This document discusses the four levels of measurement: nominal, ordinal, interval, and ratio. Nominal level involves categorical variables without numerical ordering. Ordinal level also involves categorical variables but with an ordering or ranking. Interval level involves variables where units differ by a certain amount, but the zero point is arbitrary. Ratio level also involves variables where units differ by a certain amount, and it possesses a true zero point. The document provides examples to illustrate each level of measurement and emphasizes that the level depends on the method rather than the property measured.
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• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
Creating a Coding Book in IBM SPSS StatisticsThiyagu K
The Codebook is a document containing information about each of the variables in your dataset, such as:
The name assigned to the variable
What the variable represents (i.e., its label)
How the variable was measured (e.g. nominal, ordinal, scale)
How the variable was actually recorded in the raw data (i.e. numeric, string; how many characters wide it is; how many decimal places it has)
For scale variables: The variable's units of measurement
For categorical variables: If coded numerically, the numeric codes and what they represent
This presentation explains the procedure of creating a codebook in IBM SPSS Statistics.
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Here I have tried to show how to select a statistical test for a research project based on the type of data.
initially I have given an idea about the types of data, null hypothesis, p value and the types of error
In this lesson we discuss the different levels of measurement as we continue to explore data. Knowing such will enable us to plan the data collection process we need to employ in order to gather the appropriate data for analysis.
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
Creating a Coding Book in IBM SPSS StatisticsThiyagu K
The Codebook is a document containing information about each of the variables in your dataset, such as:
The name assigned to the variable
What the variable represents (i.e., its label)
How the variable was measured (e.g. nominal, ordinal, scale)
How the variable was actually recorded in the raw data (i.e. numeric, string; how many characters wide it is; how many decimal places it has)
For scale variables: The variable's units of measurement
For categorical variables: If coded numerically, the numeric codes and what they represent
This presentation explains the procedure of creating a codebook in IBM SPSS Statistics.
Application of statistical tests in Biomedical Research .pptxHalim AS
Here I have tried to show how to select a statistical test for a research project based on the type of data.
initially I have given an idea about the types of data, null hypothesis, p value and the types of error
In this lesson we discuss the different levels of measurement as we continue to explore data. Knowing such will enable us to plan the data collection process we need to employ in order to gather the appropriate data for analysis.
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.
Describe various scales of measurements. Provide two examples of .pdfoptokunal1
Describe various scales of measurements. Provide two examples of each scale.
Describe various scales of measurements. Provide two examples of each scale.
Solution
Nominal Level
Level of measurement which classifies data into mutually exclusive, all inclusive categories in
which no order or ranking can be imposed on the data.
Example: Gender, Race
Ordinal Level
Level of measurement which classifies data into categories that can be ranked. Differences
between the ranks do not exist.
Example: class Rank, socio economic status( low, medium,high)
Interval Level
Level of measurement which classifies data that can be ranked and differences are meaningful.
However, there is no meaningful zero, so ratios are meaningless.
Example: Temperature in Celsius, time
Ratio Level
Level of measurement which classifies data that can be ranked, differences are meaningful, and
there is a true zero. True ratios exist between the different units of measure.
Example: age, height.
This Learning explains textbook classification and SPSS classification of Quantitative data and Qualitative data.
It clarifies that Quantitative data is also Continuous data and Qualitative data in SPSS is know as Categorical data.
SCALE measure in SPSS are used to set up Quantitative variables:
In SPSS, your Quantitative data is entered through creating variables with have SCALE measure and Categorical data of your research is dealt by creating variables that have ORDINAL and NOMINAL measures.
When you have data that is being measured on a standard metric scale, then it is good idea to use SCALE measurement for the variable in SPSS. For example, age, marks secured, height, weight, income etc are examples of Quantitative VARIABLE measured on SCALE measure.
ORDINAL and NOMINAL measures in SPSS are used to set up Qualitative variables:
Further is worth understanding that SCALE data can be measured in Interval Scale or a Ratio Scale.
RATIO SCALE measure of Quantitative variables:
A ratio scale has a true zero point in the measurement. For Example, Dividend paid on stock of a company say ABC Limited is $40 per share and that of XYZ is $30 per share. Here We are using Ratio scale of measurement as we can clearly not only tell that $40 is more that $30 and there is difference of $10 but there is also a true zero point as zero dividend means no dividend. So in Ratio Scale zero value makes sense and it represents absence of that particular phenomenon, for example zero dividend means absence of dividend or no dividends. So, there is a true zero in Ratio Scale.
INTERVAL SCALE measure of Quantitative variables:
In Interval Scale there is no true zero. Temperature in Fahrenheit and Celsius is a good example of interval scale because zero-degree temperature in Fahrenheit or Celsius does not mean absence of temperature.
ORDINAL measure for Qualitative variables/Categorical Data:
Nominal Measure in SPSS is used to set up categorical and Qualitative data. There are of course categories like 1-Sales, 2-Accounting, 3- Research etc. but these categories are not ranked, and we are not saying 3 (Research) is better than 2(Accounting). Similarly, we are not saying that 2 is better than 1 or Accounting is better than Sales. So, with Nominal measure data is categorized but not ranked. Moreover, variables like gender, 1-male 0-female can also be measured on Nominal Measures. Similarly, Religious affiliations and data about various geographical zone can also be measured on Nominal Scale. Similarly, marital status like 1-never married, 2-separated, 3-divorced,4-single etc can also be set on Nominal Measure.
With this understanding, you should set up your research variables accordingly in SPSS so that statistical analysis can be performed accordingly.
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2. Knowing what level of measurement each variable in your
research belongs to is highly important in order to conduct a
reliable and valid statistical analysis because the selection of
appropriate analysis can also depend on what level of
measurement your data has.
SIGNIFICANCE
4. arises when we have variables that are categorical and
nonnumeric or where the numbers have no sense of ordering.
NOMINAL LEVEL
EX:
Jersey Number
Sex
Marital status
Religious Affiliation
Room Number
Zip Code
5. also deals with categorical variables like the nominal level, but
in this level ordering is important, that is the values of the
variable could be ranked.
ORDINAL LEVEL
EX:
Social-economic status (Low-income, middle-income, high-income)
Difficulty of questions in an exam (easy, moderate, difficult)
Rank in class (top 1, top 2, top 3, etc.)
Perceptions in Likert scale (strongly disagree, disagree, neutral, Agree,
strongly agree)
6. SPECIAL NOTE
While there is a sense or ordering, there is no way to find out how
much “distance” there is between one category and another.
EX:
The difference between small and medium size may not be
the same as the difference between medium and large.
7. tells us that one unit differs by a certain amount of degree from
another unit. Knowing how much one unit differs from another is
an additional property of the interval level on top of having the
properties possessed by the ordinal level.
INTERVAL LEVEL
8. when measuring temperature in Celsius, a 10 degree difference has the
same meaning anywhere along the scale – the difference between 10
and 20 degree Celsius is the same as between 80 and 90 Celsius.
EX:
But, we cannot say that 80 degrees Celsius is twice as hot as 40 degrees
Celsius since there is no true zero, but only an arbitrary zero point. A
measurement of 0 degrees Celsius does not reflect a true “lack of
temperature.”
9. SPECIAL NOTE
Interval level allows addition and subtraction operations, but it
does not possess an absolute zero. Zero is arbitrary as it does not
mean the value does not exist. Zero only represents an additional
measurement point.
ABSOLUTE ZERO: entire absence of the variable
ARBITRARY ZERO: the value of 0 does not entail absence of
the variable.
10. Intelligence Quotient (IQ) of a person.
EX:
We can tell not only which person ranks higher in IQ but also how much
higher he or she ranks with another, but zero IQ does not mean no
intelligence.
11. RATIO LEVEL
tells us that one unit differs by a certain amount of degree from
another unit.
Also tells us that one unit has so many times as much of the
property as does another unit.
Possesses a meaningful (unique and non-arbitrary, fixed zero
point and allows all arithmetic operations (addition, subtraction,
multiplication, and division).
12. the existence of the zero point is the only difference between
ratio and interval level of measurement.
RATIO LEVEL
EX:
Mass, Heights, Weights, Energy, Electric charge
Nearly all numerical variables have ratio level of measurement.
With mass as an example, the difference between 120 grams and 135 grams is
15 grams, and this is the same difference between 380 grams and 395 grams. A
measurement of 0 reflects a complete lack of mass.
13. Amount of money
EX:
We can say that 2,000 pesos is twice more than 1,000 pesos. In addition,
money has a true zero point: if you have zero money, this implies the
absence of money.
14. SPECIAL NOTE
The levels of measurement depend mainly on the method of
measurement, not on the property measured.
The weight of primary school students measured in kilograms
has a ratio level, but the students can be categorized into
overweight, normal, underweight, and in which case, the
weight is then measured in an ordinal level.
15. CHECKPOINT!
Identify the level of measurement for each of the variable.
Variable Level of
Measurement
LRN
Grade Level
Course in College
Volume of acid
Exam scores
GWA
Monthly Salary
16. CHECKPOINT!
Identify the level of measurement for each of the variable.
Variable Level of
Measurement
LRN Nominal
Grade Level Ordinal
Course in College Nominal
Volume of acid Ratio
Exam scores Ratio
GWA Ratio
Monthly Salary Ratio
17. CHECKPOINT!
Identify the level of measurement for each of the variable.
Variable Level of
Measurement
Body temperature
Length of time (in hours)
spent jogging
Sex (M or F)
Age (Number of years
you have been alive)
Pain threshold (0 to 10)
Favorite color
Shirt size
18. CHECKPOINT!
Identify the level of measurement for each of the variable.
Variable Level of
Measurement
Body temperature Interval
Length of time (in hours)
spent jogging
Ratio
Sex (M or F) Nominal
Age (Number of years
you have been alive)
Ratio
Pain threshold (0 to 10) Interval
Favorite color Nominal
Shirt size Ordinal