DSE 212 Tutorial 3

TMA 3 – the experimental project

The projects

Quantitative project – TMA 3, deadline

February 10

Qualitative project – TMA 5, deadline April 21

Help with both projects from Day School

(January 30)

Qualitative project – focus of tutorial 5

Quantitative project – based on experimental

method, data collection and analysis

The experimental method

Psychology is trying to be scientific!

This is linked to funding – cynically, the most funding in academia goes to science-based subjects.

It is also linked to a need for the information we find out to be thorough, valid, testable, replicable, reliable and robust.

When we publish the results of an investigation, we don’t want the world and his dog telling us we were wrong!

Steps in experimentation

Find an area that is interesting and that could

generate something new to look at.

Read all the literature around the topic.

Determine a research question – this usually

then generates hypotheses.

Design an experiment that will provide

evidence to support (not prove) or refute (not

disprove) your hypotheses and answer the

question.

Steps in experimentation

Run a pilot to see whether your design works.

Run the experiment, collect the data.

Put together the raw data and work out which

statistical analysis will help you understand what’s

going on.

Draw up descriptive statistics (central tendency,

range etc).

Run an inferential test (e.g. chi-squared, t-test,

correlation).

Understand the analysis to determine whether your

hypothesis is supported or refuted.

Steps in experimentation

Discuss and interpret the analysis and

findings.

Draw on the research that you looked at right

at the beginning to see whether you agree or

disagree with everyone else.

Try to explain your findings in the light of

existing knowledge.

Suggest ideas for future research and

recognise where your method was flaky.

What experiments do…

Establish a cause and effect relationship.

By controlling variables that might influence the outcome as far as possible, we can state that the independent variable caused the effect on the dependent variable.

Reminder – the independent variable is what the researcher actually changes or manipulates.

The dependent variable is the outcome or result of that change.

Which is which?

The effect of alcohol on reaction times.

IV = alcohol, DV = reaction times

The influence of time of day on short term

memory capacity.

IV = time of day, DV = short term memory

capacity

Are men able to read maps more effectively

than women?

IV = gender, DV = ability to read maps

Which is which?

Does loud music make it more difficult to

concentrate?

IV = presence of loud music, DV = ability to

concentrate

Can women multitask better than men?

IV = gender, DV = ability to multitask

Does eating chocolate make you feel good?

IV = consumption of chocolate, DV =

measure of ‘the feel good factor’.

Some definitions…

Conditions

When the IV is changed, this will create conditions –

for example, if we want to know if loud music

influences concentration, we need more than one

condition.

We might have condition 1, loud music playing and

condition 2, no music playing.

Two conditions is easiest, however you could argue

for condition 3, soft music playing.

Three conditions makes things more complicated!

Some definitions…

Experimental and control conditions

The experimental condition would be where the IV

was changed.

This is compared to the condition where the IV is not

changed.

E.g. we introduce a new way of learning mathematics

into a primary school.

Children receiving the new method would be in the

experimental condition, children who do not receive

the new method would be in the control condition.

Some definitions…

Null hypothesis

Whenever we test something in psychology, there is always the chance that something happens because of some other factor than the IV manipulation.

The null hypothesis predicts that the IV will not affect the DV – it predicts that the differences found in an experiment happened because of chance and not because of anything we did to the IV.

Some definitions…

Null hypothesis

The statement of the null hypothesis is usually that the IV will not affect the DV.

E.g. alcohol will not have an effect on reaction times.

When applying an inferential test, we are testing whether the null hypothesis is supported – that is, whether the difference between the two samples we are testing was due to a sampling error.

Some definitions…

Null hypothesis

If we find that the results are statistically significant, then we can reject the null hypothesis.

This means the results we have found are not due to sampling error and we can accept our experimental hypothesis.

If the results are not statistically significant, we have to accept the null hypothesis.

Accepting the null hypothesis does not mean it is true, it means that we have not found sufficient evidence to show that in this particular case the null hypothesis can be rejected.

Reasons for accepting the null

Small sample

Often only having a few participants will cause a statistical test to come out as insignificant.

Larger samples are more likely to generate enough evidence to support the experimental hypothesis.

Sampling errors that may be due to confounding or extraneous variables.

A bad hypothesis!

More definitions…

Type 1 error – rejecting the null hypothesis when the DV has arisen from a variable other than the IV.

Type 2 error – accepting the null hypothesis when the sample size is really too small for us to be certain that the DV was not affected by the IV.

The most important thing to remember when looking for errors is sample size, as this has a large influence.

Populations

Confidence intervals show graphically that

two populations are the same or different.

You can compare the confidence intervals to

see whether anything other than the variable

you’re interested in is different

For example, you could tell whether the

populations are matched for age

You can tell whether you have same

populations or different populations

Populations

A population is a larger group of people that share a particular characteristic

A sample is a small group that can represent the larger group

A sample might be 20 16 year old school girls from England

The larger population would be all 16 year old school girls from England

A different population would be 16 year old school boys from England

Hypotheses

Two-tailed hypotheses (also known as non-directional hypotheses) say the IV will affectthe DV.

E.g. Time of day will affect the duration of short term memory.

One-tailed hypotheses (also known as directional hypotheses) say in which direction the IV will affect the DV.

E.g. Short term memory duration will be longer in the morning than in the afternoon.

More about hypotheses

If you choose a one-tailed hypothesis, you should have good grounds for believing the DV will be affected in the predicted direction by the IV.

The hypothesis you choose will have implications for the statistical analysis – more later.

If you choose a one-tailed hypothesis and the descriptive statistics show it is not supported, you have to accept the null…

Descriptive statistics

Descriptive statistics are the mean, standard

deviation, mode and median (depending on

the data).

If you have two sets of data, calculate the

mean and SD first.

If the means are reversed from the prediction

made by the IV, the null hypothesis is

instantly accepted without any further

analysis.

Example…

Experimental hypothesis: Eating chocolate ten

minutes before a maths test will result in higher

scores.

Null hypothesis – Eating chocolate ten minutes

before a maths test will not result in higher scores.

Two conditions – chocolate eating group, no

chocolate eating group.

Mean of score for chocolate eating group – 7.6

Mean of score for no chocolate eating group – 9.8

The null hypothesis is accepted and the experimental

hypothesis is rejected.

Probabilities

When testing hypotheses, we are looking for

the probability that the differences between

two conditions are not due to sampling errors.

Probability is a difficult concept (I think!) that

looks at the chance of something happening.

In a simple way, the chances of a coin

showing a head on any toss is 1 in 2.

The chances of winning the lottery is 1 in 14

million…

Probabilities

When looking at psychological investigations, we accept a convention of a 1 in 20 probability that the results happened by chance.

This is also expressed as 5% or 0.05 (which is actually 5/100).

This means that in any hypothesis testing we are doing, using inferential statistical tests, we accept that the experimental hypothesis is supported when the probability of it happening by chance is less than 1 in 20.

Probabilities

We write this down, conventionally, as P < 0.05

In other areas of science, for example, drug testing, it might be that the p value has to be lower, e.g. p < 0.01.

This reflects a testing of the hypothesis at a significance level of 1 in 100, that is, we are 99% sure that it didn’t happen by chance.

Some psychological testing can be seen to be significant at this level, if the sample is large enough and the results are sufficiently different.

However, p < 0.05 is enough for acceptance of the experimental hypothesis.

Probabilities and SPSS

SPSS shows the p value under the column

Sig.

This value is what you look at to see whether

the result is significant and whether you can

accept the experimental hypothesis.

It has to be less than 0.05 for the null

hypothesis to be rejected.

An example…

Chocolate and no-chocolate

Two

conditions –

children’s

scores in a

maths test

after

chocolate

(group1) and

after no

chocolate

(group 2)

Running a t-test

Click on

Analyze,

Compare

Means,

Independent-

Samples T

Test

Assigning the variables…

Assign the

Scores to

the test

variable and

the group to

the

grouping

variable –

the (? ?)

means we

haven’t

defined the

groups yet.

Defining the groups…

Define the

groups by

using 1 for

group 1 and

2 for group

2 – easy!

The output

1.00 = chocolate

2.00 = no chocolate

No of participants

in each group

Mean and SD for each group

The output

The ‘t’ value The degrees of

freedom value ‘df’

The significance for a two-

tailed hypothesis

Explaining the output

The mean for the group receiving chocolate is

5.5, the mean for the group not receiving

chocolate is 7.2.

This is telling me that if I had selected a one-

tailed hypothesis, predicting the chocolate

group to have higher scores, I would have

had to reject the null hypothesis before doing

the t-test.

Explaining the output

We have two values for t, df and Sig.

These relate to whether we can assume equal variances or not.

To determine which we choose to use, we look at the column headed ‘Levene’s Test for Equality of Variances’.

If the Sig value under this column is less than 0.05, the Levene’s test is statistically significant and this means that the two samples do not have equal variances.

Explaining the output

In our case, the Levene’s Test is not

statistically significant (the value of .370 is not

less than 0.05), so we look at the equal

variances assumed results.

These show the t value at -2.847, the

degrees of freedom as 18 and the

significance for a two-tailed hypothesis at

0.011.

Now for what these mean…

Interpreting the T test

The t statistic is a measure of the size of the effect –the higher the t value, the greater the difference between the groups.

The degrees of freedom is a difficult concept to explain – it is basically the number of observations that have been made (in this case, scores) minus the number of parameters (in this case, 2 because of the two groups).

So we have 10 + 10 – 2 = 18.

Don’t worry too much about this – SPSS tells you what it is and you just remember to quote it when you give the t-test result.

Interpreting the T test

The probability, in our case, that our result was not due to chance was 0.011, that is 11 in 100.

This is less than 0.05, so our result is significant.

However, for a two-tailed hypothesis!! This would only be the case if we have said eating chocolate will have an effect on the scores children achieve in a maths test.

It did, but it made them go down, not up.

Other things about T tests

The t statistic can be positive or negative,

depending on which group has the highest

scores.

They have the same meaning, the magnitude

is the same, it’s the direction that defines the

sign.

E.g. if group 1 have lower scores than group

2, t will be negative.

Choosing a statistical test

In my example, I used an independent T test

because I had different participants in each

condition.

That is, one group of children were given

chocolate and the other didn’t receive any.

This is a between-participants design.

Between-participants design experiments

with a continuous DV are analysed using the

independent T-test.

Choosing a statistical test

If I had carried this out differently, and used the same

children in each condition, this would be a within-

participant design.

In this case, I would use a Paired-samples T- test.

I would have tested the children firstly with no

chocolate, then later tested them again with

chocolate.

This might have been better because the children act

as their own controls and I can rule out individual

differences as causing the differences in maths

scores.

Choosing a statistical test

To remind you, when carrying out a comparison

between two continuous variables, this is a

correlational analysis.

This will require a Pearson’s correlation to analyse it -

for example, the hours revising correlated with the

scores in exams.

To analyse categorical data, where responses have

been put into categories, use chi-squared.

For example, food choices made by children of

different ages.

Back to TMA 3…

TMA 3 is asking you to undertake your own

experiment.

You will firstly conduct a Stroop Test on four

people

You will add this data to the data provided

(Assignment Booklet, on page 38, table 2).

Put it into SPSS – think about the design to

get this right

Explain the output.

The Stroop test

J.R. Stroop found, in 1932, that people asked

to identify the colour in which words were

written found it more difficult when the

information conflicted.

For example, it’s easy to identify the colour

red when the word is red.

When the word is red written in blue, it

becomes more difficult.

Why?

Automatic processing

Reading words is automatic – we can’t help reading

any word we see, even if it’s a nonsense word.

Identifying and naming colours is also automatic –

see a wall painted blue and you will automatically

know it’s blue.

However, when two automatically processed pieces

of information come together and they conflict (the

word red printed in blue), there is a hesitation in

naming the colour because the automatic process

has to be overridden consciously.

Traditional Stroop methodology

This is usually carried out using colour words printed

in the colour they refer to (congruent colour words)…

And colour words printed in colours they don’t refer to

(incongruent colour words)

Sometimes, though, variations are used

For example, colour evocative or colour related

words

Grass printed in green (congruent colour related) or

grass printed in red (incongruent colour related)

That’s what you are doing here

Stimuli materials

Available from the DSE212 course website,

course resources

You need stimulus materials, 3 sheets, one

test sheet and two conditions

Consent form

Data sheet

Notice the controls between the two

conditions for word length and letters used

Writing the report

Read carefully chapter 7 of the Exploring

Psychological Research Methods book

Full of information to help you with this

Read the assignment booklet properly

Stick to the sections recommended

Use subheadings in the methods section

Keep to the word count

Appendices and references not counted in

word count

Writing it up

The introduction should be in a filter structure

You need to read the material relating to this experiment in the course book

Chapter 6 of Book 1 Mapping Psychology, in particular section 2

Use the structure on the next slide to write your intro

Start broadly and focus down on the main purpose of the experiment – research hypothesis

Structure of the introduction

We can’t we attend to everything?

Automatic and controlled processing.

Why do we need automatic processing?

The downside of automatic

processing.

Why we want to explore

this problem.

The research

hypothesis.Start broadly, focus

down to the hypothesis

See page 204 of

Exploring

Psychological

Research Methods

Method

Needs to be fully replicable

That is, anyone else could do it from your

description

Divide into sections – Design, Participants,

Materials, Procedure

You need to mention ethics – all those that

apply to this

Ensure all the stimulus materials etc are in

the appendix and refer to them in the account

Thinking about design

You need to be absolutely clear about the design.

To remind you, the design is how the participants are allocated to conditions.

Different participants in each group –between-participants design, statistical analysis Independent T-test.

Same participants in each group – within-participants design, statistical analysis Paired-sample T-test.

Results

State the research hypothesis

Describe the data in detail, specify what the measurements were

Any calculations you carried out

Descriptive statistics (mean and SD) preferably in a table and/or chart

What do the descriptive stats say?

Inferential test (which one and why)

State the results of the inferential test

State whether it’s significant and whether you accept or reject the null hypothesis (don’t say prove or disprove)

Discussion

State the results (again, I know it feels

repetitive!)

Explain the results

Show how these results fit with research in

the field and whether they correspond with

what is already known

If the null hypothesis was accepted, was this

because of a flaw in the experiment?

Was there any issues in the data collection?

Discussion

Evaluate the experiment, be honest and

critical

Implications – are there implications of the

findings?

Ideas for further research – what other

experiments could be conducted to help find

out more or answer questions generated by

this experiment?

Title and abstract

Title should reflect the independent and dependent variables

Abstract – short (150 words) summary of the aims, methods, results and conclusion of the study and put at the front (before the introduction)

Remember ABC – Accurate, Brevity and Clarity.

Check it with a family member to see whether they understand what you did!

References and appendices

Please reference this properly

See Assignment Booklet page 8 for full detail of referencing

Include everything in the appendices –including the SPSS print outs

Please note: there may be problems with submitting this electronically for users of MS Works and Star Office – see the booklet for information on how to submit to ensure I get all the SPSS stuff!!

Approximate marks for TMA 3

Title and abstract – 10 marks

Introduction – 10 marks

Method – 24 marks (should include design,

participants, materials and procedure in

clearly defined sections)

Results – 20 marks

Discussion – 20 marks

References and appendices – 4 marks

Overall clarity and conciseness – 12 marks

Deadlines…

The deadline for this TMA is February 10

Happy to extend, for those in need!

Please email me for any other help with the

forthcoming TMA or any other course related issues.