Transcript Document 683585

Research methods
Recap: last session
1. Outline the difference between descriptive statistics
and inferential statistics?
2. The null hypothesis predicts that there will be a
significant difference? True/false.
3. Shorthand for the null hypothesis is Ho? True/false
4. What are Inferential statistics?
5. Why are Levels of measurement important?
6. Ordinal data is data that is measured on a scale?
True/false
7. Why is it necessary to have a Null hypothesis?
1.
Outline the difference between descriptive statistics and inferential
statistics?
Summarising data vs. allowing you to see whether the research hypothesis or
null hypothesis is retained
1. The null hypothesis predicts that there will be a significant difference?
True/false.
False
1. Shorthand for the null hypothesis is Ho? True/false
True
1. What are Inferential statistics?
Tests designed to assess whether we reject or retain the null hypothesis.
1. Why are Levels of measurement important?
To know which is the most appropriate descriptive statistic to calculate, which
graph to use and which inferential test to use we need to establish what the
level of measurement is.
1. Ordinal data is data that is measured on a scale? True/false
False
1. Why is it necessary to have a Null?
Eliminates bias. Forces researcher to accept the view that the two sets of data
has occurred through chance. Means there is no other conclusions that can
be made
The Null hypothesis is usually stated as :There will be no difference between X and Y or
any difference will be due to chance effects.
A team of psychologists was interested in studying the effects of alcohol on peoples'
reaction times. Earlier research suggested that an increase in reaction time was due to
the alcohol rather than peoples' expectations of alcohol. The psychologists recruited
two groups of volunteers (an independent groups design) from a local university. Each
participant's reaction time was measured by using a computer game.
The participants were then given a drink. The first group received a drink containing a
large measure of strong alcohol; the second group received an identical drink without
alcohol, but with a strong alcoholic smell. Finally, all participants were required to play
the computer game again to assess their reaction time. Once they had completed the
task, they were then thanked for their time and allowed to leave.
What is the IV?
whether the participants have had an alcoholic drink or one that is not alcoholic but
smells as if it is
What is the DV?
reaction times on a computer game
Null hypothesis:
There will be no difference between the university students‘ reaction times on a
computer game between those who have had an alcoholic drink or one that is not
alcoholic but smells as if it contains alcohol; any differences are due to chance factors.
A teacher in a small secondary school wanted to find out whether there was
any truth in her idea that students who used a computer regularly for their
homework achieved higher exam grades than those who did not. She decided
to interview a sample of 30 students taken from across the school. She taperecorded all the interviews. She later obtained their end of year exam grades
from their reports.
What is the IV?
whether the participants used a computer regularly for their homework or
didn’t use a computer regularly for their homework.
What is the DV?
Exam grade achieved
Null hypothesis:
There will be no difference between the exam grades achieved at the end of
year between those who regularly used a computer to complete homework
and those who did not regularly use a computer to complete homework; any
differences are due to chance factors.
Page 8-9 complete assessment 8a and 8b
Have you got a coin?
Probability and chance
• Read page 9
• Answer the following
1. When analysing data what can we never be certain of?
2. What do statistical tests do?
3. If results are judged to be caused by a genuine effect what
are they called?
4. Inferential tests therefore allow us to…..
5. In many experiments we are looking at a significant
difference within our results. How is a correlation
different?
Probability and chance
•
•
1.
2.
3.
4.
5.
Read page 9
Answer the following
When analysing data what can we never be certain of? That the
conclusion in true
What do statistical tests do? Calculate the statistical probability of our
results occurring through chance alone so that we can decide whether to
accept/reject our null hypothesis.
If results are judged to be caused by a genuine effect what are they
called? significant
Inferential tests therefore allow us to….. Reject or retain the null
hypothesis
In many experiments we are looking at a significant difference within our
results. How is a correlation different? The null hypothesis would state
that there is no correlation and that any relationship is due to chance
factors
Inferential statistics
• Inferential statistics are used to test hypothesis.
– Do groups differ on some outcome variable?
– Is the difference more than expected by chance
• Used to make generalisations from a sample to a
population.
• Inferential statistics take into account sampling
error (chance, random error)
P value
The reason for calculating an inferential statistic is to get a p value (p =
probability)
Inferential statistical tests work by assessing the probability of our
results occurring due to chance alone (rather than the IV)
The p value determines whether or not we reject the null hypothesis.
We use it to estimate whether or not we think the null hypothesis is
true. The p value provides an estimate of how often we would get the
obtained result by chance, if in fact the null hypothesis were true.
If the p value is small, reject the null hypothesis and accept that the
samples are truly different with regard to the outcome.
If the p value is large, accept the null hypothesis and conclude that
the treatment or the predictor variable had no effect on the outcome.
Decision rules – Levels of significance
How small is "small?“
Once we get the p value (probability) for an
inferential statistic, we need to make a decision.
Do we accept or reject the null hypothesis?
What p value should we use as a cutoff?
The one chosen is called the level of significance.
Levels of significance
Researchers can use significance levels of 10%, 5%, 1% (or
0.1% in very stringent conditions) - expressed as:
10%, 0.10, 1 in 10, p≤0.10.
5%, 0.05, 1 in 20, p≤0.05
1%, 0.01, 1 in 100, p≤0.01
If you use a 5% statistical significance level and this is achieved
you are saying that the probability of your results being a fluke
and nothing to do with your IV is less than 5%.
or you are 95% sure that your change in DV is because of your
IV
Using the 0.05 level of significance means if the null
hypothesis is true, we would get our result 5 times
out of 100 (or 1 out of 20).
We take the risk that our study is not one of those 5
out of 100.
When you use a computer program to calculate an
inferential statistic (such as a t-test, Chi-square,
correlation), the results will show an exact p value
(e.g., p = .013).
Task for homework
Read page 9-10 of booklet on significance levels.
and complete Task 8 on pages 10-11
Which type of inferential test
should be used?
• Depends on:
1) whether the researcher is testing for
differences between groups (experiment) or
a correlation between two co-variables.
1) Level of measurement (nominal, ordinal
interval)
1) Experimental design (independent groups,
matched pairs, repeated measures)
Test of difference or correlation
Nominal or at least ordinal level data?
At ordinal level
Spearman’s
Rho
Independent groups design
Chi square
Repeated measures/matched pairs or independent groups design?
Wilcoxon T
Mann-Whitney
U Test
Chi-Squared = nominal data = independent groups design
Mann-Whitney U TestIndependent groups Design
Wilcoxon T Test
Repeated measuresAt least ordinal (1st in
the race)
Activity
• Have a go at trying to draw the diagram
yourself.
• Complete pages 13 and 14 of your booklet
And Finally…
• Answers
Name of test
Difference or correlation
Level of measurement
Experimental design
Chi-Square
Difference (or
association)
Nominal (presented in a
2x2 contingency table)
Independent groups
Wilcoxon
Difference
At least Ordinal
Repeated Measures
(or matched Pairs)
Mann-Whitney
Difference
At least Ordinal
Independent groups
Spearman’s
Correlation
At least Ordinal
N/A
Now you need to justify each test
Fill in the gaps
• The Spearman’s Rho was used because the
data can be treated as at least
1)_______________ and the researchers were
studying a possible 2)_________________
between two co-variables
1 = Ordinal 2 = Correlation (or relationship)
Now you need to justify each test
Fill in the gaps
• The Chi-Square test was used because the data can be
treated as 1)_______________ and the researches had
hypothesised that there will be
2)___________________ between conditions when
using the 3) _________________________ design.
1 = Nominal 2 = a difference 3 = Independent groups
(please note that the Chi-square is also used as a test of
association)
Now you need to justify each test
Fill in the gaps
• The Wilcoxon T test was used because the data can be
treated as 1)_______________ and the researches had
hypothesised that there will be
2)___________________ between conditions when
using the 3) _________________________ design.
1 = ordinal 2 = a difference 3 = Repeated Measures (please
note that the Wilcoxon T is also used for a matched-pairs
design)
Now you need to justify each test
Fill in the gaps
• The Mann-Whitney U test was used because the
data can be treated as 1)_______________ and
the researches had hypothesised that there will
be 2)___________________ between conditions
when using the 3) _________________________
design.
1 = ordinal 2 = a difference 3 = independent groups