ASSALAM O ALAIKUM .Guys hope you all enjoying,

Normally we deal with two kinds of data.

Primary data and secondary data

This is easy for us to analyze a numerical data, like I have profitability data of hundred

companies and this data are commonly known as secondary data. Secondary data collected form

secondary sources.

There is another kind of data which is known as primary data, so primary data is little bit

difficult and different form secondary data, (my own perceptions), we collect normally primary

data through questionnaire.

So let suppose I have a questionnaire through we will discuss about Primary data analysis. In the

following questionnaire I have three variables and each variable have different items (questions)

so, here we have need to reduce these questions into single variables, like turnover intension

have six items (questions) and justice have three item, let suppose if I take justice as dependent

variable while turnover intention as independent variables so I have to run 3 time regression

because of we have three dependent variables (because three items of justice), which is too hectic

So what I have to do for converting all the items of a variable into single variable, like I have

three item of justice, what I must do for merge all three items into single variable, which is

justice?

Hence, the simple method to reducing data into smallest summary is PRINCIPAL

COMPONENT ANALYSIS (PCA)

Why we use PCA?

1. To discover or to reduce the dimensionality of the data set.

2. To identify new meaningful underlying variable

3. To test theoretical questions through empirically.

From now let’s start. First of all we will conduct reliability test

Reliability test?

Reliability is the degree to which an assessment tool produces stable and consistent results. Test-

retest reliability is a measure of reliability obtained by administering the same test twice over a

period of time to a group of individuals.

Reliability through Cronbach's alpha

Cronbach's alpha is a measure of internal consistency, that is, how closely related a set of items

are as a group. It is considered to be a measure of scale reliability. Technically speaking,

Cronbach's alpha is not a statistical test - it is a coefficient of reliability (or consistency).

What must be the value of Alpha Coefficient to conclude that items are consisting or not?

Let suppose I have the alpha coefficient for the three items (justice questions) is .839, suggesting

that the items have relatively high internal consistency. (Note that a reliability coefficient of .70

or higher is considered “acceptable" in most social science research situations.) And here is also

another reference

Cronbach's alpha Internal consistency

α ≥ 0.9 Excellent

0.9 > α ≥ 0.8 Good

0.8 > α ≥ 0.7 Acceptable

0.7 > α ≥ 0.6 Questionable

0.6 > α ≥ 0.5 Poor

0.5 > α Unacceptable

Reference

George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and

reference. 11.0 update (4th ed.). Boston: Allyn & Bacon.

How to write the results of your Cronbach’s alpha values in your article of thesis?

Table 1.Reliability of measurement

Constructs Valid number Number of items Cronbach’s alpha

Turnover intension 200 7 .778

Trust 200 3 .913

Justice 200 3 .920

Employees job satisfaction 200 9 .740

Table 1.Represent the estimated values of Cronbach’s coefficient alpha examined the reliability

and internal consistency of the measures. For the present sample, value of Cronbach’s alpha vary

from0.74 to 0.92 which indicates that each multi- item construct possesses high reliability

turnover intension (alpha = .778), trust (alpha = .913) justice (alpha = .920) employee job

satisfaction (alpha = .740) the high Cronbach’s alpha value for each construct implies that they

are internally consistent. In brief, the higher the Cronbach’s value of the construct the higher the

reliability is of measuring.

If you find Cronbach’s alpha acceptable then you can go further analysis otherwise not.

Like PCA, CORRELATION, REGRESSION ETC.

Steps for reliability test .analyze------scale----reliability and ok bellow is resulted file of

reliability test.

Step 2. Drag all items of your first variable (means drag all questions of your first variable

into right side box and ok(repeat same procedure for all your variables )

Step 3.resutls of reliability test for first variable. Check reliability of variables and write

results in above table form.

PCA

Now we are going toward PCA (principal component analysis), let suppose we have observe

reliability of all the items and we find reliability values of all items acceptable in this case

can conduct principle component analysis.

We will find two types of values in PCA, KMO and Bartlett's Test AND Eigen values.

Now why we use KMO?

The KMO measure of sampling adequate indicates the suitability of employing factor analysis.

The value of KMO varies between 0 and 1 a value of 0 indicates that there is larger dispersion in

the pattern of correlations hence, application of factor analysis become inappropriate. A value of

1 indicates that the patterns of correlation are relatively compact so the application of factor

analysis becomes appropriate. It is a general rule of thumb that a KMO value of 0.5 is poor and

0.6 is acceptable and value closer to 1 is better and more desire able further more values between

0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good values and values between 0.8

and 0.9 are great values above 0.9 are superb

Bartlett’s test sphericity

is conducted to check the significance of the relationship between the items of a construct. If

there is no relationship among the items of a construct then it will be pointless to go ahead with

the factors analysis. Bartlett’s test assumes a null hypothesis of no correlation. Generally, a p-

value <0.05 confirms the significance of the relationship among variables. In my table the p-

values of Bartlett’s test in the case of all constructs is less than 0.001 which provides evidence

against the null hypothesis of no correlation. So we can continue with factors analysis.

What values we should write for analysis in our thesis or article?

KMO and Bartlett's Test

Constructs Number

of items

Kaiser-Meyer-Olkin

Measure of Sampling

Adequacy.

Bartlett's

Test of

Sphericity

Bartlett's

Test of

Sphericity sig

Turnover intension 7 .690 771.529 .000

Trust 3 .685 674.495 .000

Justice 3 .656 634.317 .000

Employees job satisfaction 9 .858 1384.0 .000

ANALYZE----DIMENTION REDUCITON----FACTOR and ok

Step 1.

Step 2.drag all items of your first variable and note, don’t include informational variables

like gender, age etc, and below is detail about what u must click in descriptive, extraction

rotation etc. and after click all required options do ok.

Descriptive = click KMO

Extraction =no need to change any thing

Rotation=click to varimax

Scores =save as variable

Option =no need to change any thing

Step 3.

We have need of

KMO and Bartlett’s

test values, and

significance values

Step 4. You can see all five items of first variables merge and adopt a form of single

variable now go to your spss file and give the original name of your variable to new

variables , from this method convert all the variables in single variables.

What must be the KMO values?

I HAVE ALREADY TOLD ABOUT THE KMO VALUE AND BARTLET TEST VALUE

IN ABOVE PARAGHARPH.

REFRENCE.Hutcheson, G. and Sofroniou, N. (1999), The multivariate social scientist. London: Sage.

Eigen values and total variance?

Frist of all how to write Eigen values

Construct Components Total % of variance Cumulative %

Turnover Comp1 3.220 64.404 64.404

Trust Comp1 2.734 91.126 91.126

Justice Comp1 2.964 89.791 89.791

Job satisfaction Comp1 6.245 56.774 56.774

Faci_1 is new variable which is produce after

PCA, now u must give name to this variable like

you can write here justice, customer satisfaction

etc means assign name of your variable, in label

also write the name of this new variable.

Note: Constructs means our variables, component means when we run PCA

let suppose when I run PCA for first variable we saw only one variable

extract or develop , it’s not necessary that all the items produce one

component ,let suppose justice items produce 2 component means two

variable then we will write like this and remaining items same

Construct

Justice Comp1

Comp2 If I interpret above results, turnover having one component which have 64% information

of all the items of justice variables. And remain same interpretation, and if two component,

then let suppose justice have two component and one component have information of all

items x% while component 2 having information of all items of justice x%.

Correlation

Let suppose we have run PCA and summarized the items of all variables into only four

variable

The correlation analysis has been used to confirm mutual association among the items of each

construct. Then main purpose of correlation is to check the relationship strength means strong

relation or weak or moderate etc.

1. If value comes less than 0..34 we will say weak relationship among variables

2. If value comes 0.34-----.74 moderate relation

3. If value comes more than .74 strong relation

In the following window we can see 23 items are summarize into four variables , now we

will check correlation among variables , now u have no need to examine all the items u

can use only these four variables

Steps for checking correlation. Analyze --- correlate ---bivariate

Following window is showing results of correlation among variables

Here we can see that we have convert all 23

items not only four variables

I have use

only new four

variables not

all items. And

ok

How to write results of correlation

Turnover

intentions

Trust Justice Employees job

satisfaction

Employees turnover

intentions

1

Trust .134**

1

Justice .129* .798

** 1 .

Employees job

satisfaction

.891**

.524

**

.241* 1

Last regression analysis

In the following window write your dependent variable and independent variable

Your dependent

variables and all

independent

variable

Let suppose I write my T variable as dependent and other variables as independent

variable.

RESUTLS OF REGRESISON BEST OF LUCK

To see

autocorrelation in

your data you can

click on statistics and

D.W VALUE

Questionnaire used for interpretation

1. Disagree 2.Strongly disagree 3. Neutral 4. Agree 5. Strongly Agree

Turnover Intension

1 Employees understand specific needs of customers (empathy). 1 2 3 4 5

2 Employees are able to “put themselves in the customers’ place”

(empathy).

1 2 3 4 5

3 Employees are able to “tune in” to each specific customer (empathy). 1 2 3 4 5

4 Employees “surprise” customers with their excellent service (excellent

performance).

1 2 3 4 5

5 Employees do more than usual for customers (excellent performance). 1 2 3 4 5

6 Employees deliver an excellent service quality that is difficult to find in

other organizations (excellent performance).

1 2 3 4 5

Justice

1 Justice between the employees is main element in organization

1 2 3 4 5

2 Justice improve the efficiency of organization

1 2 3 4 5

3 Justice is key determent of value addition in organization

1 2 3 4 5

Employees work engagement

1 At work, I feel full of energy. 1 2 3 4 5

2 In my job, I feel strong and vigorous. 1 2 3 4 5

3 When I get up in the morning, I feel like going to work. 1 2 3 4 5

4 I can continue working for very long periods at a time. 1 2 3 4 5

5 In my job, I am mentally very resilient. 1 2 3 4 5

6 At work, I always persevere, even when things do not go well 1 2 3 4 5

7 I find the work that I do full of meaning and purpose. 1 2 3 4 5

8 I am enthusiastic about my job. 1 2 3 4 5

9 My job inspires me. 1 2 3 4 5

10 I am proud of the work I do. 1 2 3 4 5

11 I find my job challenging. 1 2 3 4 5

12 Time flies when I’m working. 1 2 3 4 5

13 .When I am working, I forget everything else around me. 1 2 3 4 5

14 I feel happy when I am working intensely. 1 2 3 4 5