Chapter 7
Measuring of data
• Reliability of measuring instruments
• The reliability* of instrument is the consistency
with which it measures the target attribute.
• Example
If a scale weighed a person at 80 kg one minute and 85 kg
the next, we would consider it unreliable. The less variation
an instrument produces in repeated measurements, the
higher its reliability. Thus, reliability can be equated with a
measure’s stability, consistency, or dependability. Reliability
also concerns a measure
Dr. Areefa
VALIDITY
The second important criterion for evaluating a quantitative
instrument is its validity.
Validity is
the degree to which an instrument measures what it
is supposed to measure. When researchers develop
an instrument to measure hopelessness, how can
they be sure that resulting scores validly reflect this
construct and not something else, like depression?
Reliability and validity are not independent
qualities of an instrument. A measuring device that
is unreliable cannot possibly be valid.
Types
Face validity refers to the degree to which an assessment or test
subjectively appears to measure the variable or construct that it is
supposed to measure. In other words, face validity is when an
assessment or test appears to do what it claims to do
example
Lila is a researcher who has just developed a new assessment that is meant to
measure mathematical ability in college students. She selects a sample of 300
college students from three local universities and has them take the test. After
the students complete the test, Lila asks all 300 participants to complete a
follow-up questionnaire.
In the questionnaire, Lila asks the participants what they think the purpose of
the test is, what construct they believe is being measured, and whether or not
they feel the assessment was an adequate measure of their mathematical
ability. After analyzing the follow-up results, Lila finds that most of the
participants agree that Lila's assessment accurately measures their
mathematical ability. Lila's has just demonstrated that her assessment has face
validity.
Content Validity
Content validity concerns the degree to which an
instrument has an appropriate sample of items for the
construct being measured. Content validity is relevant for
both affective measures. content validity (also known as
logical validity). For example,
a depression scale may lack content validity if it only assesses the
affective dimension of depression but fails to take into account
the behavioral dimension. An element of subjectivity exists in
relation to determining content validity, which requires a degree
of agreement about what a particular personality trait such as
extraversion represents. A disagreement about a personality trait
will prevent the gain of a high content validity
Construct Validity
• Validating an instrument in terms of construct Validity is a
challenging task. The key construct validity questions are: What is
this instrument really measuring? Does it adequately measure the
abstract concept of interest? involves logical analysis and tests
predicted by theoretical considerations.
Analyzing Quantitative
Data: Descriptive Statistics
•
•
•
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Statistical analysis helps researchers make
sense of quantitative information. Without
statistics, quantitative data would be useless
mass of numbers. Statistical procedures
enable researchers
• to summarize, organize, evaluate, interpret,
• and communicate numeric information.
• Statistics are either descriptive or inferential.
• Descriptive statistics are used to describe and
• synthesize data. Averages and percentages are
examples
• of descriptive statistics. Actually, when such
indexes are calculated on data from a
population, they are called parameters.
A descriptive
• index from a sample is called a statistic.
Research questions are about parameters, but
researchers calculate sample statistics to
estimate them, using inferential statistics to
make inferences about the population.
LEVELS OF MEASUREMENT
• Scientists have developed a system for
categorizing measures.
• This system is important because the analyses
that can be performed on data depend on
their measurement level.
• The four major classes, or levels, of
measurement are nominal, ordinal, interval,
• and ratio.
• Nominal Measurement
• The lowest level of measurement is nominal
measurement,
• which involves assigning numbers to
• classify characteristics into categories. Examples
• of variables amenable to nominal measurement
• include gender, blood type, and marital status.
The numeric codes assigned in nominal measurement
do not convey quantitative information.
If we classify males as 1 and females as 2, the numbers
have no inherent meaning. The number 2 clearly
does not mean “more than” 1. It would be perfectly
acceptable to reverse the code and use 1 for females
and 2 for males. The numbers are merely symbols ‫رمز‬
that represent two different values of the gender
attribute. Indeed, instead of numeric codes, we could
have used alphabetical symbols, such as M and F.
• Interval measurement
• occurs when researchers can specify the rankordering of objects on an attribute and can
assume equivalent distance between them.
• Most psychological and educational tests are
based on interval scales.
• Ratio Measurement
they tell us the exact value between units, AND
they also have an absolute zero–which allows
for a wide range of both descriptive and
inferential statistics to be applied. At the risk of
repeating myself, everything above about
interval data applies to ratio scales + ratio scales
have a clear definition of zero. Good examples
of ratio variables include height and weight.
Test your self
What type of measure?
What type of measure?
Temperature, date
interval
Wt. ht.
ratio
Constructing Frequency Distributions
• Frequency distributions are a method of
organizing numeric data.
• A frequency distribution is a systematic
• arrangement of values from lowest to highest,
• together with a count of the number of times
• each value was obtained.
The Mode
• The mode is the most frequently occurring score
• value in a distribution. The mode is simple to
determine;
• it is not computed but rather is established
• by inspecting a frequency distribution. In the
following
• distribution of numbers, we can readily see
• that the mode is 53:
• 50 51 51 52 53 53 53 53 54 55 56
The Median
The median is the point in a distribution above
which and below which 50% of cases fall.
As an example, consider the following set of values:
2 2 3 3 4 5 6 7 8 9 The value that divides the cases exactly
in half is 4.5, which is the median for this set of numbers.
The point that has 50% of the cases above and
below it is halfway between 4 and 5. An important
characteristic of the median is that it does not take into
account the quantitative values of scores. The median is an
index of average position in a distribution. It is insensitive to
extreme values.
• The Mean
The mean is equal to the sum of all scores divided
by the total number of scores. The mean is the
index usually referred to as an average. The
computational