Comments for my TA. 18/4/2012
April 16, 2012, 2:00 am
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Comparative Psychology
March 25, 2012, 1:28 am
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Comparative Psychology is the branch of psychologywhich deals with the scientific study the similarities and differences between the mental processes and behaviour of humans and other animals.  It places emphasis on cross-species comparisons, including human-to-animal comparisons. This method evaluates the similarities and differences across species to better understand the developmental and evolutionary relationships between them.

Since Darwin’s work emphasised continuity in evolution from animals to people in their mental abilities and physical characteristics, psychologists have worked to understand the basic principles and processes that underlie the behaviour of all species, human and nonhuman.  Although much research in psychology uses people as subjects, research with animal subjects continues to be essentail for answering some fundamental questions. Psychologists do research to learn more about behaviour and how knowledge of behaviour develops.  As knowledge is accumulated, identification of characteristics that are unique to different species has yielded information that contributes to understanding and advancing the welfare of animals and people.

Animal research has been the major contributor to our knowledge of;

  • Basic learning processes and motivational systems, such as hunger, thirst and reproduction.
  • Sensory processes of vision, taste hearing and pain reception.
  • Animal cognition, providing a comparative and ecological perspective on issues of the mind, intelligence and of hoe sensory functions and levels of cognition can depend on early experience.
  • Modes of adaption to change, including evolution, development and all types of learning.
  • Connections between stress disease and has suggested psychological interventions for coping with stress more effectively.
  •  Identification and refinement of the basic learning principles that have led to the development of effective methods for promoting learning and self reliance in a wide variety of populations.
  • Contribution to treatment of difficult clinical problems, such as controlling self injuries behaviour in autistic children, and adults.
  • Understanding the range of behaviour effects of psychoactive drugs and environmental toxicants.
  • Understanding of drug abuse and physical dependence.  Also to understanding the nature and extent of genetic vulnerability to drug dependence.

Recent research on the brain of processes of chemical neurotransmission, combined with behavioural research in animals has provided understanding of the function of the central nervous system, aiding understanding of;

  • The process of recovery after neural damage.
  • Biological correlates of fear, anxiety.
  • Subjective and dependence producing effects of psychotropic drugs.
  • Mechanisms that control eating and other motivational processes.
  • The biology of learning and memory.Top of Form

Comparative psychology through the use of animals in research has provided great insight into human behaviour.  But despite all the advances made I find the following studies extremely disturbing.

Warning:  the following content refers to studies involving animals which some may find upsetting.  Please do not read on if you this will upset you.

Monkey Drug Trials (1969)

In this experiment monkeys were taught how to self inject a number of horrendous drugs into their bodies. Monkeys were injected with the drugs (to include cocaine, morphine, alcohol and amphetamines), so that they became addicted to their effects. They were then taught how to inject the drugs themselves, and left with an abundance of the drug. The monkeys were so affected with the drugs, some broke their arms trying desperately to escape their torment. Some of the monkeys using cocaine even ripped their own fingers off, most likely as a result from hallucinations. Monkeys who were injecting both morphine and cocaine all died within 2 weeks.

Harlow’s Monkey Experiments.  The Well of Despair (1957 to mid-1960′s).


 Harlow is infamous for being insensitive towards animals. His studies caused disturbance to many monkeys. One of his experiments concerning social isolation, saw tiny baby rhesus monkeys, just bonded with their mothers, separated so they had no friends or family for social support. They were put in something Harlow called the “Pit of Despair” a in a stainless steel vertical chamber device, for up to an entire year. The pits were tiny and completely pitch black.  The monkeys left the pits severely disturbed and depressed.  Many were psychotic, and did not recover.  Harlow wrote that they showed “autistic self clutching and repetitive rocking”.  Dr. Harlow concluded that even a happy, normal childhood was no defence against depression, while science writer Deborah Blum called these, “common sense results.  It’s quite obvious that someone, human or not, will develop incorrectly without the social support from their family – particularly the mother”.  Another of his experiments forced baby monkeys to choose between a wire or cloth “mother”.

It is not just Psychology that conducts studies involving animals, cosmetic companies, biomedical research and many other science tests are conducted on animals. Since 1980, ethical guidelines have been developed for animals and fortunately, the use of animal studies is psychology is declining.  Even though they are still conducted, the importance of the research has to seriously outweigh the cost of the animal’s life.

We have learnt many important things with unethical animal studies, so sometimes the research is vitally important. For example it might be a rat’s life which saves millions of human’s lives from cancer. The cost of one rats life to save millions of humans would seem acceptable.

Although I can appreciate the advances made through animal testing it is a practice that does not sit well and weighs heavily with me.




Comments for my TA. 14/3/2012
March 14, 2012, 1:27 am
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Are there benefits in graphing data?
March 11, 2012, 1:32 am
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Are there benefits in graphing data or is it just more work, inputting data, computing and formatting a graph, just to replicate the results you have already written?

Florence Nightingale’s creative graph construction: Persuasive innovation and life-saving tool

Florence Nightingale, was the first woman admitted as a Fellow of the Royal Statistical Society in England.  When the Crimean War broke out, Nightingale directed the entire nursing operation at the war front for the British Army. Her legend began to grow as she instituted practices of basic hygiene, such as changing the bed sheets when new patients entered the hospital. She documented every change that she made so that she could identify what worked and succeeded at dramatically reducing the mortality rate.  Florence Nightingale’s response to bureaucratic resistance was statistics.  Her response began with a simple innovation: She kept systematic records of what happened to patients. Her simple act of using descriptive statistics to catalogue daily life in the hospital had huge consequences and is credited by some as having saved the British Army during the Crimean War.

                                 Figure 1

Nightingale’s careful record keeping did not accomplish her mission on its own. She needed a graph to tell the story. Florence Nightingale invented a “polar-area” diagram, often now referred to as a “cox comb” graph, so named because it resembled the shape of a rooster’s head. A recreation of this circular chart is shown in Figure 1, and it includes causes of death, numbers of deaths, and months of the year. This graph told her story more clearly and eloquently than descriptive statistics alone ever could.

They say a picture is worth a thousand words. The same thing could be said about a graph.  One major goal of statistics is to present data in a meaningful and manageable way.  Graphs are an excellent way to display information visually. Good graphs convey information quickly and easily to the user. It’s one thing to see a list of data, it’s another to understand the trends and details of the data.  Graphs highlight salient features of the data. They can show relationships that are not obvious from studying a list of numbers. Graphs can also provide a convenient way to compare different sets of data.  Different situations call for different types of graphs.

Brief summary of frequently used graphs

A boxplot is a concise graph showing the five point summary. Multiple boxplots can be drawn side by side to compare more than one data set.


  • Shows 5-point summary and outliers
  • Easily compares two or more data sets
  • Handles extremely large data sets easily


  • Not as visually appealing as other graphs
  • Exact values not retained

Bar graph
A bar graph displays discrete data in separate columns. A double bar graph can be used to compare two data sets. Categories are considered unordered and can be rearranged alphabetically, by size, etc.


  • Visually strong
  • Can easily compare two or three data sets


  • Graph categories can be reordered to emphasize certain effects
  • Use only with discrete data

A scatterplot displays the relationship between two factors of the experiment. A trend line is used to determine positive, negative, or no correlation.


  • Shows a trend in the data relationship
  • Retains exact data values and sample size
  • Shows minimum/maximum and outliers


  • Hard to visualize results in large data sets
  • Flat trend line gives inconclusive results
  • Data on both axes should be continuous

A histogram displays continuous data in ordered columns. Categories are of continuous measure such as time, inches, temperature, etc.


  • Visually strong
  • Can compare to normal curve
  • Usually vertical axis is a frequency count of items falling into each category


  • Cannot read exact values because data is grouped into categories
  • More difficult to compare two data sets
  • Use only with continuous data

Line graph
A line graph plots continuous data as points and then joins them with a line. Multiple data sets can be graphed together, but a key must be used.


  • Can compare multiple continuous data sets easily
  • Interim data can be inferred from graph line


  • Use only with continuous data

Stem and Leaf Plot
Stem and leaf plots record data values in rows, and can easily be made into a histogram. Large data sets can be accommodated by splitting stems.


  • Concise representation of data
  • Shows range, minimum & maximum, gaps & clusters, and outliers easily
  • Can handle extremely large data sets


  • Not visually appealing
  • Does not easily indicate measures of centrality for large data sets

The following examples of result for the same data set clearly demonstrate the benefits of graphing data.

A two-way independent-measures ANOVA (nationality: three levels, American, German andEnglish; age: two levels, younger and older) was performed on these data. There was a significant main effect of nationality (F 2, 30 = 21.03, p < .0001). Post-hoc tests revealed that, overall, the German tourists were faster to claim a sun-bed than were the English tourists, who in turn were faster than the Americans (Bonferroni tests, p < .05 for all tests). There was also a significant main effect of age (F1,30 = 14.88, p < .01): regardless of nationality, younger tourists were faster to claim a sun-bed than were older tourists. From fig. 1, it appears that the effects of age were more marked for the Germans and English than they were for the Americans. However, the ANOVA failed to support this interpretation, revealing no significant interaction between age and nationality (F 2, 30 = 2.34, n.s.)




Homework for my TA,Thandi. 22/2/2012
February 22, 2012, 12:24 am
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Faking it!
February 17, 2012, 12:15 am
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A key principle of psychology research that makes it a science is that it is based on fact and is not a fictional piece of work.

Dr. Diederik Staple was a respected psychology scientist at Tilburg University in the Netherlands, specialising in social psychology. He held the position of Dean of social and behavioural sciences at the university. His research area, studied interactions between self-image and the perceptions people hold of stimuli in the world surrounding them. He was recently expelled from his university because it was revealed by an enquiry committee that over more than ten years he had manipulated and fabricated data in his studies, putting into doubt at least 30 published papers. Dr. Staple voluntarily returned his title to the university following the interim report and his own admission of committing fraud and deception in his research work.

Staple betrayed colleagues who researched and published papers with him as well as the doctoral students he guided towards their degrees.  He destroyed his own career and adversely affected the life and work of many others around him.  He jeopardised the work of others who cited his papers in their own work, planned experiments on his findings, or relied on these findings to develop theory and support their own arguments.

Staple, a very intelligent academic with good understanding of data and high ability of numerical skills was able to simulate datasets, so they seemed as though they were genuinely collected.  Staple manipulated and fabricated data he collected in order to adapt it to the hypotheses he wanted to support.  Reportedly, Staple almost always refused to submit raw data to the inspection of colleagues and graduate students.

There has been much criticism in psychological research of the practice of keeping data in near secrecy and there has been demand from researchers to share data with other researchers in the field, allowing them to analyse the data and come to their own conclusions.   But this is a very competitive field with the race to publish papers, and the reluctance to submit or share datasets that may help others publish a paper that contradicts their original work.

This blog highlights several issues.  The importance of data in research; a research report tells a factual story, and the data collected is the factual information of a study.  The need for honesty and integrity; the results need to be genuine data, or a report is just a piece of fictional work.  To ensure these issues are addressed, access and analysis of data should be available to all members of a research group, ensuring more than one person actually analyses the data.

Dr Staple received subsidies and funding for his fraudulent projects, and no doubt he was personally paid a huge salary.  He not only disgraced himself, ruined his career, damaged the reputations and careers of others, he tainted the image of the field of psychology and psychological research.  Research is a huge undertaking, with a responsibility to carry it out honestly.


Why do we bother to conduct research and statistical analyses?
February 5, 2012, 7:19 pm
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Why do we bother to conduct research and statistical analyses? Behaviour just happens anyway, without knowing the what for’s or the why for’s.

Experimenters use inferential statistics to determine whether the results of an experiment are meaningful, and to draw inferences about a population based upon measures taken from a representative sample of that population.

Statistical tests in psychological research, test the probability of obtaining a given set of results.  They produce a statement of the probability that an observation represents a true causal relationship and not a chance occurrence. Statistical significance or a statistically significant result is unlikely to have occurred by chance, but is the result of an intervention, of the effect of the independent variable upon the dependent variable indicating a real relationship most likely exists.

Statistical significance testing tells you the probability of a particular result occurring.  Psychology often uses a significance level of p < .05. This means that the probability of an event or effect occurring by chance is less than 5%. There is less than 5 chances in 100 that the results are due to chance.  Some research, for example medical interventions such as drug trials, requires a more stringent significance level of p < .01.  This means the probability of an event occurring by chance is less than 1 %, therefore there is less than 1 chance in 100 that the results are due to chance.

Example of study with p < .05.


Example of study with p < .01.


Human beings have a natural curiosity into understanding the reasons we behave how we do, but research and statistical analyses is not just for the satisfaction of human curiosity. Many decisions in areas like psychotherapy, business and social policy etc (the list is endless) depend on, or are influenced by psychological research and the consequential results. Therefore we want to be as sure as possible that our theories about the mind and behaviour are correct.  Psychology aims to describe, explain, predict and control behaviour and much can be gained from the knowledge we gather that can be of benefit.  For example the norms of behaviour (relevant to culture, time etc) need to be established before the extremes of behaviour can be identified.


Significance testing is not perfect.  You must look at other things too, such as the effect size, the power, the theoretical underpinnings. Combined, we are better able to interpret data and gain a fully picture of the story they tell.