NUR 200 Exam
Questions Verified and
Provided with A+
Graded Answers |
Latest Updated 2026
,Levels of measurement from lowest to highest
nominal -> ordinal -> interval -> ratio
nominal
The lowest level of measurement in which values are names for categories, but not real
numbers
What can nominal data also be called?
categorical data
Nominal characteristics
- data categories must be exclusive (will fit in only one category)
- data categories must be exhaustive (will fit into at least one category)
Ex. Gender, race/ethnicity, colors, etc.
Ordinal
values can be ranked but not measured
, Ordinal characteristics
- categories must be exhaustive and mutually exclusive
- each category must be recognized as higher or lower or better or worse than another
category
- You do not know exactly how much higher or lower one subject's score is in relation to
another subject's score
Ex. pain levels, satisfaction levels
Nonparametric or Distribution-free analysis techniques
used to analyze nominal and ordinal level variables to examine relationships among
variables, and determine differences between groups in distribution-free or non-
normally distributed samples
Nonparametric or Distribution-free analysis techniques assumptions
- values need not be normally distributed (not a perfect bell curve)
- The level of measurement is usually ordinal or nominal
measurement of central tendency
mean, median, mode
Measurement of central tendency for NOMINAL level data
mode (most frequently occurring in a data set)
Measurement of central tendency for ORDINAL level data
median (middle value in a data set)
Questions Verified and
Provided with A+
Graded Answers |
Latest Updated 2026
,Levels of measurement from lowest to highest
nominal -> ordinal -> interval -> ratio
nominal
The lowest level of measurement in which values are names for categories, but not real
numbers
What can nominal data also be called?
categorical data
Nominal characteristics
- data categories must be exclusive (will fit in only one category)
- data categories must be exhaustive (will fit into at least one category)
Ex. Gender, race/ethnicity, colors, etc.
Ordinal
values can be ranked but not measured
, Ordinal characteristics
- categories must be exhaustive and mutually exclusive
- each category must be recognized as higher or lower or better or worse than another
category
- You do not know exactly how much higher or lower one subject's score is in relation to
another subject's score
Ex. pain levels, satisfaction levels
Nonparametric or Distribution-free analysis techniques
used to analyze nominal and ordinal level variables to examine relationships among
variables, and determine differences between groups in distribution-free or non-
normally distributed samples
Nonparametric or Distribution-free analysis techniques assumptions
- values need not be normally distributed (not a perfect bell curve)
- The level of measurement is usually ordinal or nominal
measurement of central tendency
mean, median, mode
Measurement of central tendency for NOMINAL level data
mode (most frequently occurring in a data set)
Measurement of central tendency for ORDINAL level data
median (middle value in a data set)
