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What is it?In psychology we have an interest in studying

human behaviour... which requires research!

Research methods (or scientific methods) involves the testing of the truth of a proposition (or idea) by using careful measurement and controlled observation

One of the most popular and powerful research tools is an experiment which is a formal trial to confirm or disconfirm a hypothesis

Statistics Descriptive Statistics:Summarise, organise and describe important

features of the data so it can be more easily interpreted eg) graps, tables, calculating the mean (average) etc.

Inferential Statistics:Allow the researcher to draw conclusions, based on

evidence, about the results in the study and whether they can be generalised to a wider population

We will look more at this later on

Selection and Allocation A participant is any person or group of

people used in any kind of research study, and how they are selected and allocated to groups is very important to the study

The process of selecting participants for research is called sampling. The participants actually selected for the research form the sample, which is the portion or subset of the larger population of interest

SampleIs a group of participants selected from, and

representative of the population of interest

EG:- 20 students from year 12- 10 17 year old boys

There are 2 different ways you can select a sample:

- Random- Stratified

PopulationIs an entire group of people or animals that

belong to a particular category

EG:- All Sale College students- All year 12 students- All year 12 psychology students- 15 year old boys- Under 5 feet tall girls

Random SamplingMeans that every member of the population

of interest has an equal chance of being selected for the sample to be used in the study

Some ways this can be done include:- Putting names in a hat and drawing them out- Giving each member of the population a

number and then choosing every 3rd or 5th number for the sample

Stratified SamplingIs where the sample contains exactly the same

proportions of participants as in the population. They are divided into distinct groups (strata) and then a sample from each stratum (each group) is chosen for the sample

EG:- If your sample is 100 people and you are looking at the

relationship between age and intelligence in Australia; the number of people in each age category in the sample should be exactly the same of the proportion of ages in the population

A Stratified Random Sample is where each member of a stratum has equal chance of being selected for the sample

Allocation to Groups Experimental group: the group that is exposed

to the treatment (the independent variable)

Control group: the group that is exposed to the controlling condition. It provides a standard against which to compare the performance of the experimental group to

EG:- Chocolate before class affects performance- Experimental group: Eats a block of chocolate

before class- Control Group: Doesn’t eat the chocolate

Random Allocation (to groups)Is where the participants basically have a

50/50 chance of being in the control group or experimental group. So they have equal chance if being in either

This means that any change in behaviour most likely has something to do with the independent variable

Placebo and Experimenter EffectsPlacebo effectOccurs when a participants response is influenced by their

expectations rather than by the treatment

Experimenter effectOccurs when there is a chance that the participant’s response is

due to the actions of the experimenter rather than the independent variable. It is possible for the experimenter to unintentionally sway the participant if they want to see a particular result

Experimenter bias: happens when the person measuring the dependent variable is aware of the purpose or hypothesis and may misread the data

Eliminating the Placebo Effect

Single-blind procedure Is where the participants do not know whether

they are in the control group or the experimental group

Eliminating the Experimenter Effect

Double-blind procedureIs when neither the participant or

experimenter knows which group the participant has been allocated to. In this case the person collecting the data is usually not the experimenter

Experimental DesignIs one of the most rigorous and controlled

methods used in psychology

Is used to test the cause-effect relationship between variables

VariablesAny event, state or condition that can be varied

to see what the outcome is

Independent variable (IV): the condition that is manipulated to see what the effect on the dependent variable will be

Dependent variable (DV): is affected by the IV

EG:- IV: Whether chocolate is eaten before class- DV: Behaviour/ability to work by the students

Extraneous Variables Is an variable other than the IV that might affect

the DV

EG:- Some of the children in the class had no sleep last

night because of weather disturbances- Other examples include temperament, attitudes,

mood, motivation

The experimenter should ensure that extraneous variables are eliminated from the experiment by pre empting them

Confounding Variables When an experimenter cannot be sure whether

changes in the dependent variable were caused by the independent variable or an uncontrolled variable, and the effects of the uncontrolled variable are confused with the effects of the independent variable, it is known as a confounding variable

It is basically a second, unintended independent variable

- Extraneous variables unaccounted for lead to confounding variables

Minimising effects of extraneous variablesThere are different experimental designs that

minimise the effects of extraneous variables, these are:

Repeated measures designMatched participants design Independent groups design

- Describe the differences between these 3 experimental designs, what are the pros and cons of each (table)?

HypothesisIs a testable prediction of the relationship

between two or more characteristics of events – its basically an educated guess about what the outcome of an experiment will be

EG:- Eating chocolate before class will have a

negative impact on a child’s ability to concentrate during class

Operational HypothesisIs a defined and precise prediction oh how each

variable is measured and the effect it is expected to have on behaviour. It states how the variables will be manipulated and measured, as well as the population from which the sample has been drawn

Operational definitions:Is where an experimenter defines all the terms and

subject matter being measured by describing precisely how they are going to measure it

- See page 334 of text for examples

Null Hypothesis States that there is NO relationship between

the variables In this case the experimenter is usually

expecting the hypothesis to be disproved

EG:- It is predicted that there will be no

relationship between the amount of sugar consumes before class and ability to concentrate on a task

Non-directional hypothesisIf an researcher is unsure of the direction of

the relationship between variables they will propose a non-directional hypothesis (two-tailed)

EG:- It is predicted that there is a relationship

between the amount of chocolate consumed before class and ability to concentrate on a task

Directional HypothesisIs used when researcher is confident of the

direction of the relationship (one-tailed) they will propose a directional hypothesis

EG:- It is predicted that the consumption of chocolate

(1 100 bar or more) of chocolate will decrease the students ability to concentrate on a task

Descriptive Statistics Includes graphs, tables, tables and calculations

of mean median, mode and correlation

Task: Read pages 335-343- What are correlations and how is the strength

of a correlation measured?- What are distributions and what do they tell us

about the data?- Define central tendency and its measures:

mean, median, mode, standard deviation

Central TendencyA measure of central tendency is a number

that describes a typical score around which other scores fall

It is a measure of the middle, or average of a data set

MeanIs the average, the sum of all the number

divided by the total number of numbers

EG:- 4, 5, 6, 7, 2, 1, 10 - The sum of all these is 35- Divided by 7 = 5- Mean = 5

MedianIs the number that falls in the middle of the data setTo get the median you arrange the data from lowest

to highest value, the value in the middle is the medianEG:- 78, 95, 97, 101, 110, 127, 129- 101 is the median as there are 3 values above and 3

below it - If there are even numbers, eg:- 78, 95, 97, 101, 110, 127, 129, 131- Then you would add 101 and 110 and divide by 2 to

get the average

ModeThe mode is the most frequently occurring

value

EG:- 1, 3, 4, 4, 6, 4, 7, 8, 7, 1, 4- The mode is 4

VariabilityRefers to how spread out the scores (data)

areRange: is the difference between the highest

and lowest score – calculated by subtracting the lowest value from the highest value

Standard deviation: describes how much a score differs from the mean

CorrelationCorrelation allows us to identify the

relationship between two variables – it indicates the extent to which two variables are related, but NOT that one causes the other

The strength of a correlation is measured by a correlation coefficient which is a score between

-1 and +1The closer to +1 or -1 the score is the greater

the strength of the correlation (relationship)

No CorrelationThere is no relationship between the x and y axis

Positive Correlation

As one variable

increases the other

variable increases

Negative CorrelationWhen one variable

increases the other

variable decreases

Distribution When graphed, the distribution of data often shows a pattern:

Normal distribution: most scores fall in the middle

Positively skewed distribution: there are lots of low scores and few high scores

Negatively skewed distribution: there are lots of high scores and few low scores

Inferential Statistics Allow us to draw conclusions, and to

generalise findings about samples to the broader research population

Task: Read pages 344-345- What is a conclusion?- What are generalisations? - What does statistical significance refer to and

how is it measured?

P- value P = probability, which refers to the likelihood of an

event occurring

Statistical significance (which is measured using the p-value), gives an estimate of how often results might have occurred through chance alone

The results of this significance test are stated as a probability, known as a p-value

The scale of the p-value ranges from 0-1

P-value An result that could have occurred through

chance 5 times or less out of 100 (5%) is considered significant

- P < 0.05 In some trials (mainly involving drugs) the p-

value needs to be much lower eg) p < 0.01 or less

- Look at summary of p-values on page 345 of text

Conclusions and GeneralisationsA conclusion is a decision or judgement about

what the results from the research mean eg) hypothesis supported or not, extraneous or confounding variables

A generalisation is a decision or judgement about how widely the findings of the study can be applied – especially to other members of the population – sampling techniques must be considered, as well as extraneous and confounding variables

Ethics in psychological researchDefine the following:- The role of the researcher- Participant’s rights- Confidentiality- Voluntary participation - Informed consent - Withdrawal rights- Deception- Debriefing- Professional conduct