This document discusses the four levels of measurement: nominal, ordinal, interval, and ratio. Nominal measurement involves categorizing variables qualitatively without numerical values. Ordinal measurement allows ranking variables but not determining degrees of difference. Interval measurement allows comparing differences but not ratios. Ratio measurement involves true quantities where ratios are meaningful and zero has a definite meaning. Knowing the level of measurement helps in interpreting and analyzing variable data appropriately.
For a detailed explanation Watch the Youtube video:
https://youtu.be/6g4tD162yhI
Hypothesis, Characteristics of a good hypothesis, contribution to research study, Types of hypothesis, Source, level of significance, two-tailed one-tailed test, types of errors
For a detailed explanation Watch the Youtube video:
https://youtu.be/6g4tD162yhI
Hypothesis, Characteristics of a good hypothesis, contribution to research study, Types of hypothesis, Source, level of significance, two-tailed one-tailed test, types of errors
Psychologist Stanley Smith Stevens (1946) developed the best-known classification with four levels, or scales of measurement such as Nominal, Ordinal, Interval, and Ratio. This presentation slide describes the four-level of scales with illustrations.
Research Report - types of reports – content of report – Style of Reporting – Steps in Drafting Reports – Qualities of a good report – Documentation – Citation - Footnotes – References – Bibliography – APA and MLA Format in writing references and bibliography
Topic: Types of Data
Student Name: Duwa
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Types of variables-Advance Research MethodologyRehan Ehsan
This Presentation states the details of types of variables for students to get help in advance research methodology. Rearchers may also get help from this work.
Validity:
Validity refers to how well a test measures what it is purported to measure.
Types of Validity:
1. Logic valididty:
Validity which is in the form of theory, statements. It has 2 types.
I. Face Validity:
It is the extent to which the measurement method appears “on its face” to measure the construct of interest.
• Example:
• suppose you were taking an instrument reportedly measuring your attractiveness, but the questions were asking you to identify the correctly spelled word in each list
II. Content Validity:
Measuring all the aspects contributing to the variable of the interest.
Example:
For physical fitness temperature, height and stamina are supposed to be assess then a test of fitness must include content about temperatures, height and stamina.
2. Criterion
It is the extent to which people’s scores are correlated with other variables or criteria that reflect the same construct
Example:
An IQ test should correlate positively with school performance.
An occupational aptitude test should correlate positively with work performance.
Types of Criterion Validity
Concurrent validity:
• When the criterion is something that is happening or being assessed at the same time as the construct of interest, it is called concurrent validity.
• Example:
Beef test.
Predictive validity:
• A new measure of self-esteem should correlate positively with an old established measure. When the criterion is something that will happen or be assessed in the future, this is called predictive validity.
• Example:
GAT, SAT
Other types of validity
Internal Validity:
It is basically the extent to which a study is free from flaws and that any differences in a measurement are due to an independent variable and nothing else
External Validity
• It is the extent to which the results of a research study can be generalized to different situations, different groups of people, different settings, different conditions, etc.
Types of Statistics Descriptive and Inferential StatisticsDr. Amjad Ali Arain
Topic: Types of Statistics Descriptive and Inferential Statistics
Student Name: Bushra
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This presentation is on Measurement and it's scales. There are four different types of scales of measurement, namely, Nominal, Ordinal, Interval and Ratio
Psychologist Stanley Smith Stevens (1946) developed the best-known classification with four levels, or scales of measurement such as Nominal, Ordinal, Interval, and Ratio. This presentation slide describes the four-level of scales with illustrations.
Research Report - types of reports – content of report – Style of Reporting – Steps in Drafting Reports – Qualities of a good report – Documentation – Citation - Footnotes – References – Bibliography – APA and MLA Format in writing references and bibliography
Topic: Types of Data
Student Name: Duwa
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Types of variables-Advance Research MethodologyRehan Ehsan
This Presentation states the details of types of variables for students to get help in advance research methodology. Rearchers may also get help from this work.
Validity:
Validity refers to how well a test measures what it is purported to measure.
Types of Validity:
1. Logic valididty:
Validity which is in the form of theory, statements. It has 2 types.
I. Face Validity:
It is the extent to which the measurement method appears “on its face” to measure the construct of interest.
• Example:
• suppose you were taking an instrument reportedly measuring your attractiveness, but the questions were asking you to identify the correctly spelled word in each list
II. Content Validity:
Measuring all the aspects contributing to the variable of the interest.
Example:
For physical fitness temperature, height and stamina are supposed to be assess then a test of fitness must include content about temperatures, height and stamina.
2. Criterion
It is the extent to which people’s scores are correlated with other variables or criteria that reflect the same construct
Example:
An IQ test should correlate positively with school performance.
An occupational aptitude test should correlate positively with work performance.
Types of Criterion Validity
Concurrent validity:
• When the criterion is something that is happening or being assessed at the same time as the construct of interest, it is called concurrent validity.
• Example:
Beef test.
Predictive validity:
• A new measure of self-esteem should correlate positively with an old established measure. When the criterion is something that will happen or be assessed in the future, this is called predictive validity.
• Example:
GAT, SAT
Other types of validity
Internal Validity:
It is basically the extent to which a study is free from flaws and that any differences in a measurement are due to an independent variable and nothing else
External Validity
• It is the extent to which the results of a research study can be generalized to different situations, different groups of people, different settings, different conditions, etc.
Types of Statistics Descriptive and Inferential StatisticsDr. Amjad Ali Arain
Topic: Types of Statistics Descriptive and Inferential Statistics
Student Name: Bushra
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This presentation is on Measurement and it's scales. There are four different types of scales of measurement, namely, Nominal, Ordinal, Interval and Ratio
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
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2. Levels of Measurement
Introduction:-
• The level of measurement refers to the
relationship among the values that are
assigned to the attributes for a variable.
• What does that mean? Begin with the idea
of the variable, in this example "party
affiliation." That variable has a number of
attributes.
3. Levels of Measurement
Introduction:-
• Let's assume that in this particular election
context the only relevant attributes are
"republican", "democrat", and "independent".
• For purposes of analyzing the results of this
variable, we arbitrarily assign the values 1, 2
and 3 to the three attributes.
• The level of measurement describes the
relationship among these three values.
4. Levels of Measurement
Introduction:-
• In this case, we simply are using the
numbers as shorter placeholders for the
lengthier text terms.
• We don't assume that higher values mean
"more" of something and lower numbers
signify "less".
• We don't assume the value of 2 means that
democrats are twice something that
republicans are.
5. Levels of Measurement
Introduction:-
• We don't assume that republicans are in
first place or have the highest priority just
because they have the value of 1.
• In this case, we only use the values as a
shorter name for the attribute.
• Here, we would describe the level of
measurement as "nominal".
6. Levels of Measurement
Why is Level of Measurement Important?
• First, knowing the level of
measurement helps you decide how to
interpret the data from that variable.
• When you know that a measure is
nominal (like the one just described),
then you know that the numerical values
are just short codes for the longer names.
7. Levels of Measurement
Why is Level of Measurement Important?
• Second, knowing the level of
measurement helps you decide what
statistical analysis is appropriate on the
values that were assigned.
• If a measure is nominal, then you know
that you would never average the data
values or do a t-test on the data.
8. Levels of Measurement
There are typically four levels of measurement
that are defined:
1. Nominal
2. Ordinal
3. Interval
4. Ratio
11. Levels of Measurement
Nominal level
The nominal type differentiates between
items or subjects based only on their names
or (meta-)categories and other qualitative
classifications they belong to; thus
dichotomous data involves the construction
of classifications as well as the classification
of items.
12. Levels of Measurement
Examples of these classifications include gender,
nationality, ethnicity, language, genre, style, biological
species, and form. In a university one could also use hall
of affiliation as an example. Other concrete examples are
• in grammar, the parts of speech: noun, verb,
preposition, article, pronoun, etc.
• in politics, power projection: hard power, soft
power, etc.
• in biology, the taxonomic ranks below domains:
Archaea, Bacteria, and Eukarya
• in software engineering, type of faults: specification
faults, design faults, and code faults
13. Levels of Measurement
Ordinal scale
The ordinal type allows for rank order (1st, 2nd,
3rd, etc.) by which data can be sorted, but still
does not allow for relative degree of
difference between them.
Examples include, on one hand, dichotomous data
with dichotomous (or dichotomized) values such
as 'sick' vs. 'healthy' when measuring health,
• 'guilty' vs. 'not-guilty' when making
judgments in courts,
14. Levels of Measurement
Ordinal scale
• 'wrong/false' vs. 'right/true' when
measuring truth value, and, on the other hand,
• Non-dichotomous data consisting of a
spectrum of values, such as 'completely agree',
'mostly agree', 'mostly disagree', 'completely
disagree' when measuring opinion.
15. Levels of Measurement
Interval scale
The interval type allows for the degree of
difference between items, but not the ratio between
them.
Examples include temperature with the Celsius scale,
which has two defined points (the freezing and boiling
point of water at specific conditions) and then separated
into 100 intervals, percentage such as a percentage
return on a stock location in Cartesian coordinates,
and direction measured in degrees from true or magnetic
north.
16. Levels of Measurement
Ratio scale
•The ratio type takes its name from the fact that
measurement is the estimation of the ratio
between a magnitude of a continuous quantity
and a unit magnitude of the same kind (Michell,
1997, 1999).
•A ratio scale possesses a meaningful (unique and
non-arbitrary) zero value.
•Most measurement in the physical sciences and
engineering is done on ratio scales.
17. Levels of Measurement
Ratio scale
•Examples include mass, length, duration, plane
angle, energy and electric charge.
•In contrast to interval scales, ratios are now
meaningful because having a non-arbitrary zero
point makes it meaningful to say, for example,
that one object has "twice the length" of another
(= is "twice as long").
18. Levels of Measurement
Ratio scale
•Very informally, many ratio scales can be
described as specifying "how much" of something
(i.e. an amount or magnitude) or "how many" (a
count). The Kelvin temperature scale is a ratio
scale because it has a unique, non-arbitrary zero
point called absolute zero.