NSE 212 CH.1-7 QUESTIONS WITH COMPLETE
SOLUTIONS
The four levels of measurement
1. Nominal
2. Ordinal
3. Interval
4. Ratio
1. Nominal
◦Dichotomous (two true values) (e.g. notion of pregnancy cause
your either pregnant or not pregant no such thing as a little bit
pregnant. One or the other no middle) or Categorical (more
than two true values) variables (martial status: divrorced,
married,etc)
- Examples:
True=1; False-2
Type of hospital: acute care=1; long term care=2; pediatric=3;
rehabilitation=4
The difference between Ordinal and Nominal is the Distance
between category
2. Ordinal Level
- Categorical, mutually exclusive categories- variables given a
number
- Numbers assigned are not arbitrary and have some meaning
,- Ranks variables based on their relative standing on an attribute
(e.g.-members of a higher ranked category have more of an
attribute than members of a lower ranked category)
Example:
- Stages of cancer diagnosis (Stage 0, I, II, III). The client who
has stage II is higher than stage 0, but we cannot say they are
twice as severe as stage 0
- Undergraduate, Masters, PhD
The difference between Ordinal and Nominal is the Distance
between category
3. Interval Level
- Variables are continuous and rank ordered
- Equal intervals between numbers i.e. temperature on the
Centigrade scale: difference between 20 and 30 and between 30
and 40 is presumed to be equivalent
- Zero point is arbitrarily assigned and not absolute (e.g., zero
on the Centigrade scale does not represent the absence of
temperature)
- 0 doesn't represent the Absence of the attribute
Many psychological/psychosocial variables and tests (e.g.,
differences between test scores represent equal intervals but a
score of zero does not indicate an absence of knowledge
**Likert scale ratings (e.g., somewhat satisfied, very satisfied,
extremely satisfied) are treated numerically and responses are at
the interval level of measurement
,The difference between Interval and Ratio is how 0 is
considered
4. Ratio Level
- Highest level of measurement
- Variables are Continuous and rank ordered and have
an Absolute, Meaningful Zero and, therefore, provide the
absolute magnitude of the attribute
- Distances between the values are numerically equal
- An absolute zero means absence of any of the attribute being
measured
◦Many physical measures are ratio measures with a real zero
E.g., weight, height, Hgb levels, pulse rate, blood pressure, age
in years, time
The difference between Interval and Ratio is how 0 is
considered
A set of data can be completely described in terms of:
1. Measures of central tendency
2. Measures of variability
3. Frequency distributions
4. Shapes of distributions
1. Measures of central tendency
mean, median, mode
formula to compute mean
, - should be little "n"
^formula for mean
^little "n" is sample size
^"N" population size
Which measure of central tendency would best represent the
population mean
Mean
mean is the
middle point in a set of values
- Mean is VERY sensitive to extreme scores that can skew or
distort findings.
median is the
middle point in a set of cases
- Because the median cares about the number of cases, extreme
scores (i.e. outliers) do NOT impact it
When is it best to use the median as the best measure of central
tendency
when you have extreme outliers or extreme scores
When is it best to use mean?
- Interval or racial level data
- When you don't have extreme scores
When is it best to use mode
nominal or ordinal data
2. measures of variability
SOLUTIONS
The four levels of measurement
1. Nominal
2. Ordinal
3. Interval
4. Ratio
1. Nominal
◦Dichotomous (two true values) (e.g. notion of pregnancy cause
your either pregnant or not pregant no such thing as a little bit
pregnant. One or the other no middle) or Categorical (more
than two true values) variables (martial status: divrorced,
married,etc)
- Examples:
True=1; False-2
Type of hospital: acute care=1; long term care=2; pediatric=3;
rehabilitation=4
The difference between Ordinal and Nominal is the Distance
between category
2. Ordinal Level
- Categorical, mutually exclusive categories- variables given a
number
- Numbers assigned are not arbitrary and have some meaning
,- Ranks variables based on their relative standing on an attribute
(e.g.-members of a higher ranked category have more of an
attribute than members of a lower ranked category)
Example:
- Stages of cancer diagnosis (Stage 0, I, II, III). The client who
has stage II is higher than stage 0, but we cannot say they are
twice as severe as stage 0
- Undergraduate, Masters, PhD
The difference between Ordinal and Nominal is the Distance
between category
3. Interval Level
- Variables are continuous and rank ordered
- Equal intervals between numbers i.e. temperature on the
Centigrade scale: difference between 20 and 30 and between 30
and 40 is presumed to be equivalent
- Zero point is arbitrarily assigned and not absolute (e.g., zero
on the Centigrade scale does not represent the absence of
temperature)
- 0 doesn't represent the Absence of the attribute
Many psychological/psychosocial variables and tests (e.g.,
differences between test scores represent equal intervals but a
score of zero does not indicate an absence of knowledge
**Likert scale ratings (e.g., somewhat satisfied, very satisfied,
extremely satisfied) are treated numerically and responses are at
the interval level of measurement
,The difference between Interval and Ratio is how 0 is
considered
4. Ratio Level
- Highest level of measurement
- Variables are Continuous and rank ordered and have
an Absolute, Meaningful Zero and, therefore, provide the
absolute magnitude of the attribute
- Distances between the values are numerically equal
- An absolute zero means absence of any of the attribute being
measured
◦Many physical measures are ratio measures with a real zero
E.g., weight, height, Hgb levels, pulse rate, blood pressure, age
in years, time
The difference between Interval and Ratio is how 0 is
considered
A set of data can be completely described in terms of:
1. Measures of central tendency
2. Measures of variability
3. Frequency distributions
4. Shapes of distributions
1. Measures of central tendency
mean, median, mode
formula to compute mean
, - should be little "n"
^formula for mean
^little "n" is sample size
^"N" population size
Which measure of central tendency would best represent the
population mean
Mean
mean is the
middle point in a set of values
- Mean is VERY sensitive to extreme scores that can skew or
distort findings.
median is the
middle point in a set of cases
- Because the median cares about the number of cases, extreme
scores (i.e. outliers) do NOT impact it
When is it best to use the median as the best measure of central
tendency
when you have extreme outliers or extreme scores
When is it best to use mean?
- Interval or racial level data
- When you don't have extreme scores
When is it best to use mode
nominal or ordinal data
2. measures of variability
