6. PPT Presentation of Role data_19_3_2021.ppt

Role of data using spss

1. Data Analysis Using SPSSData Analysis Using SPSS
ByBy
Dr. R. RAVANANDr. R. RAVANAN
Joint Director of Collegiate Education, Chennai RegionJoint Director of Collegiate Education, Chennai Region
Former Principal, Presidency College, ChennaiFormer Principal, Presidency College, Chennai
Former Head, Dept of Statistics, Presidency CollegeFormer Head, Dept of Statistics, Presidency College
E-mail: E-mail: [email protected]
Mobile: [phone]Mobile: [phone]

2. What is SPSS?What is SPSS?
Statistical Package for Social ScienceStatistical Package for Social Science
General Purpose Statistical SoftwareGeneral Purpose Statistical Software
Consists of three componentsConsists of three components
Data Window - data entry and database Data Window - data entry and database
(.sav)(.sav)
Output Window - all output from any SPSS Output Window - all output from any SPSS
session (.lst)session (.lst)
Syntax Window - commands lines (.sps)Syntax Window - commands lines (.sps)

3. Data Entry & PreparationData Entry & Preparation
Data entryData entry
New or Recalled (SPSS or non-SPSS)New or Recalled (SPSS or non-SPSS)
Data DefinitionData Definition
Data Manipulation and Variable Data Manipulation and Variable
DevelopmentDevelopment

4. Data DefinitionData Definition
Purpose: Purpose:
Give meanings to the numbers for ease of Give meanings to the numbers for ease of
reading the outputreading the output
InvolvesInvolves
Data FormatData Format
Variable NameVariable Name
Value LabelsValue Labels
Missing ValuesMissing Values
Command: Command: Data Data

Data Definition Data Definition

5. Data ManipulationData Manipulation
Recoding
To give new values to old values (especially
reversing negatively worded questions)
To form nominal variable from continuous
data
Variable Development
To form new variables combinations of old
ones or functions of old ones
Command: Transform  Recode/ Compute

6. Data Analysis - DescriptiveData Analysis - Descriptive
Purpose:
To describe each variable - What is the
current level of the variable of interest?
Command
Frequency
Means, Minimum, Maximum, Standard
Deviation, Quartiles, Standard Deviation
Analyze  Frequencies /Descriptives

7. Data Analysis - DescriptiveData Analysis - Descriptive
Frequencies for two or more nominal
variables
Analyze  Summarize  Crosstabulation
Means of variables by subgroups defined by
one or more nominal variables
Analyze  Compare Means  Means (Use of
Levels)

8. Parametric Parametric
Test of DifferencesTest of Differences
WhenWhen
dependent continuous variable and we dependent continuous variable and we
want to test differences across groupswant to test differences across groups
CommandCommand
Analyze Analyze

Compare Means Compare Means


Independent t-test/ Paired t-test/ one-Independent t-test/ Paired t-test/ one-
way ANOVAway ANOVA

9. Non-Parametric Test of Non-Parametric Test of
DifferencesDifferences
WhenWhen
dependent variable ordinal or normal dependent variable ordinal or normal
assumption not metassumption not met
CommandCommand
Analyze Analyze

Non-parametric Non-parametric

2 2
Independent/ 2 related samples/ k Independent/ 2 related samples/ k
independent samples/ k related independent samples/ k related
samplessamples

10. Parametric Two-Way ANOVAParametric Two-Way ANOVA
WhenWhen
continuous dependent variable and continuous dependent variable and
related groupsrelated groups
CommandCommand
Analyze Analyze

General Linear Model General Linear Model


Simple Simple
Note: Fixed Factor EffectNote: Fixed Factor Effect

11. Bivariate RelationshipBivariate Relationship
WhenWhen
Covariation between two variablesCovariation between two variables
Correlation: Correlation:
When both are continuous or ordinalWhen both are continuous or ordinal
CommandCommand
Analyze Analyze

Correlate Correlate

Bivariate (with Bivariate (with
option for Spearman if both ordinal)option for Spearman if both ordinal)

12. Reliability AnalysisReliability Analysis
WhenWhen
Before forming composite index to a variable Before forming composite index to a variable
from a number of items from a number of items
CommandCommand
Analyze Analyze

Scale Scale

Reliability Analysis (with Reliability Analysis (with
option for Descriptives item, scale, scale if option for Descriptives item, scale, scale if
item deleted)item deleted)
InterpretationInterpretation
alpha value greater than 0.7 is good; more alpha value greater than 0.7 is good; more
than 0.5 is acceptable; delete some items if than 0.5 is acceptable; delete some items if
necessarynecessary

13. Measures of ReliabilityMeasures of Reliability
Internal Consistency: (of items in a scale):Internal Consistency: (of items in a scale):
1. Average inter-item correlation 1. Average inter-item correlation If average inter-item If average inter-item
correlation > 0.6, then standardize items and add them correlation > 0.6, then standardize items and add them
together as an index.together as an index.
2. 2. Cronbach's alpha Cronbach's alpha , which measures " internal consistency , which measures " internal consistency
of items in a scale" Garson ,G.D.(1999) and isof items in a scale" Garson ,G.D.(1999) and is

14. Exercise 1
Test whether opinion regarding “reach the
career goal” are above average level

15. t test for Single Mean
1.It is a parametric test
2.It is Univariate Analysis
3.Data should be ordinal or scale

16. Solution:
1.Null Hypothesis: Opinion regarding all the statements of
Work related are equal to average level
1.2. Alternate Hypothesis: Opinion regarding all the
statements of Work related are
not equal to average level
3. Test Statistic: t test for single mean is

17. Exercise 2
Test whether significant difference
between Gender with regard to Factors
of Role Description of Employees

18. t -TEST FOR DIFFERENCE OF TWO MEANS
or
INDEPENDENT SAMPLE t test
1.It is a Parametric test
2.It is a Bivariate Analysis
3.It is used for comparing two group means

19. 1.Null Hypothesis: There is no significant difference between male and
female with regard to Factors of Role Description
2. Alternate Hypothesis: There is significant difference between male and
female with regard to Factors of Role
Description

3. Test Statistic: t test for difference of two means is

20. Exercise 10
Test whether significant difference
between performance and growth
opportunity

21. PAIRED ‘t’ TEST FOR DIFFERENCE OF TWO MEANS
(DEPENDENT SAMPLES)
1.It is Parametric test
2.It is used for comparing Two Dependent
Variables
3.Both variables are Ordinal or Scale
4.Both variables are equal Weightage

22. Solution:
1.Null Hypothesis: There is no significant difference in mean score of
performance and growth of employees.
2. Alternate Hypothesis: There is significant difference in mean score
of performance and growth of employees.
3. Test Statistic: Paired t test for difference of two means is

23. Exercise 3Exercise 3
Test whether significant difference among Test whether significant difference among
Age Group with regard to Factors of Role Age Group with regard to Factors of Role
Description of EmployeesDescription of Employees

24. One way ANOVA One way ANOVA
followed by followed by
Duncan Multiple Range Test (DMRT)Duncan Multiple Range Test (DMRT)
1.It is Parametric test
2.It is Bivariate Analysis
3.It is used for comparing More than Two groups
means
4.After applying ANOVA, then use Post Hoc test
(Duncan test)

25. Null Hypothesis: There is no significant difference among
age group with respect to Factors of Role
Description of Employees

Alternate Hypothesis: There is significant difference
among age group with respect to Factors
of Role Description of Employees
Procedure for One way ANOVA:
1. Find Correction Factor (CF)
2. Find Total Sum of Square (TSS)
3. Find Between Sum of Square (BSS)
4. Find Error Sum of Square (ESS)
5. Form the ANOVA table

26. Exercise 6
Test whether level of Role Description
of employees are moderate level

27. CHI SQUARE TEST FOR GOODNESS OF FIT
1.It is Non-Parametric test
2.It is Univariate Analysis
3.Data should be Nominal

28. Solution:
1.Null Hypothesis: Levels of role description of employees
are equally distributed .
2. Alternate Hypothesis: Levels of role description of
employees are not equally distributed

3. Test Statistic: Chi-square test for goodness of fit is

29. Exercise 7
Test whether association between
Educational Qualification and level of
Role Description of Employee

30. CHI-SQUARE TEST FOR
INDEPENDENCE OF ATTRIBUTES
1.It is Non-Parametric Test
2.It is Bi-variate analysis
3.Both variables are Nominal

31. Solution:
1.Null Hypothesis: There is no association between Educational
Qualification and levels of role description.
2. Alternate Hypothesis: There is association between
Educational Qualification and levels
of
role description
3. Test Statistic: Chi-square test for independence of attributes is

32. Exercise 8
Test whether significant relationship
between Factors of (work, role,
performance and growth) of Role
Descriptions

33. TEST FOR SIGNIFICANCE OF CORRELATION
COEFFICIENT
1.It is Parametric test
2.It is based on bivariate analysis
3.Both variables are scale

34. Coefficient of Correlation ( -1 ≤ r ≤ +1)
Types of Correlation:
1.Positive or Direct Correlation ( 0 < r < +1)
2. Negative or Inverse Correlation ( -1< r < 0)
3. Perfect Correlation (r = ± 1)
4. Uncorrelation or No Correlation ( r = 0 )

35. Solution:
First find the coefficient of correlation by using the formula
1.Null Hypothesis: There is no relationship between Factors of Role
Descriptions
2. Alternate Hypothesis: There is relationship between Factors of Role
Descriptions
3. Test Statistic: t test for coefficient of correlation is

36. MULTIPLE REGRESSION ANALYSIS
Problem:
The following table gives the food expenditure, annual income and family
size of 10 families. Fit a multiple regression equation of Food Expenditure
on annual family Income and family Size..

FamilyFamily Annual Food Annual Food
Expenditure (‘000)Expenditure (‘000)
Annual Income(‘000)Annual Income(‘000) Family Size (number in family)Family Size (number in family)
11 5.25.2 2828 33
22 5.15.1 2626 33
33 5.65.6 3232 22
44 4.64.6 2424 11
55 11.311.3 5454 44
66 8.18.1 2929 22
77 7.87.8 4444 33
88 5.85.8 3030 22
99 5.15.1 4040 11
1010 18.018.0 8282 66

37. The regression model is

38. Non-Parametric TestNon-Parametric Test
One sample test:One sample test:
–Binomial TestBinomial Test
–Chi-Square test for goodness of fitChi-Square test for goodness of fit
–Kolmogorov-Smirnov one sample testKolmogorov-Smirnov one sample test
Two Independent sample:Two Independent sample:
–Fisher Exact testFisher Exact test
–Chi-Square test for intendance of attributesChi-Square test for intendance of attributes
–Median testMedian test
–Mann-Whitney U testMann-Whitney U test
–Kolmogorov-Smirnov Two sample testKolmogorov-Smirnov Two sample test

39. Non-Parametric TestNon-Parametric Test
Two dependent sampleTwo dependent sample
–McNemar testMcNemar test
–Sign testSign test
–Wilcoxon Matched-Pairs signed rank testWilcoxon Matched-Pairs signed rank test
–Walsh testWalsh test
More than two independent samplesMore than two independent samples
–Krushkal_Wallis one-way analysisKrushkal_Wallis one-way analysis
–Chi-square test for k impendent sampleChi-square test for k impendent sample
–Extention of Median testExtention of Median test
More than two dependent samplesMore than two dependent samples
–Friedman Two way analysisFriedman Two way analysis
–Cochran Q testCochran Q test

40. Exercise 4Exercise 4
Test whether significant difference Test whether significant difference
between Educational Qualification with between Educational Qualification with
regard to Factors of Role Descriptionregard to Factors of Role Description

41. Mann-Whitney U testMann-Whitney U test
1. It is Non-Parametric test1. It is Non-Parametric test
2. It is equal to Independent sample t test in parametric test2. It is equal to Independent sample t test in parametric test
3. It is used for comparing two groups mean rank3. It is used for comparing two groups mean rank
Null Hypothesis: There is no significant difference between
mean rank of Professional and Non-Professional with
regard to Factors of Role Description of Employees
Alternate Hypothesis: There is significant difference between
mean rank of Professional and Non-Professional
with regard to Factors of Role Description of
Employees of Employees

42. Mann-Whitney U testMann-Whitney U test
Mann-Whitney U test isMann-Whitney U test is
which follows Standard Normal Distributionwhich follows Standard Normal Distribution
WhereWhere

43. Conditions to apply Non-Parametric testConditions to apply Non-Parametric test
1. Data not follows Normal Distribution1. Data not follows Normal Distribution
2. If the given data is ranking data, then apply Non-Parametric test2. If the given data is ranking data, then apply Non-Parametric test
3. If SD is more than Mean, then apply Non-Parametric test3. If SD is more than Mean, then apply Non-Parametric test
TEST FOR NORMALITYTEST FOR NORMALITY
Analysis, Non-parametric test, Legacy dialog, One sample K-S testAnalysis, Non-parametric test, Legacy dialog, One sample K-S test
Null Hypothesis: The data follows Normal Distribution is good Null Hypothesis: The data follows Normal Distribution is good
1.1.If P value is greater than 0.05, then the data follows Normal If P value is greater than 0.05, then the data follows Normal
DistributionDistribution
2.2.If P value is less than or equal to 0.05, then the data not follows If P value is less than or equal to 0.05, then the data not follows
Normal DistributionNormal Distribution

44. Exercise Exercise
Test whether significant difference Test whether significant difference
between work related and role claritybetween work related and role clarity

45. Wilcoxon testWilcoxon test
1.1.It is Non Parametric testIt is Non Parametric test
2.2.It is equal to Paired t test in parametric testIt is equal to Paired t test in parametric test
3. It is used for comparing two related variables3. It is used for comparing two related variables
11Null HypothesisNull Hypothesis: There is no significant difference in: There is no significant difference in
mean rank of work related and rolemean rank of work related and role
clarity of employees.clarity of employees.
2. 2. Alternate HypothesisAlternate Hypothesis: There is significant difference: There is significant difference
in mean rank of work related in mean rank of work related
and role clarity of employees.and role clarity of employees.

46. Wilcoxon testWilcoxon test
Wilcoxon test isWilcoxon test is
WhereWhere T = Sum of rank with less frequent signT = Sum of rank with less frequent sign

47. Exercise 5Exercise 5
Test whether significant difference Test whether significant difference
among Experience with regard to among Experience with regard to
Factors of Role DescriptionFactors of Role Description

48. Krushkal-Wallis TestKrushkal-Wallis Test
1. It is Non-Parametric test1. It is Non-Parametric test
2. It is equal to One Way ANOVA in parametric test2. It is equal to One Way ANOVA in parametric test
3. It is used for comparing more than two groups mean rank3. It is used for comparing more than two groups mean rank
Null Hypothesis: There is no significant difference amongNull Hypothesis: There is no significant difference among
mean rank of Experience withmean rank of Experience with
regard to all the Factors of Role descriptionregard to all the Factors of Role description
of Employeesof Employees
Alternate Hypothesis: There is significant difference amongAlternate Hypothesis: There is significant difference among
mean rank of Experience mean rank of Experience
with regard to all the Factors of Rolewith regard to all the Factors of Role
Description of EmployeesDescription of Employees

49. Krushkal-Wallis testKrushkal-Wallis test
Krushkal - Wallis Krushkal - Wallis test istest is
which follows Chi-square distribution with k-1 degrees of freedomwhich follows Chi-square distribution with k-1 degrees of freedom
WhereWhere R = Sum of rank of each groupR = Sum of rank of each group
N = Total number of observationsN = Total number of observations
n = Number of observation in each groupn = Number of observation in each group
k = Number of groupsk = Number of groups

50. Exercise 9Exercise 9
Test whether significant difference among Test whether significant difference among
mean rank towards factors on Role mean rank towards factors on Role
Descriptions Descriptions

51. Friedman TestFriedman Test
1. It is Non-Parametric test1. It is Non-Parametric test
2. It is equal to ANOVA with repeated measures in parametric test2. It is equal to ANOVA with repeated measures in parametric test
3. It is used for comparing more than two related groups of 3. It is used for comparing more than two related groups of
mean rankmean rank
4. 4. All the variables are equal weightage. If it is unequal weightage, then All the variables are equal weightage. If it is unequal weightage, then
converted into equal weightage by taking averageconverted into equal weightage by taking average
5. Any ranking data, Friedman test is most suitable test5. Any ranking data, Friedman test is most suitable test
Null Hypothesis: There is no significant difference among mean
rank towards factors of Role Description of
Employees
Alternate Hypothesis: There is significant difference among mean
rank towards factors of Role Description
of Employees

52. Friedman TestFriedman Test
Friedman Friedman test istest is
which follows Chi-square distribution with k-1 degreeswhich follows Chi-square distribution with k-1 degrees
of freedomof freedom
WhereWhere R R = Sum of rank of each items = Sum of rank of each items
N = Total number of observationsN = Total number of observations
k = Number of itemsk = Number of items