Is there a relationship between Beauty and Age? Correlational test Is there a relationship between Beauty and Age?

The hypothesis Some people say that older people get more beautiful with age? Carol Vorderman aged 49 Carol Vorderman aged 25

Is there a Correlation? 20 years 30 years 40 years 50 years 60 years

Rate Each Celebrity on a scale of 1-5 for attractiveness. Participant Age Rating

A scattergraph that displays a correlation between age (measured in years) and rating of attractiveness (measured on a 5 pt rating scale) Rating of attractiveness using a 5 pt rating scale. (1 not attractive-5 attractive) Age measured in years.

Description of Correlation: A correlation is a relationship between two variables measured on a scale. It is not suggested that one variable causes the other, just that they are related. In a correlation it is not a difference between two variables (the IV and the DV) that is looked for but a relationship between them. Correlational designs are often used when it is inappropriate or ethically unacceptable to use an experimental design. A correlation design is not really a research method; it is a tool of analysis as it makes use of statistics to test this relationship between variables. Correlational designs are not repeated measures, independent groups or matched pairs. They involve the same participants providing data for two measures, but they are not really repeated measures because correlations are a different sort of test. Correlations have two variables but both are important and there is not an independent and a dependant variable – both are measures.

Using the sources available to you find out what the following four slides are in relation to correlations.

Types of Correlations

Strength of a Correlation: How do you tell?

Correlation Coefficient: What is it? What can we tell from it?

Scattergraphs: What are they?

Positive, Negative No Correlation: What do they look like on a scattergraph?

What conclusions can we draw from our original scattergraph?

A scattergraph that displays a correlation between age (measured in years) and rating of attractiveness (measured on a 5 pt rating scale) Rating of attractiveness using a 5 pt rating scale. (1 not attractive-5 attractive) Age measured in years.

Display a Correlation

Choose your x and y carefully Choose your x and y carefully. Scientists like to say that the “independent” variable goes on the x-axis (the bottom, horizontal one) and the “dependent” variable goes on the y-axis (the left side, vertical one). Why have I put IV and DV in brackets?

What conclusions can we draw from the scattergraph?

Strengths and Weaknesses

Hypotheses for correlations Hypotheses for correlations are slightly different as they predict a relationship between 2 variables rather than a difference caused by an IV. For example: There will be a significant positive correlation between the size of a child’s feet and their score on a maths test. This is a one-tailed hypothesis as the hypothesis predicts the expected direction of results. A two-tailed hypothesis which does not predict the expected direction would be ‘there will be a significant correlation between the size of a child’s feet and their score on a maths test. There also needs to be a null hypothesis: There will be no significant correlation between the size of a child’s feet and their score on a maths test.

Correlation – Hypotheses There will be a significant [direction] correlation between [variable 1] (measured by [something]) and [variable 2] (measured by [something]) There will be a significant correlation between [variable 1] (measured by [something]) and [variable 2] (measured by [something]) 1Tailed No Direction 2Tailed

Correlation – Hypotheses There will be no significant correlation between [variable 1] (measured by [something]) and [variable 2] (measured by [something]) Null

Write a One Tailed, two Tailed and Null Hypothesis