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Need discussion response to 2 peers. Initial discussion question: After reading chapters 15,17 answer the next question.Which level of measurement would you prefer to utilize for quantitative research? Defend your answer.
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In nursing research, the rigorous analysis of data is instrumental in deriving accurate conclusions and
formulating evidence-based interventions. The ratio level of measurement, recognized as the most
precise among measurement scales, offers unparalleled advantages in this domain. Ratio scales provide
all the properties of nominal, ordinal, and interval scales, with the added benefit of a true zero point.
This zero point signifies the absence of the attribute being measured, making mathematical operations
like multiplication and division meaningful (Malhotra, 2007). A common example in nursing would be
the measurement of a patient’s weight, where zero signifies the complete absence of weight.
The distinct strength of ratio scales lies in their ability to capture exact quantities and facilitate a broad
range of statistical analyses. In nursing research, this precision is crucial when monitoring variables such
as medication dosages, blood pressure readings, or fluid intake and output. The presence of a true zero
allows for the calculation of ratios, providing deeper insights into relative differences. For instance,
understanding that one patient’s blood pressure is twice as high as another’s can be vital in determining
the urgency and nature of interventions (Burns & Grove, 2005).
However, it’s essential to acknowledge that not all variables in nursing research can be measured at the
ratio level. Some attributes, especially subjective ones like pain perception or mood, might not have a
true zero or may be more appropriately measured using other scales. Nevertheless, when the research
objective demands precise quantitative assessments and the ability to calculate proportional
differences, the ratio level of measurement stands out as the gold standard in nursing research.
References:
Malhotra, N. K. (2007). Marketing Research: An Applied Orientation (5th ed.). Prentice Hall.
Burns, N., & Grove, S. K. (2005). The practice of nursing research: Conduct, critique, & utilization (5th
ed.). Elsevier Saunders.
Levels of measurement in quantitative research are referred to as the different ways in which a variable
can be calculated. There are some limitations to consider when selecting what level of measurement to
use for a study, as some of them do not have the capacity to compute outside certain barriers. My
preferred level of measurement would be ratio measurement, due to the fact that it provides the most
precise answer.
According to an article by the Surveysparrow, It tells you about the order and the equal distance
between two adjacent values. The zero in the ratio scale also has a lot of relevance. It tells the difference
between “how much” (Williams, 2022). Ratio measurement allows for the estimation of a range
between 2 variables, such as height, weight, and age. It uses a true zero, which aids in the
representation of intervals between variables by not recognizing negative numbers.
Finally, an article by the Research Prospect states since ratio scales have an absolute zero, variables in
this type of data can be added, subtracted, multiplied, or divided (Ingram, 2023). The fact that the
variables used in a ratio measurement can be added, subtracted, or divided provides a more analytical
approach to a study, and thus making it the most detailed measurement level there is. For these
reasons, and many more, it is my level of measurement of choice for conducting a quantitate research.
References:
Williams, K. (2022). Ratio scale: Definition, characteristics & examples. SurveySparrow.
https://surveysparrow.com/blog/ratio-scale/
Ingram, O. (2023). Ratio Data: Definition, examples, and analysis. Research Prospect.
Chapter 15
Measurement and Data Quality
Copyright © 2017 Wolters Kluwer Health | Lippincott Williams & Wilkins
Question #1
❖Tell whether the following statement is true or false:
❖Measurement involves assigning numbers to objects
to represent the amount of an attribute.
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Answer to Question #1
❖True
❖Measurement involves assigning numbers to objects
to represent the amount of an attribute, using a
specified set of rules. Researchers strive to develop
or use measurements whose rules are isomorphic
with reality.
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Measurement
❖The assignment of numbers to represent the
amount of an attribute present in an object or
person, using specific rules
❖Rules are necessary to promote consistency and
interpretability
❖Advantages
o Removes guesswork
o Provides precise information
o Less vague than words
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Theories of Measurement
❖Psychometrics is a branch of psychology concerned
with the theory and methods of psychological
measurement.
o Two theories
▪
Classical test theory (CTT)
▪
Item response theory (IRT)
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Errors of Measurement
❖Obtained score = True score + Error
❖Obtained score: an actual data value for a
participant
❖True score: value that would be obtained for a
hypothetical perfect measure attribute
❖Error of measurement: represents measurement
inaccuracies
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Factors That Contribute to Errors of
Measurement
❖Situational contaminants
❖Transitory personal factors
❖Response-set biases
❖Administration variations
❖Instrument clarity
❖Item sampling
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Major Types of Measures
❖Generic
❖Specific
❖Static
❖Adaptive
❖Reflective scales
❖Formative indexes
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Measurement Taxonomy
❖Four measurement property domains
o Cross-sectional domains
▪
Reliability
▪
Validity
o Longitudinal measurement domains
▪
Reliability of change scores
▪
Responsiveness
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Measurement and Statistics
❖Correlation coefficients
❖Correlation coefficients indicate direction and
magnitude of relationships between variables.
o Pearson’s r
❖Range:
o From −1.00
(perfect negative correlation)
o Through 0.00
(no correlation)
o To +1.00
(perfect positive correlation)
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Reliability #1
❖Consistency—the absence of variation in measuring
a stable attribute for an individual
❖Reliability assessments involve computing a
reliability coefficient
o Most reliability coefficients are based on
correlation coefficients.
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Question #2
❖Tell whether the following statement is true or false:
❖Reliability coefficients usually range from .00 to
1.00, with higher values reflecting less reliability.
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Answer to Question #2
❖False
❖Reliability coefficients usually range from .00 to
1.00, with higher values reflecting greater reliability,
not less reliability.
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Reliability #2
❖Replication approaches
o Test–retest reliability: administration of the
same measure to the same people on two
occasions
o Interrater reliability: measurements by two or
more observers or raters using the same
instrument or measurements by the same
observer or rater on two or more occasions
o Parallel test reliability: measurements of the
same attribute using alternate versions of the
same instrument, with the same people
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Reliability Coefficient (R)
❖Represent the proportion of true variability to
obtained variability:
R=
VT
Vo
❖Should be at least .70; .80 preferable
❖Can be improved by making instrument longer
(adding items)
❖Are lower in homogeneous than in heterogeneous
samples
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Internal Consistency
❖The extent to which all the instrument’s items are
measuring the same attribute
❖Evaluated by administering the instrument on one
occasion
❖Appropriate for most multi-item instruments
❖Most widely used evaluation method is the
coefficient alpha
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Measurement Error
❖Unless a reliability coefficient is 1.0 (virtually never
happens), measurement error is present.
❖Used to estimate the range within which the true
score lies
o Standard error of measurement (SEM)
o Limits of agreement (LOA)
❖Measurement error is routinely estimated for multiitem measures developed with item response theory
(IRT) methods.
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Validity
❖The degree to which an instrument measures what
it is supposed to measure (resilience)
❖Four aspects of validity
o Face validity
o Content validity
o Criterion-related validity
o Construct validity
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Face Validity
❖Refers to whether the instrument looks as though it
is measuring the appropriate construct
❖Based on judgment, no objective criteria for
assessment
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Content Validity
❖The degree to which an instrument has an
appropriate sample of items for the construct being
measured
o Relevance
o Comprehensiveness
o Balance
❖Evaluated by expert evaluation, via the content
validity index (CVI)
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Criterion Validity #1
❖The degree to which the instrument correlates with
an external criterion or “gold standard”
❖Focal measures
o Expense, efficiency, risk and discomfort,
criterion unavailable, and prediction
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Criterion Validity #2
❖Two types of criterion-related validity
❖Predictive validity: the instrument’s ability to
distinguish people whose performance differs on a
future criterion
❖Concurrent validity: the instrument’s ability to
distinguish individuals who differ on a present
criterion
o Specificity, sensitivity
o Predictive values
o Likelihood ratios
o Receiver operating characteristic curve (ROC
curve), area under the curve (AUC)
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Construct Validity
❖Concerned with the questions
❖What is this instrument really measuring?
❖Does it adequately measure the construct of
interest?
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Methods of Assessing Construct Validity
❖Hypothesis-testing validity
❖Convergent validity
❖Known-groups validity
❖Divergent validity (discriminant validity)
❖Multitrait–multimethod matrix method (MTMM)
❖Structural validity
❖Cross-cultural validity
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Reliability of Change Scores
❖Change score: represents the amount of change
between two scores
❖Difference score: the difference between the
randomized groups at posttest
❖Smallest detectable change (SDC): a change in
scores that is beyond measurement error
❖Reliable change index (RCI): assesses the clinical
significance of improvement during a
psychotherapeutic intervention
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Responsiveness
❖The ability of a measure to detect change over time
in a construct that has changed, commensurate with
the amount of change that has occurred
❖Whether a change score is truly capturing a real
change in the construct
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Question #3
❖Tell whether the following statement is true or false:
❖Reliability is the degree to which an instrument
measures what it is supposed to measure.
Copyright © 2021 Wolters Kluwer Health | Lippincott Williams & Wilkins
Answer to Question #3
❖False
❖Validity is the degree to which an instrument
measures what it is supposed to measure. Reliability
is the degree of consistency or accuracy with which
an instrument measures an attribute.
Copyright © 2021 Wolters Kluwer Health | Lippincott Williams & Wilkins
Critiquing Data Quality in Quantitative
Studies
❖Can I trust the data in this study?
❖Are the measurements of key constructs reliable and
valid, and are change scores reliable and
responsive?
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Psychometric Assessment
❖Gather evidence
o Validity
o Reliability
o Other assessment criteria
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Chapter 17
Descriptive Statistics
Copyright © 2017 Wolters Kluwer Health | Lippincott Williams & Wilkins
Question #1
❖Tell whether the following statement is true or false:
❖Nominal measurement is the ranking of objects
based on their relative standing on an attribute.
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Answer to Question #1
❖False
❖Nominal measurement: the classification of
characteristics into mutually exclusive categories
❖Ordinal measurement: the ranking of objects based
on their relative standing on an attribute
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Levels of Measurement
❖Nominal measurement: the classification of
characteristics into mutually exclusive categories
❖Ordinal measurement: the ranking of objects
based on their relative standing on an attribute
❖Interval measurement: indicating not only the
ranking of objects but also the amount of distance
between them
❖Ratio measurement: distinguished from interval
measurement by having a rational zero point
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Question #2
❖Tell whether the following statement is true or false:
❖Frequency distributions impose order on raw data.
Numeric values are ordered from lowest to highest,
accompanied by a count of the number of times
each value was obtained.
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Answer to Question #2
❖True
❖Frequency distributions impose order on raw data.
Numeric values are ordered from lowest to highest,
accompanied by a count of the number (or
percentage) of times each value was obtained.
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Frequency Distributions
❖ Impose order on raw data
❖ Numeric values are ordered from lowest to highest,
accompanied by a count of the number (or percentage) of
times each value was obtained
❖ Three characteristics
o Values
o Central tendency
o Variability
❖ Common methods of display
o Histograms
o Frequency polygons
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Shapes of Distribution
❖ Symmetric: two halves are mirror images of each other
❖ Skewed: asymmetric with one tail longer than the other
o Positively skewed
o Negatively skewed
❖ Modality: number of peaks
o Unimodal
o Bimodal
o Multimodal
❖ Normal distribution
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Question #3
❖What is the median?
A. Average or typical value of a set of scores
B. Value that occurs most frequently in a distribution
C. Point above which and below which 50% of the
cases fall
D. Arithmetic average of all scores
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Answer to Question #3
❖C
❖Central tendency are indexes, expressed as a single
number, that represent the average or typical value
of a set of scores. The mode is the value that occurs
most frequently in a distribution; the median is the
point above which and below which 50% of the
cases fall; and the mean is the arithmetic average of
all scores.
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Central Tendency
❖Central tendency are indexes, expressed as a single
number, that represent the average or typical value
of a set of scores
❖Mode: value that occurs most frequently in a
distribution
❖Median: point above which and below which 50% of
the cases fall
❖Mean: arithmetic average of all scores
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Question #4
❖Tell whether the following statement is true or false:
❖Range is the distance between the highest and
lowest scores.
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Answer to Question #4
❖True
❖Range is the distance between the highest and
lowest scores.
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Variability
❖Variability: how spread out the data are
❖Range: distance between the highest and lowest
scores
❖Standard deviation: indicates how much, on
average, scores deviate from the mean
❖Calculation
o Deviation scores represent the degree to which
each person’s score deviates from the mean.
The variance is equal to the SD squared.
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Bivariate Descriptive Statistics
❖Relationships between two variables shown in
crosstab tables
❖Contingency table is a two-dimensional frequency
distribution in which the frequencies of two nominalor ordinal-level variables are crosstabulated
❖Correlation coefficients describe the direction and
magnitude of a relationship between two variables
o Product–moment correlation coefficient
(Pearson’s r)—interval or ratio
o Spearman’s rho coefficient—ordinal
❖Scatter plot
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Risk Indexes #1
❖Absolute risk reduction: expresses the estimated
proportion of people who would be spared from an
adverse outcome through exposure to an
intervention
❖Relative risk: estimated proportion of the original
risk of an adverse outcome that persists among
people exposed to an intervention
❖Relative risk reduction: estimated proportion of
untreated risk that is reduced through exposure to
the intervention
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Risk Indexes #2
❖Odds ratio: ratio of the odds for the treated versus
untreated group
❖Number needed to treat: estimate of how many
people would need to receive the intervention to
prevent one adverse outcome
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I advocate for adopting ratio measurement in quantitative research. The choice for ratio measurement
is based on its unique ability to offer a comprehensive quantitative analysis, which may be attributed to
its fundamental qualities. In contrast to nominal and ordinal measures, which provide categorized and
ranking data, ratio measurement enhances the accuracy of interval measurement by including a
reasonable zero point.
This particular attribute improves the precision and dependability of data, enabling more intricate
analysis, such as the computation of ratios, and therefore aiding in producing more thorough
understandings (Cooksey,2020). In such studies, zero value represents either an absence or a baseline
measurement. The utilization of ratio measurement enables the assessment of both relative and
absolute disparities, hence augmenting the comprehensiveness (Marateb et al.,2014).
When data exhibits category or ordinal characteristics, it is both suitable and imperative to employ
nominal and ordinal measurements, respectively. However, ratio measurement is particularly
noteworthy in quantitative research that seeks to conduct a thorough and precise analysis, mainly when
the nature of the data allows for it. This is due to its ability to provide intricate insights informed by its
distinctive features such as a rational zero point, properties of interval data, and the potential for a
more extensive statistical analysis.
References
Cooksey, R. W. (2020). Descriptive Statistics for Summarising Data. Illustrating Statistical Procedures:
Finding Meaning in Quantitative Data, 61. https://doi.org/10.1007/978-981-15-2537-7_5
Marateb, H. R., Mansourian, M., Adibi, P., & Farina, D. (2014). Manipulating measurement scales in
medical statistical analysis and data mining: A review of methodologies. Journal of Research in Medical
Sciences : The Official Journal of Isfahan University of Medical Sciences, 19(1), 47.
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