statistical infernece, corelation spss report

7/28/2019 Statistical Infernece, Corelation SPSS Report

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Transform Data into KnowledgeSPSS

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Contents:1. Context2. Introduction to SPSS

Data View Variable View Values Measures

Steps for Scatter Matrix Steps to Add Fit Line Steps for Correlation Matrix Steps for Split File

3. Putting Data into One Variable Quantitative Verbal

4. Combined Scatter Matrix5.

Combined Correlation Matrix6. Scatter Matrix Results Interpretations

7. Correlation Results Interpretations

8. Arrangement of data in different classes9. Improvement

CGPA Inter-percentage


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Context:The objective of this assignment is to transform data into an effective form of

information. Or we can also say that transforming data into knowledge or

transform data into that shape from where we can get understanding and

information.

For the analysis and learning purposes we had taken data from the batch of

BBA-16 (Both A and B) of Graduate Schools of Business at International

Islamic University, Islamabad and there are only male students. We tookthere CGPA, Intermediate percentage, medium of instruction in metric ,

intermediate institution, accounting1, accounting 2, cost accounting,

English1, English 2 and oral communication.

At first, we entered the following data i.e. CGPA, Intermediate percentage,

medium of instruction in metric , intermediate institution, accounting1,

accounting 2, cost accounting, English1, English 2 and oral communication

in Variable View by giving them appropriate value label dialogue box. After

the entrance and description of data in the variable view, we make a scatter

matrix of the following variables so that we can compare the dependency of

each variable with that of the other variable.

From the scatter matrix we can analyze whether the variables entered are

positively correlated or negatively correlated which we can see from matrix

drawn. The scatter matrix follows the correlation matrix in which we have to

report and interpret the results of validity coefficients so that we can have a

clearer view of our data.

After having all this done, we will move towards the inspection of the results

and interpretation of all six variable we use in our data i.e. CGPA,

Intermediate percentage, medium of instruction in metric , intermediate

institution, accounting1, accounting 2, cost accounting, English1, English 2

and oral communication, also Quantitative and Verbal.


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Introduction to SPSSWhenever we open the SPSS software we arrive at the Data Editor. In the

bottom left hand side we have two views

1.Data View:Columns = variable (questions, items q1, q2.)

Rows= Cases/ Respondents.

2. Variable View:Columns = Description of the variable

Rows = No. of Variables

We will avoid entering the data directly in the data view rather it is more

preferable to first give the name and the other features of the variable going

into the variable view. Label is very important in the variable view.

In the numeric area we entre the grey button and a dialogue box will appear.The description of the variable is given in the label.

Label = Var iable Description

It is meant for out put. Label utilization is in the output. But is very important

that not use too long statements because it may become a hurdle when we

plot the graphs.

Values:

Values are used whenwe have categorical or Ordinal Data. First variables areto be entered in the Variable View and then we go for the Data entry in Data

View.


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To View the Variable Attributes or Descriptions:

In the menu barUtilities Variables

In tool barIcons variable labels (red tag)

Measures:

In the measures we have three scales Nominal, Ordinal and Scale. SPSS

treats Interval and Rational as Scale.

Scale = {Interval, Ratio}

For SCATTER MATRIX:

Menu bar Graph Scatter / Dot Dialogue Box

appears Matrix Scatter Define (click)

To Add FIT Line:

Double click the scatter matrix Chart Editor

add fit line total a dialogue box appear

For Correlation Matrix:

Menu Bar Analyze Correlation

Bivariate Bivariate correlation dialogue box appears

Entre the variable (CGPA, inter-percentage, MOIM, Inter-

institute) Click OK


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If we click the SPSS then at first this type of file will appear

Data View:

The above page of SPSS (Version 17) shows the data view in data editor.

When ever we open SPSS we come in the data editor. In the bottom left

hand side we have two options Data view and Variable view. We never

entre the data directly in the data view. In the data view we information

regarding our variables like in this data we will entre CGPA, Inter-

percentage, MOIM, etc.

Data

View


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Columns = variable (questions, items q1, q2.)

Rows= Cases/ Respondents.

VARIABLE VIEW:

The following page of SPSS shows the Variable view in Data editor. It is

preferable to first give the name and other feathers of the variable going

into the variable view. In the Name column entre the variables. In the

Type column entre numeric type. Adjust the Width and Decimals

according to your data. Label is very important in the variable view. The

description of a variable is given in the label.

Label = Var iable Description

Variable

View


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It is meant for out put. Label utilization is in the output. In graphs is shows

the output.

Type:

From the above, we can see. If we click the type there come the different

options like numeric, comma, dot, scientific notation, date, dollar andstring. We select String because Names are string. And also width is

shown in the bar that how many characters a name should have.

Also the other type likes Numeric for other variable because these are

represented as numeric numbers. As the given below which is used for

the CGPA.

String

Character


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VALUES:

We use values when we have a data which is of Categorical or Ordinal

nature. For example

Medium of instruction in metric

We add value 0 for Urdu and like wise this we add other values.

And in this dialogue there is value label of CGPA, where we label asunder

1= F2= C

3= C+

4= B

5= B+6= A


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MEASUREMENT:

A. CGPA : SCALEB.INTER-PERCENTAGE : SCALEC.MOIM : NOMINALD.INSTITUTION : NOMILALE. GRADES : SCALE

Putting Data into One Variable

(Combining Variables):

We group the Variables in order to analyze the combined effect of grades

in similar course i.e. Quantitative and Verbal. We combine these variablesinto one variable by taking the mean of the selected variables.

In our study, we group the Accounting courses into one variable (QNT)

and English courses into another (Verbal).

This is how we combine different variable, detail of this given further.

QNT

Cost

Act

Act 2Act1

VERBAL

Oral

com

Eng 2Eng 1


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DATA VIEW:

Now, we will add the data in the data view form of Data Editor of SPSS. The

most important thing to remember when we add data is that we put the data

according to the labels which we defined in the variable view form. And dataentry neither should nor contradicts with the variable form of data.

For example if a person scores A grade in Act 1 we will put 6 instead of A.because we already label it.

1= F

2= C

3= C+

4= B

5= B+6= A


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Now, we will put one kind of variable into one variable. This is the process of

finding this out

Putting data into one variable

1.Transform

2.Compute

3.Computevariable dialuge

box appear

4.TargetVariable

Compute

variable

Transform


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The practical shape is as under

In the function group option click ALL and in the function and special

variable click Mean. Press the Upward arrow sign in the Numericexpression dialogue box. Write the following expression.

Mean (Act1, Act2, Act3)

Take the curser after the comma and delete the (?) sign and then press OK.

Compute

Variable

All

Mean

Arrow Sign


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QNT and Verbal will appear on the Data view of the data editor screen.

The verbal will appear like this.

The procedure is same as followed for the QNT computations.

Verbal

Mean


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After the previous steps the following two columns will appear on the SPSS

page or on the Data View page.

QNTVerbal


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Now we will draw the combined scatter matrix of the following matrix

through the followings method.

The six variable to be used in the scatter matrix are CGPA, Inter=percentage,

MOIM, Institution, QNT and Verbal.

To draw SCATTER

MATRIX we use

followings steps

Scatter Dot


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For the scatter matrix the cyclic process is as

Menu bar

Graph

Legecy

Dialuge

Matrix

Scatter

Define

(Click)


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In the scatter / Dot dialogue box click the Matrix scatter and then click define

Scatter

Matrix

Define


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Then there will open a define box, where we define the Variable as shown

below

When the scatter/ dot dialogue box appears on the data editor area click the

Matrix Scatter and click; the define a rough scatter will appear on the screen.In the scatter matrix dialogue box the six variables are entered into the matrix

variables except the NAME.

Matrix

Variable

CGPA

MOIM

Inter-Perct

Institution

QNT

Verbal


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A Rough Scatter Matrix appears on the Screen.


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To add FIT LINE:

1.

Double click the scatter matrix2. Chart editor3. Add fit line total4. A dialogue box will appear

Auto Fit

Line


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Now we can study the relationship between all six variables


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To help the study the variable we can open Utilities box

And then click the Variable toknow the variable and there labels used in

this study.

For example if we see below screen Grades of Financial Accounting 1 hasfollowings;

Measurement level= Ordinal

Value label;

1= F

2= C

3= C+4= B

5= B+

6= A

And it also helps us to find any relation between these variables.


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Now we shall write REPORTING RESULTS and interpretation ofCombined Scatter Matrix.

Correlation between medium of instruction up to metric and CGPA.

Reporting Results:

MOIM Positive Correlation CGPA

Interpretation:

As medium of instruction goes from Urdu medium to English medium, the CGPA of

students increases. The students whose medium of instruction is English, they perform

better than the students whose medium of instruction is Urdu.

Correlation between intermediate percentage and CGPA.

Reporting Results:

Inter Pct Slightly Positive Correlation CGPA

Interpretation:

As Intermediate percentage of students increases, the CGPA of students also increases.

The students whose percentage is good in intermediate their CGPA is also good.


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Correlation between institution from which inter done and CGPA.

Reporting Results:

Institution of inter Slightly Negative Correlation CGPA

Interpretation:

As institution of intermediate of students changes from public to private, the

performance of students also have a bad impact on CGPA.

Correlation between QNT and CGPA.

Reporting Results:

QNT Positive Correlation CGPA

Interpretation:

Those students who get good grades in QNT, there CGPA is also good as compare to

other students. So there is positive correlation between QNT and CGPA.

Correlation between Verbal and CGPA.

Reporting Results:

Verbal Positive Correlation CGPA

Interpretation:

Those students who get good grades in Verbal, their CGPA is also good as compare to

other students. So there is positive correlation between Verbal and CGPA. QNT affects

CGPA more than Verbal.


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CORRELATION

This is the procedure how to find out the correlation of the data and find the

result and give the interpretations.

Analyze CorrelateBivariate


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Bivariate Correlation: means the correlation between two variables

Test of Significance: significance means the chances of error in the results.

Or the probability of commuting an error.

Bivariate

Correlatio

Test of

Significance

Variables


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THE CORRELATIONS RESULTS ARE GIVEN ABOVE


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Now we shall explain the

Reporting Results and Interpretation of Correlation Matrix.

Reporting Results:

Ry2(43) = 0.237 ; p >.05

Interpretation:

As p>.05, so we accept H0 . So the result are not statistically significant at 5%

level of significance the sample data do not support the alternative hypothesis (HA ).

In other words we can say that the sample results do not hold good for the

population. We can not generalize the results of sample for the whole population.

We can also say that population correlation coefficient is not significantly from

zero.

Reporting Results:

Ry3(43) = 0.086 ; p >.05

Interpretation:

As p>.05, so we accept H0 . So the result are not statistically significant at 5%


In other words we can say that the sample results do not hold good for the

population. We can not generalize the results of sample for the whole population.


zero.


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Reporting Results:

Ry3(43) = 0.532; p .05, so we accept H0 . So the result are not statistically significant at 5%


In other words we can say that the sample results do not hold good for thepopulation. We can not generalize the results of sample for the whole population.


zero.


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Reporting Results:

Ry3(43) = 0.440; p


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To Arrange the Data in a particular order

Followings are the steps

1. Go to data menu2. Click the Short classes3. Select the class of choice4. Click ascending or descending order5. Press OK

Data

Short

Classes


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Short by

e.g. NameClass (ascending

or descending)


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The new arrangement can be seen in below picture

The arrangement is with respect to Names and in Ascending Order.


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IMPROVEMENT in CGPA and Inter-percentage:

Standardization is the method we use for the find of improvement between

two different variables which are measured on different scales.

Following are the steps for this

1. Go to Analyze menu2. Go to descriptive statistics3. Click the descriptive4. Select the variable to whom we want to standardize5. Results would appear on the data view as well as on the output view.6. Go to transform menu7. Select compute variable8. Select the CGPA-Inter percentage9. Improvement will appear at the DATA VIEW


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Always click this if you

want to save the Z-results


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This is result at the output view of SPSS when we do the above process.

Next process is the Z-intercepts which appear at the Data View in the Data

Editor form of SPSS.

The Z scores are as


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Go to Transform Menu:

The next procedure will be that where we compute our Z scores of the

variable of which we want to see the improvement.

Z CGPA- Z

Interpect

Compute

Variable

Improvemen


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Now the improvement column will appear at the data view file

This is the procedure for finding out the improvement or difference between

two different kinds of variables whose measurements are also taken in on

different scales.


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Remarks:

statistical infernece, corelation spss report

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