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User:Brent Holgate

=E-Portfolio=

What is Social Psychology?
It appears there is no definitive or agreed upon theory of Social Psychology. However, these four definitions are similar and define social psychology as how we (humans) react to and interpret external information:
 * 1) “Human Behaviour in a social context.” James Neill
 * 2) “Branch of psychology that seeks a broad understanding of how humans think, act and feel.” Baumeister and Bushman
 * 3) “Social psychology is the scientific study of how people's thoughts, feelings, and behaviors are influenced by the actual, imagined, or implied presence of others.” Floyd Allport
 * 4) “Theory that looks at human experience as a matter of interaction between the world and the self.” Dr. C. George Boeree (Social Psychology Basics: Shippensburg University. )

If this is the case it seems to me that social psychology is an incredibly broad area with many subcategories:

 * 1) Perception
 * 2) Prejudice
 * 3) Social Expectation
 * 4) Conformity
 * 5) Socio-biology
 * 6) Aggression
 * 7) Relationships
 * 8) Attraction
 * 9) Emotion
 * 10) Self
 * 11) Persuasion
 * 12) Social Learning
 * 13) Self-defence
 * 14) Obedience
 * 15) Beliefs

With such a diverse range of topics, if you’re a student wishing to pursue a career in psychology, there must be an area of social psychology that grabs your attention.

Suburban living
I’ve had a long interest in suburban living -- especially being a Canberrian which is essentially one giant suburb -- and the psychology behind it. By studying the above topics throughout the semester I hope to understand the workings of suburbia in greater detail.

Why do the new housing developments in Gungahlin, Harrison, Franklin, Casey look almost identical? (Conformity? Social Expectation?)

Why does my neighbour mow his lawn, immediately after I’ve finished mowing mine? (Social influence? Competitiveness? He has gone bonkers?)

Why do the majority of people living on my street drive four-wheel drives, and why do they park in their drive-way and not in their garage? (Fear of exclusion? Attraction? Social acceptance?)

Why do women still do the majority of house-work? (Beliefs? Gender role?)

Why do people living next to one another have contrasting beliefs (atheism vs. Christianity)? (Attitudes? Beliefs?)

These are just some of the questions that sprung to mind. I’ll leave my first portfolio entry with a suburban quote from Historians Rosalyn Baxandall and Elizabeth Ewen:

What is Communication?
What I learnt from the tutorial this week is that communication has many levels. On one level we deal with the process that takes place cognitively inside our head, at another level we deal with the complexities of the social and cultural principles in which the communication is happening. Beyond that we have levels of unpredictability. Overall, we are using communication which is based upon a code, system or language.

Johari Window Model of Communication
The Johari window model is best described as the model of communication that illustrates the factors, conscious and unconscious, present when humans interact. Joharie’s window divides personal awareness evenly into four sections: open public self, private self, blind self and unknown self. The axis that divides the window moves as the interaction unfolds. The preferred or most comfortable composition of the axis depends on the individual or situation. It is natural for the individual to push or persuade the interaction towards their most comfortable composition.

Shannon Weaver Model of Communication
The Shannon Weaver model was one of the first models of communication to be developed, around 1946. Its goal was to describe the phenomena of communication that occurred within a telephonic system. The model was later applied to human communication as a way of attempting to describe the elements and steps that occur within a communication event. One of the flaws or problems with the Shannon weaver model is that it is mechanistic by nature and linear.

Woody Allen’s ‘Sleeper’ and the Johari / Shannon Weaver Model of Communication
Sleeper’s premise has Miles (Woody Allan) a clarinet playing health food store operator from Greenwich Village New York awaking after being cryogenically frozen in 1973. Miles went into hospital for a regular operation and awakes 200 years later to find the future a hilariously sterile and mechanistic place where everybody wears white.

The scene I have looked at involves Miles’s interrogation by a Doctor from the year 2173. The doctor tries to retrieve information from 20th century artefacts that he has collected.

I’ve analyzed this piece of communication against the Johari window which deals with the conscious and unconscious and the Shannon Weaver model which gives no consideration to the unconscious.

This scene can be viewed at:

According to the Johari window
Miles expanded on his conscious panes throughout the communication. He continued to expand his consciousness by capitalizing on the Doctors large window of unconsciousness. This may have been a result of Miles preferred composition of the axis, therefore the effect of his communication on the Doctor was unintentional. If Miles was deliberately sending his answers into the Doctors unconsciousness, he was aware of the effect it had on the communication and wanted for it to continue in that direction.

According to the Shannon Weaver Model
Miles is the sender the Doctor is the receiver. Miles chooses to encode his message using language. A percentage of the language Miles uses is unfamiliar to the Doctor, this language becomes noise. Miles uses visual aids provided by the Doctor, the artefacts, and selects speech as his channel. Miles’ message is the social commentary he gives on each artefact. The Doctor fails to decode Miles’s message because he doesn’t understand satire, sarcasm, or humour, as a result these themes are also represented by noise. The feedback given by the Doctor is little and when he does contribute feedback its evident he’s failing to construct meaning from Miles’s answers. The Shannon Weaver model states that feedback should modify the communication process. However, Miles consciously or unconsciously rejects the feedback and continues communicating un-decoded information.

Racism

I finally recovered from ‘Ghosts of Rwanda’ and thought it would be appropriate to investigate racism. I read several journal articles over the weekend regarding the nature of racism and how growing anti-racist discourses have taken shape within societies aimed at refuting external and internal forms of racism and discrimination.

It appears, the nature of racism is influenced by historical, social, political and economic factors and is often the consequence of individual attitudes, institutional procedures and policies and socio-economic structures and values. Historically, these forms of racism reflected a biological ideology whereby certain individuals were deemed superior as a result of their natural genetic attributes, and as a result, racist attitudes were expressed through unequal power relationships, subsequently marginalizing those from minority groups. That description appears fitting after watching ‘Ghosts of Rwanda’ or for anyone who has seen films that depict the holocaust or slavery in South Africa.

Racism is not only a social problem that must be refuted by initiating campaigns against exclusion and exploitation but also a cultural problem of challenging ignorance and nationalist attitudes. As a result there has been the emergence of an anti-racism movement, which became specific in actions, policies and beliefs implemented to defy all approaches to racism. I was aware such actions existed however, not to the extent that I will demonstrate.

Anti-Racism

Within contemporary society, anti-racism has developed into symbolising and referring to the outlawing of explicit acts of direct discrimination, opposition to extremist racist, anti-Semitic, fascist and ultra-nationalist groups, the protection of minorities against the oppression of racial harassment and violence, and opposition to exclusionary immigration nationality and asylum policies.

Hand in Hand (HiH)

One country that has initiated anti-racist strategies through both public policy and civil society in response to increasing forms of racism has been Belgium. The anti-racist movement Hand in Hand (HiH) initiated and developed a range of strategies to counter the success of the racist extreme right-wing party Vlaams Blok. In the 1991 elections, Vlaams Blok gained substantial support for their extreme nationalist programme whereby they advocated and upheld beliefs of national insecurity caused by Non-European immigrants and contended that the deterioration of cultural values was also as a result of Non-European immigrants. Subsequently they enlarged the issue of immigration into a major problem within Belgium society that focused and targeted Muslims as the sole problem. They successfully promoted this programme with ultra-conservative, authoritarian, and anti-democratic principles. Accordingly HiH launched a broad range of strategies not only to counter the right-extremist ideas of Vlaams Blok and limit their political power but also to challenge the issue of everyday racism.

UNESCO
One prominent example of mainstream anti-racism has been the discourse of the United Nations Educational, Scientific and Cultural Organization (UNESCO). UNESCO throughout its history has aimed at promoting peace and cultural diversity through international, regional and local programmes. They have remained concerned with racism and constantly endeavoured to uphold anti-racist practices and strategies. As a result, their founding principles of anti-racism have influenced the current mainstream anti-racist discourse and been subscribed to by many European governments and also international and anti-racist organizations.

Journal Articles
Lentin, A. (2006) Racism, anti-racism and the western state. Retrieved 31 August 2008 from http://www.alanalentin.net/index.writings.php

Lerner, N. (1981), New concepts in the UNESCO Declaration on race and racial prejudice, Human Rights Quarterly, Vol.3, No. 1, pp.48-61

Lentin, A. (2000), ‘Race’, racism and anti-racism: Challenging contemporary classifications, Social Identities, Vol. 6, No. 1. pp. 91-105

Films depicting Racism

Below are some of my favourite films that illustrate racism:

Schindler's List, 12 Angry Men, American History X, Hotel Rwanda, Crash, This Is England, Rabbit-Proof Fence, Far from Heaven, The Believer and Fond Kiss..., Ae.

Relationships

Annie Hall

I watched Woody Allen’s ‘Annie Hall’ for the 6539th time this week and thought I would illustrate some of the social psychology characteristics that appear in the film – focusing primarily on relationships.

For the those of you who haven’t seen the film (I recommended stop reading this blog, drive down to blockbuster and hire it immediately) Annie Hall follows Alvy (Woody Allen) as he searches for the secret to successful relationships and ultimately concludes that love is fleeting and ridiculous but absolutely necessary.

Alvy Singer Monologue
The film opens with Woody doing a monologue.

ALVY: “There's an old joke. Uh, two elderly women are at a Catskills mountain resort, and one of 'em says: "Boy, the food at this place is really terrible." The other one says, "Yeah, I know, and such ... small portions." Well, that's essentially how I feel about life. Full of loneliness and misery and suffering and unhappiness, and it's all over much too quickly. The-the other important joke for me is one that's, uh, usually attributed to Groucho Marx, but I think it appears originally in Freud's wit and its relation to the unconscious. And it goes like this-I'm paraphrasing: Uh ... "I would never wanna belong to any club that would have someone like me for a member." That's the key joke of my adult life in terms of my relationships with women. Tsch, you know, lately the strangest things have been going through my mind, 'cause I turned forty, tsch, and I guess I'm going through a life crisis or something, I don't know. I, uh... and I'm not worried about aging. I'm not one o' those characters, you know. Although I'm balding slightly on top, that's about the worst you can say about me. I, uh, I think I'm gonna get better as I get older, you know? I think I'm gonna be the-the balding virile type, you know, as opposed to say the, uh, distinguished gray, for instance, you know? 'Less I'm neither o' those two. Unless I'm one o' those guys with saliva dribbling out of his mouth who wanders into a cafeteria with a shopping bag screaming about socialism. Annie and I broke up and I-I still can't get my mind around that. You know, I-I keep sifting the pieces of the relationship through my mind and-and examining my life and tryin' to figure out where did the screw-up come, you know, and a year ago we were... tsch, in love. You know, and-and-and ... And it's funny, I'm not-I'm not a morose type. I'm not a depressive character. I-I-I, uh, you know, I was a reasonably happy kid, I guess. I was brought up in Brooklyn during World War II.”

Social Psychology in Annie Hall

Throughout this monologue Woody touches on numerous terms that are applied in social psychology. Social acceptance – being included in a group, need to belong – the desire to form and maintain lasting relationships, self-monitoring – ability to modify ones behaviour for different situations, loneliness – wanting more human contact and connection (one of my favourite writers Paul Schrader believes true loneliness can only be felt in crowded situations and the great American novelist Thomas Wolfe says that the whole conviction of his life rests upon the belief that loneliness, far from being a rare a curious phenomenon, is a central and inevitable fact of human existence), bad apple effect – the idea that if a person breaks the rules, other people can break the rules also, unrequited love – loving someone who does not feel the same way, passionate love – feelings of longing and desire toward someone and self acceptance – regarding yourself as a decent person.

Is Love absurd or necessary?
According to Baumeister and Bushman (2008) there are two kinds of love that have emerged, passionate love and companionate love – a mutual understanding and caring to make the relationship succeed. However, Annie Hall states that a third type of love should employed, Absurdity love. Alvy resigns to the idea that relationships are absurd but that people need them, absurdity and all. He desperately tries to pinpoint what went wrong and why he broke up with Annie. Was it that book he read at age nine? His aggressive and overbearing mother? The cocaine fiasco? After coming up empty, he even asks anonymous pedestrians to identify the key to happiness in relationships. The answers, of course, are unsatisfactory and belie the arbitrariness and absurdity of love. Alvy’s relationships with his two ex-wives also underline the dilemma. How can he now feel so underwhelmed by both of these women whom he once vowed to love until death? But despite its eagerness to point out these paradoxes, the film ends by celebrating the romance between Annie and Alvy, though failed, adding weight to Alvy’s final monologue about the necessity of relationships.

Alvy Singer on Love

“It’s never something you do. That’s how people are. Love fades.”

A passerby makes this response to Alvy’s question, “Somewhere, she cooled off to me. Is it something that I did?” The pedestrian’s answer encourages Alvy to face reality and chalk up the relationship’s end as natural and inevitable. The comment suggests that we, as human beings, are helpless to control what happens to us. Alvy has nothing to do with the cooling of Annie’s feelings for him: she has just moved on to a different stage in her life that doesn’t include him. People change. Love fades. This idea doesn’t make things any easier for Alvy, who seems to want to pinpoint the exact moment and situation in which Annie’s feelings tempered. In a sense, it makes things worse, because he is left at a loss, with no one and nothing to blame for his unhappiness.

Tutorial 5
This week I thought I would elaborate on a couple of the terms that we discussed in last week’s tutorial. I will also illustrate some personal experiences concerning the terms.

National Identity
National Identity seems to be problematic. It is sometimes defined as the “imagined community of a nation” (Healey, 2006 p 41). The word ‘imagined’ suggests that identity is held to be an individual’s concept of place relative to others. Employing this theory each of us could describe ourselves with a multitude of different identities. Therefore, no single definition of ‘national identity’ can exist. Sara Cousins (2006, cited in Healey) argues that national identity is shaped by society’s contemporary ideologies with political and social ideology impacting views on morality, character, race, values and religion. That seems fair and rational. So what is an Australian National Identity? When I was backpacking around South America, Australian’s were generally thought of as being a relaxed group of people that share egalitarian, classlessness, and mateship like qualities. I would say that is a pretty good description. I was often asked why Australians are so pre-occupied with sport and leisure activities – my answer was always the same, “that’s the Australian way of life.’ If we attempt to describe an Australian national identity our convict origins, indigenous people, the gold rush era, our unique landscape etc must be considered.

Healey, J. (2006). Australian Identity and Values: issues in society Volume 230 (pp. 15-44). NSW, Australia: The Spinney Press.

Culture Shock
Dr. Carmen Guanipa from the University of San Diego believes culture shock is an emotional and physical uneasiness one suffers when living in a foreign environment. Guanipa claims culture shock originates when the way a person lived before is not accepted or considered normal in the new environment. In addition, Guanipa proposes that culture shock has five stages. The first stage, incubation, is a pleasure one feels as everything one encounters is novel and stimulating. The second stage involves one having problems concerning daily life. This could be seen as having language barriers, trouble understanding local customs and transportation difficulties. Stage three involves some understanding of the new culture. This may provide one with confidence and mental stability as they may no longer feel lost and inadequate. In the fourth stage, one comes to understand the culture they are living in with greater clarity which promotes the individual to establish goals for living. Guanipa calls the final stage ‘re-entry shock’ whereby an individual returns to their country of origin and finds that their original way of living is different as they have acquired new customs. Guanipa makes some interesting points however I believe one can experience her definition of culture shock without having to live or settle down in a foreign environment. When I backpacked around Israel for six weeks I definitely felt myself moving through the five stages. Perhaps when I arrived home I didn’t experience ‘re-entry shock’ as Guanipa describes however it did take a little time to form a routine and adjust to your original customs. Guanipa’s webpage can be found at http://edweb.sdsu.edu/people/CGuanipa/cultshok.htm.

Tutorial 6
As I have been flicking through the textbook in preparation for the exam, I stumbled across a few terms and concepts that we have discussed throughout the semester that appealed to me. The following explanations are my insights and 2 cents worth on the various ideas and terms.

Positive Psychology
Positive psychology is a branch of social psychology that I have no qualms endorsing. To quote Martin Seligman (the founder of positive psychology), “you feel the way you think” and I absolutely concur with that notion. The majority of psychology that I have studied over the past three years has focused much of its attention on negative states and I find it refreshing that some psychologists are dedicated to studying happiness, health, optimism and thriving. It is surprising that ‘positive psychology’ took so long to come to fruition. After hearing about Seligman’s work in, I think it was, the second lecture from James, I read ‘Learned Helplessness: A Theory for the Age of Personal Control’ and found it quite liberating. In a nutshell the main theory the book proposes is that an experience with uncontrollable events may give rise to the expectation that events in the future will also escape control which may lead to an interruption in motivation, emotion, and learning. In other words, if Joe fails his maths test he may attach that failure to other aspects of his life that are totally unrelated which may be detrimental to Joe’s health. This makes total sense to me. I find it incredibly interesting when the Sydney swans lose and Dad is livid for hours afterwards no matter what he is doing. This is an example of spreading bad news to every facet of your life.

Prejudice and Race
I’m still blown away that only 60 years ago Hitler killed 6 million Jews and in North America black Americans were granted permission to work in McDonalds in only the early 1970’s. Baumeister and Bushman describe prejudice as a “negative feeling toward an individual based solely on his or her membership in a particular group.” In the case described above it appears the term group translates to race. I have never understood discriminating against another group (racial) as we are told time and time again that race is a socially constructed notion and not based on any biological evidence. I read an article the other day (Smedley and Smedley, 2005) that claimed race was a term used in the 18th century as a form of classification, similar to and exchangeable with other terms like type, kind, and sort. If both statements are true they comply with the out of Africa hypothesis that those humans of whom we are descendants all lived in Africa. Disregarding any religious and spiritual mumbo jumbo about the origin of man there is evidence that our descendants first appeared 200,000 years ago and began to migrate out of Africa to all other continents, excluding Antarctica, 100,000 years later. Then as people began to migrate, they adapted to their new environment through cultural changes and genetic modification. This supports the idea that our ancestors in Africa were dark-skinned as dark skin provided superior protection against the environment. As our ancestors began to migrate to other continents, where the environment was vastly different and ultraviolet radiation was not as harmful, light pigmentation started to evolve resulting in lighter skin colour. As a result the difference in skin colour is suggested to be the result of migration and the means by which the environment contributed to changes in genetic evolution. If this is the case the concept of race serves as a social and not a biological purpose and suggests that only one human group exists. Why then does racial hatred exist in every corner of the world?

Smedley, A., & Smedley, B. (2005). Race as Biology Is Fiction, Racism as a Social Problem Is Real: Anthropological and Historical Perspectives on the Social Construction of Race. American Psychologist, 60,16-26.

Mere Exposure Effect
A term I found interesting in the final lecture last week was the ‘Mere Exposure Effect.’ James said it is basically a term that is used to describe the notion that the more we are exposed to something the more we come to like it. I assume this applies equally to both objects and people. In addition, Baumeister and Bushman describe it as people having more positive attitudes toward familiar stimuli than toward novel, unfamiliar ones and thus merely seeing or encountering something on a regular basis increases liking. The great idea regarding this theory is there are benefits to simply hanging around or being near someone that you're attracted to. For example, if you have a class with a girl you like or work with someone you like, these exposures are working to your advantage. When that special someone saw you for the first time, they may have thought you were a 4 or 5 out of 10 – which is a score that probably won’t get you a date. However, after seeing you at school or at work repeatedly over a reasonable length of time they might now rate you as 8 or 9 and you haven’t lifted a finger. How good is that?

However, I have a few qualms concerning this theory? The fact that people might have more positive attitudes toward familiar stimuli than non-familiar stimuli appears, to me, to be common sense. Of course, the more you’re exposed to something/situation, the more you will be comfortable in that situation. Is their evidence that this theory predicts longevity in romantic relationships or friendships? Does this theory originate because the more one encounters something, whether it is favourable or not, it is preferred by that person to look positive towards that stimuli because you encounter it so often. Thus it would be miserable to look unfavourable or negative at things we encounter often – basically it would make life incredibly hard.

Another problem I have with this theory is I believe the opposite can happen. When I was in South America I fell in love with every Brazilian woman I met. As it happens I had never met a Brazilian woman before the trip. Being deprived of such beauty for so long only increased my attraction. That’s why I mentioned earlier if there is evidence that this theory predicts longevity in romantic relationships or friendships. While it wasn’t practical to have a long term relationship with someone from that part of the world at that time I felt just as attracted to a women I had never seen before as to someone I had seen on a regular basis.

While I understand there are many variables to consider when it comes to relationships and attraction I’m not sure you can consider the mere exposure effect theory thorough without contemplating the preceding examples.

title: User:Brent Holgate


text:
User talk:Aisha~enwikiversity

hello

Your account will be renamed
Hello,

The developer team at Wikimedia is making some changes to how accounts work, as part of our on-going efforts to provide new and better tools for our users like cross-wiki notifications. These changes will mean you have the same account name everywhere. This will let us give you new features that will help you edit and discuss better, and allow more flexible user permissions for tools. One of the side-effects of this is that user accounts will now have to be unique across all 900 Wikimedia wikis. See the announcement for more information.

Unfortunately, your account clashes with another account also called Aisha. To make sure that both of you can use all Wikimedia projects in future, we have reserved the name Aisha~enwikiversity that only you will have. If you like it, you don't have to do anything. If you do not like it, you can pick out a different name.

Your account will still work as before, and you will be credited for all your edits made so far, but you will have to use the new account name when you log in.

Sorry for the inconvenience.

Yours, Keegan Peterzell Community Liaison, Wikimedia Foundation 23:26, 17 March 2015 (UTC)

Renamed
 This account has been renamed as part of single-user login finalisation. If you own this account you can |log in using your previous username and password for more information. If you do not like this account's new name, you can choose your own using this form after logging in: . -- Keegan (WMF) (talk) 06:02, 19 April 2015 (UTC)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: User talk:Aisha~enwikiversity


text:
User talk:Brent Holgate

Well done on setting up!
Well done, Brent, on getting your account set-up! Some official welcome to Wikiversity below - and a random mascot :) Let me know if I can do anything. -- Jtneill - Talk - c 04:35, 23 July 2008 (UTC)

 Hello Brent Holgate, 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.
 * Visit a (kind of) random random project.
 * Browse everything on Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! Jtneill - Talk - c 04:35, 23 July 2008 (UTC)

 Hi! My name is WikiversityJack. I am a pumpkin and a Wikiversity mascot. My aunt told me that fun + learning = Wikiversity. Let me know if I can help you out.

Thanks for bringing a smile on my face
>He has gone bonkers? This special sentence brought a smile on my face. Probably because of the prevoius 2 sentences and then not expecting this one. I just started reading some pages of the other participants and laughed here good (not a bad laugh, but a good laugh) - please continue like this (but keep in mind I don't give the grades - what James might think ?). Erkan Yilmaz uses the Chat (try) 10:15, 2 August 2008 (UTC)

Using images
Hi Brent, Great to see you experimenting with use of images. Please feel encouraged to do this. However, I need to share with you a bit more about image licensing. You might notice that when you edit there is a message that says "Any copyrighted works not licensed under the GFDL will be deleted." So, basically, we need to work with images which are in the public domain or which are licensed under free to use licenses such as GFDL. I've deleted some of the images you used which were clearly not copyrighted under such licenses. For self-created images, you simply need to add an appropriate license - see the options at License tags - one of these should be applied to: Image:Johari-window of Miles.jpg (also note, for this image that you may wish to correct spelling of privet -> private and re-upload). For the deleted images, I've tried to find equivalent or similar images which can be used from Wiki Commons and added them to your page - you may or may not to use these - up to you. Wiki commons is usually the first place I search when looking for images - if there's something useful there, it is usually the fastest way. Hope this helps. Let me know if you have any questions or I can do anything, since there is a bit of learning curve :). Below is a default notification message re licensing of Johari-window of Miles.jpg -- Jtneill - Talk - c 19:25, 15 August 2008 (UTC)

Image copyright problem with Image:Johari-window of Miles.jpg
Thanks for uploading Image:Johari-window of Miles.jpg. However, the image may soon be deleted unless we can determine the copyright holder and copyright status. The Wikimedia Foundation is very careful about the images included in Wikiversity because of copyright law (see Wikiversity' Copyright policy).

The copyright holder is usually the creator, the creator's employer, or the last person who was transferred ownership rights. Copyright information on images is signified using copyright templates. The three basic license types on Wikiversity are open content, public domain, and fair use. Find the appropriate template in Image copyright tags and place it on the image page like this:.

Please signify the copyright information on any other images you have uploaded or will upload. Remember that images without this important information or giving false information can be deleted by an administrator. If you have any questions, feel free to contact me, or ask them at Colloquim. Thank you. -- Jtneill - Talk - c 19:25, 15 August 2008 (UTC)

Tutorial 3
Hi Brent, Thanks for posting your tutorial 3 notes (and sharing with the discussion list). By way of a little bit of feedback, here's a few things that come to mind: Hope this helps, Sincerely, James. -- Jtneill - Talk - c 01:25, 2 September 2008 (UTC)
 * 1) It's impressive to see your independent reading/thinking/writing and particularly your delving into journal articles and referencing of them. To make this part even stronger, you might consider:
 * 2) * Why the choice of these three journal articles (by this particular author)?
 * 3) * You might cite these articles directly, where relevant (i.e., place the Author, Year(s) in brackets after statements which are based on a specific source)
 * 4) Creative use of Wiki Commons images and image sizing (well done); to extend this a little further, you may consider adding image captions to explain their relevance (e.g., see the suburbs image for how to do this)
 * 5) Did you have any reactions to the The Australian Eye, Ghosts of Rwanda, and/or their illustration of Social psychology (psychology)/Tutorials/Prejudice, aggression, and prosocial behaviour keywords from the textbook?
 * 6) Well done on sharing your e-portfolio activity with the discussion list. You mentioned wiki(impossible to use)versity - a contrast to my perception that you seemed to have it worked out :). So, what else do you think could help? Feel free to ask here or via email. The idea is that if students can just "dump" their text-based reflections, that there are plenty of others to help out and 'coach' in wikiversity ways. You've dropped a couple of humorous hints though about the difficulty-level, so I'm paying attention, but please feel free to be more direct and specific.

Hello!
Hey Brent, Just been looking through your portfolio and its fantastic. I noticed one of your favourite movies on rascism is Crash and given I had nothing better to do on the holidays I watched it and it is awesome. It made me so angry and sad at the same time! Keep up the good work, MandaG 05:33, 16 September 2008 (UTC)MandaG

Annie Hall
Hey Brent, I love this movie too! Good work on tying it in to the course work. However, basing our ideas of love and relationships on Woody Allen is probably very unhealthy (this is a man who is married to his adoptive daughter after all!). Your page is very entertaining. Keep up the good work.Bryony 00:35, 1 October 2008 (UTC)
 * Bryony, I had forgotten that interesting aspect of Woody Allen's own relationships. Perhaps, then, his exploration of a third type of 'love' (absurdity love) which Brent has mentioned (to help come to grips with relationships that seem to 'defy' social norms?) As I mentioned to Brent in a brief f2f discussion a couple of weeks, I am way out of date on Woody Allen movies. But Brent you are bringing forth interesting examples of the way that Allen plays at the edge of social assumptions/boundaries, offering much potential for socio-psychological insight. Fawlty Towers also comes to mind in this respect, as an entertaining collection of social faux pas. -- Jtneill - Talk - c 01:26, 1 October 2008 (UTC)

title: User talk:Brent Holgate


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Template:Percy

Hi , I am Percy. Nice to meet you. I love the beach, swimming, fishing, and I am currently doing some research at Wikiversity on oceanography. I am also learning to play piano. What do you like? Can I be your mascot?

title: Template:Percy


text:
Open marriage

Open marriage - what shortfall(s) in traditional marriage does this practice try to overcome or eliminate? What are the pitfalls of an open marriage?

title: Open marriage


text: Category:Relationships (interpersonal)

title: Category:Relationships (interpersonal)


text: Advanced ANOVA

title: Advanced ANOVA


text: User:Kstoker

title: User:Kstoker


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User talk:Kstoker

Well done on setting up!
Hi Kstoker, well done on getting your account set-up! Some official welcome to Wikiversity below - and a random mascot :) Let me know if I can do anything. -- Jtneill - Talk - c 11:49, 23 July 2008 (UTC)

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e-Reserve
Thanks for letting me know about e-Reserve not being available - its fixed now - take a look - via Social psychology (psychology)/Readings. -- Jtneill - Talk - c 00:08, 30 July 2008 (UTC)

title: User talk:Kstoker


text: Statistical software

title: Statistical software


text:
Testing differences

In statistics, there is often interest in comparing the data for two or more variables in order to help determine whether there are 'real' mean differences, and the size of any differences.

Such questions can be tackled using t-tests where the dependent variables are reasonably normal and with similar variances. Alternatively, several non-parametric statistics can be applied when such assumptions cannot be met.

Considerations
If you have access to the population data, then a descriptive approach could well be sufficient for meaningfully reporting on the data.

However, if you only have a sample, then inferential statistics can help in drawing conclusions about possible differences in the target population.

title: Testing differences


text:
Social entrepreneurship/Developing a Social Entrepreneurial Idea

Ideas come from many angles ... community-needs-driven, technology-inspired, business opportunities, ....

Anticipated Learning Outcomes

 * Discover options for finding or generating social entrepreneurial ideas.
 * Generating questions
 * Learn about techniques for idea elicitation and development.
 * An idea for your project.

Learn More

 * Learning Activities


text:
BCP/Mentha arvensis

Mentha arvensis

title: BCP/Mentha arvensis


text:
BCP/Mentha arvensis/Logs

2008 Logs

 * Jomegat 15:05, 23 July 2008 (UTC)
 * Jomegat 04:28, 1 August 2008 (UTC)
 * Jomegat 20:30, 9 August 2008 (UTC)
 * Jomegat 01:18, 17 August 2008 (UTC)
 * Jomegat 23:57, 23 August 2008 (UTC)
 * Jomegat 01:00, 31 August 2008 (UTC)

2009 Logs

 * Jomegat 03:54, 2 August 2009 (UTC)

Recent Logs

 * Jomegat 20:36, 10 July 2010 (UTC)

title: BCP/Mentha arvensis/Logs


text:
Introduction to Latin/Apposition

Apposition
An appositive is a noun explaining or defining another noun. In Latin, an appositive will have the same case, usually the same number, and usually the same gender as the noun it explains or defines.

A few examples:

Cicero consul saepe declamabat. - Cicero, the consul, often made speeches. (consul is in apposition with Cicero)

Amo Lesbiam, puellam meam. I love Lesbia, my girl. (puellam meam is in apposition with Lesbiam)

Read This
The following link links to a short piece written by a Latin professor from Middlebury College. It is written for the beginning student and addresses how one should approach learning Latin as a foreign language. It is an interesting read, and I hope that you consider the validity in learning how to 'think like a Roman.'

Beginning Latin

title: Introduction to Latin/Apposition


text:
Advanced ANOVA/Testing differences

This tutorial examines inferential techniques for 'testing differences' between the means for: Practical exercises are based on using SPSS.
 * 1) a single variable across two independent groups,
 * 2) two related variables, and
 * 3) one sample mean compared to a fixed value.

Types
There are three types of t-test"

1-sample t-test

 * Compares a sample mean with a known population mean
 * Non-parametric equivalent is the chi-square goodness-of-fit test

Within-subjects t-test (also dependent samples or paired sample "t"-test

 * Compares two means that are repeated measures for the same participants
 * Compares two means between matched samples
 * Compares two treatments across blocks
 * Non-parametric equivalent is the Wilcoxon t-test

Between-subjects t-test

 * Compares two means for independent groups
 * Non-parametric equivalents are Mann-Whitney U and chi-square test for two independent samples (this can be used for nominal, interval, or ratio data)

Variance

 * Within-group variance = individual differences + measurement error
 * Between-group variance = individual differences + measurement error + treatment effect

Questions

 * 1) What are the three types of t-test and when would you use each of them?
 * 2) What are the assumptions of t-tests?
 * 3) What are the non-parametric alternatives and when would you use each of them?
 * 4) What graphical techniques could accompany the different ways of testing differences?
 * 5) What measures of effect size are available for measuring differences?
 * 6) What should be included in a results section write-up for analyses which involve testing differences?
 * 7) What results might be derived from graphic displays for two dependent sample comparisons that could alter questions or comparisons?
 * 8) What information (e.g. comparing counts) might lead to non-linear transformations of the data used for comparison?

Exercises
Using the LEQ dataset, provide analyses which demonstrate use of the each of the types of parametric and non-parametric tests of differences, including:
 * 1) Assumption testing
 * 2) Graphing
 * 3) Descriptives
 * 4) Inferential analyses of differences
 * 5) Effect sizes
 * 6) APA style write-up

Tips

 * See SPSS tips
 * See Thesis tips

Readings/References

 * 1) Diekhoff Ch 6 and 7
 * 2) Howell, D. C. (2002). Statistical methods for psychology. (5th ed.). Pacific Grove CA: Duxbury. Chapter 7.
 * 3) Pruzek, R. M. and Helmreich, J. (2009) Enhancing dependent sample analyses with graphics, J. of Statistics Education

title: Advanced ANOVA/Testing differences


text:
Advanced ANOVA/One-way ANOVA

This tutorial teaches use of one-way ANOVA, a statistical technique for testing mean differences betweeen three or more independent groups on a single dependent variable. Practical exercises are based on using SPSS.

Purpose

 * Assesses the statistical significance of differences between three or more group means for a single dependent variable
 * Extension of a t-test
 * Use one-way ANOVA in preference to multiple pairwise comparisons (t-tests) because:
 * Computationally easier
 * Limits the probability of type I and type II errors.
 * With multiple comparisons, if they are all independent (which is unlikley) in a series of 100 tests we would expect to get five Type I errors with a .05 level of significance
 * By simultaneously computing all possible comparisons in a single significance test, the ANOVA avoids these inflated error rates
 * However, use of one-way ANOVA limits error rates at the expense of specificity - statistic tells us that there is a significant difference somewhere among the sample means, but does not tell us which means differ significantly (have to use post-hoc and a priori comparison procedures)

Examples

 * Experimental study: Examine reaction time under different levels of alcohol consumption by randomly assigning participants to four conditions (none, low, medium, and high alcohol)
 * Quasi-experimental study: Examine whether students with behaviour problems behave better in classrooms where teachers have a humanistic philosophy and have control of their classrooms. Classify teachers as (1) humanists with control, (2) strict disciplinarians, and (3) laissez-faire.

General steps

 * 1) Establish hypothesis/hypotheses
 * 2) Examine assumptions - If assumptions are not met, use the Kruskal-Wallis non-parametric procedure
 * 3) Examine descriptive statistics, particularly the four moments (M, SD, Skewness, Kurtosis) overall, and also for each group
 * 4) Examine graphs, e.g.,:
 * 5) * Histograms
 * 6) * Normal probability plot
 * 7) * Error-bar graph
 * 8) Conduct inferential test (ANOVA) and interpret significance of F
 * 9) Conduct follow-up tests (planned contrasts or post-hoc tests) if F is significant
 * 10) Calculate and interpret effect sizes
 * 11) * Eta-square (omnibus - equivalent to R2)
 * 12) * Standardised mean effect size (difference b/w two means) - e.g., Cohen's d

Visual ANOVA

 * Understanding ANOVA Visually (may require viewing with Internet Explorer)
 * Under what conditions would F be the smallest?
 * Under what conditions would F be the largest?
 * Now explore the same ideas with this more advanced Visualisation Tool for One-way and Two-way ANOVA Applet

Error bar graphs

 * Use any dataset
 * Conduct a one-way ANOVA and graphically present the means and confidence intervals using an Error Bar Graph - is this error bar chart consistent with the statistical results?
 * Why?
 * Why not?

Data

 * 1) AQUES.sav
 * 2) Motiv.sav

title: Advanced ANOVA/One-way ANOVA


text:
Advanced ANOVA/Factorial ANOVA

 * 1) The purpose of this tutorial is to teach use of factorial ANOVA
 * 2) Practical exercises are based on using SPSS.

Design
Factorial ANOVA involves testing of differences between group means based on two or more categorical independent variables (IVs), with a single, continuous dependent variable (DV). In other words, a factorial ANOVA could involve:
 * Two or more between-subjects categorical/ordinal IVs
 * One interval or ratio DV

The results of interest are:
 * Main effect for IV1
 * Main effect for IV2
 * Interaction between IV1 and IV2

If significant effects are found and more than two levels of an IV are involved, then provide follow-up tests, which could be either: (Note: Use appropriate control of the family-wise Type I error rate)
 * A priori (planned) contrasts
 * Post-hoc contrasts

Effect sizes should also be reported. Eta-squared provides an estimate of the percentage of variance in the DV explained by each main effect and interaction effect. Cohen's p provides an estimate of the size of differences between two groups in standard deviation groups.

Example
"What is the effect of Gender (2) and Degree Type (3) on Overall Student Satisfaction?" This could be described as a 2 (Gender) by 3 (Degree Type) factorial ANOVA.

General steps

 * 1) Establish hypothesis/hypotheses
 * 2) * Make sure you have separate hypotheses for:
 * 3) ** Main effect for each IV
 * 4) ** Interactions between IVs
 * 5) ** Planned contrasts (if warranted)
 * 6) Examine assumptions:
 * 7) * IVs (categorical; between-subjects) and DV (at least interval)
 * 8) * The data in each cell is normally distributed
 * 9) * Homogeneity of variance (the variance in each cell is similar)
 * 10) * Cells are independent
 * 11) Examine descriptive statistics, particularly the four moments (M, SD, Skewness, Kurtosis) overall, and also for each cell
 * 12) Examine graphs
 * 13) Conduct inferential test (ANOVA) and interpret significance of F scores
 * 14) Conduct follow-up tests (planned contrasts or post-hoc tests) if F is significant
 * 15) Interpret interactions
 * 16) Calculate and interpret effect sizes
 * 17) * Eta-square (omnibus - equivalent to R2)
 * 18) * Standardised mean effect size (difference b/w two means) e.g., Cohen's d

Example SPSS outputs

 * Factorial ANOVA (example) - Are there differences in University Student Satisfaction levels between Gender and Age?
 * Factorial ANOVA (example) - Are there differences in University Student Satisfaction levels between Gender and Age? - Are there differences in Locus of Control between Gender and Age?

Descriptives

 * 1) A table of descriptive statistics (M, SD, Skewness, and Kurtosis) for each cell and for each marginal total, and grand total should be presented when reporting results.
 * 2) For a 2-way ANOVA, the descriptives table it is recommended to provide a breakdown of one IV in the columns and the other IV in the rows, such as illustrated in the following tables.

Note that each of the three columns on the right should be further split into five columns to allow reporting of M, SD, Skewness, and Kurtosis, n. So, the expanded descriptive tables layout could look like this: (note that rows and columns have been swapped around from the table above - this is somewhat arbitrary):

Here's an example of such an APA style table:

Understanding interactions

 * One of the keys to understanding Factorial ANOVA is being able to intepret interactions.
 * A recommended experiential exercise for learning about interactions is to fabricate a dataset which can be used to demonstrate factorial ANOVAs in which there are:
 * No effects
 * Main effect A, no main effect B, no interaction
 * Main effect A, no main effect B, interaction
 * No main effect A, main effect B, no interaction
 * No main effect A, main effect B, interaction
 * Main effect A, main effect B, no interaction
 * Main effect A, main effect B, interaction
 * Interaction, no main effects

Francis exercises

 * 3.3 (Analysing Differences)
 * 3.3.8 Factorial ANOVA
 * AQUES.sav
 * Motiv.sav

Effect sizes

 * Cohen's d
 * Eta-squared

Write-up
A write-up checklist for a factorial ANOVA might include:
 * 1) What is the goal/purpose of the analysis?
 * 2) What is the design - i.e., what are the IVs and the DV?
 * 3) What are the relevant descriptive statistics for the cells and the marginal descriptives? (M, D, skewness, kurtosis, n)
 * 4) To what extent does the data meet the assumptions for ANOVA (e.g., independent observations (often assumed), normality, and homogeneity of variance)?
 * 5) What are the main and interaction effect statistics, including the direction, statistical significance, and size of effects (consider the merits of reporting eta-squared and/or a standardised mean difference statistic such as Cohen's d; see effect sizes)?
 * 6) Provide a figure - often a line graph is used.
 * 7) Are follow-up tests necessary and/or warranted? e.g., if there are planned contrasts and/or a significant effect with three or more levels, then appropriate follow-up tests should be conducted using a method for controlling the family-wise Type I error rate as appropriate.
 * 8) What are the conclusions?

title: Advanced ANOVA/Factorial ANOVA


text:
Advanced ANOVA/Repeated measures ANOVA

The purpose of this tutorial is teach the use of repeated measures ANOVAs, including one-way repeated measures, 2-way within-subjects ANOVA, and mixed designs. Practical exercises are based on using SPSS.

Overview
The repeated measures design is also known as a within-subject design. In this design, participants may present scores for:
 * 1) A measure repeated over time (e.g., self-confidence before, after, and following-up a psycho-social intervention), and/or
 * 2) A measure repeated cross more than one condition (e.g., experimental and control conditions), and/or
 * 3) Several related, comparable measures (e.g., sub-scales of an IQ test).

Repeated-measures designs can be thought of as an extension of the paired-samples t-test to include comparison between more than two repeated measures.

Repeated-measures designs can be combined with between-subject factors to create mixed-design ANOVAs. Multiple repeated-measures designs can also be tested using MANOVAs.

Why use?
By collecting data from the same participants under repeated conditions:
 * 1) Individual differences can be eliminated or reduced as a source of between-group differences (which helps to create a more powerful test).
 * 2) The sample size is not divided between conditions/groups and hence inferential testing becomes more powerful.

Possible designs

 * 1) One-way repeated measures - repeated measures across one IV
 * 2) Two-way repeated measures - repeated measures across two IVs
 * 3) Two-way mixed split-plot design (SPANOVA) - repeated measures on one IV, independent groups on another IV

Assumptions
Most of the assumptions for between-subjects ANOVA design apply, however the key variation is that instead of the homogeneity of variance assumption, repeated-measures designs have the assumption of Sphericity which means that the variance of the population difference scores for any two conditions should be the same as the variance of the population difference scores for any other two conditions.
 * 1) Tested by Mauchly's sphericity test.
 * 2) When the significance level of Mauchly’s test is < 0.05 then sphericity cannot be assumed.
 * 3) Note: the sphericity assumption is only relevant to the univariate (one-way) RM ANOVA. This assumption is commonly violated and so it is generally not recommended to use the univariate analyses – the p-values tend to be inaccurate to the extent that this assumption is violated. The alternative, a multivariate test, does not require the assumption of sphericity and is therefore recommended. The multivariate test is conducted on difference scores and evaluates whether the population means for the sets of difference scores are simultaneously equal to zero.

In addition, for SPANOVA, consider homogeneity of intercorrelations:
 * 1) The pattern of intercorrelations across the levels of the repeated measures factor should be consistent from level to level of the between subjects factor
 * 2) Tested using Box's M statistic.
 * 3) See also lecture slides

Exercises

 * 1) One-way repeated measures ANOVA:
 * 2) * AQUES.sav - Francis 3.3.7, p. 66 (5th ed.)
 * 3) Two-way repeated measures
 * 4) * Repmeas.sav - Francis 3.3.8.2, p. 76 (5th ed.)
 * 5) Mixed ANOVA
 * 6) * AQUES.sav - Francis 3.3.8.3, p. 81 (5th ed.)

Background
The researcher wanted to determine whether the average husband wants to express his worries to his wife more or less the longer they are married. The researcher developed the Desire to Express Worry scale (DEW) and had 30 husbands answer the questionnaire when they initially got married, and then after 5, 10, and 15 years of marriage.

Results
A one-way within-subjects analysis of variance (ANOVA) was conducted with the within-subjects factor being Time (four levels indicating the number of years married) and the dependent variable being the Desire to Express Worry Scale (DEW) scores. The means and standard deviations for the DEW scors are presented in Table 1. The assumptions for ANOVA were met (explain in more detail).

The ANOVA indicated a significant time effect, Wilks’ $$\lambda$$ = 0.62, F (3,27) = 5.57, p = .004, multivariate $$\eta^2$$ =.38. Follow-up polynomial contrasts indicated a significant linear effect with means decreasing over time, F (1,29) = 11.56, p =.002, $$\eta$$$$_p^2$$ =.29. Higher-order polynomial contrasts were not significant. Men were increasingly less likely to desire to express worry to their wives with increasing years of marriage. It should be noted that there was little change in the means from 0 to 5 years, and therefore the significant trend was due to changes after 5 years of marriage. These results suggest that men are more eager to express worry to their wives early in marriage, and that this desire decreases after 5 years of marrriage(also add a Figure and possibly pairwise, Cohen's d effect sizes).

Table 1 Means and Standard Deviations for DEW Scores

Note: Skewness and kurtosis should be added to the table. Perhaps a marginal total would also be appropriate.

University of Canberra

 * Repeated measures ANOVA (ucspace)
 * Repeated measures ANOVA Notes (Handout)

Other

 * Chapter 14 Within-Subjects ANOVA (HyperStat Online)

Effect sizes

 * Cohensd.xls

title: Advanced ANOVA/Repeated measures ANOVA


text:
Advanced ANOVA/ANCOVA

This tutorial teaches use of analysis of covariance (ANCOVA) techniques, with practical exercises based on using SPSS.

Overview

 * ANCOVA evaluates whether population means on the DV, adjusted for differences on the covariate(s) (or 'nuisance variables'), differ across the levels of the IVs. Thus, the question being tested is whether the adjusted group means vary significantly from each other.
 * ANCOVA is exactly like ANOVA, except the effects of a third variable are statistically “controlled out” (similar to use of hierarchical multiple linear regression).
 * Any number of IVs and CVs can be used to create one-way, two-way, and multivariate ANCOVA designs.

Covariates

 * Typically included to remove extraneous influences from the DV, thus decreasing the within-group variance. Particularly with small sample sizes, well-chosen and reliably measured CVs can markedly improve the power of inferential statistical tests.
 * Including covariates is appropriate in order to:
 * Eliminate some systematic variance outside the control of the researcher that can bias the results.
 * Account for differences in response due to unique characteristics of the respondents.
 * This is usually achieved in experimental designs by random assignment to groups, however, in quasi-experimental designs problems related to non-random assignment can be minimised by statistically controlling for the effects of covariates.
 * Try to minimise the number of CVs; too many covariates will reduce the statistical efficiency of the analysis - rule of thumb is that the number of CVs < (.10 x sample size) - (number of groups - 1).

Example
If you are interested in testing the effect of computer experience on the attitude towards use of internet shopping, and you suspect that those with more positive attitudes toward shopping in general are more likely to have positive attitudes towards internet shopping, you may include attitude toward shopping as a covariate so as to remove its influence from the attitude towards internet shopping measure.

Assumptions
Assumptions to be met are those for ANOVA, plus:
 * 1) Covariates must be linearly related to the DV. The stronger the correlation, the more useful the CV will be. If there is no correlation, then the inclusion of the CV will slightly weaken the power of the test by needlessly consuming a degree of freedom.
 * 2) Covariates must have a homogeneous effect on the DV across the IV groups (i.e., homogeneity of slopes or equal effects on the DV across the IV groups). If there is a significant interaction between the covariate and the IV, do not use an ANCOVA.
 * 3) The covariate should be unrelated to the IV.
 * 4) Covariates should not be overly correlated with one another.

The first two criteria can be checked via a scatterplot of the DV and the CV, with the IV as a control variable (to check for equal slopes).

The third criteria depends on experimental design (e.g., if the CV is measured prior to the IV, then it cannot be affected by the IV).

The fourth criteria can be checked via correlations and scatterplots between the CVs.

Recall that the main ANOVA assumptions are that:
 * 1) Each of the observations are independent
 * 2) The DV (and the CV) must be interval level of measurement
 * 3) The underlying populations (of adjusted scores) must be normally distributed
 * 4) Each of the underlying populations (of adjusted scores) must have the same variance

The first two assumptions are a function of experimental design. The third and fourth assumptions are more difficult to test because we do not have the "adjusted scores", so we cannot compare the variances of the adjusted scores across the IV groups, but SPSS provides a Levene's test for the homogeneity of variance for the adjusted scores. Fortunately, provided the samples are sufficiently large, the test is robust to violations of the normality assumption.

Example write-up
A one-way analysis of covariance (ANCOVA) was conducted. The independent variable, vitamin C, involved three levels: placebo, low dose, and high dose. The dependent variable was the number of days with cold symptoms during treatment and the covariate was the number of days with cold symptoms before treatment. The assumptions for ANCOVA were met. In particular, the homogeneity of the regression effect was evident for the covariate, and the covariate was linearly related to the dependent measure.

The ANCOVA was significant, F (2,26) = 6.45, p = .005. The strength of the relationship between vitamin C treatment and the dependent variable was very strong, as assessed by $$\eta$$$$_p^2$$, with the vitamin C factor accounting for 33 percent of the variance in dependent measure holding constant the number of days with pretreatment cold symptoms. The mean number of days with cold symptoms adjusted for initial differences were ordered as expected across the three vitamin C groups. The placebo group had the largest adjusted mean (M = 12.01), the low dose vitamin C group had a smaller adjusted mean (M = 7.71) and the high dose vitamin C group had the smallest adjusted mean (M = 6.67). Follow-up tests were conducted to evaluate pairwise differences among the adjusted means. The Holm’s sequential Bonferroni procedure was used to control for Type I error across the three pairwise comparisons. There were significant differences in the adjusted means between both groups that received vitamin C and the placebo, but no significant difference between the two vitamin C groups. [Standardised mean effect sizes to be added] e.g., effect size with covariate calculator

Descriptives
In presenting ANCOVA results, provide a table of means for each of the groups. If the same scale is used to measure the DV and the CV, then the unadjusted group means and SD (from Descriptives) can be presented. If a different scale is used for the DV and CV, then provide both the unadjusted mean (and SD) and the adjusted mean (and SE). The adjusted mean (controlling for the CV) is provided in the estimated marginal means table.

Teaching method
In a hypothetical educational psychology experiment, participants were randomly divided into two groups. One group was taught conventionally, and the other were taught using an innovative method. Prior to allocation to the groups, learning motivation was assessed. Improvements in academic achievement were measured as the DV.
 * Motiv.sav (p. 129; Section 5.2)
 * Achievement (achieve) (DV)
 * Teaching Method (teach) (IV - 2 levels - "conservative" and "innovative")
 * Motivation (motiv) (CV)

Positive effect
Conduct an ANCOVA to test for differences in positive effect between males and females, adjusting for differences in age.
 * Psychol.sav
 * Positive effect (DV)
 * Gender (IV)
 * Age (CV)

Vitamin C
Do low and high doses of Vitamin C reduce incidences of days suffered with a cold? Conduct an ANOVA using:
 * ANCOVA.sav
 * Group (IV; Vitamin C: placebo, low, high)
 * Days (DV; days with a cold)
 * Pre-days (CV; previous days with a cold)

Therapy and depression
What is the effect of three different therapy types on depression, taking into account pre-existing depression levels? Use:
 * Ex ANCOVA.sav
 * Group (IV; Counseling and journal therapy; Journal therapy only; Counseling only)
 * Depression prior (CV)
 * Depression after (DV)

title: Advanced ANOVA/ANCOVA


text:
Advanced ANOVA/MANOVA

 * The purpose of this tutorial is to teach use of multivariate analysis of variance (MANOVA), with practical exercises based on using SPSS.
 * Note that the MANOVA procedure is not available with the Student version of SPSS.

What is MANOVA?

 * Multivariate analysis of variance (MANOVA) was developed as a theoretical construct by Samual S. Wilks in 1932 (Biometrika).
 * An extension of univariate ANOVA procedures to situations in which there are two or more related dependent variables (ANOVA analyses only a single DV at a time). DVs should be correlated (but not overly so; otherwise they should be combined) or conceptually related.
 * The MANOVA procedure identifies (inferentially) whether:
 * Different levels of the IVs have a significant effect on a linear combination of each of the DVs
 * There are interactions between the IVs and a linear combination of the DVs.
 * There are significant univariate effects for each of the DVs separately.

Example

 * Effects of chemotherapy and memory enhancement training on cognitive functioning in Alzheimer's patients

 * IVs (factors)
 * 1) Chemotherapy (drug vs no-drug)
 * 2) Memory training (training vs no-training)

Several measures of cognitive functioning:
 * DVs
 * 1) Test of reading comprehension and retention
 * 2) Memory for names and faces
 * 3) Ratings provided by family members

Usage

 * MANOVA is appropriate when we have several DVs which all measure different aspects of some cohesive theme, e.g., several different types of academic achievement (e.g., Maths, English, Science).
 * MANOVA works well in situations where there are moderate correlations between DVs. For very high or very low correlation in DVs, it is not suitable: if DVs are too correlated, there isn’t enough variance left over after the first DV is fit, and if DVs are uncorrelated, the multivariate test will lack power (so why sacrifice degrees of freedom?)
 * Alternatively, consider use a series of univariate ANOVAs (one for each DV) or possibly Mixed ANOVA.
 * "Because of the increase in complexity and ambiguity of results with MANOVA, one of the best overall recommendations is: Avoid it if you can." (Tabachnick & Fidell, 1983, p.230). In other words - be sure it is really the best approach to use.
 * Covariates can also be included → MANCOVA

How does it work?

 * Simple explanation
 * The MANOVA procedure creates a new DV which is a linear combination of the multiple DVs. This particular combination of DVs is chosen to maximise the difference between the IV groups.
 * The MANOVA procedure then assesses whether this new DV differs significantly between the IV groups.

MANOVA combines concepts from factorial ANOVA and discriminant analysis:
 * More complex explanation
 * It examines the effect of several independent variables (main effects and interaction effects), as does univariate ANOVA
 * These IV effects are examined on several DVs that are combined to form one or more linear composites, as in discriminant analysis.
 * Factor A main effect - evaluated by combining the original DVs to form one or more orthogonal discriminant functions (roots) which provide the greatest possible separation of the groups representing the levels of Factor A.
 * Factor B main effect - evaluated by combining the original DVs to form one or more orthogonal discriminant functions (roots) which provide the greatest possible separation of the groups representing the levels of Factor B.
 * A X B Interaction - assessed by forming one or more discriminant functions that maximise the separation of cells of the factorial data matrix.
 * For each effect (A, B, and A x B) the discriminant functions will differ (so the composite DV being examined can change)

Assumptions

 * 1) Sample size
 * 2) * Rule of thumb: the n in each cell > the number of DVs
 * 3) * Larger samples make the procedure more robust to violation of assumptions
 * 4) Normality:
 * 5) * MANOVA sig. tests assume multivariate normality, however when cell size > ~20 to 30 the procedure is robust violating this assumption
 * 6) * Note that univariate normality is not a guarantee of multivariate normality, but it does help.
 * 7) * Check univariate normality via histograms, normal probability plots, skewness, kurtosis, etc. and check multivariate normality using Mahalanobis' distance. These procedures will also help to check for possible outliers.
 * 8) Outliers:
 * 9) * MANOVA is sensitive to the effect of outliers (they impact on the Type I error rate); first check for univariate outliers, then use Mahalanobis' distance to check for multivariate outliers (MVOs).
 * 10) * MVOs are cases with an unusual combination of scores for the DVs of interest.
 * 11) * The SPSS Regression menus can be used to calculate Mahalanobis' Distance, which will provide a score for each case which can be assessed according to a $$\chi$$2 distribution
 * 12) ** Analyze - Regression - Linear - Dependent (add a unique identifier e.g., ID) - Independent (add all the MANOVA DVs) - Save - MD - Paste/OK.
 * 13) * Cases which can be considered MVOs are those with MD values above the critical $$\chi$$2 value (where the number of IVs equals is the $$\chi$$2 df).
 * 14) * MANOVA can tolerate a few outliers, particularly if their scores are not too extreme and there is a reasonable N. If there are too many outliers, or very extreme scores, consider deleting these cases or transforming the variables involved (see Tabachnick & Fidell).
 * 15) Linearity
 * 16) * Linear relationships among all pairs of DVs
 * 17) * Assess via scatterplots and bivariate correlations (check for each level of the IV(s) i.e., cells - use Split File)
 * 18) Homogeneity of regression
 * 19) * This assumption is only important if using stepdown analysis, i.e., there is reason for ordering the DVs.
 * 20) * Covariates must have a homogeneity of regression effect (must have equal effects on the DV across the groups)
 * 21) Multicollinearity and singularity
 * 22) * MANOVA works best when the DVs are only moderately correlated.
 * 23) * When correlations are low, consider running separate ANOVAs
 * 24) * When there is strong multicollinearity, there are redundant DVs (singularity) which decreases statistical efficiency.
 * 25) * Correlations above .7, and particularly above .8 or .9 are reason for concern.
 * 26) * Consider removing one of the strongly correlated pairs or combining them to form a single measure.
 * 27) Homogeneity of variance-covariance matrix (Box's M)
 * 28) * The F test from Box's M statistics should be interpreted cautiously because it is a highly sensitive test of the violation of the multivariate normality assumption, particularly with large sample sizes.
 * 29) * MANOVA is fairly robust to this assumption where there are equal sample sizes for each cell.
 * 30) Homogeneity of error variances (Levene's test)
 * 31) * If this assumption is violated, use a more conservative critical $$/alpha$$ level for determining significance for that variable in the univariate F-test. Tabachnick and Fidell suggest .025 or .01 rather than the conventional .05 level.

Multivariate test statistics
Choose from among these multivariate test statistics to assess whether there are statistically significant differences across the levels of the IV(s) for a linear combination of DVs. In general Wilks' $$\lambda$$ is recommended unless there are problems with small N, unequal ns, violations of assumptions, etc. in which case Pillai's trace is more robust :
 * Roy's greatest characteristic root
 * 1) Tests for differences on only the first discriminant function
 * 2) Most appropriate when DVs are strongly interrelated on a single dimension
 * 3) Highly sensitive to violation of assumptions - most powerful when all assumptions are met

 * Wilks' lambda (λ)
 * 1) Most commonly used statistic for overall significance
 * 2) Considers differences over all the characteristic roots
 * 3) The smaller the value of Wilks' lambda, the larger the between-groups dispersion

 * Hotelling's trace
 * 1) Considers differences over all the characteristic roots

 * Pillai's criterion
 * 1) Considers differences over all the characteristic roots
 * 2) More robust than Wilks'; should be used when sample size decreases, unequal cell sizes or homogeneity of covariances is violated

Tests of between-subject effects

 * What should be done once it is found that an overall F for MANOVA is significant?
 * If there is a significant multivariate effect, examine the Tests of Between-Subjects Effects for each of the DVs.
 * Since there are multiple tests, control for the Type I error-rate (e.g., use a Bonferroni adjustment – divide the original alpha level by the number of tests).
 * However, note that the DVs are usually correlated, therefore this approach would result in confounded results.
 * Stepdown F ratios provide a similar approach, without the counfounded results. In this approach, all DVs are prioritised (by the researcher) from most to least important. The most important variable is considered first without correcting for the lower priority variables. All subsequent variables are tested after removing the effects of the higher priority variables (by specifying the higher priority variables as covariates). Thus, stepdown analysis:
 * Is used to assess IV effects on individual DVs
 * Involves computing a univariate F statistic for a DV after eliminating the effects of other DVs preceding it in the analysis.
 * Previous DVs are treated as covariates
 * Somewhat similar to hierarchical multiple linear regression
 * Researcher determines the order in which the DVs are entered, based on some theoretical conceptualisation
 * Is most appropriate when the DVs are correlated.
 * See also: Analyses Following a Significant MANOVA (uwsp.edu)

Effect sizes
Also use effect sizes to evaluate strength of the effects (particularly for significant effects):
 * Multivariate ANOVA:
 * Wilks' $$\lambda$$ - multivariate $$\eta$$$$_p^2$$: Wilks' $$\lambda$$ reflects the ratio of within-group variance across all discriminant functions to total variance across all discriminant functions.
 * Univariate ANOVA:
 * $$\eta$$2 gives the proportion of variance in the DV that is attributable to different levels of an IV.

Pros and cons

 * Advantages
 * Tests the effects of several IVs and several outcome (DVs) within a single analysis.
 * Uses the power of convergence (no single operationally defined DV is likely to capture perfectly the conceptual variable of interest)
 * IVs of interest are likely to affect a number of different conceptual variables – e.g. an organisation's non-smoking policy may affect employee satisfaction, production, absenteeism, health insurance claims, etc.
 * Can provide a more powerful test of significance than available when via univariate tests.
 * Reduced Type I error rate compared with performing a series of univariate tests.
 * Interpretive advantages over a series of separate univariate ANOVAs.

 * Disadvantages
 * Discriminant functions are not always easy to interpret - they are designed to separate groups, not to make conceptual sense. In MANOVA, each effect evaluated for significance uses different discriminant functions (Factor A may be found to influence a combination of DVs totally different from the combination most affected by Factor B or the interaction between Factors A and B).
 * Like discriminant analysis, the assumptions on which it is based are numerous and difficult to assess and meet.

 * Alternatives
 * Combine or eliminate DVs so that only one DV need be analysed.
 * Use factor analysis to find orthogonal factors that make up the DVs, then use univariate ANOVAs on each factor (because the factors are orthogonal each univariate analysis should be unrelated)

Example writeup
A one-way multivariate analysis of variance (MANOVA) was conducted to determine the effect of the three types of study strategies (thinking, writing and talking) on two dependent variables (recall and application test scores). A nonsignificant Box’s M, indicated a lack of evidence that the homogeneity of variance-covariance matrix assumption was violated. No univariate or multivariate outliers were evident and MANOVA was considered to be an appropriate analysis technique.

Significant differences were found among the three study strategies on the dependent measures, Wilks’ $$\lambda$$ = .42, F (4,52) = 7.03, p < 0.001. The multivariate Wilks' $$\lambda$$ was quite strong at .35. Table 1 presents the means and standard deviations of the dependent variables for the three strategies.

Univariate analyses of variance (ANOVAs) for each dependent variable were conducted as follow-up tests to the MANOVA. Using the Bonferroni method for controlling Type I error rates for multiple comparisons, each ANOVA was tested at the .025 level. The ANOVA of the recall scores was significant, F (2,27) = 17.11, p <.001, $$\eta^2$$ = 0.56, while the ANOVA based on the application scores was nonsignificant, F(2,27)=4.20, p = 0.026, $$\eta^2$$ = 0.24.

Post hoc analysis for the recall scores consisted of conducting pairwise comparisons to determine which study strategy affected performance most strongly. Each pairwise comparison was tested at the 0.025/3, or 0.008, significance level. The writing group produced significantly superior performance on the recall questions in comparison with either of the other two groups. The thinking and talking groups did not differ significantly from each other.

Table 1 Means and Standard Deviations for each Dependent Variable by Strategy

Note: This table should also include skewness, kurtosis, and descriptives for marginals.

Exercises

 * Data
 * Data: SCHL8.sav (Francis 5.3; p. 132 (5th ed.))
 * 1st MANOVA
 * DVs (Academic achievement):
 * Maths (mathsach)
 * English (engach)
 * IVs:
 * Socio-economic status (SES; Low, Moderate, High)

 * 2nd MANOVA
 * DVs (Classroom behaviour):
 * Attentiveness in Year 8 (attent)
 * Settledness in Year 8 (settle)
 * Sociability in Year 8 (sociab)
 * IVs:
 * Gender (Sex; Male, Female)

 * SPSS Steps
 * Analyze - General Linear Model - Multivariate (add IV(s) (fixed factors) and DVs)
 * Graphs - could use any of:
 * Clustered Bar Chart (Summaries of separate variables) or
 * Clustered Error-bar (Summaries of separate variables) or
 * Multiple Line Graph (Summaries of separate variables)

 * 3rd MANOVA (within-subjects)
 * DVs
 * Year level or Time
 * Year 7 and 8 (same participants over Time)
 * Classroom behaviour
 * Attentiveness
 * Settledness
 * Sociability

 * 4th MANOVA (within-subjects)
 * DVs
 * Students' perceptions of maths and english teachers
 * Maths and English teachers (same students assessing these)
 * Student ratings of teacher qualities
 * Responsiveness
 * Expectations
 * Enjoyable class

title: Advanced ANOVA/MANOVA


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BCP/Patrinia gibbosa

Patrinia gibbosa

title: BCP/Patrinia gibbosa


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BCP/Patrinia gibbosa/Logs

2008 Logs

 * (no archived logs yet)

Recent Logs

 * SB Johnny-LM 16:21, 23 July 2008 (UTC)
 * SB Johnny-LM 09:00, 16 August 2008 (UTC)

title: BCP/Patrinia gibbosa/Logs


title: User:Adolphus79


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Normal distribution

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Introduction
A normal distribution can be described by four moments: mean, standard deviation, skewness and kurtosis. Statistical properties of normal distributions are important for parametric statistical tests which rely on assumptions of normality. The normal distribution is often used as assumption of the underlying probability distribution in natural sciences and social sciences

Probability density function
The probability density function of the standard normal distribution (with the standard deviation and area under the curve standardized to 1 and the mean and skewness standardized to 0) is given by $$ f(x)=\frac{e^{-x^2/2}}{\sqrt{2\pi}} $$

Antiderivative
This function has no elementary antiderivative, and thus normal distribution problems are mainly limited to using numerical integration to find a probability.

Gaussian integral and Normalization
However, using multivariable calculus, the value of the Gaussian integral can be determined to be exactly $$\int^{\infty}_{-\infty} e^{-x^2}dx = \sqrt{\pi}$$, and through the change of variables $$u=x\sqrt{2}, du=dx\sqrt{2}$$, we can be assured that the total area beneath the curve is 1, and thus it is normalized correctly.

Standard Deviation
A similar approach can be used to prove that its standard deviation is also 1. By the definition of the standard deviation, $$\sigma^2 = \Sigma{(x_k-\mu)^2}$$, so we form the Riemann sum $$\Sigma (x-0)^2\frac{e^{-x^2/2}}{\sqrt{2\pi}} \Delta x $$. Taking the limit of the sum leads to the integral $$\int^{\infty}_{-\infty}x^2\frac{e^{-x^2/2}}{\sqrt{2\pi}} dx $$.

Integration by Parts
Using integration by parts, we can determine that this is in fact equal to the area under the normal curve, and thus the standard deviation is 1.

Inflection Points
Using differentiation of the probability density function, we find that the inflection points of the normal distribution curve are each exactly one standard deviation away from the mean.

Standardization
Any other normal distribution can be standardized through a change of variables (such as if the mean is not 0).

Learning Task

 * Learn about the general properties of a Probability Distribution and check why does the normal distribution fullfil these properities.
 * Create number in R programming language R e.g. with Open Source R/RStudio and plot the generated data with a histogram.
 * Analyze the Central Limit Theorem and describe, how the normal distribution is related to that theorem. Give an real world example where you would not assume a normal distribution.

Symbols

 * 1) Population mean = μ (Mu)
 * 2) Population variance = σ2 = (Sigma squared)

Testing for normality
No single indicator of normality should be overly relied upon. Graphical, descriptive, and inferential can be used, each with strengths and limitations. The most important result is to actually describe and show the distribution. Simply listing statistical properties does not demonstrate understanding.

Graphical analysis
Whether it is reported as a Figure or not, responses to interval or continuous variables should be visualised as a:
 * Histogram, with normal curve imposed

This is the single most basic and important way of examining the central tendency and shape of distribution for participants' responses.

It may also be helpful to examine a:
 * Normal Q-Q plot

Descriptive indicators of normality
A rule of thumb for assessing normality for the purposes of assumption testing for inferential statistical tests such as ANOVA is that if skewness and kurtosis are between -1 and +1 and there is a reasonable sample size (e.g., at least 20 per cell), then you are unlikely to run into issues related to violations of the assumption of normality.

Some authors suggest that variables with skewness and kurtosis values between -2 to +2 or even -3 to +3 can be treated as being drawn from a normally distributed population.

The larger the sample size, the more robust inferential tests are to departure from normality.

 * The skewness of a Normal Distribution is always 0; +ve scores indicate a tail to the right; -ve scores indicate a tail to the left.
 * The kurtosis of a Normal Distribution is always 0; +ve scores indicate a peaked distribution; -ve scores indicate a relatively flat distribution.

If concerned about non-normality, then consider recoding data to a lower level of measurement.

For more information, see: Judging severity of skewness and kurtosis

Inferential tests of normality
Significance tests of (non-)normality become overly sensitive when the sample size is large. Thus, do not rely on significance tests of normality alone in making an assessment (e.g., for assumption-testing purposes):
 * Kolmogorov-Smirnov test
 * Shapiro–Wilk test

These tests are overly sensitive to minor departures from normality, particularly with large sample samples (e.g., > 200). This doesn't mean that it should be discounted as an indicator, just that a sig. (p < .05) test value does not necessarily indicate a notable or problematic departure from normality. Also check normality using other indicators.

Take-home message
It is recommended that both graphical indicators and descriptive indicators be used for testing the assumption that a sample is derived from a normally distributed population. Inferential normality tests may also be useful.

Transformations

 * Standardise data in SPSS

Non-parametric statistics

 * Non-parametric statistics

Page Information
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title: Normal distribution


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Advanced ANOVA/Review

This tutorial aims to review the previous six tutorials about analysing differences, and to provide assistance with the assessment exercise.

Topics to review

 * Hypotheses

title: Advanced ANOVA/Review


text:
Advanced ANOVA/Data/LEQ

The LEQ dataset provides a large (N > 3000), moderately complex dataset based on the Life Effectiveness Questionnaire for analysis of group differences (e.g., for the assessment exercise).

Independent variables
Possible independent variables include:
 * 1) Gender (studsex)
 * 2) Age (ageyears)
 * 3) Any of the dependent variables listed below could be recategorised (e.g., into low-high) and used as IVs.

Dependent variables
There are two main types of dependent variables and/or within-subjects factors which can be used.

Factor
There are eight "life effectiveness" factor scores, plus an overall (average) life effectiveness score per participant. Each of these is each measured on up to four occasions (T0, T1, T2, T3).

Time
The Time administration periods were T0, T1, T2, and T3, with an outdoor education intervention program between T1 and T2:
 * 1) Time 0: Baseline, pre-program
 * 2) Time 1: First day of program (program start)
 * 3) Time 2: Last day of program (program end)
 * 4) Time 3: Followup (3 to 6 months after the program end)

title: Advanced ANOVA/Data/LEQ


text:
Advanced ANOVA/Data

Allen and Bennett

 * 1) /Allen and Bennett 2008/ (all files)

Francis

 * 1) AQUES.sav
 * 2) Motiv.sav
 * 3) Repmeas.sav
 * 4) SCHL8.sav

Neill

 * 1) LEQ_teaching2.sav - Life Effectiveness Questionnaire (LEQ)

title: Advanced ANOVA/Data


text:
Advanced ANOVA/Data/Allen and Bennett 2008

 * Data (ucspace)


text:
User:Santhip

Santhip Krishnan Kanholy

MS in Aerospace Engineering. PhD in Mechanical Engineering.

Primararily a CFD enthusiast.

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Talk:Unmanned aerial systems

Flight
http://www.youtube.com/watch?v=L17Ox4FQTkM Emesee 00:48, 24 July 2008 (UTC)

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User:Jenny O

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 * style = "vertical-align:top; margin-right:20;" |

Social Psychology E-Portfolio
Semester 2, 2008

 * This e-portfolio follows the study of social psychology over a university semester
 * Its purpose is to provide an ongoing, assessable learning activity to help students record, explore, synthesise and share their learning (James Neill)
 * Regular contributions will be made to these pages over the next 15 weeks
 * Others are invited to comment and share their views

Lecturer: James Neill Textbook: Baumeister, R. F., & Bushman, B. J. (2008). Social psychology and human nature (1st ed.) Belmont, CA: Thomson Wadsworth.

Completed
Monday 3 November 2008

 * }

 * }

Tutorial & Assessment Reflections
Tutorial reflections
 * Tutorial One: Introduction
 * Tutorial Two: Communication
 * Tutorial Three: Prejudice, aggression and prosocial behaviour
 * Tutorial Four: Cross-cultural training
 * Tutorial Five: Australian zeitgeist

Assessment reflections
 * Research Participation
 * Essay: Western domination of social psychology
 * E-portfolio

Lecture Content
Introduction
 * What is social psychology?
 * Unpacking this description of social psychology
 * Revised definition of social psychology

The Social Self
 * The conceptual definition of the self
 * The operational definition of the self

The Self in Action: Behavioural Control
 * Goals
 * Freedom to act
 * Choice
 * Reactance
 * Self-regulation
 * Irrationality and self destruction

Social Thinking
 * Attributions
 * Beliefs
 * Attitudes
 * Consistency
 * Social influence and persuasion

Aggression

Prejudice

Ghosts of Rwanda
 * Malevolence
 * Benevolance

Relationships
 * Attraction and Exclusion
 * Close Relationships

Groups
 * What is a group?
 * Concepts, theories and studies
 * Group decision-making
 * Leadership

Prosocial Behaviour
 * What is prosocial behaviour?
 * Cooperativeness, forgiveness, obedience and conformity
 * Why do people help others?

The Environment
 * Environmental psychology
 * Built environments
 * Natural environments

title: User:Jenny O