Showing posts with label variable. Show all posts
Showing posts with label variable. Show all posts

Quantitative variable

Quantitative variable

·         Quantitative or numerical data arise when the observations are frequencies or measurements.

·         The data are said to be discrete if the measurements are integers (e.g. number of  employees of a company, number of incorrect answers on a test, number of participants in a program…)

·         The data are said to be continuous if the measurements can take on any value, usually within some range (e.g. weight).   Age and income are continuous quantitative variables. For continuous variables, arithmetic operations such as differences and averages make sense.

Analysis can take almost any form:

Þ      Create groups or categories and generate frequency tables.

Þ      All descriptive statistics can be applied.

Þ      Effective graphs include: Histograms, Stem-and-Leaf plots, Dot Plots, Box plots, and XY Scatter Plots (2 variables).

·         Some quantitative variables can be treated only as ranks; they have a natural order, but  these values are not strictly measured.  Examples are:  1) age group (taking the values child, teen, adult, senior), and 2) Likert Scale data (responses such as strongly agree, agree, neutral, disagree, strongly disagree).  For these variables, the distinction between adjacent points on the scale is not necessarily the same, and the ratio of values is not meaningful.

Analyze using:

Þ      Frequency tables

Þ      Mode, Median, Quartiles

Qualitative variable

Qualitative variable

·         This data describes the quality of something in a non-numerical format.

·         Counts can be applied to qualitative data, but you cannot order or measure this type of variable. Examples are gender, marital status, geographical region of an organization, job title….

·         Qualitative data is usually treated as Categorical variable.

With categorical data, the observations can be sorted according into non-overlapping categories or by characteristics. For example, shirts can be sorted according to color; the characteristic 'color' can have non-overlapping categories: white, black, red, etc.  People can be sorted by gender with categories male and female. Categories should be chosen carefully since a bad choice can prejudice the outcome. Every value of a data set should belong to one and only one category.

·         Analyze qualitative data  using:

1      Frequency tables

2      Modes - most frequently occurring

         3      Graphs:  Bar Charts and Pie Charts