2. Lesson objectives
Recap SPSS
Data entry
Data view
Variable view
Descriptive analysis
Determining reliability
Inferential Statistics with SPSS
3. Inferential Statistics
Based on the assumption that the
sample is random
Types of tests
Chi Squared
Correlation
T test
4. Example research
Purpose : To determine if V-ROTAN method of
teaching will lead to higher achievement and
learning satisfaction among visual learners
5. Design
Population: EDU Research student (100)
Sample ( chosen at random, 3 lessons taught by
the same person using the ‘method’)
Class 1 (40)
Class 2 (40)
Dependent VariableIndependent Variable
Achievement
Satisfaction
Learning styles
INTERVENTION
Teaching method
6. Instruments
Learning style inventory
Scores will determine learning styles
Can categorize as visual, tactile or auditory
Questionnaire
Satisfaction regarding the teaching method
higher score – higher lesson satisfaction
Test
Scores will determine achievement
7. Establishing causality
To establish causality, one must use
an experimental or quasi-
experimental design.
True experimental designs include:
Pre-test/Post-test control group design
Solomon Four-Group design
Post-test only control group design
9. What to describe?
Descriptive stats
Age
Gender
Program
Learning styles
Cross tabulate?
Gender and learning styles
10. Significance
If significant, unlikely to have
occurred by chance (kebetulan)
there is statistical evidence that
there is a difference, a correlation,
an association between etc….
12. Significance level
Significance levels show you how likely a result is due to
chance.
The most common level, used to mean something is good
enough to be believed, is 0.95
The finding has a 95% chance of being true.
No statistical package will show you "95%" or ".95" to
indicate this level. Instead it will show you ".05," meaning
that the finding has a five percent (.05) chance of not
being true, which is the converse of a 95% chance of
being true.
To find the significance level, subtract the number shown
from one. For example, a value of ".01" means that there
is a 99% (1-.01=.99) chance of it being true
13. Hypothesis testing
The Null hypothesis states there is no
true difference/no relationship between
parameters in the population
We reject or fail to reject the null hypothesis
It is rejected only when it becomes evidently false,
that is, when the researcher has a certain degree of
confidence, usually 95% to 99%, that the data do
not support the null hypothesis
Example
There is no significant difference between the
mean test scores of visual and tactile learners
15. Significance
Test of significance
To decide whether to reject the null
hypothesis
Select probability
5 out of 100 times the difference did not
occur by chance ( Significance level: 0.05)
1 out of 100 times the difference did not
occur by chance ( Significance level: 0.01)
Confidence level?
95% or 99%
16. Example
Null hypothesis
There is no relationship between variables..
Significance level : 0.05
Test statistic
Probability value 0.009 or Sig. 0.009 (smaller
than 0.05)
What does that mean?
very unlikely that there’s no relationship
between the variables
Variables not independent of each other
REJECT Null hypothesis
17. Example
Null hypothesis
There is no relationship between variables..
Significance level : 0.01
Test statistic
Probability value 0.12 or Sig. 0.12(greater
than 0.01)
What does this mean?
Higher likelihood that there’s no relationship
between the variables
Variables are independent of each other
Fail to reject (accept?) Null hypothesis
19. Now.. What to infer?
Independence/ Association
Correlation
Differences
20. Independence test –Chi squared
Chi squared test is used in situations
where you have two categorical
variables
Gender and employment sector
Gender and learning styles
Chi-square test of independence
tests the null hypothesis that there
is no association between the two
variables
21. Example: Test for independence
Gender
Female
Male
Learning styles
Visual
Tactile
Auditory
Null Hypothesis: No association between
gender and learning styles
22. Using SPSS for chi squared
Click
Analyze
Descriptive
Crosstabs
Statistics
23. Using SPSS for chi squared
Low chi squared statistic
Sig.961
Fail to reject the null
hypothesis
There is no association…
Variables independent of
each other
Chi-Square Tests
.079a
2 .961
.080 2 .961
10
Pearson Chi-Square
Likelihood Ratio
N of Valid Cases
Value df
Asymp. Sig.
(2-sided)
6 cells (100.0%) have expected count less than 5. The
minimum expected count is .90.
a.
24. Correlation
Measure of the linear relationship between two variables.
A correlation coefficient has a value ranging from -1 to 1.
Values that are closer to the absolute value of 1 indicate
that there is a strong relationship between the variables
being correlated whereas values closer to 0 indicate that
there is little or no linear relationship.
The sign of a correlation coefficient describes the type of
relationship between the variables being correlated.
A positive correlation coefficient indicates that there is a
positive linear relationship between the variables: as one
variable increases in value, so does the other.
A negative value indicates a negative linear relationship
between variables: as one variable increases in value, the
other variable decreases in value.
26. Correlation in SPSS
Start at the Analyze menu.
Select the Correlate option from this
menu. You will see three options for
correlating variables:
Bivariate
Partial
Distances.
The bivariate correlation is for situations
where you are interested only in the
relationship between two variables
27.
28. Correlation in SPSS
Then, consider is the type of correlation coefficient.
Pearson's is appropriate for continuous data
Kendall's tau-b and Spearman's, are designed for ranked
data.
The choice between a one and two-tailed significance test
in the Test of Significance box should be determined by
the hypothesis you are testing
if you are making a prediction that there is a negative or
positive relationship between the variables, then the one-
tailed test is appropriate
if you are not making a directional prediction, you should use
the two-tailed test (there is not a specific prediction about
the direction of the relationship between the variables)
29. Output
Correlations
1.000 .498**
. .003
30 30
.498** 1.000
.003 .
30 30
Pearson Correlation
Sig. (1-tailed)
N
Pearson Correlation
Sig. (1-tailed)
N
LSVISUAL
TEST
LSVISUAL TEST
Correlation is significant at the 0.01 level (1-tailed).**.
30. Output
Correlation is not statistically significant
Correlations
1.000 .127
. .252
30 30
.127 1.000
.252 .
30 30
Pearson Correlation
Sig. (1-tailed)
N
Pearson Correlation
Sig. (1-tailed)
N
LSVISUAL
QNAIRE
LSVISUAL QNAIRE
32. Differences: Using t test
The t test is a useful technique for
comparing mean values of two sets of
numbers.
Statistic for evaluating whether the difference
between two means is statistically significant.
t tests can be used either
to compare two independent groups
(independent-samples t test)
to compare observations from two
measurement occasions for the same group
(paired-samples t test).
33. Remember
t test - tests the null hypothesis / that
there is no difference …
34. t test
If you are using the t test to compare two
groups, the groups should be randomly
drawn from normally distributed and
independent populations.
Using SPSS
Analyze
Compare Means
One-Sample T test...
Independent-Samples T test...
Paired-Samples T test...
35. Types of t-test
The one-sample t test is used compare a single sample
with a population value.
Example, a test could be conducted to compare the average
test scores of U5C with a value that was known to represent
the whole EDU 540 population.
The independent-sample t test is used to compare two
groups' scores on the same variable.
Example : Compare the test scores of U5C and PKPG to
evaluate whether there is a difference in their scores.
The paired-sample t test is used to compare the means of
two variables within a single group.
Example, it could be used to see if there is a statistically
significant difference between test 1 and test 2 among the
members of U5C
37. Output
Notice the two parts of the output
Equal variances assumed
Equal variance not assumed
Which to use?
Look at Levene’s test for equality of variance
If small Sig. - groups have unequal variances
Independent Samples Test
.814 .378 -6.024 19 .000 -11.528 1.914 -15.533 -7.523
-5.483 10.805 .000 -11.528 2.103 -16.166 -6.890
Equal variances
assumed
Equal variances
not assumed
TEST
F Sig.
Levene's Test for
Equality of Variances
t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference Lower Upper
95% Confidence
Interval of the
Difference
t-test for Equality of Means
38. Output
t-statistics is -6.024
Sig. level : .000
The significance level tells us that the probability that
(there is no difference between visual and tactile
learners) – the “NULL” is very small
Hence, there is a significant difference in the test
scores between visual and tactile learners
Independent Samples Test
.814 .378 -6.024 19 .000 -11.528 1.914 -15.533 -7.523
-5.483 10.805 .000 -11.528 2.103 -16.166 -6.890
Equal variances
assumed
Equal variances
not assumed
TEST
F Sig.
Levene's Test for
Equality of Variances
t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference Lower Upper
95% Confidence
Interval of the
Difference
t-test for Equality of Means
39. Have fun with SPSS!
Proceed to Qualitative Analysis and Ethics in
Research
