presentation of quantitative data

• ways to present data


  • Summary Tables: descriptive stats
  • Scattergrams: correlations 
  • Bar Graphs: discrete data in categories (nominal)
  • Histograms: continuous data (interval)
  • Normal & Skewed Distributions: distributions (extreme values)
• summary tables

  Results tables summarise the main findings of the data: they present raw, unprocessed scores (ones that haven’t been subject to a statistical analysis) from research studies, or the descriptive stats (mean, range etc). 

  Presenting data in this way will allow the reader to easily compare the most important values, without needed to interpret the data.

  In your exams, you will most likely get asked questions that ask you what the data in a summary table is revealing about mean average and standard deviation/range.
• correlational study/scattergrams 


  correlational studies involve measuring the strength and direction of relationships between co-variables.

  They are not referred to as the IV and DV because the study is investigating the relationship between them,

  A scattergram/graph shows the correlation between two sets of data by plotting points to represent each pair of scores: it indicates the degree and direction of the correlation between the co-variables, one indicated on the Y axis and the other on the X axis (it doesn’t matter which).
• positive/weak positive correlation

  occurs where one co-variable increases as the other co-variable increases. Example- ice cream sales increase as the temperature increases. 
• negative/weak negative correlation

  is where one co-variable increases while another co-variable decreases. Example- raincoat sales decrease as sunny weather increases.
• zero correlation


  is where there is no distinct relationship shown between the two variables. The individual participant marks randomly appear on the scattergram.
• bar graph/chart


  show data in the form of categories to be compared
• histograms

  Histograms and bar charts are similar, but the main difference is that histograms are used for continuous data, such as test scores.

  The continuous scores are placed along the x-axis, the frequency of these scores are shown on the y-axis (vertical).

  There are no spaces between the bars since the data are continuous and the column width for each value on the x-axis should be same width per equal category interval.
• normal distribution


  There are several ways the data can be checked to see if it is normally distributed.

  1. Examine visually- look at the data to see if most scores are clustered around the mean.
  2. Calculate the measures of central tendency- calculate mean, mode, and median to see if they are similar.
  3. Plot the frequency of distribution.
• skewed distribution 


  Not all distributions form a balanced symmetrical pattern, some data sets may produce skewed distributions: this means that the distributions appear to lean to one side or the other due to extreme values/scores in a dataset effecting the mean (this means it is dragged across the bell curve. These are referred to as positive and negative skews.
• positive skew


  where most of the distribution is concentrated towards the left of the graph, resulting in a long tail on the right. Imagine a very difficult test in which most people got low marks with only a handful of students at the higher end: this would cause a positive skew.
• negative skew


  where most of the distribution is concentrated towards the right of the graph, resulting in a long tail on the left. A very easy test would produce a distribution where the bulk of scores are concentrated to the right.