Describing data using statistics

Structured analysis of the collected data can actually act wonders for a research document. The exploratory data analysis approach emphasised the use of diagrams to understand your data. Gathering professional Dissertation Statistics Consultation has proved to be extremely beneficial for the success of the dissertation writers residing in different corners of the world. Descriptive statistics enables the research scholars to describe variables numerically. Your research question(s) and objectives, although limited by the type of data should guide your choice of statistics.

Statistics is being used for describing a variable as per two important parameters including:

  • The central tendency
  • The dispersion

When describing data for both samples and populations quantitatively, it is usual to provide some general impression of values that could be seen as common, middling or average. These are termed measures of central tendency and are discussed in virtually all statistics textbooks. The three effective ways of measuring the central tendency most used in business research are the:

  • value that occurs most frequently(mode);
  • middle value or mid-point after the data have been ranked(median);
  • value, often known as the average, that includes all data values in its calculation (mean).

Grabbing professional SPSS help can ensure the effective analysis of data that has been collected for the research document. As well as describing the central tendency for a variable, it is quite important to describe how the data values are dispersed around the central tendency. Two of the most frequently used ways of describing the dispersion are the:

  • difference within the middle 50 per cent of values(inter-quartile range);
  • extent to which values differ from the mean(standard deviation).

A more frequently used statistic is the inter-quartile range. The range can be further divided into four equal sections called quartiles. The lower quartile is the value below which a quarter of your data values will fail; the upper quartile is the value below which a quarter of your data values will fall. You can also calculate the range for other fractions of a variable’s distribution. One alternative is to divide your distribution using percentiles. These split your distribution into 100 equal parts. Obviously, the lower quartile is the 25th percentile and the upper quartile the 75th percentile.