Scope of research - Research Methodology - Manu Melwin Joymanumelwin
Technological innovations: Research is conducted to know & adapt new technological innovations, developments in machinery, method, etc. used . For e.g., to know level of use of information technology e.g. Networking, Tally, SAP, etc. in the organization.
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.
Scope of research - Research Methodology - Manu Melwin Joymanumelwin
Technological innovations: Research is conducted to know & adapt new technological innovations, developments in machinery, method, etc. used . For e.g., to know level of use of information technology e.g. Networking, Tally, SAP, etc. in the organization.
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.
Data Collection tools: Questionnaire vs ScheduleAmit Uraon
Questionnaire is one of the important method of data collection in which a researcher distributes a questionnaire to the respondents and requests them to fill up the questionnaire and return.
Same way Schedule is also a set of structured questions and the answers in questionnaire is not filled up by respondents themselves but by enumerators.
A research design is the overall plan or programme of research. It is the general blueprint for the collection, measurement and analysis of data.
Research design is nothing but a scheme of work to be undertaken by a researcher at various stages.
Editing is the essential thing for any type of the things. whether it is a movie, report, research project, short films. But while coming to the point of research methodology it will play an important role.....
Its a fully detailed topic about Editing , Coding, Tabulation o Data in research work.
The editing , coding , tabulation of data is been explained in this ppt.
Methods of data collection (research methodology)Muhammed Konari
Included all types of data collection.Includes primary data collection and secondary data collection. Described each and every classification of Data collections which are included in KTU Kerala.
Research Methods vs Research MethodologySundar B N
This ppt elaborate Research Methods vs Research Methodology which covers Research Methods Versus Methodology, Research Methods, Research Methodology, Difference Between Research Methods and Methodology.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Characteristics of a Good Sample
Representativeness
Absence of sampling error
Economically viable
Generalized and applicable
Goal oriented
Proportional
Randomly Selected
Actual information provider
Practical
Data Collection tools: Questionnaire vs ScheduleAmit Uraon
Questionnaire is one of the important method of data collection in which a researcher distributes a questionnaire to the respondents and requests them to fill up the questionnaire and return.
Same way Schedule is also a set of structured questions and the answers in questionnaire is not filled up by respondents themselves but by enumerators.
A research design is the overall plan or programme of research. It is the general blueprint for the collection, measurement and analysis of data.
Research design is nothing but a scheme of work to be undertaken by a researcher at various stages.
Editing is the essential thing for any type of the things. whether it is a movie, report, research project, short films. But while coming to the point of research methodology it will play an important role.....
Its a fully detailed topic about Editing , Coding, Tabulation o Data in research work.
The editing , coding , tabulation of data is been explained in this ppt.
Methods of data collection (research methodology)Muhammed Konari
Included all types of data collection.Includes primary data collection and secondary data collection. Described each and every classification of Data collections which are included in KTU Kerala.
Research Methods vs Research MethodologySundar B N
This ppt elaborate Research Methods vs Research Methodology which covers Research Methods Versus Methodology, Research Methods, Research Methodology, Difference Between Research Methods and Methodology.
Subscribe to Vision Academy for Video assistance
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
Characteristics of a Good Sample
Representativeness
Absence of sampling error
Economically viable
Generalized and applicable
Goal oriented
Proportional
Randomly Selected
Actual information provider
Practical
Scaling is the process of measuring or ordering entities with respect to quantitative attributes or traits. With comparative scaling, the items are directly compared with each other .In non -comparative scaling each item is scaled independently of the others.
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.
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Research methodlogy unit-iv-measurement and data preperation_For BBA_B.com_M...Manoj Kumar
This PPT will be helpful understanding Research Methodology concepts like
Measurement
Types of Scales
Scaling Technique
Data Processing
Data Analysis & Interpretation
Displaying of Data
Links for other units are also given below kindly use that too.
Unit-I
https://www2.slideshare.net/ManojKumar730/research-methodology-unitiresearch-and-its-various-process
Unit-II
https://www2.slideshare.net/ManojKumar730/research-methodology-unit-iidata-collection
Unit-iii
https://www2.slideshare.net/ManojKumar730/research-methodlogy-unitiiisampling
Unit-IV
https://www2.slideshare.net/ManojKumar730/research-methodlogy-unitivmeasurement-and-data-preperationfor-bbabcommba-and-for-other-ug-and-pg-students
Unit-V
https://www2.slideshare.net/ManojKumar730/research-methodlogy-unitvreseach-report-for-bcom-bba-mba-and-other-ug-and-pg-courses
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
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Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
2. Objectives
Introduction
Measurement and Scaling
Issues in Attitude Measurement
Levels of Measurement Scales
Types of Scaling Techniques
# Comparative Scales
# Non-comparative Scales
Selection of an Appropriate Scaling Technique
Conclusion
Key Words
3. The Measurement & Scaling Technique helps us to :
explain the concepts of measurement and scaling,
discuss four levels of measurement scales,
classify and discuss different scaling techniques, and
select an appropriate attitude measurement scale for
our research problem.
4. As we discussed earlier, the data consists of quantitative
variables, like price, income, sales etc., and qualitative
variables like knowledge, performance , character etc. The
qualitative information must be converted into numerical
form for further analysis. This is possible through
measurement and scaling techniques. A common feature of
survey based research is to have respondent’s
feelings, attitudes, opinions, etc. in some measurable
form.
5. Before we proceed further it will be worthwhile to understand
the following two terms: (a) Measurement, and (b) Scaling.
a) Measurement: Measurement is the process of observing and
recording the observations that are collected as part of research. The
recording of the observations may be in terms of numbers or other
symbols to characteristics of objects according to certain prescribed rules.
The respondent’s, characteristics are feelings, attitudes, opinions etc.
The most important aspect of measurement is the specification of rules
for assigning numbers to characteristics. The rules for assigning numbers
should be standardized and applied uniformly. This must not change over
time or objects.
b) Scaling: Scaling is the assignment of objects to numbers or semantics
according to a rule. In scaling, the objects are text statements, usually
statements of attitude, opinion, or feeling.
6. When a researcher is interested in measuring the
attitudes, feelings or opinions of respondents he/she should
be clear about the following:
a) What is to be measured?
b) Who is to be measured?
c) The choices available in data collection techniques
7. The level of measurement refers to the relationship among
the values that are assigned to the attributes, feelings or
opinions for a variable.
Typically, there are four levels of measurement scales or
methods of assigning numbers:
(a) Nominal scale,
(b) Ordinal scale,
(c) Interval scale, and
(d) Ratio scale.
8. Nominal Scale is the crudest among all measurement
scales but it is also the simplest scale. In this scale the different
scores on a measurement simply indicate different categories.
The nominal scale does not express any values or relationships
between variables.
The nominal scale is often referred to as a categorical scale.
The assigned numbers have no arithmetic properties and act
only as labels. The only statistical operation that can be
performed on nominal scales is a frequency count. We cannot
determine an average except mode.
For example: labeling men as ‘1’ and women as ‘2’ which is the
most common way of labeling gender for data recording
purpose does not mean women are ‘twice something or other’
than men. Nor it suggests that men are somehow ‘better’ than
women.
9. Ordinal Scale involves the ranking of items along the
continuum of the characteristic being scaled. In this scale, the
items are classified according to whether they have more or
less of a characteristic.
The main characteristic of the ordinal scale is that the
categories have a logical or ordered relationship. This type of
scale permits the measurement of degrees of
difference, (i.e. ‘more’ or ‘less’) but not the specific amount
of differences (i.e. how much ‘more’ or ‘less’). This scale is
very common
in marketing, satisfaction and attitudinal research.
Using ordinal scale data, we can perform statistical analysis
like Median and Mode, but not the Mean.
For example, a fast food home delivery shop may wish to ask
its customers:
How would you rate the service of our staff?
(1) Excellent • (2) Very Good • (3) Good • (4) Poor • (5) Worst •
10. Interval Scale is a scale in which the numbers are used to
rank attributes such that numerically equal distances on the scale
represent equal distance in the characteristic being measured. An
interval scale contains all the information of an ordinal scale, but it
also one allows to compare the difference/distance between
attributes. Interval scales may be either in numeric or semantic
formats.
The interval scales allow the calculation of averages like
Mean, Median and Mode and dispersion like Range and Standard
Deviation.
For example, the difference between ‘1’ and ‘2’ is equal to
the difference between ‘3’ and ‘4’. Further, the difference between
‘2’ and ‘4’ is twice the difference between ‘1’ and ‘2’.
Measuring temperature is an example of interval scale. But, we
cannot say 40°C is twice as hot as 20°C.
11.
12. Ratio Scale is the highest level of measurement scales. This
has the properties of an interval scale together with a fixed (absolute)
zero point. The absolute zero point allows us to construct a
meaningful ratio.
Ratio scales permit the researcher to compare both differences in
scores and relative magnitude of scores. Examples of ratio scales
include weights, lengths and times.
For example, the number of customers of a bank’s ATM in the last
three months is a ratio scale. This is because you can compare this
with previous three months.
For example, the difference between 10 and 15 minutes is the same as
the difference between 25 and 30 minutes and 30 minutes is twice as
long as 15 minutes
13.
14. In comparative scaling, the respondent is asked to
compare one object with another.
The comparative scales can further be divided into the
following four types of scaling techniques:
(a) Paired Comparison Scale,
(b) Rank Order Scale,
(c) Constant Sum Scale, and
(d) Q-sort Scale.
15. Paired Comparison Scale:
This is a comparative scaling technique in which a
respondent is presented with two objects at a time and
asked to select one object according to some criterion. The
data obtained are ordinal in nature.
For example, there are four types of cold drinks Coke, Pepsi, Sprite, and Limca. The respondents can prefer
Pepsi to Coke or Coke to Sprite, etc.
16. Rank Order Scale:
This is another type of comparative scaling technique in
which respondents are presented with several items
simultaneously and asked to rank them in the order of
priority. This is an ordinal scale that describes the
favoured and unfavoured objects, but does not reveal
the distance between the objects.
The resultant data in rank order is ordinal data. This
yields better results when direct comparison are
required between the given objects.
The major disadvantage of this technique is that only
ordinal data can be generated.
17.
18. Constant Sum Scale:
In this scale, the respondents are asked to allocate a constant
sum of units such as points, rupees, or chips among a set of
stimulus objects with respect to some criterion.
For example, you may wish to determine how important the
attributes of price, fragrance, packaging, cleaning power, and
lather of a detergent are to consumers. Respondents might
be asked to divide a constant sum to indicate the relative
importance of the attributes.
The advantage of this technique is saving time.
However, main disadvantages are the respondents may
allocate more or fewer points than those specified. The
second problem is respondents might be confused.
19.
20. Q-Sort Scale:
This is a comparative scale that uses a rank order procedure to sort
objects based on similarity with respect to some criterion. The
important characteristic of this methodology is that it is more
important to make comparisons among different responses of a
respondent than the responses between different respondents.
Therefore, it is a comparative method of scaling rather than an
absolute rating scale. In this method the respondent is given
statements in a large number for describing the characteristics of a
product or a large number of brands of a product.
21.
22. In non-comparative scaling respondents need only
evaluate a single object. Their evaluation is independent
of the other object which the researcher is studying.
The non-comparative scaling techniques can be further
divided into:
(a)Continuous Rating Scale, and
(b)Itemized Rating Scale.
23. Continuous Rating Scales :
It is very simple and highly useful. In continuous rating scale, the
respondent’s rate the objects by placing a mark at the
appropriate position on a continuous line that runs from one
extreme of the criterion variable to the other.
Example :
Question: How would you rate the TV advertisement as a guide
for buying?
24.
25. Itemized Rating Scales :
Itemized rating scale is a scale having numbers or brief
descriptions associated with each category. The categories are
ordered in terms of scale position and the respondents are
required to select one of the limited number of categories
that best describes the product, brand, company, or product
attribute being rated. Itemized rating scales are widely used in
marketing research.
Itemised rating scales is further divided into three parts, namely
(a) Likert scale,
(b) Semantic Differential Scale, and
(c) Stapel Scale.
26. The itemised rating scales can be in the form of : (a) graphic, (b)
verbal, or (c) numeric as shown below :
27.
28.
29. Likert Scale:
Likert, is extremely popular for measuring attitudes, because, the
method is simple to administer. With the Likert scale, the
respondents indicate their own attitudes by checking how strongly
they agree or disagree with carefully worded statements that range
from very positive to very negative towards the attitudinal
object. Respondents generally choose from five alternatives (say
strongly agree, agree, neither agree nor disagree, disagree, strongly
disagree).
A Likert scale may include a number of items or statements.
Disadvantage of Likert Scale is that it takes longer time to complete
than other itemised rating scales because respondents have to read
each statement.
Despite the above disadvantages, this scale has several advantages.
It is easy to construct, administer and use.
30.
31. Semantic Differential Scale:
This is a seven point rating scale with end points associated with
bipolar labels (such as good and bad, complex and simple) that
have semantic meaning. It can be used to find whether a
respondent has a positive or negative attitude towards an object.
It has been widely used in comparing brands, products and
company images. It has also been used to develop advertising and
promotion strategies and in a new product development study.
32.
33. Staple Scale:
The Stapel scale was originally developed to measure the
direction and intensity of an attitude simultaneously. Modern
versions of the Stapel scale place a single adjective as a substitute
for the Semantic differential when it is difficult to create pairs of
bipolar adjectives. The modified Stapel scale places a single
adjective in the centre of an even number of numerical Values.
34.
35.
36. A number of issues decide the choice of scaling technique. Some
significant issues are:
1) Problem Definition and Statistical Analysis,
2) The Choice between Comparative and Non-comparative
Scales,
3) Type of Category Labels,
4) Number of Categories,
5) Balanced versus Unbalanced Scale, and
6) Forced versus Non-forced Categories
37. 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.