Let's Review

Remember, a variable is a measurable characteristic of some event, object, or person that can take on different values or amounts from one situation to the next. Variables can be either quan-titative (can be counted or expressed numerically) or qual-itative (not numerical and subject to interpretation).

 

There are four types of data, or scales of measurement: Nominal, Ordinal, Interval, and Ratio.

 

Once raw data is collected through research; actual scores, or values on a questionnaire, survey, or assessment, we must decide how best to organize this distribution. This can first be done through a series of data tables. Two of the most common examples are frequency distributions and grouped frequency distribution.

 

Graphing data is the first and often most important step in data analysis. So now that we have our data set up in frequency tables, we can move forward to illustrating this data in graphs. Four important types of graphs are the frequency polygon, the histogram, the bar graph, and the scatterplot.

 

A measure of central tendency is a calculated value that is typical of the entire distribution. There are three primary measures of central tendency: mean, median, and mode.

 

The shape of the distribution is another important characteristic of a distribution of data. Some important ideas include:

 

Without looking back through the lesson, can you envision the shape of the distributions listed above?

 

Variability is a measure of dispersion; how disperse a group of scores are from each other; the degree to which individual scores in a data set differ from one another. The three measures of variability are the range, standard deviation, and variance.