This document discusses research objectives, questions, hypotheses, and variables. It provides examples of how to formulate objectives for quantitative and qualitative studies in 3 sentences or less. It also discusses how to write research questions for quantitative and qualitative studies. Finally, it explains the differences between independent and dependent variables, and how hypotheses involve predicting relationships between variables.
This document provides an overview of key statistical concepts needed to understand nursing research. It discusses why knowledge of statistics is important for both qualitative and quantitative research. Some key points covered include the importance of identifying appropriate statistical tests for the study design and hypotheses, evaluating statistical and clinical significance of results, and how to perform critical appraisal of quantitative study results. The document also defines important statistical terms like types of errors, power, inference, and parametric vs nonparametric tests. Consulting a statistician early in the research process can help with developing the statistical analysis plan and properly interpreting results.
This document discusses effect size, statistical power, and the relationship between statistical and practical significance in research studies. It defines effect size as a measure of the difference between populations due to an experimental manipulation. Statistical power is the probability that a study will produce a statistically significant result when the research hypothesis is true. A study's power depends on its effect size, sample size, significance level, and whether it uses a one-tailed or two-tailed test. While a result may be statistically significant, it still may not have practical significance if the effect size is too small to make a meaningful difference. Evaluating both statistical and practical significance is important, especially for studies with practical implications.
Inference about means and mean differencesAndi Koentary
This document discusses hypothesis testing and introduces the t-statistic. It explains that hypothesis testing is used to make inferences about populations based on sample data. The t-statistic can be used like the z-score when the population standard deviation is unknown. It describes the steps of a hypothesis test including stating hypotheses, finding critical values, collecting sample data, computing the t-statistic, and making a decision. Directional and non-directional hypotheses are also discussed.
This document discusses concepts related to measurement in research, including definitions of measurement, instrumentation, levels of measurement (nominal, ordinal, interval, ratio), types of measurement error (random, systematic), reliability, and validity. It provides examples of different types of reliability (test-retest, internal consistency) and validity (face validity, content validity, convergent validity). The key concepts covered include assigning quantitative values to concepts being studied, developing tools to accurately measure concepts, reducing errors, and ensuring tools consistently and actually measure the intended constructs.
This document is a student assignment applying exploratory factor analysis to survey data on the importance of supermarket features. It includes an introduction outlining the study's purpose and structure. The document then reviews the theory of exploratory factor analysis and applies it to analyze survey data on 14 items measuring the importance of supermarket features. The analysis identifies underlying dimensions or factors in the data. The document presents the results of the factor analysis and discusses implications for marketing grocery stores to students.
Here is a piece of detailed information about the experimental design used in the field of statistics. This also features some information on the three most widely accepted and most widely used designs.
This document provides an overview of key statistical concepts needed to understand nursing research. It discusses why knowledge of statistics is important for both qualitative and quantitative research. Some key points covered include the importance of identifying appropriate statistical tests for the study design and hypotheses, evaluating statistical and clinical significance of results, and how to perform critical appraisal of quantitative study results. The document also defines important statistical terms like types of errors, power, inference, and parametric vs nonparametric tests. Consulting a statistician early in the research process can help with developing the statistical analysis plan and properly interpreting results.
This document discusses effect size, statistical power, and the relationship between statistical and practical significance in research studies. It defines effect size as a measure of the difference between populations due to an experimental manipulation. Statistical power is the probability that a study will produce a statistically significant result when the research hypothesis is true. A study's power depends on its effect size, sample size, significance level, and whether it uses a one-tailed or two-tailed test. While a result may be statistically significant, it still may not have practical significance if the effect size is too small to make a meaningful difference. Evaluating both statistical and practical significance is important, especially for studies with practical implications.
Inference about means and mean differencesAndi Koentary
This document discusses hypothesis testing and introduces the t-statistic. It explains that hypothesis testing is used to make inferences about populations based on sample data. The t-statistic can be used like the z-score when the population standard deviation is unknown. It describes the steps of a hypothesis test including stating hypotheses, finding critical values, collecting sample data, computing the t-statistic, and making a decision. Directional and non-directional hypotheses are also discussed.
This document discusses concepts related to measurement in research, including definitions of measurement, instrumentation, levels of measurement (nominal, ordinal, interval, ratio), types of measurement error (random, systematic), reliability, and validity. It provides examples of different types of reliability (test-retest, internal consistency) and validity (face validity, content validity, convergent validity). The key concepts covered include assigning quantitative values to concepts being studied, developing tools to accurately measure concepts, reducing errors, and ensuring tools consistently and actually measure the intended constructs.
This document is a student assignment applying exploratory factor analysis to survey data on the importance of supermarket features. It includes an introduction outlining the study's purpose and structure. The document then reviews the theory of exploratory factor analysis and applies it to analyze survey data on 14 items measuring the importance of supermarket features. The analysis identifies underlying dimensions or factors in the data. The document presents the results of the factor analysis and discusses implications for marketing grocery stores to students.
Here is a piece of detailed information about the experimental design used in the field of statistics. This also features some information on the three most widely accepted and most widely used designs.
This document provides an overview of exploratory factor analysis (EFA). It defines EFA as a technique used to identify clusters of inter-correlated variables and empirically test theoretical data structures. The document outlines the assumptions, steps, and examples of EFA. It discusses determining the number of factors, rotating factor loadings for interpretation, and interpreting the factor structure. The goal of EFA is to simplify data and develop theoretical models through identification of underlying factors.
This document provides an introduction to regression analysis. It discusses correlation as a technique to measure relationships between two variables. Regression allows using the value of one variable to predict the value of another when a consistent relationship exists. The goal of regression is to find the equation of the best fitting straight line for a set of data. This line can be expressed as an equation relating the total cost (Y) variable to the number of hours (X) variable. The best fitting line minimizes the sum of the squared distances between the data points and the line. This process results in a regression equation that can be used to predict Y values given X values. However, predictions should only be made within the range of the original data.
Chp5 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
The document discusses the research process and developing a theoretical framework and hypotheses. It describes identifying variables, developing relationships between variables through a theoretical framework, and generating testable hypotheses. The theoretical framework explains expected relationships between independent and dependent variables. Hypotheses are then developed to empirically test relationships between variables. Examples are provided to demonstrate identifying variables and how they relate in theoretical frameworks.
The independent variable in the Stroop Effect experiment is whether the color word matches or mismatches the color of the ink. The dependent variable is reaction time. It is hypothesized that reaction time will be lower for color-word matches than mismatches. Descriptive statistics on the dataset show higher mean and variability for the incongruent condition. A t-test was conducted and found a statistically significant difference between conditions, rejecting the null hypothesis. This matches expectations that it takes longer to name a color when it mismatches the written word.
This document discusses factors that can threaten the internal and external validity of experimental research designs. It identifies six main threats to internal validity: history effects, maturation effects, instrumentation effects, selection bias, statistical regression, and mortality. It also discusses how randomization and matching groups can help control for contaminating variables. The trade-off between internal and external validity is addressed, as well as types of experimental designs, simulation as an alternative, and ethical issues.
The document discusses key elements of research design including the purpose of studies, types of investigations, study settings, populations, time horizons, and units of analysis. It also covers measurement scales, reliability, and validity. The purpose can be exploratory, descriptive, or for hypothesis testing. Studies can be causal, correlational, contrived or non-contrived. Populations can be individuals, groups, organizations or cultures. Studies can also be cross-sectional or longitudinal. Proper research design ensures the purpose is effectively addressed.
Exploratory factor analysis (EFA) is a statistical technique used to identify the underlying relationships between measured variables. EFA can group variables into a smaller number of factors and reduce complexity in the data. The document discusses EFA methodology, including conducting EFA in SPSS, determining the number of factors, rotating factors, and interpreting results. Assumptions of EFA and different extraction and rotation methods are also covered.
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
Factor analysis is a statistical technique used to reduce a large set of variables into a smaller set of underlying factors or dimensions. It examines the interrelationships among variables to define common dimensions called factors that can help explain correlations. Factor analysis is used to identify the underlying structure in a data set and reduce many variables into a smaller number of factors for subsequent analysis like regression or discriminant analysis.
Predictive analytics in Information Systems Research (TSWIM 2015 keynote)Galit Shmueli
Slides from keynote presentation at 3rd Taiwan Summer Workshop in Information Management (TSWIM) by Galit Shmueli on "To Explain or To Predict? Predictive Analytics in Information Systems Research"
This document discusses the differences between explanatory modeling and predictive modeling in statistical analysis. Explanatory modeling in social sciences aims to test causal theories by developing statistical models based on theoretical constructs. The goal is explanatory power rather than prediction. Predictive modeling develops models for accurately predicting new observations without necessarily understanding the underlying causes. While explanatory models can have predictive value by checking relevance and predictability, explanatory power does not guarantee predictive power. The document argues for incorporating predictive modeling more in social sciences research to strengthen theories and make predictions.
Statistics and experimental design are important for drawing valid conclusions from research. Well-designed experiments produce unbiased comparisons, precise estimates, and account for variability. Hypothesis tests answer yes/no questions about population values and aim to reject false null hypotheses. P-values indicate the likelihood of obtaining extreme data if the null is true. Multiple testing increases chances of false positives, requiring adjustments. Sample size impacts power to detect effects and precision of estimates. Both statistical and practical significance must be considered.
Attribute prioritization in choice experiment pre designAlexander Decker
This document discusses a proposed method for prioritizing attributes in the design of choice experiments. The method involves calculating a Design Attribute Relative Importance Index (DARII) based on surveys of stakeholders to assess the relative importance of different attributes. This allows researchers to reach larger samples of stakeholders compared to traditional interview methods. The DARII is adapted from the Relative Importance Index approach used in engineering risk management. It transforms Likert scale responses into a continuum from 0 to 1 to indicate weak to strong preference for attributes. An application to solid waste management attributes demonstrated the method was simple and applicable to large samples, which could help reduce the risk of attribute non-attendance in choice experiments.
The document discusses key concepts in developing a theoretical framework and hypotheses for research. It defines a theoretical framework as identifying the important variables and relationships between them. Hypotheses are testable statements developed based on this framework. Variables can be dependent (outcome), independent (predictor), moderating, or intervening. The theoretical framework forms the basis of the research by conceptualizing these relationships between variables.
This document provides an introduction to structural equation modeling (SEM) through a series of definitions and explanations. It discusses key concepts in SEM including latent versus measured variables, covariance versus correlation, and the history and development of SEM. Sample size requirements and software for conducting SEM are also covered. The document is intended as introductory material for postgraduate students learning about SEM.
PSYCH 625 Effective Communication - snaptutorial.comdonaldzs41
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
The document discusses factor analysis as an exploratory and confirmatory multivariate technique. It explains that factor analysis is commonly used for data reduction, scale development, and evaluating the dimensionality of variables. Factor analysis determines underlying factors or dimensions from a set of interrelated variables. It reduces a large number of variables to a smaller number of factors. The key steps in factor analysis include computing a correlation matrix, extracting factors, rotating factors, and making decisions on the number of factors.
This document provides an overview of quantitative research methods. It defines basic and applied research, as well as key concepts like rigor and control. It also outlines the steps of the quantitative research process, including formulating research problems, developing a framework, defining variables, selecting appropriate designs and methods of measurement, and communicating findings. Descriptive, correlational, quasi-experimental and experimental research designs are also briefly discussed.
This document provides an overview of quantitative research methods. It defines basic and applied research, as well as key concepts like rigor and control. It also outlines the steps of the quantitative research process, including formulating research problems, developing a framework, defining variables, selecting appropriate designs and methods of measurement, and communicating findings. Descriptive, correlational, quasi-experimental and experimental research designs are also briefly discussed.
This document provides an overview of quantitative research methods. It defines basic and applied research, as well as key concepts like rigor and control. It also outlines the steps of the quantitative research process, including formulating research problems, developing a framework, defining variables, selecting appropriate designs and methods of measurement, and communicating findings. Descriptive, correlational, quasi-experimental and experimental research designs are also briefly discussed.
This document provides an overview of exploratory factor analysis (EFA). It defines EFA as a technique used to identify clusters of inter-correlated variables and empirically test theoretical data structures. The document outlines the assumptions, steps, and examples of EFA. It discusses determining the number of factors, rotating factor loadings for interpretation, and interpreting the factor structure. The goal of EFA is to simplify data and develop theoretical models through identification of underlying factors.
This document provides an introduction to regression analysis. It discusses correlation as a technique to measure relationships between two variables. Regression allows using the value of one variable to predict the value of another when a consistent relationship exists. The goal of regression is to find the equation of the best fitting straight line for a set of data. This line can be expressed as an equation relating the total cost (Y) variable to the number of hours (X) variable. The best fitting line minimizes the sum of the squared distances between the data points and the line. This process results in a regression equation that can be used to predict Y values given X values. However, predictions should only be made within the range of the original data.
Chp5 - Research Methods for Business By Authors Uma Sekaran and Roger BougieHassan Usman
The document discusses the research process and developing a theoretical framework and hypotheses. It describes identifying variables, developing relationships between variables through a theoretical framework, and generating testable hypotheses. The theoretical framework explains expected relationships between independent and dependent variables. Hypotheses are then developed to empirically test relationships between variables. Examples are provided to demonstrate identifying variables and how they relate in theoretical frameworks.
The independent variable in the Stroop Effect experiment is whether the color word matches or mismatches the color of the ink. The dependent variable is reaction time. It is hypothesized that reaction time will be lower for color-word matches than mismatches. Descriptive statistics on the dataset show higher mean and variability for the incongruent condition. A t-test was conducted and found a statistically significant difference between conditions, rejecting the null hypothesis. This matches expectations that it takes longer to name a color when it mismatches the written word.
This document discusses factors that can threaten the internal and external validity of experimental research designs. It identifies six main threats to internal validity: history effects, maturation effects, instrumentation effects, selection bias, statistical regression, and mortality. It also discusses how randomization and matching groups can help control for contaminating variables. The trade-off between internal and external validity is addressed, as well as types of experimental designs, simulation as an alternative, and ethical issues.
The document discusses key elements of research design including the purpose of studies, types of investigations, study settings, populations, time horizons, and units of analysis. It also covers measurement scales, reliability, and validity. The purpose can be exploratory, descriptive, or for hypothesis testing. Studies can be causal, correlational, contrived or non-contrived. Populations can be individuals, groups, organizations or cultures. Studies can also be cross-sectional or longitudinal. Proper research design ensures the purpose is effectively addressed.
Exploratory factor analysis (EFA) is a statistical technique used to identify the underlying relationships between measured variables. EFA can group variables into a smaller number of factors and reduce complexity in the data. The document discusses EFA methodology, including conducting EFA in SPSS, determining the number of factors, rotating factors, and interpreting results. Assumptions of EFA and different extraction and rotation methods are also covered.
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
Factor analysis is a statistical technique used to reduce a large set of variables into a smaller set of underlying factors or dimensions. It examines the interrelationships among variables to define common dimensions called factors that can help explain correlations. Factor analysis is used to identify the underlying structure in a data set and reduce many variables into a smaller number of factors for subsequent analysis like regression or discriminant analysis.
Predictive analytics in Information Systems Research (TSWIM 2015 keynote)Galit Shmueli
Slides from keynote presentation at 3rd Taiwan Summer Workshop in Information Management (TSWIM) by Galit Shmueli on "To Explain or To Predict? Predictive Analytics in Information Systems Research"
This document discusses the differences between explanatory modeling and predictive modeling in statistical analysis. Explanatory modeling in social sciences aims to test causal theories by developing statistical models based on theoretical constructs. The goal is explanatory power rather than prediction. Predictive modeling develops models for accurately predicting new observations without necessarily understanding the underlying causes. While explanatory models can have predictive value by checking relevance and predictability, explanatory power does not guarantee predictive power. The document argues for incorporating predictive modeling more in social sciences research to strengthen theories and make predictions.
Statistics and experimental design are important for drawing valid conclusions from research. Well-designed experiments produce unbiased comparisons, precise estimates, and account for variability. Hypothesis tests answer yes/no questions about population values and aim to reject false null hypotheses. P-values indicate the likelihood of obtaining extreme data if the null is true. Multiple testing increases chances of false positives, requiring adjustments. Sample size impacts power to detect effects and precision of estimates. Both statistical and practical significance must be considered.
Attribute prioritization in choice experiment pre designAlexander Decker
This document discusses a proposed method for prioritizing attributes in the design of choice experiments. The method involves calculating a Design Attribute Relative Importance Index (DARII) based on surveys of stakeholders to assess the relative importance of different attributes. This allows researchers to reach larger samples of stakeholders compared to traditional interview methods. The DARII is adapted from the Relative Importance Index approach used in engineering risk management. It transforms Likert scale responses into a continuum from 0 to 1 to indicate weak to strong preference for attributes. An application to solid waste management attributes demonstrated the method was simple and applicable to large samples, which could help reduce the risk of attribute non-attendance in choice experiments.
The document discusses key concepts in developing a theoretical framework and hypotheses for research. It defines a theoretical framework as identifying the important variables and relationships between them. Hypotheses are testable statements developed based on this framework. Variables can be dependent (outcome), independent (predictor), moderating, or intervening. The theoretical framework forms the basis of the research by conceptualizing these relationships between variables.
This document provides an introduction to structural equation modeling (SEM) through a series of definitions and explanations. It discusses key concepts in SEM including latent versus measured variables, covariance versus correlation, and the history and development of SEM. Sample size requirements and software for conducting SEM are also covered. The document is intended as introductory material for postgraduate students learning about SEM.
PSYCH 625 Effective Communication - snaptutorial.comdonaldzs41
For more classes visit
www.snaptutorial.com
PSYCH 625 Assignment Week 1 Descriptive and Inferential Statistics Worksheet (New Syllabus)
PSYCH 625 Assignment Week 2 Probability and Statistical Analysis Worksheet (New Syllabus)
The document discusses factor analysis as an exploratory and confirmatory multivariate technique. It explains that factor analysis is commonly used for data reduction, scale development, and evaluating the dimensionality of variables. Factor analysis determines underlying factors or dimensions from a set of interrelated variables. It reduces a large number of variables to a smaller number of factors. The key steps in factor analysis include computing a correlation matrix, extracting factors, rotating factors, and making decisions on the number of factors.
This document provides an overview of quantitative research methods. It defines basic and applied research, as well as key concepts like rigor and control. It also outlines the steps of the quantitative research process, including formulating research problems, developing a framework, defining variables, selecting appropriate designs and methods of measurement, and communicating findings. Descriptive, correlational, quasi-experimental and experimental research designs are also briefly discussed.
This document provides an overview of quantitative research methods. It defines basic and applied research, as well as key concepts like rigor and control. It also outlines the steps of the quantitative research process, including formulating research problems, developing a framework, defining variables, selecting appropriate designs and methods of measurement, and communicating findings. Descriptive, correlational, quasi-experimental and experimental research designs are also briefly discussed.
This document provides an overview of quantitative research methods. It defines basic and applied research, as well as key concepts like rigor and control. It also outlines the steps of the quantitative research process, including formulating research problems, developing a framework, defining variables, selecting appropriate designs and methods of measurement, and communicating findings. Descriptive, correlational, quasi-experimental and experimental research designs are also briefly discussed.
This document discusses the process of interpreting research outcomes. It involves examining study evidence, determining findings, forming conclusions, identifying limitations, generalizing findings, considering implications, and suggesting further research. Key steps include evaluating the research plan, measurements, data collection, analysis, results, and previous studies to determine what the evidence shows and how to interpret the findings. Limitations must be identified and conclusions should not overgeneralize beyond the scope of the study. The implications and need for additional research are also considered.
This document discusses the evolution of nursing research from Florence Nightingale's time to the present day. It traces how nursing research has developed from data collection on patient outcomes, to a focus on nursing education, clinical research and the nursing process. More recently, research has emphasized evidence-based practice and using various research methods and studies to synthesize the best evidence to guide nursing practice.
This document discusses the evolution of nursing research from Florence Nightingale's pioneering work in the 1850s to the current emphasis on evidence-based practice. It traces how nursing research has developed from a focus on education, clinical studies, and the nursing process to encompass a variety of quantitative and qualitative methodologies. The document also introduces key concepts in nursing research including outcomes research, intervention research, systematic reviews, and evidence-based practice guidelines.
This document discusses the evolution of nursing research from Florence Nightingale's pioneering work in the 1850s to the current emphasis on evidence-based practice. It traces how nursing research has developed from a focus on education, clinical studies, and the nursing process to encompass a variety of quantitative and qualitative methodologies. The document also introduces key concepts in nursing research such as outcomes research, intervention research, systematic reviews, and evidence-based practice guidelines.
This document discusses evidence-based practice and strategies for synthesizing evidence, including conducting systematic reviews and meta-analyses. It describes the 10 step process for systematic reviews, which includes formulating a clinical question, searching for and selecting studies, critically appraising studies, and developing a final report. It also discusses how to conduct meta-analyses by statistically combining data from multiple studies to determine the overall effectiveness of an intervention.
This document discusses the process of disseminating research findings, including developing a research report and communicating results through presentations and publications. It covers the typical sections of a research report such as the introduction, methods, results, and discussion. It also discusses strategies for targeting different audiences and outlets for sharing results, such as publishing in journals, presenting at conferences, or communicating to consumers. The goal is to share findings with others in order to advance science and nursing practice.
This document discusses the process of disseminating research findings, including developing a research report and communicating results through presentations and publications. It covers the typical sections of a research report such as the introduction, methods, results, and discussion. It also discusses strategies for targeting different audiences and outlets for sharing results, such as publishing in journals, presenting at conferences, or communicating to consumers. The goal is to share findings with others in order to advance science and nursing practice.
This document discusses different study designs used in research methodology. It begins with definitions of key concepts like independent and dependent variables. It explains the need for well-designed studies and important features. Different types of studies are described, including descriptive, analytical, experimental and observational designs. Experimental designs are discussed in more detail, covering principles of replication, randomization and local control. Different classification systems for epidemiological studies are also presented.
The document discusses critical appraisal of nursing research. Critical appraisal involves systematically and objectively examining all aspects of a research study to judge its strengths, weaknesses, and significance. It is an important skill for nurses to have in order to evaluate research and determine how findings can inform nursing practice. The document outlines the steps involved in critically appraising quantitative research, including identifying the research process used in a study, assessing its strengths and limitations, and evaluating the credibility and meaning of the findings.
How to create Chapter One of Your Thesis 1.pptJessaBejer1
This document provides an overview of the key components that should be included in Chapter 1 of a thesis. Chapter 1 typically includes an introduction, background and setting, identification of the problem, purpose statement, research questions or objectives, assumptions, limitations, definition of terms, and significance of the study. Each section is described in detail to guide the writer in developing this important introductory chapter.
This document discusses nursing research and its importance. It defines nursing research as a scientific process that validates existing knowledge and generates new knowledge to improve nursing practice. The document outlines different roles nurses can play in research based on their level of education. These roles range from critically appraising studies for BSN nurses to leading research teams for PhD nurses. The document also discusses the purposes of nursing research as description, explanation, prediction, and control. It provides examples of potential research questions that could address these purposes. Finally, it briefly introduces different research methods used in nursing research.
This document discusses nursing research and its importance. It defines nursing research as a scientific process that validates existing knowledge and generates new knowledge to improve nursing practice. The document outlines different roles nurses can play in research based on their level of education. These roles range from critically appraising studies for BSN nurses to leading research teams for PhD nurses. The document also discusses the purposes of nursing research as description, explanation, prediction, and control. It provides examples of potential research questions that could address these purposes. Finally, it briefly introduces different research methods used in nursing research.
A framework is an abstract structure that guides nursing research. Frameworks are usually present in quantitative research and sometimes in qualitative research. A framework includes concepts, constructs, relational statements, and theories. Concepts are the building blocks of theories. Constructs are highly abstract concepts. Relational statements declare relationships between concepts. Theories integrate concepts, existence statements, and relational statements. Grand theories are highly abstract while middle-range theories address more specific phenomena. Variables are measurable forms of concepts. Frameworks link concepts, constructs, and variables to guide nursing research.
This document discusses frameworks and theoretical concepts in nursing research. It defines key terms like framework, concept, construct, theory, and variables. A framework provides structure and guidance for a study. Quantitative research usually has an explicit framework while qualitative research may not. The document reviews different types of theories from grand theories, which are highly abstract, to middle range theories which are more specific. It also discusses how to evaluate existing theories and develop new research frameworks by identifying relevant theories, synthesizing from research, or proposing based on clinical experience.
This document discusses frameworks and theoretical concepts in nursing research. It defines key terms like framework, concept, construct, theory, and variables. A framework provides structure and logic to guide a study and link findings to existing nursing knowledge. Quantitative research usually has an explicit framework, while qualitative research may not. The document reviews different types of theories from grand theories to middle-range theories and how they can be adapted or synthesized to develop a framework for a new study. It also provides guidance on evaluating existing frameworks for appropriate application in research.
The slides will help you in knowing the components of research design in brief what is research design, components of research design, differnt types of research design
The document discusses the different types of quantitative research, including survey research and experimental research. It defines survey research as using questionnaires to collect data from a sample at one or more points in time, while experimental research examines causal relationships by applying treatments to experimental and control groups. The key components of each type are described, such as survey design, variables, and data analysis techniques. Examples are provided of how quantitative research is applied across various fields like medicine, psychology, business, and more.
The document discusses various microbiology techniques for culturing microbes including inoculation, isolation, incubation, inspection, and identification. It describes how to produce pure cultures through methods like streak plating and describes different types of culture media including solid, liquid, enriched, selective, and differential media. The goals are to transfer microbes to produce isolated colonies, grow them under proper conditions, observe characteristics, and identify organisms through comparing data.
The document provides instructions for creating a research poster, including reviewing sample posters and an article on best practices. It discusses font size, logo placement, poster size, image and graphic quality, and elements that make a poster engaging. A sample student research poster is also included, with sections on the problem, methodology, results, conclusions, and references. The poster summarizes a study on the occupations of school-aged children who have siblings with cognitive or behavioral disabilities.
The document provides instructions for creating an effective research poster. It discusses reviewing sample posters to understand best practices like font size, logo placement, size of the poster, and quality of images. It also recommends considering what makes sample posters visually engaging and how one's own poster could be improved.
Position Your Body for Learning implements evidence-based measurements to assess optimal positioning for learning. The document describes three simple assessments - "roll", "rattle", and "rumble" - to determine if desk height matches elbow rest height and chair height matches popliteal height. It explains that proper ergonomic positioning through adjustments can improve students' attention, fine motor skills, and performance on standardized tests. The document provides a form called "Measuring for Optimal Positioning" to document student measurements and identify furniture adjustments needed.
The agenda outlines a thesis dissemination meeting that will include welcome and introductions, a syllabus review, project summaries from students, breaks, a presentation on APA style and thesis document preparation from the writing center, library resources overview, and discussion of thesis resources and dismissal. The document also lists various thesis course, poster, article, and conference resources that will be made available to students.
This document discusses program evaluation, outlining key concepts and approaches. It describes the purposes of program evaluation as determining if objectives are met and improving decision making. Formative and summative evaluations are explained, with formative used for ongoing improvement and summative to determine effects. Both quantitative and qualitative methods are appropriate, including experimental, quasi-experimental and non-experimental designs. Stakeholder involvement, utilization of results, and addressing ethical considerations are important aspects of program evaluation.
The document outlines topics from Chapter 6 of a course, including similarities and differences between intervention planning for individuals and community programs, best practices for developing mission statements and effective teams, and issues related to program sustainability. It also provides examples and activities for developing SMART goals, vision and mission statements, and sustainability plans for a fall prevention program. Resources and considerations are presented for each step of the program development process.
Compliance, motivation, and health behaviors stanbridge
This document provides information about compliance, motivation, and health behaviors as they relate to learners. It introduces several occupational therapy students and their backgrounds. The objectives cover defining key terms and discussing theories of compliance, motivation concepts, and strategies to facilitate motivation. The document then matches vocabulary terms to their definitions and discusses several theories of behavior change, including the health belief model, self-efficacy theory, protection motivation theory, stages of change model, and theory of reasoned action. Motivational strategies and the educator's role in health promotion are also outlined.
Ch 5 developmental stages of the learnerstanbridge
This document provides an overview of developmental stages of the learner from infancy through older adulthood. It begins with introductions of the presenters and learning objectives. Key terms are defined. Development is discussed in terms of physical, cognitive, and psychosocial characteristics at each stage: infancy/toddlerhood, early childhood, middle/late childhood, adolescence, young adulthood, middle-aged adulthood, and older adulthood. Teaching strategies are outlined for each developmental stage. The role of family in patient education is also addressed.
This document summarizes the content covered in Week 2 of a course on community-based occupational therapy practice. Chapter 3 discusses using theories from related disciplines in community practice and identifying strategies for organizing communities to meet health needs. Chapter 4 covers understanding relevant federal legislation, including laws supporting reimbursement and those focused on education, medical rehabilitation, consumer rights, and environmental issues. The document also lists vocabulary terms and guest speakers for the week.
This document outlines the topics and activities to be covered in Week 3 of a course on community health and health promotion program development. It will describe processes of environmental scanning, trend analysis, and the key steps of community health program development. Students will learn about needs assessments, theories in health promotion planning, goals and objectives, and the ecological approach. They will develop implementation strategies at different levels of intervention and learn the purposes of program evaluation. Readings, discussions, and activities are planned, including a scenario analyzing a sheltered workshop using SWOT analysis. Key terms and concepts are defined.
This document outlines the topics that will be covered in the first two chapters of a course on community-based occupational therapy practice. Chapter 1 will discuss the history and roles of OT in community-based practice as well as characteristics of effective community-based OTs. It will also cover paradigm shifts in OT. Chapter 2 will address concepts in community and public health, determinants of health, and strategies for prevention. It will discuss OT's contributions to Healthy People 2020 and its role in health promotion. The schedule includes lectures, small group work, and a guest speaker.
This document discusses how to critically appraise quantitative studies for clinical decision making. It covers evaluating the validity, reliability, and applicability of studies. Key points include assessing for bias, determining if results are statistically and clinically significant, and considering how well study findings can be applied to patients. Study designs like randomized controlled trials, case-control studies, and cohort studies are examined. The importance of systematic reviews and meta-analyses in evidence-based practice is also covered.
This document discusses the importance of clinical judgment in evidence-based nursing practice. It states that research evidence must be considered alongside patient concerns and preferences. Good clinical judgment requires carefully examining the validity of evidence and how it is applied to specific patients. The fit between evidence and each patient's unique situation is rarely perfect. Nurses must understand patients narratively and use judgment over time to determine the most appropriate care based on evidence and the patient's needs. Experiential learning and developing expertise in caring for particular patient populations enhances a nurse's clinical grasp and judgment.
This document discusses qualitative research and its application to clinical decision making. It describes how qualitative evidence can inform understanding of patient experiences and perspectives, which are important components of evidence-based practice. The document outlines different qualitative research traditions like ethnography, grounded theory, and phenomenology. It also discusses techniques for appraising qualitative studies based on their credibility, transferability, dependability, and confirmability. The key point is that qualitative evidence provides insights into human experiences, values, and meanings that can help inform clinical decisions.
This document discusses critically appraising knowledge for clinical decision making. It explains that practice should be based on unbiased, reliable evidence rather than tradition. The three main sources of knowledge for evidence-based practice are valid research evidence, clinical expertise, and patient choices. Clinical practice guidelines are the primary source to guide decisions as they synthesize research evidence. Internal evidence from quality improvement projects applies specifically to the setting where it was collected, unlike external evidence which is more generalizable. Both internal and external evidence should be combined using the PDSA (Plan-Do-Study-Act) cycle for continuous improvement.
This document discusses implementing evidence-based practice (EBP) in clinical settings. It emphasizes that engaging all stakeholders, including clinical staff, administrators, and other disciplines, is key. It also stresses that assessing and addressing barriers like knowledge, attitudes, and resources is important. Finally, it highlights that evaluating outcomes through quantifiable measures can help determine the impact of EBP changes on patient care.
This document discusses clinical practice guidelines (CPGs), including how they are developed based on evidence, how they can standardize care while allowing flexibility, and how to evaluate and implement them. It notes that CPGs systematically develop statements to guide regional diagnosis and treatment based on the best available evidence. While CPGs provide time-effective guidance, the commitment of caregivers is most important for successful implementation.
This document discusses key aspects of writing a successful grant proposal. It explains that grant proposals request funding for research or evidence-based projects by outlining specific aims, background, significance, methodology, budget, and personnel. Successful grant writers are passionate, meticulous planners who can persuade reviewers of a project's importance and address potential barriers. The most important initial question is whether a project meets the funding organization's application criteria. Proposals need compelling abstracts that explain why a project deserves funding and clearly written background and methodology sections. Common weaknesses that can lead to rejection are a lack of significance or novel ideas and inadequate description of study design.
The document discusses ethical considerations for evidence implementation and generation in healthcare. It outlines key ethical principles like beneficence, nonmaleficence, autonomy and justice. These principles form the foundation for core dimensions of healthcare quality according to the Institute of Medicine. The document also differentiates between clinical research, quality improvement initiatives, and evidence-based practice. It notes some controversies around applying different ethical standards to research versus quality improvement. Overall, the document provides an overview of how ethical principles guide evidence-based healthcare practices and quality improvement efforts.