Research Methods Pt4 (Quantitatuve Data - Correlational Analysis)

Research Methods Pt4 (Quantitatuve Data - Correlational Analysis)

1. Analysis of Quantitative Data

   • Fractions
   • Percentages
   • Ratios
   • Estimate results
   • Significant figures
   • Order of magnitude
     • When dealing with very large number it may be easier to give two significant figures and say how many zeros there are

2. Descriptive Statistics

   • Used to summarise large amounts of data into one typical or average value for ease of comparison.
   • Mean: The sum of scores divided by the number of scores
   • Median: The central score in a list of rank-ordered scores
   • Mode: The most frequently occurring value in a set of scores
   • Measures of Dispersion
     • These are used to indicate the amount of variability, or spread in a sample of scores.
     • Range: The difference between the highest and lowest scores in a sample of scores.
   • Standard deviation
     • The amount by which each score in a sample differs (deviates) from the sample mean and then finding the amount by which the scores typically differ from the mean. Around 68% of the scores in a sample will fall with ±1SD from the mean.

3. Display of Quantitative Data

   • Tables and graphs should be simple so they can be read clearly.
     • They should clearly show the findings from a study
     • There should be a short but informative table (eg. A line graph to show…)
     • Table columns and rows should be labelled and in a graph both axes should be clearly labelled. The horizontal x-axis (across) is usually the independent variable and the vertical y-axis the frequency.
     • Always use squared paper, a pencil and rules for drawing graphs.
   • Tables
   • Bar Charts
   • Histograms
   • Line graph
   • Scattergraphs

4. Data Distributions

   • The normal distribution of data is a classic bell-shaped curve it has no skew. However in some populations scores data is not distributed equally around the mean – this is a skewed distribution. Data is "skewed", meaning it tends to have a long tail on one side or the other.
     • A Normal Distribution is not skewed. It is perfectly symmetrical. And the Mean is exactly at the peak.
     • Why is it called negative skew? Because the long "tail" is on the negative side of the peak. People sometimes say it is "skewed to the left" (the long tail is on the left hand side) The mean is also on the left of the peak.
     • And positive skew is when the long tail is on the positive side of the peak, and some people say it is "skewed to the right". The mean is on the right of the peak value.

5. Correlational Analysis

   • Correlations measure the relationship between two variables. The variables are each referred to as co-variables and are believed to influence each other in some way (e.g. heights and weight). Each co-variable must be OPERATIONALISED.
   • It is a technique of data analysis that is especially useful for detecting patterns in data. To apply correlations, it is necessary to collect scores that are paired. We may even to conduct another research methods to gain our data for a study.
   • We use the data to establish whether:
     • * High values on one variable are associated with high values on another variable (a positive correlation)
     • * High values on one variable are associated with low values on another (a negative correlation)
     • * Whether the values on each variable are completed unrelated (no correlation)