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Instructional design/User testing of e-learning courses/Check Your Knowledge 5

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Analyzing the Results

Knowledge Check 1
Read the user testing data and then indicate whether it is quantitative or qualitative by selecting the appropriate radio button. When you are finished making your selection, click the "Submit" button.

{Scoring trend on a particular exam item. + QUALITATIVE - QUANTITATIVE
 * type=""}

{Comment on the use of periods at the end of items on a bulleted list. - QUALITATIVE + QUANTITATIVE
 * type=""}

{Average seat-time on a particular lesson. + QUALITATIVE - QUANTITATIVE
 * type=""}

{Summative results of a satisfaction survey. + QUALITATIVE - QUANTITATIVE
 * type=""}

{Comment on how slow the learning management system loads each page of a course. - QUALITATIVE + QUANTITATIVE
 * type=""}

{Comment on how the learner struggles to figure out what button to click during a software application simulation practice task. - QUALITATIVE + QUANTITATIVE
 * type=""}

Knowledge Check 2
Read the user testing data and then indicate whether its severity by selecting the appropriate radio button. For the purposes of this exercise, you have already confirmed each item's veracity. When you are finished making your selection, click the "Submit" button.

{It would be nice to know where the midline torus is reflected in the computer record (although it is shown later in the training). This is an opportunity to reinforce this concept. - Deal Breaker + Serious - Minor - N/A
 * type=""}
 * This user has indicated a possible improvement to the training's content. A big clue is the "...nice to know..." comment. Your decision to designate a FIX or NO FIX to this item will depend on the level of effort for the revision. If the decision is "NO FIX," be sure an keep this suggestion on file for future revisions.

{Is this an error? Shouldn't you select the “US HUMERUS” to navigate to the Additional Details page instead of the "Procedures" button? + Deal Breaker - Serious - Minor - N/A
 * type=""}
 * Because this tester is an actual user of the system in the field, he has caught an error in the content. You never want to convey wrong information to the learner, so this is something that should definately be fixed during final revisions to the course.

{I personally feel that this lesson needs to be divided in two. Provider Access and then the separate one call Beneficiary Resources. You could actually tailor beneficiary resources directly to patients. This could be put on Kiosks at MTF, send out CD’s with the lesson. - Deal Breaker - Serious + Minor - N/A
 * type=""}
 * Remember that by the time a course gets to the user testing phase, it is at the end of the development cycle. In most cases, returning to the storyboard phase to rechunk a lesson's content will be out of scope. In addition, the suggestion to provide the information on Kiosks/CDs is out of scope in this case. This type of action would have needed to be indicated during front-end analysis during requirements gathering. Although assigned a "NO FIX" status, these are both great suggestions and should be documented for future revisions to the e-learning course.

{This does an excellent job of explaining the different roles. I wasn’t clear on my role until now. Didn’t know we could modify the webpage for the MTF - good info. This may be the best WBT I have ever used. It shows you what you need to know with out a lot of unnecessary information. - Deal Breaker - Serious - Minor + N/A
 * type=""}
 * Comments like this one require no action on your part. However, they should remain a part of your formal user testing documentation. They also provide you and your team-members a great pat on the back for a well-designed course.

{When you typed in the last name, you typed it in the ‘Find NAS by Patient SSN box’ which is wrong and then hit the wrong search button. + Deal Breaker - Serious - Minor - N/A
 * type=""}
 * Because this tester is an actual user of the system in the field, he has caught an error in the content. You never want to convey wrong information to the learner, so this is something that should definately be fixed during final revisions to the course.

{When I returned to the course the next day, all my bookmarks were gone. + Deal Breaker - Serious - Minor - N/A
 * type=""}
 * This either a problem with the content or the delivery platform and needs to be resolved prior to deployment of the course.

Knowledge Check 3
In the previous lesson, Conducting User Testing, you conducted your own user test on an e-learning page from Wikiversity. In this knowledge check, practice what you know about analyzing results by judging the severity of each your findings. If you believe that the item needs resolution, think about how you might go about fixing it.

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Analyzing the Results


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Portal:Non-formal Education/To-do

 * Locate more content on Wikiversity which relates to this portal and add it to the dynamic category system by categorising it under Category:Non-formal Education
 * Reorganize the category system which this portal feeds off so that the guide looks better.

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User:Jtneill/Sandbox/Tables and boxes

To sort
Demonstration tables and boxes. This page demonstrates some different kinds of tables and boxes using MediaWiki. See also:
 * Help:Table
 * ../../Wikiversity.

Content here

The Wikibooks community is developing a policy on the use of generative AI. Please review the draft policy and provide feedback on its talk page.

Basic
align=center border=1 cellspacing=0 cellpadding=5

2 columns, plain
Note: Also has style="width: 50%; vertical-align: top;" | (for cells)

Sortable

 * 3x2

See also: w:Help:Table

Collapsible
Content

Content

Grey box
 A grey box (using div):

with centred content

Dotted border
This box has a dotted green border.

Border and shading
 This box has a red border and background shading.

 A free and open textbook authored by students

Heading box (right-aligned)
Here's a way to make a right-hand 'heading'

Multicoloured boxes

Green header box A floating green header box

Green header box A floating green header box

Green header box A floating green header box

Floating box
 A float right box... using this code:
 * Float right box

 * See also
 * LessonPlan
 * Help:Pretty boxes
 * Help:Project boxes}}

Fixed box
 2024 July 29  UTC
 * This is an example of a fixed box e.g., for notification/navigation

Scrollable box

Highlight boxes
some highlighted text some highlighted text

Bar

Square boxes
 Metawiki commons species MediaWiki Betawiki  Wikipedia Wikinews Wiktionary Wikibooks Wikiquote Wikisource Wikiversity <br style="clear:both;" />

title: User:Jtneill/Sandbox/Tables and boxes


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Template:Tertiary Education/To-do

 * Locate more content on Wikiversity which relates to this portal and add it to the dynamic category system by categorising it under Category:Tertiary Education
 * Reorganize the category system which this portal feeds off so that the guide looks better.

title: Template:Tertiary Education/To-do


text:
Portal:Secondary Education/To-do

 * Locate more content on Wikiversity which relates to this portal and add it to the dynamic category system by categorising it under Category:Secondary Education
 * Reorganize the category system which this portal feeds off so that the guide looks better.
 * Help with the Highschool Help Forum

title: Portal:Secondary Education/To-do


text:
Portal:Primary Education/To-do

 * Locate more content on Wikiversity which relates to this portal and add it to the dynamic category system by categorising it under Category:Primary Education
 * Reorganize the category system which this portal feeds off so that the guide looks better.
 * Add links to 2007 Wikipedia Selection for schools as appropriate.
 * Create the Passenger Pigeon page

title: Portal:Primary Education/To-do


text:
Portal:Pre-school Education/To-do

 * Locate more content on Wikiversity which relates to this portal and add it to the dynamic category system by categorising it under Category:Pre-school Education.
 * Reorganize the category system which this portal feeds off so that the guide looks better.

title: Portal:Pre-school Education/To-do


text:
Portal:Pre-school Education/Featured

 * The Crocodile project for young children.
 * Solar System
 * Big Cats
 * Wikiversity mascot contest
 * Learning fun
 * Using Tux Paint:

title: Portal:Pre-school Education/Featured


text:
Portal:Primary Education/Featured

 * English as a second language at primary school
 * Primary school mathematics

title: Portal:Primary Education/Featured


text:
Portal:Tertiary Education/Featured

 * Bell's theorem

Called "the most profound discovery of science", Bell's theorem is accessible to any person with a strong background in high school physics. A study of Bell's theorem guides the student through vectors, Venn diagrams, and basic probability theory. It leads to discussions of the Weeping Angels of Doctor Who, and topics ordinarily associated with pseudoscience, but in a way consistent with scientific skepticism by showing us that the laws that seem to apply to the macroscopic world do not hold at the quantum level. More...

title: Portal:Tertiary Education/Featured


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Portal:Tertiary Education/Intro

Welcome to Wikiversity's Tertiary Education Portal, an entry-point for Wikiversity's university-related content.

The words used to describe tertiary level education vary a lot from one country to another so here's a quick guide. Tertiary education is also known as third stage, third level, and post-secondary education and includes higher education and further education. It is the educational level following the completion of a school providing a secondary education, such as a high school or secondary school. Higher education is normally taken to include undergraduate and postgraduate education, while vocational education and training beyond secondary education is known as further education. Tertiary education may take place at colleges, universities, institutes of technology and polytechnics (among other institutions).

Wikiversity is by no means "only" a university in terms of its content - it is much more than this and its content extends to all educational levels from preschool through primary and secondary education. Wikiversity is definitely not a university in the administrative sense at all - for example, Wikiversity cannot confer degrees or offer credits, nor is Wikiversity affiliated with any traditional accredited university. Wikiversity is simply the Wikimedia Foundation's project for global learning.

title: Portal:Tertiary Education/Intro


text:
User talk:Reedy

Hello Reedy, welcome around here. You have a reply here. Below more info about Wikiversity, Erkan Yilmaz uses the Wikiversity:Chat (try) 14:26, 23 March 2008 (UTC)

Welcome
Hello Reedy Boy, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Erkan Yilmaz uses the Wikiversity:Chat (try) 14:26, 23 March 2008 (UTC)

rename
Changing_username is done. --mikeu talk 16:45, 23 March 2008 (UTC)

title: User talk:Reedy


text:
User talk:ReevesAstronomy

Hello Reeves, welcome around here. Have fun in the Computer Science section. Below more info about Wikiversity, Erkan Yilmaz uses the Wikiversity:Chat (try) 14:28, 23 March 2008 (UTC)

Welcome
Hello ReevesAstronomy, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Erkan Yilmaz uses the Wikiversity:Chat (try) 14:28, 23 March 2008 (UTC)

title: User talk:ReevesAstronomy


text:
Successful E-Learning Tips

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Conducting User Testing

12 Tips to Successful E-Learning
The following tips can be helpful conducting and/or observing the evaluation of an e-learning course.

Use technology that’s easy to access such as an easy synchronous technology,, i.e. WebEx, if it’s a virtual evaluation to Microsoft Office products and e-mail. Many synchronous technologies contain built in recording features. Other tools that can be used are Captivate which will create a recording of the learner’s actions and SnagIt is a static screen capture utility similar to Print-Screen for static pictures that can be pasted into a document. Make sure each person performing the evaluation has their own computer. Use virtual recording within the synchronous technology, screen captures, or complete paper or online evaluations. Virtual evaluations cannot observe body language but they can hear what the learner is saying. Examples of positive trigger words include wow, cool, and great. Examples of negative trigger words include hmm, just a minute, and I don’t understand, where do I go now. If an observer is not present, a background program such as WebTrends can report how many times a user clicks on a certain page within the course or runs a particular interface element. Time may be a factor in the evaluation process so it’s important to prioritize up-front on which activities/content the learner should spend their time. Learners have a more positive evaluation experience if they can see their suggestions implemented while they’re testing. Provide additional feedback tools as links within the course they’re testing. This feedback can be submitted directly to a database or via email to the designer.
 * Select effortless technology
 * Have a means of capturing/recording screens.
 * Focus on a single subject per session
 * Describe the learning Scenario
 * 1) Always communicate to the learner the goals of the test. For user testing, the goal is to assess the course usability and learner attitudes while completing the instruction. It’s important to a ssure them they are not being tested, the instruction.
 * 2) Don’t provide too much detail or commentary about the course to explain various parts because it should be self explanatory. If the learner runs into problems, that’s a sign there’s something wrong with the instruction.
 * 3) Ask the learner to talk out loud while performing the testing. Not only the steps they’re taking but also what they’re thinking about the course itself and various parts such as activities, images, etc.
 * Record the test
 * If the evaluation includes an observer, listen for “trigger words” and read body language
 * Watch for “mouse mania”. If the learner is being observed virtually or in person, the following actions from the learner are considered “mouse mania” and signal the learner might be confused or impatient with the e-learning course:
 * 1) Rapid, undirected movement of the mouse.
 * 2) Frequently scrolling up and down the screen.
 * 3) Rapidly clicking on buttons and other interface elements within the course
 * Cherry-pick key learning activities
 * Allow the client to “listen in”
 * 1) If it’s a virtual evaluation, the client can be in the same room as the observer listening to the conversation and watching the computer screen.
 * 2) If the evaluation is in-person, the learner may be uncomfortable with an extra person in the room but they can listen to audio or view screen captures of the completed evaluation.
 * On-The-Fly Updates
 * Feedback and Subsequent Engagement

Resources
Adapted from...

I'm trying to upload it but don't have the licensing, emailing Dr. H.

Also having problem linking the file to a different name..

For more information and a complete description of each tip, read, “” by Dr. Peter Honebein and Darryl Sink.

title: Successful E-Learning Tips


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Instructional design/User testing of e-learning courses/Steps of User Testing

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Conducting User Testing

When performing user testing, the following steps provide a useful process to conducting a user test. Using these steps provides a uniform way to help ensure consistency in testing results. Whether the test is virtual, observed in the same room with the tester, or self-paced, each of these steps should be addressed, where applicable. For example, if the test is self-paced and you are not using an audio recording, there's no need to explain to the tester how to think out loud. Instead of explaining how to think out loud, explain to the tester they should write what they're think.

Steps to Conducting a User Test

 * 1) Introduce yourself
 * 2) Describe the purpose of the observation and set the participant at ease.
 * 3) Tell the participant that it’s okay to quit at any time.
 * 4) Talk about the video equipment in the room (if applicable).
 * 5) Explain how to think out loud.
 * 6) Explain that you cannot provide help.
 * 7) Introduce the product.
 * 8) Ask if there are any questions before you start; then begin the observation.
 * 9) Describe the task.  At this point, you or a volunteer will hand the task description documents (readme files, forms to fill out, etc., to the evaluators.)
 * 10) Conclude the observation.

Scenario
Below is a sample scenario of the ten steps.

Hi, my name's John Smith and I will be conducting this user test today in an observed user test. The purpose of this exercise is to test the usability of the 'Introduction to Psychomotor Behaviors' lesson and your attitudes about the lesson. What does this mean to you? I'd like you to go through the lesson, read the text, click on the links, take the activities and knowledge checks. Don't worry, the knowledge checks are not graded. Remember, this will help us evaluate how easy it is to use and navigate through the lesson, NOT you personally or how well you perform on the activities and knowledge checks. The activities and knowledge checks do not keep track of who you are so please, be as honest as possible. I encourage you to talk out loud and say what you're thinking, both the good and bad. If something is confusing, say it's confusing. If you like a particular scenario or the way the information is displayed, say that also. (Don't worry about the video equipment, it's to help me with my observation notes.) Since I'm conducting the test and the results are to test the usability of the lesson, I'm not able to help.

As you can see, the Wikiversity page that you will be testing is on your screen. It's a one page lesson with tables, images, and a knowledge check. As you're testing the lesson, please complete the evaluation forms either printed on your desk or on the computer. Be sure to write down what happens while you're testing, your thoughts and comments, screen prints, and anything else you would like known about the lesson.

Do you have any questions before you start? (Answer the questions if possible.) If there are no more questions, you can begin testing.'

If this were a self-paced user test, the same type of information is presented in an e-mail, applicable documents, and/or a conversation with the tester.

Before you get started on your own user test, read the evaluation tips in the next topic to assist in your evaluation.

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Conducting User Testing


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text:
User talk:Lerdsuwa

Welcome
Hello Lerdsuwa, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Erkan Yilmaz uses the Wikiversity:Chat (try) 15:13, 23 March 2008 (UTC)

title: User talk:Lerdsuwa


text:
User:Lerdsuwa

Me at Wikipedia:
 * en
 * th

title: User:Lerdsuwa


text: File:Ws key.png

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text:
Portal:Tertiary Education/Related

This box can be used to enter cross-references to related material on Wikiversity. You can also add more boxes for different purposes - contact a custodian if you need help with this.
 * /We need an action plan/

title: Portal:Tertiary Education/Related


text:
Portal:Non-formal Education/Related

This box can be used to enter cross-references to related material on Wikiversity. You can also add more boxes for different purposes - contact a custodian if you need help with this.

title: Portal:Non-formal Education/Related


text:
Portal:Secondary Education/Related

This box can be used to enter cross-references to related material on Wikiversity. You can also add more boxes for different purposes - contact a custodian if you need help with this.

title: Portal:Secondary Education/Related


text:
Template:Primary Education/Related

This box can be used to enter cross-references to related material on Wikiversity. You can also add more boxes for different purposes - contact a custodian if you need help with this.

title: Template:Primary Education/Related


text:
Portal:Pre-school Education/Related

This box can be used to enter cross-references to related material on Wikiversity. You can also add more boxes for different purposes - contact a custodian if you need help with this.

title: Portal:Pre-school Education/Related


text: Portal:Pre-school Education/Intro

title: Portal:Pre-school Education/Intro


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text:
Portal:Primary Education/Intro

Welcome to Wikiversity's Primary Education Portal, an entry-point for Wikiversity's primary education content.

Wikiversity is the Wikimedia Foundation's project which specifically aims to collect and share open educational resources. Wikiversity aims to be a critical global player in the use of the internet and information technologies to improve global education. We provide equally for all ages and stages of education and view primary education as a key component of our mission. Through the creation of this portal dedicated to primary education, we hope to encourage growth of our resource collection in this area. If you are a primary school educator (or parent) and need any help with the use of Wikiversity, or with modifying Wikiversity to make it a more primary-friendly environment, please feel welcome to ask.

Primary education is also known as elementary education. The transition from primary to secondary education varies widely internationally, and averages at about 11 to 12 years of age.

title: Portal:Primary Education/Intro


text:
Survey research and design in psychology/Lectures/Exploratory factor analysis/Notes

Exploratory factor analysis lecture notes

About these notes
These lecture notes:
 * 1) were converted from .odp slides to mediawiki syntax.
 * 2) subsequently copyedited and further wikified (ongoing)
 * 3) they do not yet include all:
 * 4) images
 * 5) additional notes (i.e., provide slide text only)
 * 6) notes from updated slides

To see an embedded version of this presentation:
 * Exploratory factor analysis (slideshare)

What is factor analysis?

 * 1) A multivariate statistical technique for identifying clusters of inter-correlated variables (or 'factors').
 * 2) A family of techniques to examine linear correlations amongst variables.
 * 3) Aim is to identify groups of variables which are relatively homogeneous.
 * 4) Groups of related variables are called 'factors'.
 * 5) Involves empirical testing of theoretical data structures

Purposes
There are two main applications of factor analytic techniques; to:

 * 1) reduce the number of variables, and
 * 2) detect structure in the relationships between variables, that is, to classify variables.

Factor analysis is commonly used in psychometric instrument development.

History

 * 1) Invented by Spearman (1904)
 * 2) Usage was hampered by onerousness of hand calculation
 * 3) Since the advent of computers, usage has thrived, esp. to develop:
 * 4) Theory-e.g., determining the structure of personality
 * 5) Practice-e.g., development of 10,000s+ of psychological screening & measurement tests
 * 6) IQ -is it separate but multiple, related factors, e.g,.
 * 7) Verbal
 * 8) Mathematical
 * 9) Interpersonal, etc.
 * ...or is it one global factor (g)?
 * ...or is it hierarchically structured?

 * 1) "Introduced and established by Pearson in 1901 and Spearman three years thereafter, factor analysis is a process by which large clusters and grouping of data are replaced and represented by factors in the equation. As variables are reduced to factors, relationships between the factors begin to define the relationships in the variables they represent (Goldberg & Digman, 1994). In the early stages of the process' development, there was little widespread use due largely in part to the immense amount of hand calculations required to determine accurate results, often spanning periods of several months. Later on a mathematical foundation would be developed aiding in the process and contributing to the later popularity of the methodology. In present day, the power of super computers makes the use of factor analysis a simplistic process compared to the 1900's when only the devoted researchers could use it to accurately attain results (Goldberg & Digman, 1994)." (from http://www.personalityresearch.org/papers/fehringer.html)
 * 2) Terminology was introduced by Thurstone (1931) - http://www.statsoft.com/textbook/stfacan.html

Conceptual models
Here are some visual ways of conceptualising factor analytic models:

Hierarchical

 * 1) [[Image:FactorAnalysis SimpleModel.png|center|thumb|700px|A simple factor analytic model (2-d) ... e.g., 12 items testing might actually tap only 3 underlying factors]]
 * 2) Figure 2.1 (DeCoster, 1998) (pdf)

Cluster

 * 1) [[Image:FactorAnalysis ConceptualModel DotsRings.png|center|thumb|450px|Factor analysis uses correlations among many items to search for common clusters...Exploratory factor analysis is a tool to help a researcher ‘throw a hoop’ around clusters of related items, to distinguish between clusters, and to identify and eliminate irrelevant or indistinct (overlapping) items.]]

3-d

 * 1) Figure 3 (Clemants & Moore, 2003) (gif/html).

Factor analysis process

 * 1) FA can be conceived of as a method for examining a matrix of correlations in search of clusters of highly correlated variables.
 * 2) A major purpose of factor analysis is data reduction, i.e., to reduce complexity in the data, by identifying underlying (latent) clusters of association.

Intelligence
IQ – does intelligence consist of separate factors, e.g,.
 * 1) Verbal
 * 2) Mathematical
 * 3) Interpersonal, etc.?

...or is it one global factor (g)?

...or is it hierarchically structured?

Personality
Personality – does it consist of 2, 3, or 5, 16, etc. factors? e.g., the “Big 5”?

 * 1) Neuroticism
 * 2) Extraversion
 * 3) Agreeableness
 * 4) Openness
 * 5) Conscientiousness

Essential facial features
Six orthogonal factors, represent 76.5 % of the total variability in facial recognition (in order of importance):

 * 1) upper-lip
 * 2) eyebrow-position
 * 3) nose-width
 * 4) eye-position
 * 5) eye/eyebrow-length
 * 6) face-width.

Problems
Problems with factor analysis include:

 * 1) Mathematically complicated
 * 2) Technical vocabulary
 * 3) Results usually absorb a dozen or so pages
 * 4) Students do not ordinarily learn factor analysis
 * 5) Lay people find the results incomprehensible

“The very power of FA to create apparent order from real chaos contributes to its somewhat tarnished reputation as a scientific tool” - Tabachnick & Fidell (2001)

“It is mathematically complicated and entails diverse and numerous considerations in application. Its technical vocabulary includes strange terms such as eigenvalues, rotate, simple structure, orthogonal, loadings, and communality. Its results usually absorb a dozen or so pages in a given report, leaving little room for a methodological introduction or explanation of terms. Add to this the fact that students do not ordinarily learn factor analysis in their formal training, and the sum is the major cost of factor analysis: most laymen, social scientists, and policy-makers find the nature and significance of the results incomprehensible.” Rummell - http://www.hawaii.edu/powerkills/UFA.HTM

EFA = Exploratory Factor Analysis

 * 1) explores & summarises underlying correlational structure for a data set

CFA = Confirmatory Factor Analysis

 * 1) tests the correlational structure of a data set against a hypothesised structure and rates the 'goodness of fit'

Data reduction

 * 1) Simplifies data by revealing a smaller number of underlying factors
 * 2) Helps to eliminate:
 * 3) redundant variables - (e.g, items which are highly correlated are unnecessary)
 * 4) unclear variables - (e.g., items which don’t load cleanly on a single factor)
 * 5) irrelevant variables - (e.g., variables which have low loadings)

Steps

 * 1) Test assumptions
 * 2) Select type of analysis
 * 3) Extraction (PC/PAF)
 * 4) Rotation (Orthogonal/Oblique)
 * 5) Determine no. of factors
 * 6) Identify which items belong in each factor
 * 7) Drop items as necessary and repeat steps 3 to 4
 * 8) Name and define factors
 * 9) Examine correlations amongst factors
 * 10) Analyse internal reliability

Sample size
Here are some guidelines about sample size for exploratory factor analysis. Factor analysis requires a reasonable sample size in order to be effective - and requires a larger sample size than for multivariate analyses such as multiple linear regression and ANOVA. Also note that factor analysis is based on correlations amongst the items, so a good estimate of each pair-wise correlation is needed - e.g., check the scatterplots.

Typical guidelines for factor analysis sample size requirements reported in the research method literature are:
 * 1) A Total N > 200 is recommended. Comrey and Lee (1992) provide this description of total sample sizes' adequacy for factor analysis:
 * 2) 50 = very poor,
 * 3) 100 = poor,
 * 4) 200 = fair,
 * 5) 300 = good,
 * 6) 500 = very good
 * 7) 1000+ = excellent
 * 8) Min/Ideal sample size based on variable:factor ratio
 * 9) Min. N > 5 cases per variable (item)
 * e.g., if I have 30 variables, I should have at least 150 cases (i.e., 1:5)
 * 1) Ideal N > 20 cases per variable
 * e.g., if I have 30 variables, I would ideally have at least 600 cases (1:20)

For more information, see EFA assumptions.

Example factor analysis - Classroom behaviour

 * 1) Based on Francis Section 5.6, which is based on the Victorian Quality Schools Project (google search).
 * 2) 15 classroom behaviours of high-school children were rated by teachers using a 5-point scale.
 * 3) Task: Identify groups of variables (behaviours) that are strongly inter-related & represent underlying factors.

Items

 * 1) Cannot concentrate ↔ can concentrate
 * 2) Curious & enquiring ↔ little curiousity
 * 3) Perseveres ↔ lacks perseverance
 * 4) Irritable ↔ even-tempered
 * 5) Easily excited ↔ not easily excited
 * 6) Patient ↔ demanding
 * 7) Easily upset ↔ contented
 * 8) Control ↔ no control
 * 9) Relates warmly to others ↔ provocative,disruptive
 * 10) Persistent ↔ easily frustrated
 * 11) Difficult ↔ easy
 * 12) Restless ↔ relaxed
 * 13) Lively ↔ settled
 * 14) Purposeful ↔ aimless
 * 15) Cooperative ↔ disputes

LOM

 * 1) All variables must be suitable for correlational analysis, i.e., they should be ratio/metric data or at least Likert data with several interval levels.

Normality

 * 1) FA is robust to assumptions of normality(if the variables are normally distributed then the solution is enhanced)

Linearity

 * 1) Because FA is based on correlations between variables, it is important to check there are linear relations amongst the variables (i.e., check scatterplots)

Outliers

 * 1) FA is sensitive to outlying cases
 * 2) Bivariate outliers(e.g., check scatterplots)
 * 3) Multivariate outliers (e.g., Mahalanobis' distance)
 * 4) Identify outliers, then remove or transform

Factorability

 * 1) It is important to check the factorability of the correlation matrix (i.e., how suitable is the data for factor analysis?)
 * 2) Check correlation matrix for correlations over .3
 * 3) Check the anti-image matrix for diagonals over .5
 * 4) Check measures of sampling adequacy (MSAs)
 * 5) Bartlett's
 * 6) KMO

Correlations

 * 1) The most manual and time consuming but thorough and accurate way to examine the factorability of a correlation matrix is simply to examine each correlation in the correlation matrix
 * 2) Take note whether there are SOME correlations over .30 -if not, reconsider doing an FA-remember garbage in, garbage out

Anti-image correlation matrix

 * 1) Anti-image: Medium effort, reasonably accurate
 * 2) Examine the diagonals on the anti-image correlation matrix to assess the sampling adequacy of each variable
 * 3) Variables with diagonal anti-image correlations of less that .5 should be excluded from the analysis -they lack sufficient correlation with other variables

Measures of sampling adequacy

 * 1) Quickest method, but least reliable
 * 2) Global diagnostic indicators - correlation matrix is factorable if:
 * 3) Bartlett's test of sphericity is significant and/or
 * 4) Kaiser-Mayer Olkin (KMO) measure of sampling adequacy > .5

Summary

 * 1) Are there several correlations > .3?
 * 2) Are the anti-image matrix diagonals > .5?
 * 3) Is Bartlett's test significant?
 * 4) Is KMO > .5 to .6?(depends on whose rule of thumb)

Extraction method
There are two main approaches to EFA based on:

 * 1) Analysing only shared variancePrinciple Axis Factoring (PAF)
 * 2) Analysing all variancePrinciple Components (PC)

Principal components (PC)

 * 1) More common
 * 2) More practical
 * 3) Used to reduce data to a set of factor scores for use in other analyses
 * 4) Analyses all the variance in each variable

Principal axis factoring (PAF)

 * 1) Used to uncover the structure of an underlying set of p original variables
 * 2) More theoretical
 * 3) Analyses only shared variance(i.e. leaves out unique variance)

Total variance explained

 * 1) Often there is little difference in the solutions for the two procedures.
 * 2) It's a good idea to check your solution using both techniques
 * 3) If you get a different solution between the two methods try to work out why and decide on which solution is more appropriate

Communalities
p

 * 1) The proportion of variance in each variable which can be explained by the factors
 * 2) Communality for a variable = sum of the squared loadings for the variable on each of the factors
 * 3) Communalities range between 0 and 1
 * 4) High communalities (> .5) show that the factors extracted explain most of the variance in the variables being analysed
 * 5) Low communalities (< .5) mean there is considerable variance unexplained by the factors extracted
 * 6) May then need to extract MORE factors to explain the variance

Eigen values

 * 1) EV = sum of squared correlations for each factor
 * 2) EV = overall strength of relationship between a factor and the variables
 * 3) Successive EVs have lower values
 * 4) Eigen values over 1 are 'stable'

Explained variance

 * 1) A good factor solution is one that explains the most variance with the fewest factors
 * 2) Realistically happy with 50-75% of the variance explained

How many factors?
A subjective process ... Seek to explain maximum variance using fewest factors, considering:

 * 1) Theory -what is predicted/expected?
 * 2) Eigen Values > 1? (Kaiser's criterion)
 * 3) Scree Plot -where does it drop off?
 * 4) Interpretability of last factor?
 * 5) Try several different solutions?
 * 6) Factors must be able to be meaningfully interpreted & make theoretical sense?

 * 1) Aim for 50-75% of variance explained with 1/4 to 1/3 as many factors as variables/items.
 * 2) Stop extracting factors when they no longer represent useful/meaningful clusters of variables
 * 3) Keep checking/clarifying the meaning of each factor and its items.

Scree plot

 * 1) A bar graph of Eigen Values
 * 2) Depicts the amount of variance explained by each factor.
 * 3) Look for point where additional factors fail to add appreciably to the cumulative explained variance.
 * 4) 1st factor explains the most variance
 * 5) Last factor explains the least amount of variance
 * 6) Factor loadings (FLs) indicate the relative importance of each item to each factor.
 * 7) In the initial solution, each factor tries â€œselfishlyâ€ to grab maximum unexplained variance.
 * 8) All variables will tend to load strongly on the 1st factor
 * 9) Factors are made up of linear combinations of the variables (max. poss. sum of squared rs for each variable)

Initial solution - Unrotated factor structure
1st factor extracted: Second factor extracted:
 * 1) best possible line of best fit through the original variables
 * 2) seeks to explain maximum overall variance
 * 3) a single summary of the main variance in set of items
 * 4) Each subsequent factor tries to maximise the amount of unexplained variance which it can explain.
 * 1) orthogonal to first factor - seeks to maximize its own eigen value (i.e., tries to gobble up as much of the remaining unexplained variance as possible)
 * 2) Vectors = lines of best fit
 * 3) Seldom see a simple unrotated factor structure
 * 4) Many variables will load on 2 or more factors
 * 5) Some variables may not load highly on any factors
 * 6) Until the FLs are rotated, they are difficult to interpret.
 * 7) Rotation of the FL matrix helps to find a more interpretable factor structure.

Two basic types

 * 1) Orthogonal (Varimax): Minimises factor covariation, produces factors which are uncorrelated
 * 2) Oblique (Oblimin): allows factors to covary, allows correlations between factors.

Why rotate a factor loading matrix?

 * 1) After rotation, the vectors (lines of best fit) are rearranged to optimally go through clusters of shared variance
 * 2) Then the FLs and the factor they represent can be more readily interpreted
 * 3) A rotated factor structure is simpler & more easily interpretable
 * 4) each variable loads strongly on only one factor
 * 5) each factor shows at least 3 strong loadings
 * 6) all loading are either strong or weak, no intermediate loadings

Orthogonal versus oblique rotations

 * 1) Think about purpose of factor analysis
 * 2) Try both
 * 3) Consider interpretability
 * 4) Look at correlations between factors in oblique solution - if >.32 then go with oblique rotation (>10% shared variance between factors)

Factor structure
Factor structure is most interpretable when:

(Loadings of > +.40 are generally OK)
 * 1) Each variable loads strongly on only one factor
 * 2) Each factor has three or more strong loadings
 * 3) Most factor loadings are either high or low with few of intermediate value

How do I eliminate items?
Elminating items from an EFA is a subjective process, but consider:
 * 1) Communalities (each ideally > .5)
 * 2) Size of main loading (bare min > |.4|, preferably > |.5|, ideally > |.6|)
 * 3) Meaning of item (face validity)
 * 4) Contribution it makes to the factor (i.e., is a better measure of the latent factor achieved by including or not including this item?)
 * 5) Number of items already in the factor (i.e., if there are already many items (e.g., > 6) in the factor, then the researcher can be more selective about which ones to include and which ones to drop)
 * 6) Eliminate 1 variable at a time, then re-run, before deciding which/if any items to eliminate next
 * 7) (Size of cross loadings max ~ |.3|)

How many items per factor?

 * 1) Bare min. = 2
 * 2) Recommended min. = 3
 * 3) Max. = unlimited
 * 4) More items
 * 5) -> greater reliability
 * 6) -> more 'roundedness'
 * 7) -> Law of diminishing returns
 * 8) Typically = 4 to 10 is reasonable

Interpretability

 * 1) The researcher must be able to understand and interpret a factor if it is going to be extracted.
 * 2) Be guided by theory and common sense in selecting factor structure.
 * 3) However, watch out for 'seeing what you want to see' when factor analysis evidence might suggest a different solution.
 * 4) There may be more than one good solution! e.g.,
 * 5) 2 factor model of personality
 * 6) 5 factor model of personality
 * 7) 16 factor model of personality

Factor loadings & item selection
A factor structure is most interpretable when:
 * 1) Each variable loads strongly on only one factor (strong is > +.40).
 * 2) Each factor shows 3 or more strong loadings, more = greater reliability.
 * 3) Most loadings are either high or low, few intermediate values.
 * 4) These elements give a 'simple' factor structure.

Factor loading guidelines (Comrey & Lee, 1992)
Loadings:
 * 1) > .70 - excellent
 * 2) > .63 - very good
 * 3) > .55 - good
 * 4) > .45 - fair
 * 5) > .32 - poor

Example - Condom use

 * 1) The Condom Use Self-Efficacy Scale (CUSES) was administered to 447 multicultural college students.
 * 2) PC FA with a varimax rotation.
 * 3) Three distinct factors were extracted:
 * 4) 'Appropriation'
 * 5) 'Sexually Transmitted Diseases'
 * 6) 'Partners' Disapproval'
 * 7) Barkley, T. W. Jr., & Burns, J. L. (2000). Factor analysis of the Condom Use Self-Efficacy Scale among multicultural college students. Health Education Research, 15(4), 485-489.
 * 8) Condom Use Self-Efficacy Scale (CUSES)

Summary

 * 1) Factor analysis is a family of multivariate correlational data analysis methods for summarising clusters of covariance.
 * 2) FA analyses and summarises correlations amongst items
 * 3) These common clusters (the factors) can be used as summary indicators of the underlying construct

Assumptions

 * 1) 5+ cases per variables (ideal is 20 per)
 * 2) N > 200
 * 3) Outliers
 * 4) Factorability of correlation matrix
 * 5) Normality enhances the solution

Steps

 * 1) Communalities
 * 2) Eigen Values & % variance
 * 3) Scree Plot
 * 4) Number of factors extracted
 * 5) Rotated factor loadings
 * 6) Theoretical underpinning

Type of FA

 * 1) PC vs. PAF
 * 2) PC for data reduction e.g., computing composite scores for another analysis ( uses all variance )
 * 3) PAF for theoretical data exploration (uses shared variance)
 * 4) Choose technique depending on the goal of your analysis.

Rotation

 * 1) Rotation
 * 2) orthogonal -perpendicular vectors
 * 3) oblique -angled vectors
 * 4) Try both ways -are solutions different?

Factor analysis in practice
To find a good solution, most researchers, try out each combination of The above methods would then be commonly tried out on a range of possible/likely factors, e.g., for 2, 3, 4, 5, 6, and 7 factors No. of factors to extract?
 * 1) PC-varimax
 * 2) PC-oblimin
 * 3) PAF-varimax
 * 4) PAF-oblimin
 * 1) Try different numbers of factors
 * 2) Try orthogonal & oblimin solutions
 * 3) Try eliminating poor items
 * 4) Conduct reliability analysis
 * 5) Check factor structure across sub-groups if sufficient data
 * 6) You will probably come up with a different solution from someone else!
 * 1) Inspect EVs - look for > 1
 * 2) % of variance explained
 * 3) Inspect scree plot
 * 4) Communalities
 * 5) Interpretability
 * 6) Theoretical reason


text:
Portal:Secondary Education/Intro

Welcome to Wikiversity's Secondary Education Portal, an entry-point for Wikiversity's secondary education content.

Wikiversity is the Wikimedia Foundation's project which specifically aims to collect and share open educational resources. Wikiversity aims to be a critical global player in the use of the internet and information technologies to improve global education. We provide equally for all ages and stages of education and view secondary education as a key component of our mission. Through the creation of this portal dedicated to secondary education, we hope to encourage growth of our resource collection in this area.

title: Portal:Secondary Education/Intro


text:
User talk:Softtest123

Hello, thank you for they typo fixes. Below more info about Wikiversity, Erkan Yilmaz uses the Wikiversity:Chat (try) 17:36, 23 March 2008 (UTC)

Welcome
Hello Softtest123, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Erkan Yilmaz uses the Wikiversity:Chat (try) 17:36, 23 March 2008 (UTC)

title: User talk:Softtest123


text:
Instructional design/User testing of e-learning courses/Check Your Knowledge 4

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Analyzing the Results

Knowledge Check 1
Read the user testing data and then indicate whether it is quantitative or qualitative by selecting the appropriate radio button. When you are finished making your selection, click the "Submit" button.

{Scoring trend on a particular exam item. + QUALITATIVE - QUANTITATIVE
 * type=""}

{Comment on the use of periods at the end of items on a bulleted list. - QUALITATIVE + QUANTITATIVE
 * type=""}

{Average seat-time on a particular lesson. + QUALITATIVE - QUANTITATIVE
 * type=""}

{Summative results of a satisfaction survey. + QUALITATIVE - QUANTITATIVE
 * type=""}

{Comment on how slow the learning management system loads each page of a course. - QUALITATIVE + QUANTITATIVE
 * type=""}

{Comment on how the learner struggles to figure out what button to click during a software application simulation practice task. - QUALITATIVE + QUANTITATIVE
 * type=""}

Knowledge Check 2
Read the user testing data and then indicate whether its severity by selecting the appropriate radio button. For the purposes of this exercise, you have already confirmed each item's veracity. When you are finished making your selection, click the "Submit" button.

{It would be nice to know where the midline torus is reflected in the computer record (although it is shown later in the training). This is an opportunity to reinforce this concept. - Deal Breaker + Serious - Minor - N/A
 * type=""}
 * This user has indicated a possible improvement to the training's content. A big clue is the "...nice to know..." comment. Your decision to designate a FIX or NO FIX to this item will depend on the level of effort for the revision. If the decision is "NO FIX," be sure an keep this suggestion on file for future revisions.

{Is this an error? Shouldn't you select the “US HUMERUS” to navigate to the Additional Details page instead of the "Procedures" button? + Deal Breaker - Serious - Minor - N/A
 * type=""}
 * Because this tester is an actual user of the system in the field, he has caught an error in the content. You never want to convey wrong information to the learner, so this is something that should definately be fixed during final revisions to the course.

{I personally feel that this lesson needs to be divided in two. Provider Access and then the separate one call Beneficiary Resources. You could actually tailor beneficiary resources directly to patients. This could be put on Kiosks at MTF, send out CD’s with the lesson. - Deal Breaker - Serious + Minor - N/A
 * type=""}
 * Remember that by the time a course gets to the user testing phase, it is at the end of the development cycle. In most cases, returning to the storyboard phase to rechunk a lesson's content will be out of scope. In addition, the suggestion to provide the information on Kiosks/CDs is out of scope in this case. This type of action would have needed to be indicated during front-end analysis during requirements gathering. Although assigned a "NO FIX" status, these are both great suggestions and should be documented for future revisions to the e-learning course.

{This does an excellent job of explaining the different roles. I wasn’t clear on my role until now. Didn’t know we could modify the webpage for the MTF - good info. This may be the best WBT I have ever used. It shows you what you need to know with out a lot of unnecessary information. - Deal Breaker - Serious - Minor + N/A
 * type=""}
 * Comments like this one require no action on your part. However, they should remain a part of your formal user testing documentation. They also provide you and your team-members a great pat on the back for a well-designed course.

{When you typed in the last name, you typed it in the ‘Find NAS by Patient SSN box’ which is wrong and then hit the wrong search button. + Deal Breaker - Serious - Minor - N/A
 * type=""}
 * Because this tester is an actual user of the system in the field, he has caught an error in the content. You never want to convey wrong information to the learner, so this is something that should definately be fixed during final revisions to the course.

{When I returned to the course the next day, all my bookmarks were gone. + Deal Breaker - Serious - Minor - N/A
 * type=""}
 * This either a problem with the content or the delivery platform and needs to be resolved prior to deployment of the course.

Knowledge Check 3
In the previous lesson, Conducting User Testing, you conducted your own user test on an e-learning page from Wikiversity. In this knowledge check, practice what you know about analyzing results by judging the severity of each your findings. If you believe that the item needs resolution, think about how you might go about fixing it.

Back to Topic: Instructional Design > User Testing of E-Learning Courses > Analyzing the Results


text:
Talk:Java Tutorial/Hello World!

How do you run it?
I got everything installed, including notepad++ (neato!), saved HelloWorld.java and compiled it and now have a file HelloWorld.class. So how do I run it? I thought it was just: "java HellowWorld.class", but I am getting a traceback of a "class doesn't exist" exception:

Exception in thread "main" java.lang.NoClassDefFoundError: HelloWorld/class Caused by: java.lang.ClassNotFoundException: HelloWorld.class at java.net.URLClassLoader$1.run(Unknown Source) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(Unknown Source) at java.lang.ClassLoader.loadClass(Unknown Source) at sun.misc.Launcher$AppClassLoader.loadClass(Unknown Source) at java.lang.ClassLoader.loadClass(Unknown Source) at java.lang.ClassLoader.loadClassInternal(Unknown Source)

--Softtest123 18:02, 23 March 2008 (UTC)

 * You need to use class name as the first parameter, not the file name. In your case, you want to type java HelloWorld . --Sigma 7 21:08, 24 November 2009 (UTC)

title: Talk:Java Tutorial/Hello World!


text:
User:Softtest123

See about me on my [w:User:Softtest123|Wikipedia page]]. --Softtest123 18:05, 23 March 2008 (UTC)

title: User:Softtest123


text:
Portal:Non-formal Education/Intro

Welcome to Wikiversity's Non-formal Education Portal. Non-formal education is the official umbrella term for all education which takes place outside the formal sector, where formal education comprises the provision of education at pre-school, primary, secondary and tertiary levels. Non-formal education includes learning alongside and outside schools and universities, such as sports and hobbies. It also includes much of professional and vocational education, adult literacy training and lifelong learning. Opportunities for non-formal education may be provided by libraries, companies and clubs as well as by schools and colleges in the form of extra-curricular activities. On the international scale, non-formal education is critical in developing countries as a method of overcoming inherited skills deficits.

Wikiversity is a project of the Wikimedia Foundation (which also runs Wikipedia) and our Non-formal Education Portal offers everyone, from all over the world, the opportunity to create and share open resources. Everything you place here will be immediately available to others, and you can immediately use or adapt anything which others have placed here. If Wikiversity is able to emulate the success of its sister project at Wikipedia, then we can hope to build one of the world's largest centres for non-formal education here.

title: Portal:Non-formal Education/Intro


text:
User:Remi/Barnstars

now i need a barn

title: User:Remi/Barnstars


text:
Alternating current circuits/Capacitance values

The SI unit of Capacitance is the Farad. It was named after Michael Faraday. It is the charge in coulombs which a capacitor will accept for the potential across it to change by 1 volt.

The Farad was coined by Josiah Latimer Clark in 1861 in honor of Michael Faraday, but it was for a unit of quantity of charge.

An abfarad is an obsolete electromagnetic (CGS) unit of capacitance equal to 109 farads (1,000,000,000 F or 1 GF). This very large unit is used in medical terminology only.

The statfarad was the comparable obsolete electrostatic (CGS) unit of capacitance, and was equal to 1 microfarad.


text:
Instructional design/Learning objectives/Performance/I got them both right

Great job!

1. Which of these describes an overt action? ...identify a red blood cell that contains the sickle cell.. (identify is a covert action without an indicator) ...recognize a red blood cell that contains the sickle cell.. (recognize is a covert action without an indicator) ...circle a red blood cell that contains the sickle cell. (circle specifies an overt, observable, and measurable action)

2. Which of these is the best example of an observable action verb? ...determine (determine is a covert action without an indicator) ...diagram (diagram is an overt and observable action ...reflect (reflect is a covert action without an indicator)


text:
Instructional design/Learning objectives/Performance/I missed one or both

Remember that the best performance objectives use verbs that are
 * overt
 * observable
 * measurable

When we want to use a covert action, then we need to include an indicator (verb) that is observable and measurable.

1. Which of these describes an overt action? ...identify a red blood cell that contains the sickle cell.. (identify is a covert action without an indicator) ...recognize a red blood cell that contains the sickle cell.. (recognize is a covert action without an indicator) ...circle a red blood cell that contains the sickle cell. (circle specifies an overt, observable, and measurable action)

While it is important for learners to be able to identify, recognize, and distinguish certain information; they are not observable behavior unless we add an indicator that can be measured. "Identify" by itself may cause some misunderstanding without a little clarification about how to identify (Point, Circle, etc). "Circle" is an overt behavior that we can measure.

2. Which of these is the best example of an observable action verb? ...determine (determine is a covert action without an indicator) ...diagram (diagram is an overt and observalbe action ...reflect (reflect is a covert action without an indicator)

Determine and reflect are behaviors that we may want to develop in our students, but they are not observable unless we add an indicator. When a learner diagrams a sentence or a system it is an overt, observable, and measurable action.


text:
More explanation

The following identifies the verbs with the cognitive level of Bloom's Taxonomy.

1. Which of the following is a performance verb for knowledge ? ...Name (Knowledge) ...Examine (Analysis) ...Validate (Evaluation) ...Compose (Synthesis)

2. Which of the following is a performance verb for comprehension ? ...Choose (Knowledge) ...Create (Synthesis) ...Diagnose (Evaluation) ...Explain (Comprehension)

3. Which of the following is a performance verb for application ? ...Demonstrate (Application) ...Test (Analysis) ...Write (Knowledge) ...Match (Knowledge)

title: More explanation


text:
Security

Security may refer to:
 * School:Computer forensics and security
 * School:Security, surveillance and law enforcement
 * Security engineering
 * Information security
 * IT Security

title: Security


text:
User talk:Eroka

Welcome
Hello Eroka, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! --Remi 22:55, 23 March 2008 (UTC)

title: User talk:Eroka


text:
User talk:Juan Karlovy Vary

Hello Juan, thank you for your contribution at Bloom Clock. I am not sure about this edit. There seems to have disappeared previous info ? btw: Below more info about Wikiversity, Erkan Yilmaz uses the Wikiversity:Chat (try) 23:32, 23 March 2008 (UTC)

 * Dear, Erkan! Be sure it was me and it was correct. I had edited before the information for Karlovy Vary to the profile of Český Krumlov.--Juan Karlovy Vary 17:21, 20 April 2008 (UTC)

Welcome
Hello Juan Karlovy Vary, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Erkan Yilmaz uses the Wikiversity:Chat (try) 23:32, 23 March 2008 (UTC)

 * Huh, wv introduction is nice, but could you translate it to the Czech language, please!--Juan Karlovy Vary 17:21, 20 April 2008 (UTC)

title: User talk:Juan Karlovy Vary


text:
Survey research and design in psychology/Participants

Online participation

 * 1) Learning support is available from James Neill - email and let him know about your interest
 * 2) Browse the individual topics for what interests you or join in and work through the material and tasks sequentially, recording your journey and asking questions along the way

 * 1) Online participants are encouraged to create an account and sign below with four tildes ( ~ )

Online participants

 * U3017048 01:16, 18 January 2011 (UTC)
 * Deathlaser (talk) 19:13, 13 May 2012 (UTC)

Facilitators

 * James Neill


text:
Instructional design/Learning objectives/Conditions Self Test

Click Next to continue.

Self-Test

Click Next to continue. Phonebein 17:37, 30 March 2008 (UTC) There is no next button. Provide one, or fix the navigation instruction.

Good stuff thus far. This lesson is coming together nicely. I like the navigation, the length of the pages, and the writing style.


text:
United States currency/Large denominations

Today, the United States prints the U.S. Dollar ($). The current denominations that are printed in bills are $1, $2, $5, $10, $20, $50, and $100.

At one time a while ago, it also included five larger denominations. High denomination currency was common from the very beginning of United States Government issue (1861). $500, $1,000, and $5,000 interest bearing notes were issued in 1861, and $10,000 gold certificates arrived in 1865. There are many different designs and types of these notes.

All of the dollar bills were issued in small size in 1929. The high denomination designs were as follows:

 * The $500 bill featured a portrait of William McKinley.
 * The $1,000 bill featured a portrait of Grover Cleveland.
 * The $5,000 bill featured a portrait of James Madison.
 * The $10,000 bill featured a portrait of Salmon P. Chase.
 * The $100,000 bill featured a portrait of Woodrow Wilson.

The reverse designs featured abstract scrollwork with ornate denomination identifiers. All were printed in green, except for the $100,000. The $100,000 is an odd bill, in that it was not generally issued, and printed only as a gold certificate of Series of 1934. These gold certificates (of denominations $100, $1,000, $10,000, and $100,000) were issued after the gold standard was repealed and gold was compulsorily purchased by presidential order of Franklin Roosevelt on March 9, 1933 (see United States Executive Order 6102), and thus were used only for intra-government transactions. They are printed in orange on the reverse. This series was discontinued in 1940. The other bills are printed in black and green as shown by the $10,000 example (pictured at right).

Although they are still technically legal tender in the United States, high-denomination bills were last printed in 1945 and officially discontinued on July 14, 1969, by the Federal Reserve System. The $5,000 and $10,000 effectively disappeared well before then: there are only about 200 $5,000 and 300 $10,000 bills known, of all series since 1861. Of the $10,000 bills, 100 were preserved for many years by Benny Binion, the owner of Binion's Horseshoe casino in Las Vegas, Nevada, where they were displayed in a glass case. The case is no longer there, and the bills were sold to collectors.

Circulation of high-denomination bills was halted in 1969 by executive order of President Richard Nixon, in an effort to combat organized crime.

For the most part, these bills were used by banks and the Federal Government for large financial transactions. This was especially true for gold certificates from 1865 to 1934. However, the introduction of the electronic money system has made large-scale cash transactions obsolete; when combined with concerns about counterfeiting and the use of cash in unlawful activities such as the illegal drug trade, it is unlikely that the U.S. government will re-issue large denomination currency in the near future. According to the US Department of Treasury website, "The present denominations of our currency in production are $1, $2, $5, $10, $20, $50 and $100....Neither the Department of the Treasury nor the Federal Reserve System has any plans to change the denominations in use today."

-km 00:53, 24 March 2008 (UTC)


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United States currency/Fake denominations

Fake denominations of United States currency have been created by individuals as practical jokes, by money artists such as J. S. G. Boggs, or as genuine attempts at counterfeiting.

Before the passage of the National Banking Act of 1863, individual banks in the United States were permitted to issue their own currency. Many banks did so, resulting in a proliferation of banknotes of various denominations, and the need for merchants to have books explaining the characteristics of various notes.

$3
Various $3 bills have been released, generally poking fun at politicians or celebrities such as Richard Nixon, Bill Clinton, Michael Jackson, or Hillary Clinton.

In the 1960s, Mad magazine magazine printed a three-dollar bill. This was not counterfeiting, but the Federal Bureau of Investigation complained to the magazine's editors, because people were cutting the bill out of the magazine in |Las Vegas] and successfully using it to obtain change in bill changers. On the bill, which had a portrait of Alfred E. Neuman, a line read: "This is not legal tender—nor will Meat tenderizer help it.

Prior to the creation of the Federal Reserve System, individual banks offered their own currencies. The faces on the various currencies were standardized across the country, but designs varied per bank. Several banks issued three dollar bills, with the face of Santa Claus gracing the front of the note. Such banks included Howard Banking Company of Boston, the Central Bank of Troy, the Pittsfield Bank and the White Mountain Bank, and perhaps coincidentally, the St. Nicholas Bank of Manhattan.

$6
Shortly after the Lewinsky scandal, parody $6 bills (or "sex" dollars) appeared in the market. They can still be found for sale.

$200
In 2001, a man bought a sundae at a Danville, Kentucky, Kentucky Dairy Queen with a United States 200 dollar bill featuring President of the United States George W. Bush and received $197.88 in change. In September 2003, a North Carolina man named Travis Martin used a $200 bill at a Food Lion to purchase $150 in groceries; the cashier accepted the fake bill and presented Martin with $50 in change.

$1,000,000
Many businesses print million dollar bills. They're sold as novelties, and they do not assert that they are legal tender; the Federal Reserve has declared them legal to print or own as long as they are not used fraudulently, and does not consider them counterfeit because no genuine million dollar bill exists or ever has existed.

In March 2004, Alice Regina Pike attempted to use a $1,000,000 bill with a picture of the Statue of Liberty on the front to purchase $1671.55 in goods from a Walmart in Covington, Georgia, for which she was arrested.

In November 2007, Alexander D. Smith tried to open a bank account in Aiken County, South Carolina by depositing a $1,000,000 bill. The bank employee refused to deposit the bill and called the police. Smith was immediately arrested on a charge of forgery.

The Libertarian Party (United States) makes an annual tradition of handing out informational fliers made to look like $1,000,000 bills on April 15th to draw attention to its anti-income tax platform.

Though not meant to be used as actual legal tender, Christian Evangelism Ray Comfort's ministry, Living Waters Publications, produces a fake $1,000,000 bill featuring Grover Cleveland, which is in reality a Tract (literature). It appears to be based on the United States twenty-dollar bill, with the gospel message around the back, and also includes some of their Web site addresses on the bill with the statement "This is NOT legal tender for all debts, public and private."

After someone attempted to deposit one of the fake bills in North Carolina, the Secret Service raided The Great News Network, a sister ministry to LWP based in Denton, Texas, Texas, on June 2, 2006. The USSS told workers at GNN they would locate and seize all of the million dollar bills at LWP's Bellflower, California, headquarters. Comfort has been advised by his lawyers to refuse such an action, and no warrants yet appear to have been issued for the tracts. However, in a precautionary move LWP also produced an enlarged "Secret Service version".

$1,000,000,000
In July 2006, Comfort's ministry developed and began printing a similar $1,000,000,000 bill (one billion USD). Its color scheme more closely resembles the United States ten-dollar bill, although the background resembles the series 2004 $20 bill (like their "million-dollar bill"). The tract contains a similar gospel message and features to the million-dollar tract, but the picture is instead that of 19th century Great Britain evangelist Charles Spurgeon, whose portrait obscures the last two zeros on the upper-left corner of the "bill". There have yet to be any repercussions from the Secret Service regarding this new tract.

In March 2006, agents from U.S. Immigration and Customs Enforcement and the United States Secret Service seized 250 counterfeit Federal Reserve notes, each bearing a denomination of $1,000,000,000 (one billion USD) from a West Hollywood, California apartment. The suspect had previously been arrested on federal charges for attempting to smuggle more than $37,000 in currency into the U.S. following a trip to Korea in 2002.

$8,000,000,000,000
An $8,000,000,000,000 (eight trillion USD) "Federal Deficit Note" was designed in 2005 by Alexander S. Peak to acknowledge what was at that time the United States public debt.

The president at that time, George W. Bush, appears in the center of the bill. Over the seal on the right-hand side of the bill appears the word BROKE. In place of the usual claim that a given bill is legal tender, this bill reads, "This note represents the legal tender stolen from you, the American people, and now constitutes the current federal debt, which increases every fiscal year."

In pop culture
In the film Superbad, a character has a daydream in which he buys liquor with an $80 bill. The Simpsons episode "The Trouble with Trillions" featured a one trillion dollar bill intended for reconstruction of post-war Europe. In the Netflix series Richie Rich in the intro scene the there's a picture of a trillion dollar bill with all of the main characters featured on it.

TWE dollars
In the 1970s, a fake bill dispensed in gumball machines had a denomination of "TWE DOLLARS". Much of the artwork was duplicated from the real twenty-dollar bill, including the portrait of Andrew Jackson, but the name "Jefferson" was printed under Jackson's picture. The country's name was printed as "The Untied States of Anemia." The "twe-dollar bill" has 3s in the corners.

-km 01:10, 24 March 2008 (UTC)

Another novelty fake bill showing ten hundred dollars as the denomination and 1,000 dollars on the corners with a portrait of Benjamin Franklin also uses the "United States of Anemia" for the country name.


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President of the United States/Quiz on The Washington Presidency 1789-1799

Back to America's First Five Presidents: 1789 to 1828

{Who was elected as George Washington's Vice President in 1789? - Thomas Jefferson - Benjamin Franklin - James Madison + John Adams - Alexander Hamilton
 * type=""}

{The great majority of representatives and senators in the first Congress were + Federalists. - Antifederalists.
 * type=""}

{George Washington was inaugurated in which city?
 * type=""}

- Philadelphia - Washington D.C. - Baltimore + New York


text:
User talk:FreshScholar

Welcome
Hello FreshScholar, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Jtneill - Talk 02:48, 24 March 2008 (UTC)

America's First Five Presidents: 1789 to 1828
Hello, welcome around here. I just saw you blanked the above page. Was there any problem ? Can we help ? Erkan Yilmaz uses the Wikiversity:Chat (try) 08:26, 24 March 2008 (UTC)

title: User talk:FreshScholar


text:
Least-Squares Method

Learning project summary

 * Project code: Least-Squares Method
 * School: Computer Science
 * Department: Scientific_Computing

Content summary
A brief introduction to Least-Squares method, and its statistic meaning.

Goals
This learning project offers learning activities and some application for Least-Squares Method. With this project, one should understand the intention of Least-Squares Method, and what it means. Moreover, one should be able to apply some simple Least-Squares methods to find a good approximation for any functions. For more mathematical explanation, one should visit the following page: "Least squares" to obtain more information.

Texts
[1] Numerical Mathematics and Computing Chapter 12.1

[2] Numerical Method for Engineers: With Software and Programming Applications Chapter 17.3

[3] Statistics for Management and Economics Chapter 17.1

[4] T.Strutz: Data Fitting and Uncertainty. A practical introduction to weighted least squares and beyond. 2nd edition, Springer Vieweg, 2016, ISBN 978-3-658-11455-8.

Lesson 1: Introduction to Least-Squares Method
The goal of Least-Squares Method is to find a good estimation of parameters that fit a function, f(x), of a set of data, $$x_1 ... x_n$$. The Least-Squares Method requires that the estimated function has to deviate as little as possible from f(x) in the sense of a 2-norm. Generally speaking, Least-Squares Method has two categories, linear and non-linear. We can also classify these methods further: ordinary least squares (OLS), weighted least squares (WLS), and alternating least squares (ALS) and partial least squares (PLS).

To fit a set of data best, the least-squares method minimizes the sum of squared residuals (it is also called the Sum of Squared Errors, SSE.)

$$ S=\sum_{i=1}^{i=n}r_i^2$$,

with, $$r_i$$, the residual, which is the difference between the actual points and the regression line, and is defined as

$$ r_i= y_i - f(x_i)$$

where the m data pairs are $$ (x_i, y_i)\! $$, and the model function is $$ f(x_i) $$.

At here, we can choose n different parameters for f(x), so that the approximated function can best fit the data set.

For example, in the right graph, R3 = Y3 - f(X3), and $$(R1^2 + R2^2 + R3^2+ R4^2 +R5^2)$$, the sum of the square of each red line's length, is what we want to minimize.

Lesson 2: Linear Least-Squares Method
Linear Least-Squares (LLS) Method assumes that the data set falls on a straight line. Therefore, $$ f(x) = ax + b $$, where a and b are constants. However, due to experimental error, some data might not be on the line exactly. There must be error (residual) between the estimated function and real data. Linear Least-Squares Method (or $$ l_2 $$ approximation) defined the best-fit function as the function that minimizes $$ S=\sum_{i=1}^{i=n}(y_i - (ax_i + b) )^2$$

The advantages of LLS:

1. If we assume that the errors have a normal probability distribution, then minimizing S gives us the best approximation of a and b.

2. We can easily use calculus to determine the approximated value of a and b.

To minimize S, the following conditions must be satisfied $$ \frac{\partial S}{\partial a}=0 $$, and $$ \frac{\partial S}{\partial b}=0 $$

Taking the partial derivatives, we obtain $$ \sum_{i=0}^{i=n}(2*((ax_i + b) - y_i))*x_i = 0$$, and $$ \sum_{i=0}^{i=n}(2*((ax_i + b) - y_i)) = 0$$.

This system actually consists of two simultaneous linear equations with two unknowns a and b. (These two equations are so-called normal equations.)

Based on the simple calculation on summation, we can easily find out that

$$ a = \frac{1}{c}*[(n+1)*\sum_{i=0}^{i=n}(x_i*y_i)-(\sum_{i=0}^{i=n}(x_i))(\sum_{i=0}^{i=n}(y_i))] $$ and $$ b = \frac{1}{c}*[(\sum_{i=0}^{i=n}((x_i)^2))*(\sum_{i=0}^{i=n}(y_i))-(\sum_{i=0}^{i=n}(x_i))(\sum_{i=0}^{i=n}(x_i*y_i))] $$

where

$$ c = (n+1)*(\sum_{i=0}^{i=n}((x_i)^2))-(\sum_{i=0}^{i=n}(x_i))^2 $$.

Thus, the best estimated function for data set $$ (i, y_i) $$, for i is an integer between [1, n], is

$$ y = ax + b $$, where $$ a = \frac{1}{n^3-n}*[12*\sum_{i=1}^{i=n}(i*y_i)-6*(n+1)(\sum_{i=1}^{i=n}(y_i))] $$ and $$ b = \frac{1}{n^2-n}*[(4*n+2)*\sum_{i=1}^{i=n}(y_i)-6*(\sum_{i=1}^{i=n}(i*y_i))] $$.

Lesson 3: Linear Least-Squares Method in matrix form
We can also represent estimated linear function in the following model: $$ y_i = a_1*x_1 + a_2*x_2 + ... + a_m*x_m + r $$. It can be also represented in the matrix form: $${Y}=[X]{A}+{R}$$, where [X] is a matrix containing coefficients that are derived from the data set (It might not be a square matrix based on the number of variables (m), and data point (n).); Vector $${Y}$$ contains the value of dependent variable, which is $$ {Y}^T = \begin{bmatrix} y_1 & y_2 & ... & y_n \end{bmatrix}$$; Vector $${A}$$ contains the unknown coefficients that we'd like to solve, which is $$ {A}^T = \begin{bmatrix} a_1 & a_2 & ... & a_m \end{bmatrix}$$; Vector {R} contains the residuals, which is $$ {R}^T = \begin{bmatrix} r_1 & r_2 & ... & r_n \end{bmatrix}$$.

To minimize $$ {R}^T $$, we follow the same method in lesson 2, obtaining partial derivative for each coefficient, and setting it equal zero. As a result, we have a system of normalized equations, and they can be represented in the following matrix form: $$ X]^T[X{A} = {[X]^T[Y]} $$.

To solve the system, we have many options, such as LU method, Cholesky method, inverse matrix, and Gauss-Seidel. (Generally, the equations might not result in diagonal dominated matrices, so Gauss-Seidel method is not recommended.)

182.56.115.73 (discuss) 11:40, 23 August 2014 (UTC)KB

Lesson 4: Least-Squares Method in statistical view
From equation $$ X]^T[X{A} = {[X]^T[Y]} $$, we can derive the following equation: $${A} = X]^T[X^{-1}{[X]^T{Y}}$$.

From this equation, we can determine not only the coefficients, but also the approximated values in statistic.

Using calculus, the following formulas for coefficients can be obtained:

$$a = $$$$S_{xy} \over{S^2_x} $$ and $$ b = \bar{y} - a*\bar{x} $$

where

$$ S_{xy} = $$$$\sum_{i=1}^{i=n}((x_i-\bar{x})*(y_i-\bar{y})) \over{n-1}$$

$$ S^2_x = $$$$\sum_{i=1}^{i=n}((x_i-\bar{x})^2) \over{n-1}$$

$$ \bar{x} = $$$$\sum_{i=1}^{i=n}(x_i) \over{n}$$

$$ \bar{y} = $$$$\sum_{i=1}^{i=n}(y_i) \over{n}$$.

Moreover, the diagonal values and non-diagonal values matrix $$X]^T[X^{-1}$$ represents variances and covariances of coefficient $$ a_i $$, respectively.

Assume the diagonal values of $$X]^T[X^{-1}$$ is $$ x_{i,i} $$ and the corresponding coefficient is $$ a_i $$, then

$$var(a_{i-1}) = x_{i,i}*s^2_{y/x}$$ and $$cov(a_{i-1}, a_{j-1}) = x_{i,j}*s^2_{y/x}$$

where $$ s_{y/x} $$ is called stand error of the estimate, and $$ s_{y/x} = \sqrt{S \over {n - 2}} $$.

(Here, lower index, y/x, means that the error of certain x is caused by the inaccurate approximation of corresponding y.)

We have many application on these two information. For example, we can derive the upper and lower bound of intercept and slope.

Assignments
To better understand the application of Least-Squares application, the first question will be solved by applying the LLS equations, and the second one will be solved by Matlab program.

Question1: Linear Least-Square Example
The following are 8 data points that shows the relationship between the number of fishermen and the amount of fish (in thousand pounds) they can catch a day.

According to this data set, what is the function between the number of fishermen and the amount of fish caught? hint: let the number of fisherman be x, and the amount of fish caught be y, and use LLS to find the coefficients.

Calculation
By the simple calculation and statistic knowledge, we can easily find out:
 * 1) $$\bar{X}$$ = 13
 * 2) $$\bar{Y}$$ = 20.625, and
 * 3) the following chart

Thus, we have $$\Sigma{(X-\bar{X})*(Y-\bar{Y})} = 412$$, and $$\Sigma{(X-\bar{X})^2} = 198$$, so the slope, a, = $$412 \over{198}$$$$ = 2.\bar{08}$$.

And last the intercept, b, = $$20.625-2.\bar{08}*13 = -6.4255$$.

Therefore, the linear least-squares line is $$Y = f(X) = -6.4255 + 2.\bar{08}*X$$.

Question2: Nonpolynomial example
We have the following data $$(x_i, y_i)$$, where $$ 1 \le i \le n$$, by a function of the form $$a*e^x+b*lnx+c*sinx+d*cosx$$.

Write a Matlab program that uses Least-Squares method to obtain the estimated function. hint: input the data in the matrix form, and solve the system to obtain the coefficients.

The blue spots are the data, the green spots are the estimated nonpolynomial function.

Active participants
Active participants in this Learning Group

 * ltseng3

title: Least-Squares Method


text:
Correlation

Correlation (co-relation) refers to the degree of relationship (or dependency) between two variables.

Linear correlation refers to straight-line relationships between two variables.

A correlation can range between -1 (perfect negative relationship) and +1 (perfect positive relationship), with 0 indicating no straight-line relationship.

The earliest known use of correlation was in the late 19th century.

Introduction
When we ask questions such as "Is X related to Y?", "Does X predict Y?", and "Does X account for Y"?, we are interested in measuring and better understanding the relationship between two variables.

Correlation measures the extent to which variables:
 * 1) covary
 * 2) depend on one another
 * 3) predict one another

The extent of correlation between two variables, by convention, is denoted r, and the correlation between variable X and variable Y is indicated by rXY.

Correlations are standardised to vary between -1 and +1, with 0 representing no relationship, -1 a perfect negative relationship, and +1 a perfect positive relationship.

A variety of bivariate correlational statistics are available, the choice of which depends on the variables' level of measurement:
 * Nominal by nominal: Contingency table, Pearson's chi-square test, Phi/Cramer's V
 * Ordinal by ordinal: Spearman's rho, Kendall's tau-b
 * Dichotomous by interval/ratio: Point biserial correlation coefficient
 * Interval/ration by interval/ratio: Pearson product-moment correlation coefficient

Correlational analyses should be accompanied by appropriate bivariate graphs, such as:
 * Nominal by nominal: Clustered bar charts
 * Ordinal by ordinal: Scatterplot (with point bins)
 * Interval/ratio by interval/ratio: Scatterplot

The world is made of covariation
Responses which vary can be measured as a variable (i.e., responses are distributed across a range).

Responses to two or more variables may covary. These variables share some variation. When the value of one variable is high, the value of other variable tends to be high (positive correlation) or low (negative correlation).

If you look around, you may notice that the world is made of covariation! e.g.,
 * pollen count is positively correlated with bee activity
 * rainfall is positively correlated with amount of vegetation
 * hours of study is positively correlated with test performance
 * number of fire trucks attending a fire is correlated with cost of repairs for the fire
 * Sibling's IQ is positively correlated
 * perceived air temperature is negatively correlated with amount of clothing worn

The more you look, the more you'll see that there are many predictable patterns of co-occurrence between phenomena (i.e., things tend to occur together).

Scatterplots
Independent variable (IV) (predictor) is placed on the X axis and dependent variable (DV) is placed on the Y axis. Each case is plotted according to its X and Y value.

Visual inspection of scatterplots is essential
It is unwise to rely solely on correlation as a statistic that indicates the nature of the relationship between variables without also examining a visualisation of the data such as through a scatterplot.

For example, the linear (straight-line) correlation in each of these four scatterplots is .82, yet the nature of what the data indicated about the relationship between the variables is very different for each.

Homoscedasticity
If the data are normally distributed, then scatterplots should be homoscedastistic (even spread about the line of best fit).

If data are not normally distributed (e.g., skewed), then the bivariate distribution may be heteroscedastic (uneven spread about the line of best fit). This violate the assumption of homoscedasticity for correlation.

For more information, see Homoscedasticity and Heteroscedasticity (Wikipedia).

Correlation does not equal causation
Correlation does not prove causation, although it may be consistent with causation. It is important to understand that correlation does not equal causation. A relationship between two variables may be caused by a third variable.

See Correlation does imply causation (Wikipedia)

Range restriction

For more info, see the effect of range restrictions (Howell, 2009) and restricted range (Lane, n. d.).

For a practical tutorial, see outliers and restricted range.

Coefficient of determination
When a correlation coefficient (r) is squared (r2), this gives the coefficient of determination which is the percentage of variance shared between the two variables.

 * See also
 * Lecture slide
 * References
 * Allen & Bennett, 2010, p. 173
 * Howell, 2010, p. 344
 * Coefficient of determination (Wikipedia)

Interactive activity
Correlation guess: Correlation guess

Quiz
Test yourself: This is a pre-quiz to see what you already know - Introductory quiz

title: Correlation


text: File:Telephoto 135mm lens example.jpg

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User talk:Ltseng3

Hello Ltseng3, welcome around here. Thanks for your contribution in the Statistics. Below more info about Wikiversity, Erkan Yilmaz uses the Wikiversity:Chat (try) 08:28, 24 March 2008 (UTC)

Welcome
Hello Ltseng3, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. To get started, you may
 * Take a guided tour and learn to edit;
 * Explore our learning projects;
 * Browse our portals, schools, and research activities;
 * Read and help develop our community policies；or
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold, and see you around Wikiversity! Erkan Yilmaz uses the Wikiversity:Chat (try) 08:28, 24 March 2008 (UTC)

title: User talk:Ltseng3


text: File:Noia 64 apps knode m.png

title: File:Noia 64 apps knode m.png


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Template:Global help

 * Creating educational content at Wikiversity (teacher-oriented tour).
 * Starting out at Wikiversity (student-oriented tour).

title: Template:Global help


text: Category:Help with portals

title: Category:Help with portals


text:
Help:Portal

This is the help page for building and maintaining portals.

Creating a new portal
There are a number of ready-made templates, the latest of which is this one: Template:Box portal skeleton 2. To use this template, create your new page and type in (or better, copy and paste in):

The remainder of this help page assumes you are using Template:Box portal skeleton 2.

What goes in a portal?
It is important to realise that portals can overlap with each other. A portal does not have an exclusive claim over its content. A portal primarily serves as an access aid or navigation aid, helping specific kinds of user find the content they are interested in.

Example: there are portals for both primary education and secondary education. However the age boundary between primary and secondary is anything but clear. If a child has 10 years of compulsory schooling, then usually the first four will be primary and the last four secondary, and the middle two will vary from one jurisdiction or schooling area to another. On top of that, certain topics may be common to both levels, such as some issues of teaching methodology.

Overlapping is therefore necessary and desirable, but this does not mean that a merger should take place.

This is different from real-world universities and institutions, where territory is valuable and claims to exclusivity are often made by superordinate bodies. In the real world, joint ventures between levels of educational administration are more the exception than the norm.

Updating featured content
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Updating the category system
You'l' see that the category system ("guide to..." box) does not have an edit link. That's because you can't edit the category tree directly. The tree is a dynamic record of the current structure of the category system linked to the portal. The only way to change it is to edit the categories themselves.

 * To move (or remove) any item, click on its page in the category tree, then edit the page's categories (and perhaps their parent categories). See: Help:Category.
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Adding more boxes
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 * 2) Change the title of the box. It can be hyperlinked with wiki syntax if required.
 * 3) Choose an icon and enter the name of the icon where it says.
 * 4) The content page for the content of the box must be specified, and you do this by changing the subpage name - in two places - once for the main inclusion, and once for the edit link.

If you need help with this, ask a custodian.

title: Help:Portal


text: Template:Atp/delimiter

title: Template:Atp/delimiter


text:
Microscope

Parts

 * Ocular lens - Magnifies the image form the objective lens 10x.
 * Body tube - Houses (holds) the lenses and maintains the proper distance between the ocular lens and the objective lens.
 * Coarse adjustment - Moves either the stage or body tube up and down for focusing on low power only (usually the larger of the 2 adjustment knobs).
 * Fine adjustment knob - Used to sharpen the image of the specimen under high and low power (usually the smaller of the 2 adjustment knobs).
 * Revolving nose piece - Spins to allow a change from one objective power to another.
 * Objective lenses - Magnify the specimen/object being viewed.
 * red: 4x yellow: 10x blue: 40x
 * Arm - supports the body tube and connects the body tube to the base.
 * Stage clip - (One of a pair of clamps) that hold the slide in place on the stage.
 * Stage - Platform upon which the slide is placed for viewing.
 * Diaphragm - regulates the amount of light that enters the body tube.
 * Light Sources - collects and sends light rays upward through the diaphragm, stage, specimen, and body tube so the specimen can be seen.
 * Base - Supports the microscope in an upright position.

Resources

 *
 * Optical microscope

title: Microscope


text: Category:Wikiversity vision

title: Category:Wikiversity vision


text:
Template:Major Portal Right Column

''This template forms the right column on many major portals and serves to link major portals in to critical global Wikiversity facilities such as navigation, tutorials and news. Editing this template could have massive implications all across Wikiversity.''

title: Template:Major Portal Right Column


text:
Category:BCP/Mid winter

Tracking:
 * 16 as of May 2, 2008, matched to Category:BCP/SEPA/2 only
 * Switched to Category:BCP/SEPA/1 7 December, 2008

title: Category:BCP/Mid winter