, Descriptive Statistics, Descriptives. The Descriptives dialogue
box will appear.

Select and transfer all the loyalty variables into Variable(s) box.

Click OK.

The output will appear. See Means Output.

BASIC ANALYSIS: 57


Note

There is an Options button. Click on it and it will depict the following.

Normally we are satisfied with this though sometimes there may be a need to
test for Distribution. Click Continue.

BASIC ANALYSIS: 58



Means can be presented in a variety of ways, of which the easiest is to make
it into a Table as shown here. Or you can just show the summation loyalty
variables.

Table 1: Means for Loyalty Variables
Variables Mean Std. Dev.
There is a high probability that you will dine at this
restaurant again. 6.43 2.31
You have recommended other people to patronize this
restaurant. 5.66 2.26
You will say positive thing to other people about the
service provided by this restaurant. 5.82 2.26
You will give positive feedback to this restaurant. 5.43 2.30
You will try the new food or drinks that are
recommended by this restaurant. 6.14 2.44
Behavioral Loyalty 5.88 1.97
You will continue to dine at this restaurant even if the
price or service charge is increased somewhat. 4.99 2.23
You have strong preference on this restaurant. 5.51 2.12
You will keep dining at this restaurant; regardless of
everything being changed somewhat. 5.06 2.18
Attitude Loyalty 5.20 1.91
This restaurant is the first choice in your mind when
you consider having dinner outside. 4.88 2.31
Assume that you have only three choices when you
are in need of having dinner, this restaurant must be
one of them. 5.50 2.49
You have regularly dined at this restaurant for a long
period of time. 5.18 2.51
Cognition Loyalty 5.20 2.15
Overall Loyalty 5.50 1.78

Or:

Table 1: Means for Loyalty Variables
Variables Mean Std. Dev.
Behavioral Loyalty 5.88 1.97
Attitude Loyalty 5.20 1.91
Cognition Loyalty 5.20 2.15
Overall Loyalty 5.50 1.78

BASIC ANALYSIS: 59


Assignment

1. Open the file Example Loyalty. Now try by yourself to run a Means from
the above dataset for all the Service Recovery variables.


BASIC ANALYSIS: 60


10. How to Analyze: t-test


T-test is normally used when there are only two values in a variable. An
Anova is used when there are three or more values in a variable. SPSS offers
3 types of t-test:

1. One Sample T-Test
2. Independent Sample T-Test
3. Paired Samples T-Test

One Sample T-Test

A One Sample T-Test compares the mean score of a sample to a known
value. Lets say you want to know whether in your education variable, that the
respondent’s education level is different from the known population mean. In
this case, the mean for education level, let say is 4.

Click on Analyze, Compare Means, One Sample T-Test. The following
dialogue box will appear.

Click and transfer Education Level variable to the Test Variable Box. Then
type in the Test Value, which refers to the known population mean. In this
case, lets assume it is 4.

BASIC ANALYSIS: 61


Click OK.

The output file will appear. See Output One Sample T Test.

The T value is –1.218, with 248 degrees of freedom. The significance value is
0.224. This means that there is no significance difference between the two
groups (the significance is more than 0.05).

BASIC ANALYSIS: 62



T-Test findings can be presented as a sentence or a Table. If in a sentence,
We would say:

T-test findings indicate that the Education Level variable (t = -1.218, p =
0.224) is not significantly different from the population mean.

Or, it could also be presented in Table form as follows:

Table 1: One-Sample Test for Educational Level
Variable Test Value = 4
t df Sig. (2-tailed)
Education Level -1.218 248 .224

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a One
Sample T-Test from the above dataset for all the Service Recovery variables,
with a Test Value of 5.


Independent Sample T-Test

An Independent Samples T Test compares the mean scores of two groups on
a given variable. Lets say you want to know whether the means for apology to
be used as a service recovery is similar or different between men and women.

Click on Analyze, Compare Means, Independent Samples T Test. The
dialogue box will appear.

BASIC ANALYSIS: 63


You will see that there is a Test Variable box and Grouping Variable box.
Move the dependent variable, in this case Apology, to the Test Variable box.
Move the Independent Variable, in this case Gender, to the Grouping
Variable.

When you have done so, you will notice that the Define Groups button pops
up. Click on it and define your groups.

BASIC ANALYSIS: 64


As you know, we have only two values here, that is 1 and 2. Type it in.

Click on Continue.

There is an Options button in the main Independent Samples T Test dialogue
box. This is to indicate the Confidence Interval that you wish to use. We
normally leave it at 95%.

The Output file will appear. See Output for Independent Sample T-Test.

BASIC ANALYSIS: 65


The Output depicts the Means. Here we can see that men score higher than
women for the variable apology.

Group Statistics

Std. Error
Gender N Mean Std. Deviation Mean
apology Male 105 7.5810 1.99413 .19461
Female 144 6.9444 2.39398 .19950

The next output that is important is to note the Levene’s Test.

Independent Samples Test
Levene's Test for Equality
of Variances
F Sig.
Lower Upper
apology Equal variances assumed
4.994 .026
Equal variances not
assumed

BASIC ANALYSIS: 66


This is important, as this is part of the assumptions for running this test, that
the variances are approximately equal. If the Levene test is significant (the
value in Sig. is less than 0.05) then this indicates that the variance of the two
samples are significantly different. If the Levene test is not significant (the
value in Sig. is more than 0.05) then this indicates that the variance of the two
samples are approximately equal.

However in this example, the Levene test is significant, indicating that the
variance of the two samples are significantly different.

The next portion to note is the results of the Independent T-Test.

Independent Samples Test

t-test for Equality of Means
Mean Std. Error 95% Confidence Interval
t df Sig. (2-tailed) Difference Difference of the Difference

Lower Upper Lower Upper Lower Upper Lower

2.220 247 .027 .63651 .28673 .07175 1.20126

2.284 242.594 .023 .63651 .27870 .08753 1.18548

Read the BOTTOM line when the Levene test indicates that the variances of
the two samples are significantly different.

Read the TOP line when the Levene test indicates that the variances of the
two samples are approximately equal.

In this case, we read the BOTTOM line. There is a significance difference
between the two groups (the significance level is less than 0.05). Therefore
this indicates that how men and women see the possibility of apology being
used as a service recovery effort is different.


We would say:

How men and women see the possibility of apology being used as a service
recovery effort is different (t = 2.284, p = 0.023).


BASIC ANALYSIS: 67


Table 1: Independent Sample Test for Apology
Variable t df Sig. (2-tailed)
Apology 2.284 242.594 .023

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a Independent
Sample T-Test from the above dataset for all the Service Recovery
variables, against gender.


Paired Samples T-Test

The Paired Samples T-Test compares the means of two variables. This T-
Test measures the difference between the two variables for each case, and
then tests to see if the average difference is significantly different from zero.
Lets say you want to see if there is any difference between the variable
apology and apology1 as methods of service recovery.

Click on Analyze, Compare Means, Paired Samples T Test. The dialogue box
will appear.

Click on apology and apolgy1. You will notice that when you click the
variables, it will appear in the Current Selections. You can only choose two
variables at a time.

BASIC ANALYSIS: 68


Then transfer it to the Paired Variables box.

The Options button is similar to the previously discussed button.

Click OK and the Output will appear. See Output Paired Samples T-Test.

BASIC ANALYSIS: 69


The first output depicts the Means, which in this case indicates that apology is
seen as more probable response for service recovery.

Paired Samples Statistics

Std. Error
Mean N Std. Deviation Mean
Pair apology 7.2129 249 2.25199 .14271
1 Apology 1 6.4699 249 2.16462 .13718

The second output depicts correlation between the two variables. Apparently
there is a high correlation between apology and assistance.

Paired Samples Correlations

N Correlation Sig.
Pair 1 apology & Apology 1 249 .601 .000

The last part that we need to see is the difference. In this case there is a clear
difference. If the significance value is less than .05, there is a significant
difference. If the significance value is greater than. 05, there is no significant
difference.

BASIC ANALYSIS: 70


Paired Samples Test

Paired Differences
95% Confidence
Interval of the
Std. Error Difference
Mean Std. Deviation Mean Lower Upper t df Sig. (2-tailed)
Pair 1 apology - Apology 1 .74297 1.97316 .12504 .49669 .98925 5.942 248 .000


We would say:

Apology and Assistance correlates well (Correlation = 0.601, p = 0.000) yet
the paired samples t-test indicates that there is significant difference between
the two variables (t = 5.942, p = 0.000).


Table 1: Paired Sample Test for Apology-Assistance
Variable t df Sig. (2-tailed)
Apology-Assistance 5.942 248 .000

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a Paired Sample
T-Test from the above dataset for assist and assist1.


BASIC ANALYSIS: 71


11.. How to Analyze: Correlation

Correlation is used when you want to know how two variables are associated
with each other and how strong that association is. Correlation can also tell
you the direction of the association.

Pearson R Correlation is used when the data that you are using is normally
distributed. When the data that you are using is not normally distributed, then
you use Spearman Rho.

SPSS offers three types of correlations:
1. Bivariate
2. Partial
3. Distance

The normally used correlation is Bivariate, which will be discussed here.


Lets say you want to know the correlation between apology and assist
variables. These are two variables that are used in service recovery.

Select Analyze, Correlations, Bivariate. A dialogue box will appear.

Remember, if your data is normally distributed then use Pearson R
Correlation and if it is not normally distributed, then you use Spearman Rho.
This can be seen under the Table Correlation Coefficients. You can also
choose whether to use Two-tailed or One-tailed significance test.

BASIC ANALYSIS: 72


Select apology and assist and transfer it to the Variables box.

The Options button here allows you to choose if you require added statistics.
Normally we don’t use it, as the statistics would have been calculated earlier.

Click OK and the output will appear. See Output Correlation.

The output provides us with the correlation coefficient, significance and
number of cases (N). The correlation coefficient is shown as a number
between +1 and -1. The strength of the correlation can be seen as when it
gets nearer to either +1 or –1. The correlation coefficient also provides the

BASIC ANALYSIS: 73


direction of the relationship, either positive (one increase, so does the other)
or negative (one increase the other decrease). In this case, the correlation
coefficient is 0.601, which is quite acceptable and positive. So in this case, as
the probability of apology increase, there is also an increase in the probability
of assistance.


Correlation findings can be presented as a sentence or a Table. If in a
sentence, we would say:

Apology and Assistance correlates well (Correlation = 0.601, p = 0.000).


Table 1: Correlations
Variable Apology
Assist .601(*)
* Correlation is significant at the 0.01 level (2-tailed).

Assignment

1. Open the file Example Loyalty. Now try by yourself to run Correlations
from the above dataset for all the Service Recovery variables.


BASIC ANALYSIS: 74


12. How to Analyze: One-Way ANOVA

The One-Way ANOVA compares the mean of one or more groups based on
one independent variable.


Lets say you want to know the One-Way Anova between the variable apology
and age level. In simple terms, you want to know whether there is any
difference in how the various age groups look at the variable apology.

Click on Analyze, Compare Means, One-Way Anova. The dialogue box will
appear.

Click on apology and move it to the Dependent List box. Click on Age and
move it to the Factor box.

BASIC ANALYSIS: 75


There are three buttons at the bottom, Contrast, Post Hoc, and Options. You
will need to look at Options and Post Hoc. Click on Options and click on the
boxes for Descriptives and Homogeneity of Variance. Click continue.

Click on Post Hoc and when it opens, it will show you various post hoc tests.
Normally we would use either Tukey or Bonferroni. If there are equal numbers
of cases in each group, choose Tukey. If there are not equal numbers of
cases in each group, choose Bonferroni. You can also click on more than one,
but this is just a post hoc test so there is no need to do so. In this case we
choose Bonferroni. You will also note that the significance level is maintained
at 95% level of confidence or shown here as .05. Click continue.

Click OK and the output should appear.

BASIC ANALYSIS: 76


Now lets look back at the One-Way Anova analysis that we had run.

The output would have appeared as such. Refer to Output One Way Anova.

We can see the Means by the age group for apology. The means look similar,
with those in the age bracket of 25 – 34 scoring highest.

Descriptives

apology
95% Confidence Interval for
Mean
Lower Upper Std.
Lower Upper Lower Upper Lower Maximu
Upper
N
Bound Mean
Bound Deviation
Bound Std. Error
Bound Bound Bound Minimum
Bound m
Bound
15-24 173 7.1214 2.32335 .17664 6.7727 7.4701 .00 10.00
25-34 63 7.5079 2.09356 .26376 6.9807 8.0352 2.00 10.00
35-44 13 7.0000 2.04124 .56614 5.7665 8.2335 4.00 10.00
Total 249 7.2129 2.25199 .14271 6.9318 7.4939 .00 10.00

Then the Levene test, as have been discussed earlier. If the Levene test is
significant (the value in Sig. is less than 0.05) then this indicates that the
variances of the samples are significantly different. If the Levene test is not

BASIC ANALYSIS: 77


significant (the value in Sig. is more than 0.05) then this indicates that the
variances of the samples are approximately equal.

Test of Homogeneity of Variances

apology
Levene
Statistic df1 df2 Sig.
1.280 2 246 .280

We note that the variances are equally distributed. Then we can see the
Anova findings. The findings here indicate that the significance value is 0.478,
which is more than 0.05. This indicates that there is no significant difference
between the groups.

ANOVA

apology
Sum of
Squares df Mean Square F Sig.
Between Groups 7.522 2 3.761 .740 .478
Within Groups 1250.197 246 5.082
Total 1257.719 248

Lastly the Bonferroni test is shown. SPSS will highlight with an asterisk (*) if
there is any significant differences. In this case there is none.

Multiple Comparisons

Dependent Variable: apology
Bonferroni

95% Confidence Interval
Mean
Difference (I-
Lower Upper Lower
(I) Age (J) Age J)
Bound Std. Error
Bound Sig.
Bound Upper Bound Lower Bound
15-24 25-34 -.38655 .33173 .735 -1.1862 .4131
35-44 .12139 .64831 1.000 -1.4413 1.6841
25-34 15-24 .38655 .33173 .735 -.4131 1.1862
35-44 .50794 .68673 1.000 -1.1474 2.1633
35-44 15-24 -.12139 .64831 1.000 -1.6841 1.4413
25-34 -.50794 .68673 1.000 -2.1633 1.1474


One-Way Anova findings can be presented as a sentence or a Table. If in a
sentence, we would say:

BASIC ANALYSIS: 78


There was no significant difference in how the age group saw apology (F =
0.740, p = 0.478).


Table 1: One Way Anova by Age Scale
Variable F Sig.
Apology .740 .478

Assignment

1. Open the file Example Loyalty. Now try by yourself to run One-Way Anova
from the above dataset for all the Service Recovery variables by Age
scale.


BASIC ANALYSIS: 79


13. How to Analyze: Regression

General linear model can be used to analyze designs with categorical
predictor (e.g., name, rank) and continuous predictor (e.g., interval scales,
ratio scales). The purpose of running a simple regression is for hypotheses
testing about whether the predictor variables are related to the criterion
variable.

A simple regression equation is normally written as such:

Y=b0 + b1X

Basically, there are three types of major regression models, which are known
as standard regression, stepwise regression, and hierarchical regression.

Standard Regression

Simple linear regression is used to predict the impact of independent variable
on the values of a continuous, interval-scaled dependent variable. It depicts
the strength of the predictor variables in order to make a better conclusion
about others.

Open the file Example Regression.

Lets say if you want to know the relationship between the predictors (affect,
loyalty, respect, contribute are the important dimensions in LMX) and soft
tactic (management influence methods). In other words, you want to find out
which of the independent variables is the best predictor of the use of soft
tactic.

Click on Analyze, Regression, Linear…. The dialogue box will appear as
follows.

BASIC ANALYSIS: 80


Click on contribute, respect, affect and loyalty and move it to the
Independent(s) box. Click on soft_T and move it to the Dependent box. The
following dialogue box will appear.

BASIC ANALYSIS: 81


Then click on the Statistics… button, and you will come to the Linear
Regression Statistics box. Select casewise diagnostics in the Residuals box.
The purpose of using the casewise diagnostics is to make sure that all
observations outside the range of 3 standard deviations were considered as
outliers and should be excluded for further analyses. Click continue.

BASIC ANALYSIS: 82


If you plan to display the model in a scatterplot, standard practice is to make
the independent variable as X-axis, and dependent variable as Y-axis.

Click on Plots which is located at the bottom of Linear Regression box, and
you will open the following box. Click on ZPRED at the left hand side and
move it to the X-axis, and click on ZRESID and move it to the Y-axis. Click on
the normal probability plot in the Standardized Residual Plots and click
continue.

You will return to the main Linear Regression box. Click on OK and you will
obtain your Output. Refer to output std regressions.

BASIC ANALYSIS: 83


The first Table that you will see is Variables Entered/Removed(b) which
shows the variables that have been used in this design and the method used.

b
Variables Entered/Removed

Variables Variables
Model Entered Removed Method
1 affect,
respect,
. Enter
loyalty, a
contribute
a. All requested variables entered.
b. Dependent Variable: soft_T

The next Table shows the Model Summary, which indicates R. The important
thing to note here is R Square, which measures the percentage of explanatory
power of the independents used. Therefore, in this example, it is evident that
the variables explain 10% of the variance in soft tactic.

BASIC ANALYSIS: 84


b
Model Summary

Adjusted Std. Error of
Model R R Square R Square the Estimate
1 .323a .104 .081 1.04953
a. Predictors: (Constant), affect, respect, loyalty, contribute

The next table shows that the model is significant (p< .00) with F value equals
to 4.462.

b
ANOVA

Sum of
Model Squares df Mean Square F Sig.
1 Regression 19.660 4 4.915 4.462 .002a
Residual 168.530 153 1.102
Total 188.190 157
a. Predictors: (Constant), affect, respect, loyalty, contribute

The unstandardized coefficient of an independent variable is known as
which measures the strength of the predictors and the criterion variables. We
are using the unstandardized coefficients in view of the fact that they can be
measured on different scales. For example, we cannot compare the value for
gender with the value for soft tactic.

Coefficientsa

Unstandardized Standardized
Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 2.366 .569 4.158 .000
contribute -.444 .171 -.353 -2.595 .010
respect .292 .128 .247 2.288 .023
loyalty .123 .131 .111 .939 .349
affect .338 .180 .256 1.879 .062
a. Dependent Variable: soft_T

The table above has shown that there is significant relationship between
respect and contribute with soft tactic at p<.05.

BASIC ANALYSIS: 85

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