Measuring and scaling of quantitative data khalidKhalid Mahmood
This document discusses measuring and scaling of quantitative data. It defines measurement as assigning numbers to describe a phenomenon's properties. Scaling involves aligning responses on a continuum to measure a construct's intensity. There are four levels of measurement: nominal, ordinal, interval, and ratio. The document also outlines the process of measurement development and different scaling methods and types of scales like Likert and semantic differential scales. Reliability ensures a scale is consistent, while validity ensures a scale accurately measures the intended concept.
There are two main types of attitudinal scales: rating scales and ranking scales. Rating scales measure responses regarding an object using categories, while ranking scales elicit preferences by comparing objects. Ten common rating scales are described, including Likert, semantic differential, and numerical scales. Ranking scales include paired comparisons and forced choice methods. Goodness of measures is ensured through item analysis, reliability, and validity testing. Reliability examines consistency over time through methods like test-retest and internal consistency. Validity assesses measuring the intended concept using techniques such as content, criterion, and construct validity.
This document discusses various methods of attitude measurement and scaling. It defines measurement as assigning numbers or symbols to characteristics of objects. Scaling extends measurement by creating a continuum on which measurements are located. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales. Attitude is defined as a disposition to respond consistently to the world and has affective, cognitive and behavioral components. Various methods are presented for measuring attitudes including single and multiple item scales, comparative scales using paired comparisons or rankings, non-comparative scales, and semantic differential or Likert scales. Good measurement considers reliability, validity and sensitivity.
This document discusses measurement and scaling techniques used in marketing research. It defines different types of measurement scales including nominal, ordinal, interval, and ratio scales. It also describes various scaling techniques such as paired comparison scaling, ranking scaling, constant sum scaling, Q-sort scaling, non-comparative scaling, continuous rating scales, Likert scales, semantic differential scales, and Stapel scales. The document emphasizes that reliability refers to a scale's ability to produce consistent results over multiple measurements, while validity is the extent to which a scale measures what it is intended to measure.
The document discusses measurement and scaling in marketing research. It defines key concepts like measurement, scales, and reliability and validity. It explains the four basic levels of measurement scales - nominal, ordinal, interval, and ratio scales. It also describes different scaling techniques like Likert scales, semantic differential scales, and behavioral intention scales. Scale development and evaluation of reliability and validity are important aspects of gathering primary data.
Scaling involves assigning quantitative values or symbols to subjective concepts or attributes. There are four main types of scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers as labels while ordinal scales indicate ranking or order. Interval scales show equal distances between scale points and ratio scales have an absolute zero point. Common scaling techniques include rating scales, which assign qualitative ratings, and ranking scales, which compare items. Paired comparisons and rank ordering are two approaches used in ranking scales.
Attitude scales and measures of emotions can be used to understand consumer evaluations and feelings toward products and advertising. Rating scales can measure overall attitude or specific attributes, and can be comparative (e.g. blind taste tests) or non-comparative. Common scaling techniques include paired comparisons, ranking, rating scales (e.g. Likert, semantic differential), and measuring response latency. Emotions are also important to measure, as feelings can impact behavior, and can be gauged using semantic differentials, pictures, or other scales. Reliability and validity are key in evaluating measurement scales.
This document discusses measuring variables and scales of measurement, including nominal, ordinal, interval, and ratio scales. It also discusses psychometric properties of reliability and validity. Reliability refers to the consistency or stability of scores and is measured through test-retest reliability, equivalent forms reliability, internal consistency, and interrater reliability. Validity refers to whether a test accurately measures what it intends to measure and is obtained through content validity, construct validity, and criterion validity. Reliability is necessary for validity but not sufficient on its own.
Measuring and scaling of quantitative data khalidKhalid Mahmood
This document discusses measuring and scaling of quantitative data. It defines measurement as assigning numbers to describe a phenomenon's properties. Scaling involves aligning responses on a continuum to measure a construct's intensity. There are four levels of measurement: nominal, ordinal, interval, and ratio. The document also outlines the process of measurement development and different scaling methods and types of scales like Likert and semantic differential scales. Reliability ensures a scale is consistent, while validity ensures a scale accurately measures the intended concept.
There are two main types of attitudinal scales: rating scales and ranking scales. Rating scales measure responses regarding an object using categories, while ranking scales elicit preferences by comparing objects. Ten common rating scales are described, including Likert, semantic differential, and numerical scales. Ranking scales include paired comparisons and forced choice methods. Goodness of measures is ensured through item analysis, reliability, and validity testing. Reliability examines consistency over time through methods like test-retest and internal consistency. Validity assesses measuring the intended concept using techniques such as content, criterion, and construct validity.
This document discusses various methods of attitude measurement and scaling. It defines measurement as assigning numbers or symbols to characteristics of objects. Scaling extends measurement by creating a continuum on which measurements are located. There are different types of measurement scales including nominal, ordinal, interval, and ratio scales. Attitude is defined as a disposition to respond consistently to the world and has affective, cognitive and behavioral components. Various methods are presented for measuring attitudes including single and multiple item scales, comparative scales using paired comparisons or rankings, non-comparative scales, and semantic differential or Likert scales. Good measurement considers reliability, validity and sensitivity.
This document discusses measurement and scaling techniques used in marketing research. It defines different types of measurement scales including nominal, ordinal, interval, and ratio scales. It also describes various scaling techniques such as paired comparison scaling, ranking scaling, constant sum scaling, Q-sort scaling, non-comparative scaling, continuous rating scales, Likert scales, semantic differential scales, and Stapel scales. The document emphasizes that reliability refers to a scale's ability to produce consistent results over multiple measurements, while validity is the extent to which a scale measures what it is intended to measure.
The document discusses measurement and scaling in marketing research. It defines key concepts like measurement, scales, and reliability and validity. It explains the four basic levels of measurement scales - nominal, ordinal, interval, and ratio scales. It also describes different scaling techniques like Likert scales, semantic differential scales, and behavioral intention scales. Scale development and evaluation of reliability and validity are important aspects of gathering primary data.
Scaling involves assigning quantitative values or symbols to subjective concepts or attributes. There are four main types of scales: nominal, ordinal, interval, and ratio. Nominal scales use numbers as labels while ordinal scales indicate ranking or order. Interval scales show equal distances between scale points and ratio scales have an absolute zero point. Common scaling techniques include rating scales, which assign qualitative ratings, and ranking scales, which compare items. Paired comparisons and rank ordering are two approaches used in ranking scales.
Attitude scales and measures of emotions can be used to understand consumer evaluations and feelings toward products and advertising. Rating scales can measure overall attitude or specific attributes, and can be comparative (e.g. blind taste tests) or non-comparative. Common scaling techniques include paired comparisons, ranking, rating scales (e.g. Likert, semantic differential), and measuring response latency. Emotions are also important to measure, as feelings can impact behavior, and can be gauged using semantic differentials, pictures, or other scales. Reliability and validity are key in evaluating measurement scales.
This document discusses measuring variables and scales of measurement, including nominal, ordinal, interval, and ratio scales. It also discusses psychometric properties of reliability and validity. Reliability refers to the consistency or stability of scores and is measured through test-retest reliability, equivalent forms reliability, internal consistency, and interrater reliability. Validity refers to whether a test accurately measures what it intends to measure and is obtained through content validity, construct validity, and criterion validity. Reliability is necessary for validity but not sufficient on its own.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
This document discusses various concepts related to measurement in research including defining measurement, different types of measurement scales (nominal, ordinal, interval, ratio), sources of error in measurement, tests of sound measurement including validity, reliability and practicality. It also covers scaling techniques like rating scales and different scaling methods. Rating scales can be graphic or itemized and have benefits but also limitations like errors of leniency, central tendency and halo effect. Measurement is important for accurately assessing both physical and abstract concepts in research.
Nominal scales categorize objects without order, while ordinal scales rank objects with order but unknown differences. Interval scales have order and known differences, and ratio scales have a true zero point. Comparative scales require comparing objects, while non-comparative scales evaluate objects independently. Common non-comparative scales include line marking scales, itemized rating scales like Likert scales, and semantic differential scales. Reliability ensures consistency and validity ensures accuracy in measuring constructs. Reliability is necessary for validity.
Comparative and Non-Comparative Scaling TechniquesVarsha Prakash
This document discusses and compares various scaling techniques used in business research methods. It describes comparative scaling techniques like pairwise comparison and rank-ordering that directly compare items, as well as non-comparative techniques like Likert scales and semantic differential scales that independently scale each item. The document also discusses different data types that can be measured, including nominal, ordinal, interval and ratio levels, and how this influences scale construction. It concludes by emphasizing the importance of testing scales for reliability, validity and generalizability.
This document discusses research tools and techniques used for data collection. It describes the research process and defines a research tool as a mechanism used to collect, manipulate, or interpret data. Some common tools for data collection include questionnaires, rating scales, checklists, attitude scales, tests, and inventories. Techniques include interviews and observation. The document then provides details on questionnaires, structured vs. unstructured questionnaires, closed vs. open forms, and levels of measurement including interval and ratio scales. It concludes by outlining characteristics of a good research tool, including validity, reliability, objectivity, adequacy, and usability.
This document discusses various attitude scaling techniques used in business research. It covers:
1) Comparative scales like paired comparison, rank order rating, and constant sum scales which compare objects simultaneously.
2) Non-comparative scales like continuous rating and itemized rating (e.g. Likert scales) which rate characteristics of a single object.
3) Specific scales discussed include semantic differential scales, Stapel scales, Thurston scales, and Guttman scales - each with their own unique approach to measuring attitudes.
Chapter 13 Measuremen and Scaling Concept Slides.pptRajjaRashad1
The document discusses key concepts in measurement and scaling for business research. It covers determining what to measure based on the research question, different levels of scale measurement (nominal, ordinal, interval, ratio), how to operationalize concepts, methods for assessing reliability and validity of measures, and how to develop composite or index measures. The goal is to understand how to properly measure concepts both concretely and abstractly for research purposes.
The document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale and the types of numerical operations that can be performed on data for each scale. Nominal scales involve simple sorting into categories while ratio scales allow for absolute comparisons between values. The document also covers various rating scale formats researchers can use to measure attributes, including Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are discussed as important aspects of ensuring measurement instruments accurately measure the intended constructs.
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.
This document discusses establishing validity and reliability in test papers. It provides definitions and methods for ensuring reliability and validity. Reliability refers to consistency of test results and is measured through various methods like test-retest reliability and internal consistency. Validity refers to a test measuring what it intends to measure and includes content validity, predictive validity, concurrent validity, and construct validity which are established through statistical analyses and expert evaluation.
The document discusses the reliability of language tests. It defines reliability as the ability of a test to consistently produce the same results under the same conditions. There are different types of reliability: test-retest reliability measures consistency over time; parallel forms reliability uses different but comparable test forms; and internal consistency examines consistency between parts of the same test using methods like split-half reliability and Cronbach's alpha. Reliability can be affected by factors like test length, range of scores, and item similarity. Ensuring high reliability is important so tests accurately measure constructs without measurement error.
The document discusses various concepts related to measurement and scaling in research. It defines measurement as assigning numbers or symbols to characteristics of objects based on rules. Scaling is locating measured objects on a continuum according to these rules. There are four main types of measurement scales discussed - nominal, ordinal, interval, and ratio scales - which differ in the meanings assigned to numbers. The document also covers topics like variables, constructs, indexes, types of scales like comparative and non-comparative, and decisions to consider when constructing scales.
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 method. c17 2nd sem IISWBM.pptxSagnikSanyal2
Hey there! I just wanted to share this awesome PowerPoint presentation with you. It has all the answers to the theoretical questions for the second semester C17 paper at IISWBM, Calcutta University. This material was taught by Sumanti Mam and JD Sir, and it really helped our whole batch ace the theory questions in the 2nd semester of 2024. I hope it will be just as helpful for the new junior students too.
By the way, I'm an MBA day student for the 2023-2025 academic year.
Connect with me: www.linkedin.com/in/sagnik-sanyal
This document discusses measurement and scaling techniques used in research. It defines measurement as associating numbers or symbols to observations. Measurement can be qualitative or quantitative, and some characteristics like motivation are more difficult to measure than tangible properties like weight.
Scaling involves placing measured objects on a continuum based on how they differ. There are four main types of measurement scales - nominal, ordinal, interval, and ratio scales - which differ in the types of statistical analyses that can be used. The document also discusses various scaling techniques like paired comparisons, ranking, and rating scales. Sources of error in measurement can come from respondents, the measurement situation, the measurer, or the instrument itself. Successful measurement development involves concept development, specifying dimensions, selecting indicators
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
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale type and explains their key characteristics. Nominal scales involve categorization while ordinal scales allow for ranking. Interval and ratio scales are quantitative with equal intervals between scale points. Common scaling techniques like Likert scales, semantic differentials, and Q-sorting are also overviewed.
Measurement involves assigning numbers or symbols to characteristics of objects to provide an accurate description. Scaling extends measurement by creating a continuum on which measurements are located. There are four main types of measurement scales: nominal, ordinal, interval, and ratio scales. Scales can be single item or multiple item, and comparative or non-comparative. Common scales include Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are important criteria for evaluating the quality of measurement scales.
This document discusses reliability and validity in psychological testing. It defines reliability as the consistency and repeatability of test scores. There are several types of reliability: test-retest, parallel forms, inter-rater, and internal consistency. Validity refers to how well a test measures what it intends to measure. There are different aspects of validity including internal, external, content, face, criterion, construct, convergent, and discriminant validity. Reliability is a necessary but not sufficient condition for validity - a test can be reliable without being valid if it does not accurately measure the intended construct.
Concept of Measurements in Business ResearchCS PRADHAN
Measurement is a fundamental concept in business research used to quantify variables and enable comparison. It requires defining what is to be measured and how through operational definitions. There are four levels of measurement - nominal, ordinal, interval, and ratio - determined by the characteristics of order, distance, and origin represented. Validity and reliability are important criteria for any measurement and various techniques like rating, ranking, and sorting are used depending on whether the concept is simple or complex.
This document discusses various methods of measurement and scaling used in research. It describes four main types of measurement scales: nominal, ordinal, interval, and ratio scales. It also discusses potential sources of error in measurement, ways to test the validity and reliability of measurement tools, and different types of scales including comparative scales like paired comparisons and non-comparative scales like Likert scales. Finally, it outlines the process of developing a new measurement tool, including concept development, indicator selection, and index formation.
This document discusses various concepts related to measurement in research including defining measurement, different types of measurement scales (nominal, ordinal, interval, ratio), sources of error in measurement, tests of sound measurement including validity, reliability and practicality. It also covers scaling techniques like rating scales and different scaling methods. Rating scales can be graphic or itemized and have benefits but also limitations like errors of leniency, central tendency and halo effect. Measurement is important for accurately assessing both physical and abstract concepts in research.
Nominal scales categorize objects without order, while ordinal scales rank objects with order but unknown differences. Interval scales have order and known differences, and ratio scales have a true zero point. Comparative scales require comparing objects, while non-comparative scales evaluate objects independently. Common non-comparative scales include line marking scales, itemized rating scales like Likert scales, and semantic differential scales. Reliability ensures consistency and validity ensures accuracy in measuring constructs. Reliability is necessary for validity.
Comparative and Non-Comparative Scaling TechniquesVarsha Prakash
This document discusses and compares various scaling techniques used in business research methods. It describes comparative scaling techniques like pairwise comparison and rank-ordering that directly compare items, as well as non-comparative techniques like Likert scales and semantic differential scales that independently scale each item. The document also discusses different data types that can be measured, including nominal, ordinal, interval and ratio levels, and how this influences scale construction. It concludes by emphasizing the importance of testing scales for reliability, validity and generalizability.
This document discusses research tools and techniques used for data collection. It describes the research process and defines a research tool as a mechanism used to collect, manipulate, or interpret data. Some common tools for data collection include questionnaires, rating scales, checklists, attitude scales, tests, and inventories. Techniques include interviews and observation. The document then provides details on questionnaires, structured vs. unstructured questionnaires, closed vs. open forms, and levels of measurement including interval and ratio scales. It concludes by outlining characteristics of a good research tool, including validity, reliability, objectivity, adequacy, and usability.
This document discusses various attitude scaling techniques used in business research. It covers:
1) Comparative scales like paired comparison, rank order rating, and constant sum scales which compare objects simultaneously.
2) Non-comparative scales like continuous rating and itemized rating (e.g. Likert scales) which rate characteristics of a single object.
3) Specific scales discussed include semantic differential scales, Stapel scales, Thurston scales, and Guttman scales - each with their own unique approach to measuring attitudes.
Chapter 13 Measuremen and Scaling Concept Slides.pptRajjaRashad1
The document discusses key concepts in measurement and scaling for business research. It covers determining what to measure based on the research question, different levels of scale measurement (nominal, ordinal, interval, ratio), how to operationalize concepts, methods for assessing reliability and validity of measures, and how to develop composite or index measures. The goal is to understand how to properly measure concepts both concretely and abstractly for research purposes.
The document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale and the types of numerical operations that can be performed on data for each scale. Nominal scales involve simple sorting into categories while ratio scales allow for absolute comparisons between values. The document also covers various rating scale formats researchers can use to measure attributes, including Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are discussed as important aspects of ensuring measurement instruments accurately measure the intended constructs.
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.
This document discusses establishing validity and reliability in test papers. It provides definitions and methods for ensuring reliability and validity. Reliability refers to consistency of test results and is measured through various methods like test-retest reliability and internal consistency. Validity refers to a test measuring what it intends to measure and includes content validity, predictive validity, concurrent validity, and construct validity which are established through statistical analyses and expert evaluation.
The document discusses the reliability of language tests. It defines reliability as the ability of a test to consistently produce the same results under the same conditions. There are different types of reliability: test-retest reliability measures consistency over time; parallel forms reliability uses different but comparable test forms; and internal consistency examines consistency between parts of the same test using methods like split-half reliability and Cronbach's alpha. Reliability can be affected by factors like test length, range of scores, and item similarity. Ensuring high reliability is important so tests accurately measure constructs without measurement error.
The document discusses various concepts related to measurement and scaling in research. It defines measurement as assigning numbers or symbols to characteristics of objects based on rules. Scaling is locating measured objects on a continuum according to these rules. There are four main types of measurement scales discussed - nominal, ordinal, interval, and ratio scales - which differ in the meanings assigned to numbers. The document also covers topics like variables, constructs, indexes, types of scales like comparative and non-comparative, and decisions to consider when constructing scales.
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 method. c17 2nd sem IISWBM.pptxSagnikSanyal2
Hey there! I just wanted to share this awesome PowerPoint presentation with you. It has all the answers to the theoretical questions for the second semester C17 paper at IISWBM, Calcutta University. This material was taught by Sumanti Mam and JD Sir, and it really helped our whole batch ace the theory questions in the 2nd semester of 2024. I hope it will be just as helpful for the new junior students too.
By the way, I'm an MBA day student for the 2023-2025 academic year.
Connect with me: www.linkedin.com/in/sagnik-sanyal
This document discusses measurement and scaling techniques used in research. It defines measurement as associating numbers or symbols to observations. Measurement can be qualitative or quantitative, and some characteristics like motivation are more difficult to measure than tangible properties like weight.
Scaling involves placing measured objects on a continuum based on how they differ. There are four main types of measurement scales - nominal, ordinal, interval, and ratio scales - which differ in the types of statistical analyses that can be used. The document also discusses various scaling techniques like paired comparisons, ranking, and rating scales. Sources of error in measurement can come from respondents, the measurement situation, the measurer, or the instrument itself. Successful measurement development involves concept development, specifying dimensions, selecting indicators
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
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It provides examples of each scale type and explains their key characteristics. Nominal scales involve categorization while ordinal scales allow for ranking. Interval and ratio scales are quantitative with equal intervals between scale points. Common scaling techniques like Likert scales, semantic differentials, and Q-sorting are also overviewed.
Measurement involves assigning numbers or symbols to characteristics of objects to provide an accurate description. Scaling extends measurement by creating a continuum on which measurements are located. There are four main types of measurement scales: nominal, ordinal, interval, and ratio scales. Scales can be single item or multiple item, and comparative or non-comparative. Common scales include Likert scales, semantic differential scales, and graphic rating scales. Reliability and validity are important criteria for evaluating the quality of measurement scales.
This document discusses reliability and validity in psychological testing. It defines reliability as the consistency and repeatability of test scores. There are several types of reliability: test-retest, parallel forms, inter-rater, and internal consistency. Validity refers to how well a test measures what it intends to measure. There are different aspects of validity including internal, external, content, face, criterion, construct, convergent, and discriminant validity. Reliability is a necessary but not sufficient condition for validity - a test can be reliable without being valid if it does not accurately measure the intended construct.
Concept of Measurements in Business ResearchCS PRADHAN
Measurement is a fundamental concept in business research used to quantify variables and enable comparison. It requires defining what is to be measured and how through operational definitions. There are four levels of measurement - nominal, ordinal, interval, and ratio - determined by the characteristics of order, distance, and origin represented. Validity and reliability are important criteria for any measurement and various techniques like rating, ranking, and sorting are used depending on whether the concept is simple or complex.
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These lecture slides, by Dr Sidra Arshad, offer a quick overview of the physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...Donc Test
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Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
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Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
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Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
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3. Scaling
Scaling is a procedure for the assignment
of numbers (or other symbols) to a
property of objects in order to import
some of the characteristics of numbers to
properties in question
4. Methods of Scaling
Rating scales
Have several response categories and are
used to elicit responses with regard to the
object, event, or person studied.
Ranking scales
Make comparisons between or among
objects, events, persons and elicit the
preferred choices and ranking among
them.
7. Rating Scales (Cont’d)
Category scale
Uses multiple items to elicit a single
response.
Nominal scale
8. Category Scale
Where in northern California do you reside?
North Bay
South Bay
East Bay
Peninsula
Other (specify:_____________)
9. Rating Scales (Cont’d)
Likert scale
Is designed to examine how strongly
subjects agree or disagree with statements
on a 5-point scale.
Interval scale
10. Likert Scale
My work is very interesting
Strongly disagree
Disagree
Neither agree nor disagree
Agree
Strongly agree
11. Rating Scales (Cont’d)
Semantic differential scale
Several bipolar attributes are identified at
the extremes of the scale, and respondents
are asked to indicate their attitudes.
Interval scale
13. Rating Scales (Cont’d)
Numerical scale
Similar to the semantic differential scale,
with the difference that numbers on a 5-
point or 7-point scale are provided, with
bipolar adjectives at both ends.
Interval scale
14. Numerical Scale
How pleased are you with your new real estate
agent?
Extremely 7 6 5 4 3 2 1 Extremely
Pleased Displeased
15. Rating Scales (Cont’d)
Itemized rating scale
A 5-point or 7-point scale with anchors, as
needed, is provided for each item and the
respondent states the appropriate number
on the side of each item, or circles the
relevant number against each item.
Interval scale
16. Itemized Rating Scale
1 2 3 4 5
Very Unlikely Unlikely Neither Unlikely Likely Very Likely
Nor Likely
1. I will be changing my job within the next 12 months
17. Rating Scales (Cont’d)
Fixed or constant sum scale
The respondents are here asked to
distribute a given number of points across
various items.
Ordinal scale
19. Rating Scales (Cont’d)
Stapel scale
This scale simultaneously measure both the
direction and intensity of the attitude
toward the items under study.
Interval data
21. Rating Scales (Cont’d)
Graphic rating scale
A graphical representation helps the
respondents to indicate on this scale their
answers to particular question by placing a
mark at the appropriate point on the line.
Ordinal scale
27. Ranking Scales (Cont’d)
Comparative Scale
Provides a benchmark or a point of reference
to assess attitudes toward the current object,
event, or situation under study.
29. Goodness of Measures
Reliability
Indicates the extent to which it is without
bias (error free) and hence ensures
consistent measurement across time and
across the various items in the instrument.
31. Goodness of Measures
(Cont’d)
Validity
Ensures the ability of a scale to measure the
intended concept.
Content validity
Criterion related validity
Construct validity
32. Validity
Content validity
Ensures that the measure includes an
adequate and representative set of items
that tap the concept.
A panel of judges
33. Validity (Cont’d)
Criterion related validity
Is established when the measure
differentiates individuals on a criterion it is
expected to predict
Concurrent validity: established when the
scale differentiates individuals who are known
to be different
Predictive validity: indicates the ability of
measuring instrument to differentiate among
individuals with reference to future criterion
Correlation
34. Validity (Cont’d)
Construct validity
Testifies to how well the results obtained from the
use of the measure fit the theories around which the
test is designed.
Convergent validity: established when the scores
obtained with two different instrument measuring the
same concept are highly correlated
Discriminant validity: established when, based on
theory, two variables are predicted to be uncorrelated,
and the scores obtained by measuring them are indeed
empirically found to be so
Correlation, factor analysis, convergent-discriminant
techniques, multitrait-multimethod analysis