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Lecture 2:
Basic steps in SPSS and some
tests of statistical inference
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Basic steps in SPSS
Error checking
Missing values analysis
Grouping variables
Graphical representation
Break
Hypothesis testing, inferential statistics and
parametric testing in SPSS
Exercises
• Exercise data can be found on the website:
www.ex.ac.uk/ebrg
Statistical inference: the null and
alternative hypotheses (H0, H1)
• Statistical inference is concerned with
differences between samples / populations
• Null hypothesis:
“There is no significant difference between
A and B”
• Alternative hypothesis:
“There is a significant difference between A
and B”
One and Two tailed tests
• Very often, we will want to examine the
‘direction’ of a difference
• One-tailed test: specifying the direction
• Two-tailed test: no direction specified
• Alternative hypothesis changes for onetailed test:
• “A is significantly greater or less than B”
Determining your data types
• Population: all possible cases
• Sample: your selection of the population
• Tied or ‘paired’ samples:
Samples that are linked, perhaps in time
(e.g. before/after samples)
What type of test is appropriate?
• Parametric:
Classical tests
• Non-parametric:
Less powerful tests
• Knowing your data
Test selection
• The choice of statistical test is crucial
• Various tests depending on test
requirements:
Parametric
Non-parametric
• Test calculation can be undertaken in SPSS
Significance testing
• How significant is the test result? Does it
show a ‘real’ difference, or could it have
occurred by chance?
• With every test, there will be a probability
distribution, which looks like the normal
distribution
• This distribution specifies the probability of
the result (test statistic) occurring by chance
or random variation
Significance - continued
• Probability is measured as 100% = 1.00
• Each test has a critical ‘rejection’ area on its
distribution where test statistics must be
rejected, as they are too large than to have
occurred by chance, according to the
number in the sample
• Accordingly, all tests have critical values
for different levels of significance and
differently tailed tests
Interpreting significance
• To simplify matters, we use significance
‘levels’ to determine if a test is significant
• We usually use 0.05 (5%) or you could use
0.01 (1%)
• This means that there is a 5% probability
that the result occurred by chance, i.e. we
can be 95% confident in its importance