This summary analyzes a document describing the use of discriminant analysis in SPSS to predict whether graduate students will successfully complete their PhD programs based on information collected before acceptance. It identifies key outputs of the discriminant analysis including tests showing one predictor variable significantly differs between groups, coefficients of the discriminant function, group centroids, and a classification results table showing around 80% of students were correctly classified.
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.In this presentation a brief introduction about SLR and MLR and their codes in R are described
Linear Discriminant Analysis and Its Generalization일상 온
The brief introduction to the linear discriminant analysis and some extended methods. Much of the materials are taken from The Elements of Statistical Learning by Hastie et al. (2008).
It is on Conjoint Analysis presented by Radhika Gupta, Shivi Agarwal, Neha Arya, Neha Kasturia, Mudita Maheshwari, Dhruval Dholakia, Chinmay Jaggan Anmol Sahani and Madhusudan Partani of FMG-18A, FORE School of Management
Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.In this presentation a brief introduction about SLR and MLR and their codes in R are described
Linear Discriminant Analysis and Its Generalization일상 온
The brief introduction to the linear discriminant analysis and some extended methods. Much of the materials are taken from The Elements of Statistical Learning by Hastie et al. (2008).
This presentation discusses the application of discriminant analysis in sports research. One can understand the steps involved in the analysis and testing its assumptions.
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.
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.
You cannot just do a Google search for the topic! Academic sources are required. You may use Google Scholar or other libraries.
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.
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Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
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http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
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Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. Chapter 6 Discriminant Analyses
SPSS - Discriminant Analyses
Data file used: graduate.sav
In this example the topic is criteria for acceptance into a graduate program. Every year, selectors miss-
guess and select students who are unsuccessful in their efforts to finish the degree. A wealth of information
is collected about each applicant prior to acceptance, and department records indicate whether that
student was successful in completing the course. This example uses the information prior to acceptance to
predict successful completion of a graduate program. The file consists of 50 students admitted into the
program between 7 and 11 years ago. The dependent variable is category (1 = finished the Ph.D., 2 = did
not finish), and 17 predictor variables are utilized to predict category membership in on of these groups.
How to get there: Analyze Classify ….
Discriminant …
Discriminant Analysis is used primarily to predict membership in two or more mutually exclusive groups.
This menu selection opens the following dialog box:
First enter the grouping variable (here: variable category). Then, define the lowest and highest coded value
for the grouping variable by clicking on Button Define Range. As the variable category has only two
levels you enter 1 and 2 in the boxes. See the second figure standing above.
Then, select the independent variables (choose gender, motive and stable) in the ‘Independents:’ box.
There are several methods for discriminant analyses, but here we will only use ‘Enter independents
together’, which is standard selected.
Button Statistics…
Here you can indicate those statistics that are desired in discriminant analysis. Often these include:
Means: The means and standard deviations for each variable for each group (the two levels of category in
this case), and for the entire sample.
Univariate ANOVAs: This compares the mean values for each group for each variable to see if there are
significant univariate differences between means.
Box’s M: A test for the equality of the group covariance matrices. For sufficiently large samples, a non-
significant p value means there is insufficient evidence that the matrices differ. The test is sensitive to
departures from multivariate normality.
Unstandardized Function Coefficients: The unstandardized coefficients of the discriminant equation
based on the raw scores of discriminating variables.
2. Button Classify…
Many classification options can be selected here, such as prior probabilities and plots. Also, a summary
table can be requested.
Button Save…
This option allows you to save as new variables: Predicted group membership, Discriminant Scores and
Probabilities of group membership.
Output of running Discriminant Analyses
We performed a discriminant analysis selecting ‘Enter independents together’. The descriptives Univariate
Anova’s Box’s M and unstandardized function coefficients are requested. A within-groups covariance
matrix is used, and a summary table and a leave-one-out classification are requested (under Button
Classify…).
The SPSS output is enormous, so we only indicate some of the relevant information here.
Tests of Equality of Group Means
1,000 ,007 1 48 ,934
,938 3,200 1 48 ,080
,679 22,722 1 48 ,000
STUDENTS EMOTIONAL
STABILITY
1=FEMALE 2=MALE
STUDENTS MOTIVATION
Wilks'
Lambda F df1 df2 Sig.
In the table ‘Tests of Equality of Group Means’ the results of univariate ANOVA’s, carried out for each
independent variable, are presented. Here, only student’s motivation (variable motive) differ (Sig. = ,000)
for the two groups (PhD completed and PhD not completed).
Box's Test of Equality of Covariance Matrices
Log Determinants
3 -,952
3 -1,023
3 -,890
1=COMPLETED
PHD, 2=DID NOT
COMPLETE PHDFINISH
NOT FINISH
Pooled within-groups
Rank
Log
Determinant
The ranks and natural logarithms of determinants
printed are those of the group covariance matrices.
Test Results
4,679
,727
6
16693,132
,628
Box's M
Approx.
df1
df2
Sig.
F
Tests null hypothesis of equal population covariance matrices.
The significance value of 0,628 indicates that the data do not differ significantly from multivariate normal.
This means one can proceed with the analysis.
Summary of Canonical Discriminant Functions
Eigenvalues
,514a 100,0 100,0 ,583
Function
1
Eigenvalue % of Variance Cumulative %
Canonical
Correlation
First 1 canonical discriminant functions were used in the
analysis.
a.
An eigenvalue indicates the proportion of variance explained. (Between-groups sums of squares divided by
within-groups sums of squares). A large eigenvalue is associated with a strong function.
3. The canonical relation is a correlation between the discriminant scores and the levels of the dependent
variable. A high correlation indicates a function that discriminates well. The present correlation of 0.583 is
not extremely high (1.00 is perfect).
Wilks' Lambda
,661 19,286 3 ,000
Test of Function(s)
1
Wilks'
Lambda Chi-square df Sig.
Wilks’ Lambda is the ratio of within-groups sums of squares to the total sums of squares. This is the
proportion of the total variance in the discriminant scores not explained by differences among groups. A
lambda of 1.00 occurs when observed group means are equal (all the variance is explained by factors other
than difference between those means), while a small lambda occurs when within-groups variability is small
compared to the total variability. A small lambda indicates that group means appear to differ. The
associated significance value indicate whether the difference is significant. Here, the Lambda of 0,661 has
a significant value (Sig. = 0,000); thus, the group means appear to differ.
The ‘Canonical Discriminant Function Coefficients’ indicate
the unstandardized scores concerning the independent
variables. It is the list of coefficients of the unstandardized
discriminant equation. Each subject’s discriminant score
would be computed by entering his or her variable values (raw
data) for each of the variables in the equation.
‘Functions at Group Centroide’ indicates the average
discriminant score for subjects in the two groups. More
specifically, the discriminant score for each group when the
variable means (rather than individual values for each
subject) are entered into the discriminant equation. Note that
the two scores are equal in absolute value but have opposite
signs.
‘Classification Results’ is a
simple summary of number
and percent of subjects
classified correctly and
incorrectly. The ‘leave-one-
out classification’ is a
cross-validation method, of
which the results are also
presented.
Canonical Discriminant Function Coefficients
-,001
-,595
1,169
-8,327
STUDENTS EMOTIONAL
STABILITY
1=FEMALE 2=MALE
STUDENTS MOTIVATION
(Constant)
1
Function
Unstandardized coefficients
Functions at Group Centroids
,702
-,702
1=COMPLETED
PHD, 2=DID NOT
COMPLETE PHDFINISH
NOT FINISH
1
Function
Unstandardized canonical discriminant
functions evaluated at group means
Classification Resultsb,c
20 5 25
6 19 25
80,0 20,0 100,0
24,0 76,0 100,0
20 5 25
7 18 25
80,0 20,0 100,0
28,0 72,0 100,0
1=COMPLETED
PHD, 2=DID NOT
COMPLETE PHD
FINISH
NOT FINISH
FINISH
NOT FINISH
FINISH
NOT FINISH
FINISH
NOT FINISH
Count
%
Count
%
Original
Cross-validateda
FINISH NOT FINISH
Predicted Group
Membership
Total
Cross validation is done only for those cases in the analysis. In cross validation,
each case is classified by the functions derived from all cases other than that
case.
a.
78,0% of original grouped cases correctly classified.b.
76,0% of cross-validated grouped cases correctly classified.c.