1) The document provides an overview of measurement and scaling techniques used in business research including nominal, ordinal, interval, and ratio scales.
2) It also discusses comparative scaling methods like paired comparison, rank order, and constant sum scales as well as non-comparative methods like Likert scales and semantic differential scales.
3) Finally, the document covers important concepts in research like reliability, validity, and sources of primary and secondary data.
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.
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.
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 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.
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.
This presentation is on Measurement and it's scales. There are four different types of scales of measurement, namely, Nominal, Ordinal, Interval and Ratio
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 Scaling in Research.
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
Show drafts
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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.
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 Scaling in Research.
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
Show drafts
volume_up
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.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. Contents
Measurement and Scaling
What Do We Measure
Measurement Scales
Nominal
Ordinal
Interval
Ratio
Scaling Methods
Comparative
Non-Comparative
Reliability and Validity
Primary and Secondary Sources of
Data
3. 01
Step 1
Selecting observable
empirical events
02
Step 2
Developing a set of
mapping rules
03
Step 3
Applying the mapping rules to
each observation of that event
Measurement and Scaling
Measurement in research consists of assigning numbers to empirical
events,objects or properties or activities in compliance with a set of rules.
This definition implies that measurement is a three step process :
Tushar 20BC140
4. What Do We Measure?
Variables being studied in
research may be classified
into objects or properties.
Objects
Tangible Items like people, automobiles or
furniture
Things that are not as concrete like genes,
peer pressure
They include concepts of ordinary experiences:
Properties
Physical like weight, height, posture
Psychological like attitude, intelligence
Social like leadership ability, status
They inculde characteristics of the objects:
For Example - Properties of a person are
Tushar 20BC140
5. Measurement
Scales
In measuring one devises some
mapping rules and then translates
the observation of property indicants
using this rule.Each one has its own
set of underlying assumptions about
how the numerical symbols
corresponds to real-world
observations.
Nominal Ordinal
Interval Ratio
Tushar 20BC140
6. Nominal Scale
A nominal scale is a scale of measurement used to assign events or objects into
discrete categories.
This form of scale does not require the use of numeric values or categories
ranked by class, but simply unique identifiers to label each distinct category.
Often regarded as the most basic form of measurement, nominal scales are
used to categorize and analyze data in many disciplines.
From a statistics point of view this scale is one of the easiest to understand
measurement scale. It is assigned to items that are not quantitative or number
oriented.
Tushar 20BC140
7. Examples of Nominal Scale
Illustration 1
Illustration 2
Illustration 3
Let’s assume we have 5 colors, orange, blue, red, black and yellow. We could
number them in any order we like either 1 to 5 or 5 to 1 in ascending or descending
order. Here numbers are assigned to colors only to identify them.
In the case of a gender scale, an individual can be categorized either as male or
female. In this case, all objects in the category will have the same number, for
example, all males can be no. 1 and all females can be no. 2. Please note, that
nominal is purely used for counting purposes.
Another example of nominal scale from a research activity point to view is YES/NO
scale. It essentially has no order.
Tushar 20BC140
8. Ordinal Scale
Ordinal scale is the 2nd level of measurement that reports the ranking and
ordering of the data without actually establishing the degree of variation
between them.
“Ordinal” indicates “order”. Ordinal data is quantitative data which have
naturally occurring orders and the difference between is unknown. It can be
named, grouped and also ranked.
An ordinal scale is used as a comparison parameter to understand whether the
variables are greater or lesser than one another using sorting. The central
tendency of the ordinal scale is Median..
Tushar 20BC140
9. Examples of Ordinal Scale
Survey respondents will choose between these options of satisfaction but the
answer to “how much?” will remain unanswered.
Likert Scale is an example of why the interval difference between ordinal
variables cannot be concluded. In this scale the answer options are usually
polar such as, “Totally satisfied” to “Totally dissatisfied”.
The intensity of difference between these options can’t be related to specific
values as the difference value between totally satisfied and totally dissatisfied
will be much larger than the difference between satisfied and neutral.
“How satisfied are you with our products?"
1- Totally Satisfied 2- Satisfied 3- Neutral 4- Dissatisfied 5- Totally Dissatisfied
Tushar 20BC140
10. Interval Scale
The interval scale is a quantitative measurement scale where there is order, the
difference between the two variables is meaningful and equal, and the
presence of zero is arbitrary.
It measures variables that exist along a common scale at equal intervals. The
measures used to calculate the distance between the variables are highly
reliable.
The interval scale is preferred to nominal scale or ordinal scale because the
latter two are qualitative scales. The interval scale is quantitative in the sense
that it can quantify the difference between values.
This is a preferred scale in statistics because you can assign a numerical value to
any arbitrary assessment, such as feelings and sentiments.
Tushar 20BC140
11. Examples of Interval Scale
Illustration 1
Illustration 2
Some examples of interval scale are Likert Scale, Bipolar Matrix and Net
Promoter Score (NPS)
For instance, for an IQ test, one may use an interval scale. The use of an interval
scale can help measure that the difference between an IQ of 80 and 90 is the same
as an IQ of 90 and 100. At the same time, the score cannot be zero, because the
minimum level for IQ is 40.
The scale may show the value as zero but it does not mean true zero or absence.
For example, in the case of temperature, the indication for zero degrees Fahrenheit
and Celsius does not mean the absence of temperature. The value is used to
describe the phenomena where equal parts of ice, water, and salt freeze.
Tushar 20BC140
12. Ratio Scale
Ratio scale is a type of variable measurement scale which is quantitative in
nature. It allows any researcher to compare the intervals or differences. Ratio
scale possesses a zero point or character of origin.
It is the most informative scale as it tends to tell about the order and number of
the object between the values of the scale. The most common examples of this
scale are height, money, age, weight etc.
In this scale, variables can be systematically added, subtracted, multiplied and
divided (ratio). All statistical analysis including mean, mode, the median can be
calculated using it.
Ratio scale has units which have several unique and useful properties. One of
them is they allow unit conversion.
Tushar 20BC140
13. Examples of Ratio Scale
Less than 5 ft
5 ft 1 in - 5 ft 5 in
5 ft 6 in - 6 ft
More than 6 ft
Take an example of calculation of energy flow. Several units of energy occur like
Joules, gram-calories, kilogram-calories, British thermal units. Still more units of
energy per unit time (power) exist kilocalories per day, liters of oxygen per hour,
ergs, and Watts. This conversion of units is made possible by ratio scale.
However, the temperature cannot be measured on this scale because zero
degree celsius doesn’t mean there is no cold or heat for that matter. But most of
the scientific variables can be measured on this scale.
“What is your height in feet and inches?"
Tushar 20BC140
14. Scaling Methods
Comparative
Methods
Paired Comparison
Rank Order
Constant Sum
For comparing two or more variables, a
comparative scale is used by the
respondents. Following are the different
types of comparative scaling techniques:
Likert Scale
Semantic Differential Scale
Stapel Scale
A non-comparative scale is used to
analyse the performance of an individual
product or object on different
parameters. Following are some of its
most common types:
Non-Comparative
Methods
Nishant 20BC519
15. 01
Paired Comparison
A paired comparison
symbolizes two variables
from which the respondent
needs to select one.
02
Rank Order
In rank order scaling
the respondent needs
to rank or arrange the
given objects
according to his or
her preference.
03
Constant Sum
It is a scaling technique where
a continual sum of units like
points, is given to the features,
attributes and importance of a
particular product or service by
the respondents.
Comparative Methods
Nishant 20BC519
16. Paired Comparison Scale
The Paired Comparison Scaling is a comparative scaling technique wherein the
respondent is shown two objects at the same time and is asked to select one
according to the defined criterion. The resulting data are ordinal in nature.
It is often used when the stimulus objects are physical products. The
comparison data so obtained can be analyzed.
The paired comparison method is effective when the number of objects is
limited because it requires the direct comparison, and with a large number of
stimulus objects the comparison becomes cumbersome.
Nishant 20BC519
17. Examples of Paired Comparison Scale
Fig: Comparing employees with each other for performance appraisal by using paired
comparison scaling method
Nishant 20BC519
18. Rank Order Scale
The Rank Order Scaling is a yet another comparative scaling technique wherein
the respondents are presented with numerous objects simultaneously and are
required to order or rank these according to some specified criterion.
It is one of the most commonly used survey question types when the market
researcher would like to understand the order of importance of items when
there are multiple items.
The ranking is by a simple ordinal position where one variable or option is
higher than another or a relative position where one variable has a higher
relative rating than another. It could be on the basis of features, needs, likes or
dislikes, effectiveness, etc.
Nishant 20BC519
20. Constant Sum Scale
A constant sum scale is a type of question used in a market research survey in
which respondents are required to divide a specific number of points or
percents as part of a total sum. The allocation of points are divided to detail the
variance and weight of each category.
Constant sum scales are a less frequently used question in surveys when
compared to basic likert scales, single radio responses, or checklists. They are an
excellent way to create variance among a data set and truly understand which
factors are key and which are not for customers or respondents.
They are especially helpful if you need to ask a question to a customer or
respondent where you believe several factors are critical or of high importance.
Nishant 20BC519
22. 01
Likert Scale
The researcher provides
some statements and ask
the respondents to mark
their level of agreement or
disagreement over these
statements by selecting any
one of the options from the
five or seven given
alternatives.
02
Semantic
Differential Scale
A bi-polar non-comparative
rating scale is where the
respondent can mark on any
of the points for each given
attribute of the object as per
personal choice.
03
Stapel Scale
It is that itemized rating scale
which measures the response,
perception or attitude of the
respondents for a particular
object through a unipolar
rating.
Non-Comparative Methods
Nishant 20BC519
23. Likert Scale
A Likert scale is a unidimensional scale that researchers use to collect
respondents’ attitudes and opinions. Researchers often use this psychometric
scale to understand the views and perspectives towards a brand, product, or
target market.
Items are easily related to the sentence’s answers, regardless of whether the
relationship between item and sentence is evident. Also, the items have two
extreme positions and an intermediate answer option that serves as graduation
between the extremes.
It is better to use a scale as wide as possible like a 5 point or a 7 point scale
because one can always collapse the answers into concise groups, if
appropriate, for analysis.
Nishant 20BC519
25. Semantic Differential Scale
A semantic differential scale is a survey or questionnaire rating scale that asks
people to rate a product, company, brand, or any 'entity' within the frames of a
multi-point rating option.
These survey answering options are grammatically on opposite adjectives at
each end.
Respondents can express their opinions about the matter in hand more
accurately and entirely due to the polar options provided in the semantic
differential. The researcher declares a statement and expects respondents to
either agree or disagree with that.
For example, love-hate, satisfied-unsatisfied, and likely to return-unlikely to
return with intermediate options in between.
Nishant 20BC519
27. Stapel Scale
Stapel scale is defined as a rating scale that is close-ended with a single
adjective (unipolar), developed to gather respondent insights about a particular
subject or event. The survey question is comprised of an even number of
response options without a neutral point.
The stapel scale can be indicated as standalone questions or even as a matrix
question type where each line item is one unique adjective.
Since this is a non-comparative and categorical scale, it makes it very similar to
a semantic differential scale with the sole difference being the presence of only
adjective in between a positive and negative category rather than having two
opposing adjectives.
Nishant 20BC519
29. Reliability
Research reliability is the degree to
which research method produces
stable and consistent results. A
specific measure is considered to be
reliable if its application on the same
object of measurement number of
times produces the same results.
For Example:
Think of a scale to measure weight. You
would expect this scale to be consistent
from one time to the next. If you stepped
on the scale and it read 140 pounds, then
got off and back on, you would expect it
to again read 140. If it read 110 the second
time, the scale would not be reliable.
Kushal 20BC786
30. Validity
Validity is the accuracy of a measure or the extent to which
a score truthfully represents a concept. In other words, are
we accurately measuring what we think we are
measuring?
01
Content Validity
It refers to the degree that a
measure covers the domain of
interest. Do the items capture the
entire scope, but not go beyond,
the concept we are measuring?
02
Criterion Validity
It is the ability of a measure to
correlate with other standard
measures of similar constructs
or established criteria. It
addresses the question, “How
well does my measure work in
practice?”
03
Construct Validity
It exists when a measure reliably
measures and truthfully
represents a unique concept. It
is a great way of ensuring that
the measurement method
aligns well with the construct
that you aim to measure.
Kushal 20BC786
31. Primary Data
Primary Data refers to the first hand data
gathered by the researcher himself Primary
data are usually collected from the source—
where the data originally originates from and
are regarded as the best kind of data in
research.
Direct personal investigation
Indirect oral investigation
Information through
correspondents
Telephonic interview
Mailed questionnaire
The questionnaire filled by
enumerators
Sources of Data
Collection
Bharat 20BC778
32. Secondary Data
Secondary data is the data that has already
been collected through primary sources and
made readily available for researchers to use for
their own research. It is a type of data that has
already been collected in the past.
Books
Published Sources
Unpublished Personal
Sources
Journals
Newspapers
Blogs
Government Records
Sources of Data
Collection
Bharat 20BC778