Handling Data

• Primary data is original data that has been collected by the researcher.

  Strengths

  • The researcher has full control of the data gathered

  • The collection is designed to fit the aims of the study

  Weaknesses

  • Lengthy process in terms of planning, creating resources etc.

  • expensive
• Secondary data is data that has been collected by someone else, other than the researcher and already exists before the research begins.

  Strengths

  • Simpler so takes less time to access

  • Cheaper

  • Data has normally been tested so we know if the results are significant

  Weaknesses

  • May not fit the aims of the study completely

  • There may be a lot of variation in the quality and accuracy of the data.
• Measures of central tendency are ‘averages’ which give us information about the most typical values in a set of data.

  Mean

  Strengths

  • representative of the data as a whole

  Weaknesses

  • easily distorted by extreme scores (ones that vary significantly from the average)

  Median

  Strengths

  • Not affected by extreme scores

  • Easy to calculate

  Weaknesses

  • Less sensitive as not all scores are included in the final calculation

  Mode

  Strengths

  • Easy to calculate

  • Sometimes the only method that can be used

  Weaknesses

  • Crude measure so not usually representative of the total scores
• Measures of dispersion are the spread of scores (how much they differ from one another).

  Range

  Strengths

  • easy to calculate

  Weaknesses

  • affected by extreme scores

  • doesn’t tell you if most of the scores are grouped around a mean or spread out evenly

  Standard deviation

  • average distance of each score above or below the mean.

  • shows the amount of variation in a data set.

  Strengths

  • More precise as it takes all the values into account

  Weaknesses

  • It may hide extreme values which can distort it

  • More difficult to calculate
• Scattergrams

  • These are used to represent correlational data.

  Bar chart

  • The height of each bar represents the frequency of each item.

  Histogram

  • Similar to a bar chart but the bars touch each other to show the data is continuous rather than discrete.
• If the distribution is not symmetrical we call this skewed distribution. This can be a positively skewed distribution where most of the distribution is concentrated towards the left of the graph, whilst a negatively skewed distribution is concentrated towards the right of the graph.
• Quantitative data can be divided into different levels of measurement.

  Nominal data

  • Data which is in categories.

  • It is discrete data because the data can only go in one category.

  Ordinal data

  • Data which is in order in some way.

  • It tells us the position of an item in the group.

  • It does not have equal intervals between it.

  Interval data

  • Data based on numerical scales.

  • It has equal, precisely defined intervals between each set of data.