Spss for research

1. What is SPSS?
 “Statistical Package for the Social Science”
 One of the most popular statistical
packages which can perform highly
complex data and analysis with simple
instructions.

2.  SPSS Inc. founded in 1968
 2009 acquired by IBM
 SPSS is now owned b y IBM

3. Opening SPSS
 Start → All Programs → SPSS Inc→ SPSS
21.0 → SPSS 21.0

4. Opening SPSS
 The default window will have the data editor
 There are two sheets in the window:
1. Data view 2. Variable view

6. Variable View window
Name
 The first character of the variable
name must be alphabetic
 less than 64 characters.
 Spaces are NOT allowed.

7. Type
 The two basic types of variables that you will use are
numeric and string.

8. Width
 Width allows you to determine the number of
characters SPSS will allow to be entered for
the variable

9. Decimals
 Number of decimals
 It has to be less than or equal to 16
3.14159265

10. Label
 Describing the variable
 You can write characters with spaces up
to 256 characters

11. Values(coding)
This is used and to suggest which
numbers represent which categories
when the variable represents a
category

12. Defining the value labels
 Click the cell in the values column
 For the value, and the label, you can put up to 60
characters.
 After defining the values click add and then click OK.

13. MISSING
Missing value:
• Not captured in the data set: errors in
feeding ...
•Empty value - No value in the population

14. Measure
Nominal,
Ordinal
 and scale (Interval & ratio )

15. Sample Questionnaire
 Gender : Male / Female
 Age : below 25/ 26-35/ 36-45/ 46-55
 Category : student / Faculty / research scholars
 How do you come to know about this
program(multiple choice)
Mail / WhatsApp/ friends/website

16. Data entry Exercise
 Data entry

17. Saving the data
 To save the data file -click ‘file’ and click
‘save as.’ You can save the file in different
forms by clicking “Save as type.”
Click

18. Opening the sample data
 Open ‘Employee data.sav’ from the SPSS
 Go to “File,” “Open,” and Click Data

19. Descriptive statistics
 Percentage analysis
 Frequency analysis
 Charts/graphs
 Mean , median and mode ( Numercial scale)
 Cross tabs

20. Descriptive statistics
Frequencies
 Click ‘Analyze,’ ‘Descriptive statistics,’ then click
‘Frequencies’

21. Frequencies
 Click gender and put it into the variable box.
 Click ‘Charts.’
 Then click ‘Bar charts’ and click ‘Continue.’
Click Click

23. Descriptives
 Click ‘Analyze,’ ‘Descriptive statistics,’ then
click ‘Descriptives…’
 Select the variables
 Click Options
Click

25. Cronbach's alpha is the
most common measure of
internal consistency

26. STEPS
Click Analyze > Scale > Reliability
Analysis...

28. Reliability Analysis

29. Transfer the variables Qu1 to
Qu9 into the Items:

30.  Leave the Model: set as "Alpha", which represents
Cronbach's alpha in SPSS Statistics. If you want to
provide a name for the scale, enter it in the Scale label.

32.  Click the CONTINUE button,
 Click the OK button to generate the output.

33. OUTPUT

35. Cronbach's Alpha
 .00-1.0
 00 = no consistency in measurement (negative
consistency)
 1.0= perfect consistency in measurement(positive
consistency)
 .70= 70% of the variance in score is reliable
(acceptable level .7)

36. Sample -1 case

37. Testing hypotheses about two means

38. Cont..
The independent-samples t-test
compares the means between two
unrelated groups on the dependent
variable.
Eg :
Did male and female in the study
have same academic score?

39. Hypotheses
 Ha = Male and female have same academic score.
 Ho = Male and female don’t have same academic score.

40. Rule of Thumb
If sig. value is > .05 = Accept Ho
If sig. value is < .05 = Accept Ha

41. Conditions :
 dependent variable should be measured on a
interval or ratio scale.
 Independent variable should consist of two
categorical group.(Nominal )
 There needs to be homogeneity of variances.
Levene’s test for homogeneity

46. OUTPUT
 If the levene’s sig value > .05 t-test should be based on
equal variance.

48. Paired Samples t Test
 Detect a difference between the mean of two
dependent variables.
 Eg :
Measure the employees performance before and after
training .

49. Conditions
 Dependent variable should be interval or ratio scale.
 Two groups(dependent)
 Normality
 Homogeneity of variances

51.  Click on OK

53.  The sig. value is .000
 which is < .05
 Reject Ho
 There is a significant difference between before and
after training.
 We can conclude that the training program is effective

54. Paired Samples t Test
Sample .2

55. Closeness of relationship
between two variables

56. Eg
How strongly the sales are related with
advertising expenditure?
Work experience and their output.
Exam performance and preparation
time.
Online test score and students work
experience.

57. Conditions
Two variables should be measured at
the interval or ratio level.

61. Output

62. Pearson correlation
coefficient, r, is 0.777, and
that this is statistically
significant (p < 0.05).

63. Exercise
 How strongly online test score is related to work
experience?

65. ANOVA is used to determine whether
there are any significant differences
between the means of more than two
independent groups.
understand whether exam performance
differed based on their community type
( urban, rural, semi-urban)

66. Conditions :
Dependent variable should be
measured at the interval or ratio
level.
Independent variable should consist
of three or more categorical group.
Normality

70. Post hoc
 ANOVA result will tell us whether there is a difference
among at least two of the group , but it will not tell us
which of the group exhibited this difference.
 Post hoc help us to identify which group are different.

72.  Click continue then options

73.  Click continue then ok

74. Exercise 1
Do students in each of the three
groups of community type have
similar academic score?

75. Two way ANOVA
SIGNIFICANT DIFFRENCE
BETWEEN TWO VARIABLES .
EXAMPLE
Scores – dependent
Age - Independent
Gender – Independent

76. Conditions :
 Dependent variable should be ratio scale
 Independent variables should be nominal
scale.
 Two independent variable
 Homogeneity (equality) of
variances(levene’s model)

77. Hypotheses
H1= Gender have no significant effect
on students academic score
H2= age have no significant effect on
score.
H3 = gender and age interaction have
no significant effect on score.

78. Analyse / General Linear Model / Univariate

79. Dependent Variable : Score
Fixed Factors: duration + modality

80. Displays overall mean,
means for each level of
duration, mean for each
level of modality and the
means for each
combination of duration by
modality (= the interaction
means).
Means

81. Levene’s test. This significant result means
the assumption of equal group variances
has not been met.
Output

82. In this case the analysis is not valid !.
Output

84. Exercise : sample 3
Does the place of operations influences
ROCE?
Does the Industry type influences
ROCE?

85. Association between two
variables

86. Eg
Is there a significant association
between mentor evaluation and
community type?

87. Conditions
Two variables should be measured at
an ordinal or nominal level
Minimum expected cell count should
be 5 (using cross tap)

91. Clock on statistics

92.  Click continue then OK

93. OUTPUT

95. p = .485 there is no statistically
significant association between two
variables.
P> .05 Accept Ho

96. The next step up after correlation

97.  It is used when we want to predict the value of a
variable(dependent) based on the value of another
variable.(independent).
 Use multiple regression if independent variable are
more than one.
 Example: How well can we predict a test score based
on work experience?

98. Conditions
 variables should be measured at the interval or ratio
scale.
 There needs to be a linear relationship between the
two variables(use scatter plot)
 No significant outliers (observed data should not be
deviated from rest of the data).
 One independent and one dependent variable.

99. Eg
 Exam performance can be predicted based on revision
time.
 Cigarette consumption can be predicted based on
smoking duration.
 sales can be predicted based on ad.expenditure.

104.  R is correlation value
 R square = How much of the total variation in the
dependent variable can be explained by the
independent variable.
 76.2% which is very large.

106.  Price = 8287 + 0.564(Income)

107.  How well can we predict a test score if we know the
work experience?

108. EXAMPLESALES
(UNITS)
120 140 190 200 220 170 180 190 220 250
ADV.EX
(in
1000s)
12 15 17 20 23 23 21 22 21 25

109. An extension of simple linear regression

110. Cause and effect relationship
 Eg you could use multiple regression to understand
whether exam performance can be predicted based on
revision time, lecture attendance and work exp.

111.  It helps you to determine the overall fit (variance
explained) of the model .
 Eg 2: sales can be predicted by sales men exp. And ad
exp.

112. Conditions
 Dependent variable should be an interval scale.
 You have two or more independent variables.
 There needs to be a linear relationship between
independent and dependent variable.(scatter plot)
 Your data must not show multicollinearity(R)
 There should be no significant outliers

118. Determining how well the model
fits

119.  R, the multiple correlation coefficient,
 R2 value ,which is the proportion of variance in the
dependent variable that can be explained by the
independent variables.

120. Statistical significance

121.  The table shows that the independent variables
statistically significantly predict the dependent
variable, F(4, 95) = 32.393, p < .0005 (i.e., the
regression model is a good fit of the data).

122. Statistical significance of the
independent variables

123. Exploratory Factor Analysis
Basic Concepts
What is Factor
Analysis?
The basic assumption of factor analysis is that for a
collection of observed variables there are a set
of underlying variables called factors (smaller than
the observed variables), that can explain the
interrelationships among those variables.

124. Exploratory Factor Analysis
Basic Concepts
Sample Size to Run a Factor
Analysis
 10-15 Respondents per
variable
 300 or more than 300 is a
good sample size
 Run Kaiser-Meyer-Olkin
Measure of Sampling
Adequacy(KMO) test

139. KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .930
Bartlett's Test of
Sphericity
Approx. Chi-
Square
19334.49
2
df 253
Sig. .000
Interpreting
Exploratory Factor Analysis Output

140. Communalities
Initial Extraction
Statiscs makes me cry 1.000 .435
My friends will think I'm stupid for not
being able to cope with SPSS
1.000 .414
Standard deviations excite me 1.000 .530
I dream that Pearson is attacking me with
correlation coefficients
1.000 .469
I don't understand statistics 1.000 .343
I have little experience of computers 1.000 .654
All computers hate me 1.000 .545
I have never been good at mathematics 1.000 .739
My friends are better at statistics than me 1.000 .484
Computers are useful only for playing
games
1.000 .335
I did badly at mathematics at school 1.000 .690
People try to tell you that SPSS makes
statistics easier to understand but it doesn't
1.000 .513
I worry that I will cause irreparable
damage because of my incompetenece with
computers
1.000 .536
Computers have minds of their own and
deliberately go wrong whenever I use them
1.000 .488
Computers are out to get me 1.000 .378
I weep openly at the mention of central
tendency
1.000 .487
I slip into a coma whenever I see an
equation
1.000 .683
SPSS always crashes when I try to use it 1.000 .597
Everybody looks at me when I use SPSS 1.000 .343
I can't sleep for thoughts of eigen vectors 1.000 .484
I wake up under my duvet thinking that I
am trapped under a normal distribtion
1.000 .550
My friends are better at SPSS than I am 1.000 .464
Interpreting
Exploratory
Factor Analysis
Output

141. Total Variance Explained
Compon
ent
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulati
ve %
Total
% of
Variance
Cumulati
ve %
Total
% of
Variance
Cumulati
ve %
1 7.290 31.696 31.696 7.290 31.696 31.696 3.730 16.219 16.219
2 1.739 7.560 39.256 1.739 7.560 39.256 3.340 14.523 30.742
3 1.317 5.725 44.981 1.317 5.725 44.981 2.553 11.099 41.841
4 1.227 5.336 50.317 1.227 5.336 50.317 1.950 8.476 50.317
5 .988 4.295 54.612
6 .895 3.893 58.504
7 .806 3.502 62.007
8 .783 3.404 65.410
9 .751 3.265 68.676
10 .717 3.117 71.793
11 .684 2.972 74.765
12 .670 2.911 77.676
13 .612 2.661 80.337
14 .578 2.512 82.849
15 .549 2.388 85.236
16 .523 2.275 87.511
17 .508 2.210 89.721
18 .456 1.982 91.704
19 .424 1.843 93.546
20 .408 1.773 95.319
21 .379 1.650 96.969
22 .364 1.583 98.552
23 .333 1.448 100.000
Interpreting
Exploratory
Factor
Analysis
Output

142. Rotated Component Matrixa
Component
1 2 3 4
I have little experience of computers .800
SPSS always crashes when I try to use it .684
I worry that I will cause irreparable damage because
of my incompetence with computers
.647
All computers hate me .638
Computers have minds of their own and deliberately
go wrong whenever I use them
.579
Computers are useful only for playing games .550
Computers are out to get me .459
I can't sleep for thoughts of Eigen vectors .677
I wake up under my duvet thinking that I am trapped
under a normal distribution
.661
Standard deviations excite me -.567
People try to tell you that SPSS makes statistics easier
to understand but it doesn't
.473 .523
I dream that Pearson is attacking me with correlation
coefficients
.516
I weep openly at the mention of central tendency .514
Statiscs makes me cry .496
I don't understand statistics .429
I have never been good at mathematics .833
I slip into a coma whenever I see an equation .747
I did badly at mathematics at school .747
My friends are better at statistics than me .648
My friends are better at SPSS than I am .645
If I'm good at statistics my friends will think I'm a
nerd
.586
My friends will think I'm stupid for not being able to
cope with SPSS
.543
Everybody looks at me when I use SPSS .428
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Interpreting
Exploratory
Factor
Analysis
Output

143. Factors
Fear of
Compute
rs
Fear of
Statistics
Fear of
Maths
Peer
Evaluatio
n
I have little experience of computers .800
SPSS always crashes when I try to use it .684
I worry that I will cause irreparable damage because
of my incompetence with computers
.647
All computers hate me .638
Computers have minds of their own and deliberately
go wrong whenever I use them
.579
Computers are useful only for playing games .550
Computers are out to get me .459
I can't sleep for thoughts of Eigen vectors .677
I wake up under my duvet thinking that I am trapped
under a normal distribution
.661
Standard deviations excite me -.567
People try to tell you that SPSS makes statistics
easier to understand but it doesn't
.473 .523
I dream that Pearson is attacking me with
correlation coefficients
.516
I weep openly at the mention of central tendency .514
Statiscs makes me cry .496
I don't understand statistics .429
I have never been good at mathematics .833
I slip into a coma whenever I see an equation .747
I did badly at mathematics at school .747
My friends are better at statistics than me .648
My friends are better at SPSS than I am .645
If I'm good at statistics my friends will think I'm a
nerd
.586
My friends will think I'm stupid for not being able to .543
Interpreting
Exploratory
Factor
Analysis
Output