This document discusses different types of scales used in measurement, including nominal, ordinal, interval, and ratio scales. Each scale has different properties in terms of magnitude, equal intervals between numbers, and whether it has an absolute zero point. Nominal scales only allow categorization while ratio scales have all properties, allowing calculations like ratios. The document also covers different rating techniques used to measure constructs, such as Likert scales, semantic differential scales, Thurstone scales, and Guttman scaling.
This variable is nominal. It classifies respondents into categories (married, widowed, divorced, etc.) without implying any rank among them. The numbers assigned to the categories (1, 2, 3, etc.) have no mathematical meaning.
The document discusses various concepts related to measurement, scaling, instrument design, and sampling. It defines measurement as assigning numbers to objects or observations according to specific rules. There are four main types of measurement scales discussed - nominal, ordinal, interval, and ratio scales - which differ in the types of mathematical operations and statistical analyses that can be conducted. Good measurement is reliable, valid, and practical. Reliability refers to consistency over time, validity is the ability to measure what is intended, and practicality considers cost, convenience and interpretability.
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.
The level of measurement for gender in this case study is nominal. Gender (male or female) consists of distinct categories without quantitative properties that allow comparisons in terms of magnitude.
Case 2: The effect of Project-Based Learning (PBL)
on the grades of students was studied among
college students. Grades were either A, B, C, D, or F.
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 document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It explains the key properties and appropriate statistical analyses for each scale type. Nominal scales involve simple categorization while ratio scales allow for all types of mathematical comparisons. The document also outlines important aspects of measurement such as validity, reliability, practicality, and potential sources of error. Overall, it provides an overview of measurement fundamentals for research studies.
This document provides an introduction to statistics and research design. It defines key terms like descriptive statistics, inferential statistics, populations, samples, variables, hypothesis testing, reliability and validity, research designs, correlation analysis, regression analysis, and outlier analysis. Descriptive statistics involves organizing and summarizing data, while inferential statistics allows estimating characteristics of populations based on samples. Samples are subsets of populations used to make inferences. The goal of research is to accurately measure concepts in a reliable and valid way to test hypotheses and understand relationships between variables.
The document discusses various methods for scaling and measurement in research. It describes four main types of scales: nominal, ordinal, interval, and ratio scales. It also discusses sources of error in measurement and characteristics of sound measurement, including validity, reliability, and practicality. The document provides examples of different scaling techniques used in research, including rating scales, attitude scales, Thurstone scales, Likert scales, and semantic differential scales.
This variable is nominal. It classifies respondents into categories (married, widowed, divorced, etc.) without implying any rank among them. The numbers assigned to the categories (1, 2, 3, etc.) have no mathematical meaning.
The document discusses various concepts related to measurement, scaling, instrument design, and sampling. It defines measurement as assigning numbers to objects or observations according to specific rules. There are four main types of measurement scales discussed - nominal, ordinal, interval, and ratio scales - which differ in the types of mathematical operations and statistical analyses that can be conducted. Good measurement is reliable, valid, and practical. Reliability refers to consistency over time, validity is the ability to measure what is intended, and practicality considers cost, convenience and interpretability.
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.
The level of measurement for gender in this case study is nominal. Gender (male or female) consists of distinct categories without quantitative properties that allow comparisons in terms of magnitude.
Case 2: The effect of Project-Based Learning (PBL)
on the grades of students was studied among
college students. Grades were either A, B, C, D, or F.
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 document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It explains the key properties and appropriate statistical analyses for each scale type. Nominal scales involve simple categorization while ratio scales allow for all types of mathematical comparisons. The document also outlines important aspects of measurement such as validity, reliability, practicality, and potential sources of error. Overall, it provides an overview of measurement fundamentals for research studies.
This document provides an introduction to statistics and research design. It defines key terms like descriptive statistics, inferential statistics, populations, samples, variables, hypothesis testing, reliability and validity, research designs, correlation analysis, regression analysis, and outlier analysis. Descriptive statistics involves organizing and summarizing data, while inferential statistics allows estimating characteristics of populations based on samples. Samples are subsets of populations used to make inferences. The goal of research is to accurately measure concepts in a reliable and valid way to test hypotheses and understand relationships between variables.
The document discusses various methods for scaling and measurement in research. It describes four main types of scales: nominal, ordinal, interval, and ratio scales. It also discusses sources of error in measurement and characteristics of sound measurement, including validity, reliability, and practicality. The document provides examples of different scaling techniques used in research, including rating scales, attitude scales, Thurstone scales, Likert scales, and semantic differential scales.
This document discusses measurement of variables in research design, including operational definition, scales of measurement, and assessing the reliability and validity of measurement instruments. It defines operational definition as reducing abstract concepts to measurable behaviors or properties. It describes four types of scales - nominal, ordinal, interval, and ratio - and provides examples. It emphasizes that reliability ensures consistent measurement and addresses test-retest and parallel form reliability for assessing stability over time.
This document discusses measurement as a tool for research. It outlines four measurement scales - nominal, ordinal, interval, and ratio scales - and describes what can be done with data on each scale. Nominal scales use numbers as labels, ordinal scales indicate order or ranking, interval scales show differences between values but lack a true zero point, and ratio scales have an absolute zero and allow values to be multiplied and divided. The document also discusses the importance of reliability, which is about consistency of measurement, and validity, which is about accuracy and whether a method truly measures what it intends to measure. High reliability does not guarantee high validity, but a highly valid measurement will generally also be reliable.
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.
QUANTITATIVE RESEARCH DESIGN AND METHODS.pptBhawna173140
This document discusses key concepts in quantitative research design and methods. It covers types of quantitative research including exploratory, descriptive, and causal research. It also discusses measurement fundamentals such as concepts, variables, levels of measurement including nominal, ordinal, interval and ratio. Additionally, it covers research validity including construct validity, internal validity, external validity, and statistical validity. The document provides examples and definitions to explain these important quantitative research concepts.
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 document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
eeMba ii rm unit-3.1 measurement & scaling aRai University
This document discusses various types of measurement scales used in business research including nominal, ordinal, interval, and ratio scales. It also describes commonly used scaling techniques such as rating scales, attitude scales, Thurstone scales, Likert scales, and semantic differential scales. The key aspects of valid and reliable measurement are highlighted, including selecting observable events, developing a mapping rule to assign numbers, and applying the rule consistently.
Research methods 2 operationalization & measurementattique1960
The document discusses key concepts in research methods including operationalization, hypotheses generation, units of analysis, measurement, levels of measurement, and reducing errors. It explains that a hypothesis is a proposed relationship between variables that can be tested. Good hypotheses should be empirical, general, plausible, specific, and relate to collected data. Measurement involves systematically observing variables and assigning numerical values. There are four levels of measurement - nominal, ordinal, interval, and ratio - that determine appropriate statistical analyses. Error can be reduced through pilot testing, thorough training, and using multiple measures.
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
1. Measurement involves assigning numbers to objects or observations based on established rules. There are different scales of measurement that determine what statistical analyses can be used.
2. The scales of measurement from least to most powerful are nominal, ordinal, interval, and ratio scales. Nominal scales simply categorize data while ratio scales have a true zero point and allow comparisons of ratios.
3. Each scale of measurement is associated with different statistical analyses that can appropriately be used. For example, only nominal data allows the use of the mode as a measure of central tendency while more powerful scales like interval and ratio allow the use of more sophisticated tests.
Chp9 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
This document discusses measurement scales and establishing the reliability and validity of measures. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are introduced as ways to develop measures using these scales. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the concepts they are intended to. Item analysis, reliability testing, and validity assessment are presented as key ways to evaluate the quality of developed measures.
This document discusses various techniques for measuring attitudes, including scales and methods. It describes:
1) Rating scales like Likert scales that measure agreement with statements and semantic differential scales that rate objects on bipolar adjective scales.
2) Other methods like Thurstone scales where judges rank statements and Guttman scales where responses are expected to follow a deterministic pattern.
3) Issues in attitude measurement like defining the object and respondents of study, desired accuracy, and available techniques. Measurement involves assigning numbers or symbols to characterize objects while scaling places measurements on a continuum.
This document discusses scales of measurement and standardized testing. It covers four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples of variables that fall under each scale. It also discusses key assumptions underlying testing and measurement, such as the idea that traits and states can be quantified, that multiple data sources should be used for important decisions, and that tests can predict non-test behaviors. The document focuses on reliability and validity as important factors for identifying good tests and assessments. It defines reliability as consistency or stability of scores and validity as the accuracy of interpretations from scores. It covers four types of reliability - test-retest, equivalent forms, internal consistency, and inter-scorer - and three types of validity evidence
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are also discussed, along with specific scales like Likert scales. The document stresses the importance of establishing the reliability and validity of measures to ensure the instruments accurately measure the intended constructs. Item analysis is presented as the first step, followed by assessing reliability and validity.
200 chapter 7 measurement :scaling by uma sekaran Irfan Sheikh
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. It also discusses developing rating scales and ranking scales to measure attitudes. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the constructs they are intended to measure.
This document discusses measurement scales and how to establish the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating scales and ranking scales are presented as two categories for developing attitudinal scales. The document emphasizes the importance of establishing the goodness of measures through item analysis and testing the reliability and validity of instruments.
This document discusses different types of scales used to measure variables in research: nominal, ordinal, interval, and ratio scales. It provides examples of each scale and explains their properties. Nominal scales categorize subjects into groups without rank. Ordinal scales rank order categories. Interval scales measure the distance between scale points. Ratio scales have a true zero point. The document also discusses developing scales through rating scales like Likert scales, which use numbered categories to indicate agreement, and ranking scales, which compare preferences.
This document discusses measurement of variables in research design, including operational definition, scales of measurement, and assessing the reliability and validity of measurement instruments. It defines operational definition as reducing abstract concepts to measurable behaviors or properties. It describes four types of scales - nominal, ordinal, interval, and ratio - and provides examples. It emphasizes that reliability ensures consistent measurement and addresses test-retest and parallel form reliability for assessing stability over time.
This document discusses measurement as a tool for research. It outlines four measurement scales - nominal, ordinal, interval, and ratio scales - and describes what can be done with data on each scale. Nominal scales use numbers as labels, ordinal scales indicate order or ranking, interval scales show differences between values but lack a true zero point, and ratio scales have an absolute zero and allow values to be multiplied and divided. The document also discusses the importance of reliability, which is about consistency of measurement, and validity, which is about accuracy and whether a method truly measures what it intends to measure. High reliability does not guarantee high validity, but a highly valid measurement will generally also be reliable.
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.
QUANTITATIVE RESEARCH DESIGN AND METHODS.pptBhawna173140
This document discusses key concepts in quantitative research design and methods. It covers types of quantitative research including exploratory, descriptive, and causal research. It also discusses measurement fundamentals such as concepts, variables, levels of measurement including nominal, ordinal, interval and ratio. Additionally, it covers research validity including construct validity, internal validity, external validity, and statistical validity. The document provides examples and definitions to explain these important quantitative research concepts.
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 document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
eeMba ii rm unit-3.1 measurement & scaling aRai University
This document discusses various types of measurement scales used in business research including nominal, ordinal, interval, and ratio scales. It also describes commonly used scaling techniques such as rating scales, attitude scales, Thurstone scales, Likert scales, and semantic differential scales. The key aspects of valid and reliable measurement are highlighted, including selecting observable events, developing a mapping rule to assign numbers, and applying the rule consistently.
Research methods 2 operationalization & measurementattique1960
The document discusses key concepts in research methods including operationalization, hypotheses generation, units of analysis, measurement, levels of measurement, and reducing errors. It explains that a hypothesis is a proposed relationship between variables that can be tested. Good hypotheses should be empirical, general, plausible, specific, and relate to collected data. Measurement involves systematically observing variables and assigning numerical values. There are four levels of measurement - nominal, ordinal, interval, and ratio - that determine appropriate statistical analyses. Error can be reduced through pilot testing, thorough training, and using multiple measures.
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
Levels of Measurement: Nominal = Data one collects when doing a wide-open descriptive or exploratory study, however, it is not limited to these kinds of studies. We can count this data, but we can’t order it. We need to be able to put this data into categories that are mutually exclusive, i.e. it can’t be in more than one category at a time. An example would be looking at age, race, sex, or some other type of data that you either are or aren’t. The categories need to be exhaustive – there need to be enough categories to cover the data you collect. Ordinal = this category has mutually exclusive categories, but with ordinal data you can order the data within each category. The ratings of poor, fair, good are an example of ordinal information. Note that you can order the ratings, but you can’t really tell how far apart each of these descriptors are from each other. You could also look at who finishes a task first, second, third, and so on. Again, you can rank this data, but you don’t know how much faster the first person was in relation to the second person or subsequent people. Interval-ratio data = this type of data allows you to measure the difference between each of your rankings. Data is ordered (as with ordinal data) and you can tell how much difference there is between each observation because there is a scale that is divided into equal units. You can measure a race with a stopwatch in terms of seconds or tenths of seconds. A thermometer gives you data with measurements in degrees. Ratio data is like interval data (and is often lumped together with it because they are usually handled the same way statistically). Its primary difference is that there is a zero point on the scale so that you can do multiplication and division. Money is an example of a ratio scale – two dollars are exactly twice one dollar. Volume, area, and distance measures are also ratio scales (2 times 1 liter equals 2 liters). This is different from a strict interval scale like a thermometer – we can’t say that 10 degrees Fahrenheit is twice as warm as 5 degrees Fahrenheit. Statistical Distributions: According to Shi, “a distribution organizes the values of a variable into categories. Frequency Distribution (aka Marginal Distribution): Displays the number of cases that falls into each category. Percentage Distribution: Found by dividing the number of frequency of cases in the category by the total N. Measures of Central Tendency: Mean: The most common measure of central tendency. It simply the sum of the numbers divided by the number of numbers. Median: It is defined as the middle position or midpoint of a distribution. Mode: Is defined as the most frequently occurring value. What is variability? Amount of spread or dispersion within a distribution of scores within a set of data. Measures of Variability: Range: The difference between the highest and lowest values in a distribution. Interquartile Range: Known as the ‘midspread’ or ‘middle fifty.” It contains the middle 50% of
1. Measurement involves assigning numbers to objects or observations based on established rules. There are different scales of measurement that determine what statistical analyses can be used.
2. The scales of measurement from least to most powerful are nominal, ordinal, interval, and ratio scales. Nominal scales simply categorize data while ratio scales have a true zero point and allow comparisons of ratios.
3. Each scale of measurement is associated with different statistical analyses that can appropriately be used. For example, only nominal data allows the use of the mode as a measure of central tendency while more powerful scales like interval and ratio allow the use of more sophisticated tests.
Chp9 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
This document discusses measurement scales and establishing the reliability and validity of measures. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are introduced as ways to develop measures using these scales. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the concepts they are intended to. Item analysis, reliability testing, and validity assessment are presented as key ways to evaluate the quality of developed measures.
This document discusses various techniques for measuring attitudes, including scales and methods. It describes:
1) Rating scales like Likert scales that measure agreement with statements and semantic differential scales that rate objects on bipolar adjective scales.
2) Other methods like Thurstone scales where judges rank statements and Guttman scales where responses are expected to follow a deterministic pattern.
3) Issues in attitude measurement like defining the object and respondents of study, desired accuracy, and available techniques. Measurement involves assigning numbers or symbols to characterize objects while scaling places measurements on a continuum.
This document discusses scales of measurement and standardized testing. It covers four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples of variables that fall under each scale. It also discusses key assumptions underlying testing and measurement, such as the idea that traits and states can be quantified, that multiple data sources should be used for important decisions, and that tests can predict non-test behaviors. The document focuses on reliability and validity as important factors for identifying good tests and assessments. It defines reliability as consistency or stability of scores and validity as the accuracy of interpretations from scores. It covers four types of reliability - test-retest, equivalent forms, internal consistency, and inter-scorer - and three types of validity evidence
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating and ranking scales are also discussed, along with specific scales like Likert scales. The document stresses the importance of establishing the reliability and validity of measures to ensure the instruments accurately measure the intended constructs. Item analysis is presented as the first step, followed by assessing reliability and validity.
200 chapter 7 measurement :scaling by uma sekaran Irfan Sheikh
This document discusses measurement scales and establishing the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. It also discusses developing rating scales and ranking scales to measure attitudes. The document emphasizes the importance of establishing the reliability of measures through assessing stability and internal consistency, as well as validity, to ensure the measures accurately capture the constructs they are intended to measure.
This document discusses measurement scales and how to establish the reliability and validity of measurement instruments. It describes the four main types of scales - nominal, ordinal, interval, and ratio - and provides examples of each. Rating scales and ranking scales are presented as two categories for developing attitudinal scales. The document emphasizes the importance of establishing the goodness of measures through item analysis and testing the reliability and validity of instruments.
This document discusses different types of scales used to measure variables in research: nominal, ordinal, interval, and ratio scales. It provides examples of each scale and explains their properties. Nominal scales categorize subjects into groups without rank. Ordinal scales rank order categories. Interval scales measure the distance between scale points. Ratio scales have a true zero point. The document also discusses developing scales through rating scales like Likert scales, which use numbered categories to indicate agreement, and ranking scales, which compare preferences.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
2. Measurement
• The process of assigning numbers to objects in such a
way that specific properties of the objects are faithfully
represented by specific properties of the numbers.
• Such ways of assigning numbers do not attempt to
measure the total phenomenon, but only a specific set of
attributes.
2
3. Measurement (cont.)
•Measurement is used to capture some “construct”
- For example, if research is needed on the construct of
“depression”, it is likely that some systematic
measurement tool will be needed to assess depression.
3
4. Measurement
Measurement--defined as application of rules to
assign numbers to objects (or attributes).
Measurement rules--the procedures used to
transform the qualities of attributes into
numbers (e.g., type of scale used).
4
5. Why bother assigning numbers?
quantifying something that is expected to vary.
individual differences -- premise that people will
vary (get different scores) on the attribute
5
6. Scales of measurement
Three important properties:
Magnitude--property of “moreness”. Higher
score refers to more of something.
Equal intervals--is the difference between any
two adjacent numbers referring to the same
amount of difference on the attribute?
Absolute zero--does the scale have a zero point
that refers to having none of that attribute?
6
8. Levels of Measurement
Nominal Scales - there must be distinct classes but these classes
have no quantitative properties. Therefore, no comparison can be made
in terms of one category being higher than the other.
For example - there are two classes for the variable gender -- males and
females. There are no quantitative properties for this variable or these
classes and, therefore, gender is a nominal variable.
Other Examples:
country of origin
biological sex (male or female)
animal or non-animal
married vs. single
8
9. Nominal Scale
Sometimes numbers are used to designate
category membership
Example:
Country of Origin
1 = United States 3 = Canada
2 = Mexico 4 = Other
However, in this case, it is important to keep in
mind that the numbers do not have intrinsic
meaning 9
10. Levels of Measurement
Ordinal Scales - there are distinct classes but these
classes have a natural ordering or ranking. The
differences can be ordered on the basis of magnitude.
For example - final position of horses in a
thoroughbred race is an ordinal variable. The horses
finish first, second, third, fourth, and so on. The
difference between first and second is not necessarily
equivalent to the difference between second and third,
or between third and fourth.
10
11. Ordinal Scales
Does not assume that the intervals between numbers are equal
Example:
finishing place in a race (first place, second place)
1 hour 2 hours 3 hours 4 hours 5 hours 6 hours 7 hours 8 hours
1st place 2nd place 3rd place 4th place
11
12. Levels of Measurement (cont.)
Interval Scales - it is possible to compare differences in magnitude,
but importantly the zero point does not have a natural meaning. It
captures the properties of nominal and ordinal scales -- used by most
psychological tests.
Designates an equal-interval ordering - The distance between, for
example, a 1 and a 2 is the same as the distance between a 4 and a 5
Example - Celsius temperature is an interval variable. It is meaningful to
say that 25 degrees Celsius is 3 degrees hotter than 22 degrees Celsius,
and that 17 degrees Celsius is the same amount hotter (3 degrees) than 14
degrees Celsius. Notice, however, that 0 degrees Celsius does not have a
natural meaning. That is, 0 degrees Celsius does not mean the absence
of heat!
12
13. Levels of Measurement (cont.)
Ratio Scales - captures the properties of the other types of
scales, but also contains a true zero, which represents the
absence of the quality being measured.
For example - heart beats per minute has a very natural zero
point. Zero means no heart beats. Weight (in grams) is also a
ratio variable. Again, the zero value is meaningful, zero grams
means the absence of weight.
Example:
the number of intimate relationships a person has had
0 quite literally means none
a person who has had 4 relationships has had twice as
many as someone who has had 2 13
14. Levels of Measurement Scales (cont.)
• Each of these scales have different properties (i.e.,
difference, magnitude, equal intervals, or a true zero point)
and allows for different interpretations.
• The scales are listed in hierarchical order. Nominal scales
have the fewest measurement properties and ratio having the
most properties including the properties of all the scales
beneath it on the hierarchy.
• The goal is to be able to identify the type of measurement
scale, and to understand proper use and interpretation of the
scale.
14
15. Types of scales
Nominal scales--qualitative, not quantitative
distinction (no absolute zero, not equal intervals,
not magnitude)
Ordinal scales--ranking individuals (magnitude,
but not equal intervals or absolute zero)
Interval scales--scales that have magnitude and
equal intervals but not absolute zero
Ratio scales--have magnitude, equal intervals,
and absolute zero (so can compute ratios)
15
16. 16
Type of Scale Numerical Operation Descriptive Statistics
Nominal Counting Frequency in each
category, percentage in
each category, mode
Ordinal Rank Ordering Median, range,
percentile ranking
Interval Arithmetic Operations on
Intervals between
numbers
Mean, standard
deviation, variance
Ratio Arithmetic Operations on
actual quantities
Geometric mean,
coefficient of variation
17. Rating Scales for Measurement
A scale represents a composite measure of
a variable;
it is based on more than one item.
Scales are generally used with complex
variables that do not easily lend
themselves to single-item or single-
indicator measurements.
17
18. Rating Techniques for
Measurement
Some items, such as age, newspaper
circulation, or number of radios in the
house, can be adequately measured
without scaling techniques.
Measurement of other variables, such as
attitude toward TV news or gratification
received from going to a movie theater,
generally requires the use of scales.
18
19. Simple Rating Scales
Rating scales are common in mass
media research.
Researchers frequently ask respondents to
rate a list of items such as a list of
programming elements that can be
included in a radio station’s weekday
morning show,
or to rate how much respondents like
radio or TV on-air personalities. 19
20. Simple Rating Scales
The researcher’s decision is to decide
which type of scale to use: 1 to 3? 1 to 5?
1 to 7?
1 to 10? 1 to 100? Or even a 0 to 9 scale,
which is commonly used by researchers
who don’t have computer software to
accept double-digit numbers (like 10).
Selecting a type of scale is largely a
matter of personal preference, 20
21. SPECIALIZED RATING
SCALES
Thurstone Scales
Thurstone scales are also called equal
appearing interval scales because of the
technique used to develop them and are
typically used to measure the attitude
toward a given concept or construct.
21
22. SPECIALIZED RATING
SCALES
Reserahcer first collects a large number of
statements (Thurstone recommends at
least 100) that relate to the concept or
construct to be measured.
Next, judges rate these statements along
an 11-category scale in which each
category expresses a different degree of
favorableness toward the concept.
22
23. SPECIALIZED RATING
SCALES
Reserahcer first collects a large number of
statements (Thurstone recommends at
least 100) that relate to the concept or
construct to be measured.
Next, judges rate these statements along
an 11-category scale in which each
category expresses a different degree of
favorableness toward the concept.
23
24. SPECIALIZED RATING
SCALES
The items are then ranked according to
the mean or median ratings assigned by
the judges and are used to construct a
questionnaire of 20 to 30 items that are
chosen more or less evenly from across
the range of ratings.
The statements are worded so that a
person can agree or disagree with them.
24
25. SPECIALIZED RATING
SCALES
The scale is then administered to a sample
of respondents whose scores are
determined by computing the mean or
median value of the items agreed with.
A person who disagrees with all the items
has a score of zero.
Thurstone scales are not often used in
mass media research, but they are common
in psychology and education research.
25
26. SPECIALIZED RATING
SCALES
Guttman Scaling
Guttman scaling, also called scalogram
analysis, is based on the idea that items
can be arranged along a continuum in such
a way that a person who agrees with an
item or finds an item acceptable will also
agree with or find acceptable all other items
expressing a less extreme position.
26
27. SPECIALIZED RATING
SCALES
a hypothetical four-item Guttman scale:
1. Indecent programming on TV is
harmful to society.
2. Children should not be allowed to
watch indecent TV shows.
3. Television station managers should
not allow indecent programs on their
stations.
4. The government should ban indecent
programming from TV. 27
28. SPECIALIZED RATING
SCALES
A Guttman scale requires a great deal of
time and energy to develop.
Although they do not appear often in mass
media research,
Guttman scales are common in political
science, sociology, public opinion research,
and anthropology.
28
29. SPECIALIZED RATING
SCALES
Likert Scales
Perhaps the most commonly used scale in
mass media research is the Likert scale,
also called the summated rating approach.
A number of statements are developed with
respect to a topic, and respondents can
strongly agree, agree, be neutral, disagree,
or strongly disagree with the statements
29
30. SPECIALIZED RATING
SCALES
Each response option is weighted, and each
subject’s responses are added to produce a
single score on the topic.
1. Only U.S. citizens should be allowed to
own broadcasting stations.
Strongly agree 5
Agree 4
Neutral 3
Disagree 2
Strongly disagree 1 30
31. SPECIALIZED RATING
SCALES
2. Prohibiting foreign ownership of
broadcasting stations is bad for business.
Strongly agree 1
Agree 2
Neutral 3
Disagree 4
Strongly disagree 5
31
32. SPECIALIZED RATING
SCALES
Semantic Differential Scales
Another commonly used scaling procedure
is the semantic differential technique.
As originally conceived by Osgood, Suci,
and Tannenbaum (1957), this technique is
used to measure the meaning an item has
for an individual.
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33. SPECIALIZED RATING
SCALES
To use the technique, a name or a concept
is placed at the top of a series of seven-
point scales anchored by bipolar attitudes.
The bipolar adjectives that typically “anchor”
such evaluative scales are pleasant/
unpleasant, valuable/worthless, honest/
dishonest, nice/awful, clean/dirty, fair/unfair,
and good/bad.
Unique set of anchoring adjectives be
developed for each particular measurement
33