Workshop on SPSS1

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Workshop on SPSS (Hands-On) for Beginnersby Assoc. Prof. Dr. Ernest Cyril De Run

Venue: Computer Laboratory, Faculty of Computer Science andInformation Technology, West Campus, UNIMAS.

Date: 26 October 2007 (Friday)

The workshop will introduce participants to a basic hands-on use of SPSS.Issues such as how to key in, compute, and transform data fromquestionnaires and interviews will be presented. Tips on syntax use will alsobe provided. Basic explanation on using SPSS to calculate Means, ANOVA,Manova, Regression, Factor Analysis, Frequency, Correlation, and Crosstabulation will also be discussed. Please note that the workshop will focus onhow to use SPSS, and will not discuss the various methods of statistical

analysis.

What is SPSS?

How to open the program

An Overview of the Program

How to key in Interview based data

How to key in Questionnaire based Data and to Transform.

Syntax

How to Analyze

Frequency

Crosstabulation

Means

ANOVA

Manova

Correlation Regression (Enter for confirmation, stepwise for prediction)

Exploratory Factor Analysis

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What is SPSS?

SPSS refers to computer software named Statistical Program for Social

Sciences and it comes in various versions and add ons. It is software and nota method of analysis. Therefore please do not state that you are using SPSSto analysis whatever in your research paper. You may state that you use thisstatistical package in order to run a certain analysis such as ANOVA or anyother method.

SPSS is statistical and data management software that is widely used. This ispartly because it is simple to use, user friendly, and does not require codingas by SAS. You may use code in Syntax, but thats another story. In mostcases, you can just copy and paste code from SPSS output into Syntax thusnot requiring you to write your own code.

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

See output example in Example of Output.

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

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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, SPSS15.0 for Windows.

A window will appear asking you what you would like to do, with a fewchoices. I normally just click on Cancel. Then I will open an existing file that Iwant or start keying in data.

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

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



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

A window will appear asking you what you would like to do, with a fewchoices. I normally just cancel it and then open an existing file that I want or

start keying in data.



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An Overview of the Program

Now that you have a SPSS program open, lets look into its components.

Lets start from the bottom upwards to the top.

At the very bottom, you will see a note, SPSS Processor is Ready. This is aneat feature that tells you what you already know. And what more, when youwork 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 yourcomputer is on) and Variable view show you the inner working or the meaningof that words or numbers.

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

Data View

Next you will notice this empty box (where soon your data will be placed in). Itis cordoned by a series of numbers (rows) and var in the column. This will

change once you keyed in the appropriate terms in the Variable view later.



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The next line shows the various buttons that you may use. Most importantly,the SAVE button. Please do use this regularly. Others refer to go to buttonsor Insert buttons. The Value Label (also known as toe tag icon) willdetermine whether your screen shows numeric values or their labels asdictated 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 onSPSS. 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 ona different file. If you choose Data, a new Data file (similar to what youare 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 areworking 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 windowthat 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 inLog is ticked. This will then display the codes for all the commands thatyou use, which you can later incorporate into your Syntax.

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



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Variable View

Lets look into Variable View. Click on it.

Lets start from the bottom upwards to the top.

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

Lets look at each column in the Variable View.

1. Name. Refers to the name of the column in the Data View. Normallythis will coincide with the questions in your questionnaire so that it willbe easier to track down once you run an analysis. The name must beunique, start with a letter, and up to 8 characters. Use short terms, asthis will make life easier when running analysis later on. Plus you canplace a longer explanation in the Label. Click on the appropriate box,and type in the name. SPSS is kind of tricky here, especially if youwant to use the hyphen, as SPSS thinks its a minus sign. You can useunderscore. Also, dont have space between terms.

2. Type. This refers to the type of data that you will be typing in. There are8 types (Numeric, Comma, Dot, Scientific notation, Date, Dollar,



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Custom Currency, String), but the most common are Numeric andString. Numeric refers to the use of numbers and String refers to theuse of alphabets or alphabets and numbers. There are implications tothis choice. If you choose String, data there cannot be analyzed bynumeric operations (i.e. Means). Even if your data is in Ringgit

Malaysia, I would still suggest that you use Numeric instead of Dollar.

3. Width. This refers to the number of characters that SPSS will allow tobe 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 thatexplains the column. The maximum space is for 255 characters but Ido suggest that you be brief as this will appear in your analysis andwould make your tables look ugly.

6. Values. This is where you assign meaning to the numbers that you areusing. Clicking up the Values box (where the three dots are), will openanother window that allows you to key in the appropriate meanings foreach value. In the Values Label dialogue box, you can click in the

Value field the appropriate number, then click in the Value Label field totype in what that number represents. Always click on Add after that,otherwise its not kept in the list. You can also change or remove valuesby clicking the appropriate box.



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7. Missing. You may inform SPSS that certain data should be treated asmissing by using certain numerical code. This can be done by filling inthe Discrete missing values with values of your choice. You may alsojust leave the field blank, where SPSS will display that is known asSYSTEM MISSING data.

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

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

10.Measure. This is important as you decide what type of data that youhave. As the saying goes, rubbish in, rubbish out. SPSS does notdifferentiate between interval and ratio, so these two are placedtogether as scale. The other two forms of data measurement remains,which are ordinal and nominal.

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How to Key in Interview-based Data

Many have told me that SPSS cant 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 toanalyze it. However I beg to differ, as there is more than one way to skin acat.

Planning for Interview Key In

Refer to the attached Interview Transcripts.

I would normally open an individual file for each research question of theinterview. In this case there are two research questions, why do they join andwhy do they stay on in a Multi Level Marketing Company. You would also

notice that there are some demographics and ancillary questions that wouldbe nice to have in a data form to help analyze the data that is found.

Therefore I would first and foremost arrange the data in the interviewaccording to how I would want to key it in SPSS. The first section would bethe demographics and ancillary data and the second section would be therelevant research questions.

Key in Interview Data

Open SPSS.

Create variables in the Variable view that can represent the demographics. Isee 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 I would do it is to note the answers givenby the fifteen respondents. I would code it accordingly, or even get a secondcoder 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 isdone, I am ready to enter the data into SPSS. In many cases, I do this on the

fly, which is to code and key in immediately.

Key in Interview Data - Example

Lets do Interview 1.

For demographics, I would key in gender in the Name first, and type in thelabel, Gender. For Values, I would key in 1 for Male and 2 for Female. If younotice, after you typed in gender in name, all other variables automaticallyappear. After doing so, when you open Data View, you can see the firstcolumn named gender.

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Do this for all the other variables.

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

See SPSS file Interview Demographics.

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For the first research question, why do they join MLM companies, I would look

at the answer given to the question, code it, and key it in as a Yes/No.

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

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

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Delete). This will leave me with the demographic file. I will then save it asanother file and proceed to key in the answers / codes to the second researchquestion.

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

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Assignment

Now try by yourself to key in the remaining interviews.

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How to key in Questionnaire based Data and to Transform.

The process to key in for a Questionnaire based data is also similar. Exceptthat 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 ofkeying into SPSS so that when the data comes, you can immediately post thedata into SPSS. This means questionnaire design must take into account helimitations of SPSS and the requirements of the method of analysis.

The demographics section will pretty well be the same as the earlierdiscussion. The only difference will be the data coding for the questionnairesand 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,ExampleQuestionnaire 1.

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Look at how the coding in the SPSS file mirrors the questionnaire. In thiscase, 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.

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 importantfor later stages of checking data.

Checking for Mistakes

Once completed keying in all the data, check if there were any mistakes inwhat was keyed in. How to do so? Two ways. The first is to select Analyze,Descriptive Statistics, Frequencies. A dialogue box would appear. Select allthe 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, ifyou used a Likert Scale with 5 anchors then you shouldnt have any othernumbers 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 theVariables box. Then click OK.

Once you have done so, the output would appear. Check if there are anynumbers that do not represent the Minimum and Maximum in the dataset. Asan example, if you used a Likert Scale with 5 anchors then you shouldnt haveany other numbers aside from 1, 2, 3, 4, and 5. So if you find number 11, or22, or 6, there must have been a mistake in keying in the data. Check also ifany 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 willappear. Key in the relevant number or item that was wrong to find out whichrow 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 thatrepresents the row that contains the wrong key in. Type in the right number.

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Recode

Lets now look into Recode.

Lets assume the researcher wants to recode the Educational Level data ofrespondents from the current 7 values (1 = SPM, 2 = STPM, 3 = Matriculation,4 = Diploma, 5 = Undergrad, 6 = Degree, 7 = Master) into only 3 values, thatis 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 issufficient numbers to do such a recode. Running a frequency does this. Thiswill 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 twotypes of Recode instructions. They are:

1. Recode into Same Variable, and2. Recode into Different Variable.

The choice is yours depending on what you intend to do. I would nearlyalways recode into a different variable, as I prefer to leave my initial dataintact so that I may return to it at a later stage.

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

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 berenamed Numeric Variable -> Output Variable.

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If you notice on the Output variable box, there is Name and Label, whichcorresponds to the new name and label that you wish for this variable. Type infor name, newedu and for label type in, New Education Level. Click onChange.

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

Now click on Old and New Values.

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

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

We wanted to create 3 values, which are those with an educational level up toschool 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 iswithin a range, key in number 1 to 2 in Range in the old data section and inthe new data section type in 1. Click on Add, otherwise it will not be addedinto 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.

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

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

Click OK.

SPSS will run the data and an Output table will appear with the code. Youmay 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.

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 onthe 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 toschool level, 2 for those with pre-university, and 3 for those with an Universityeducation.

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

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

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

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 followingsituation.

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

2. Use the datasetAssignment 1. Recode into a different variable for thecurrent variable by the name Age to your own determination of values. Thismust reflect the data.

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Compute

Compute refers to a method where SPSS runs a computation for you in orderto 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 theVariable View and as shown here, Table 1.

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Table 1. Loyalty Items by Rows

Row Name Label

23 highprob There is a high probability that you will dine at thisrestaurant again.

24 recomend You have recommended other people to patronize thisrestaurant.

25 sayptive You will say positive thing to other people about the serviceprovided 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 recommendedby this restaurant.

28 pricrise You will continue to dine at this restaurant even if the priceor service charge is increased somewhat.

29 prefer You have strong preference on this restaurant.

30 changed You will keep dining at this restaurant; regardless ofeverything being changed somewhat.

31 firstcho This restaurant is the first choice in your mind when youconsider having dinner outside.

32 oneofcho Assume that you have only three choices when you are inneed of having dinner, this restaurant must be one of them.

33 regular You have regularly dined at this restaurant for a long periodof 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 allrows creates a measurement for Loyalty.

Lets say we wish to create a variable named Behavioral Loyalty. We know it isthe average sum of rows 23 to rows 27.

Click on Transform, Compute Variable.

A Compute Variable dialogue box will appear.

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Target Variable refers to the new variable name that we wish to create; in thiscase lets name it behloy.

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Numeric Expression refers to the mathematical formula that we intend to useto create this new Target Variable. In this case it is the average of the sum ofrows 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 onthe appropriate variable and bringing it to the Numeric Expression (click onthe arrow). Do this for all the variables required and then place the ). Thenplace the divide sign ( / ) followed by the number to be divided by to obtain the

average.

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Click OK.

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

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Open the Data View and have a look at the behloy data column. You willnotice 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 andreducing it to 0 decimals. However, I dont prefer this as when you run afrequency SPSS will still show the different decimal points.

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I prefer to open a Microsoft Excel file. Copy all the variables from SPSS andpaste it in the Excel file. Highlight all the numbers in the Excel file. Then clickon 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 singledecimal. Copy this and paste it back onto SPSS.

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Select all the data in SPSS. Copy.

Paste the data in Microsoft Excel.

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

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Copy the data and paste in back in SPSS.

Assignment

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

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

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

See answer here in Example Loyalty.

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Syntax

SPSS is run on a program language that most of us will not even use or befamiliar with, Nevertheless, by knowing some simple tricks of the trade, it will

make life easier especially when running repetitive analysis. Syntax in SPSSis the program language. I do not recommend that you learn it, but if you wishto do so you may look in the Help topics in SPSS or in its manuals. When youare running syntax, you can find out what are the commands, subcommands,and keywords by pressing on F1. For me, and for most researchers, it wouldbe sufficient enough that you know how to create the command language andhow to run it again and again for your task.

By now you will realize that I have used most of the commands found in themenu and dialogue boxes. This is because it is easy to use and easier tounderstand. 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 laterdate or to repeat various analyses.

A syntax file is just a file that carries the SPSS language commands. You cantype 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 aredisplayed, make sure Syntax (*.sps) is selected in the Files of type drop-downlist. Click Open.

How to get the Commands

As discussed earlier, the normal ways are by reading the manuals and Helpsection. I 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 forthe analysis that you intended to do.

Open the file Example Loyalty.

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

You will see a Syntax window appear with the commands for the analysis that

you wanted to do.

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You can save the syntax file, as Example Syntax.

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

You will obtain an output.

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

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

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

Assignment

Now try by yourself to create your own syntax file.

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Output

We have been discussing quite a number of matters while looking at theOutput file, yet without discussing this rather important file. As you may havenoticed, 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 personaltaste and need. I would normally close it as I prefer to have the new syntaxcommands and without the clutter of past work. However, sometimes the pastwork in itself is essential. Therefore the choice is yours.

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

See example of an output.

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

From the output, you can copy whatever data that is relevant to your studyand paste it onto other programs such as MSWord. This is what moststudents do. Please dont do this, as it indicates a lack of analysis on yourpart.

This is how students normally present such findings.

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Gender

Frequenc

y Percent

Valid

Percent

Cumulative

Percent

Valid Male 105 42.2 42.2 42.2

Female 144 57.8 57.8 100.0Total 249 100.0 100.0

Race

Frequenc

y Percent

Valid

Percent

Cumulative

Percent

Valid Malay 57 22.9 22.9 22.9

Chinese 148 59.4 59.4 82.3

Iban 15 6.0 6.0 88.4Others 29 11.6 11.6 100.0

Total 249 100.0 100.0

Age

Frequenc

y Percent

Valid

Percent

Cumulative

Percent

Valid 15-24 173 69.5 69.5 69.5

25-34 63 25.3 25.3 94.8

35-44 13 5.2 5.2 100.0Total 249 100.0 100.0

Education Level

Frequenc

y Percent

Valid

Percent

Cumulative

Percent

Valid SPM 60 24.1 24.1 24.1

STPM 33 13.3 13.3 37.3

Matriculation 6 2.4 2.4 39.8

Diploma 29 11.6 11.6 51.4

Undergraduat

e34 13.7 13.7 65.1

Degree 83 33.3 33.3 98.4

Master 4 1.6 1.6 100.0

Total 249 100.0 100.0

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Gender

FemaleMale

Frequency

150

100

50

0

Gender

Age35-4425-3415-24

Frequency

200

150

100

50

0

Age

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Race

OthersIbanChineseMalay

Frequency

150

100

50

0

Race

Education Level

MasterDegreeUndergraduateDiplomaMatriculationSTPMSPM

Frequency

100

80

60

40

20

0

Education Level

Again, please dont do this.

This is common in most students presentation of SPSS findings from anoutput. A direct cut and paste of the output file. Plus the graphs and the dataset are redundant. Students do this even when the output is for a regressionor a factor analysis. Please note, and we will discuss this, that there arenorms of presentation for various types of analysis.

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

Table 1: Respondents ProfileVariable 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 5.2EducationLevel

SPM 60 24.1

STPM 33 13.3

Matriculation 6 2.4

Diploma 29 11.6

Undergraduate 34 13.7

Degree 83 33.3

Master 4 1.6

Aside from cut and paste, one can also export what was found in the output

file to MS Word. This is done by right clicking the mouse and selecting Export.

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

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

Click OK.

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The document should appear as OUTPUT.DOC.

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

Assignment

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

2. Then try to run the Export function.

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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 thatyour Test Distribution is Normal.

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

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

Variances are Equal

You can check whether the variances of all variables used are equal by notingthe 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 thisindicates 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) thenthis indicates that the variance of the two samples are approximately equal.

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How to Analyze: Frequency


Lets say you want to know the frequency of your respondents gender.

Select Analyze, Descriptive Statistics, Frequencies. The Frequencies dialoguebox will appear.

Select Gender and transfer it to the Variables box.

Click on OK.

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

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Note

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

When you click on Statistics, a Statistics dialogue box will appear. It has fourmini boxes, Percentiles Values, Central Tendencies, Dispersion, andDistribution.

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

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

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How to Present the Findings

Refer to Output discussion.

Assignment

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

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

the Quartiles.

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

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How to Analyze: Crosstabulation


Lets say you want to know the relationship between gender and itsrelationship 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 forcolumns. 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 I would rather haveGender 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 thedata later on.

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

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

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

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

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

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

As for Format, I normally just let it be.

Click OK.

The Output file will appear. See Crosstab Output.

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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 caneasily be shown for either one by itself.

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Table 1: Crosstabulation of Possibility of Apology by Gender

Apology

Gender

Male Female

No chance

Frequency 0 1% .0% .7%

Very slightpossibility

Frequency 0 1

% .0% .7%

Slightpossibility

Frequency 0 5

% .0% 3.5%

Somepossibility

Frequency 4 7

% 3.8% 4.9%

Fair possibility Frequency 4 7

% 3.8% 4.9%

Fairly goodpossibility

Frequency 8 19% 7.6% 13.2%

Goodpossibility

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,practicallycertain

Frequency 25 26

%23.8% 18.1%

Assignment

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

2. Prepare a Table to depict your findings.

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How to Analyze: Means


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

Select Analyze, Descriptive Statistics, Descriptives. The Descriptives dialoguebox will appear.

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

Click OK.

The output will appear. See Means Output.

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Note

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

Normally I am satisfied with this though sometimes there may be a need to

test for Distribution. Click Continue.

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Means can be presented in a variety of ways, of which the easiest is to makeit 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 thisrestaurant again. 6.43 2.31

You have recommended other people to patronize thisrestaurant. 5.66 2.26

You will say positive thing to other people about theservice 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 arerecommended by this restaurant. 6.14 2.44

Behavioral Loyalty 5.88 1.97

You will continue to dine at this restaurant even if theprice 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 ofeverything being changed somewhat. 5.06 2.18

Attitude Loyalty 5.20 1.91

This restaurant is the first choice in your mind whenyou consider having dinner outside. 4.88 2.31

Assume that you have only three choices when youare in need of having dinner, this restaurant must beone of them. 5.50 2.49

You have regularly dined at this restaurant for a longperiod of time. 5.18 2.51

Cognition Loyalty 5.20 2.15

Overall Loyalty 5.50 1.78

Or:

Table 1: Means for Loyalty Variables

Variables Mean Std. Dev.

Behavioral Loyalty 5.88 1.97

Attitude Loyalty 5.20 1.91

Cognition Loyalty 5.20 2.15

Overall Loyalty 5.50 1.78

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Assignment

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


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How to Analyze: t-test


T-test is normally used when there are only two values in a variable. AnAnova is used when there are three or more values in a variable. SPSS offers3 types of t-test:

1. One Sample T-Test2. Independent Sample T-Test3. Paired Samples T-Test

One Sample T-Test

A One Sample T-Test compares the mean score of a sample to a knownvalue. Lets say you want to know whether in your education variable, that therespondents education level is different from the known population mean. Inthis case, the mean for education level, let say is 4.

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

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

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Click OK.

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

The T value is 1.218, with 248 degrees of freedom. The significance value is0.224. This means that there is no significance difference between the twogroups (the significance is more than 0.05).

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T-Test findings can be presented as a sentence or a Table. If in a sentence, Iwould 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 = 4t df Sig. (2-tailed)

Education Level -1.218 248 .224

Assignment

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


Independent Sample T-Test

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

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

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

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

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

Click on Continue.

There is an Options button in the main Independent Samples T Test dialoguebox. This is to indicate the Confidence Interval that you wish to use. I normallyleave it at 95%.

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

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

Group Statistics

Gender N Mean Std. DeviationStd. 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 Levenes Test.

Independent Samples Test

Levene's Test for Equality

of Variances

F Sig.Lower Upper

apology Equal variances assumed4.994 .026

Equal variances notassumed

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

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

Independent Samples Test

t-test for Equality of Means

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference

95% Confidence Intervalof 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 ofthe two samples are significantly different.

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

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



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


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Table 1: Independent Sample Test for Apology

Variable t df Sig. (2-tailed)

Apology 2.284 242.594 .023

Assignment

1. Open the file Example Loyalty. Now try by yourself to run aIndependent Sample T-Test from the above dataset for all the ServiceRecovery 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, andthen 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 variableapology and assist as methods of service recovery.

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

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

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Then transfer it to the Paired Variables box.

The Options button is similar to the previously discussed button.

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

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

Paired Samples Statistics

Mean N Std. DeviationStd. Error

Mean

Pair 1 apology 7.2129 249 2.25199 .14271

assist 6.4699 249 2.16462 .13718

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

Paired Samples Correlations

N Correlation Sig.

Pair 1 apology & assist 249 .601 .000

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

difference.

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Paired Samples Test

t df Sig. (2-tailed)

Pair 1 apology - assist 5.942 248 .000



Apology and Assistance correlates well (Correlation = 0.601, p = 0.000) yetthe paired samples t-test indicates that there is significant difference betweenthe 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 all the Service Recoveryvariables.


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How to Analyze: Correlation

Correlation is used when you want to know how two variables are associatedwith 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 normallydistributed. When the data that you are using is not normally distributed, thenyou use Spearman Rho.

SPSS offers three types of correlations:1. Bivariate2. Partial3. Distance

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


Lets say you want to know the correlation between apology and assistvariables. 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 RCorrelation and if it is not normally distributed, then you use Spearman Rho.This can be seen under the Table Correlation Coefficients. You can also

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

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

The Options button here allows you to choose if you require added statistics.Normally I dont 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 andnumber 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 itgets nearer to either +1 or 1. The correlation coefficient also provides the

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


Correlation findings can be presented as a sentence or a Table. If in asentence, I 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 Correlationsfrom the above dataset for all the Service Recovery variables.


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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 apologyand age level. In simple terms, you want to know whether there is anydifference in how the various age groups look at the variable apology.

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

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

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There are three buttons at the bottom, Contrast, Post Hoc, and Options. Youwill need to look at Options and Post Hoc. Click on Options and click on theboxes 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 I would use either Tukey or Bonferroni. If there are equal numbers ofcases in each group, choose Tukey. If there are not equal numbers of casesin 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 I chooseBonferroni. You will also note that the significance level is maintained at 95%level of confidence or shown here as .05. Click continue.

Click OK and the output should appear.

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

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

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

Descriptives

apology

N MeanStd.

DeviationStd.Error

95% Confidence Interval forMean

Minimum MaximumLowerBound

UpperBound

15-24 173 7.1214 2.32335 .17664 6.7727 7.4701 .00 10.00

25-34 63 7.5079 2.09356 .26376 6.9807 8.0352 2.00 10.00

35-44 13 7.0000 2.04124 .56614 5.7665 8.2335 4.00 10.00

Total 249 7.2129 2.25199 .14271 6.9318 7.4939 .00 10.00

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

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

Test of Homogeneity of Variances

apology

LeveneStatistic df1 df2 Sig.

1.280 2 246 .280

We note that the variances are equally distributed. Then we can see theAnova 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 differencebetween the groups.

ANOVA

apology

Sum ofSquares 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 (*) ifthere is any significant differences. In this case there is none.

Multiple Comparisons

Dependent Variable: apologyBonferroni

(I) Age (J) Age

MeanDifference

(I-J) Std. Error Sig.

95% Confidence Interval

Upper Bound Lower Bound

15-24 25-34 -.38655 .33173 .735 -1.1862 .413135-44 .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 1.441325-34 -.50794 .68673 1.000 -2.1633 1.1474


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

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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-WayAnova from the above dataset for all the Service Recovery variables byAge scale.


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How to Analyze: Manova

MANOVA or Multivariate analysis of variance is simply an Anova that runs onseveral dependent variables. MANOVA is used to assess whether an overalldifference exist between groups, and the differences among the combinations

that is presented by the researcher. Normally, after a Manova is run, then onlydoes the researcher carry out Univariate GLM analysis.

As an example, in our One-way Anova, we ran the analysis only on age andapology. What if we wanted to test all the various methods available forservice recovery and add on education level as well? Running a MANOVAcan 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 DependentVariables. Transfer age and education level to Fixed Factors.

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The buttons on the right side are for further consideration by the researcher.For me, I would leave the Model, Contrast and Save buttons. As for Plots, itdepend 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. Thiscan be done by clicking on age and placing it on the Horizontal Axis. Thenclick on Add.

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To have both age by edu, click age and place it on the Horizontal Axis andclick 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 ofoptions.

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. Inthis case, Bonferroni.

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For the Options button, when you click on it, he following will display.

If you wish to display the Means, select all and transfer to the Display Meansfor: box. I would normally click on descriptive Statistics, Spread vs. level plots,and Residual plots. Then click on Continue.

You will return to the main Multivariate dialogue box. Click OK.

The output will appear. Refer to Output Manova.

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You will find displayed initially the Between Subject Factors and theDescriptive Statistics. The next box will depict the Multivariate Test, which isof greatest importance to you.

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Multivariate Testsc

.752 36.541a 18.000 217.000 .000

.248 36.541a 18.000 217.000 .000

3.031 36.541a

18.000 217.000 .0003.031 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 TraceRoy'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' LambdaHotelling'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 I would normally look at is the Pillais Trace as it has been noted asmore robust and appropriate. Nevertheless, many also use Wilks Lambda.

Note whether it is significant or not for the overall interaction and by eachvariable. In this case, all are not significant indicating that there are nodifferences in how service recovery variables is seen by age, education, andits interactions.

Then you may look at the Test Between Subjects, which will indicatesignificance at individual levels. Running a Univariate analysis cancorroborate this.

This is followed by a table on the means and then followed by the post hoctest. In this case, you may note the significant variables highlighted by SPSSin 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.Pillais trace for the variable Age (P = 0.194, F = 1.300, Sig = 0.120),

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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 recoveryvariables.

It could also be presented in a Table.

Table 1: Multivariate Tests for Service recovery Variables by Age andEducation Level

Effect Value F Sig.

Age

Pillai's Trace .194 1.300 .120

Wilks' Lambda .814 1.309(a) .114

Hotelling's Trace .220 1.318 .108

Roy's Largest Root .163 1.978(b) .012

Education Level

Pillai's Trace .400 .880 .802

Wilks' Lambda .658 .877 .807

Hotelling's Trace .439 .875 .812

Roy's Largest Root .148 1.819(b) .024

Age *Education Level

Pillai's Trace .346 .754 .970

Wilks' Lambda .695 .758 .967

Hotelling's Trace .383 .763 .964

Roy's Largest Root .159 1.960(b) .013a Exact statisticb 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 MANOVAfrom the above dataset for all the Loyalty variables by Age, Education, andGender.


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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 hypothesestesting about whether the predictor variables are related to the criterionvariable.

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 variableon the values of a continuous or interval-scaled dependent variable. It depictsthe strength of the predictor variables in order to make a better conclusionabout others.

Open the file Example Regression.sav

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

Click on Analyze, Regression, Linear. The dialogue box will appear asfollows.

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

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

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

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

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

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

Variables Entered/Removed(b)

Model Variables Entered Variables Removed Method

1

affect, respect, loyalty,contribute(a)

. Enter

a All requested variables entered.b Dependent Variable: soft_T

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

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Model Summary(b)

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .323(a) .104 .081 1.04953

a Predictors: (Constant), contribute, respect, loyalty, affectb Dependent Variable: soft_T

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

ANOVA(b)

ModelSum ofSquares df Mean Square F Sig.

1 Regression 19.660 4 4.915 4.462 .002(a)

Residual 168.530 153 1.102

Total 188.190 157

a Predictors: (Constant), contribute, respect, loyalty, affectb Dependent Variable: soft_T

The unstandardized coefficient of an independent variable is known as ,which measures the strength of the predictors and the criterion variables. Weuse the unstandardized coefficients in view of the fact that they can be

measured on different scales. For example, we cannot compare the value forgender with the value for soft tactic.

Coefficients(a)

ModelUnstandardized

CoefficientsStandardizedCoefficients t Sig.

B Std. Error Beta B Std. Error

1 (Constant) 2.366 .569 4.158 .000

affect .338 .180 .256 1.879 .062

loyalty .123 .131 .111 .939 .349

respect .292 .128 .247 2.288 .023

contribute -.444 .171 -.353 -2.595 .010

a Dependent Variable: soft_T

The table above has shown that there is significant relationship betweenrespect and contribute with soft tactic atp


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Observed Cum Prob

1.00.80.60.40.20.0

ExpectedCumProb

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 asentence, I would say:

There was a significant relationship between respect and soft tactic ( = .292,p < .05).

Assignment

1. Open the file Example Regression.sav. Now try by yourself to run standardregression from the above dataset for all the independent variables (affect,loyalty, respect, and contribute) on rational_T.

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How to Analyze: Exploratory Factor Analysis

Exploratory Factor Analysis (EFA) is done when one wants to know if thereare any simple patterns among the variables studies. This is especially truewhen 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 reflectsthe theory that places it into three factors, that is Behavior, Attitude, andCognition.

Click on Analyze, Data Reduction, Factor. The following dialogue box willappear.

Select and transfer all the relevant loyalty items into the Variables box.

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You will notice a few buttons at the bottom of the screen, Descriptives,Extraction, Rotation, Scores, and Options. Lets look at them now.

Click on Descriptives. I would normally then click on KMO and Bartletts Testof sphericity. You may also click on Anti-image. Click on Continue.

Click on Extraction. I would normally click on Scree Plot and maintain theothers. Do note that there are many options of Method of factor analysisextraction that is offered by SPSS, but the usual is by Principal Components.Factor analysis also allows you to extract as many factors as possible basedon Eigenvalues or for you to pre-determine the number of factors that yourequire. In this case, lets leave it at Eigenvalues over 1. As for Iterations, the

set limit here is 25. You can place a higher number if you want to. Click onContinue.

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Click on Rotation. I normally use Varimax. Nevertheless, as you may noticethere are many other methods that can be used. Click on Continue.

Ignore Scores. Click on Options. For the Coefficient Display Format, I would

normally click on both boxes and I choose the suppress absolute values lessthan 0.4999. Nevertheless there are those that argue than anything less than0.3999 could also be suppressed. Click on Continue.

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Click on OK. The output file should appear. See Output factor analysis.

The first thing that you will note is the KMO and Bartletts test. There arearguments as to what level of KMO is acceptable. For me, the higher thebetter of course, but the absolute minimum in 0.60.

Bartletts Test is used to test equality of variance.

KMO and Bartlett's Test

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Kaiser-Meyer-Olkin Measure of SamplingAdequacy. .908

Bartlett's Test ofSphericity

Approx. Chi-Square 1812.110

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 thediagonal of the matrix is greater than 0.5 to indicate that the sample isadequate. The next box is on Communalities.

Then look at the Total Variance Explained box. This will tell you how manyComponents or Factors there are, and how many for you to keep. Normally

one will take any variable that has an Eigenvalue above 1. Sometimes theremay be a lot of components with an Eigenvalue over 1, so keep those whichcontribute to 70% to 80% of the variance. The other way is by examining theScree Plot, which will be discussed later. In this case, there are only 2components.

Total Variance Explained

Component Initial Eigenvalues

Total % of Variance Cumulative %

1 6.406 58.240 58.240

2 1.155 10.501 68.7413 .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. Inthis case the first two values are acceptable.

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Component Number

1110987654321

Eigenvalue

6

4

2

0

Scree Plot

Then look at the Rotated Component Matrix and try to name the componentsbased on the variables shown.

Rotated Component Matrix(a)

Component

1 2

This restaurant is the first choice in your mind when you consider having dinneroutside.

.834

Assume that you have only three choices when you are in need of having dinner, thisrestaurant 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 changedsomewhat.

.709

You have strong preference on this restaurant..662

You will continue to dine at this restaurant even if the price or service charge isincreased somewhat. .640

You have recommended other people to patronize this restaurant..847

There is a high probability that you will dine at this restaurant again..828

You will say positive thing to other people about the service provided by thisrestaurant.

.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.

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Note:

You will need to do a reliability test for your findings. This can be done byclicking on Analyze, Scale, Reliability Analysis. The dialogue box will appear.

Select all the variables in the first component of your factor analysis andtransfer it to the box named Items. Make sure the Model selected is Alpha.Click on OK.

The following output will appear. See Output Reliability.

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The first box will detail the case summaries. The second box will detail theCronbach Alphas 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 furtherexplanations. 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 thatthe analysis is meritorious. Bartletts test is significant (Chi Square = 1812.11,p


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Table 1: Rotated Component Matrix for Loyalty

Variable Attitudeand

Cognition

Behavior

This restaurant is the first choice in your mind when youconsider having dinner outside.

.834

Assume that you have only three choices when you arein need of having dinner, this restaurant must be one ofthem.

.806

You have regularly dined at this restaurant for a longperiod of time. .784

You will keep dining at this restaurant, regardless ofeverything being changed somewhat.

.709

You have strong preference on this restaurant. .662

You will continue to dine at this restaurant even if theprice or s

Documents

Workshop on SPSS1