Canonical correlation analysis was used to detect potential bias in faculty promotion scoring at the American University of Nigeria (AUN). The analysis compared scores from three promotion committees and tested whether any committee showed bias that influenced candidates' promotability. The analysis found:
1) It could discriminate between candidates deemed promotable versus non-promotable, rejecting the hypothesis that it couldn't do so.
2) There were no significant differences in scoring between committees, rejecting the hypothesis that it couldn't detect bias.
3) Only the president's committee showed significant score weight influence on promotability, rejecting the hypothesis that it couldn't detect overbearing influences.
The study demonstrated canonical correlation analysis can be an effective tool for unbiased faculty
This Presentation is on recommended system on question paper predication using machine learning techniques. We did literature survey and implement using same technique.
This Presentation is on recommended system on question paper predication using machine learning techniques. We did literature survey and implement using same technique.
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
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
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
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
Article defines social media for the B2C real estate sector and identifies effective social media tools that can be used to communicate effectively to create awareness and generate leads
this activity is designed for you to explore the continuum of an a.docxhowardh5
this activity is designed for you to explore the continuum of an addictive behavior of your choice.
Addictive behavior appears in stages. The earliest stage is non-use, which finally leads up to out-of-control dependence. The stages in between are important to identify, as it is much easier to correct an early-stage issue as opposed to a late-stage problem.
After reviewing the module readings and tasks, use the module notes as a reference and alcohol or substance abuse addiction as an example to identify the various levels of addiction.
You may choose to develop a time line identifying the stages or develop a written essay (no more than 500 words in Word format) to describe the escalation of addictive behaviors.
You are to include at least two references from academic sources that you have researched on this topic in the Excelsior College Library and use appropriate citations in American Psychological Association (APA) style.
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Chapter 13
Qualitative Data Analysis
1
Process of Qualitative Data Analysis
Preparing the Qualitative Data
Transform the data into readable text
Check for and resolve transcription errors
Manage the data
Organize by attribute coding
Two Separate Processes
5
Coding: Involves labeling and breaking down the data to find:
Patterns
Themes
Interpretation: Giving meaning to the identified patterns and themes
Coding
Starts with identifying the unit of analysis
Coding categories may reflect realms of meaning or different activities.
Coding categories can be theoretically-based or inductively created emerging from the data.
Use of Analytical Memos
7
Analytical memos help researchers w/ process of breaking down the data
Personal reflections on the research experience, methodological issues, or patterns in the data
Comes in 3 varieties:
Code notes
Operational notes
Theoretical notes
Data Displays
Taxonomy: system of ordered classification
Data matrix: individuals or other units represent columns and coding categories represent rows
Typologies: representation of findings based on the interrelationship between two or more ideas, concepts, or variables
Flow charts: diagrams that display processes
Taxonomy of Survival Strategies
Data Matrix: Homeless Individuals by Dimensions
Drawing and Evaluating Conclusions
Conclusions may result in:
Rich descriptions
Identification of themes
Inferences about patterns and concepts
Theoretical propositions
Evaluation of the data can occur by:
Comparing notes among observers
Using multiple sources of data
Examining exceptions to the data patterns
Member checking
Variations in Qualitative Data Analysis: Grounded Theory
Objective is to develop theory from data
Emphasizes people’s actions and voices as the main sources of d.
Discriminant analysis is a technique that is used by the researcher to analyze the research data when the criterion or the dependent variable is categorical and the predictor or the independent variable is the interval in nature. The term categorical variable means that the predictor variable is divided into a number of categories.
DA is typically used when the groups are already defined prior to the study.
The end result of DA is a model that can be used for the prediction of group memberships. This model allows us to understand the relationship between the set of selected variables and the observations. Furthermore, this model will enable one to assess the contributions of different variables.
Data Processing and Statistical Treatment: Spreads and CorrelationJanet Penilla
A hyperlinked presentation. The objectives of the topic were written. The presentation was started with the variance and then the standard deviation provided with examples. It also answers on when to use the sample standard deviation and the population standard deviation or what type of data should we use when we calculate a standard deviation. The presentation also includes Correlations and other correlation techniques(Pearson-product moment correlation; Spearman - rank order correlation coefficient; t-test for correlation).
Welcome to this comprehensive presentation on regression analysis, a fundamental technique in predictive modeling. In this slide deck, we will embark on a journey through the intricate world of regression, exploring its essence, types, applications, systematic process, underlying assumptions, diagnostic tools, and real-world significance.
Regression analysis is a powerful statistical tool that enables us to understand and quantify the relationships between variables. By examining the interplay between a dependent variable and one or more independent variables, regression unveils patterns and trends that can drive informed decision-making. Whether you're working in finance, marketing, healthcare, or any other field, regression empowers analysts to extract valuable information from their data and make accurate predictions.
During our exploration, we will delve into various types of regression models. Simple Linear Regression establishes a linear relationship between two variables, serving as a foundation for understanding more complex models. Multiple Linear Regression expands this concept by incorporating multiple predictors, allowing us to account for multiple factors influencing the dependent variable. Polynomial Regression goes beyond linear relationships, capturing non-linear associations between variables. Logistic Regression, on the other hand, is specifically designed for predicting categorical outcomes, making it an invaluable tool for classification problems.
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Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
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A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
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Recruiting in the Digital Age: A Social Media MasterclassLuanWise
In this masterclass, presented at the Global HR Summit on 5th June 2024, Luan Wise explored the essential features of social media platforms that support talent acquisition, including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok.
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Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
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3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
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4. Sustainability Implementation & Best Practices
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The key differences between the MDR and IVDR in the EUAllensmith572606
In the European Union (EU), two significant regulations have been introduced to enhance the safety and effectiveness of medical devices – the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR).
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2. Introduction If we have two sets of variables, x1,...., xn and y1,….., ym, and there are correlations among the variables, then canonical correlation analysis will enable us to find linear combinations of the x's and the y's which have maximum correlation with each other. Canonical correlation begin with the observed values of two sets of variables relating to the same set of areas, and a theory or hypothesis that suggests that the two are interrelated. The overriding concern is with the structural relationship between the two sets of data as a whole, rather than the associations between individual variables
3. Canonical correlation is the most general form of correlation. Multiple regression analysis is a more specific case in which one of the sets of data contains only one variable, while product moment correlation is the most specific case in that both sets of data contain only one variable. Canonical correlation analysis is not related to factor/principal components analysis despite certain conceptual and terminological similarities. Canonical correlation analysis is used to investigate the inter-correlation between two sets of variables, whereas factor/principal components analysis identifies the patterns of relationship within one set of data.
4. Difficulties in Canonical Correlation Canonical correlation is not the easiest of techniques to follow, though the problems of comprehension are conceptual rather than mathematical. Unlike multiple regression and principal components analysis, we cannot provide a graphic device to illustrate even the simplest form. For with canonical correlation analysis we are dealing with two sets of data. Even the most elementary example must, therefore, have at least two variables on each side and so we require 2 + 2 = 4 dimensions. Tied as we are, however, to a three dimensional world, a true understanding of the technique in the conventional cognitive/visual sense of the term, is beyond our grasp.
5. Conceptual Overview Data Input The size of the matrices : There is no requirement in canonical analysis that there must be the same number of variables (columns) in each matrix, though there must be the same number of areas (rows). (There must of course be more than one variable in each set otherwise we would be dealing with multiple regression analysis) The order of the matrices : Neither set of data is given priority in the analysis so it does not matter which we term the criteria and which the predictors. Unlike simple linear regression there is no concept of a 'dependent' set or an 'independent' set. But in practice the smaller set is always taken second as this simplifies the calculation enormously
6. Advantages Useful and powerful technique for exploring the relationships among multiple dependent and independent variables. Results obtained from a canonical analysis should suggest answers to questions concerning the number of ways in which the two sets of multiple variables are related, the strengths of the relationships. Multiple regressions are used for many-to-one relationships, canonical correlation is used for many-to-many relationships. Canonical Correlation- More than one such linear correlation relating the two sets of variables, with each such correlation representing a different dimension by which the independent set of variables is related to the dependent set.
7.
8. The Canonical Problem Latent Roots and weights Canonical Scores Results and Interpretation Latent Roots Canonical Weights Canonical Scores
9. Mathematical Model The partitioned intercorrelation matrix where R11 is the matrix of intercorrelations among the p criteria variables R22 is the matrix of intercorrelations among the q predictor variables R12 is the matrix of intercorrelations of the p criteria with the q predictors R21 is the transpose of R12
14. Canonical correlation analysis-promotion bias scoring detector(a case study of American university of Nigeria(AUN)) Researchers-A. O. Unegbu & James J. Adefila `
15. Introduction Problem: AUN bids to keep with her value statement i.e. highest standards of integrity, transparency and academic honest. Solution: Appraise & select Faculties for promotion based on various promotion committees’ scores. Issues : Dwindling funding, need for a bias free selection technique,
16.
17. H02: CCA cannot detect significantly whether or not score-weights of each of the Promotion Assessors have over bearing influence on the promotability of candidates.
18.
19. Steps of the Research Data collection Manual computations SPSS analysis Test the Hypothesis
20. AUN promotion procedure Weights: The benchmark for promotion is securing a weighted average score should be more than 65%age.
21. Each of the Committee’s point allocation will be based on the below criteria
22. Supporting documents for Teaching Effectiveness Peer evaluation Student evaluation Course Syllabi Record of participation in teaching seminars, workshops, etc Contributions to the development of new academic programs Faculty awards for excellence in teaching
23. Scholarship, Research and Creative Works Terminal degrees/Professional qualifications At least Five publications, three of which shall be journal articles Computer Software and Program development Creative work in the areas of advertising, public relations, layout design, photography and graphics, visual arts etc.
24. Service to the University, Profession and Community Membership/leadership in departmental, school-wide or university-wide committees Planning or participation in workshops, conferences, seminars . Evidence of participation in mentoring or career counseling of students. Membership in Civil Society organizations Evidence of service as external assessor or external examiner on examination committees
28. Data Input The data input view containing the three groups of assessors and individual assessors
29. SPSS Results Analyze ⇒General Linear Model⇒Multivariate SPSS classified candidates into two groups of promotable and non promotable of 5 and 9 respectively. The result leads to the rejection of Null hypothesis Ho3 which states that Canonical Correlation Analysis cannot with 90% confidence level discriminate between promotable and non promotable candidates
30.
31.
32. Candidate’s status determination resulting from scores across the assessors and those that might result from bias scoring are very insignificant(Wilk’s lambda value =0.041)
33. There is no between-status differences in the scores between assessors of both group and individuals
34.
35. The results of the table show that the scores of each assessor had a significant effect on the determination of each Candidate Status as the significance is 0.135.
36. Test for homogeneity of variance Overbearing score weight influence test hypothesis is aimed at detecting across the individual assessors’ mark allocations and weights assigned to each. In this test, the assessors having low significance value mean that there is homogeneity of variance.
37.
38. This Leads to rejection of null hypothesis (Ho2) which states that Canonical Correlation Analysis cannot detect significantly whether or not score-weights of each of the promotion assessors has overbearing influence on the promotability of candidates.
39.
40.
41. Continued…………. Rejection of Null Hypothesis(Ho1):Pillar’s trace of 0.041, Wilk’s Lambda of 0.041, Hotelling’s trace of 0.041 and Roy’s Largest Root of 0.041 - all of them showed that p<0.05, it means that there is no between-status differences in the scores between assessors of both group and individuals, thereby leading to the rejection of Null hypothesis (Ho1) which states that Canonical Correlation Analysis cannot detect bias. Rejection of Null Hypothesis(Ho2):For Group Assessors - Internal Assessors with p=0.096, External Academic Assessors with p=0.526 and The President’s Assessment with p=0.0001, shows that except that of the President, the weight assigned to scores of other two are group assessors are insignificant- lead us to reject the Null hypothesis (Ho2) which states that Canonical Correlation Analysis cannot detect significantly whether or not score-weights of each of the promotion assessors has overbearing influence on the promotability of candidates.
In employment example the area was different zones, and in another example the area were particular people ( 3 psychological variables , 4 academic variables and 1 gender variable and area were 600 students )