NUR 200: EXAM 1 UPDATED ACTUAL Questions and CORRECT Answers
Levels of measurement from lowest to highest nominal -> ordinal -> interval -> ratio
The lowest level of measurement in which values are
nominal
names for categories, but not real numbers
What can nominal data also be called? categorical data
- data categories must be exclusive (will fit in only one
category)
- data categories must be exhaustive (will fit into at least
Nominal characteristics
one category)
Ex. Gender, race/ethnicity, colors, etc.
Ordinal values can be ranked but not measured
- categories must be exhaustive and mutually exclusive
- each category must be recognized as higher or lower or
better or worse than another category
Ordinal characteristics - 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
used to analyze nominal and ordinal level variables to ex-
amine relationships among variables, and determine dif-
Nonparametric or Distribution-free analysis techniques
ferences between groups in distribution-free or non-nor-
mally distributed samples
- values need not be normally distributed (not a perfect
Nonparametric or Distribution-free analysis techniques
bell curve)
assumptions
- 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)
, The distances between intervals of the scale are numeri-
Interval
cally equal, but there is no absolute zero
- There is no absolute zero
- The score of zero does not indicate that the property
being measured is absent
Interval characteristic
- continuous variables
Ex. temperature (Celsius)
The highest form of measurement, numerically equal in-
Ratio
tervals on the scale, with the presence of an absolute zero
- numerically equal intervals of a scale
- There is an absolute zero
ratio characteristics - continuous variables
Ex. Pulse, blood pressure, age
Analysis techniques are conducted on interval and ratio
levels of data to describe variables, examine relationships
parametric statistics
among variables, and determine the difference between
groups
- distribution of scores is expected to be a normal distrib-
ution or approximately normal
- variables are continuous, measured at the interval or
parametric statistics assumptions
ratio level
- data can be treated as though they were obtained from
a random sample
results that align with the outcomes predicted by the re-
Significant Results
searcher and their team
by * or p values that are less than or equal to alpha, which
How are significant results identified
is 0.05!! (d0.05)
population
, A particular group of elements or individuals who are be-
ing studied (ex., people with diabetes, central catheters,
or overweight children)
the whole group you want the study to describe, deter-
mined by sampling criteria
target population
the available potential study participants within the target
population
accessible population
focus of a particular study
sample
participants, subjects, objects, or events that might be
included in the sample for a study
study elements
The extent to which research findings can be applied to
Generalization
the target population
The number of individuals or elements that participate in
sample size
a study
Adequate sample size is essential for identifying signif-
What is the importance of an adequate sample size? icant relationships among variables or differences be-
tween groups
Power within statistical research
Levels of measurement from lowest to highest nominal -> ordinal -> interval -> ratio
The lowest level of measurement in which values are
nominal
names for categories, but not real numbers
What can nominal data also be called? categorical data
- data categories must be exclusive (will fit in only one
category)
- data categories must be exhaustive (will fit into at least
Nominal characteristics
one category)
Ex. Gender, race/ethnicity, colors, etc.
Ordinal values can be ranked but not measured
- categories must be exhaustive and mutually exclusive
- each category must be recognized as higher or lower or
better or worse than another category
Ordinal characteristics - 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
used to analyze nominal and ordinal level variables to ex-
amine relationships among variables, and determine dif-
Nonparametric or Distribution-free analysis techniques
ferences between groups in distribution-free or non-nor-
mally distributed samples
- values need not be normally distributed (not a perfect
Nonparametric or Distribution-free analysis techniques
bell curve)
assumptions
- 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)
, The distances between intervals of the scale are numeri-
Interval
cally equal, but there is no absolute zero
- There is no absolute zero
- The score of zero does not indicate that the property
being measured is absent
Interval characteristic
- continuous variables
Ex. temperature (Celsius)
The highest form of measurement, numerically equal in-
Ratio
tervals on the scale, with the presence of an absolute zero
- numerically equal intervals of a scale
- There is an absolute zero
ratio characteristics - continuous variables
Ex. Pulse, blood pressure, age
Analysis techniques are conducted on interval and ratio
levels of data to describe variables, examine relationships
parametric statistics
among variables, and determine the difference between
groups
- distribution of scores is expected to be a normal distrib-
ution or approximately normal
- variables are continuous, measured at the interval or
parametric statistics assumptions
ratio level
- data can be treated as though they were obtained from
a random sample
results that align with the outcomes predicted by the re-
Significant Results
searcher and their team
by * or p values that are less than or equal to alpha, which
How are significant results identified
is 0.05!! (d0.05)
population
, A particular group of elements or individuals who are be-
ing studied (ex., people with diabetes, central catheters,
or overweight children)
the whole group you want the study to describe, deter-
mined by sampling criteria
target population
the available potential study participants within the target
population
accessible population
focus of a particular study
sample
participants, subjects, objects, or events that might be
included in the sample for a study
study elements
The extent to which research findings can be applied to
Generalization
the target population
The number of individuals or elements that participate in
sample size
a study
Adequate sample size is essential for identifying signif-
What is the importance of an adequate sample size? icant relationships among variables or differences be-
tween groups
Power within statistical research
