Notes on SPSS

Clementine

Amoss

DATA VIEW OF SPSS

Row – for a respondant

Column- for a variable

VARIABLE VIEW OF SPSS

Measure – nominal, ordinal, interval and ratio

In spss, thr r only 3 measures…nominal, ordinal and scale..intrval and ratio are treated as scale.

Variable 1 – current brand

Though it is a brand, Nominal which can be coded are marked as numeric itself. As the brands given here can be coded.

String is used when we have some open ended qtns like others, givce suggestions etc..

LABEL – the abbreviation which we have used , the expansion of the abrveiation

VALUES - Coding procedure is used when the variable is nominal or scale

Here current brand is qualitivative and hence for coding purposes we have coded closeup-1 , colgate – 2 pepsodant – 3 others- 4

Type 1, 2 3, 4 in value column and then corresponding labels in the label column.

MISSING – in case a respondant dint aswer a particular qtn, hence we will do a diffrnt analysis

Here, in this we assume that ther is no missing variable

2nd and 3rd qtn, value and label are not needed, as the answer is in numeric form itself

In 4th qtn , using dental health it mite give a wrong contradiction if the coding is given as 1- xtremely healthy, 2- healthy, 3 – etc..

So we need to give it as 5- xtremely hlthy, 4- healthy 3 – unhealthy 2 – extreamlyunhlthy

DATA entering is done in data view.

Do the data entering as coded

NOTES :

Normally it is advisable to enter in the order of the qtns itself

WORKING MOTHERS

Breast milk is a complete food if the resp types true, then it is true but if someone has typed falso, then its taken as 0

The statement correctness is checked as the survey if for the awareness of the breast feeding

Cant say = o as the stdy is on awareness, and it shows the ignorance of the ladies

When working with an existing DB, have a look at the variable and the codes of each sectors

Go to transform compute variable and add the data variables

Go to analyze

Taking mean

Awareness = 0-18 mean is 9

If >9 high awareness

If we shoud have low awarensss, medium awarenss and high awareness, then dif=vide it into 3 classes of equal intervals 0-6,7-12 and 13- 18

Hence prgrm of awareness ws successful

tAking median

50% is above 12 and 50% below 12

Hence prgrm is successful

3 steps

Checking the relationships Testing Strength of relationship

TO CHECK WHETHER THERE IS RELNSHIP BTWEEN VARIABLES

Which sector – indpt variable ,nominal measurement

Awareness level – dept variable, scale measurement

There r diff methods of measurement types, for investigative purposes. Eg: correlation, regression etc.

If indpt variable is nominal and dept variable is scale, then compare AM.

If indpt variable is nominal and dept variable is ordinal, then we do cross tabulation, to find out whether there is relationship or not (nominalXordinal)

If indpt variable is ordinal and dept variable is nominal, then we do cross tabulation, to find out whether there is relationship or not (ordinal x nominal)

If indpt variable is ordinal and dept variable is ordinal, then we do cross tabulation, to find out whether there is relationship or not (ordinal x ordinal) ( we tell idepedendt variable 1st and dept variable 2nd )

Also sign of rank correlation can also be used if the data can be ranked

If nominal X nominal, we can use cross tab as well.( but its wrong we use correspondence analysis)

If ordinal X scale , comparison of AM to find out relationship

If scale X scale , then we use sign of correlation coefficient and sign of regression correlation to find out the relationship between variables.

Tools for measuring the Strength of relationship

Nominal Ordinal scaleNominal Not applicable for

beginnersContingency coefficientCramers vPhi coefficientLambdaUncertainity coefficient

Eta

Ordinal Contingency coefficientCramers vPhi coefficientLambdaUncertainity coefficient

Gamma Somers D Kendalls tau-b Kendall’s tau-c Rank order

correlation

Eta

Scale Not applicable for beginners

Not applicable for beginners

Correlation coefficientRegression coefficientR2 (coefficient of determination)

To find the relation :Analyze means AM

Descriptive statistics frequency ; now checking whether the frequency has a relation with the sector

Report

sum_awareness

sector Mean N Std. Deviation

bank 12.5581 43 2.60313

IT 12.0000 32 2.48868

nurse 11.5750 40 7.00508

contract labours 9.2553 47 2.90029

teacher 12.5915 71 2.89126

Total 11.6567 233 4.01212

INTERPRETATIONIf there are significant difference between AM between sectors , then only we can say that there is relationship between variables.

Here only contract variables are the only sectors showing a low awareness level

Bank, IT, teachers we can say that these people have a high level of awareness (keeping in mind the avg mean is 9 i.e between 0-18)

Go to descriptive statistics -cross tab ..now move the required variables to row and columns..(usually indpt is in row and dept is in column) -- cells and click on ROWS under percentages as the indpt variable here (sector) is in ROWS

sector * Do/did you BF baby on demand Crosstabulation

Do/did you BF baby on demand

TotalNo Yes

sector bank Count 18 25 43

% within sector 41.9% 58.1% 100.0%

IT Count 16 16 32

% within sector 50.0% 50.0% 100.0%

nurse Count 9 31 40

% within sector 22.5% 77.5% 100.0%

contract labours Count 25 22 47

% within sector 53.2% 46.8% 100.0%

teacher Count 24 47 71

% within sector 33.8% 66.2% 100.0%

Total Count 92 141 233

% within sector 39.5% 60.5% 100.0%

In interpreting cross tab data use only percentage , not frequency

The above table shows a 50% prediction accuracy in all sectors except for teachers. Since there are not much of high percentage the prediction accuracy is less.If only there have been above 80%, then we can say the prediction accuracy is high and we can say that there are sector wise difference in awarenessThis is strength of reln btween variable when either dept or indpt variable is NOMINAL

Analysing/interpreting cross tabFor example :

Fail passMale 50 50

female 50 50

Fail/pass cant be predicted using gender.hence this is called no relationship in cross tab.

Male 100 0

female

0 100

100 % prediction is possible provided one variable is nominal. So whatever statiticscal analysis we do in cross tab, almost everything will lie between these two situations

Report

ATTITUDE TOWARDS EDUCATION

Recoded

SES Mean N Std. Deviation

Low 77.47 73 12.544

Middle 69.68 151 12.264

High 61.41 76 8.950

Total 69.48 300 12.868

INTERPRETATION : as socioeconomic status increases the attitude towards edn decreases

HOW TO CATEGORISE FROM SCORE

Transform recoded to different variables - change the variable and label old and new values

Change the variable name and change the range in old new value range column ( in these case we have decided that 30-60 its code is 1 and 61-99 is code 2 . now in the new column there will be values either 1 or 2 . (1- low attitude and 2 is high attitude)

Tools for testing the significance

Nominal Ordinal scaleNominal Chi- square t- test

ANNOVAOrdinal Chi- square Chi- square t- test

ANNOVAScale Test- correlation

Test – regressionTest – R2

Monday, October 08, 2012

SPSS

ordinalXordinal

Strength of relationship can be explained using sign

This happens when the ordinal variable is increasing/decreasing. Depends on the selection of the The variables being tested .

Low Mod HighLow 100 0 0Mod 0 100 0High 0 0 100SE increases, the performance increases - +ve reln

Low Mod HighLow 0 0 100Mod 0 100 0High 100 0 100SE increases, the performance decreases - -ve reln

Low Mod HighLow 33 33 34Mod 0 100 0High 100 0 100Theres no prediction accuracy in any row or column. Reltionship is hence zero here.

And the strength of relnship is also zero

How to recode? Transformrecode into diffrenet variables

Recoded SES * recoded attitude towards education Crosstabulation

recoded attitude towards education

Totallow attitude high attitude

Recoded SES Low Count 5 68 73

% within Recoded SES 6.8% 93.2% 100.0%

Middle Count 26 125 151


High Count 32 44 76


Total Count 63 237 300


Interpretation

Ses low , performance is 93.2% high

SES mod , performance is 82.8%

SES high, performance is 79%

i.e. –ve reln as SES increseas, the perf is decreasing

analyse descriptive stat crosstab statitsics Gamma ( since its ordinalXordinal)

Symmetric Measures

Value

Asymp. Std.

Errora Approx. Tb Approx. Sig.

Ordinal by Ordinal Gamma -.600 .087 -5.350 .000

N of Valid Cases 300

a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

Interpretation ( as interpreting the correlation data)

-1 perfect negative correlation

+1 perfect positive correlation

0 non linear relationship

0-1/3 low

1/3-2/3 mod

2/3-1 high linear correlation

When u click on somer’s D( analyse descriptive stat crosstab stat somer’s d)

Directional Measures

Value

Asymp. Std.

Errora Approx. Tb Approx. Sig.

Ordinal by Ordinal Somers' d Symmetric -.277 .047 -5.350 .000

Recoded SES Dependent -.399 .067 -5.350 .000

recoded attitude towards

education Dependent

-.212 .039 -5.350 .000

a. Not assuming the null hypothesis.

b. Using the asymptotic standard error assuming the null hypothesis.

Interpretation

Meaning there is an assumption behind reln between indpt and dept variables .

Here the attitude is depnt variable and recoded SES is indpt variable in this study, hence we need only take the 3rd result in Somer’s d.

TESTING OF HYPOSTHESIS

To check wther the sample is a characterstic of the population H0- no reln ; H1 – there is reln between variables WHICH CHARACTERESTIC? Can be relan between two variables Can be strength of reln btween two variables This being found in sample, we need to chek wther this can be projected to the population Hence , checking the above mentioned characteristic in population is called testing of

hypothesis The method is characterized by ‘p’ value –or significant value If p value<0.05 , H1 is accepted If p>0.05, H1 is rejected i.e, h0 is accepted

ANOVA T- testUsed when there is comparison between more than 2 groups

Used when there is comparison between 2 groups i.e AM of Male vs AM of Female

Example :

1. Female students have a better attitude towards education.Comparing the means , as there are only 2 values and going to test the hypothesisAnalyse compare means Indpt sample t-test Variable to be tested ( attitude towards edn) is test variable and the indpt variable is sex

Group Statistics

SEX N Mean Std. Deviation Std. Error Mean

ATTITUDE TOWARDS

EDUCATION

MALE 168 68.52 13.054 1.007

FEMALE 132 70.70 12.572 1.094

Shows that male has less attitude twrds edn and females have more attitude towards ednBut, now checking whthr this is a representative of the sample, we check the significance level using t-test

0.146 > 0.05, hence H1 is rejected. i.e there is no significant diffrnce in attitude for males or females

One smple t-test – used in industriesIndpt sample t-test - when 2 samples are independentaly chosen Paired sample t-test – used when one group doesn’t have an indpt choice ; its not random choice ; i.e mother-child ( it’s a dept sample)

ANOVA

When more than 2 groups are there for indent var, we use anova.

For anova, we use f-distrbtn, where as for t-test we use t-distrbn. There is no concept of sign in f-distrbn.

For example.

Ho: mean(male)=mean(female)

H1: mean(male) not equal to mean(female)

Or mean(male)>mean(female) or men(male)<mean(female).

Using anova , we will be able to prove only not equal to, > or < cannot be tested using anova.

To test direction hypotheses, we will use only z-distrn or t-distrbn.

We can change the sign of t-distribn, by accordingly changing the group definition. For example, if we have given hypothesis, mean(male)>mean(female). Then we should give group definition as group1 and group 2. Alternatively, we should give group definition as group2 and group 1.

t-value can be made +ve and –ve by changing the definition of hypothesis.

If only 2 groups are there, best method is to use t-test always.

Example:

Independent Samples Test

Levene's Test for

Equality of

Variances t-test for Equality of Means

95% Confidence

Interval of the

Difference

F Sig. t df

Sig. (2-

tailed)

Mean

Differenc

e

Std. Error

Differenc

e Lower Upper

ATTITUDE

TOWARDS

EDUCATION

Equal

variances

assumed

.479 .489 -

1.45

9

298 .146 -2.179 1.494 -5.119 .761

Equal

variances not

assumed

-

1.46

5

285.97

3

.144 -2.179 1.487 -5.106 .748

Here we can see t value is -1.459, hence, we can say the direction of hypothesis

Anova

Analyzecomapre meansone way anova

Factor represents the indpnt var

Eg:

ANOVA


Sum of Squares df Mean Square F Sig.

Between Groups 351.011 1 351.011 2.128 .146

Within Groups 49159.825 298 164.966

Total 49510.837 299

Here we can see sig value(p-value) is.146, hence we will not be able to say direction, but instead we can say that there is no significant difference between mean

If p-value<0.05, accept H1

If p-value >0.05, accept H0

PA 765:type in google, to get notes for interpretations on spss output.

If sig value for one way anova is >0.05, no need for subgroup analysis..the following post-hoc analysis is used for subgroup analysis

Post-hoc analysis:

Tukey and LSD are commonly used. Tukey is much better.

This is done to check the varitaions within the groups.(for eg, to know which group has variation etc..)

Multiple Comparisons


Tukey HSD

(I) STANDARD (J) STANDARD

Mean Difference

(I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

VII STD - EARLY IX STD - MIDDLE 4.270* 1.804 .049 .02 8.52

XI STD - LATE 4.230 1.804 .051 -.02 8.48

IX STD - MIDDLE VII STD - EARLY -4.270* 1.804 .049 -8.52 -.02

XI STD - LATE -.040 1.804 1.000 -4.29 4.21

XI STD - LATE VII STD - EARLY -4.230 1.804 .051 -8.48 .02

IX STD - MIDDLE .040 1.804 1.000 -4.21 4.29

*. The mean difference is significant at the 0.05 level.

From the above table, w can see the difference btn 3 groups-7th,9th and 11th. Here we can see, 7th and 9th has sig value .049, which is <0.05 and hence accept h1., where as, for the other groups, we can see sig value>0.05 and hence accept h0 hypothesis.

ANOVA


Sum of Squares df Mean Square F Sig.

Between Groups 367.106 4 91.776 .551 .698

Within Groups 49143.731 295 166.589

Total 49510.837 299

Here, we need not do subgroup analysis since sig value>0.05.

Chi-square test

Use crosstabs, take row percentage, and then statistics, choose chi square, choose accordingly for nominal and ordinal,which all tests to use.(ex;nominal: contingency coeef etc, ordinal:gamma etc)

Chi-Square Tests

Value df

Asymp. Sig. (2-

sided)

Exact Sig. (2-

sided)

Exact Sig. (1-

sided)

Pearson Chi-Square .603a 1 .437

Continuity Correctionb .402 1 .526

Likelihood Ratio .607 1 .436

Fisher's Exact Test .477 .264

Linear-by-Linear Association .601 1 .438

N of Valid Cases 300

a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 27.72.

b. Computed only for a 2x2 table

Here we can see, that pearson chi-square sig value =.498 which is >0.05, hnce accept null hypo, here we cans ay there is no significant diff btn gender and attitude.

Correlation

Analyzecorrelatebivariate

Correlation is symmetric. Hence, keep both the variables in the variable list.

For large samples, correlation will always be significant.(dnt prove hypothesis using correlation)

For pilot study, if we use correlation to interpret data, we get a good idea of the relationship between the variables.

Partial correlation

To control the relationship btn 2 variable using another var. we can use any number of variables to control the effect of the variables.

REGRESSION

When both dept and indept variables are scale.

Measures assymetric reln , whereas correlation measures symmetric reln

Correlation tells strength of reln and direction of reln.

This is applicable in regression also.

Another characteristic is the predictive power of regression ( which is not applicable in correlation)

Hence regression is found to be a highly used method in research

Simple regression

Y= a+bx

where, y= dept variable and x= indpt variable

A= y intercept ( if indept variable is absent, the value of the dept variable is called y intercept; its also called CONSTANT OF REGRESSION)

B= regression coefficient or slope

xy denotes , x will influence y

change produced in dept variable when the indept variable changes by one unit is called coefficient of regression

example :Attitude towards edn and parental encouragement

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 35.314 3.134 11.266 .000

PARENTAL

ENCOURAGEMENT SCALE

.840 .075 .542 11.123 .000

a. Dependent Variable: ATTITUDE TOWARDS EDUCATION

INTERPRETATION

Y=a+bx

Attitude twrds edn = 35.314+0.840(parental encouragement)

Consider a situation where parental encouragement= 0, attitude twrds edn= 35.315

When parental encouragement is increased by 1 unit, the attitude twrds edn increases by 0.84 units

TESTING

Regression coeff

In general the rule is , H0= model is not valid for the problem

H1 = model is valid for the entire population

OR

H0 : R 2 =0

H1 : R 2 not equal to 0

The developed equation from sample, can I claim that it is applicable for the entire population

This can be said by interpreting the ANOVA table

Here p=0.00 which is <0.05 which means that the model is valid for the population

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .542a .293 .291 10.835

a. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE

If its simple regression, the R will be correlation score it self

R 2 = coefficient of determination

= explains the percentage of variance of dept variable explained by indept variable

Interpretation of above table

R 2 = is always explained as a percentage

= 29.3%

= reln of parental encouragement on attitude twrds education can be explained only 29.3%

If coefficient is negative , an example by doing simple regression

Parental encouragement

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .509a .260 .257 11.091

a. Predictors: (Constant), SOCIO-ECONOMIC-STATUS

Can be explained 26%

But,

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 87.648 1.890 46.384 .000

SOCIO-ECONOMIC-

STATUS

-.239 .023 -.509 -10.221 .000


Hence, when one unit SES increases, -0.239 decrease in attitude towards education

MULTIPLE REGRESSION

Y= a+b1x1+ b2x2

B1 = change in y when x1 increases by 1 unit , provided x2 is constant

B2 = change in y when x2 increases by 1 unit, provided x1 is constant

X1 and x2 influence y

For example,

Marks = 5+3(hrs of preprn)+4(no of bookd referred)

Preprn =0, books =0 then M= 5

Prepr =1, books =1 then M = 12

Preprn =1, books = 2 , then M= 16

Preprn =1 , books = 3, M = 20

It’s a change produced in the dpt variable(y) corresponding to one unit change in a indept variable(of books ) when other indept variable( preparation ) remains constant

X1

X2

y

INTERPRETATION

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .612a .375 .370 10.210

a. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE,

HOME CLIMATE SCALE SCORE

Can be explained 37%

When we add more indpt variable into the model, the decimal of R2 value seems to increase. Hence to nullify that effect, the adjusted R2 value is introduced.

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 19.893 3.858 5.156 .000

HOME CLIMATE SCALE

SCORE

.321 .052 .298 6.212 .000

PARENTAL

ENCOURAGEMENT SCALE

.707 .074 .456 9.524 .000


B coefficients are not directly comparable as the 2 indpt variables may be measured in different scales.

Hence we depend on Standardised regr coefficents or beta. Its used for comparison purpose to find which indpt variable is having more effect on the dept variable.

Now, increasing the indept variables in SPSS, here we took 8 variables

Coefficient of multiple determinations( when all indpt variable are used)

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .746a .557 .545 8.680

a. Predictors: (Constant), Non Verbal Intelligence Score, PARENTAL

ENCOURAGEMENT SCALE, SOCIO-ECONOMIC-STATUS,

PERSONAL DEVELOPMENT, HOME CLIMATE SCALE SCORE,

SOCIO-CULTURAL INFLUENCE SCORE, INFLUENCE OF MASS

MEDIA, RELATIONSHIP DIMENSION

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 23.206 6.232 3.724 .000

HOME CLIMATE SCALE

SCORE

.201 .046 .186 4.326 .000

PARENTAL

ENCOURAGEMENT SCALE

.525 .071 .339 7.428 .000

INFLUENCE OF MASS

MEDIA`

.016 .052 .014 .309 .757

SOCIO-CULTURAL

INFLUENCE SCORE

.039 .053 .033 .748 .455

RELATIONSHIP

DIMENSION

.253 .067 .175 3.765 .000

PERSONAL

DEVELOPMENT

.121 .095 .055 1.282 .201

SOCIO-ECONOMIC-

STATUS

-.158 .020 -.337 -7.894 .000

Non Verbal Intelligence

Score

.153 .053 .121 2.873 .004


Here it has been found that its >0.05 and hence these variables cannot be taken to the entire population

Since 3 of these indpt variables got rejected , it is better to go fwrd to SEM

Changing the “method” to stept wise and doing analyse

Model Summary

Model R R Square

Adjusted R

Square

Std. Error of the

Estimate

1 .542a .293 .291 10.835

2 .688b .473 .469 9.376

3 .720c .518 .513 8.978

4 .734d .539 .533 8.793

5 .744e .554 .546 8.669

a. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE

b. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE,

SOCIO-ECONOMIC-STATUS

c. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE,

SOCIO-ECONOMIC-STATUS, HOME CLIMATE SCALE SCORE

d. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE,

SOCIO-ECONOMIC-STATUS, HOME CLIMATE SCALE SCORE,

RELATIONSHIP DIMENSION

e. Predictors: (Constant), PARENTAL ENCOURAGEMENT SCALE,

SOCIO-ECONOMIC-STATUS, HOME CLIMATE SCALE SCORE,

RELATIONSHIP DIMENSION, Non Verbal Intelligence Score

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 35.314 3.134 11.266 .000

PARENTAL

ENCOURAGEMENT SCALE

.840 .075 .542 11.123 .000

2 (Constant) 55.255 3.360 16.443 .000

PARENTAL

ENCOURAGEMENT SCALE

.726 .066 .468 10.956 .000

SOCIO-ECONOMIC-

STATUS

-.202 .020 -.430 -10.050 .000

3 (Constant) 41.723 4.113 10.145 .000

PARENTAL

ENCOURAGEMENT SCALE

.636 .066 .410 9.675 .000

SOCIO-ECONOMIC-

STATUS

-.183 .020 -.391 -9.389 .000

HOME CLIMATE SCALE

SCORE

.244 .046 .226 5.283 .000

4 (Constant) 35.682 4.349 8.205 .000

PARENTAL

ENCOURAGEMENT SCALE

.539 .070 .347 7.737 .000

SOCIO-ECONOMIC-

STATUS

-.169 .020 -.361 -8.683 .000

HOME CLIMATE SCALE

SCORE

.207 .046 .192 4.481 .000

RELATIONSHIP

DIMENSION

.247 .067 .172 3.686 .000

5 (Constant) 27.940 4.970 5.621 .000

PARENTAL

ENCOURAGEMENT SCALE

.533 .069 .344 7.771 .000

SOCIO-ECONOMIC-

STATUS

-.166 .019 -.353 -8.601 .000

HOME CLIMATE SCALE

SCORE

.210 .046 .194 4.595 .000

RELATIONSHIP

DIMENSION

.264 .066 .183 3.971 .000

Non Verbal Intelligence

Score

.153 .050 .121 3.079 .002


Here everything is significant

Effectively, it means that its only selecting only those variables which is significant and the insignificant ones are removed

Documents

Notes on SPSS