IJCA - Volume I - Flipbook - Page 34
34 The International Journal of Conformity Assessment
2022 | Volume 1, Issue 1
Author Biographies
Osman Vural manages the
International Accreditation Service
(IAS) accreditation program and
personnel certification bodies (ISO/
IEC 17024). He earned a Bachelor of
Science degree in civil engineering
from Gazi University in Turkey and has worked for
many years as a consultant, auditor, and trainer in
conformity assessment markets. Osman’s expertise
extends to the accreditation of testing/calibration
laboratories, personnel certification bodies, and
management systems certification bodies. Other
professional accomplishments include serving as
director of the International Personnel Certification
Association (IPC) and leading many relevant technical
committees.
Ioannis Anastasopoulos received
his Bachelor of Science degree in
mathematics from the University of
California at Berkeley, where he is
currently pursuing a master’s degree
in the field of education technology. He
has participated in research projects in
both the United States and Europe during his course of
studies, specifically related to OpenITS, credentialing
examinations, and effective implementation of
statistical methods and tools. Additionally, Ioannis
has co-authored a series of publications related
to conformity assessment, testing validation, and
education technologies.
David S. Nelson, PE, Ph.D., is the
president of Quality Psychometric
Services in Alabama and boasts 30plus years’ experience developing
and managing state and national
licensing and certification programs
for construction-sector professions. Registered as
a professional engineer in Florida, David earned a
Bachelor of Science degree in civil engineering from
North Carolina State University and received master’s
and doctorate degrees in educational psychology from
the University of Southern California. Career highlights
include serving as vice president of certification and
testing for the International Code Council (ICC) and
the International Conference of Building Officials. He
was also the senior policy advisor of the International
Accreditation Service (IAS) and managed the IAS
accreditation program for personnel certification
bodies (ISO/IEC 17024).
Annex 1
between” categorical and quantitative data.
35
Arithmetic Mean
The sum of a collection of numbers divided by the
count of numbers in the collection. In simple terms,
it is known as an “average.”
Statistical Terms and Definitions That Examiners
Need to Know
Data
Data are obtained by measurement, counting,
experimentation, observation, or research. Data
collected by measurement or counting and reporting
a numerical value are called quantitative data, and
data that do not report a numerical value are called
qualitative (categorical) data.
Weighted Arithmetic Mean
The weighted arithmetic mean is similar to an
ordinary arithmetic mean, except that instead of
each of the data points contributing equally to the
final average, some data points contribute more than
others.
Dichotomous Variables
Dichotomous variables are nominal variables that
have only two categories or levels. They have only
two possible values (e.g., 0/1, Yes/No, True/False,
etc.).
Variance
The expectation of the squared deviation of a
random variable from its mean.
Quantitative Variables
A variable that reflects a notion of magnitude—
that is, if the values it can take are numbers. A
quantitative variable, thus, represents a measure
and is numerical.
Qualitative (Categorical) Variables
Discrete Variables
Variables that are not numerical and which do not fit
into categories.
Variables for which the values it can take are
countable and have a finite number of possibilities.
The values are often (but not always) integers.
Nominal Variables
Continuous Variables
Nominal variables are variables that have two or
more categories, but which do not have an intrinsic
order.
Variables for which the values are not countable and
have an infinite number of possibilities.
Standard Deviation
Standard deviation is a measure of statistical
dispersion. “Dispersion” indicates how much of
the data is spread out. Specifically, it shows how
the data is spread across the mean or average. For
example, are all of the scores close to the average?
Or are lots of scores way above (or way below) the
average score?
Note: Misleading data encoding
In datasets, it is very often the case that numbers are used for
qualitative variables. For instance, a person doing statistical
analysis may assign the number “0” to the answer “False” and
“1” to the answer “True.” Despite the numerical classification,
the variable answer is still a qualitative variable and not a
discrete variable as it may look. The numerical classification is
only used to facilitate data collection and data management.
Median
Covariance
A measure of the joint variability of two random
variables. In other words, a measure of how much
two random variables vary together. It’s similar
to variance, but where variance tells how a single
variable varies, covariance tells how two variables
vary together.
The value separating the higher half from the lower
half of a data sample
Correlation
Ordinal Variables
A categorical variable for which the possible values
are ordered. Ordinal variables can be considered “in
Correlation is a statistical technique that measures
the relationship between two variables, such as X
and Y, in terms of the units of measurement results
for the variables.
