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2007 guide book on SPSS / AMOS for my 2nd year students by myself, Dr Lo, and Heriyadi, who have agreed to share this online. A bit outdated now, but can still be used.
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1BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
ASSOCIATE PROFESSOR DR ERNEST CYRIL DE RUN
DR LO MAY CHIUN
HERIYADI KUSNARYADI
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2BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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
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3BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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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4BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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5BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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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6BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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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7BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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.’
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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.
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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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16BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
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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17BASIC ANALYSIS:
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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
cy Percent
Valid
Percent
Cumulativ
e Percent
Valid Male 105 42.2 42.2 42.2
Femal
e
144 57.8 57.8 100.0
Total 249 100.0 100.0
Race
Frequen
cy Percent
Valid
Percent
Cumulativ
e Percent
Valid Malay 57 22.9 22.9 22.9
Chines
e
148 59.4 59.4 82.3
Iban 15 6.0 6.0 88.4
Others 29 11.6 11.6 100.0
Total 249 100.0 100.0
Age
Frequen
cy Percent
Valid
Percent
Cumulativ
e Percent
Valid 15-2
4
173 69.5 69.5 69.5
25-3
4
63 25.3 25.3 94.8
35-4
4
13 5.2 5.2 100.0
Total 249 100.0 100.0
Education Level
Frequen
cy Percent
Valid
Percent
Cumulativ
e Percent
Valid SPM 60 24.1 24.1 24.1
STPM 33 13.3 13.3 37.3
Matriculatio
n
6 2.4 2.4 39.8
Diploma 29 11.6 11.6 51.4
Undergradu
ate
34 13.7 13.7 65.1
Degree 83 33.3 33.3 98.4
Master 4 1.6 1.6 100.0
Total 249 100.0 100.0
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Gender
FemaleMale
Fre
qu
en
cy
150
100
50
0
Gender
Age
35-4425-3415-24
Freq
uen
cy
200
150
100
50
0
Age
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Race
OthersIbanChineseMalay
Freq
uen
cy
150
100
50
0
Race
Education Level
MasterDegreeUndergraduateDiplomaMatriculationSTPMSPM
Fre
qu
en
cy
100
80
60
40
20
0
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.
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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
Level
SPM 60 24.1
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.
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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.
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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.
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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.
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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.
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7. How to Analyze: Frequency
Open the file Example Loyalty.
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.
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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.
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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.
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8. How to Analyze: Crosstabulation
Open the file Example Loyalty.
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.
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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.
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If click on row, the output will be as such.
If click on column, the output will be as such.
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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.
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How to Present the Findings
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.
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Table 1: Crosstabulation of Possibility of Apology by Gender
Apology
Gender
Male Female
No chance
Frequency 0 1
% .0% .7%
Very slight
possibility
Frequency 0 1
% .0% .7%
Slight
possibility
Frequency 0 5
% .0% 3.5%
Some
possibility
Frequency 4 7
% 3.8% 4.9%
Fair possibility
Frequency 4 7
% 3.8% 4.9%
Fairly good
possibility
Frequency 8 19
% 7.6% 13.2%
Good
possibility
Frequency 18 24
% 17.1% 16.7%
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,
practically
certain
Frequency 25 26
% 23.8% 18.1%
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.
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9. How to Analyze: Means
Open the file Example Loyalty.
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.
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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.
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How to Present the Findings
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
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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.
2. Prepare a Table to depict your findings.
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10. How to Analyze: t-test
Open the file Example Loyalty.
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.
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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).
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How to Present the Findings
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.
2. Prepare a Table to depict your findings.
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.
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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.
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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.
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The Output depicts the Means. Here we can see that men score higher than
women for the variable apology.
Group Statistics
Gender N Mean Std. Deviation
Std. Error
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
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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
t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
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.
How to Present the Findings
T-Test findings can be presented as a sentence or a Table. If in a sentence,
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).
Or, it could also be presented in Table form as follows:
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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.
2. Prepare a Table to depict your findings.
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.
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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.
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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
7.2129 249 2.25199 .14271
6.4699 249 2.16462 .13718
apology
Apology 1
Pair
1
Mean N Std. Deviation
Std. Error
Mean
The second output depicts correlation between the two variables. Apparently
there is a high correlation between apology and assistance.
Paired Samples Correlations
249 .601 .000apology & Apology 1Pair 1
N Correlation Sig.
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.
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Paired Samples Test
.74297 1.97316 .12504 .49669 .98925 5.942 248 .000apology - Apology 1Pair 1
Mean Std. Deviation
Std. Error
Mean Lower Upper
95% Confidence
Interval of the
Difference
Paired Differences
t df Sig. (2-tailed)
How to Present the Findings
T-Test 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) yet
the paired samples t-test indicates that there is significant difference between
the two variables (t = 5.942, p = 0.000).
Or, it could also be presented in Table form as follows:
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.
2. Prepare a Table to depict your findings.
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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.
Open the file Example Loyalty.
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.
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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
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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.
How to Present the Findings
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).
Or, it could also be presented in Table form as follows:
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.
2. Prepare a Table to depict your findings.
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12. How to Analyze: One-Way ANOVA
The One-Way ANOVA compares the mean of one or more groups based on
one independent variable.
Open the file Example Loyalty.
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.
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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.
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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
N Mean
Std.
Deviation Std. Error
95% Confidence Interval for
Mean
Minimum
Maximu
m
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
15-24173 7.1214 2.32335 .17664 6.7727 7.4701 .00 10.00
25-3463 7.5079 2.09356 .26376 6.9807 8.0352 2.00 10.00
35-4413 7.0000 2.04124 .56614 5.7665 8.2335 4.00 10.00
Total249 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
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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
(I) Age (J) Age
Mean
Difference (I-
J) Std. Error Sig.
95% Confidence Interval
Lower
Bound
Upper
Bound
Lower
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
How to Present the Findings
One-Way Anova findings can be presented as a sentence or a Table. If in a
sentence, we would say:
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There was no significant difference in how the age group saw apology (F =
0.740, p = 0.478).
Or, it could also be presented in Table form as follows:
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.
2. Prepare a Table to depict your findings.
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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.
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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.
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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.
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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.
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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.
Variables Entered/Removedb
affect,
respect,
loyalty,
contribute
a
. Enter
Model
1
Variables
Entered
Variables
Removed Method
All requested variables entered.a.
Dependent Variable: soft_Tb.
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.
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Model Summaryb
.323a
.104 .081 1.04953
Model
1
R R Square
Adjusted
R Square
Std. Error of
the Estimate
Predictors: (Constant), affect, respect, loyalty, contributea.
Dependent Variable: soft_Tb.
The next table shows that the model is significant (p< .00) with F value equals
to 4.462.
ANOVAb
19.660 4 4.915 4.462 .002a
168.530 153 1.102
188.190 157
Regression
Residual
Total
Model
1
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), affect, respect, loyalty, contributea.
Dependent Variable: soft_Tb.
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
2.366 .569 4.158 .000
-.444 .171 -.353 -2.595 .010
.292 .128 .247 2.288 .023
.123 .131 .111 .939 .349
.338 .180 .256 1.879 .062
(Constant)
contribute
respect
loyalty
affect
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: soft_Ta.
The table above has shown that there is significant relationship between
respect and contribute with soft tactic at p<.05.
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The following is the Normal P-P Plot of Regression Standardized Residual.
The rationale for showing the graph is to confirm the normality and linearity of
the data.
Observed Cum Prob
1.00.80.60.40.20.0
Ex
pe
cte
d C
um
P
ro
b
1.0
0.8
0.6
0.4
0.2
0.0
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: soft_T
How to Present the Findings
Standard Regression findings can be presented as a sentence. If in a
sentence, we would say:
There was a significant relationship between respect and soft tactic = .292,
p < .05).
Assignment
Open the file Example Regression Now try by yourself to run standard
regression from the above dataset for all the independent variables (affect,
loyalty, respect, and contribute) on rational_T.
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Stepwise Regression
The main purpose of running a stepwise regression is to identify a better
subset of predictors by adding and removing variables to the regression
model. There are three basic procedures to observe namely, (i) identifying an
initial model, (ii) repeat altering the previous model by adding or removing a
predictor variable, and lastly, (iii) terminate the search when stepping is no
longer needed or when the number of steps have reached the maximum. In
fact, stepwise selection is a combination of forward and backward procedures.
First, click on Analyze, Regression, and then on Linear…. The dialogue box
will appear. In the Method box, select stepwise from the drop-down list. Again,
make sure that casewise diagnostics has been selected in the Statistics box
as explained previously. Click continue.
The output would have appeared as such. Refer to output Stepwise
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The first Table that you will see is Variables Entered/Removed(b) which
shows the variables that have been found to be significant in this design and
the method used.
Variables Entered/Removeda
respect .
Stepwise
(Criteria:
Probabilit
y-of-
F-to-enter
<= .050,
Probabilit
y-of-
F-to-remo
ve >= .
100).
Model
1
Variables
Entered
Variables
Removed Method
Dependent Variable: soft_Ta.
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As shown in the Coefficients(a) table, it was found that only one component in
LMX, namely respect dimension is found to have significant impact on soft
tactic.
Coefficientsa
2.531 .484 5.229 .000
.281 .092 .237 3.051 .003
(Constant)
respect
Model
1
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig.
Dependent Variable: soft_Ta.
We note that in Excluded Variables(b) table, the other three variables are
excluded as it could not meet the selection criteria (e.g., probability of F <=
.05) and the tolerance level in Collinearity Statistics have exceeded the value
of .10.
Excluded Variablesb
.122a
1.195 .234 .096 .576
.076a
.815 .416 .065 .697
-.126a
-1.197 .233 -.096 .546
affect
loyalty
contribute
Model
1
Beta In t Sig.
Partial
Correlation Tolerance
Collinearity
Statistics
Predictors in the Model: (Constant), respecta.
Dependent Variable: soft_Tb.
How to Present the Findings
Stepwise Regression findings can be presented as a sentence. If in a
sentence, we would say:
There was a significant relationship between respect and soft tactic = .281,
p < .05).
The other two types of stepwise regressions are Forward Selection and
Backward Selection. The procedures for using these two methods are the
same as the Enter stepwise method. For except the fact that, Forward
Selection, starts with no variables in the model, and after that variables are
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entered one by one until it reaches the statistical significant level. On the other
hand, Backward Selection starts with all variables in the model, and after that
take out one by one until the model is not significant.
Forward Selection
First, click on Analyze, Regression, and then on Linear…. The dialogue box
will appear. In the Method box, select forward from the drop-down list. Again,
make sure that casewise diagnostics has been selected in the Statistics box
as explained previously. Click continue.
Backward Selection
First, click on Analyze, Regression, and then on Linear…. The dialogue box
will appear. In the Method box, select backward from the drop-down list.
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Assignment
Open the file Example Regression. Now try by yourself to run stepwise
regression from the above dataset for all the independent variables (affect,
loyalty, respect, and contribute) on rational_T.
Hierarchical Multiple Regression
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Generally, hierarchical multiple regression is used to explore the patterns of
relationship between a number of predictor variables and one criterion
variable. Normally, theoretical considerations will be used as a guide to
determine the order of entry.
Open the file Example Regression.
Lets say you want to use the hierarchical multiple regression to find out
whether gender of the supervisor would affect the relationship between LMX
and soft influence tactics.
Click on Analyze, Regression, Linear…. The dialogue box will appear.
Click on soft_T and move it to the Dependent box. Click on contribute,
respect, loyalty and affect and move it to the independent(s) box.
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Click on Next and move gender of the supervisors into Block 2 of 2.
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Click on Statisctics…and you will see Linear Regression: Statistics box.
Select R squared change and collinearity diagnostics and also click on Case
diagnostics at the Residuals box.
R squared change is used to asses the contribution of new predictors, in this
case is leader’s gender, to the variance in the dependent variable.
Multicollinearity happens when there are two or more variables are found to
have close to perfect linear combinations of one another. This has to be done
to make sure that regression model estimates of the coefficients will not
become unstable and the standard errors are not wildly inflated.
The reason for selecting Case Diagnostic is to make sure that those influential
outliers outside the range of 3 standard deviations can be identified and
subsequently omitted and dropped from the regression. This has to be done
to make sure that regression model estimates of the coefficients will not be
come unstable and the standard errors are not wildly inflated. Click continue.
Click OK and the output should appear. Now lets look back at the regression
analysis that we had run. The output would have appeared as such. Refer to
output hierarchical regression.
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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.
Variables Entered/Removedb
affect,
respect,
loyalty,
contribute
a
. Enter
Leader
gender
a. Enter
Model
1
2
Variables
Entered
Variables
Removed Method
All requested variables entered.a.
Dependent Variable: soft_Tb.
Next table shows the Model Summary(c). There are two models shown in this
table. Model 1 refers to the first stage in the regression where only four
dimensions of LMX namely, contribute, respect, loyalty, and affect are used
as predictors. Model 2 refers to the final model where leader gender has been
included.
It was found that LMX (affect, respect, loyalty, contribute) accounts for 11.7%
of the variability in explaining soft_T as shown in R Square. Whereas as for
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the second model, R Square increases to 11.8% indicating that the additional
variable entered in this model, namely leader gender has contributed for an
extra .10% .
Model Summaryc
.342a
.117 .086 1.05416 .117 3.837 4 116 .006
.344b
.118 .080 1.05778 .002 .209 1 115 .649
Model
1
2
R R Square
Adjusted
R Square
Std. Error of
the Estimate
R Square
Change F Change df1 df2 Sig. F Change
Change Statistics
Predictors: (Constant), affect, respect, loyalty, contributea.
Predictors: (Constant), affect, respect, loyalty, contribute, Leader genderb.
Dependent Variable: soft_Tc.
The next table is the Anova(c) Table which demonstrates whether the two
models are significantly predicting the outcome. For the first model, the F
value is 3.837 (p < .00) which shows that the model is significant. The second
model posited a F value of 3.09 (p < .05). Thus, it is interpreted that the first
model has a better significant result in explaining soft_T.
ANOVAc
17.055 4 4.264 3.837 .006a
128.906 116 1.111
145.961 120
17.288 5 3.458 3.090 .012b
128.673 115 1.119
145.961 120
Regression
Residual
Total
Regression
Residual
Total
Model
1
2
Sum of
Squares df Mean Square F Sig.
Predictors: (Constant), affect, respect, loyalty, contributea.
Predictors: (Constant), affect, respect, loyalty, contribute, Leader genderb.
Dependent Variable: soft_Tc.
The following table is the Coefficients(a) Table. Beta value tells us about the
positive/negative relationship between predictors and the outcome. We should
look out for significant level which is associated with t value. In this case, only
one dimension of LMX, namely affect, has significant positive impact on
soft_T (t = 1.665, p< .10). The tolerance value and VIF under collinearity
statistics column shows a value of more than 1.0 and less than 10,
respectively, indicating that all the predictors do not have multicollinearity.
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Coefficientsa
2.070 .610 3.393 .001
-.277 .192 -.232 -1.444 .152 .296 3.379
.159 .153 .142 1.038 .301 .406 2.463
.180 .143 .168 1.263 .209 .431 2.319
.333 .200 .261 1.665 .099 .309 3.234
1.957 .661 2.963 .004
-.281 .193 -.235 -1.461 .147 .295 3.388
.163 .154 .146 1.060 .291 .405 2.472
.171 .144 .160 1.189 .237 .424 2.359
.339 .201 .265 1.683 .095 .308 3.245
.101 .221 .041 .457 .649 .972 1.029
(Constant)
contribute
respect
loyalty
affect
(Constant)
contribute
respect
loyalty
affect
Leader gender
Model
1
2
B Std. Error
Unstandardized
Coefficients
Beta
Standardized
Coefficients
t Sig. Tolerance VIF
Collinearity Statistics
Dependent Variable: soft_Ta.
The last table is Excluded Variables Table. It tells us which variable that
entered into the model does not have significant impact on the model’s ability
to predict soft_T. In this case, it was found that leader gender does not predict
soft_T (t = .457, p >.05).
Excluded Variablesb
.041a
.457 .649 .043 .972 1.029 .295Leader gender
Model
1
Beta In t Sig.
Partial
Correlation Tolerance VIF
Minimum
Tolerance
Collinearity Statistics
Predictors in the Model: (Constant), affect, respect, loyalty, contributea.
Dependent Variable: soft_Tb.
How to Present the Findings
Hierarchical Regression findings can be presented as a sentence. If in a
sentence, we would say:
There was a significant relationship between affect and soft tactic t = 1.665,
p< .10).
Assignment
Open the file Example Regression. Now try by yourself to run hierarchical
regression from the above dataset for all the independent variables (affect,
loyalty, respect, and contribute), enter as first block, and leader gender as
second block on rational_T.
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14. How to Analyze: Manova
MANOVA or Multivariate analysis of variance is simply an Anova that runs on
several dependent variables. MANOVA is used to assess whether an overall
difference exist between groups, and the differences among the combinations
that is presented by the researcher. Normally, after a Manova is run, then only
does the researcher carry out Univariate GLM analysis.
As an example, in our One-way Anova, we ran the analysis only on age and
apology. What if we wanted to test all the various methods available for
service recovery and add on education level as well? Running a MANOVA
can do this.
Open the file Example Loyalty.
Select Analyze, click on General Linear Model, Multivariate. The dialogue box
will appear.
Click on all the service recovery variables and transfer them to Dependent
Variables. Transfer age and education level to Fixed Factors.
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The buttons on the right side are for further consideration by the researcher.
For me, We would leave the Model, Contrast and Save buttons. As for Plots, it
depend on the researcher as to whether do you require graphs. If you do,
select Plots and the following box will appear.
Select the plots that you require, lets say by age, edu, and its interaction. This
can be done by clicking on age and placing it on the Horizontal Axis. Then
click on Add.
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To have both age by edu, click age and place it on the Horizontal Axis and
click on edu and place it on Separate Lines. Click Add. Click on Continue.
As for Post Hoc, when you click on it you will be provided with a variety of
options.
Select and transfer the relevant factors to the Post Hoc Tests for: box.
Choose and click on the relevant post hoc test that you wish to carry out. In
this case, Bonferroni.
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For the Options button, when you click on it, he following will display.
If you wish to display the Means, select all and transfer to the Display Means
for: box. We would normally click on descriptive Statistics, Spread vs. level
plots, and Residual plots. Then click on Continue.
You will return to the main Multivariate dialogue box. Click OK.
The output will appear. Refer to Output Manova.
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You will find displayed initially the Between Subject Factors and the
Descriptive Statistics. The next box will depict the Multivariate Test, which is
of greatest importance to you.
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Multivariate Testsc
.752 36.541a
18.000 217.000 .000
.248 36.541a
18.000 217.000 .000
3.031 36.541a
18.000 217.000 .000
3.031 36.541a
18.000 217.000 .000
.194 1.300 36.000 436.000 .120
.814 1.309a
36.000 434.000 .114
.220 1.318 36.000 432.000 .108
.163 1.978b
18.000 218.000 .012
.400 .880 108.000 1332.000 .802
.658 .877 108.000 1250.817 .807
.439 .875 108.000 1292.000 .812
.148 1.819b
18.000 222.000 .024
.346 .754 108.000 1332.000 .970
.695 .758 108.000 1250.817 .967
.383 .763 108.000 1292.000 .964
.159 1.960b
18.000 222.000 .013
Pillai's Trace
Wilks' Lambda
Hotelling's Trace
Roy's Largest Root
Pillai's Trace
Wilks' Lambda
Hotelling's Trace
Roy's Largest Root
Pillai's Trace
Wilks' Lambda
Hotelling's Trace
Roy's Largest Root
Pillai's Trace
Wilks' Lambda
Hotelling's Trace
Roy's Largest Root
Effect
Intercept
age
edu
age * edu
Value F Hypothesis df Error df Sig.
Exact statistica.
The statistic is an upper bound on F that yields a lower bound on the significance level.b.
Design: Intercept+age+edu+age * educ.
What We would normally look at is the Pillai’s Trace as it has been noted as
more robust and appropriate. Nevertheless, many also use Wilks’ Lambda.
Note whether it is significant or not for the overall interaction and by each
variable. In this case, all are not significant indicating that there are no
differences in how service recovery variables is seen by age, education, and
its interactions.
Then you may look at the Test Between Subjects, which will indicate
significance at individual levels. Running a Univariate analysis can
corroborate this.
This is followed by a table on the means and then followed by the post hoc
test. In this case, you may note the significant variables highlighted by SPSS
in the Bonferroni test.
This is finally followed by the various plots that depict the interactions.
How to Present the Findings
MANOVA findings are usually presented as a sentence.
A MANOVA was carried out to determine if there was any interaction effect
between age and education level with the various service recovery methods.
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Pillai’s trace for the variable Age (P = 0.194, F = 1.300, Sig = 0.120),
Education Level (P = 0.400, F = 0.880, Sig = 0.802) and its interaction (P =
0.346, F = .7540, Sig = 0.970) was not significant for service recovery
variables.
It could also be presented in a Table.
Table 1: Multivariate Tests for Service recovery Variables by Age and
Education Level
Effect Value F Sig.
Age
Pillai's Trace .194 1.300 .120
Wilks' Lambda .814 1.309(a) .114
Hotelling's Trace .220 1.318 .108
Roy's Largest Root .163 1.978(b) .012
Education Level
Pillai's Trace .400 .880 .802
Wilks' Lambda .658 .877 .807
Hotelling's Trace .439 .875 .812
Roy's Largest Root .148 1.819(b) .024
Age *Education Level
Pillai's Trace .346 .754 .970
Wilks' Lambda .695 .758 .967
Hotelling's Trace .383 .763 .964
Roy's Largest Root .159 1.960(b) .013
a Exact statistic
b The statistic is an upper bound on F that yields a lower bound on the significance level.
c Design: Intercept+age+edu+age * edu
Assignment
1. Open the file Example Loyalty. Now try by yourself to run a MANOVA
from the above dataset for all the Loyalty variables by Age, Education,
and Gender.
2. Prepare a Table to depict your findings.
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15. How to Analyze: Exploratory Factor Analysis
Exploratory Factor Analysis (EFA) is done when one wants to know if there
are any simple patterns among the variables studies. This is especially true
when there are a lot of observed variables, from a minimum of 3 to as many
as possible.
Open the file Example Loyalty.
Lets say we want to see if the data that we obtained on loyalty, really reflects
the theory that places it into three factors, that is Behavior, Attitude, and
Cognition.
Click on Analyze, Data Reduction, Factor. The following dialogue box will
appear.
Select and transfer all the relevant loyalty items into the Variables box.
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You will notice a few buttons at the bottom of the screen, Descriptives,
Extraction, Rotation, Scores, and Options. Lets look at them now.
Click on Descriptives. We would normally then click on KMO and Bartlett’s
Test of sphericity. You may also click on Anti-image. Click on Continue.
Click on Extraction. We would normally click on Scree Plot and maintain the
others. Do note that there are many options of Method of factor analysis
extraction that is offered by SPSS, but the usual is by Principal Components.
Factor analysis also allows you to extract as many factors as possible based
on Eigenvalues or for you to pre-determine the number of factors that you
require. In this case, lets leave it at Eigenvalues over 1. As for Iterations, the
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107BASIC ANALYSIS:
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set limit here is 25. You can place a higher number if you want to. Click on
Continue.
Click on Rotation. We normally use Varimax. Nevertheless, as you may notice
there are many other methods that can be used. Click on Continue.
Ignore Scores. Click on Options. For the Coefficient Display Format, we would
normally click on both boxes and we choose the suppress absolute values
less than 0.4999. Nevertheless there are those that argue than anything less
than 0.3999 could also be suppressed. Click on Continue.
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108BASIC ANALYSIS:
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Click on OK. The output file should appear. See Output factor analysis.
The first thing that you will note is the KMO and Bartlett’s test. There are
arguments as to what level of KMO is acceptable. For me, the higher the
better of course, but the absolute minimum in 0.60. Bartlett’s Test is used to
test equality of variance.
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109BASIC ANALYSIS:
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KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of
Sampling Adequacy. .908
Bartlett's Test of
Sphericity
Approx. Chi-
Square
1812.11
0
df 55
Sig. .000
The next thing you will notice is the Anti-Image. Look at the Correlation box.
Look for the Measure of Sampling Adequacy. Check if all elements on the
diagonal of the matrix is greater than 0.5 to indicate that the sample is
adequate. The next box is on Communalities.
Then look at the Total Variance Explained box. This will tell you how many
Components or Factors there are, and how many for you to keep. Normally
one will take any variable that has an Eigenvalue above 1. Sometimes there
may be a lot of components with an Eigenvalue over 1, so keep those which
contribute to 70% to 80% of the variance. The other way is by examining the
Scree Plot, which will be discussed later. In this case, there are only 2
components.
Total Variance Explained
Compone
nt Initial Eigenvalues
Total
% of
Variance
Cumulative
%
1 6.406 58.240 58.240
2 1.155 10.501 68.741
3 .735 6.679 75.420
4 .485 4.410 79.829
5 .454 4.128 83.958
6 .426 3.874 87.832
7 .346 3.141 90.973
8 .324 2.942 93.915
9 .297 2.704 96.620
10 .192 1.748 98.368
11 .180 1.632 100.000
Extraction Method: Principal Component Analysis.
Lets look at the Scree Plot. The rule is to keep all factors before the elbow. In
this case the first two values are acceptable.
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110BASIC ANALYSIS:
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Component Number
1110987654321
Eig
en
valu
e
6
4
2
0
Scree Plot
Then look at the Rotated Component Matrix and try to name the components
based on the variables shown.
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111BASIC ANALYSIS:
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Rotated Component Matrix(a)
Component
1 2
This restaurant is the first choice in your mind when you
consider having dinner outside.
.834
Assume that you have only three choices when you are in need
of having dinner, this restaurant must be one of them.
.806
You have regularly dined at this restaurant for a long period of
time.
.784
You will keep dining at this restaurant, regardless of everything
being changed somewhat.
.709
You have strong preference on this restaurant..662
You will continue to dine at this restaurant even if the price or
service charge is increased somewhat.
.640
You have recommended other people to patronize this
restaurant.
.847
There is a high probability that you will dine at this restaurant
again.
.828
You will say positive thing to other people about the service
provided by this restaurant.
.772
You will give positive feedback to this restaurant. .764
You will try the new food or drinks that are recommended by this
restaurant.
.639
Extraction Method: Principal Component Analysis. Rotation Method: Varimax
with Kaiser Normalization.a Rotation converged in 3 iterations.
Note:
You will need to do a reliability test for your findings. This can be done by
clicking on Analyze, Scale, Reliability Analysis. The dialogue box will appear.
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112BASIC ANALYSIS:
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Select all the variables in the first component of your factor analysis and
transfer it to the box named Items. Make sure the Model selected is Alpha.
Click on OK.
The following output will appear. See Output Reliability.
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The first box will detail the case summaries. The second box will detail the
Cronbach Alpha’s figure for the number of variables that you have keyed in.
In this case, the reliability is quite high at 0.897.
How to Present the Findings
Factor Analysis is usually presented in a Table, with some further
explanations. It normally also includes Reliability of the various components.
A factor analysis was carried out on the various variables that describe
loyalty. The initial Kaiser-Mayer-Olkin (KMO) was 0.908, which indicate that
the analysis is meritorious. Bartlett’s test is significant (Chi Square = 1812.11,
p<0.001). Two components were extracted with a cumulative variance of
68.74%. Please refer to Table 1.
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114BASIC ANALYSIS:
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Table 1: Rotated Component Matrix for Loyalty
Variable
Attitu
de
a
nd
Co
gn
itio
n
Be
ha
vio
r
This restaurant is the first choice in your mind when you
consider having dinner outside.
.834
Assume that you have only three choices when you are
in need of having dinner, this restaurant must be one of
them.
.806
You have regularly dined at this restaurant for a long
period of time.
.784
You will keep dining at this restaurant, regardless of
everything being changed somewhat.
.709
You have strong preference on this restaurant..662
You will continue to dine at this restaurant even if the
price or service charge is increased somewhat.
.640
You have recommended other people to patronize this
restaurant.
.847
There is a high probability that you will dine at this
restaurant again.
.828
You will say positive thing to other people about the
service provided by this restaurant.
.772
You will give positive feedback to this restaurant..764
You will try the new food or drinks that are recommended
by this restaurant.
.639
Eigenvalue6.406 1.155
% of Variance58.240 10.501
Cumulative % of Variance58.240 68.741
Reliability0.897 0.890
Extraction Method: Principal Component Analysis. Rotation Method: Varimax
with Kaiser Normalization. a Rotation converged in 3 iterations.
Assignment
1. Open the file Example Loyalty. Now try by yourself to run a Factor
Analysis from the above dataset for all the Loyalty variables but to limit the
components to 3.
2. Prepare a Table to depict your findings.
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115BASIC ANALYSIS:
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16. How to Analyze: Confirmatory Factor Analysis
Confirmatory Factor Analysis (CFA) is normally done through the use of
another computer program called AMOS 6. It runs on SPSS data. You can
open the file similarly as that of SPSS, except that you will need the
appropriate software. AMOS is basically software for drawing of models
instead of a key in data type of software. This means that you must draw the
relevant model in the canvas provided, fill it in from the data that you have
(normally from SPSS) and then run the analysis.
Once you have opened the file, it should appear as follows.
On the left hand are all the relevant short forms for the various tasks that you
have to do. In the middle is the analysis output and the right side is your
canvas.
A brief discussion of what some specific icon tools do is as follows:
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116BASIC ANALYSIS:
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Let’s say we want to run the findings from our earlier Exploratory Factor
Analysis (EFA) to obtain a CFA.
The first thing to do is to have the EFA data on hand and transfer it to AMOS.
Go to Select Data Files and click on it. The following dialogue box will
appear.
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117BASIC ANALYSIS:
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Click on File Name and the following box will appear. Select the database that
you wish to use. In this case, select Example Loyalty CFA.
The following should be displayed. Click OK.
The data is now recorded and saved for later use.
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118BASIC ANALYSIS:
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Then go back to the earlier EFA discussion and look at the findings. It is
displayed here.
Table 1: Rotated Component Matrix for Loyalty
Variable
Attitu
de
a
nd
Co
gn
itio
n
Be
ha
vio
r
This restaurant is the first choice in your mind when you
consider having dinner outside.
.834
Assume that you have only three choices when you are
in need of having dinner, this restaurant must be one of
them.
.806
You have regularly dined at this restaurant for a long
period of time.
.784
You will keep dining at this restaurant, regardless of
everything being changed somewhat.
.709
You have strong preference on this restaurant..662
You will continue to dine at this restaurant even if the
price or service charge is increased somewhat.
.640
You have recommended other people to patronize this
restaurant.
.847
There is a high probability that you will dine at this
restaurant again.
.828
You will say positive thing to other people about the
service provided by this restaurant.
.772
You will give positive feedback to this restaurant..764
You will try the new food or drinks that are recommended
by this restaurant.
.639
Eigenvalue6.406 1.155
% of Variance58.240 10.501
Cumulative % of Variance58.240 68.741
Reliability0.897 0.890
Extraction Method: Principal Component Analysis. Rotation Method: Varimax
From here we can see that there is two components. The first has 6 variables
and the second has 5.
This means that we must draw on our canvas what reflects this situation.
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119BASIC ANALYSIS:
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In this case our objective is to draw a graphical model to represent the EFA
above (Table 1) as follows:
Select “Draw a Latent Variable” by clicking on it once. Move your cursor to
the canvas. Your cursor should appear as the “Draw a Latent Variable”
symbol. Click once. The following should appear.
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120BASIC ANALYSIS:
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Click again. The following should appear. You should remember that the first
component had 6 variables and the second component had 5 variables.
Continue drawing.
In SEM there is a nomenclature, where:
Components and errors are drawn as circle or oval, and considered as
unobserved variables.
The variables that reflective the components called as indicators will be drawn
as rectangles or squares, and they are considered as observed variables.
Then (one headed arrow) shows regression path or effect
And (double headed arrow) shows covariance or correlations
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121BASIC ANALYSIS:
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You should get this.
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122BASIC ANALYSIS:
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Then continue on till for the next component.
We would normally save the file at this stage. Click on File, Save As. The
dialogue box will appear as below. Save the document in an appropriate file
(Create a new folder for it).
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123BASIC ANALYSIS:
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Now, lets make this drawing look good. We will click on Select all objects .
You will notice that the drawings have changed color to blue.
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124BASIC ANALYSIS:
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Click on “Rotate the indicators...” Click once on the top drawing. This will
happen.
So click on all till it appears as such.
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125BASIC ANALYSIS:
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Then click on “Resize the path diagram...” . Everything will come into
place in the middle of your canvas.
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126BASIC ANALYSIS:
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Click on the “Deselect all objects ” The drawing will now return to black.
Amos automatically set the loading factor of one indicator variable to be fixed
to 1. However, according to our objective this loading should be free (not be
fixed as 1) as this is just the Measurement Model stage. Place the mouse
pointer to the load factor line that is to be changed.
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127BASIC ANALYSIS:
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Right click on the mouse and the “Objective Properties…” box will appear,
and then click on it.
Make sure that we are in the “Parameter” section.
Delete the number 1 in Regression weight, and then click to finish. Do the
same thing to the other component.
Then our graphic model will be as follows:
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128BASIC ANALYSIS:
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Next we are to draw the line known as covariance or correlation, that
connects both components by clicking then direct the mouse pointer to the
first (top) component, click, hold then drag it to the second component
(below).
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129BASIC ANALYSIS:
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The result of this process should be as follow:
Next, fix the variances of the components (latent variables) to 1 by right clicking on
the oval.
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130BASIC ANALYSIS:
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Make sure that we are in the “Parameter” section. Write 1 on the Variance
section.
Do the same thing to the other component, now our graphic should appear as
such:
Next step is to fill in the variables into the graphics (see our objective and EFA
finding), for ease of use the following codebook of data is provided:
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131BASIC ANALYSIS:
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Table 2: Codebook of EFA Data
No Variables Labels
1 highprob There is a high probability that you will dine at this restaurant
again.
2 Recomend You have recommended other people to patronize this restaurant.
3 sayptive You will say positive thing to other people about the service
provided by this restaurant.
4 Feedback You will give positive feedback to this restaurant.
5 trynew You will try the new food or drinks that are recommended by this
restaurant.
6 pricrise You will continue to dine at this restaurant even if the price or
service charge is increased somewhat.
7 Prefer You have strong preference on this restaurant.
8 changed You will keep dining at this restaurant; regardless of everything
being changed somewhat.
9 firstcho This restaurant is the first choice in your mind when you consider
having dinner outside.
10 oneofcho Assume that you have only three choices when you are in need of
having dinner, this restaurant must be one of them.
11 regular You have regularly dined at this restaurant for a long period of
time.
Click on List variables Tool to name the observe variables. The following will
appear.
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132BASIC ANALYSIS:
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Click and drag the appropriate variable name from Variables in Dataset box to the
appropriate Observed variables one by one (the position for the variable placement is
based on our objective model or as detailed in Table 1). The final product is shown
below.
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Then click on the “Variables in Data..” to finish.
Next is to name the unobserved variables, because we already have names
for the two components “Attitude and Cognition” for first component and
“Behavior” for the second component. This is depicted as the two ovals in the
diagram.
Right click the top oval ( component), click the “Object Properties…”.
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134BASIC ANALYSIS:
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The following table will appear. Go to Text and write “Attitude and Cognition”
in Variable Name box. Do the same for the second oval (component) and
write “Behavior”.
The following should be the graphical model on your computer.
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135BASIC ANALYSIS:
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Then for the error variables (the remaining small ovals that have not been
named), we can use the “Plugin” menu bar or we can write in the names
manually using “Object Properties” menu as described earlier.
For the plugin, go to the menu bar, click on Plugins, Name Unobserved
Variables.
The result will be as follows.
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136BASIC ANALYSIS:
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Now we are ready to run the analysis. Nevertheless, it is important to first
predetermine the output that we desire to obtain from the analysis. This is
done by setting the “View, Analysis Properties - Output” in the Menu bar.
Or you may also use the “Analysis Properties” Icon , which we find easier.
When you click on it, the following box will appear.
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Tick the “Standardized estimates” and “Modification indices”
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Click to finish
Now we can run the Analysis by using the Calculate Estimates Tool… or
using “Analyze – Calculate Estimates” in the Menu bar.
The Result:
Then Amos will calculate the model estimates. You can watch the progress of
calculations in the panel to the left of the path diagram, but the calculations
happen so quickly that you may see only the summary after calculations are
complete
Amos have two options for viewing the output: graphics and text output
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139BASIC ANALYSIS:
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To view the graphic output, Click the Show the output path diagram
You can change the view of graphics output between Unstandardized
estimates and Standardized estimates by click on them
Unstandardized estimates
Standardized estimates
Unstandardized
Regression
estimates
Covariance
Variance of
Errors
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140BASIC ANALYSIS:
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The complete result can be found in “Text Output” that can be executed by
clicking or using “View – Text Output” in menu Bar.
An example is shown below.
Corelation
Standardized
Regression
estimates or
Loading Factors
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141BASIC ANALYSIS:
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Regression Weights: (Group number 1 - Default model)
Estimate S.E. C.R. P
Lab
el
pricrise <--- Attitude and_Cognition 1.691 .124 13.689 ***
prefer <--- Attitude and_Cognition 1.670 .115 14.542 ***
changed <--- Attitude and_Cognition 1.629 .122 13.402 ***
regular <--- Attitude and_Cognition 1.753 .143 12.232 ***
oneofcho <--- Attitude and_Cognition 1.960 .135 14.519 ***
firstcho <--- Attitude and_Cognition 1.963 .121 16.233 ***
trynew <--- Behavior 1.740 .139 12.534 ***
feedback <--- Behavior 1.795 .126 14.247 ***
sayptive <--- Behavior 1.797 .123 14.641 ***
highprob <--- Behavior 1.877 .124 15.108 ***
recomend <--- Behavior 1.909 .119 16.073 ***
Standardized Regression Weights: (Group number 1 - Default model)
Estimate
pricrise <--- Attitude and_Cognition .759
prefer <--- Attitude and_Cognition .791
changed <--- Attitude and_Cognition .747
regular <--- Attitude and_Cognition .700
oneofcho <--- Attitude and_Cognition .790
firstcho <--- Attitude and_Cognition .850
trynew <--- Behavior .714
feedback <--- Behavior .782
sayptive <--- Behavior .796
highprob <--- Behavior .813
recomend <--- Behavior .846
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142BASIC ANALYSIS:
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Covariances: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
Attitude and_Cognition <--> Behavior .774 .033 23.499 ***
Correlations: (Group number 1 - Default model)
Estimate
Attitude and_Cognition <--> Behavior .774
Variances: (Group number 1 - Default model)
Estimate S.E. C.R. P Label
Attitude and_Cognition 1.000
Behavior 1.000
e1 2.108 .217 9.702 ***
e2 1.670 .178 9.365 ***
e3 2.097 .214 9.799 ***
e4 3.208 .317 10.127 ***
e5 2.312 .247 9.375 ***
e6 1.480 .177 8.370 ***
e7 2.916 .293 9.964 ***
e8 2.052 .219 9.353 ***
e9 1.865 .203 9.166 ***
e10 1.806 .203 8.913 ***
e11 1.441 .175 8.250 ***
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143BASIC ANALYSIS:
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Modification Indices:
Covariances: (Group number 1 - Default model)
M.I. Par Change
E11 <--> Attitude and_Cognition 4.035 -.129
E10 <--> e11 21.086 .573
E9 <--> e10 7.605 -.378
E8 <--> e11 5.719 -.314
E8 <--> e9 7.289 .389
E7 <--> Behavior 9.814 -.266
E7 <--> Attitude and_Cognition 16.466 .346
E7 <--> e11 5.599 -.363
E6 <--> Behavior 6.362 -.164
E6 <--> e11 5.891 -.291
E5 <--> e11 4.473 -.301
E5 <--> e9 6.907 .407
E5 <--> e6 26.057 .718
E4 <--> e5 22.665 .921
E3 <--> e11 4.479 .282
E3 <--> e9 7.555 -.398
E3 <--> e7 9.279 .532
E3 <--> e5 15.537 -.624
E2 <--> Behavior 8.284 .191
E2 <--> Attitude and_Cognition 4.835 -.144
E2 <--> e6 8.411 -.347
E2 <--> e5 7.769 -.400
E2 <--> e3 11.406 .454
E1 <--> Behavior 5.959 .180
E1 <--> e7 16.341 .711
E1 <--> e5 7.974 -.450
E1 <--> e4 5.803 -.439
E1 <--> e2 4.825 .297
Regression Weights: (Group number 1 - Default model)
M.I. Par Change
recomend <--- highprob 6.259 .094
recomend <--- firstcho 4.217 -.077
recomend <--- oneofcho 4.249 -.072
highprob <--- recomend 4.900 .092
trynew <--- Attitude and_Cognition 5.164 .271
trynew <--- changed 12.500 .185
trynew <--- prefer 5.933 .131
trynew <--- pricrise 17.016 .211
firstcho <--- recomend 5.223 -.089
firstcho <--- oneofcho 8.798 .105
oneofcho <--- firstcho 5.889 .110
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144BASIC ANALYSIS:
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M.I. Par Change
oneofcho <--- regular 10.782 .137
oneofcho <--- changed 6.236 -.119
regular <--- oneofcho 7.414 .130
changed <--- trynew 4.237 .082
changed <--- oneofcho 5.109 -.089
prefer <--- feedback 4.580 .083
prefer <--- changed 4.579 .087
pricrise <--- trynew 13.145 .146
Model Fit
CMIN
Model NPAR CMIN DF P CMIN/DF
Default model 23 202.899 43 .000 4.719
Saturated model 66 .000 0
Independence model 11 1845.599 55 .000 33.556
RMR, GFI
Model RMR GFI AGFI PGFI
Default model .307 .868 .798 .566
Saturated model .000 1.000
Independence model 2.649 .250 .101 .209
Baseline Comparisons
Model
NFI
Delta1
RFI
rho1
IFI
Delta2
TLI
rho2
CFI
Default model .890 .859 .911 .886 .911
Saturated model 1.000 1.000 1.000
Independence model .000 .000 .000 .000 .000
Parsimony-Adjusted Measures
Model PRATIO PNFI PCFI
Default model .782 .696 .712
Saturated model .000 .000 .000
Independence model 1.000 .000 .000
NCP
Model NCP LO 90 HI 90
Default model 159.899 119.302 208.034
Saturated model .000 .000 .000
Independence model 1790.599 1653.967 1934.598
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FMIN
Model FMIN F0 LO 90 HI 90
Default model .818 .645 .481 .839
Saturated model .000 .000 .000 .000
Independence model 7.442 7.220 6.669 7.801
RMSEA
Model RMSEA LO 90 HI 90 PCLOSE
Default model .122 .106 .140 .000
Independence model .362 .348 .377 .000
AIC
Model AIC BCC BIC CAIC
Default model 248.899 251.238 329.800 352.800
Saturated model 132.000 138.712 364.152 430.152
Independence model 1867.599 1868.717 1906.291 1917.291
ECVI
Model ECVI LO 90 HI 90 MECVI
Default model 1.004 .840 1.198 1.013
Saturated model .532 .532 .532 .559
Independence model 7.531 6.980 8.111 7.535
HOELTER
Model
HOELTER
.05
HOELTER
.01
Default model 73 83
Independence model 10 12
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Evaluate the measurement model
There is a rule of thumb to evaluate the measurement model. It is suggested
that a measurement model is good when its loading factor exceed 0.5 and its
critical ratio is above 1.96.
Table : Standardized Regression Weights: (Loading factors)
Estimate
pricrise <--- Attitude and_Cognition .759
prefer <--- Attitude and_Cognition .791
changed <--- Attitude and_Cognition .747
regular <--- Attitude and_Cognition .700
oneofcho <--- Attitude and_Cognition .790
firstcho <--- Attitude and_Cognition .850
trynew <--- Behavior .714
feedback <--- Behavior .782
sayptive <--- Behavior .796
highprob <--- Behavior .813
recomend <--- Behavior .846
Table : Regression Weights and Critical Ratio
Estimate S.E. C.R.
pricrise <--- Attitude and_Cognition 1.691 .124 13.689
prefer <--- Attitude and_Cognition 1.670 .115 14.542
changed <--- Attitude and_Cognition 1.629 .122 13.402
regular <--- Attitude and_Cognition 1.753 .143 12.232
oneofcho <--- Attitude and_Cognition 1.960 .135 14.519
firstcho <--- Attitude and_Cognition 1.963 .121 16.233
trynew <--- Behavior 1.740 .139 12.534
feedback <--- Behavior 1.795 .126 14.247
sayptive <--- Behavior 1.797 .123 14.641
highprob <--- Behavior 1.877 .124 15.108
recomend <--- Behavior 1.909 .119 16.073
Now, we can conclude that all loading factors is good and acceptable,
because they all exceed 0.5 and have critical ratio above 1.96.
Evaluating the Goodness of Fit of the model
There are many test for evaluating the goodness of fit, but here we only show
the commonly used test, such as: Chi-Square, degree of freedom (df),
CMIN/df, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index
(AGFI), Comparative Fit Index (CFI), and the Root Mean Square Error of
Approximation (RMSEA).
All of loading
factors exceed 0.5
All critical ratio
(C.R.) above 1.96
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147BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Table X: Goodness of FitT
est*
Marker
Ch
i S
q
df
P
CM
IN
/d
f
GF
I
AG
FI
CF
I
RM
SE
A
MM1 - 202.882 43 .000 4.719 .868 .798 .911 .122
Note: MM – Measurement Model
To evaluate this result we will use the rule of thumb of cut off point as follows:
Evaluating the goodness of fit using Cut off Point
MM Test Cut off Good Fit
File name Example Loyalty CFA
Interpretation From EFA
Marker -
Chi Square 202.882 Smaller to 0 Not
df 43
p 0.000 >= 0.05 Not
CMIN/df 4.719 <= 2 - 5 Yes
GFI 0.868 >= 0.90 Not
AGFI 0.798 >= 0.90 Not
CFI 0.911 >= 0.95 Not
RMSEA 0.122 <= 0.08 Not
The measurement model turned out to be a poor model. This is very common
occurrence for any first time run of a model that we are evaluating, especially
when it’s directly from a Exploratory Factor Analysis (EFA).
Since all loading factors are good and acceptable, then we go to next step of
the model that is named Configural Invariance Model (CIM). In CIM we will fix
the highest loading factor in each component to 1 and let the variance of
component free.
Go to Amos window, we will fix “firstcho” and “recommend” to 1, because
these variables have the highest loading factor in its component (firstcho =
0.850, and recommend = 0.846).
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148BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Right click the arrow that point to “firstcho”, the Object Properties will appear
and make sure the parameter tab is chosen
Right click here
Type 1 here
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149BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Do the same to “recommend”, then we make the variance of the component
“Attitude and Cognition” to free by deleting 1 in variance box:
Also do the same to “Behavior” component, now our graphics should be
looked like as follows:
Now, we are ready to run the analysis. Click Calculate Estimate
The result is almost same with first model.
Delete 1
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150BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Here the summary of goodness of fit
Test*
Mark
er
Ch
i S
q
df
P
CM
IN
/d
f
GF
I
AG
FI
CF
I
RM
SE
A
CIM - 202.899 43 0.000 4.719 0.868 0.798 0.911 0.122
The next step model is Metric Invariance Model (MIM), in this model we will
constraint all the loading factors that are not fixed to 1 by typing “f_2” and so
on to its arrow line.
Go to Amos windows, and bring back the CIM model before calculate
estimate by clicking on input path diagram button
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151BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Right click on the arrow to call the Object Properties, then type f_2 in
Regression weight box
Do the same to rest of variables, then our graphics should like this:
Right click here
Type f_2 here
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152BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Now, we are ready to run the analysis. Click Calculate Estimates
The result
Summary of Goodness of Fit
Table : Based on Metric Invariance Model (MIM)
Test*
Mark
er
Ch
i S
q
df
P
CM
IN
/d
f
GF
I
AG
FI
CF
I
RM
SE
A
MIM - 202.899 43 0.000 4.719 0.868 0.798 0.911 0.122
The last model is Scalar Invariance Model (SIM)
In this model we will add Estimate means and intercept to the model of MIM.
Go to Amos windows and make sure we have MIM first model before
Calculate estimates
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153BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Go to View Menu and choose “Analysis Properties”
Or just click the Analysis Properties icon in left side
Then Analysis Properties menu appear, choose Estimation Tab, then tick the
Estimate means and intercept
Then click to finish, then our graphics will be like this
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154BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Then click Calculate estimates to run the analysis.
The result
Table : Based on Scalar Invariance Model (SIM)
Test*
Mark
er
Ch
i S
q
df
P
CM
IN
/d
f
GF
I
AG
FI
CF
I
RM
SE
A
SIM - 202.899 43 0.000 4.719 0.868
(RFI)
0.798 0.911
(IFI)
0.122
How to Present the Findings
Presenting the result in publication we can summarize all results of the
models as follows
Table : Summary for Goodness of Fit
Test*
Mark
er
Ch
i S
q
df
P
CM
IN
/d
f
GF
I
AG
FI
CF
I
RM
SE
A
MM1 - 202.899 43 0.000 4.719 0.868 0.798 0.911 0.122
CIM - 202.899 43 0.000 4.719 0.868 0.798 0.911 0.122
MIM - 202.899 43 0.000 4.719 0.868 0.798 0.911 0.122
SIM - 202.899 43 0.000 4.719 0.868
(RFI)
0.798 0.911
(IFI)
0.122
Note MM – Measurement Model, CIM – Configural Invariance Model, MIM – Metric
Invariance Model, SIM – Scalar Invariance Model
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155BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Assignment
1. Open the file Example Loyalty. Run an Exploratory Factor Analysis for the
Service Recovery Methods (Apology, assist, compensa, reinstat, empathy,
symbolic, follow, acknowle, explain, discount, correcti, mngment, replace,
coreplus, refund, freefood, coupon, empower). Then run a Confirmatory
Factor Analysis for your findings.
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156BASIC ANALYSIS:
A GUIDE FOR STUDENTS AND RESEARCHERS
Dr. De Run, Ernest Cyril
is an Associate Professor
of Marketing and the
Deputy Dean (Research
and Postgraduate) at
Universiti Malaysia
Sarawak, Sarawak,
Malaysia. He earned his
BBA majoring in
Marketing from Universiti
Kebangsaan Malaysia
and completed his MMS
(Distinction) at the
University of Waikato in
New Zealand. He
obtained his PhD from
the Marketing
Department, School of
Business, University of
Otago, New Zealand.
Dr. Lo, May-Chiun is a
lecturer of Economics
and Business at
Universiti Malaysia
Sarawak, Sarawak,
Malaysia. She earned her
BBA (First Class Honors)
majoring in Finance from
Universiti Kebangsaan
Malaysia and completed
her MBA at Herriot-Watt
University in the UK. She
obtained her PhD from
the School of
Management at Universiti
Sains Malaysia, Penang,
Malaysia.
Mr. Heriyadi
Kusnaryadi, SE, ME, is
a PhD Candidate at the
Faculty of Economics
and Business, Universiti
Malaysia Sarawak. He is
also a Lecturer at the
Economics Faculty and
MBA Programs at
Tanjungpura University
Pontianak Kalimantan
Barat Indonesia. He
teaches Marketing
Management, Marketing
Research and Statistics
for Business.
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