correlations

• Outline the correlational method

  Correlational research examines the relationship between two continuous co-variables, such as age and shoe size. Data is collected through observations or questionnaires and plotted on a scattergram, where the x and y axes represent each co-variable. The graph is then analyzed to determine the direction and strength of the relationship.
• What is the difference between correlations and experiments?

  • Correlations: Measure whether there is a relationship, do not involve manipulation of a variable, and cannot establish cause and effect.
  • Experiments: Measure whether there is a difference, involve manipulating an independent variable, and can establish cause and effect.
• What is a strength of correlational research?

  🟢 Correlations allow researchers to study relationships between variables that cannot be manipulated for ethical or practical reasons, such as stress and depression.
• What is a limitation of correlational research?

  🔴 Correlations only show relationships, not cause and effect, as a third variable may influence both (e.g., temperature affecting ice cream sales and shark attacks).
• Positive correlation:

  As one co-variable increases, so too does the other co-variable. This leads to an upward trend in a scattergram.

  
• Negative correlation

  As one co-variable increases, the other co-variable decreases. This leads to a downward trend in a scattergram.

  
• Zero correlation

  There is no relationship between the two co-variables. This leads to no clear trend in a scattergram.

  
• How do you find the Strength of a correlation

  By imagining a line of best fit on a scattergram, you can see how strong the relationship is: if the data fit closely around the line, it is a strong correlation. The more that the data points deviate from the line of best fit, the weaker the correlation.

  The presence of strong outliers can affect the strength of the correlation. For example, a scattergram

  that shows a strong correlation may actually be weak if there are strong outliers. Outliers are particularly influential when a small sample is used.

  
• Explain the purpose of correlation coefficients.

  A correlation coefficient is a number between -1 and 1 that shows the strength and direction of a correlation. A negative number means a negative correlation, 0 means no correlation, and a positive number means a positive correlation. The closer the number is to -1 or 1, the stronger the correlation (e.g., -0.89 is a strong negative correlation, while 0.48 is a medium positive correlation).