Basic Analysis

1BASIC ANALYSIS:

A GUIDE FOR STUDENTS AND RESEARCHERS

BASIC ANALYSIS:


ASSOCIATE PROFESSOR DR ERNEST CYRIL DE RUN

DR LO MAY CHIUN

HERIYADI KUSNARYADI

(CIB pdf formfields Demoversion)

2BASIC ANALYSIS:


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


3BASIC ANALYSIS:


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.


4BASIC ANALYSIS:



5BASIC ANALYSIS:


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.


6BASIC ANALYSIS:


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.


7BASIC ANALYSIS:


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.


8BASIC ANALYSIS:


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.


9BASIC ANALYSIS:


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


10BASIC ANALYSIS:


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.


11BASIC ANALYSIS:


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.


12BASIC ANALYSIS:


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.’


13BASIC ANALYSIS:


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.


14BASIC ANALYSIS:


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.


15BASIC ANALYSIS:


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


16BASIC ANALYSIS:


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.


17BASIC ANALYSIS:


Assignment

Now try by yourself to key in the remaining interviews.


18BASIC ANALYSIS:


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.


19BASIC ANALYSIS:


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


20BASIC ANALYSIS:


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


21BASIC ANALYSIS:


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.


22BASIC ANALYSIS:


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.


23BASIC ANALYSIS:


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.


24BASIC ANALYSIS:


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.


25BASIC ANALYSIS:


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


26BASIC ANALYSIS:


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


27BASIC ANALYSIS:


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.


28BASIC ANALYSIS:


Target Variable refers to the new variable name that we wish to create; in this

case let’s name it behloy.


29BASIC ANALYSIS:


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.


30BASIC ANALYSIS:


Click OK.

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


31BASIC ANALYSIS:


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.


32BASIC ANALYSIS:


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.


33BASIC ANALYSIS:


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.


34BASIC ANALYSIS:


Select all the data in SPSS. Copy.

Paste the data in Microsoft Excel.


35BASIC ANALYSIS:


Select Format, Cells. This is the Format Cells dialogue box. Change the

decimals to 0 and click OK.


36BASIC ANALYSIS:


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.


37BASIC ANALYSIS:


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.


38BASIC ANALYSIS:


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.


39BASIC ANALYSIS:


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.


40BASIC ANALYSIS:


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


41BASIC ANALYSIS:


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


42BASIC ANALYSIS:


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.


43BASIC ANALYSIS:


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.


44BASIC ANALYSIS:


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.


45BASIC ANALYSIS:


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.


46BASIC ANALYSIS:


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.


47BASIC ANALYSIS:


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.


48BASIC ANALYSIS:


7. How to Analyze: Frequency


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

Select Analyze, Descriptive Statistics, Frequencies. The Frequencies dialogue

box will appear.

Select Gender and transfer it to the Variables box.

Click on OK.

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


49BASIC ANALYSIS:


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.


50BASIC ANALYSIS:


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.


51BASIC ANALYSIS:


8. How to Analyze: Crosstabulation


Lets say you want to know the relationship between gender and its

relationship with the use of apology in service recovery.

Select Analyze, Descriptive Statistics, Crosstabs.

The Crosstabs dialogue box will appear.

You will notice that there are two boxes, one for rows and the other for

columns. This is how you data will be shown, so careful planning has to be

done in order to present your data nicely. In this case we would rather have

Gender in the column and the use of apology in service recovery in the rows.

Why? This is because it would make it easier to see as well as to present the

data later on.


52BASIC ANALYSIS:


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.


53BASIC ANALYSIS:


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

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


54BASIC ANALYSIS:


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.


55BASIC ANALYSIS:



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.


56BASIC ANALYSIS:


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.


57BASIC ANALYSIS:


9. How to Analyze: Means


Lets say you want to know the Means of the various measurements of loyalty

that you have used, from the variables to the summations.

Select Analyze, Descriptive Statistics, Descriptives. The Descriptives dialogue

box will appear.

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

Click OK.

The output will appear. See Means Output.


58BASIC ANALYSIS:


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.


59BASIC ANALYSIS:



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


60BASIC ANALYSIS:


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.



61BASIC ANALYSIS:


10. How to Analyze: t-test


T-test is normally used when there are only two values in a variable. An

Anova is used when there are three or more values in a variable. SPSS offers

3 types of t-test:

1. One Sample T-Test

2. Independent Sample T-Test

3. Paired Samples T-Test

One Sample T-Test

A One Sample T-Test compares the mean score of a sample to a known

value. Lets say you want to know whether in your education variable, that the

respondent’s education level is different from the known population mean. In

this case, the mean for education level, let say is 4.

Click on Analyze, Compare Means, One Sample T-Test. The following

dialogue box will appear.

Click and transfer Education Level variable to the Test Variable Box. Then

type in the Test Value, which refers to the known population mean. In this

case, lets assume it is 4.


62BASIC ANALYSIS:


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).


63BASIC ANALYSIS:



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

We would say:

T-test findings indicate that the Education Level variable (t = -1.218, p =

0.224) is not significantly different from the population mean.

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

Table 1: One-Sample Test for Educational Level

Variable

Test Value = 4

t df Sig. (2-tailed)

Education Level -1.218 248 .224

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a One

Sample T-Test from the above dataset for all the Service Recovery variables,

with a Test Value of 5.


Independent Sample T-Test

An Independent Samples T Test compares the mean scores of two groups on

a given variable. Lets say you want to know whether the means for apology to

be used as a service recovery is similar or different between men and women.

Click on Analyze, Compare Means, Independent Samples T Test. The

dialogue box will appear.


64BASIC ANALYSIS:


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.


65BASIC ANALYSIS:


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.


66BASIC ANALYSIS:


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


67BASIC ANALYSIS:


This is important, as this is part of the assumptions for running this test, that

the variances are approximately equal. If the Levene test is significant (the

value in Sig. is less than 0.05) then this indicates that the variance of the two

samples are significantly different. If the Levene test is not significant (the

value in Sig. is more than 0.05) then this indicates that the variance of the two

samples are approximately equal.

However in this example, the Levene test is significant, indicating that the

variance of the two samples are significantly different.

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

Independent Samples Test

t-test for Equality of Means


Mean

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.



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).



68BASIC ANALYSIS:


Table 1: Independent Sample Test for Apology

Variable t df Sig. (2-tailed)

Apology 2.284 242.594 .023

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a Independent

Sample T-Test from the above dataset for all the Service Recovery

variables, against gender.


Paired Samples T-Test

The Paired Samples T-Test compares the means of two variables. This T-

Test measures the difference between the two variables for each case, and

then tests to see if the average difference is significantly different from zero.

Lets say you want to see if there is any difference between the variable

apology and apology1 as methods of service recovery.

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

will appear.

Click on apology and apolgy1. You will notice that when you click the

variables, it will appear in the Current Selections. You can only choose two

variables at a time.


69BASIC ANALYSIS:


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.


70BASIC ANALYSIS:


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.


71BASIC ANALYSIS:


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




We would say:

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

the paired samples t-test indicates that there is significant difference between

the two variables (t = 5.942, p = 0.000).


Table 1: Paired Sample Test for Apology-Assistance

Variable t df Sig. (2-tailed)

Apology-Assistance 5.942 248 .000

Assignment

1. Open the file Example Loyalty. Now try by yourself to run a Paired Sample

T-Test from the above dataset for assist and assist1.



72BASIC ANALYSIS:


11.. How to Analyze: Correlation

Correlation is used when you want to know how two variables are associated

with each other and how strong that association is. Correlation can also tell

you the direction of the association.

Pearson R Correlation is used when the data that you are using is normally

distributed. When the data that you are using is not normally distributed, then

you use Spearman Rho.

SPSS offers three types of correlations:

1. Bivariate

2. Partial

3. Distance

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


Lets say you want to know the correlation between apology and assist

variables. These are two variables that are used in service recovery.

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

Remember, if your data is normally distributed then use Pearson R

Correlation and if it is not normally distributed, then you use Spearman Rho.

This can be seen under the Table Correlation Coefficients. You can also

choose whether to use Two-tailed or One-tailed significance test.


73BASIC ANALYSIS:


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


74BASIC ANALYSIS:


direction of the relationship, either positive (one increase, so does the other)

or negative (one increase the other decrease). In this case, the correlation

coefficient is 0.601, which is quite acceptable and positive. So in this case, as

the probability of apology increase, there is also an increase in the probability

of assistance.


Correlation findings can be presented as a sentence or a Table. If in a

sentence, we would say:

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


Table 1: Correlations

Variable Apology

Assist .601(*)

* Correlation is significant at the 0.01 level (2-tailed).

Assignment

1. Open the file Example Loyalty. Now try by yourself to run Correlations

from the above dataset for all the Service Recovery variables.



75BASIC ANALYSIS:


12. How to Analyze: One-Way ANOVA

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

one independent variable.


Lets say you want to know the One-Way Anova between the variable apology

and age level. In simple terms, you want to know whether there is any

difference in how the various age groups look at the variable apology.

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

appear.

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

move it to the Factor box.


76BASIC ANALYSIS:


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.


77BASIC ANALYSIS:


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


78BASIC ANALYSIS:


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


One-Way Anova findings can be presented as a sentence or a Table. If in a



79BASIC ANALYSIS:


There was no significant difference in how the age group saw apology (F =

0.740, p = 0.478).


Table 1: One Way Anova by Age Scale

Variable F Sig.

Apology .740 .478

Assignment

1. Open the file Example Loyalty. Now try by yourself to run One-Way Anova

from the above dataset for all the Service Recovery variables by Age

scale.



80BASIC ANALYSIS:


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.


81BASIC ANALYSIS:


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.


82BASIC ANALYSIS:


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.


83BASIC ANALYSIS:


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.


84BASIC ANALYSIS:


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.


85BASIC ANALYSIS:


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.


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




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.


86BASIC ANALYSIS:


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


Standard Regression findings can be presented as a sentence. If in a


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.


87BASIC ANALYSIS:


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


88BASIC ANALYSIS:



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



89BASIC ANALYSIS:


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.


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.



Stepwise Regression findings can be presented as a sentence. If in a


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


90BASIC ANALYSIS:


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


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


will appear. In the Method box, select backward from the drop-down list.


91BASIC ANALYSIS:


Assignment

Open the file Example Regression. Now try by yourself to run stepwise


loyalty, respect, and contribute) on rational_T.

Hierarchical Multiple Regression


92BASIC ANALYSIS:


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.


93BASIC ANALYSIS:


Click on Next and move gender of the supervisors into Block 2 of 2.


94BASIC ANALYSIS:


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.


95BASIC ANALYSIS:



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.


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


96BASIC ANALYSIS:


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, 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



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.


97BASIC ANALYSIS:


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


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.



Hierarchical Regression findings can be presented as a sentence. If in a


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


loyalty, respect, and contribute), enter as first block, and leader gender as

second block on rational_T.


98BASIC ANALYSIS:


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.


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.


99BASIC ANALYSIS:


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.


100BASIC ANALYSIS:


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.


101BASIC ANALYSIS:


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.


102BASIC ANALYSIS:


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.


103BASIC ANALYSIS:


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.


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.


104BASIC ANALYSIS:


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


Age *Education Level

Pillai's Trace .346 .754 .970

Wilks' Lambda .695 .758 .967

Hotelling's Trace .383 .763 .964


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.



105BASIC ANALYSIS:


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.


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.


106BASIC ANALYSIS:


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


107BASIC ANALYSIS:


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.


108BASIC ANALYSIS:


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.


109BASIC ANALYSIS:


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.


110BASIC ANALYSIS:


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.


111BASIC ANALYSIS:


Rotated Component Matrix(a)

Component

1 2

This restaurant is the first choice in your mind when you


.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


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


.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.


112BASIC ANALYSIS:


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.


113BASIC ANALYSIS:


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.


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.


114BASIC ANALYSIS:


Table 1: Rotated Component Matrix for Loyalty

Variable

Attitu

de

a

nd

Co

gn

itio

n

Be

ha

vio

r



.834

Assume that you have only three choices when you are

in need of having dinner, this restaurant must be one of

them.

.806


period of time.

.784

You will keep dining at this restaurant, regardless of


.709



price or service charge is increased somewhat.

.640


restaurant.

.847


restaurant again.

.828


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


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.



115BASIC ANALYSIS:


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:


116BASIC ANALYSIS:


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.


117BASIC ANALYSIS:


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.


118BASIC ANALYSIS:


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



.834

Assume that you have only three choices when you are

in need of having dinner, this restaurant must be one of

them.

.806


period of time.

.784

You will keep dining at this restaurant, regardless of


.709



price or service charge is increased somewhat.

.640


restaurant.

.847


restaurant again.

.828


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


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.


119BASIC ANALYSIS:


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.


120BASIC ANALYSIS:


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


121BASIC ANALYSIS:


You should get this.


122BASIC ANALYSIS:


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).


123BASIC ANALYSIS:


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.


124BASIC ANALYSIS:


Click on “Rotate the indicators...” Click once on the top drawing. This will

happen.

So click on all till it appears as such.


125BASIC ANALYSIS:


Then click on “Resize the path diagram...” . Everything will come into

place in the middle of your canvas.


126BASIC ANALYSIS:


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.


127BASIC ANALYSIS:


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:


128BASIC ANALYSIS:


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).


129BASIC ANALYSIS:


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.


130BASIC ANALYSIS:


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:


131BASIC ANALYSIS:


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


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.


132BASIC ANALYSIS:


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.


133BASIC ANALYSIS:


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…”.


134BASIC ANALYSIS:


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.


135BASIC ANALYSIS:


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.


136BASIC ANALYSIS:


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.


137BASIC ANALYSIS:


Tick the “Standardized estimates” and “Modification indices”


138BASIC ANALYSIS:


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


139BASIC ANALYSIS:


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


140BASIC ANALYSIS:


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


141BASIC ANALYSIS:


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


142BASIC ANALYSIS:


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 ***


143BASIC ANALYSIS:


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


144BASIC ANALYSIS:


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


145BASIC ANALYSIS:


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


ECVI

Model ECVI LO 90 HI 90 MECVI

Default model 1.004 .840 1.198 1.013

Saturated model .532 .532 .532 .559


HOELTER

Model

HOELTER

.05

HOELTER

.01

Default model 73 83

Independence model 10 12


146BASIC ANALYSIS:


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


147BASIC ANALYSIS:


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).


148BASIC ANALYSIS:


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


149BASIC ANALYSIS:


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


150BASIC ANALYSIS:


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


151BASIC ANALYSIS:


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


152BASIC ANALYSIS:


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


153BASIC ANALYSIS:


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


154BASIC ANALYSIS:


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


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


155BASIC ANALYSIS:


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.


156BASIC ANALYSIS:


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.


Basic Analysis

Education

Transcript of Basic Analysis