The document provides an overview of confirmatory factor analysis (CFA). It defines CFA and explains that CFA requires specifying the number of factors and which variables load on which factors before analysis. The document outlines the 6 stages of CFA: 1) defining constructs, 2) developing the measurement model, 3) designing a study, 4) assessing the measurement model, 5) specifying the structural model, and 6) assessing the structural model. It emphasizes that CFA confirms or rejects preconceived theories about relationships between observed and latent variables.
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
Multivariate data analysis regression, cluster and factor analysis on spssAditya Banerjee
Using multiple techniques to analyse data on SPSS. A basic software that can easily help run the numbers. Multivariate Data Analysis runs regressions models, factor analyses, and clustering models apart from many more
These are some slides I use in my Multivariate Statistics course to teach psychology graduate student the basics of structural equation modeling using the lavaan package in R. Topics are at an introductory level, for someone without prior experience with the topic.
Multivariate data analysis regression, cluster and factor analysis on spssAditya Banerjee
Using multiple techniques to analyse data on SPSS. A basic software that can easily help run the numbers. Multivariate Data Analysis runs regressions models, factor analyses, and clustering models apart from many more
Introduction to SEM (Structural Equation Models) - invited talk at the seminar "Analyzing and Interpreting Data" organized by the Finnish Doctoral Programme in Education and Learning (15 May 2013) in Vuosaari, Helsinki, Finland. Acknowledgements to Barbara Byrne for an excellent intro book of SEM.
This presentation covers the intricacies of the Item Response Theory. I made this presentation to explain the concepts of IRT to my lab research group at the University of Minnesota. I have taken the contents from various sources so apologies for the poor design of the presentation.
Multiple Linear Regression II and ANOVA IJames Neill
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.
Introduction to SEM (Structural Equation Models) - invited talk at the seminar "Analyzing and Interpreting Data" organized by the Finnish Doctoral Programme in Education and Learning (15 May 2013) in Vuosaari, Helsinki, Finland. Acknowledgements to Barbara Byrne for an excellent intro book of SEM.
This presentation covers the intricacies of the Item Response Theory. I made this presentation to explain the concepts of IRT to my lab research group at the University of Minnesota. I have taken the contents from various sources so apologies for the poor design of the presentation.
Multiple Linear Regression II and ANOVA IJames Neill
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.
This is an elaborate presentation on how to predict employee attrition using various machine learning models. This presentation will take you through the process of statistical model building using Python.
Tutorial given at LAK13 conference, Leuven, April, 9th, 2013. The presentation is informed by WP2 of the LinkedUp-project.eu that develops an Evaluation Framework for Open Web Data (Linked Data) Applications for Education purposes.
This Presentation is on recommended system on question paper predication using machine learning techniques. We did literature survey and implement using same technique.
PREDICTING BANKRUPTCY USING MACHINE LEARNING ALGORITHMSIJCI JOURNAL
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problem. This paper provides a unique methodology to classify companies as bankrupt or healthy by applying predictive analytics. The prediction model stated in this paper yields better accuracy with standard parameters used for bankruptcy prediction than previously applied prediction methodologies.
International Journal of Mathematics and Statistics Invention (IJMSI) inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Abstract The main purpose of this paper is to investigate whether stock prices and exchange rates are related to each
other or not. Both the short term and the long term association between these variables are discovered. The study applies
monthly and quarterly data on two gulf countries, including Kingdom Saudi Arabia (KSA) and United Arab Emirate (UAE)
for the period January 2008 to December 2009. The results of this study in the short term found that the exchange rate
influence positively on the stock market price index for United Arab Emirate and there is no association between them for
Kingdom Saudi Arabia. Moreover the study in the long term found that the exchange rate influence negatively on stock
market price index for the United Arab Emirate. While no association between these variables in Kingdom Saudi Arabia.
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• Communication Mining Overview
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• Phases in Communication Mining
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
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Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
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Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
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1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
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- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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22. STAGE 3: DESIGNING A STUDY TO
PRODUCE EMPIRICAL RESULTS
In this stage the researcher's measurement
theory will be tested.
We should note that initial data analysis
procedures should first be performed to
identify any problems in the data, including
issues such as data input errors.
In this stage the researcher must make some
key decisions on designing the CFA model.
23. • 1-Measurement Scales in CFA
• CFA models typically contain reflective
indicators measured with an ordinal or better
measurement scale. Meaning Indicators with
ordinal responses of at least four response
categories can be treated as interval, or at least
as if the variables are continuous.
• 2-SEM and Sampling.(Many times CFA requires
the use of multiple samples. Meaning
sample(s) should be drawn to perform the CFA.
Even after CFA results are obtained.)
24. 3-Specifying the Model
• distinction between CFA and EFA
• the researcher does not specify cross
loadings, which fixes the loadings at
zero.
• One unique feature in specifying the
indicators for each construct is the
process of "setting the scale" of a
latent factor.
25. 4-Issues in Identification
• overidentification is the desired state
for CFA and SEM models in general.
• During the estimation process, the most
likely cause of the computer program
"blowing up" or producing meaningless
results is a problem with statistical
identification. As SEM models become
more complex.
26. AVOIDING IDENTIFICATION PROBLEMS
(Several guidelines can help determine the
identification status of a SEM model and assist the
researcher in avoiding identification problems)
• Meeting the Order and Rank
Conditions.(required mathematical properties)
• THREE-INDICATOR RULE.(It is satisfied when all
factors in a congeneric model have at least three
significant indicators)
• RECOGNIZING IDENTIFICATION PROBLEMS(Many
times the software programs will provide some
form of solution)
27. SOURCES AND REMEDIES OF
IDENTIFICATION PROBLEMS
Does the presence of identification problems mean
your model is invalid? Although many times
identification issues arise from common mistakes
in specifying the model and the input data.
• Incorrect Indicator Specification. (4 mistakes e.g.)
• "Setting the Scale" of a Construct.(each construct
must have one value specified)
• Too Few Degrees of Freedom.(Small sample size
(fewer than 200) increases the likelihood of
problems )
28. Problems in Estimation
most SEM programs will complete the estimation
process in spite of these issues.
It then becomes the responsibility of the researcher
to identify the illogical results and correct the
model to obtain acceptable results.
• ILLOGICAL STANDARDIZED PARAMETERS. (when
correlation estimates between constructs exceed
|1.0| or even standardized path coefficients exceed
|1.0|. Meaning there is problem with SEM results.
• HEYWOOD CASES A SEM. (solution that produces
an error variance estimate of less than zero (a
negative error variance) is termed a Heywood case.
29. STAGE 4: ASSESSING MEASUREMENT
MODEL VALIDITY
Once the measurement model is correctly
specified, a SEM model is estimated to provide
an empirical measure of the relationships
among variables and constructs represented by
the measurement theory.
The results enable us to compare the theory
against reality as represented by the sample
data.
we see how well the theory fits the data.
30. a-Assessing Fit
The sample data are represented by a
covanance matrix of measured items, and
the theory is represented by the
proposed measurement model. These
equations enable us to estimate reality
by computing an estimated covariance
matrix based on our theory. Fit compares
the two covariance matrices.
31. b-Path Estimates
One of the most fundamental assessments of construct
validity involves the measurement relationships
between items and constructs
• SIZE OF PATH ESTIMATES AND STATISTICAL
SIGNIFICANCE.
loadings should be at least .5 and ideally .7 or higher
meaning Loadings of this size or larger confirm that the
indicators are strongly related to their associated
constructs and are one indication of construct validity.
• IDENTIFYING PROBLEMS.
means(Loadings also should be examined for offending
estimates as indications of overall problems)
32. C- CFA and Construct Validity
One of the biggest advantages of CFA/SEM is its ability
to assess the construct validity of a proposed
measurement theory. Construct validity
Construct validity is made up of four important
components:
1. Convergent validity – three approaches:
o Factor loadings.
o Variance extracted.
o Reliability.
2. Discriminant validity.
3. Nomological validity.
4. Face validity.
33. Construct Validity
Construct validity is the extent to which a set of measured items
actually reflects the theoretical latent construct those items are
designed to measure.
1- CONVERGENT VALIDITY.
The items that are indicators of a specific construct should converge
• Factor Loadings.
• At a minimum, all factor loadings should be statistically
significant.(standardized loading estimates should be .5 or
higher, and ideally .7 or higher)
• Average Variance Extracted.
• The Li represents the standardized factor
loading, and i is the number of items.
• AVE estimates for two factors also should be greater than the
square of the correlation between the two factors to provide
evidence of discriminant validity.
34. • Reliability.
• Reliability estimate is that .7 or higher
suggests good reliability. Reliability between
.6 and .7 may be acceptable, provided that
other indicators of a model's construct validity
are good.
35. 2- DISCRIMINANT VALIDITY.
the extant to which a construct is truly distinct from
other construct. (The high discriminant validity provides
evidence that a construct is Unique)
3- NOMOLOGICAL VALIDITY AND FACE VALIDITY
(Constructs also should have face validity and
nomological validity)
• face validity: must be established prior to any
theoretical testing when using FA.
• nomological validity: is then tested by examining
whether the corrections among the constructs in a
measurement theory make sense.
36. D- Model Diagnostics
• the process of testing using CFA provides
additional diagnostic information that may
suggest modifications for either addressing
unresolved problems or improving the
model's test of measurement theory.
• Some areas that can be used to identify
problems with measures as following:
37. 1- STANDARDIZED RESIDUALS:
• Residuals: are the individual differences
between observed covariance terms and the
fitted (estimated) covariance terms.
• The standardized residuals: are simply the raw
residuals divided by the standard error of the
residual.
• Residuals: can be either positive or negative,
depending on whether the estimated
covariance is under or over the corresponding
observed covariance.
38. 2- MODIFICATION INDICES:
(is calculated for every possible relationship that
is not estimated in a model)
(of approximately 4.0 or greater suggest that the
fit could be improved significantly) e.g. HBAT
3- SPECIFICATION SEARCHES:
(is an empirical trial-and-error approach that
uses model diagnostics to suggest changes in
the model)
(SEM programs such as AMOS and LISREL can
perform specification searches automatically)
39. 4- CAVEATS IN MODEL RESPECIFICATION:
• CFA results suggesting more than minor
modification should be reevaluated with
a new data set.
• (e.g., if more than 20% of the measured
variables are deleted, then the
modifications cannot be considered
minor)