3.4 types of validity
•Download as PPTX, PDF•
4 likes•2,118 views
A
A MThe document discusses two types of validity: internal validity and external validity. Internal validity refers to the extent to which a study's findings reflect the true causal effect in the target population. External validity refers to the extent to which findings from a study population can be generalized to an external population beyond the target population. A key point is that internal validity is a prerequisite for external validity - a study must first be internally valid before its findings can be generalized to other populations.
Report
Share
Report
Share
Recommended
3.3 hierarchy of populations
This document outlines the hierarchy of populations in research studies, including:
1. The study population or sample, which is the population that participated in the study.
2. The actual population that met eligibility criteria and could have participated.
3. The target population, which is broader than the actual population and is what inferences are aimed to be made about.
4. One or more external populations, which are broader than the target population and may or may not overlap with it, and are what the investigator wants to generalize findings to.
Recall bais
Irrespective of study design, the first step in the process of avoiding any type of bias is the proper definition and articulation of the research question.
Consequently, this step will lead to a number of questions that need to be adequately addressed by the investigator during the planning stage of research:
what kind of information are required to answer this question in the study in terms of exposure, outcome, and possible confounders?
what is the most appropriate method to collect these information?
how to achieve comparable accuracy of data collection between the study groups?
Cross sectional research
Cross-sectional research involves analyzing data from a population or subset at a single point in time. It provides a snapshot of populations by gathering data through repeated cross-sectional surveys that sample different individuals each time, rather than following the same individuals over time like longitudinal studies. Cross-sectional research is time efficient and requires no long term cooperation since it does not assess individual changes, but it cannot determine cause and effect or rule out alternative explanations.
observational studies in research methodology
The document discusses different qualitative research methods including field observations, focus groups, and intensive interviews. It provides details on how field observations are classified based on the degree of participation and concealment. The advantages and disadvantages of different field observation techniques are outlined. Focus groups involve interviewing 6-12 people simultaneously to understand attitudes and behaviors on a topic. Key steps in conducting focus groups include defining the problem, selecting a representative sample, and determining the appropriate number of groups. The document emphasizes that qualitative methods like field observations and focus groups are aimed at understanding descriptions and explanations rather than making quantitative measurements.
Longitudinal research
Longitudinal research involves studying the same sample of individuals over multiple points in time. There are three main types of longitudinal studies: trend studies sample different groups over time from the same population; cohort analyses measure characteristics of groups that experience the same life events; and panel studies measure the same sample at different points to reveal changes. Combining qualitative and quantitative data in longitudinal research allows qualitative data to aid interpretation and suggest new hypotheses. Conducting longitudinal research online through internet surveys panels has advantages like easier recruitment but also disadvantages like lack of representation.
unit 6 historical research
This document provides an overview of historical research methods. It defines historical research as a systematic attempt to study past life experiences by gathering and analyzing data. The goals are to understand what occurred in the past and use those findings to modify current practices to prevent unwanted past experiences. Historical research analyzes primary sources like eyewitness accounts and secondary sources more removed from the event. The researcher evaluates the authenticity and value of the evidence using external and internal criticism. An example is given of a historical study analyzing nursing literature from 1895-1935 to understand how nurses worked to prevent sepsis in surgical wounds before antibiotics.
Cross sectional study
This document discusses cross-sectional studies. It defines a cross-sectional study as an observational study that measures exposure and health outcomes in a population at a single point in time, providing a "snapshot" of prevalence. It describes key characteristics, including simultaneously collecting exposure and outcome data, estimating prevalence rather than incidence, and inability to determine temporal relationships between variables. The document outlines advantages as being quick and inexpensive but also limitations such as inability to establish causation.
Recommended
3.3 hierarchy of populations
This document outlines the hierarchy of populations in research studies, including:
1. The study population or sample, which is the population that participated in the study.
2. The actual population that met eligibility criteria and could have participated.
3. The target population, which is broader than the actual population and is what inferences are aimed to be made about.
4. One or more external populations, which are broader than the target population and may or may not overlap with it, and are what the investigator wants to generalize findings to.
Recall bais
Irrespective of study design, the first step in the process of avoiding any type of bias is the proper definition and articulation of the research question.
Consequently, this step will lead to a number of questions that need to be adequately addressed by the investigator during the planning stage of research:
what kind of information are required to answer this question in the study in terms of exposure, outcome, and possible confounders?
what is the most appropriate method to collect these information?
how to achieve comparable accuracy of data collection between the study groups?
Cross sectional research
Cross-sectional research involves analyzing data from a population or subset at a single point in time. It provides a snapshot of populations by gathering data through repeated cross-sectional surveys that sample different individuals each time, rather than following the same individuals over time like longitudinal studies. Cross-sectional research is time efficient and requires no long term cooperation since it does not assess individual changes, but it cannot determine cause and effect or rule out alternative explanations.
observational studies in research methodology
The document discusses different qualitative research methods including field observations, focus groups, and intensive interviews. It provides details on how field observations are classified based on the degree of participation and concealment. The advantages and disadvantages of different field observation techniques are outlined. Focus groups involve interviewing 6-12 people simultaneously to understand attitudes and behaviors on a topic. Key steps in conducting focus groups include defining the problem, selecting a representative sample, and determining the appropriate number of groups. The document emphasizes that qualitative methods like field observations and focus groups are aimed at understanding descriptions and explanations rather than making quantitative measurements.
Longitudinal research
Longitudinal research involves studying the same sample of individuals over multiple points in time. There are three main types of longitudinal studies: trend studies sample different groups over time from the same population; cohort analyses measure characteristics of groups that experience the same life events; and panel studies measure the same sample at different points to reveal changes. Combining qualitative and quantitative data in longitudinal research allows qualitative data to aid interpretation and suggest new hypotheses. Conducting longitudinal research online through internet surveys panels has advantages like easier recruitment but also disadvantages like lack of representation.
unit 6 historical research
This document provides an overview of historical research methods. It defines historical research as a systematic attempt to study past life experiences by gathering and analyzing data. The goals are to understand what occurred in the past and use those findings to modify current practices to prevent unwanted past experiences. Historical research analyzes primary sources like eyewitness accounts and secondary sources more removed from the event. The researcher evaluates the authenticity and value of the evidence using external and internal criticism. An example is given of a historical study analyzing nursing literature from 1895-1935 to understand how nurses worked to prevent sepsis in surgical wounds before antibiotics.
Cross sectional study
This document discusses cross-sectional studies. It defines a cross-sectional study as an observational study that measures exposure and health outcomes in a population at a single point in time, providing a "snapshot" of prevalence. It describes key characteristics, including simultaneously collecting exposure and outcome data, estimating prevalence rather than incidence, and inability to determine temporal relationships between variables. The document outlines advantages as being quick and inexpensive but also limitations such as inability to establish causation.
Observational Studies: Case Control & Cohort Studies
Cohort and case-control studies are two common types of observational analytical studies used in medical research. Cohort studies follow groups of individuals over time and compare outcomes between those exposed and unexposed to a particular risk factor. Case-control studies identify cases with an outcome and compare their exposures to controls without the outcome. Both study designs allow researchers to examine multiple exposures and outcomes simultaneously while accounting for potential confounding factors.
PUBLICATION BIAS & NEGATIVE RESULTS
This document discusses issues with publishing negative or null results in scientific literature. There is pressure in academia to publish only statistically significant, positive results in high impact journals. However, not publishing negative results can skew the overall body of evidence and waste resources as other researchers pursue lines of inquiry that have already been investigated. The document argues for changing the scientific culture to value negative results as much as positive results, and for correcting the literature when previous findings are refuted by new evidence. Publishing all results, regardless of outcome, would lead to a more systematic and truthful understanding of what is known.
Introduction to ebm_2011-07
Evidence-based medicine involves integrating the best available research evidence with clinical expertise and patient values when making medical decisions. Key aspects of EBM include formulating clear clinical questions, thoroughly searching the literature for relevant evidence, critically appraising the evidence and determining its applicability. Randomized controlled trials provide the highest level of evidence, followed by well-designed observational studies. Different study designs like meta-analyses, randomized trials, cohort and case-control studies each have strengths and limitations in assessing treatment effectiveness.
presentation on Sampling
This document discusses sampling in research. It defines sampling as selecting a subset of a population to study and generalize findings to the larger group. Key terms are defined, like population, target population, sample, and sampling frame. The purposes of sampling are described as making research more economical, improving data quality, allowing for quicker study results, and increasing precision and accuracy. Characteristics of a good sample and factors influencing the sampling process are also outlined. In conclusion, sampling is an important part of research that aims to select a representative portion of a population to study.
Survey research
This document defines and describes survey research. It notes that a survey collects superficial information from a population to provide extensive rather than intensive results about phenomena prevalence, distribution, and relationships. It discusses the main features of surveys including gathering current data from a sample representing the total population. The document also outlines different types of surveys based on the phenomenon studied, such as descriptive, exploratory, comparative, and correlational surveys. Additionally, it describes data collection methods for surveys including written, oral, and electronic surveys and provides advantages and disadvantages of non-experimental research.
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
This document provides an overview of research methods and sampling techniques. It discusses the differences between probability and non-probability sampling, and describes common probability sampling methods like simple random sampling. The document also summarizes different approaches to systematic reviews, including traditional systematic reviews primarily used in medicine, mixed methods reviews that incorporate qualitative research, and integrative reviews focused on theory building. Finally, it outlines the typical steps involved in undertaking a systematic review project.
Sampling
This document discusses different types of sampling methods used in research studies. It describes non-probability sampling methods like accessibility sampling and quota sampling which do not give a true representation of the population. It then explains various probability sampling methods that give each individual an equal chance of being selected, including simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. For each method, it provides a brief definition and example to illustrate how it is implemented in research.
Observational Research designs: detailed description
This document provides an overview of observational research designs presented by Professor Tarek Tawfik Amin. It outlines the objectives, methodology, contents and plan of the workshop on observational research designs. Descriptive designs such as case reports, case series, and cross-sectional studies are discussed. Case reports describe individual cases while case series report aggregates of individual cases. Cross-sectional studies observe a population at a single point in time to describe disease prevalence and other characteristics. Analytical observational designs like case-control and cohort studies are also introduced.
Study designs
This document discusses different types of study designs used in evidence-based research: quantitative research, qualitative research, nonexperimental studies, cross-sectional studies, cohort studies, and case-control studies. It provides details on the purpose, design, advantages, and disadvantages of cohort studies and case-control studies. Cohort studies follow groups over time to examine causes and effects, while case-control studies compare cases with a control group to identify predictors and generate hypotheses.
3 cross sectional study
This document provides an overview of cross-sectional studies, including what they are, their uses, methodology, advantages, and disadvantages. A cross-sectional study involves observing a population at a single point in time to determine prevalence of disease. It is a quick and inexpensive way to describe characteristics of a population and identify associations between variables. However, it cannot determine causation due to its observational nature.
Types of research
This document outlines different types of research. Basic research includes experimental studies like animal experiments to develop new knowledge. Clinical research involves interventional studies like clinical trials as well as observational studies like case series. Epidemiological research investigates disease distribution and causes through experimental and observational designs such as cohort and case-control studies. Qualitative research explores human experiences and attitudes through methods like interviews, while quantitative research uses tools like questionnaires and tests hypotheses. Operational research aims to improve health programs by identifying implementation issues and testing solutions through primary data collection or record reviews. Organizational research examines organizations without focusing on individuals.
Ethics, reliability, validity, sampling
Here are potential responses to these exam questions on ethics:
1) A psychologist could maintain confidentiality when reporting a case study by:
- Using a pseudonym for the participant
- Omitting or changing any identifying details
2) Two ethical issues that arise in research involving children are:
- Informed consent - children may not fully understand what they are consenting to. Parental consent is needed.
- Protection - there is a duty of care to protect children from potential harm, embarrassment or distress. Research methods need to be age-appropriate.
Research methods in sociology
This document outlines different methods that sociologists use to conduct research, including quantitative and qualitative approaches. Quantitative methods involve gathering measurable data through surveys and experiments, while qualitative methods involve field research approaches like participant observation, ethnographic studies, and case studies. The goal of sociological research is to investigate how human societies function using empirical evidence and the scientific method or interpretive frameworks. Researchers aim to design studies that are reliable, valid, and value neutral in disclosing results.
Bias in clinical research
This is an interesting ppt discussing in detail about bias in clinical research and how to overcome it...
Apa method section
The document provides guidance on writing the Method section of a research paper. The Method section should describe the study in enough detail that other researchers could replicate it. It includes subsections for participants, design, materials, apparatus, procedure, and participant characteristics. The participants subsection describes the number, demographics, and source of participants. The design subsection describes the research design. The materials subsection lists what was used to collect data. The procedure subsection provides a step-by-step description of how the study was conducted. The goal is to give readers a clear understanding of how the research was performed.
Longitudinal Design and its Types
This document discusses longitudinal design and its types. A longitudinal study observes the same subjects over an extended period of time to study changes. There are several types of longitudinal designs, including panel studies that follow the same individuals, cohort studies that follow groups based on a shared characteristic, and retrospective studies that examine past records. Longitudinal research can measure outcomes like risk factors, treatment effects, patterns of change and stability, and historical or developmental impacts. Key types are prospective panel studies with single or multiple cohorts, retrospective panel designs, record linkage studies, and repeated cross-sectional trend studies.
Survey designs
Survey research designs are procedures used in quantitative research where investigators administer surveys to describe populations. There are two main types of survey designs - cross-sectional and longitudinal. Cross-sectional designs collect data at one point in time to measure current attitudes or practices, while longitudinal designs collect data about trends over time within the same population. Key characteristics of survey research include sampling from a population, collecting data through questionnaires or interviews, designing instruments, and obtaining a high response rate.
How To Read A Medical Paper: Part 2, Assessing the Methodological Quality
This document outlines five essential questions to ask when assessing the methodological quality of papers: 1) Was the study original? 2) Whom is the study about? 3) Was the design of the study sensible? 4) Was systematic bias avoided or minimized? 5) Was the study large enough and long enough to make the results credible? It discusses factors to consider for each question when evaluating a study's methods section such as sample size, duration of follow up, and completeness of follow up.
Research methods
The document provides information on various aspects of research methodology. It defines key terms such as research, theory, data types, data collection methods, research design and sampling. It discusses primary and secondary data sources and the advantages and limitations of each. Various data collection techniques for qualitative and quantitative data are also outlined.
Validity, its types, measurement & factors.
This document discusses the different types of validity in psychological testing: face validity, content validity, criterion validity (including predictive and concurrent validity), and discriminant validity. It provides examples for each type of validity. Criterion validity refers to how a test correlates with other measures of the same construct. Discriminant validity shows a test does not correlate with measures of different constructs. Validity is determined through empirical evidence over many studies, and is not an all-or-none concept. Factors like history, maturation, testing, and selection can threaten a test's validity if not controlled.
Aspects of Validity
An Argument-Based, Systematic Framework to Study Validity and Reliability of Unit and Program Assessments
More Related Content
What's hot
Observational Studies: Case Control & Cohort Studies
Cohort and case-control studies are two common types of observational analytical studies used in medical research. Cohort studies follow groups of individuals over time and compare outcomes between those exposed and unexposed to a particular risk factor. Case-control studies identify cases with an outcome and compare their exposures to controls without the outcome. Both study designs allow researchers to examine multiple exposures and outcomes simultaneously while accounting for potential confounding factors.
PUBLICATION BIAS & NEGATIVE RESULTS
This document discusses issues with publishing negative or null results in scientific literature. There is pressure in academia to publish only statistically significant, positive results in high impact journals. However, not publishing negative results can skew the overall body of evidence and waste resources as other researchers pursue lines of inquiry that have already been investigated. The document argues for changing the scientific culture to value negative results as much as positive results, and for correcting the literature when previous findings are refuted by new evidence. Publishing all results, regardless of outcome, would lead to a more systematic and truthful understanding of what is known.
Introduction to ebm_2011-07
Evidence-based medicine involves integrating the best available research evidence with clinical expertise and patient values when making medical decisions. Key aspects of EBM include formulating clear clinical questions, thoroughly searching the literature for relevant evidence, critically appraising the evidence and determining its applicability. Randomized controlled trials provide the highest level of evidence, followed by well-designed observational studies. Different study designs like meta-analyses, randomized trials, cohort and case-control studies each have strengths and limitations in assessing treatment effectiveness.
presentation on Sampling
This document discusses sampling in research. It defines sampling as selecting a subset of a population to study and generalize findings to the larger group. Key terms are defined, like population, target population, sample, and sampling frame. The purposes of sampling are described as making research more economical, improving data quality, allowing for quicker study results, and increasing precision and accuracy. Characteristics of a good sample and factors influencing the sampling process are also outlined. In conclusion, sampling is an important part of research that aims to select a representative portion of a population to study.
Survey research
This document defines and describes survey research. It notes that a survey collects superficial information from a population to provide extensive rather than intensive results about phenomena prevalence, distribution, and relationships. It discusses the main features of surveys including gathering current data from a sample representing the total population. The document also outlines different types of surveys based on the phenomenon studied, such as descriptive, exploratory, comparative, and correlational surveys. Additionally, it describes data collection methods for surveys including written, oral, and electronic surveys and provides advantages and disadvantages of non-experimental research.
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
This document provides an overview of research methods and sampling techniques. It discusses the differences between probability and non-probability sampling, and describes common probability sampling methods like simple random sampling. The document also summarizes different approaches to systematic reviews, including traditional systematic reviews primarily used in medicine, mixed methods reviews that incorporate qualitative research, and integrative reviews focused on theory building. Finally, it outlines the typical steps involved in undertaking a systematic review project.
Sampling
This document discusses different types of sampling methods used in research studies. It describes non-probability sampling methods like accessibility sampling and quota sampling which do not give a true representation of the population. It then explains various probability sampling methods that give each individual an equal chance of being selected, including simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multistage sampling. For each method, it provides a brief definition and example to illustrate how it is implemented in research.
Observational Research designs: detailed description
This document provides an overview of observational research designs presented by Professor Tarek Tawfik Amin. It outlines the objectives, methodology, contents and plan of the workshop on observational research designs. Descriptive designs such as case reports, case series, and cross-sectional studies are discussed. Case reports describe individual cases while case series report aggregates of individual cases. Cross-sectional studies observe a population at a single point in time to describe disease prevalence and other characteristics. Analytical observational designs like case-control and cohort studies are also introduced.
Study designs
This document discusses different types of study designs used in evidence-based research: quantitative research, qualitative research, nonexperimental studies, cross-sectional studies, cohort studies, and case-control studies. It provides details on the purpose, design, advantages, and disadvantages of cohort studies and case-control studies. Cohort studies follow groups over time to examine causes and effects, while case-control studies compare cases with a control group to identify predictors and generate hypotheses.
3 cross sectional study
This document provides an overview of cross-sectional studies, including what they are, their uses, methodology, advantages, and disadvantages. A cross-sectional study involves observing a population at a single point in time to determine prevalence of disease. It is a quick and inexpensive way to describe characteristics of a population and identify associations between variables. However, it cannot determine causation due to its observational nature.
Types of research
This document outlines different types of research. Basic research includes experimental studies like animal experiments to develop new knowledge. Clinical research involves interventional studies like clinical trials as well as observational studies like case series. Epidemiological research investigates disease distribution and causes through experimental and observational designs such as cohort and case-control studies. Qualitative research explores human experiences and attitudes through methods like interviews, while quantitative research uses tools like questionnaires and tests hypotheses. Operational research aims to improve health programs by identifying implementation issues and testing solutions through primary data collection or record reviews. Organizational research examines organizations without focusing on individuals.
Ethics, reliability, validity, sampling
Here are potential responses to these exam questions on ethics:
1) A psychologist could maintain confidentiality when reporting a case study by:
- Using a pseudonym for the participant
- Omitting or changing any identifying details
2) Two ethical issues that arise in research involving children are:
- Informed consent - children may not fully understand what they are consenting to. Parental consent is needed.
- Protection - there is a duty of care to protect children from potential harm, embarrassment or distress. Research methods need to be age-appropriate.
Research methods in sociology
This document outlines different methods that sociologists use to conduct research, including quantitative and qualitative approaches. Quantitative methods involve gathering measurable data through surveys and experiments, while qualitative methods involve field research approaches like participant observation, ethnographic studies, and case studies. The goal of sociological research is to investigate how human societies function using empirical evidence and the scientific method or interpretive frameworks. Researchers aim to design studies that are reliable, valid, and value neutral in disclosing results.
Bias in clinical research
This is an interesting ppt discussing in detail about bias in clinical research and how to overcome it...
Apa method section
The document provides guidance on writing the Method section of a research paper. The Method section should describe the study in enough detail that other researchers could replicate it. It includes subsections for participants, design, materials, apparatus, procedure, and participant characteristics. The participants subsection describes the number, demographics, and source of participants. The design subsection describes the research design. The materials subsection lists what was used to collect data. The procedure subsection provides a step-by-step description of how the study was conducted. The goal is to give readers a clear understanding of how the research was performed.
Longitudinal Design and its Types
This document discusses longitudinal design and its types. A longitudinal study observes the same subjects over an extended period of time to study changes. There are several types of longitudinal designs, including panel studies that follow the same individuals, cohort studies that follow groups based on a shared characteristic, and retrospective studies that examine past records. Longitudinal research can measure outcomes like risk factors, treatment effects, patterns of change and stability, and historical or developmental impacts. Key types are prospective panel studies with single or multiple cohorts, retrospective panel designs, record linkage studies, and repeated cross-sectional trend studies.
Survey designs
Survey research designs are procedures used in quantitative research where investigators administer surveys to describe populations. There are two main types of survey designs - cross-sectional and longitudinal. Cross-sectional designs collect data at one point in time to measure current attitudes or practices, while longitudinal designs collect data about trends over time within the same population. Key characteristics of survey research include sampling from a population, collecting data through questionnaires or interviews, designing instruments, and obtaining a high response rate.
How To Read A Medical Paper: Part 2, Assessing the Methodological Quality
This document outlines five essential questions to ask when assessing the methodological quality of papers: 1) Was the study original? 2) Whom is the study about? 3) Was the design of the study sensible? 4) Was systematic bias avoided or minimized? 5) Was the study large enough and long enough to make the results credible? It discusses factors to consider for each question when evaluating a study's methods section such as sample size, duration of follow up, and completeness of follow up.
Research methods
The document provides information on various aspects of research methodology. It defines key terms such as research, theory, data types, data collection methods, research design and sampling. It discusses primary and secondary data sources and the advantages and limitations of each. Various data collection techniques for qualitative and quantitative data are also outlined.
What's hot (20)
Observational Studies: Case Control & Cohort Studies
Observational Studies: Case Control & Cohort Studies
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
SOCIAL WORK RESEARCH: Sampling and Systematic review (Incomplete)
Observational Research designs: detailed description
Observational Research designs: detailed description
How To Read A Medical Paper: Part 2, Assessing the Methodological Quality
How To Read A Medical Paper: Part 2, Assessing the Methodological Quality
Viewers also liked
Validity, its types, measurement & factors.
This document discusses the different types of validity in psychological testing: face validity, content validity, criterion validity (including predictive and concurrent validity), and discriminant validity. It provides examples for each type of validity. Criterion validity refers to how a test correlates with other measures of the same construct. Discriminant validity shows a test does not correlate with measures of different constructs. Validity is determined through empirical evidence over many studies, and is not an all-or-none concept. Factors like history, maturation, testing, and selection can threaten a test's validity if not controlled.
Aspects of Validity
An Argument-Based, Systematic Framework to Study Validity and Reliability of Unit and Program Assessments
3.5.2 selection bias
This document discusses selection bias, which occurs when there are systematic differences in the characteristics of those selected for a study compared to the overall population from which they were selected. It provides examples of selection bias in case-control and cohort studies. Selection bias can be introduced when the probability of being included in the study depends on both the exposure and outcome of interest. This distorts the association observed in the study sample compared to the true association in the population. Differential participation or loss to follow up based on exposure and outcome status are common causes of selection bias.
3.1 big picture
The document discusses threats to validity from information bias and selection bias in epidemiology studies. It explains key concepts like the difference between causal and error-based associations, internal and external validity, types of populations, and sources of information and selection bias. Specifically, it covers identifying sources of bias, understanding differential and non-differential misclassification and their implications, and strategies for minimizing biases in study design and analysis.
Bias and validity
This document discusses bias and validity in clinical research. It defines clinical epidemiology as the study of health-related states and events in populations to control health problems. It describes how epidemiologic studies compare outcomes like disease rates between exposed and unexposed groups. Validity is important, with internal validity indicating good construct free from bias/errors, and external validity showing generalizability. Bias and confounding can threaten validity and lead to erroneous associations if not avoided or controlled for.
Validity Study Barbados NVQ-B
The document summarizes a study examining the validity of assessments for the Barbados NVQ-B Level I in Amenity Horticulture. The study had two phases: [1] an exploratory study examining how the NVQ-B is used to measure employee competence, and [2] an assessment validation to evaluate the validity of the NVQ-B assessment system, content, and decisions. Key findings from Phase I included that the NVQ-B assessment process involves gathering evidence of competence, and that assessments presented challenges. Phase II found generally good coherence between assessment content and standards, but identified threats to validity from measurement of some constructs and ambiguity in assessment criteria. The study concluded with validity arguments that assessments provide a reasonable indication
Uses of epidemiology
Epidemiology is defined as the study of the distribution and determinants of health-related states in populations. It differs from clinical medicine in that it focuses on groups rather than individuals and uses quantitative tools to study communities. Epidemiology has many uses including healthcare management by making community diagnoses, understanding disease processes, guiding public health practice through disease investigation and surveillance, and informing clinical practice and research through evaluating treatments and assessing effectiveness of diagnostic procedures. Overall, epidemiology provides essential population-level insights that aid in healthcare planning, policy development, and clinical decision-making.
Randomized Controlled Trial
This study aims to prevent childhood obesity through early childhood feeding and parenting guidance. It will randomize pregnant women into an intervention and control group. The intervention group will receive home visits and guidance from community health workers, while the control group will only receive measurements. The study hypothesizes that fewer children in the intervention group will be overweight or obese due to differences in feeding practices and responsiveness to infant cues. Data on infant growth, diet, sleep and more will be collected to assess the effectiveness of the early intervention program in preventing childhood obesity.
Randomised controlled trials
Randomized controlled trials (RCTs) are considered the gold standard for clinical research. An RCT involves randomly assigning participants into experimental and control groups to receive different interventions. Randomization aims to make the groups comparable to limit bias. It reduces the influence of unknown factors and ensures the only difference between groups is the intervention being tested. RCTs can be single blind, double blind, or triple blind depending on who is aware of group assignments. They provide the most powerful and least biased assessments of clinical interventions.
validity its types and importance
The document discusses the importance of validity in test construction and identifies three main types of validity: content validity, which refers to how well the test items align with the objectives being measured; criterion-related validity, which examines the correlation between test scores and external criteria; and construct validity, which refers to how well test scores are explained by theoretical constructs. Validity is specific to each test administration and is determined through evidence rather than absolute measures, with the most important type for classroom teachers being content validity.
Clinical Trial Phases
Clinical trials involve several phases:
- Phase I trials involve small groups of healthy volunteers or patients and aim to determine the safety and tolerability of a new drug.
- Phase II trials involve larger groups of patients and aim to determine efficacy and further evaluate safety. These trials provide preliminary data on effectiveness.
- Phase III trials involve even more patients and aim to confirm effectiveness, monitor side effects, and compare the new treatment to standard treatment. These trials provide the primary data to support effectiveness. Regulatory approval is based on positive Phase III results showing safety and effectiveness.
Presentation Validity & Reliability
The document discusses key qualities of measurement devices: validity, reliability, practicality, and backwash effect. It defines each quality and provides examples. Validity refers to what a test measures, and includes content, construct, criterion-related, concurrent, and predictive validity. Reliability is how consistent measurements are, including equivalency, stability, internal, and inter-rater reliability. Practicality means a test is easy to construct, administer, score and interpret. Backwash effect is a test's influence on teaching and learning.
Clinical Trials - An Introduction
This presentation will give an overview of the Clinical Research/Drug Development and licensing(US FDA) process. Please feel free to provide feedback.
Validation
This document discusses test validity and its various types. It defines validity as measuring what a test is designed to measure. There are different types of validity evidence including rational, empirical, and construct validity. Internal validity looks at a test's content and effect, while external validity compares test scores to outside criteria. Construct validity determines what test scores represent by comparing them to other data about test takers. Gathering multiple forms of validity evidence establishes that a test is accurately measuring the intended construct. Reliability is necessary for validity, but a test can be reliable without being valid.
3.5 types of biases
The document discusses three main types of biases that can occur in studies: information bias, which arises from mismeasurement or misclassification of variables; selection bias, which comes from how study subjects are selected and factors influencing participation; and confounding bias, which is considered a form of bias where true causal associations exist in the population, unlike other biases. The document notes there is disagreement around whether confounding should be called a bias, and that approaches to handling confounding differ from information and selection biases.
3.6 comparing biases
Selection bias relates to how participants are recruited into a study and their likelihood of remaining in the study or dropping out, which can affect the exposure-outcome association seen in the study compared to the overall population. Information bias relates to how exposure and outcome data are collected once participants are enrolled, such as through self-report, study personnel, records, or a registry, and whether misclassification is differential between exposed and unexposed or diseased and non-diseased groups.
3.2 sourced of error
This document discusses sources of error in epidemiologic studies, including random error and systematic error (bias). Random error is due to chance and relates to precision, as estimates from random samples may differ from the true population value. Systematic error is a systematic difference between observed and true effects due to factors like measurement error. Studies can be precise but not valid (if biased) or precise and valid (if minimizing bias). The direction and magnitude of bias must be considered when interpreting study results.
3.5.1 information bias
Information bias can occur from flawed measurement of study variables, resulting in misclassification of subjects. There are two types of misclassification: non-differential, where the degree of misclassification is unrelated to exposure/outcome status, and differential, where the degree depends on status. Non-differential misclassification typically biases results towards the null, while differential can bias in either direction. Specific biases include recall bias, interviewer bias, observer bias, and respondent bias. Proper evaluation of measurement error and its direction is important to understand potential biases.
Maryam Bolouri
This document discusses the history and definitions of validity in educational assessment. It covers several key points:
1) Validity has been defined and conceptualized in many different ways over the past 100 years, leading to confusion. It involves both theoretical frameworks and practical validation processes.
2) Validity refers to the appropriateness of test interpretation and use. It is considered the most important criterion for evaluating tests.
3) The document outlines different phases in the history of validity's definition, from early conceptions to more unified modern frameworks. It also discusses the ongoing debate around how to substantiate claims of validity through logical and empirical means.
4) Validity involves making accurate decisions about attributes being measured,
Randomized Controlled Trials
This document provides an overview of randomized controlled trials (RCTs). It begins with an introduction to RCTs, describing them as the gold standard for evaluating health care technologies. It then covers key aspects of designing and conducting RCTs, including developing a protocol, selecting and randomizing study populations, manipulating variables, follow-up, assessment, types of RCTs based on design and use, and ethical considerations. RCTs aim to provide scientific evidence of causal relationships and evaluate interventions through random assignment and control groups.
Viewers also liked (20)
Similar to 3.4 types of validity
POP77034 Experimental Methods HT2023 week 2 slides.pdf
This document provides an overview of experimental methods for social scientists. It discusses key concepts in causal inference and experimental design such as treatment, randomization, and measurement. Randomization techniques covered include simple random assignment, block randomization, and cluster randomization. Ethical considerations in experimentation are also reviewed. The document aims to help researchers design effective experiments and interpret their results causally.
Research Methods
This document provides an overview of a course on research methods in public health. The course aims to expose students to fundamentals of public health research and acquaint them with various methods used. It is divided into 3 modules over 9 units that cover: introduction to public health research; research methods and data analysis; and topic selection and research proposal components. Key concepts discussed include defining public health research, sampling techniques, reliability/validity, study designs, quantitative/qualitative methods, and data analysis. Examples of public health surveys from Nigeria are also provided.
4 Epidemiological Study Designs 1.pdf
The document discusses different epidemiological study designs. It distinguishes between observational and experimental studies. Observational studies include descriptive designs like case reports, case series, cross-sectional studies and analytical designs like cohort and case-control studies. Experimental designs involve assigning subjects to intervention and control groups. The key factors in choosing a study design are the research question, current knowledge on the problem, and practical considerations. Each design has strengths and limitations for establishing causality that must be considered.
Sampling for Quantities & Qualitative Research Abeer AlNajjar.docx
Sampling for Quantities & Qualitative Research
Abeer AlNajjar
1
Population
Target group (universe in texts)
Census (to study every member of a population)
because measuring every member of a population usually is not feasible most researchers employ a Sample
Sample ( a subgroup of the population)
2
Communication researchers are interested in a population (also called a universe when applied to texts) of communicators, all the people who posses a particular characteristic, or, in the case of those who study texts, all the messages that share a characteristic of interest.
The population of interest to researchers (often called the target group) might be members of a business, communication majors at a university, all students at a university, all people living in a city, all eligible voters in a country.
Texts ( editorials published in a specific newspaper for a week, or a large universe such as every editorial published In every newspaper in the UAE, or even larger such as all persuasive messages).
The best way to generalize to a population is to study every member of a population (Census)
If every member is studied, we know, by definition, the population’s response at the point in time the study was done
Sample
The results from the sample are then generalized back to (used to represent) the population
Representative sample ( population validity)
Its similarity to its parent population
3
The results from the sample are then generalized back to (used to represent) the population). For such generalization to be valid (demonstrate population validity), the sample must be representative of its population. That is, it must accurately approximate the population.
Types of sampling
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
Nonrandom sampling (nonprobability sampling)
Is what ever researchers do instead of using procedures that ensure that each member of a population has an equal chance of being selected
Sampling error
Is a number that express how much the characteristic of a sample probably differ from the characteristics of a population
5
There are 2 different types of sampling procedures, and differ in terms of how confident we are about the ability of the selected sample to represent the population from which it is drawn
Random sampling (probability sampling)
Involves selecting a sample in such a way that each person in the population of interest has an equal chance of being included
By giving everyone an equal chance , random sampling eliminates the danger of researchers biasing the selection process because of their own opinions or desires. By eliminating bias, random sampling provides the best assurance that the same characteristics of the population exist in the sample, and, therefore, that the sample represents the population.
Nonrandom sampling: it sometimes is .
Evidence based psychiatry
This document provides an overview of evidence-based psychiatry. It defines evidence-based medicine and outlines the key steps, including formulating an answerable clinical question using PICO, finding the best evidence, appraising the evidence, and applying it to individual patients. The document discusses different types of studies and how to evaluate their results, focusing on sensitivity, specificity, odds ratios, and other statistical concepts. It emphasizes integrating individual clinical expertise with the best available external evidence.
An evidence-based approach to research support: applying systematic review me...
An evidence-based approach to research support: applying systematic review me...IL Group (CILIP Information Literacy Group)
This document provides an overview of evidence-based approaches to research support. It discusses the development of systematic reviews and Cochrane reviews. Key aspects of developing a systematic search strategy are covered, including defining the research question using PICO/PECO, developing search terms, combining concepts, and testing the search strategy. The importance of searching multiple sources and record keeping are emphasized to ensure transparency and reproducibility. The goal is to identify all relevant evidence to answer the research question. cohort study
This document summarizes a presentation on cohort studies. It defines a cohort study as one that identifies a group of people who share a common characteristic or experience within a defined time period and follows them over time to determine outcomes. The key features of cohort studies are identified as cohorts being defined prior to disease appearance and studying the frequency of disease in groups over time. Measures of frequency used in cohort studies such as cumulative incidence and incidence density are also defined.
1. unit 3 part I- intro with (a) Observational studies – descriptive and anal...
This document provides an overview of epidemiological study designs and research ethics. It begins by acknowledging the sources used to prepare the presentation. The objective is to understand epidemiological study design types, including observational and interventional studies. Observational studies are further broken down into descriptive and analytical designs, as well as ecological studies. Descriptive designs aim to simply describe health-related events, while analytical designs seek to establish relationships between exposures and outcomes. Ecological studies analyze data at the group level rather than the individual level. The document discusses various study design considerations and limitations. It emphasizes the importance of choosing appropriate study designs and addressing potential biases and confounding. Finally, it recommends further readings on epidemiological study design topics.
Ch04 General Issues in Research Design
Maxfield, Michael G. & Babbie, Earl R. (2011). Research Methods for Criminal Justice and Criminology, 6th Edition. Belmont, CA: Wadsworth Publishing.
chapter_5.ppt
This document discusses sampling methods and sample size calculations. It begins by defining key terms like population, sample, sampling unit, and sampling frame. It then covers the advantages and disadvantages of sampling, as well as sources of error in sampling like sampling error and non-sampling error. The document discusses both non-probability and probability sampling methods. For non-probability methods it covers convenience sampling, volunteer sampling, judgement sampling, quota sampling, and snowball sampling. For probability methods it discusses simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multi-stage sampling.
20100928150918 lecture 14 (analysis of data)
This document defines key concepts in research methods, including variables, populations, samples, sampling methods, instruments, and data analysis. It discusses that a variable is a measurable characteristic that varies, and examples include age, gender, and income. A population is the entire group being studied, while a sample is a subset of the population. Random and non-random sampling methods are outlined. Instruments are used to collect data, such as questionnaires, tests, and scales. Qualitative data involves themes and observations, while quantitative data is analyzed statistically using software.
Cohort Studies by Dr. Mohammed Jawad 2020
A cohort study compares groups of individuals who are exposed or unexposed to a certain factor over long periods of time to determine outcomes. The document discusses key aspects of cohort studies including: prospective cohorts follow exposed and unexposed groups into the future while retrospective cohorts look back in time; cohorts are established based on a common characteristic and followed to measure outcomes; advantages are direct risk estimation and studying rare exposures, while disadvantages include large sample sizes and potential for bias.
EVIDENCE BASED PUBLIC HEALTH.pptx
EBD is sequential, systematic process of addressing a clinical or community relevant problems.
EBD components are;
Clinical expertise
Patient’s preferences & values
Highest level of evidence
Classroom Obsevation- 4 SAMPLING METHODS.pptx
This document discusses various sampling methods used in research. It begins by introducing the objectives of understanding different sampling techniques and distinguishing between probability and non-probability sampling. It then presents several interactive activities to recall key terms related to research methodology. The document proceeds to define sampling and describe different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also outlines various non-probability sampling techniques including quota sampling, voluntary sampling, purposive sampling, availability sampling, and snowball sampling. The document concludes by discussing how to avoid sampling errors and providing an assignment for groups to develop research instruments.
STUDY DESIGN in health and medical research .pptx
This document provides an overview of observational (non-experimental) study designs used in epidemiology. It describes descriptive studies such as case reports, case series, cross-sectional studies, and correlational studies. It also describes analytical studies including case-control and cohort studies. Case-control studies compare exposure history in cases versus controls, while cohort studies follow groups exposed versus not exposed over time to compare incidence rates. Both are used to test hypotheses about risk factors but cohort studies provide a direct measure of risk through relative risk. Bias is a potential limitation of all observational studies.
Epidemiological study designs
This document discusses various epidemiological study designs. It begins by defining descriptive studies, which involve systematically collecting and presenting data to describe a situation, and analytical studies, which attempt to establish causes or risk factors by comparing exposed and unexposed groups. The main types of descriptive studies covered are cross-sectional (examining a population at a single point in time), longitudinal (following a population over time), and ecological (examining population-level associations between exposures and outcomes). Advantages and disadvantages of each design are provided.
11 epidemiology
1. Social and economic factors may have changed between the time periods studied, such as increases in unemployment, poverty, or family disruption, which could impact rates of psychiatric morbidity.
2. Changes may have occurred in how psychiatric disorders are diagnosed and classified between 1977 and 1985 that could influence prevalence findings.
3. Increased awareness, destigmatization of mental health issues, and expansion of treatment services between the periods may have impacted help-seeking behaviors and the proportion of cases identified.
3 Research methods and materials (1).pptx
This document provides an overview of various research methods and study designs, including:
- Descriptive studies like ecological, case report, and cross-sectional studies which describe disease occurrence.
- Analytical observational studies like case-control studies which compare exposure histories of cases and controls to identify potential risk factors.
- The key components of research methods are discussed, such as study area, population, variables, sample size calculation, and data collection and analysis plans. Strengths and limitations of different study designs are also reviewed.
Seminar case control study
This document provides an overview of case control studies in epidemiology. It defines a case control study as a retrospective study that compares cases (people with a disease or condition) to controls (people without the disease or condition) to determine risk factors for the disease. The key steps outlined are: selection of cases and controls from the same study base; matching cases and controls on important characteristics; measuring exposure to suspected risk factors in the same way for both groups; and analyzing the data to compare exposure rates between cases and controls and estimate disease risk associated with exposure. Advantages are that case control studies are efficient for rare diseases, but limitations include potential for selection and recall bias.
Similar to 3.4 types of validity (20)
POP77034 Experimental Methods HT2023 week 2 slides.pdf
POP77034 Experimental Methods HT2023 week 2 slides.pdf
Sampling for Quantities & Qualitative Research Abeer AlNajjar.docx
Sampling for Quantities & Qualitative Research Abeer AlNajjar.docx
An evidence-based approach to research support: applying systematic review me...
An evidence-based approach to research support: applying systematic review me...
1. unit 3 part I- intro with (a) Observational studies – descriptive and anal...
1. unit 3 part I- intro with (a) Observational studies – descriptive and anal...
More from A M
Transparency7
This document contains slides from a lecture on public health given by Alasdair Cohen at UC Berkeley on October 17, 2016. The slides discuss various topics related to research quality including publication bias, p-hacking, negligent science, Cochrane reviews, transparent reporting, and the CONSORT checklist. The CONSORT checklist is explained in detail over several slides, outlining the key elements that should be reported in randomized controlled trials to improve transparency and replication.
Transparency6
This document summarizes the key points from a lecture on public health research methods. It discusses recommendations for organizing data analysis workflows, including using annotated text files for code, GitHub for version control and collaboration, and having others replicate analyses using different software. It also covers citation management options, ethics review boards, and some Berkeley-specific resources. The overall lecture provided an overview of best practices for conducting and documenting public health research.
Transparency5
This document summarizes a lecture on data de-identification and anonymization given by Alasdair Cohen at UC Berkeley on October 17. The lecture covered why data de-identification is important, definitions of direct identifiers and quasi-identifiers, issues around de-identification including an example of Governor Weld being re-identified, techniques to reduce de-identification risk such as suppression, generalization and swapping, estimating re-identification risk, and briefly mentioned data repositories and journal policies. The majority of the content in the lecture was sourced from a 2016 presentation by Simson Garfinkel of NIST on data de-identification issues.
Transparency4
This document summarizes a lecture about transparency and openness in research. It discusses the CONSORT statement, which was developed to standardize the reporting of randomized controlled trials. CONSORT began in the 1990s and has been revised over time, with the 2010 statement providing a checklist of essential items to include in trial reports. The lecture also notes that CONSORT has been expanded to provide guidelines for specifically reporting different types of trials and that hundreds of other reporting guidelines have been developed for other research areas as well.
Transparency3
This document summarizes a lecture on protocol registration and pre-specified analysis plans. It discusses the history of clinical trial registration requirements, highlighting milestones like the establishment of ClinicalTrials.gov. It also reviews reasons for registration like ethical principles from the Belmont Report. Additionally, it covers types of registration platforms, issues with registration alone, debates around registering observational studies, and templates for developing pre-analysis plans. The key takeaways are that registration and pre-specifying analysis plans can help reduce bias, while allowing flexibility like registering changes to protocols.
Transparency2
Alasdair Cohen gave a lecture on replication science at UC Berkeley on October 17, 2016 for a public health class. The lecture introduced the concept of replication by discussing two examples: a story about computer worms and how an Excel error impacted austerity policies. The remainder of the lecture covered the topic of replication more broadly.
Transparency1
This presentation outline covers issues with scientific research including publication bias, substandard science, p-hacking, and scientific misconduct. It then discusses approaches to address these problems such as replication science, pre-registration of study protocols and analysis plans, transparent reporting, and responsible data management. Tools for organizing research and ensuring ethical standards are also reviewed. The presentation aims to improve the reliability and integrity of scientific research.
5.3.5 causal inference in research
1. The document outlines steps for conducting causal inference research including defining a hypothesis, study design, drawing a directed acyclic graph (DAG) to identify confounders, analyzing data while controlling for confounders, and making scientific inferences.
2. An example is provided on estimating associations between deworming, hygienic latrines, finished floors, and soil-transmitted helminth infections using a cross-sectional survey design.
3. Results show the adjusted prevalence ratios for single and combined exposures on deworming and household characteristics on infection, including any additive interaction effects.
5.3.4 reporting em
Causal inference involves using directed acyclic graphs (DAGs) and the potential outcomes model to assess effect modification (EM) from a causal perspective. DAGs can help identify confounding and selection bias. The potential outcomes model distinguishes statistical from causal interaction and allows estimating average risk or risk differences to determine if causal interaction is present on the additive scale. Studies should minimize bias, control confounding, and quantify uncertainty to best assess causal EM.
5.3.2 sufficient cause em
This document discusses causal inference and effect modification. It outlines key concepts like directed acyclic graphs, confounding, and selection bias. It then explains the potential outcomes model for causal interaction using counterfactual types. These types represent what would happen to individuals under different exposure scenarios. The document shows that an additive interaction implies causal effect modification if certain assumptions hold, by deriving expected risks under different exposure combinations with and without interacting types.
5.2.3 dags for selection bias
This document discusses causal inference and outlines key concepts including:
1) Using directed acyclic graphs (DAGs) to identify confounding and selection bias
2) Different types of interaction and effect modification that can be modeled causally
3) Integrating causal concepts like DAGs, effect modification, and addressing biases into research by defining hypotheses, study design, analysis, and scientific inferences
5.1.2 counterfactual framework
1. The document discusses approaches for identifying confounding using directed acyclic graphs (DAGs). It outlines two main approaches: (1) removing direct effects and checking for common causes, and (2) checking for open back-door paths and blocking them.
2. An example applies these approaches to assess whether adjusting for prenatal care is sufficient to control for confounding between vitamin use and birth defects. It determines that additional adjustment is needed based on remaining connections in the DAG.
3. Traditional criteria for confounding may sometimes disagree with the DAG approach, and the DAG approach is more reliable as it can identify situations like conditioning on a collider that may bias results.
5.1.1 sufficient component cause model
This document outlines concepts related to causality in epidemiology. It discusses different models of causality including the sufficient-component cause model (Rothman's pies), counterfactual framework, Hill's criteria, and graphical models. The sufficient-component cause model depicts disease causation as occurring through combinations of component causes that are sufficient to result in the disease outcome. It illustrates concepts like necessary and component causes. The model contributes to understanding how the strength of associations can depend on the prevalence of other component causes in the population.
5.2.1 dags
This document discusses causal inference in epidemiology. It begins by defining what causal inference entails, including careful definition of estimation goals and a set of assumptions that link observed data to ideal causal data. Directed acyclic graphs (DAGs) are introduced as a way to visually depict causal assumptions and identify potential confounders. Conditioning on variables in DAGs is discussed, as are different DAG configurations like chains, forks, and colliders. Limitations of DAGs are noted. Non-parametric structural equation models provide a link between DAGs and counterfactual causal analysis.
4.4. effect modification
This document discusses effect modification and how it differs from confounding. It defines effect modification as a change in the magnitude of the effect of an exposure on an outcome according to levels of a third variable. Effect modification provides a more detailed description of the relationship between exposure and outcome, whereas confounding is a bias to be eliminated. The document contrasts effect modification and confounding, and provides examples to illustrate the concepts. It also discusses testing for effect modification using tests of homogeneity and how the interpretation of effect modification depends on the choice of effect measure.
4.5. logistic regression
This document describes using logistic regression to analyze data on smoking, matches use, and lung cancer while adjusting for potential confounding. It presents sample data stratified by smoking and matches use, then develops a logistic regression model with smoking and matches as predictors. The model indicates smoking significantly increases lung cancer risk but matches use does not modify this relationship. The document concludes by noting logistic regression can simultaneously adjust for multiple variables and derive coefficient estimates using maximum likelihood.
4.3.2. controlling confounding stratification
Stratification and multivariate analysis are methods used in epidemiologic studies to reduce confounding. Stratification involves dividing the data into subgroups based on potential confounding variables, and examining the relationship between exposure and outcome separately within each subgroup. The Mantel-Haenszel method provides a pooled estimate that averages the stratum-specific estimates to provide an overall effect estimate adjusted for confounding. Evaluating differences between adjusted and non-adjusted estimates helps determine if confounding is present.
More from A M (20)
Recently uploaded
How to Setup Warehouse & Location in Odoo 17 Inventory
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
A Visual Guide to 1 Samuel | A Tale of Two Hearts
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Walmart Business+ and Spark Good for Nonprofits.pdf
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
How to deliver Powerpoint Presentations.pptx
"How to make and deliver dynamic presentations by making it more interactive to captivate your audience attention"
Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
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
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
● What is event processing in MuleSoft?
● Types of event processing models in Mule 4
● Distinction between the reactive, parallel, blocking & non-blocking processing
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shivani Yasaswi - https://www.linkedin.com/in/shivaniyasaswi/
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"National Information Standards Organization (NISO)
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...Nguyen Thanh Tu Collection
https://app.box.com/s/y977uz6bpd3af4qsebv7r9b7s21935vdRecently uploaded (20)
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
How to Setup Warehouse & Location in Odoo 17 Inventory
How to Setup Warehouse & Location in Odoo 17 Inventory
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
Mule event processing models | MuleSoft Mysore Meetup #47
Mule event processing models | MuleSoft Mysore Meetup #47
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
3.4 types of validity
- 1. 1 Types of validity • Overall definition of validity: A study design and analysis plan are valid if, on average (aside from random error), results generated from the study design reflect the true causal effect in the source population* • A faulty study design and/or analysis plan will, on average, produce biased results...meaning that results will not reflect the true causal effect in the source population*
- 2. Populations and types of validity Study population (sample) Actual Population Target Population External Population Statistical inference Internal validity External validity JC: “Sample” JC: ”Available” JC: “RQ” JC: “Generalizable” 33 Adapted from Kleinbaum, Kupper and Morgenstern, 1982
- 3. 3 Types of validity • There are two types of validity depending on the source population of interest – Internal validity – External validity • Can understand these types of validity in the context of the hierarchy of populations
- 4. 4 Types of validity • * Type of validity depends on the source population of interest for generalization • Internal validity: – The extent to which findings from the study population reflect the true underlying parameters/causal effect in the target population • External validity: – The extent to which findings from the study population reflect the true underlying parameters/causal effect in a defined external population beyond the target population
- 5. Populations and types of validity Study population (sample) Actual Population Target Population External Population Statistical inference Internal validity External validity JC: “Sample” JC: ”Available” JC: “RQ” JC: “Generalizable” 36 Adapted from Kleinbaum, Kupper and Morgenstern, 1982
- 6. 6 Types of validity • Internal validity is a pre-requisite for external validity, and is the primary concern • A study may lack external validity, but even if we can’t generalize results to some external population, the findings are still meaningful if the study is internally valid • If a study lacks internal validity then it necessarily lacks external validity as well • As such, lack of internal validity is the primary concern