Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.statswork.com)
•Download as PPTX, PDF•
2 likes•2,594 views
Statswork (Statswork.com), Lecture:2 – Basic concepts of Latent, Manifest and Observed Variables using in SEM analysis. SEM consists of two parts: 1. Measurement model(s) for each latent variable 2.2. Path analysis between the latent and observed variables. Latent Variables: These phenomena are termed as latent variables, or common factors. Examples of latent variables: ·Psychology: Self concept and Motivation. ·Education:Verbal ability& Teacher Expectancy. Manifest and Observed Variables: The unobserved variable is linked to one that is observable, thereby measurement is possible. Examples: ·Self-report to an attitudinal scale. ·Scores on achievement test. Watch more. Contact Us: UK NO:+44-1143520021 India No: +91-8754446690 US NO:+1-972-502-9262 Email: info@statswork.com Website: http://www.statswork.com/ Landline: +91-44-42124284
Report
Share
Report
Share
Recommended
Sampling Methods in Qualitative and Quantitative Research
This document discusses different types of sampling methods used in qualitative and quantitative research. It outlines the different assumptions researchers make regarding sampling in qualitative versus quantitative studies. A variety of sampling techniques are described for different research contexts such as ethnographic fieldwork, interviews, and content analysis.
Descriptive Research Method
Descriptive research method is used to obtain information about the current status of phenomena without manipulating the environment. It describes "what exists" with respect to variables in a situation. There are several types of descriptive research methods including survey method, content analysis method, and correlation method. Developmental study method examines changes and trends over time using longitudinal studies, cross-sectional studies, or trend studies. Longitudinal studies follow the same subjects over a long period of time while cross-sectional studies observe different subjects of various developmental stages at the same point in time. Both methods aim to evaluate changes occurring within the context of time.
Causal comparative research
This document discusses causal-comparative research, which studies cause-and-effect relationships between variables that are not manipulated by the researcher. It examines how causal-comparative research explores how independent variables influence dependent variables after the fact, without experimenter control. Some key points made are that this method is used when manipulation is impossible for ethical or practical reasons, and helps study problems that can't be replicated experimentally, though it is limited by a lack of control over variables.
Quantitative Research Design - Module 1 provides a basic understanding of qua...
Quantitative Research Design - Module 1 provides a basic understanding of qua...Linfield Christian School
A basic understanding of quantitative design. The first module covers some terms used when conducting this type of study.Population & sample lecture 04
This document provides an overview of key concepts in sampling and statistics. It defines population as the entire set of items from which a sample can be drawn. It discusses different types of sampling methods including probability sampling (simple random, stratified, cluster, systematic) and non-probability sampling (convenience, judgmental, quota, snowball). It also defines key terms like bias, precision, randomization. The document discusses the sampling process and compares advantages and disadvantages of sampling. It provides examples of calculating standard error of mean and proportion. Finally, it distinguishes between standard deviation and standard error.
Correlational research design
This document discusses correlational research. It defines correlational research as using correlation statistics to examine relationships between two or more variables. There are two main types of correlational designs: explanatory and predictive. Explanatory design aims to explain relationships between variables, while predictive design aims to forecast outcomes. Key characteristics of correlational research include displaying scores in scatterplots and matrices, analyzing the direction, form, and strength of associations between variables using correlation coefficients, and using techniques like partial correlation and multiple regression for multiple variable analysis.
content analysis
Content analysis is a research technique used to objectively, systematically, and quantitatively analyze the manifest content of communications. It can be used to analyze any type of recorded media, such as text, images, or videos. There are two main types: conceptual analysis, which establishes the frequency of concepts, and relational analysis, which examines relationships between concepts. Content analysis is useful for reducing large amounts of unstructured data, identifying important aspects of content, and making inferences about messages, authors, and cultural contexts. While it provides an unobtrusive means of analysis, it can also be time-consuming and reductive when dealing with complex materials.
Inferential statistics.ppt
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
Recommended
Sampling Methods in Qualitative and Quantitative Research
This document discusses different types of sampling methods used in qualitative and quantitative research. It outlines the different assumptions researchers make regarding sampling in qualitative versus quantitative studies. A variety of sampling techniques are described for different research contexts such as ethnographic fieldwork, interviews, and content analysis.
Descriptive Research Method
Descriptive research method is used to obtain information about the current status of phenomena without manipulating the environment. It describes "what exists" with respect to variables in a situation. There are several types of descriptive research methods including survey method, content analysis method, and correlation method. Developmental study method examines changes and trends over time using longitudinal studies, cross-sectional studies, or trend studies. Longitudinal studies follow the same subjects over a long period of time while cross-sectional studies observe different subjects of various developmental stages at the same point in time. Both methods aim to evaluate changes occurring within the context of time.
Causal comparative research
This document discusses causal-comparative research, which studies cause-and-effect relationships between variables that are not manipulated by the researcher. It examines how causal-comparative research explores how independent variables influence dependent variables after the fact, without experimenter control. Some key points made are that this method is used when manipulation is impossible for ethical or practical reasons, and helps study problems that can't be replicated experimentally, though it is limited by a lack of control over variables.
Quantitative Research Design - Module 1 provides a basic understanding of qua...
Quantitative Research Design - Module 1 provides a basic understanding of qua...Linfield Christian School
A basic understanding of quantitative design. The first module covers some terms used when conducting this type of study.Population & sample lecture 04
This document provides an overview of key concepts in sampling and statistics. It defines population as the entire set of items from which a sample can be drawn. It discusses different types of sampling methods including probability sampling (simple random, stratified, cluster, systematic) and non-probability sampling (convenience, judgmental, quota, snowball). It also defines key terms like bias, precision, randomization. The document discusses the sampling process and compares advantages and disadvantages of sampling. It provides examples of calculating standard error of mean and proportion. Finally, it distinguishes between standard deviation and standard error.
Correlational research design
This document discusses correlational research. It defines correlational research as using correlation statistics to examine relationships between two or more variables. There are two main types of correlational designs: explanatory and predictive. Explanatory design aims to explain relationships between variables, while predictive design aims to forecast outcomes. Key characteristics of correlational research include displaying scores in scatterplots and matrices, analyzing the direction, form, and strength of associations between variables using correlation coefficients, and using techniques like partial correlation and multiple regression for multiple variable analysis.
content analysis
Content analysis is a research technique used to objectively, systematically, and quantitatively analyze the manifest content of communications. It can be used to analyze any type of recorded media, such as text, images, or videos. There are two main types: conceptual analysis, which establishes the frequency of concepts, and relational analysis, which examines relationships between concepts. Content analysis is useful for reducing large amounts of unstructured data, identifying important aspects of content, and making inferences about messages, authors, and cultural contexts. While it provides an unobtrusive means of analysis, it can also be time-consuming and reductive when dealing with complex materials.
Inferential statistics.ppt
This document discusses inferential statistics, which uses sample data to make inferences about populations. It explains that inferential statistics is based on probability and aims to determine if observed differences between groups are dependable or due to chance. The key purposes of inferential statistics are estimating population parameters from samples and testing hypotheses. It discusses important concepts like sampling distributions, confidence intervals, null hypotheses, levels of significance, type I and type II errors, and choosing appropriate statistical tests.
Developmental Research
This document discusses three methods of developmental research: longitudinal, cross-sectional, and cross-sequential studies. Longitudinal studies involve repeated observations of the same items over long periods of time. Cross-sectional studies observe groups at different ages at the same time. Cross-sequential studies combine longitudinal and cross-sectional methods by comparing two separate longitudinal studies covering different time periods. The document outlines advantages and disadvantages of longitudinal and cross-sectional studies, as well as definitions and examples of these research methods.
Anova (f test) and mean differentiation
ANOVA (analysis of variance) and mean differentiation tests are statistical methods used to compare means or medians of multiple groups. ANOVA compares three or more means to test for statistical significance and is similar to multiple t-tests but with less type I error. It requires continuous dependent variables and categorical independent variables. There are different types of ANOVA including one-way, factorial, repeated measures, and multivariate ANOVA. Key assumptions of ANOVA include normality, homogeneity of variance, and independence of observations. The F-test statistic follows an F-distribution and is used to evaluate the null hypothesis that population means are equal.
Causal comparative study
This document discusses causal-comparative research, which investigates potential causes of existing differences between groups. Key points include: causal-comparative research is a form of associative research that examines nonexperimental group differences; threats to internal validity like subject characteristics must be evaluated; and data analysis may involve t-tests, ANCOVAs, or crossbreak tables to compare group means or associations between categorical variables.
Parametric vs Non-Parametric
This document compares parametric and non-parametric statistical analyses. Parametric analyses make assumptions about the population distribution and variance, are applicable to interval/ratio data, and can be affected by outliers. Non-parametric analyses make no assumptions, can be used with ordinal/nominal data, and are not affected by outliers. The document provides examples of common parametric tests (t-tests, ANOVA) and non-parametric alternatives (Mann-Whitney, Kruskal-Wallis), and guidelines for determining whether a parametric or non-parametric approach is more appropriate.
Multiple Linear Regression II and ANOVA I
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Survey research
This document describes descriptive research and survey research methods. Descriptive research aims to describe characteristics of a population without determining causes. Survey research involves asking questions of respondents using methods like questionnaires and interviews. The document outlines approaches like case studies and surveys, and survey designs including cross-sectional, before-after, and longitudinal studies. It also discusses steps to conduct surveys, question formats, data collection modes, and advantages and limitations of interviews and questionnaires.
Causal comparative n survey
This document provides information about quantitative research methodology, specifically causal-comparative research. Causal-comparative research aims to determine the cause of existing differences among groups by comparing two or more groups in which the independent variable is not manipulated. It differs from experimental research in that the independent variable has already occurred. Survey research is also discussed as a popular technique for collecting primary data through interaction with people to obtain facts, opinions, and attitudes. Various types of surveys like cross-sectional, longitudinal, and their designs are explained.
Relational and correlational research
Correlational research investigates the connection between two or more variables that are already present in a group. The aim is to identify if changes in one variable result in changes in another. There are three types: positive correlation where increases in one variable produce increases in another; negative correlation where increases in one produce decreases in another; and no correlation where the variables are independent. Common data collection methods include naturalistic observation, archival data, and surveys. Correlational research is non-experimental, backward-looking, and dynamic as relationships can change over time. The correlation coefficient indicates the strength and direction of relationships between variables.
Cross and longitudinal studies
This document compares and contrasts cross-sectional and longitudinal study designs. Cross-sectional studies take a snapshot to compare different population groups at a single point in time, allowing comparison of multiple variables. However, they do not show cause-and-effect relationships. Longitudinal studies observe the same subjects over long periods of time, which allows researchers to detect changes in populations and establish sequences of events, making them better able to suggest cause-and-effect relationships. Researchers may start with a faster cross-sectional study and then conduct a longitudinal study to further examine causes and effects.
Explanatory research - Research Methodology - Manu Melwin Joy
This document discusses explanatory research and provides examples. Explanatory research aims to explain why events occur and test theories. It allows testing of specific theories and amendments to previous theories. One example tests a theory about reducing campus crime by limiting library access. Another analyzes the correlation between a region's migrant population share and support for anti-immigration initiatives in a Swiss referendum to see if attitudes towards migration relate to exposure to migrants. The research questions examine relationships between variables to help explain phenomena.
Evaluation research-resty-samosa
valuation is a methodological area that is closely related to, but distinguishable from more traditional social research. Evaluation utilizes many of the same methodologies used in traditional social research, but because evaluation takes place within a political and organizational context, it requires group skills, management ability, political dexterity, sensitivity to multiple stakeholders and other skills that social research in general does not rely on as much.
ex post fact research - ppt
This document discusses ex post facto research. Ex post facto research is a type of non-experimental research where the independent variable has already occurred and the researcher studies the effects. Some key points:
1) Ex post facto research uses groups that differ on the independent or dependent variable and compares them.
2) The independent variable cannot be manipulated. The research focuses on analyzing the effects or causes of events.
3) There are strengths like relevance when variables cannot be manipulated, and weaknesses like inability to randomly assign groups or manipulate variables.
4) The steps of ex post facto research include determining the problem, literature review, formulating hypotheses, research design, validity, and interpreting conclusions.
Chapter 5 variables and their types
It gives you insight into the meaning of variables and their types such as Independent variables
Dependent variables
Intervening variables
Moderating variables
Control variables
Extraneous variables
Quantitative variables
Qualitative variables
Confounding variables
Composite variables
Longitudinal Research Design
This document discusses longitudinal survey research design. It describes longitudinal research as involving repeated observations of the same variables over time. The document outlines the main types of longitudinal research: trend studies, which examine changes within a population over time; cohort studies, which identify a subgroup sharing a characteristic to study over time; and panel studies, which examine the same individuals repeatedly. Strengths are identified as the ability to establish sequences of events and trace development. Drawbacks include high costs and risk of participants dropping out.
Regression Analysis
Regression analysis measures the average relationship between two or more variables using their original data units. There are two main types: simple regression involving two variables, and multiple regression involving more than two variables. Regression can be linear, following a straight line, or non-linear/curvilinear. A simple linear regression model relates a dependent variable Y to an independent variable X plus an error term. Estimating the model involves calculating the slope/regression coefficient and intercept. Multiple regression relates a dependent variable to two or more independent variables using a multiple correlation coefficient.
ANOVA II
This document provides an overview of various statistical analyses that can be used in psychological research, including t-tests, analysis of variance (ANOVA), repeated measures ANOVA, factorial ANOVA, mixed ANOVA, analysis of covariance (ANCOVA), and multivariate analysis of variance (MANOVA). Examples of research questions that could be addressed with each type of analysis are provided. Key assumptions, procedures, and interpretations for each analysis are discussed at a high level.
Structural equation-models-introduction-kimmo-vehkalahti-2013
The document provides an introduction to structural equation modeling (SEM). It discusses key concepts such as latent and observed variables, and measurement models. It also presents examples of confirmatory factor analysis output to illustrate model fitting and interpretation. Specifically, it analyzes a four-factor CFA model with academic self-concept variables and reports various goodness-of-fit statistics and parameter estimates to assess how well the hypothesized model fits the sample data.
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.
Quantitative and Qualitative Research
This document provides an overview of the key differences between quantitative and qualitative research methods. Quantitative research aims to test hypotheses and make predictions by studying specific variables through structured data collection from large randomly selected groups, which is then analyzed statistically. Qualitative research seeks to understand social phenomena through descriptive data like words and images collected from smaller non-random groups via open-ended questions, interviews and observations, with the goal of gaining insights rather than making generalized predictions.
Qualitative, Quantitative (PowerPoint)
Distinguish between QUALITATIVE and QUANTITATIVE - data/information/evidence/research.
Examples are given.
You need to download PowerPoint to view the animations.
There is a WORKSHEET with this PowerPoint at:
http://www.slideshare.net/yaryalitsa/worksheet-qualitative-quantitative-powerpoint
ANSWERS TO THE ABOVE WORKSHEET AT:
http://www.slideshare.net/yaryalitsa/worksheet-answers-qualitative-quantitative-powerpoint
Exogenous and Endogenous Latent Variables in SEM analysis | statswork.com
Statswork (Statswork.com), Lecture 3: Exogenous and Endogenous Latent Variables in SEM analysis.
1. Exogenous latent variables
2. Endogenous latent variables
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
US NO: +1-972-502-9262
Email: info@statswork.com
Website: http://www.statswork.com/
Landline: +91-44-42124284
Learning session on research
This document provides an overview of research concepts including:
- The objectives of learning about research which are to draw conceptual frameworks, draft research problems and hypotheses, and design data gathering procedures.
- An introduction to what constitutes good research and the basic research process.
- How to formulate a research proposal including choosing topics, drafting conceptual frameworks, and developing measurement tools.
- The different types of variables in research including independent, dependent, moderating, and intervening variables.
- Measurement scales used in research such as nominal, ordinal, interval and ratio.
- Considerations for developing measurement instruments.
More Related Content
What's hot
Developmental Research
This document discusses three methods of developmental research: longitudinal, cross-sectional, and cross-sequential studies. Longitudinal studies involve repeated observations of the same items over long periods of time. Cross-sectional studies observe groups at different ages at the same time. Cross-sequential studies combine longitudinal and cross-sectional methods by comparing two separate longitudinal studies covering different time periods. The document outlines advantages and disadvantages of longitudinal and cross-sectional studies, as well as definitions and examples of these research methods.
Anova (f test) and mean differentiation
ANOVA (analysis of variance) and mean differentiation tests are statistical methods used to compare means or medians of multiple groups. ANOVA compares three or more means to test for statistical significance and is similar to multiple t-tests but with less type I error. It requires continuous dependent variables and categorical independent variables. There are different types of ANOVA including one-way, factorial, repeated measures, and multivariate ANOVA. Key assumptions of ANOVA include normality, homogeneity of variance, and independence of observations. The F-test statistic follows an F-distribution and is used to evaluate the null hypothesis that population means are equal.
Causal comparative study
This document discusses causal-comparative research, which investigates potential causes of existing differences between groups. Key points include: causal-comparative research is a form of associative research that examines nonexperimental group differences; threats to internal validity like subject characteristics must be evaluated; and data analysis may involve t-tests, ANCOVAs, or crossbreak tables to compare group means or associations between categorical variables.
Parametric vs Non-Parametric
This document compares parametric and non-parametric statistical analyses. Parametric analyses make assumptions about the population distribution and variance, are applicable to interval/ratio data, and can be affected by outliers. Non-parametric analyses make no assumptions, can be used with ordinal/nominal data, and are not affected by outliers. The document provides examples of common parametric tests (t-tests, ANOVA) and non-parametric alternatives (Mann-Whitney, Kruskal-Wallis), and guidelines for determining whether a parametric or non-parametric approach is more appropriate.
Multiple Linear Regression II and ANOVA I
Explains advanced use of multiple linear regression, including residuals, interactions and analysis of change, then introduces the principles of ANOVA starting with explanation of t-tests.
Survey research
This document describes descriptive research and survey research methods. Descriptive research aims to describe characteristics of a population without determining causes. Survey research involves asking questions of respondents using methods like questionnaires and interviews. The document outlines approaches like case studies and surveys, and survey designs including cross-sectional, before-after, and longitudinal studies. It also discusses steps to conduct surveys, question formats, data collection modes, and advantages and limitations of interviews and questionnaires.
Causal comparative n survey
This document provides information about quantitative research methodology, specifically causal-comparative research. Causal-comparative research aims to determine the cause of existing differences among groups by comparing two or more groups in which the independent variable is not manipulated. It differs from experimental research in that the independent variable has already occurred. Survey research is also discussed as a popular technique for collecting primary data through interaction with people to obtain facts, opinions, and attitudes. Various types of surveys like cross-sectional, longitudinal, and their designs are explained.
Relational and correlational research
Correlational research investigates the connection between two or more variables that are already present in a group. The aim is to identify if changes in one variable result in changes in another. There are three types: positive correlation where increases in one variable produce increases in another; negative correlation where increases in one produce decreases in another; and no correlation where the variables are independent. Common data collection methods include naturalistic observation, archival data, and surveys. Correlational research is non-experimental, backward-looking, and dynamic as relationships can change over time. The correlation coefficient indicates the strength and direction of relationships between variables.
Cross and longitudinal studies
This document compares and contrasts cross-sectional and longitudinal study designs. Cross-sectional studies take a snapshot to compare different population groups at a single point in time, allowing comparison of multiple variables. However, they do not show cause-and-effect relationships. Longitudinal studies observe the same subjects over long periods of time, which allows researchers to detect changes in populations and establish sequences of events, making them better able to suggest cause-and-effect relationships. Researchers may start with a faster cross-sectional study and then conduct a longitudinal study to further examine causes and effects.
Explanatory research - Research Methodology - Manu Melwin Joy
This document discusses explanatory research and provides examples. Explanatory research aims to explain why events occur and test theories. It allows testing of specific theories and amendments to previous theories. One example tests a theory about reducing campus crime by limiting library access. Another analyzes the correlation between a region's migrant population share and support for anti-immigration initiatives in a Swiss referendum to see if attitudes towards migration relate to exposure to migrants. The research questions examine relationships between variables to help explain phenomena.
Evaluation research-resty-samosa
valuation is a methodological area that is closely related to, but distinguishable from more traditional social research. Evaluation utilizes many of the same methodologies used in traditional social research, but because evaluation takes place within a political and organizational context, it requires group skills, management ability, political dexterity, sensitivity to multiple stakeholders and other skills that social research in general does not rely on as much.
ex post fact research - ppt
This document discusses ex post facto research. Ex post facto research is a type of non-experimental research where the independent variable has already occurred and the researcher studies the effects. Some key points:
1) Ex post facto research uses groups that differ on the independent or dependent variable and compares them.
2) The independent variable cannot be manipulated. The research focuses on analyzing the effects or causes of events.
3) There are strengths like relevance when variables cannot be manipulated, and weaknesses like inability to randomly assign groups or manipulate variables.
4) The steps of ex post facto research include determining the problem, literature review, formulating hypotheses, research design, validity, and interpreting conclusions.
Chapter 5 variables and their types
It gives you insight into the meaning of variables and their types such as Independent variables
Dependent variables
Intervening variables
Moderating variables
Control variables
Extraneous variables
Quantitative variables
Qualitative variables
Confounding variables
Composite variables
Longitudinal Research Design
This document discusses longitudinal survey research design. It describes longitudinal research as involving repeated observations of the same variables over time. The document outlines the main types of longitudinal research: trend studies, which examine changes within a population over time; cohort studies, which identify a subgroup sharing a characteristic to study over time; and panel studies, which examine the same individuals repeatedly. Strengths are identified as the ability to establish sequences of events and trace development. Drawbacks include high costs and risk of participants dropping out.
Regression Analysis
Regression analysis measures the average relationship between two or more variables using their original data units. There are two main types: simple regression involving two variables, and multiple regression involving more than two variables. Regression can be linear, following a straight line, or non-linear/curvilinear. A simple linear regression model relates a dependent variable Y to an independent variable X plus an error term. Estimating the model involves calculating the slope/regression coefficient and intercept. Multiple regression relates a dependent variable to two or more independent variables using a multiple correlation coefficient.
ANOVA II
This document provides an overview of various statistical analyses that can be used in psychological research, including t-tests, analysis of variance (ANOVA), repeated measures ANOVA, factorial ANOVA, mixed ANOVA, analysis of covariance (ANCOVA), and multivariate analysis of variance (MANOVA). Examples of research questions that could be addressed with each type of analysis are provided. Key assumptions, procedures, and interpretations for each analysis are discussed at a high level.
Structural equation-models-introduction-kimmo-vehkalahti-2013
The document provides an introduction to structural equation modeling (SEM). It discusses key concepts such as latent and observed variables, and measurement models. It also presents examples of confirmatory factor analysis output to illustrate model fitting and interpretation. Specifically, it analyzes a four-factor CFA model with academic self-concept variables and reports various goodness-of-fit statistics and parameter estimates to assess how well the hypothesized model fits the sample data.
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.
Quantitative and Qualitative Research
This document provides an overview of the key differences between quantitative and qualitative research methods. Quantitative research aims to test hypotheses and make predictions by studying specific variables through structured data collection from large randomly selected groups, which is then analyzed statistically. Qualitative research seeks to understand social phenomena through descriptive data like words and images collected from smaller non-random groups via open-ended questions, interviews and observations, with the goal of gaining insights rather than making generalized predictions.
Qualitative, Quantitative (PowerPoint)
Distinguish between QUALITATIVE and QUANTITATIVE - data/information/evidence/research.
Examples are given.
You need to download PowerPoint to view the animations.
There is a WORKSHEET with this PowerPoint at:
http://www.slideshare.net/yaryalitsa/worksheet-qualitative-quantitative-powerpoint
ANSWERS TO THE ABOVE WORKSHEET AT:
http://www.slideshare.net/yaryalitsa/worksheet-answers-qualitative-quantitative-powerpoint
What's hot (20)
Explanatory research - Research Methodology - Manu Melwin Joy
Explanatory research - Research Methodology - Manu Melwin Joy
Structural equation-models-introduction-kimmo-vehkalahti-2013
Structural equation-models-introduction-kimmo-vehkalahti-2013
Similar to Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.statswork.com)
Exogenous and Endogenous Latent Variables in SEM analysis | statswork.com
Statswork (Statswork.com), Lecture 3: Exogenous and Endogenous Latent Variables in SEM analysis.
1. Exogenous latent variables
2. Endogenous latent variables
Contact Us:
UK NO: +44-1143520021
India No: +91-8754446690
US NO: +1-972-502-9262
Email: info@statswork.com
Website: http://www.statswork.com/
Landline: +91-44-42124284
Learning session on research
This document provides an overview of research concepts including:
- The objectives of learning about research which are to draw conceptual frameworks, draft research problems and hypotheses, and design data gathering procedures.
- An introduction to what constitutes good research and the basic research process.
- How to formulate a research proposal including choosing topics, drafting conceptual frameworks, and developing measurement tools.
- The different types of variables in research including independent, dependent, moderating, and intervening variables.
- Measurement scales used in research such as nominal, ordinal, interval and ratio.
- Considerations for developing measurement instruments.
Objective and Characteristics of Scientific Research, Nature and types of Var...
Research Method -
Objective of scientific research, characteristics of scientific research, nature and types of variables
Pentaho Meeting 2008 - Statistics & BI
The document discusses the importance of data quality, proper use of statistics, and correct interpretation of results in statistical analysis. It provides a 3 step approach: 1) Ensuring high quality data by addressing issues like missing values and outliers. 2) Appropriate use of statistical techniques after defining the variables and objectives clearly. Considering issues like correlation, normality, and model assumptions. 3) Careful interpretation of results while preserving the multidimensional nature of phenomena and considering partial correlations between variables. It emphasizes the need for collaboration between data miners, statisticians and domain experts for successful knowledge discovery.
4793325
This document provides an introduction to basic concepts in biostatistics including:
1. Biostatistics is the analysis and interpretation of data to objectively evaluate conclusions.
2. The unit of analysis refers to what or who is being studied, such as individuals in a study.
3. Variables are logical groupings of attributes that can take on different values or categories. The level of measurement of a variable, such as nominal or interval, determines what types of analyses can be used.
Applied statistics lecture_5
This document provides an overview of reliability analysis and factor analysis. It discusses the concepts of validity, reliability, and their importance in scientific research. Reliability is defined as the consistency or dependability of measurement, and is assessed using reliability analysis techniques like Cronbach's alpha. Factor analysis is introduced as a technique to simplify complex constructs into underlying dimensions or factors. The key steps of factor analysis include examining the correlation matrix, extracting initial factors using methods like principal component analysis, and rotating factors to arrive at the final factor solution. Decisions on the number of factors are based on statistical criteria like eigenvalues and scree plots, as well as conceptual grounds.
Confirmatory Factor Analysis
This document discusses various techniques for measuring latent variables using factor analysis, including exploratory factor analysis (EFA), confirmatory factor analysis (CFA), fixing the scale of latent variables, modeling mean structures, the use of formative indicators, item parcelling, and higher-order factor models. EFA is described as an initial, inductive approach to uncover the underlying factor structure in observed variables, while CFA provides a confirmatory, theory-driven approach to explicitly test a hypothesized measurement model. Additional topics covered include identifying latent variables, modeling means of observed and latent variables, and distinguishing between reflective and formative indicator specifications.
Chapter15
This document discusses ex post facto research, which investigates causes and effects after they have been manifested. It describes two approaches - starting with subjects differing on an independent variable or dependent variable. Key points include characteristics like examining causes retrospectively, advantages like exploring relationships when experiments aren't possible, and disadvantages like inability to prove causation. The stages of an ex post facto investigation are outlined, including defining the problem, hypotheses, data collection methods, and analysis. Controls in this type of research include matching groups and accounting for extraneous variables.
Class lecture notes #1 (statistics for research)
This document defines key concepts in statistics and research methods. It discusses the difference between descriptive and inferential statistics, and explains variables, parameters, and statistics. It also outlines three research methods: correlational, experimental, and quasi-experimental. The correlational method looks for relationships between variables but does not prove causation. The experimental method establishes cause-and-effect by manipulating an independent variable. The quasi-experimental method uses a non-manipulated variable like gender or time to define comparison groups.
Factor analysis (1)
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.
BRM_ Instrument Preparation and Data Collection.ppt
This document discusses instrument preparation and data collection. It covers identifying variables and concepts, and converting concepts into measurable variables through operationalization. Different types of variables are described based on their role in causal relationships, study design, and unit of measurement. Categorical and quantitative variables are defined. Measurement scales are also introduced, with the importance of measuring variables discussed in relation to research methodology and analysis.
Variables, Theory and Sampling Map
This lecture will help Research scholars at the starting of their research issues regarding definitions of variables, what is theory and creating a sapling map..
English
Researchers must define the variables being studied, whether qualitative or quantitative. Independent variables are those controlled by researchers, like whether a course includes service-learning, to determine their effect on dependent variables - the measured outcomes. Common dependent variables in service-learning research include effects on students, such as learning outcomes, personal growth, and sense of civic responsibility.
2-nature.pptx
This document provides an overview of quantitative research methods. It begins by defining quantitative research as objective and empirical, utilizing numerical data and analysis. It then describes key characteristics of quantitative research like clearly defined research questions, structured instruments, and large sample sizes. Strengths are listed as being objective, replicable, and allowing for statistical analysis, while weaknesses include high costs and potential inaccuracy. Different types of quantitative research are defined, including descriptive, correlational, experimental, and survey research. The document concludes by explaining key concepts in quantitative research like variables, and distinguishing between independent, dependent, intervening, and confounding variables.
Research Methodology
This chapter discusses research design and methodology. It defines research design as the plan or strategy for conducting a study to answer research questions. A good research design identifies the study population, sampling method, data collection tools, and how variables will be measured and analyzed. It ensures validity, objectivity and accuracy of the study. Key components of research design include determining the study population, sample size and method, data collection tools, and addressing ethical issues. The chapter also discusses how to establish causal relationships between variables and minimize the effects of extraneous variables through randomization, matching, and eliminating confounding variables from the study design.
Unit4 systemdynamics
This document provides an overview of system dynamics and systems thinking. It defines key terms like systems, static vs dynamic systems, feedback loops, stocks and flows. Systems thinking focuses on how parts of a system interrelate and how systems work over time. System dynamics uses feedback loops and stocks/flows to model how complex systems change over time. It involves conceptualizing the system, formulating stock/flow diagrams, testing the model, and implementing it to test policies. Causal loop diagrams and stock/flow diagrams are introduced as tools to understand system structure and behavior.
Mba2216 week 11 data analysis part 03 appendix
Multivariate analysis involves simultaneously analyzing multiple variables to understand relationships. This document discusses key concepts in multivariate analysis including:
1. Defining multivariate analysis and when it is appropriate to use.
2. Describing specific techniques like multiple regression, discriminant analysis, logistic regression, MANOVA, canonical correlation analysis, conjoint analysis, factor analysis, cluster analysis, multidimensional scaling, and correspondence analysis.
3. Providing guidelines for selecting the appropriate technique based on the measurement scales and relationship between variables.
It also covers important considerations like measurement error, statistical power, and a structured approach to multivariate model building.
AGRICULTURAL-STATISTICS.pptx
This document provides an overview of key concepts in statistics. It discusses that statistics involves collecting, organizing, analyzing and interpreting data. It also defines important statistical terms like population, sample, parameter, statistic, qualitative and quantitative data, independent and dependent variables, discrete and continuous variables, and different levels of measurement for variables. The different levels of measurement are nominal, ordinal, interval and ratio. Descriptive statistics are used to summarize and describe data, while inferential statistics allow making inferences about populations from samples.
Lecture 7.ppt
1. The document discusses the components of developing a theoretical framework for research, including identifying variables, relationships between variables, and formulating hypotheses.
2. It defines key terms like dependent and independent variables, and directional and non-directional hypotheses. Examples are provided to illustrate different types of variables and hypotheses.
3. Developing a theoretical framework is important as it provides the logical foundation for a research study and determines what will be measured and relationships explored.
Similar to Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.statswork.com) (20)
Exogenous and Endogenous Latent Variables in SEM analysis | statswork.com
Exogenous and Endogenous Latent Variables in SEM analysis | statswork.com
Objective and Characteristics of Scientific Research, Nature and types of Var...
Objective and Characteristics of Scientific Research, Nature and types of Var...
Variables and conceptual framework [Compatibility Mode].pdf
Variables and conceptual framework [Compatibility Mode].pdf
BRM_ Instrument Preparation and Data Collection.ppt
BRM_ Instrument Preparation and Data Collection.ppt
More from Stats Statswork
Exploring the impact and evolution of Advanced Analytics Tools.pdf
The impact and evolution of advanced analytics tools have transformed how businesses operate, offering unprecedented insights and decision-making capabilities. Statstwork has been at the forefront of this evolution, providing cutting-edge solutions that leverage big data, machine learning, and AI. These tools enable companies to analyze vast amounts of data in real-time, identify trends, and predict future outcomes with high accuracy. As a result, businesses can optimize their operations, enhance customer experiences, and drive innovation. The continuous advancement of these tools promises even greater efficiencies and opportunities, making them indispensable in the modern data-driven landscape.
For more information contact:
https://www.statswork.com
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
Exploring the impact and evolution of Advanced Analytics Tools.pdf
The impact and evolution of advanced analytics tools have transformed how businesses operate, offering unprecedented insights and decision-making capabilities. Statstwork has been at the forefront of this evolution, providing cutting-edge solutions that leverage big data, machine learning, and AI. These tools enable companies to analyze vast amounts of data in real-time, identify trends, and predict future outcomes with high accuracy. As a result, businesses can optimize their operations, enhance customer experiences, and drive innovation. The continuous advancement of these tools promises even greater efficiencies and opportunities, making them indispensable in the modern data-driven landscape.
For more information contact:
https://www.statswork.com
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
SEO Asset (PDF) Comparing Python, R, and SAS Overcoming Training Data Set Cha...
Statswork enables researchers to gather insightful information, contributing to robust research outcomes.
https://www.statswork.com/services/data-collection-coding-management/
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
SEO Asset (PPT) Comparing Python, R, and SAS Overcoming Training Data Set Cha...
https://www.statswork.com/services/data-collection-coding-management/
& https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
SW-Asset-Predictive Analytics Models.pdf
Our cutting-edge predictive analytics models and algorithms transform raw data into actionable insights, empowering businesses to stay ahead of the curve.Trust StatsWork to optimize operations, minimize risks, and drive growth through predictive analytics. Experience the transformative impact of our solutions and propel your business towards success today.
For More Information, Please refer to our services : https://statswork.com/blog/business-success-predictive-analytics/ & https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
Empowering Business Growth with Predictive Analytic - Statswork
Our cutting-edge predictive analytics models and algorithms transform raw data into actionable insights, empowering businesses to stay ahead of the curve.Trust StatsWork to optimize operations, minimize risks, and drive growth through predictive analytics. Experience the transformative impact of our solutions and propel your business towards success today.
For More Information, Please refer to our services : https://statswork.com/blog/business-success-predictive-analytics/ & https://www.statswork.com/contact-us/
Contact our Experts:
Our Email id: info@statswork.com
Contact No: +91 8754467066
How to establish and evaluate clinical prediction models - Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modelling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Read More With Us: https://bit.ly/3dxn32c
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
United Kingdom: 44-1143520021
India: 91-4448137070
WhatsApp: 91-8754446690
How to establish and evaluate clinical prediction models - Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modeling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Read More With Us: https://bit.ly/3dxn32c
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
United Kingdom: 44-1143520021
India: 91-4448137070
WhatsApp: 91-8754446690
7 excellent reasons why statistics are important statswork
Statistics are used to analyze what's happening within the world around us. In this data-driven world, all activities of ours are monitored by someone else every time. Statistics help us to convert whatever occurs in the past can be used in predicting the future. Statswork Is A Premier Statistics Consulting Company That Spearheaded Online Statistics Consultancy Service With Clientele Ranging From Educational Institutions, Academics, Corporations And Ngos. We Provide End-To-End Service And Assistance For Your Statistical Research And Analytical Needs From Data Collection, Data Mining, Data Analysis To Research Framework And Research Methodology.
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com/
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Research design decisions and be competent in the process of reliable data co...
Research Design may be described as the researchers scheme of outlining the flow of his project. It is based on research design, that the researcher goes about gathering data to answer his research question. It enables the researcher to prioritize his work, create better questionnaires and arrive at conclusions with greater clarity. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Learn More: http://bit.ly/2S312hb
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com/
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Panel data analysis a survey on model based clustering of time series - stats...
The Clustering technique in Statistical Analysis is used to determine the subsets as clusters in the data using the specified distance measure. However, this technique cannot be applied easily for longitudinal or time-series data. In this blog, I will discuss some of the methods used for modeling longitudinal or panel data using the Clustering Analysis technique as explained in Schmatter (2011). Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: http://www.statswork.com/
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Application of time series analysis in financial economics
This document discusses time series analysis and its application in financial economics. It begins by defining time series analysis and its main objective of developing accurate models to describe patterns in financial data. It then provides examples of different types of time series and various models used for time series forecasting. Finally, it outlines three applications of time series analysis including forecasting stock performance, understanding price trends, and predicting sales.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Choosing a qualitative data analysis Plan
CHOOSING A QUALITATIVE DATA ANALYSIS (QDA) PLAN
Data Analysis should change what you do, not just how you do it. - Matin Movassate
If you are to choose the right data analysis plan for your study, it is first pertinent to collect qualitative data. Since Qualitative analysis is more about the meaning of the analysis, it is too confusing with unstructured and huge data. For conducting Data Analysis for any research, it is also important to have the right methodology. If the data and methods of data analysis plan are right, it will have numerous benefits, including taking the right decisions.
But before that, there are certain fundamental details to know before choosing the right data analysis plan, which includes:
What is a qualitative data analysis?
QDA is based on interpretative policy to examine the symbolic and meaningful content of data. In other words, it is interpreting the qualitative data by many processes and procedures to transform them into great insights for taking dynamic decisions.
What is qualitative data?
Descriptive data that are non-numerical and capturing concepts and opinions, values and behaviors of people in a social context is called Qualitative data Collection. It is the data from observation of audio and video recordings and also reading the transcripts of interviews and copies of documents.
What purpose does the qualitative data analysis plan perform?
Unlike Quantitative data analysis, which is more of numbers and statistics, qualitative analysis is analysis of subjective and non-numerical qualitative data. Hence it performs many functions including:
• Organizing data
• Interpreting data
• Identifying patterns
• Forming the basis for informed and verifiable conclusions
• Ties research objectives to data
After knowing the above fundamentals of Qualitative data analysis, it is time to choose the right data analysis plan. The plans can be selected for specific research design and can also be applied for a variety of research designs.
Methods used for qualitative data collection
Data plays an important role in any research or study conducted. It aids in bringing about a breakthrough in the respective field as well as for future researches. The collection of data is carried out in two forms viz: Qualitative Data and Quantitative Data which includes further bifurcation under it.
What is Qualitative Data?
Qualitative research can be defined as the method of research which focuses on gaining relevant information through observational, open-ended and communication method. They are more exploratory which concentrates on gaining insights about the situation and dig a bit deeper to find the underlying reason. The central idea behind using this method is to find the answer to Why and How rather than How many. Data gathered during a qualitative research is what is termed as qualitative data.
What is the purpose?
A qualitative data is non-numerical and more textual which comprises mostly of images, written texts, recorded audios and spoken words by people. Moreover, one can conduct qualitative research online as well as offline too. Apart from this, the varied purpose of qualitative research is as follows:
- To examine the purpose or reason for the situation
- Gain an understanding of the experience of people
- Understanding of relations and meaning
- Varied norms including social and political as well as contextual and cultural practice which impact the cause.
Methods of qualitative data analysis
Data is a source of great information which can enable informed decision making for businesses. Data is divided into Quantitative Data and Qualitative Data. Qualitative data refers to those non-numerical, explanatory data. Herein, we will have a detailed look into the various methods of qualitative data analysis.
Design of Engineering Experiments Part 5
The document discusses the 2k factorial design, which is a special case of the general factorial design with k factors at two levels. It provides examples of using 2k factorial designs to investigate how multiple factors affect a response. For an unreplicated 2k design with no replication, there are challenges in statistical testing due to having zero degrees of freedom for error. Various methods are discussed for analyzing the effects in an unreplicated 2k design, such as normal probability plotting, Lenth's method, and conditional inference charts. Transformation of the response may also be needed to meet assumptions of the model such as equal variance.
Module 6: Outlier Detection for Two Sample Case
Two sample plot, also known as youden’s plot, is a scatter plot with a confidence region. Youden used it for detecting labs with unusual testing results when two samples are tested in n different lab. Youden plot is a special case of the bivariate control chart, and the idea behind is the principal component analysis.
United Kingdom: +44-1143520021
India: 044 3318-2000
Email: info@statswork.com
Website. www.statswork.com
SPSS Step-by-Step Tutorial and Statistical Guides by Statswork
This document provides an overview of cluster analysis techniques used in marketing research. It defines cluster analysis as classifying cases into homogeneous groups based on a set of variables. Cluster analysis can be used for market segmentation, understanding buyer behaviors, and identifying new product opportunities in marketing research. The document outlines the steps to conduct cluster analysis, including selecting a distance measure and clustering algorithm, determining the number of clusters, and validating the analysis. It provides examples of hierarchical and non-hierarchical clustering methods like k-means and discusses choosing between these approaches. SPSS is used to demonstrate a cluster analysis example analyzing supermarket customer data.
Statistical Meta Analysis Sample Work - Statswork
Statswork is a pioneer statistical consulting company providing full statistical assistance including academic, educational institutions and non-government organizations across the globe. We provide end-to-end solutions for all your analytical needs that include creating hypothetical framework to power point presentation. The objective is to provide prompt, reliable, and understandable information about data analysis to our clients.
United Kingdom: +44-1143520021
India: +044 3318-2000
Email: info@statswork.com
Website: www.statswork.com
More from Stats Statswork (20)
Exploring the impact and evolution of Advanced Analytics Tools.pdf
Exploring the impact and evolution of Advanced Analytics Tools.pdf
Exploring the impact and evolution of Advanced Analytics Tools.pdf
Exploring the impact and evolution of Advanced Analytics Tools.pdf
SEO Asset (PDF) Comparing Python, R, and SAS Overcoming Training Data Set Cha...
SEO Asset (PDF) Comparing Python, R, and SAS Overcoming Training Data Set Cha...
SEO Asset (PPT) Comparing Python, R, and SAS Overcoming Training Data Set Cha...
SEO Asset (PPT) Comparing Python, R, and SAS Overcoming Training Data Set Cha...
Empowering Business Growth with Predictive Analytic - Statswork
Empowering Business Growth with Predictive Analytic - Statswork
How to establish and evaluate clinical prediction models - Statswork
How to establish and evaluate clinical prediction models - Statswork
How to establish and evaluate clinical prediction models - Statswork
How to establish and evaluate clinical prediction models - Statswork
7 excellent reasons why statistics are important statswork
7 excellent reasons why statistics are important statswork
Research design decisions and be competent in the process of reliable data co...
Research design decisions and be competent in the process of reliable data co...
Panel data analysis a survey on model based clustering of time series - stats...
Panel data analysis a survey on model based clustering of time series - stats...
Application of time series analysis in financial economics
Application of time series analysis in financial economics
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
SPSS Step-by-Step Tutorial and Statistical Guides by Statswork
SPSS Step-by-Step Tutorial and Statistical Guides by Statswork
Recently uploaded
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...National Information Standards Organization (NISO)
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneISO/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
How to Fix the Import Error in the Odoo 17
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Hindi varnamala | hindi alphabet PPT.pdf
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
How to Manage Your Lost Opportunities in Odoo 17 CRM
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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/y977uz6bpd3af4qsebv7r9b7s21935vdHow to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience Wahiba Chair Training & Consulting
Wahiba Chair's Talk at the 2024 Learning Ideas Conference. clinical examination of hip joint (1).pdf
described clinical examination all orthopeadic conditions .
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!"
Main Java[All of the Base Concepts}.docx
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
PCOS corelations and management through Ayurveda.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
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.
Recently uploaded (20)
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
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...
How to Manage Your Lost Opportunities in Odoo 17 CRM
How to Manage Your Lost Opportunities in Odoo 17 CRM
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
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...
How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience
Liberal Approach to the Study of Indian Politics.pdf
Liberal Approach to the Study of Indian Politics.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
Lecture 2: Latent/Manifest/Observed Variables using in SEM Analysis (www.statswork.com)
- 1. www.statswork.com +91-8754446690 info@statswork.com LECTURE SERIES STRUCTURAL EQUATION MODELING REAL TIME APPLICATION WITH EXAMPLES
- 2. Latent Vs. Manifest/ Observed Variables in SEM analysis LECTURE - 2 BASIC INTRODUCTION- II Website: www.statswork.com 2 Contact: +91-8754446690
- 3. Table of Content Contact: 8754446690 3 Website: www.statswork.com 1. Latent Variables 1. Observed or Manifest Variables
- 4. What is Latent & Observed in SEM? 1. SEM consists of two parts o Measurement model(s) for each latent variable o Path analysis between the latent and observed variables 1. Variable Labels o Latent variable factor construct o Observed variable measured variable manifest variable indicator generally considered endogenous
- 5. Latent Variables In the behavioural sciences researchers often interested in studying theoretical constructs that cannot be observed directly. ✓ These phenomena are termed as latent variables, or common factors. ✓ Unmeasurable “Pure” Variables or true score ✓ not directly observed ✓ Free of measurement errors Examples of latent variables: 1. Psychology: Self concept and Motivation. 2. Education:Verbal ability& Teacher Expectancy. 4
- 6. Observed or manifest variables Latent variables are not observed directly, it follows that they cannot be measured directly. ❖ The unobserved variable is linked to one that is observable, thereby measurement is possible. ❖ directly measurable (through questionnaire) ❖ include measurement error ❖ The term behaviour is used here in the very broadest sense to include scores on a particular measuring instrument. Examples: 1. Self-report to an attitudinal scale. 2. Scores on achievement test. 5
- 7. Figure shows the Latent & Observed Variables
- 8. Lecture -3 6 1. Exogenous and Endogenous latent variables Contact us: Website: www.statswork.com Contact: +91-8754446690 E-mail: info@statswork.com Upcoming Weeks….. Structural Equation Modeling (SEM) using AMOS