# Internal Assessment Overview Mr. Freeman - A word from Mr.pptx by hcj

VIEWS: 7 PAGES: 35

• pg 1
```									Internal Assessment
Overview
Mr. Freeman

1
Workshop Overview
 Null and Experimental Hypothesis
 Design of study
 Sampling of study
 Choosing the right Inferential Test for your study
 Questions/Comments

2
Null Hypothesis
 The logic of traditional hypothesis testing requires
that we set up two competing statements or
hypotheses referred to as the null hypothesis and
the alternative(experimental) hypothesis. These
hypotheses are mutually exclusive and exhaustive.

 Ho: The finding occurred by chance

 H1: The finding did not occur by chance

3
Null Hypothesis
 For testing, you will be analyzing and comparing
your results against the null hypothesis, so your
research must be designed with this in mind. It is
vitally important that the research you design
produces results that will be analyzable using
statistical tests.

4
Hypothesis testing
 Most social scientist are very afraid of statistics, due to
obscure mathematical symbols, and worry about not
understanding the processes or messing up the
experiments. There really is no need to fear!

 Most psychologists understand only the basic principles
of statistics, and once you have these, modern
computing technology (SPSS, Microsoft excel) gives a
whole battery of software for hypothesis testing.

 Designing your research only needs a basic
understanding of the best practices for selecting
samples, isolating testable variables and randomizing
groups, and choosing the right inferential testing
measures.

5
Hypothesis testing
 A common statistical method is to compare a population to the
mean.
 For example, you might have come up with a measurable
hypothesis that IB students have a higher IQ if they take IB
Psychology for two years.

 Your alternative hypothesis, H1 would be

 “Students who take IB Psychology for two years (IV) will show a
higher IQ increase (DV) than students who have not.”

 Therefore, your null hypothesis, H0 would be

 “Students who take IB Psychology for two years do not show a
higher IQ increase than students who do not.”

6
Hypothesis testing

 In other words, with the experiment design, you
will be measuring whether the IQ increase of
student who take IB Psychology deviate from the
mean, assumed to be the normal condition.

“H0 = No increase. The students will show no
increase in mean intelligence.”

7
Hypothesis testing
 From IQ testing of the control group, you find that the
students who did not take IB Psychology (control
group) have a mean IQ of 100 before the experiment
and 100 afterwards, or no increase. This is the mean
against which the sample group will be tested.

 The students who took IB Psychology show an
increase from 100 to 106. This appears to be an
increase, but here is where the statistics enters the
hypothesis testing process. You need to test whether
the increase is significant, or if experimental error and
standard deviation could account for the difference.

8
Hypothesis testing
 Using an appropriate test (an inferential test-depending on the
design chosen), the researcher compares the two means, taking
into account the increase, the number of data samples and the
relative randomization of the groups. A result showing that the
researcher can have confidence in the results allows rejection of
the null hypothesis.

 Remember, not rejecting the null is not the same as accepting it.
It is only that this particular experiment showed that IB
Psychology had no affect upon IQ. This principle lies at the very
heart of hypothesis testing. Just because you failed to prove that
IB Psych increases your IQ does not mean that you prove that it
does not!

 In other words, just because your experiment failed to proved
that IB Psychology increases IQ, does not mean that it is not the
case.

9
Significance

 The exact type of statistical test used depends
upon many things, including the field, the type of
data and sample size, amongst other things.

 The vast majority of scientific research is ultimately
tested by statistical methods, all giving a degree
of confidence in the results.

10
Significance
 For psychology, the researcher looks for a
significance level of 0.05, signifying that there is
only a 5% probability that the observed results
and trends occurred by chance.

 The significance level determines whether the null
or alternative is rejected, a crucial part of
hypothesis testing.

11
Writing a Hypothesis

 The entire experiment and research revolves
around the research hypothesis (H1) and the null
hypothesis (H0), so this is a very intricate part of
the experiment.

 Needless to say, it can all be a little intimidating,
and many international students have found this
to be the most difficult stage of the IA.

12
Example (borrowed from my
college professor)
 A worker on a fish-farm notices that his trout seem
to have more fish lice in the summer, when the
water levels are low, and wants to find out why.

 His research leads him to believe that the amount
of oxygen is the reason – fish that are oxygen
stressed tend to be more susceptible to disease
and parasites.

13
Example (borrowed from my
college professor)

He proposes a general hypothesis.

 “Water levels affect the amount of lice suffered by
rainbow trout.”

 This is a good general hypothesis, but it gives no guide
to how to design the research or experiment. The
hypothesis must be refined to give a little direction.

14
Example    (borrowed from my college
professor-Dr. Jackson-Lowman)

Rainbow trout suffer more lice when water levels are low.”

Now there is some directionality, but the hypothesis is not really
testable, so the final stage is to design an experiment around which
research can be designed, a testable hypothesis.

Rainbow trout suffer more lice in low water conditions because there is
less oxygen in the water.”

This is a testable hypothesis – he has established variables, and by
measuring the amount of oxygen in the water, eliminating other
controlled variables, such as temperature, he can see if there is a
correlation against the number of lice on the fish.

This is an example of how a gradual focusing of research helps to
define how to write a hypothesis.

15
Example    (borrowed from my college
professor-Dr. Jackson-Lowman)

Once you have your hypothesis, the next stage is to
design the experiment, allowing a statistical analysis of
data, and allowing you to test your hypothesis.
The statistical analysis will allow you to reject either the
null or the alternative hypothesis. If the alternative is
rejected, it is OK! Remember the study is not intended to
be ground breaking.

This is part of the scientific process, striving for greater
accuracy and developing ever more refined hypotheses.

16
Design of your study
 Repeated measure design
 Independent measure design

17
Repeated measure design
 A repeated measures design consists of testing the
same individuals on two or more conditions.
 The key advantage of the repeated measures
design is that individual differences between
participants are removed as a potential
confounding variable.
 Also the repeated measures0 design requires fewer
participants, since data for all conditions derive from
the same group of participants.

18
Repeated measure design
 The design also has its disadvantages. The range of
potential uses is smaller than for the independent
groups design. For example, it is not always possible
to test the same participants twice.
 There is also a potential disadvantage resulting from
order effects, although these order effects can be
minimized.
 Order effects occur when people behave differently
because of the order in which the conditions are
performed. For example, the participant’s
performance may be enhanced because of a
practice effect, or performance may be reduced
because of a boredom or fatigue effect.

19
Repeated measure design
 Order effects act as a confounding variable but
can be reduced by using counterbalancing.
 If there are two conditions in an experiment the
first participant can do the first condition first and
the second condition second.
 The second participant can do the second
condition first and the first condition second and
so on. Therefore any order effects should be
randomized.

20
Independent measure design
 If two groups in an experiment consist of different
individuals then this is an independent measures
design.

 The main advantage of an independent measures
design is that there is no problem with order effects.

 However, the design also has disadvantages. The
most serious is the potential for error resulting from
individual differences between the groups of
participants taking part in the different conditions.

21
Sampling
 One of the most important issues about any type of
method is how representative of the population the
results are.

 The population is the group of people from whom the
sample is drawn. For example if the sample of
participants is taken from sophomore IB students, the
findings of the study can only be applied to that group of
people and not all Rickards High School students and
certainly not all people in the world.

 Obviously it is not usually possible to test everyone in the
target population so therefore you will use sampling
techniques to choose people who are representative
(typical) of the population as a whole.

22
Opportunity Sampling
 Opportunity sampling is the sampling technique most
used by IB psychology students. It consists of taking
the sample from people who are available at the
time the study is carried out and fit the criteria your
are looking for.

 This may simply consist of choosing the first 20
students in your class to fill in your sample quota.

 It is a popular sampling technique as it is easy in
terms of time.
 It can also be seen as adequate when investigating
processes which are thought to work in similar ways
for most individuals such as memory processes

23
Opportunity Sampling
 However, there are many weaknesses of opportunity
sampling. Opportunity sampling can produce a
biased sample as it is easy for the researcher to
choose people from their own social and cultural
group. This sample would therefore not be
representative of your target population as you
friends may have different qualities to people in
general.

 A further problem with opportunity sampling is that
participants may decline to take part and your
sampling technique may turn into a self selected
sample.

24
Self-Selected Sampling
 Self selected sampling (or volunteer sampling)
consists of participants becoming part of a study
because they volunteer when asked or in response
to an advert. This sampling technique is used in a
number of the core studies, for example Milgram
(1963).

 This technique, like opportunity sampling, is useful as
it is quick and relatively easy to do. It can also reach
a wide variety of participants. However, the type of
participants who volunteer may not be
representative of the target population for a number
of reasons. For example, they be more obedient,
more motivated to take part in studies and so on.

25
Choosing the right Inferential
test
 Choosing the right statistical test may at times, be a
very challenging task for a young psychology
student.
 In order to choose the right statistical test, when
analyzing the data from an experiment, we must
have at least:
§ a decent understanding of some basic statistical terms and
concepts;
§ some knowledge about few aspects related to the data you
collected during the research/experiment (e.g. what types of
data we have - nominal, ordinal, interval or ratio, how the
data are organized, how many study groups (usually
experimental and control at least) you have, are the groups
paired or unpaired, and are the sample(s) extracted from a
normally distributed.

26
Choosing the right Inferential
test your IAs will fall in the following design
Most of
type:
 Repeated measures
 1 sample or 2 sample
 Parametric

As your advisor, I can suggest inferential test
associated with your sample but it is your
responsibility to choose the right inferential test.

27
Choosing the right Inferential
test also depends on the type of data that
 Your test
you have.
 The following terms are used are used to
describe types of data and by some to dictate
the appropriate statistical test to use:
 Nominal
 Ordinal
 Interval
 Ratio

28
Choosing the right Inferential
test
 Interval Data: Temperature, Dates (data that has
an arbitrary zero)
 Ratio Data: Height, Weight, Age, Length (data
that has an absolute zero)

 Nominal Data: Male, Female, Race, Political
Party (categorical data that cannot be ranked)

 Ordinal Data: Degree of Satisfaction at
Restaurant (data that can be ranked)

29
Choosing the right Inferential
test
Potential inferential test:
 T-Test
 Wilcoxon matched pair test
 Chi Square
 Mann-Whitney U test.

30
Wilcoxon signed rank sum
test
The Wilcoxon signed-rank test is a non-parametric
statistical hypothesis test used when comparing
two related samples or repeated measurements
on a single sample to assess whether their
population mean ranks differ (i.e. it's a paired
difference test).
How to use the Wilcoxon signed rank sum test:
http://faculty.vassar.edu/lowry/ch12a.html

31
T-Test
Use Student's t-test when you have one nominal
variable and one measurement variable, and you
want to compare the mean values of the
measurement variable. The nominal variable must
have only two values, such as "male" and "female"
or "treated" and "untreated.“ It is usually used to:
(1) To test hypothesis about the population mean
(2) To test whether the means of two independent samples are
different.
(3) To test whether the means of two dependent samples are
different.
(4) To construct a confidence interval for the population mean.

32
Chi square
 The Chi Square (X2) test is undoubtedly the most
important and most used member of the
nonparametric family of statistical tests.
 Chi Square is employed to test the difference
between an actual sample and another
hypothetical or previously established distribution
such as that which may be expected due to
chance or probability.
 Chi Square can also be used to test differences
between two or more actual samples

33
Mann-Whitney U test

 The Mann-Whitney U-test is used to test whether
two independent samples of observations are
drawn from the same or identical distributions.
 An advantage with this test is that the two
samples under consideration may not necessarily
have the same number of observations.

34
Mann-Whitney U test

 For more info on statistical test:
http://www.statsoft.com/textbook/nonparametric-
statistics/
http://www.fon.hum.uva.nl/Service/Statistics/Signe
d_Rank_Test.html
http://statistics-help-for-
students.com/How_do_I_report_paired_samples_T_
test_data_in_APA_style.htm

35

```
To top