This document discusses the process of scale development. It outlines key steps in scale development including construct definition, item generation, content validity testing, measurement purification through techniques like exploratory factor analysis, and verification of dimensionality through assessing convergent and discriminant validity. It provides examples and guidelines for each step to help ensure a rigorous process for developing a valid and reliable measurement scale.
Validity and Reliability - Research MangementVinu Arpitha
How to Know data gathering instrument being used will measure what it is supposed to measure and will do this in a consistent manner - Through Validity And Reliability
Types of Validity and Reliability
The document discusses reliability and validity in research studies. It defines key terms like validity, reliability, and objectivity. There are different types of validity including internal, external, logical, statistical, and construct validity. Threats to validity are also outlined such as maturation, history, pre-testing, selection bias, and instrumentation. Reliability refers to consistency of measurements and is a prerequisite for validity. Absolute and relative reliability are discussed. Threats to reliability include fatigue, habituation, and lack of standardization. Measurement error also impacts reliability.
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
This document outlines the criteria for critiquing a research study. It breaks the critique down into 12 sections: (1) problem statement and purpose, (2) literature review and theoretical framework, (3) hypotheses/research questions, (4) sample, (5) research design, (6) instruments, (7) data analysis, (8) conclusions/implications. Each section lists several questions to guide the evaluation of that aspect of the study, such as whether the problem statement specifies the variables and population, if the sample and design match the problem/purpose, and if the conclusions are supported by the results. The overall purpose is to provide a systematic way to assess the strengths and limitations of a research undertaking.
Construct validity assesses how accurately theories and ideas have been translated into procedures and measures. It is established by defining terms, proving control over variables, and supporting theory with empirical evidence. Content validity ensures test items adequately represent the domain being measured. It is achieved through thorough literature review, constructing procedures accordingly, and expert evaluation. Both validity types are important for drawing conclusive results from studies, though establishing validity can be challenging for complex studies with many interacting variables.
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.
This document discusses experimental design in statistics. It defines experimental design as a planned interference by the researcher to manipulate events rather than just observe them. It discusses key principles of experimental design like replication and randomization. It also describes different types of experimental designs like completely randomized design, randomized block design, and Latin square design; and notes that researchers use experimental designs to make causal inferences and rule out alternative explanations. The goal of experimental design is to gain unambiguous information about what factors cause the effects being studied.
This document discusses the process of scale development. It outlines key steps in scale development including construct definition, item generation, content validity testing, measurement purification through techniques like exploratory factor analysis, and verification of dimensionality through assessing convergent and discriminant validity. It provides examples and guidelines for each step to help ensure a rigorous process for developing a valid and reliable measurement scale.
Validity and Reliability - Research MangementVinu Arpitha
How to Know data gathering instrument being used will measure what it is supposed to measure and will do this in a consistent manner - Through Validity And Reliability
Types of Validity and Reliability
The document discusses reliability and validity in research studies. It defines key terms like validity, reliability, and objectivity. There are different types of validity including internal, external, logical, statistical, and construct validity. Threats to validity are also outlined such as maturation, history, pre-testing, selection bias, and instrumentation. Reliability refers to consistency of measurements and is a prerequisite for validity. Absolute and relative reliability are discussed. Threats to reliability include fatigue, habituation, and lack of standardization. Measurement error also impacts reliability.
This presentation is for educational purpose only. I do not own the rights to written material or pictures or illustrations used.
This is being uploaded for students who are in search of, or trying to understand how a quasi-experimental research design should look like.
This document outlines the criteria for critiquing a research study. It breaks the critique down into 12 sections: (1) problem statement and purpose, (2) literature review and theoretical framework, (3) hypotheses/research questions, (4) sample, (5) research design, (6) instruments, (7) data analysis, (8) conclusions/implications. Each section lists several questions to guide the evaluation of that aspect of the study, such as whether the problem statement specifies the variables and population, if the sample and design match the problem/purpose, and if the conclusions are supported by the results. The overall purpose is to provide a systematic way to assess the strengths and limitations of a research undertaking.
Construct validity assesses how accurately theories and ideas have been translated into procedures and measures. It is established by defining terms, proving control over variables, and supporting theory with empirical evidence. Content validity ensures test items adequately represent the domain being measured. It is achieved through thorough literature review, constructing procedures accordingly, and expert evaluation. Both validity types are important for drawing conclusive results from studies, though establishing validity can be challenging for complex studies with many interacting variables.
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.
This document discusses experimental design in statistics. It defines experimental design as a planned interference by the researcher to manipulate events rather than just observe them. It discusses key principles of experimental design like replication and randomization. It also describes different types of experimental designs like completely randomized design, randomized block design, and Latin square design; and notes that researchers use experimental designs to make causal inferences and rule out alternative explanations. The goal of experimental design is to gain unambiguous information about what factors cause the effects being studied.
The document discusses what constitutes a research problem and how to formulate one. A research problem is defined as any question an researcher aims to answer or assumption they want to challenge. However, not all questions can be research problems - it takes considerable knowledge and effort to develop a meaningful research problem. The key steps to formulating a research problem are to identify a broad topic of interest, narrow it down to a specific sub-area, develop research questions within that area, and define clear objectives to address the research questions. Properly formulating the research problem is crucial as it determines all subsequent research design choices.
This document discusses quasi-experimental research design. Quasi-experimental research involves manipulating an independent variable to observe its effects, but unlike true experiments, it lacks random assignment or a control group. The two main types discussed are non-randomized control group design, where groups are not randomly assigned but a control receives no treatment, and time series design, where a treatment is applied and removed over multiple time periods to a small group. Quasi-experimental designs are more practical than true experiments when randomization is not possible but allow evaluation of treatment effects under natural conditions.
- The document describes a study that examined whether cursing focuses or distracts from pain using an ice water test.
- Participants submerged their hands in ice water under two conditions - when swearing aloud and when not swearing. The length of time each group kept their hands in the water was recorded.
- This is an example of a repeated measures or within-subjects design where the same participants undergo both conditions and their results are compared to see if swearing impacted pain tolerance.
Reliability refers to the consistency or repeatability of measurement results. There are four types of reliability: inter-rater, parallel forms, test-retest, and internal consistency. Reliability can be estimated using external consistency procedures, which compare results from independent data collection processes, or internal consistency procedures, which assess consistency across items in the same test.
When to use, What Statistical Test for data Analysis modified.pptxAsokan R
This document discusses choosing the appropriate statistical test for data analysis. It begins by defining key terminology like independent and dependent variables. It then discusses the different types of variables, including quantitative, categorical, and their subtypes. Hypothesis testing and its key steps are explained. The document outlines assumptions that statistical tests make and categorizes common parametric and non-parametric tests. It provides guidance on choosing a test based on the research question, data structure, variable type, and whether the data meets necessary assumptions. Specific statistical tests are matched to questions about differences between groups, association between variables, and agreement between assessment techniques.
This document discusses various statistical tests used to analyze agreement between raters or tests, including intraclass correlation, Cohen's kappa, and Bland-Altman plots. It explains how to perform intraclass correlation, Cohen's kappa, receiver operating characteristic curves, and other tests on SPSS. These statistical analyses are used to evaluate rater agreement, compare tests to a gold standard, and determine if tests provide predictions better than chance. The document provides guidance on interpreting the results of these analyses and choosing appropriate cut-off values.
This document discusses research questions and their importance in guiding rigorous research. It defines what a research question is and explains that it focuses a study, determines the methodology, and guides all stages of inquiry. There are three main types of research questions: descriptive, comparative, and causal. Characteristics of good research questions are that they are feasible, clear, ethical, and significant. Rigorous research questions are focused on a specific research area and topic. They identify the right research paradigm and drive an appropriate research design. Non-rigorous questions are too broad, narrow, yes/no, or include presumptions. The choice of research question is important as the wrong question can waste time and effort.
This document discusses criteria for good measurement in research. It identifies three key criteria: validity, reliability, and sensitivity. Validity refers to a measure accurately reflecting what it intends to measure. There are four types of validity: face validity, content validity, criterion-related validity (which has concurrent and predictive validity), and construct validity (which has convergent and discriminant validity). Reliability indicates a measure is free of bias and consistently measures a concept over time. Two aspects of reliability are stability (via test-retest and parallel-form reliability) and internal consistency. Sensitivity refers to how well a measure distinguishes between variables it is intended to measure.
Questionnaire validation is a process in which the creators review the questionnaire to determine whether the questionnaire measures what it was designed to measure. If a questionnaire's validation succeeds, the creators label the questionnaire as a valid questionnaire. This validity comes in different forms, all relying on the method used for the validation procedure
This document discusses validity and reliability in research. It defines validity as the extent to which a test measures what it claims to measure. Reliability is defined as the extent to which a test shows consistent results on repeated trials. The document then discusses various types of validity including content, face, criterion-related, construct, and ecological validity. It also discusses types of reliability including equivalency, stability, internal consistency, inter-rater, and intra-rater reliability. Factors affecting validity and reliability are presented along with how validity and reliability are related concepts in research.
The document discusses selecting a research problem and provides guidance on various factors to consider. It begins by defining what constitutes a research problem and some common types of problems. It then covers important considerations for selecting a problem such as ensuring the topic is significant, feasible to study, ethical, and of personal interest to the researcher. Additionally, it emphasizes the need for a clear research question and discusses how to write effective questions and define key terms. The document also provides an overview of the research process and common components of a research proposal or report.
The document discusses formulating a research problem and hypothesis. It begins by explaining that identifying a research problem is the first step of the research process. A research problem refers to a difficulty experienced in a field that indicates gaps in current knowledge. There are three types of research problems: theoretical, which provides a theoretical explanation; applied, which puts theoretical knowledge into practical use; and action, which requires an immediate solution. Sources of research problems can come from experience, observations, theories, literature reviews, contradictory results, and meetings. Selecting a research problem considers factors like interest, magnitude, expertise, relevance, data availability, and ethics. The document concludes by defining a hypothesis as a tentative explanation of the research problem, and noting there are
It is a Presentation on the Meaning, types, methods of establishing validity, the factors influencing validity and how to increase the validity of a tool
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
1. Reliability in psychological testing refers to the consistency or accuracy of measurement - the degree to which a test is free from errors of measurement.
2. Several models of reliability are used to evaluate the error associated with administering a test, including test-retest, parallel forms, split-half, Kuder-Richardson, and coefficient alpha methods.
3. The level of reliability considered "high enough" depends on the purpose and use of the test, with clinical settings requiring higher reliability indices greater than .95 compared to basic research where .70-.80 may be sufficient.
The 8 steps in scale development are: 1) determine the construct, 2) generate item pool, 3) determine response format, 4) have experts review items, 5) include validation items, 6) administer items to a sample, 7) evaluate items using correlations, means, variances, and coefficient alpha, and 8) optimize the scale length. The goal is to end up with a concise, reliable scale that accurately measures the intended construct.
The document discusses the importance of validating research questionnaires through establishing reliability and validity. It outlines the key steps in translating and adapting questionnaires for other cultures, including forward translation, expert reviews, back translation, pilot testing, and confirming psychometric properties. Validity is subdivided into content, construct, criterion, and other types that should be assessed through both qualitative and quantitative methods. Reliability can be evaluated through measures like internal consistency, test-retest reliability and more. The document emphasizes the need to carefully validate any adapted questionnaires to ensure cross-cultural relevance and accurate measurement.
This document discusses the process of developing and validating a questionnaire. It begins by defining what a questionnaire is and noting that developing a good questionnaire takes significant time and effort, often involving multiple drafts. It then covers types of questionnaires, advantages and disadvantages of self-administered versus interviewer-administered questionnaires, and key steps in the development process including formulating objectives, conducting a literature review, designing initial drafts, and pre-testing drafts. The document provides guidance on question wording and types, testing questionnaires, and ensuring reliability and validity. It concludes by discussing important elements of covering letters.
Descriptive Research Design - Techniques and TypesSundar B N
This ppt includes Introduction to Descriptive Research, Meaning of Descriptive Research Design and Methods used in Descriptive Research, Types of Descriptive Research and DIFFERENCE B/W EXPLORATORY AND CONCLUSIVE RESEARCH.
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
This document discusses content validity, which refers to how well a test measures an intended content area. It is determined by expert judgment of item validity and sampling validity. A table of specifications is used to define content areas and ensure all areas are measured. To measure content validity, experts evaluate how essential each item is, then the content validity ratio and index are calculated. The ratio indicates validity of individual items while the index averages ratios to measure overall test validity.
1. The document outlines the development of a new self-report instrument called the "SRC VOC" to measure self-regulatory capacity in vocabulary acquisition.
2. It involved creating items to assess the five facets of self-regulation, piloting the instrument, confirming its reliability and validity through various statistical analyses, and refining it to include 20 final items.
3. The instrument demonstrates good psychometric properties and reliability in measuring self-regulatory capacity for vocabulary learning, providing a valid tool for learners and researchers.
The document discusses what constitutes a research problem and how to formulate one. A research problem is defined as any question an researcher aims to answer or assumption they want to challenge. However, not all questions can be research problems - it takes considerable knowledge and effort to develop a meaningful research problem. The key steps to formulating a research problem are to identify a broad topic of interest, narrow it down to a specific sub-area, develop research questions within that area, and define clear objectives to address the research questions. Properly formulating the research problem is crucial as it determines all subsequent research design choices.
This document discusses quasi-experimental research design. Quasi-experimental research involves manipulating an independent variable to observe its effects, but unlike true experiments, it lacks random assignment or a control group. The two main types discussed are non-randomized control group design, where groups are not randomly assigned but a control receives no treatment, and time series design, where a treatment is applied and removed over multiple time periods to a small group. Quasi-experimental designs are more practical than true experiments when randomization is not possible but allow evaluation of treatment effects under natural conditions.
- The document describes a study that examined whether cursing focuses or distracts from pain using an ice water test.
- Participants submerged their hands in ice water under two conditions - when swearing aloud and when not swearing. The length of time each group kept their hands in the water was recorded.
- This is an example of a repeated measures or within-subjects design where the same participants undergo both conditions and their results are compared to see if swearing impacted pain tolerance.
Reliability refers to the consistency or repeatability of measurement results. There are four types of reliability: inter-rater, parallel forms, test-retest, and internal consistency. Reliability can be estimated using external consistency procedures, which compare results from independent data collection processes, or internal consistency procedures, which assess consistency across items in the same test.
When to use, What Statistical Test for data Analysis modified.pptxAsokan R
This document discusses choosing the appropriate statistical test for data analysis. It begins by defining key terminology like independent and dependent variables. It then discusses the different types of variables, including quantitative, categorical, and their subtypes. Hypothesis testing and its key steps are explained. The document outlines assumptions that statistical tests make and categorizes common parametric and non-parametric tests. It provides guidance on choosing a test based on the research question, data structure, variable type, and whether the data meets necessary assumptions. Specific statistical tests are matched to questions about differences between groups, association between variables, and agreement between assessment techniques.
This document discusses various statistical tests used to analyze agreement between raters or tests, including intraclass correlation, Cohen's kappa, and Bland-Altman plots. It explains how to perform intraclass correlation, Cohen's kappa, receiver operating characteristic curves, and other tests on SPSS. These statistical analyses are used to evaluate rater agreement, compare tests to a gold standard, and determine if tests provide predictions better than chance. The document provides guidance on interpreting the results of these analyses and choosing appropriate cut-off values.
This document discusses research questions and their importance in guiding rigorous research. It defines what a research question is and explains that it focuses a study, determines the methodology, and guides all stages of inquiry. There are three main types of research questions: descriptive, comparative, and causal. Characteristics of good research questions are that they are feasible, clear, ethical, and significant. Rigorous research questions are focused on a specific research area and topic. They identify the right research paradigm and drive an appropriate research design. Non-rigorous questions are too broad, narrow, yes/no, or include presumptions. The choice of research question is important as the wrong question can waste time and effort.
This document discusses criteria for good measurement in research. It identifies three key criteria: validity, reliability, and sensitivity. Validity refers to a measure accurately reflecting what it intends to measure. There are four types of validity: face validity, content validity, criterion-related validity (which has concurrent and predictive validity), and construct validity (which has convergent and discriminant validity). Reliability indicates a measure is free of bias and consistently measures a concept over time. Two aspects of reliability are stability (via test-retest and parallel-form reliability) and internal consistency. Sensitivity refers to how well a measure distinguishes between variables it is intended to measure.
Questionnaire validation is a process in which the creators review the questionnaire to determine whether the questionnaire measures what it was designed to measure. If a questionnaire's validation succeeds, the creators label the questionnaire as a valid questionnaire. This validity comes in different forms, all relying on the method used for the validation procedure
This document discusses validity and reliability in research. It defines validity as the extent to which a test measures what it claims to measure. Reliability is defined as the extent to which a test shows consistent results on repeated trials. The document then discusses various types of validity including content, face, criterion-related, construct, and ecological validity. It also discusses types of reliability including equivalency, stability, internal consistency, inter-rater, and intra-rater reliability. Factors affecting validity and reliability are presented along with how validity and reliability are related concepts in research.
The document discusses selecting a research problem and provides guidance on various factors to consider. It begins by defining what constitutes a research problem and some common types of problems. It then covers important considerations for selecting a problem such as ensuring the topic is significant, feasible to study, ethical, and of personal interest to the researcher. Additionally, it emphasizes the need for a clear research question and discusses how to write effective questions and define key terms. The document also provides an overview of the research process and common components of a research proposal or report.
The document discusses formulating a research problem and hypothesis. It begins by explaining that identifying a research problem is the first step of the research process. A research problem refers to a difficulty experienced in a field that indicates gaps in current knowledge. There are three types of research problems: theoretical, which provides a theoretical explanation; applied, which puts theoretical knowledge into practical use; and action, which requires an immediate solution. Sources of research problems can come from experience, observations, theories, literature reviews, contradictory results, and meetings. Selecting a research problem considers factors like interest, magnitude, expertise, relevance, data availability, and ethics. The document concludes by defining a hypothesis as a tentative explanation of the research problem, and noting there are
It is a Presentation on the Meaning, types, methods of establishing validity, the factors influencing validity and how to increase the validity of a tool
univariate and bivariate analysis in spss Subodh Khanal
this slide will help to perform various tests in spss targeting univariate and bivariate analysis along with the way of entering and analyzing multiple responses.
1. Reliability in psychological testing refers to the consistency or accuracy of measurement - the degree to which a test is free from errors of measurement.
2. Several models of reliability are used to evaluate the error associated with administering a test, including test-retest, parallel forms, split-half, Kuder-Richardson, and coefficient alpha methods.
3. The level of reliability considered "high enough" depends on the purpose and use of the test, with clinical settings requiring higher reliability indices greater than .95 compared to basic research where .70-.80 may be sufficient.
The 8 steps in scale development are: 1) determine the construct, 2) generate item pool, 3) determine response format, 4) have experts review items, 5) include validation items, 6) administer items to a sample, 7) evaluate items using correlations, means, variances, and coefficient alpha, and 8) optimize the scale length. The goal is to end up with a concise, reliable scale that accurately measures the intended construct.
The document discusses the importance of validating research questionnaires through establishing reliability and validity. It outlines the key steps in translating and adapting questionnaires for other cultures, including forward translation, expert reviews, back translation, pilot testing, and confirming psychometric properties. Validity is subdivided into content, construct, criterion, and other types that should be assessed through both qualitative and quantitative methods. Reliability can be evaluated through measures like internal consistency, test-retest reliability and more. The document emphasizes the need to carefully validate any adapted questionnaires to ensure cross-cultural relevance and accurate measurement.
This document discusses the process of developing and validating a questionnaire. It begins by defining what a questionnaire is and noting that developing a good questionnaire takes significant time and effort, often involving multiple drafts. It then covers types of questionnaires, advantages and disadvantages of self-administered versus interviewer-administered questionnaires, and key steps in the development process including formulating objectives, conducting a literature review, designing initial drafts, and pre-testing drafts. The document provides guidance on question wording and types, testing questionnaires, and ensuring reliability and validity. It concludes by discussing important elements of covering letters.
Descriptive Research Design - Techniques and TypesSundar B N
This ppt includes Introduction to Descriptive Research, Meaning of Descriptive Research Design and Methods used in Descriptive Research, Types of Descriptive Research and DIFFERENCE B/W EXPLORATORY AND CONCLUSIVE RESEARCH.
Subscribe to Vision Academy
https://www.youtube.com/channel/UCjzpit_cXjdnzER_165mIiw
This document discusses content validity, which refers to how well a test measures an intended content area. It is determined by expert judgment of item validity and sampling validity. A table of specifications is used to define content areas and ensure all areas are measured. To measure content validity, experts evaluate how essential each item is, then the content validity ratio and index are calculated. The ratio indicates validity of individual items while the index averages ratios to measure overall test validity.
1. The document outlines the development of a new self-report instrument called the "SRC VOC" to measure self-regulatory capacity in vocabulary acquisition.
2. It involved creating items to assess the five facets of self-regulation, piloting the instrument, confirming its reliability and validity through various statistical analyses, and refining it to include 20 final items.
3. The instrument demonstrates good psychometric properties and reliability in measuring self-regulatory capacity for vocabulary learning, providing a valid tool for learners and researchers.
The document provides guidance on developing assessments. It instructs participants to create a purpose statement, target content standards, and develop a test blueprint in three steps. The purpose statement outlines what the assessment measures, how scores will be used, and why it was developed. Targeted content standards are selected that represent key concepts and prepare students for future learning. The test blueprint identifies the standards and number of items/tasks to sufficiently measure student knowledge across different cognitive levels. The next module will cover developing items/tasks, scoring methods, and assembling test forms.
Using the test process improvement models. Case study based on TPI Next model...Sigma Software
The document discusses using the TPI Next test process improvement model. It provides an overview of the TPI Next model, which evaluates test processes across 16 key areas and 4 maturity levels. It then presents a case study example of implementing TPI Next on a project. The case study involves evaluating the current test process maturity, identifying improvement priorities, creating a test process improvement plan, implementing improvements, and planning the next improvement cycle. While most improvements were successfully implemented, one faced resistance from management.
The document provides guidance on designing assessments by outlining three key steps:
1. Creating a purpose statement that clearly defines what the assessment will measure, how scores will be used, and why the assessment was developed.
2. Targeting relevant content standards that represent essential knowledge and skills within an enduring concept.
3. Developing a test blueprint that identifies the targeted standards and number of items/tasks needed to sufficiently measure student mastery of the standards across different cognitive levels.
The next module will focus on building the actual assessment by developing rigorous items/tasks, associated scoring materials, and assembling test forms for administration.
The document describes the three-part Student Learning Objective (SLO) process of design, build, and review. It provides information about reviewing SLOs, including conducting a three-level quality assurance review to ensure completeness, comprehensiveness, and coherency. Key resources and rubrics are referenced to aid in the review of SLO templates, performance measures, and the overall coherence of the SLO.
The document provides an overview of the formal technical review (FTR) process. It discusses the objectives and benefits of FTR, which include improving quality and reducing defects and costs. The document outlines the basic principles of review, including a general inspection process with phases for planning, orientation, preparation, review meeting, rework, and verification. It also discusses critical success factors for effective reviews, such as using detailed checklists to guide inspection and allocating sufficient time for preparation.
Arrogance or Apathy: The Need for Formative Evaluation + Current & Emerging S...Michael M Grant
Formative evaluation involves getting user feedback during the development process to improve an interactive learning environment (ILE). This document discusses three key methods of formative evaluation: expert review, user review, and usability testing. User review involves getting feedback from users through one-on-one observations and small group trials of prototypes to identify strengths, weaknesses, and needed improvements. Usability testing directly observes representative users attempting typical tasks to evaluate ease of use and identify usability issues. Both methods provide valuable feedback to refine the ILE before full implementation.
R.M Evaluation Program complete research.pptxtalhaaziz78
The document outlines 12 steps for conducting a qualitative program evaluation: 1) define purpose and scope, 2) review program goals, 3) identify stakeholders, 4) identify available time and resources, 5) revisit evaluation purpose, 6) decide if the evaluation will be in-house or contracted, 7) specify the evaluation design, 8) create a data collection plan, 9) determine sampling and recruitment strategies, 10) summarize and analyze data, 11) disseminate information, and 12) provide feedback for program improvement. Qualitative evaluation is used to understand why a program works or not and its unintended consequences by considering small, purposefully selected samples. Ensuring credibility, transferability, dependability, and confirmability strengthen
This document provides guidance to educators on developing performance measures to assess student learning in three phases: design, build, and review. The design phase involves creating a purpose statement, identifying targeted content standards, and developing a test blueprint. The build phase consists of writing item stems and tasks, developing scoring keys and rubrics, and organizing items into test forms. Finally, the review phase includes evaluating items for alignment and quality, analyzing assessment data, and refining the performance measure. Educators use an online platform called Homeroom to access training, templates, and resources to guide them through each step of the process for creating valid and reliable assessments of student mastery of content standards.
Scale Development Techniques Presentation.pptxAnjaliUpadhye1
This document outlines the process and methodology for developing and validating a new scale. It discusses the objectives to assess scale development techniques and understand validity and reliability of data in scale measurements. The methodology involves 3 phases - item generation, scale development, and scale evaluation. Phase 1 involves domain identification, item generation, and expert evaluation. Phase 2 involves pre-testing questions, survey administration, and item reduction. Phase 3 involves tests of dimensionality, reliability, and validity. Statistical analyses like CFA, EFA, and reliability tests are used for scale evaluation. The document provides an example of developing a scale to measure emotional intelligence and validating it using the outlined process.
This document provides an overview of quantitative research, including its key characteristics, strengths, weaknesses, and types. It discusses how quantitative research uses numbers and statistical analysis to make generalizations about variables. The strengths include reliability and validity, while weaknesses include cost, time requirements, and limiting results to what is proved or unproved. The types of quantitative research designs covered are experimental (pre-experimental, quasi-experimental, true experimental) and non-experimental (survey, correlational, ex-post facto, comparative, evaluative, methodological). Examples of different quantitative research methods are also provided.
How to develop self reported scale pptMahesh Chand
The document provides an overview of the steps involved in developing a high-quality self-report scale. It discusses conceptualizing the construct, generating items, preliminary evaluation of items including input from experts and the target population, field testing the instrument, analyzing scale development data through item analysis and factor analysis, refining and validating the scale, interpreting scale scores, and providing norms and cut-off marks. The key steps involve thoroughly understanding the construct, generating a large initial item pool, evaluating and refining items both qualitatively and quantitatively, and validating the factor structure and reliability of the scale.
The document outlines the steps to develop a self-reported scale. It discusses conceptualizing the construct, generating items, preliminary evaluation of items including input from experts and the target population, and refining the scale. The key steps are:
1. Conceptualizing the underlying construct and deciding on the type and features of scale items.
2. Generating an initial large pool of items and preliminary evaluation through internal review and input from target groups.
3. External review by expert panels to refine items and assess content validity through multiple rounds of feedback and revisions.
4. Further field testing, analysis of scale data, and refinements to finalize the scale.
This document provides guidance on reviewing assessment items, tasks, and tests to ensure quality and alignment. It outlines the following key review components:
1. Item/task reviews examine design fidelity (content, bias/fairness, accessibility) and editorial soundness.
2. Alignment reviews check if each item matches content standards and cognitive demand, and if the overall test distribution matches the blueprint.
3. Performance level reviews evaluate if score categories accurately reflect expectations in the content standards.
The document provides procedures and checklists to guide the review process in multiple steps to refine items, tasks, test forms, and performance level expectations.
This document provides guidance for developing quality student performance measures. It introduces a rubric to help teachers self-assess measures they create. The rubric examines measures across three strands: design, build, and review. It outlines steps to rate measures in each strand and provides examples of quality indicators like clearly stating the measure's purpose, aligning items to standards, and establishing cut scores for performance levels. The goal is to help teachers build rigorous, valid and reliable measures of student achievement.
Антон Мужайло, «Using the test process improvement models. Case study based o...Sigma Software
The document discusses implementing the TPI Next test process improvement model on a case study project. It begins by introducing TPI Next and its key areas and maturity levels. It then outlines the implementation process, which includes evaluating the current situation, planning improvements, implementing them, and re-evaluating. As part of the case study, the document shows how to use TPI Next tools to assess maturity across 16 areas, prioritize improvements, and create a test process improvement plan to address priorities. Resistance was encountered in fully implementing one improvement around tester involvement in risk analysis.
The mission statement sets the direction and priority for developing and implementing the quality plan. It clearly states the nature of the organization’s commitment to quality and should then be tied to the organizational operations through programs, projects, actions and rewards/recognition.
1. The document outlines the process of test construction which involves preliminary considerations, reviewing the content domain, item/task writing, assessing content validity, revising items/tasks, field testing, revising based on field testing results, test assembly, selecting performance standards, pilot testing, and preparing manuals.
2. Key steps include specifying test purposes and intended examinees, reviewing content standards/objectives, drafting and editing items/tasks, evaluating items for validity and potential biases, conducting item analysis after field testing, revising or deleting weak items, assembling the final test, and collecting ongoing reliability and validity data.
3. Item analysis involves both qualitative review of item content and format as well as quantitative analysis
Dr. Ara Tekian discusses the process of blueprinting national examinations. A blueprint outlines the content and proportion of questions to be included in an exam. It is developed through a multi-step process involving committees of content experts. They identify important content areas and assign weightings. A draft is created and surveyed to a broader group of experts for feedback. Considering survey results and ensuring coverage of important topics, the committees finalize the blueprint to guide exam construction. The National Board of Medical Examiners and American Board of Internal Medicine were provided as examples of organizations that follow rigorous blueprinting processes.
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Steps in Developing A Valid and Reliable Scale.pdf
1. Steps In Developing AValid And Reliable
Scale of Measurement
BY:
Omnia Samir Elseifi
Assistant Professor of Public Health and Community Medicine.
Faculty of Medicine
Zagazig University
23 January 2020
2. Scale development process
• Measurement scales are useful tools to get scores about certain health aspects that cannot be measured directly, such as
measuring quality of life.
• The researcher must pass through many steps to reach the ultimate goal; which is the developing of a valid and reliable
scale to support the application of the test results.
Phase I
Item
Development
1-
Identification
of domain
2- Item
generation
3- Content
validity
Phase II
Scale
Development
4- Pretesting
(Pilot testing of
the Items)
5- Item
reduction
6- Extraction of
factors
Phase III
Scale Evaluation
7- Test of
dimensionality
8-Test of
reliability
9-Test of validity
(1,2,3)
3. Scale development process Scheme
1- Identification of
domain(s)
1- Purpose
2- Justification
2- Item generation
1- Appropriate
questions
2- Number of items
3- Item wording
4- Translation of items
3- ContentValidity
CVR
CVI
FaceValidity
3- Describing
domains
4- Specify the
dimensions
5- Define each
dimension
5-Types of questions
6- Response to items
• To Specify the boundaries of
the domain.
• To Select Which Items to
Ask.
• To Assess if the Items
Adequately Measure the
Content of The Domain of
Interest.
4. Scale development process Scheme
4- Pretesting
1- Interview with
target population
2- Sample size
5- Item reduction
1- Item difficulty index
2- Item discrimination
index
3- Item- item
correlation and Item –
total correlation
4- Distractor Efficiency
Analysis
6- Extraction of
factors
Exploratory
Factor Analysis
(EFA)
Confirmatory
Factor Analysis
(CFA)
3- Distribution of
scale
• To Gather Enough Data from
the Right People.
• To Identify Items That Are
Not Related To The Domain,
So, They Can Be Deleted Or
Modified.
• To Explore the Number of
Latent Constructs that Fit
The Observed Data.
5. Scale development process Scheme
7-Test of
Dimensionality
Using Factor
analysis
Unidimensional
scale
8-Test of
Reliability
1- Test- Retest
Reliability
2- Internal
Consistency
3- Parallel form
Reliability
4- Inter-Rater
Reliability
9-Test ofValidity
CriterionValidity:
Concurrent validity
Predictive validity
ConstructValidity:
ConvergentValidity.
DivergentValidity
Known groupValidity
Multidimensional
scale
• To Identify The Number Of
Latent Variables That Are
Measured By The Scale.
• To Establish if Responses Are
ConsistentWhen Repeated.
• To Ensure the scale
Measures The intended
Latent Dimension.
6. Example Of Validated Scale Development
Research
A research conducted In Pakistan for “Development of a
stress scale for pregnant women in the South Asian context:
the A–Z Stress Scale.”
Will be an example in most of steps.
7. Phase 1: Item development
Step 1: Identification of the Domain(s)
Identification
of the
Domain(s)
5-Define each
dimension
1-The purpose: is to
develop a scale based on
stressors to measure
stress among pregnant in
developing countries
2- Justification: They found
preexisting scales record the
somatic and psychological
symptoms of the stressors not
the stressors themselves
3- Describing
domains: They agreed
about defining the
different stressors the
pregnant exposed to.
4- Specify the
dimensions :They decided
the scale will be consisted
from three dimensions;
daily, life event and
pregnancy related stressors.
The purpose : To specify the boundaries of the domain and facilitate item generation.
(4,5)
8. Pitfalls
1. This step is often neglected or dealt with in a superficial manner.
2. Construct underrepresentation (focus on narrow aspect of the
domain).
These troubles lead to a significant number of problems later in the
validation process(6,7).
Phase 1: Item development
Step 1: Identification of the Domain(s)
9. Phase 1: Item development
Step 2 Item Generation
The purpose : To create an appropriate questions that fit to identified domain.
Item
Generation
6- Response to
questions
1-Appropriate
questions
2- Number of
items
(must be 2-5 times the
number in final scale)
Item pool of 235
items
3-Item
wording
4-Translation
of the items
5-Types of
questions
Deductive methods
Literature review
Inductive methods:
interviews with 25
experts from different
specialties” Psychiatry,
Gynecology and
Sociology”.
They conducted
interview with 79
pregnant women asking
them about the possible
stressors.
(5,8-11)
10. Pitfalls
1. Presence of irrelevant items to the defined domain can lead to failure of validation
of the measuring scale, poor quality of data and invalid conclusion regarding the
results and the relationship with other constructs.
2. Improper response to the items as too short scale can affect the reliability of the
instrument this is also for too many responses (more than 7) (12).
Phase 1: Item development
Step 2 Item Generation
11. Phase 1: Item development
Step 3: ContentValidity
Content validity:
• Content validity is to be sure that
the items of the generated scale
measure what they are presumed to
measure (all contents domain of
interest) (2)
Content validity is assessed by:
• Experts,
• Target population (2)
12. Purpose: To evaluate the items constituting the domain regarding; content relevance, and technical
quality .
Phase 1: Item development
Step 3: ContentValidity
Expert evaluation
ContentValidity
Ratio
(CVR)
Kappa coefficient
ContentValidity
Index
(CVI)
• >0.74 it’s considered excellent.
• Between 0.60 and 0.74 is considered good.
• Between 0.40 and 0.59 are considered fair.
(2)
I-CVIs
S-CVI
13. ContentValidity Ratio (CVR):
• The experts are requested to specify whether an item is necessary for the construct or not.
-Score 1 for: [not necessary] item.
-Score 2 for: [useful but not essential] item.
- Score 3 for: [essential] item.
.
Phase 1: Item development
Step 3: ContentValidity
(Number of experts indicating essential - The
total number of experts/2) / The total number
of experts / 2.
• For minimum number of expert (5 or 6 experts) CVR must be not
less than 0.99,
• for 8 experts not less than 0.85
• for 10 experts not less than 0.62
otherwise the item should be eliminated from the scale .
CVR
(13)
14. Content validity index (CVI):
Panel members are asked to rate instrument items in terms of clarity and relevancy to the construct on a 4-point scale:
-Score 1 for: [not relevant or not clear] items.
-Score 2 for: [somewhat relevant or item somewhat clear and need some revision] items.
-Score 3 for: [quite relevant or quite clear] items.
-Score 4 for: [highly relevant or highly clear] items
Phase 1: Item development
Step 3: ContentValidity
For each item:
Experts giving 3 or 4 score / the
Total number of experts
I-CVIs
• >79%, the item is appropriate and retained
within the scale.
• If between 70 and 79 % it will need
revision.
• <70 percent, it is eliminated from the scale
The number of relevant items by
agreement of all experts / Total
number of items
S-CVI/UA
Should be not less than 0.80
Sum of I-CVIs for the items
/ Total number of items
S-CVI/Ave
Should be not less than 0.90
(14)
15. Phase 1: Item development
Step 3: ContentValidity
Face
Validity
Readability
Feasibility
Layout
Clarity of
words
Face validity means the degree at which the
designed measuring instrument is apparently
appropriate and related to the domain under
study.
The target population share with expert in
evaluating the face validity of the scale of
measurement (15).
16. Example for this step:
A research conducted for the development of a stress scale for pregnant women in the South Asian
context: the A–Z Stress Scale (5). The researchers stated that they evaluate the content validity of the scale:
By experts and target pregnant (face validity) . According to that the items selected from the item pool were 78
items.
Pitfalls
• Some researches usually fail to assess the content validity, this may be due to lack of resources or skills. This is
expected to affects the final collected data conducted by the scale and the statistical analysis.
• Limited numbers of the developing scales undergo target population evaluation which is important step as those
population are the target of the newly developed scale (16).
Phase 1: Item development
Step 3: ContentValidity
17. Phase 2: Scale Development
Step 4: Pre-testing Questions
Pre-
testing
Questions
1- Cognitive
Interviews with
pregnant
2- Sample size
Golden rule of
thumb is10
respondents per
survey item (10:1)
They interviewed
70 pregnant
3-Distribution of
the scale;
Paper based survey or
Online survey
(they used Paper
based face to face
interview)
The purpose :
•To ensure the availability of sufficient data for scale development with
minimum level of error.
(5,17,18)
Pitfalls
• Sample size in many validation
studies is usually less than the
golden role, this may be due to this
type of studies may be difficult to
be funded.
• Missing data increase the risk of
inaccurate conclusions due to
increasing occurrence of errors.
19. Inter-item correlations:
Examine the correlation between each item in the
scale and the other items.
Phase 2: Scale Development
Step 5: Item Reduction
Inter-item and Item-Total Correlations
Purpose: To determine the correlations between scale items, as well as the correlations between each item and sum score of scale
items.
Item-total correlations:
Examine the relationship between each item score and
the total scale score.
In both techniques, items with low correlations (r <0.30) are less desirable and could be deleted.
(19,20)
20. Example:
A research conducted for the development of a stress scale for pregnant women in the South Asian context: the A–Z
Stress Scale (5).
Phase 2: Scale Development
Step 5: Item Reduction
The researchers conducted item- total analysis
ranged from r = 0.2 to r = 0.8.
As a result the items were reduced to final 30 items.
21. Item Difficulty Index
Purpose: To assess the difficulty level of the scale test items.
Phase 2: Scale Development
Step 5: Item Reduction
Item correct answers for the item /
the total answers on that item
Ranges between 0.0 to 1.0
Item difficulty index Difficulty level
0.86 and above Very easy.
0.71 to 0.85 Easy
0.30 to 0.70 Moderate
0.15 to 0.29 Difficult
0.14 and below Very difficult
High difficulty index score means a
greater proportion of the sample
population answered the question
correctly.
Lower difficulty index score means a
smaller proportion of the sample
understood the question and
answered correctly.
(2,21)
22. Item Discrimination test
Purpose: to identify the degree to which an item can correctly differentiates between respondents .
Phase 2: Scale Development
Step 5: Item Reduction
The upper group
(with high scores)
proportion of responders who got
the item correct in the upper group
- proportion of responders with
correct answer in the lower group.
Ranges between -1 to +1
The lower group
(with low scores) Item discrimination index Discrimination level
0.19 and below Poor item; should be eliminated
or revised.
0.20 to 0.29 Marginal items; need revision
0.30 to 0.39 Good item; may need some
improvement
0.4 or above Very good item
(22,23)
23. Distractor Efficiency Analysis:
Purpose:
To determine the distribution of incorrect options “distractors” and how they contribute to the quality of items.
Phase 2: Scale Development
Step 5: Item Reduction
The upper group
(with high scores)
The middle group
(with middle
scores)
The lower group
(with low scores)
• 100% of participants in the high
group
• about 50% of participants in the
middle
• few or none of those in the
lower group
Correct
option
Appropriate
item
If those with adequate knowledge “the high group” can’t differentiate between the right option of the item and
the distractors, the question may need to be modified or deleted.
(24,25)
24. Factor analysis:
It is a method for explaining the construction of data by
explaining the correlations between variables. It
summarizes data into a few dimensions by condensing many
variables into a smaller set of latent variables or factors .
• Exploratory Factor Analysis (EFA) it’s the interrelation
between items in the construct. It is used to reduce the set
of observed variables to a smaller, more close set of
variables.
• Confirmatory Factor Analysis (CFA) and is used to
determine the factors by statistically testing the hypothesis of
the expected factor loading (FL) of the observed items on
underlying (latent) factors and the correlation between latent
variables.
• Items having factor loading or slope coefficients
below 0.30 are considered inadequate “Unrelated
items” that should be eliminated.
• Items with cross loading > 0.4 should be eliminated.
Phase 2: Scale Development
Step 6: Extraction of Factors
(4,23,26)
25. Phase 2: Scale Development
Step 6: Extraction of Factors
Example:
In a research for Developing a disease-
specific tool for assessment of quality of
life of patients with hepatitis C virus
associated chronic liver disease (27).
They conducted CFA and calculated Factor
loading, any item with factor loading less than
0.3 is eliminated.
Pitfalls:
Many of scale developers are hesitating to use
factor analysis either because:
• it needs large sample size to be conducted
• because it involves many confusing and
complicated steps and interpretations (16)
26. • Purpose: A scale’s dimensionality, to identify the number of latent variables that are measured by the scale.
• It’s usually depends on the factor’s extraction and analysis.
Phase 3: Scale Evaluation
Step 7:Test dimensionality
(12)
Start
27. Example:
A research conducted for the development of a stress scale for pregnant women in the South Asian
context: the A–Z Stress Scale (5)
The researchers stated that their scale has two dimension by multidimension scaling;
1- socioenvironmental related hassles dimension (includes items from 1-26).
2- chronic illness dimension (items 27-30).
Phase 3: Scale Evaluation
Step 7:Test dimensionality
Pitfalls
• Failure to effectively calculate EFA and CFA will lead to miss classification of the dimensions of the
construct.
• Many of the researchers depend on literature and expert view to divide the dimensions of the construct
rather than using factors analysis (12).
28. Reliability is the ability to reproduce same result consistently under the same conditions.
Purpose: To measure reliability regarding; stability, internal consistency, equivalence and inter-rater reliability.
Phase 3: Scale Evaluation
Step 8:Tests of Reliability
Stability
The test is administered
twice or more to the same
participant to ensure that
same results are obtained.
Testing the developing
scale on 43 pregnant
twice one week interval
(r = 0.86).
It measures whether items measuring the same
general construct produce the same scores
(Homogeneity).It’s assessed by:
• Cronbach’s α;(value 0-1, ≥0.7 is acceptable)
• Kuder-Richardson
• Split halves reliability (two equal halves of the
scale then compare).
• Cronbach’s alpha (0.82 for the scale and
was ranged between 0.75 to 0.86 for
different items).
Equivalence
It determines the
correlation of level of
agreement between two or
more instruments at the
same point of time.
It assesses the degree of
agreement between two or
more raters in assessing
certain phenomena at the
same point of time.
The developing scale was
applied on 50 pregnant
and two interviewers (r =
0.91).
(22, 28, 29)
29. Pitfalls:
• Test – retest reliability should be used with caution as the score of values could be changed over
time in some types of studies (e.g., intervention studies), here the change isn’t due to low reliable
measure, but it’s a true change in the participants.
• Number of items in the scale below 10, could lead to decrease Cronbach’s alpha
• Lack of standardization between the observers leads to decrease interrater agreement (1,2).
Phase 3: Scale Evaluation
Step 8:Tests of Reliability
30. Phase 3: Scale Evaluation
Step 9:Tests ofValidity
Validity
The ability of
the
measuring
scale to
evaluate the
domain that
was intended
to be
measured.
Content
validity
Including face
validity
Criterion
validity
Concurrent
validity
Compare at
the same time
Gold
standard
Predictive
validity
Gold
standard
or
Behavior
Predict
after time
Construct
validity
Convergent
validity
Same
result
Two related
measures
Divergent
validity
(Discriminate)
Different
result
Two
different
measures
Known-groups
validity
Two
different
groups
Different
result
Same group
Same group
Same
measurement
New
measure
New
measure
(22. 28, 30)
31. Phase 3: Scale Evaluation
Step 9:Tests ofValidity
Criterion
validity
Concurrent
validity
Compare at
the same time multicultural
validated
depression scale
New
A–Z Stress
Scale
Moderate correlation
between the two scales
(r = 0.56)
Example: In the study conducted for the development of a stress scale for pregnant women in the South Asian
context: the A–Z Stress Scale (5)
Pitfalls for validity calculation:
1- Criterion validity can’t be assessed with small sample size due to presence of sampling
error.
2- Criterion validity cannot be used in all circumstances, especially in social sciences as a
relevant criterion “gold standard” may be not present, So, it’s usually ignored and not
calculated in most of the validation studies.
3- Lack of sufficient resources or skills for calculation and assessment (22).
32. Pitfalls for validity calculation: (cont.)
4- The scale developers usually use homogeneous group from the population in the pilot study which
limit calculation of construct validity, so recruiting of heterogenous group or random sample of the
population is recommended.
5- Single time calculation of validity is inaccurate if the variable under study changed with time, so, it’s
recommended to conduct longitudinal studies during scale development to get accurate validity
measures especially predictive validity, as it will lead to pseudo correlations between variables.
6- Social desirability bias: which is a systematic error present in self-reporting measures in which the
participants want to keep good image. This is considered as one of the important threats to the
validity (22).
Phase 3: Scale Evaluation
Step 9:Tests ofValidity
33. Conclusion
• Valid research results begin with valid and reliable measurement. This can be
achieved if a systematic and scientific based process is followed.
• Developing a valid and reliable scale is a multiphasic procedure that need a
researcher with adequate knowledge and proper level of skills.
• Poor scale development will be had effect on the validity and reliability of the results
and therefore, the applicability in practice. So, the availability of a comprehensive
guide for scale development is essential.
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