This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. Nominal scales assign categories with no numerical difference between them. Ordinal scales order categories but do not specify numerical distance. Interval scales have equal numerical distance between values but no absolute zero. Ratio scales have all the qualities of the previous scales plus an absolute zero point. Measurement scales are important for categorizing and quantifying variables in research and other applications such as market transactions.
caling is the branch of measurement that involves the construction of an instrument that associates qualitative constructs with quantitative metric units. Scaling evolved out of efforts in psychology and education to measure “unmeasurable” constructs like authoritarianism and self-esteem. In many ways, scaling remains one of the most arcane and misunderstood aspects of social research measurement. And, it attempts to do one of the most difficult of research tasks – measure abstract concepts.
Most people don’t even understand what scaling is. The basic idea of scaling is described in General Issues in Scaling, including the important distinction between a scale and a response format. Scales are generally divided into two broad categories: unidimensional and multidimensional. The unidimensional scaling methods were developed in the first half of the twentieth century and are generally named after their inventor. We’ll look at three types of unidimensional scaling methods here:
Thurstone or Equal-Appearing Interval Scaling
Likert or “Summative” Scaling
Guttman or “Cumulative” Scaling
In the late 1950s and early 1960s, measurement theorists developed more advanced techniques for creating multidimensional scales. Although these techniques are not considered here, you may want to look at the method of concept mapping that relies on that approach to see the power of these multivariate methods.
This presentation is on Measurement and it's scales. There are four different types of scales of measurement, namely, Nominal, Ordinal, Interval and Ratio
caling is the branch of measurement that involves the construction of an instrument that associates qualitative constructs with quantitative metric units. Scaling evolved out of efforts in psychology and education to measure “unmeasurable” constructs like authoritarianism and self-esteem. In many ways, scaling remains one of the most arcane and misunderstood aspects of social research measurement. And, it attempts to do one of the most difficult of research tasks – measure abstract concepts.
Most people don’t even understand what scaling is. The basic idea of scaling is described in General Issues in Scaling, including the important distinction between a scale and a response format. Scales are generally divided into two broad categories: unidimensional and multidimensional. The unidimensional scaling methods were developed in the first half of the twentieth century and are generally named after their inventor. We’ll look at three types of unidimensional scaling methods here:
Thurstone or Equal-Appearing Interval Scaling
Likert or “Summative” Scaling
Guttman or “Cumulative” Scaling
In the late 1950s and early 1960s, measurement theorists developed more advanced techniques for creating multidimensional scales. Although these techniques are not considered here, you may want to look at the method of concept mapping that relies on that approach to see the power of these multivariate methods.
This presentation is on Measurement and it's scales. There are four different types of scales of measurement, namely, Nominal, Ordinal, Interval and Ratio
Formulating Hypothesis
Hypothesis Formulation is –
-the process of creating possible, tentative explanations for a given set of information.
-the whole Process of creating and formulating the hypothesis
How is Hypothesis Formulated
Reichenbach (1938) made a distinction between the two processes found commonly in any hypothesis formulation -
-Context of Discovery:
--Hypotheses is ‘discovered’ from earlier research findings, existing theories and personal observations, and experience.
-Context of justification:
--When a Researcher reconstructs his thoughts and communicates them in the form of a hypothesis to others, he uses the context of justification –
Steps in Formulation of Hypothesis
-Understand the area of problem
-Consider goal
-Identify variables
-identify the relationship between the variables.
-Think critically about hypothesis
-Express the idea as own hypothesis
Process of Hypothesis Formulation
-Understand the area of problem
Understand the problem that is being worked on.
-Consider goal
After selecting the problem & understanding the problem, objectives have to be selected according to the problem
-Identify variables
Must be define the variables.
Variables in hypothesis are testable not ?
Specify dependent and independent & others variables.
-Identify the relationship between the variables.
Variables are influence each other or not?
-Think critically about hypothesis
Hypothesis are testable, verifiable or not ? Which will make able to confirm the hypothesis.
-Express the idea as own hypothesis
Here researcher made the hypothesis in a Tentative Solution Statement manner
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.
Formulating Hypothesis
Hypothesis Formulation is –
-the process of creating possible, tentative explanations for a given set of information.
-the whole Process of creating and formulating the hypothesis
How is Hypothesis Formulated
Reichenbach (1938) made a distinction between the two processes found commonly in any hypothesis formulation -
-Context of Discovery:
--Hypotheses is ‘discovered’ from earlier research findings, existing theories and personal observations, and experience.
-Context of justification:
--When a Researcher reconstructs his thoughts and communicates them in the form of a hypothesis to others, he uses the context of justification –
Steps in Formulation of Hypothesis
-Understand the area of problem
-Consider goal
-Identify variables
-identify the relationship between the variables.
-Think critically about hypothesis
-Express the idea as own hypothesis
Process of Hypothesis Formulation
-Understand the area of problem
Understand the problem that is being worked on.
-Consider goal
After selecting the problem & understanding the problem, objectives have to be selected according to the problem
-Identify variables
Must be define the variables.
Variables in hypothesis are testable not ?
Specify dependent and independent & others variables.
-Identify the relationship between the variables.
Variables are influence each other or not?
-Think critically about hypothesis
Hypothesis are testable, verifiable or not ? Which will make able to confirm the hypothesis.
-Express the idea as own hypothesis
Here researcher made the hypothesis in a Tentative Solution Statement manner
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.
Measurement is a procedure for assigning symbols, letters, or numbers to empirical properties of variables according to rules. A Scale is a tool or mechanism by which individuals are distinguished as to how they differ from one another on the variables of interest to our study There are four levels of measurements: nominal, ordinal, interval, and ratio. The measurement scales, commonly used in marketing research, can be divided into two types; comparative and non-comparative scales. A number of scaling techniques are available for measurement of attitudes. There is no unique way that you can use to select a particular scaling technique for your research study.
Research Methodology: Questionnaire, Sampling, Data Preparationamitsethi21985
As per PTU's MBA Syllabus, Unit No. 2: Sources Of Data: Primary And Secondary; Data Collection Methods; Questionnaire Designing: Construction, Types And Developing A Good Questionnaire. Sampling Design and Techniques, Scaling Techniques, Meaning, Types, Data Processing Operations, Editing, Coding, Classification, Tabulation. Research Proposal/Synopsis Writing. Practical Framework
Understanding the Scales of MeasurementDrShalooSaini
This Power Point Presentation has been made while referring to the research books written by eminent, renowned and expert authors as mentioned in the references section. The purpose of this Presentation is to help the research students in developing an insight about the Scales of Measurement.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
1. TYPES OF SCALES
Pioneer Institute of Professional Studies
Submitted to:
Prof. Shweta Mogre
Submitted by:
Ms. Nikita Agrawal B.Sc. (CS )IV Sem
2. Content
Introduction
Measurement Scales
Types of Scales
Nominal Scale
Ordinal Scale
Interval Scale
Ratio Scale
Example
Some other types of Scales
Importance of Measurement Scales
3. Introduction
Measurement :-
The term ‘measurement’ means assigning numbers or some other
symbols to the characteristics of certain objects.
Scaling :-
Scaling is a process of measuring.
6. Nominal Scale
Examples
Gender
Marital status
State of residence
Assign responses to different categories
No numerical difference between categories
7. Ordinal Scale
Examples
Miss America Results
Military rank
Class rank
Set of categories that are ordered according to preference or ranking.
Don’t know numerical distance from each category to the next.
8. Interval Scale
Examples
How appealing is the cereal box to children?
Current temperature
IQ
Scale with values, and there is the same numerical distance between each
value.
This scale has no absolute zero and multiples of measures are not
meaningful.
9. Ratio Scale
Examples
Weight
Sales figures
Quantity purchased
Highest and more informative scale
Contains the qualities of the nominal, ordinal and interval scales with the
addition of an absolute zero point.
11. Some other types of Scales
Self Rating Scale
1. Graphic Rating Scale
2. Itemized Rating Scale
Likert Scale
Thurstone Scale
Staple’s Scale
Guttman Scale
12. Importance of Measurement Scale
Measurement is the basics need in any research work in science.
Exact, careful and precise work of measurement leads to the discovery of very
important laws.
Measurement plays an important role in the market. Preparation of bills, money
transaction related to it etc. cannot be completed without measurement.
The quality of measurement depends upon the instrument used for the purpose.
Editor's Notes
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