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Internal Assessment Overview Mr. Freeman - A word from Mr.pptx

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					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

				
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