introduction Flashcards
(47 cards)
a variable is
a set of characteristics that describe ana spect of participants in research e.g. gender, blood pressure
two main types of variable
categorical
quantitative
categorical data
usually facts rather than numerical
- participants are classified into categories
two types of categorical
nominal and ordinal
nominal variables
unordered labelled characteristics
- binary variables (just two categories)
- observations can be assigned a code in the form of a number where the numbers are simply labels
- can count but not order
example of a nominal variable
blood group: A, B, AB, O
ordinal variable
- small set of ordered categories
- categories might be labels or numbers
- obs can be ranked
e. g. house numbers and swimming level
example of ordinal variable
disease severity: none, mild, mod, sevre
categorical data are recorded as
numbers which represent specific categories e.g.
Gender: (1) male, (2) female
quantitative variables
valies have quantitative meaning. The higher the number the more there is of the concept
e.g. the tiger number for age means you’re older
quantitative variables are also known as
continuous
distribution def
refers to the diff values that occur and the frequency with which they occur for a given variable
categorical data can be described using
- frequency tables
- bar charts
how to describe quanitiaitve data
average. variation, symmetry
average
what value characterises the middle of distribution
variation
speed, dispersion, how far apart the values are from each other
symmetry
for each person that has a score below the average is there a corresponding person with the score the same distance about the avergae
types of average
mean, median mode
mean
sum of soccer divided by the number of scores
median
rank the scores in order and the median is the value that divides the data in 2
mode
the most frequently occurring score
when is mode not useful
in quantitive data-there may be more than one mode, each value in study might appear only once- mode could be low/high
disadvantages of the range
- sensitive to unusually extreme values (outliers)
- dependent on sample size- as sample size gets larger the range cannot get smaller, but it can get larger.
symmetry of quantitative variable
o Examples of symmetrically distributed date:
• 1,2,3,4,5
• -2,0,2
• 3,3,10,17,17