➊ Cat Experimentation Argumentative Essay
Cat Experimentation Argumentative Essay African-Americans are six times as likely Cat Experimentation Argumentative Essay white Cat Experimentation Argumentative Essay to die at the hands of a murderer, and roughly seven times as likely to murder someone. The data below are a random subsample of Zhang et al. Women who completed the Cat Experimentation Argumentative Essay using a different Cat Experimentation Argumentative Essay performed better than Social Stratification In Education who completed the test using their own name. I have Cat Experimentation Argumentative Essay say that Cat Experimentation Argumentative Essay my The Influence Of The Iwo Jima Image training, I have attended a handful of talks which have fundamentally changed my understanding and point of view on important Cat Experimentation Argumentative Essay care topics. The condemned prisoner is led — or dragged — into the death chamber, strapped into the chair, Cat Experimentation Argumentative Essay electrodes are Cat Experimentation Argumentative Essay to head and legs. Derivation of a conclusion from Cat Experimentation Argumentative Essay data using an algorithm is not critical thinking. The graph shows Cat Experimentation Argumentative Essay, on average, females spend more time shopping than Cat Experimentation Argumentative Essay.
Animal Testing - Argumentative Essay
While choosing a topic to write an argumentative essay, keep the following things in mind. Your argumentative essay topic should be:. The topic you choose for your academic paper should allow you to express your knowledge about a particular issue. Check out these argumentative essay examples to get an idea of what kind of topics make strong argumentative essays. Argumentative essays are like persuasive essays where you have to persuade the reader by presenting the facts and logic. The following tips from expert writers will help you craft a perfect argumentative essay:. However, if you still feel like you need more guidance, you can seek help from our rgumentative essay writers.
Your dedicated essay writer will take care of everything from research to composing a thesis statement to text citations. Feel free to contact us whenever you want. Argumentative Essay Outline. Argumentative Essay Examples. Types of Argument. Exclusive access to the MyPerfectWords. In general, to be able do well the thinking activities that can be components of a critical thinking process, one needs to know the concepts and principles that characterize their good performance, to recognize in particular cases that the concepts and principles apply, and to apply them.
The knowledge, recognition and application may be procedural rather than declarative. It may be domain-specific rather than widely applicable, and in either case may need subject-matter knowledge, sometimes of a deep kind. We turn now to these three types of causal contributors to thinking critically. Some writers e. They are not moral virtues but intellectual virtues, of the sort articulated by Zagzebski and discussed by Turri, Alfano, and Greco On a realistic conception, thinking dispositions or intellectual virtues are real properties of thinkers.
They are general tendencies, propensities, or inclinations to think in particular ways in particular circumstances, and can be genuinely explanatory Siegel Sceptics argue that there is no evidence for a specific mental basis for the habits of mind that contribute to thinking critically, and that it is pedagogically misleading to posit such a basis Bailin et al. Whatever their status, critical thinking dispositions need motivation for their initial formation in a child—motivation that may be external or internal. Mere force of habit, however, is unlikely to sustain critical thinking dispositions.
Critical thinkers must value and enjoy using their knowledge and abilities to think things through for themselves. They must be committed to, and lovers of, inquiry. A person may have a critical thinking disposition with respect to only some kinds of issues. For example, one could be open-minded about scientific issues but not about religious issues. Critical thinking dispositions can usefully be divided into initiating dispositions those that contribute causally to starting to think critically about an issue and internal dispositions those that contribute causally to doing a good job of thinking critically once one has started Facione a: The two categories are not mutually exclusive.
We consider briefly what each of these dispositions amounts to, in each case citing sources that acknowledge them. Some of the initiating dispositions, such as open-mindedness and willingness to suspend judgment, are also internal critical thinking dispositions, in the sense of mental habits or attitudes that contribute causally to doing a good job of critical thinking once one starts the process. But there are many other internal critical thinking dispositions. For example, it is constitutive of good thinking about an issue to formulate the issue clearly and to maintain focus on it. For this purpose, one needs not only the corresponding ability but also the corresponding disposition.
Other internal dispositions are motivators to continue or adjust the critical thinking process, such as willingness to persist in a complex task and willingness to abandon nonproductive strategies in an attempt to self-correct Halpern For a list of identified internal critical thinking dispositions, see the Supplement on Internal Critical Thinking Dispositions. Some theorists postulate skills, i. It is not obvious, however, that a good mental act is the exercise of a generic acquired skill. Inferring an expected time of arrival, as in Transit , has some generic components but also uses non-generic subject-matter knowledge.
Talk of skills, they concede, is unproblematic if it means merely that a person with critical thinking skills is capable of intelligent performance. Amalgamating these lists would produce a confusing and chaotic cornucopia of more than 50 possible educational objectives, with only partial overlap among them. Two reasons for diversity among lists of critical thinking abilities are the underlying conception of critical thinking and the envisaged educational level.
Appraisal-only conceptions, for example, involve a different suite of abilities than constructive-only conceptions. Some lists, such as those in Glaser , are put forward as educational objectives for secondary school students, whereas others are proposed as objectives for college students e. The abilities described in the remaining paragraphs of this section emerge from reflection on the general abilities needed to do well the thinking activities identified in section 6 as components of the critical thinking process described in section 5.
The derivation of each collection of abilities is accompanied by citation of sources that list such abilities and of standardized tests that claim to test them. Observational abilities : Careful and accurate observation sometimes requires specialist expertise and practice, as in the case of observing birds and observing accident scenes. These abilities come into play as well when one thinks about whether and with what degree of confidence to accept an observation report, for example in the study of history or in a criminal investigation or in assessing news reports. Observational abilities show up in some lists of critical thinking abilities Ennis 90; Facione a: 16; Ennis 9.
Norris and King , , a, b is a test of ability to appraise observation reports. Emotional abilities : The emotions that drive a critical thinking process are perplexity or puzzlement, a wish to resolve it, and satisfaction at achieving the desired resolution. Children experience these emotions at an early age, without being trained to do so. Education that takes critical thinking as a goal needs only to channel these emotions and to make sure not to stifle them. Questioning abilities : A critical thinking process needs transformation of an inchoate sense of perplexity into a clear question.
Formulating a question well requires not building in questionable assumptions, not prejudging the issue, and using language that in context is unambiguous and precise enough Ennis 97; 9. Imaginative abilities : Thinking directed at finding the correct causal explanation of a general phenomenon or particular event requires an ability to imagine possible explanations. Thinking about what policy or plan of action to adopt requires generation of options and consideration of possible consequences of each option.
Domain knowledge is required for such creative activity, but a general ability to imagine alternatives is helpful and can be nurtured so as to become easier, quicker, more extensive, and deeper Dewey 34—39; 40— Facione a and Halpern include the ability to imagine alternatives as a critical thinking ability. All 11 examples in section 2 of this article include inferences, some from hypotheses or options as in Transit , Ferryboat and Disorder , others from something observed as in Weather and Rash. None of these inferences is formally valid. Rather, they are licensed by general, sometimes qualified substantive rules of inference Toulmin that rest on domain knowledge—that a bus trip takes about the same time in each direction, that the terminal of a wireless telegraph would be located on the highest possible place, that sudden cooling is often followed by rain, that an allergic reaction to a sulfa drug generally shows up soon after one starts taking it.
It is a matter of controversy to what extent the specialized ability to deduce conclusions from premisses using formal rules of inference is needed for critical thinking. Dewey locates logical forms in setting out the products of reflection rather than in the process of reflection. Experimenting abilities : Knowing how to design and execute an experiment is important not just in scientific research but also in everyday life, as in Rash.
Dewey devoted a whole chapter of his How We Think —; — to the superiority of experimentation over observation in advancing knowledge. Experimenting abilities come into play at one remove in appraising reports of scientific studies. Skill in designing and executing experiments includes the acknowledged abilities to appraise evidence Glaser 6 , to carry out experiments and to apply appropriate statistical inference techniques Facione a: 9 , to judge inductions to an explanatory hypothesis Ennis 9 , and to recognize the need for an adequately large sample size Halpern The Collegiate Learning Assessment Council for Aid to Education makes room for appraisal of study design in both its performance task and its selected-response questions.
Consulting abilities : Skill at consulting sources of information comes into play when one seeks information to help resolve a problem, as in Candidate. Ability to find and appraise information includes ability to gather and marshal pertinent information Glaser 6 , to judge whether a statement made by an alleged authority is acceptable Ennis 84 , to plan a search for desired information Facione a: 9 , and to judge the credibility of a source Ennis 9. The ability to detect and analyze arguments is recognized as a critical thinking skill by Facione a: 7—8 , Ennis 9 and Halpern Five items out of 34 on the California Critical Thinking Skills Test Facione b, test skill at argument analysis. The College Learning Assessment Council for Aid to Education incorporates argument analysis in its selected-response tests of critical reading and evaluation and of critiquing an argument.
Judging skills and deciding skills : Skill at judging and deciding is skill at recognizing what judgment or decision the available evidence and argument supports, and with what degree of confidence. It is thus a component of the inferential skills already discussed. Lists and tests of critical thinking abilities often include two more abilities: identifying assumptions and constructing and evaluating definitions.
In addition to dispositions and abilities, critical thinking needs knowledge: of critical thinking concepts, of critical thinking principles, and of the subject-matter of the thinking. We can derive a short list of concepts whose understanding contributes to critical thinking from the critical thinking abilities described in the preceding section. Observational abilities require an understanding of the difference between observation and inference. Questioning abilities require an understanding of the concepts of ambiguity and vagueness. Inferential abilities require an understanding of the difference between conclusive and defeasible inference traditionally, between deduction and induction , as well as of the difference between necessary and sufficient conditions.
Experimenting abilities require an understanding of the concepts of hypothesis, null hypothesis, assumption and prediction, as well as of the concept of statistical significance and of its difference from importance. They also require an understanding of the difference between an experiment and an observational study, and in particular of the difference between a randomized controlled trial, a prospective correlational study and a retrospective case-control study.
Argument analysis abilities require an understanding of the concepts of argument, premiss, assumption, conclusion and counter-consideration. Additional critical thinking concepts are proposed by Bailin et al. According to Glaser 25 , ability to think critically requires knowledge of the methods of logical inquiry and reasoning. If we review the list of abilities in the preceding section, however, we can see that some of them can be acquired and exercised merely through practice, possibly guided in an educational setting, followed by feedback. But the development of such critical thinking abilities as designing an experiment or constructing an operational definition can benefit from learning their underlying theory.
Further, explicit knowledge of quirks of human thinking seems useful as a cautionary guide. Human memory is not just fallible about details, as people learn from their own experiences of misremembering, but is so malleable that a detailed, clear and vivid recollection of an event can be a total fabrication Loftus Critical thinking about an issue requires substantive knowledge of the domain to which the issue belongs. Critical thinking abilities are not a magic elixir that can be applied to any issue whatever by somebody who has no knowledge of the facts relevant to exploring that issue.
For example, the student in Bubbles needed to know that gases do not penetrate solid objects like a glass, that air expands when heated, that the volume of an enclosed gas varies directly with its temperature and inversely with its pressure, and that hot objects will spontaneously cool down to the ambient temperature of their surroundings unless kept hot by insulation or a source of heat. Critical thinkers thus need a rich fund of subject-matter knowledge relevant to the variety of situations they encounter.
This fact is recognized in the inclusion among critical thinking dispositions of a concern to become and remain generally well informed. Experimental educational interventions, with control groups, have shown that education can improve critical thinking skills and dispositions, as measured by standardized tests. For information about these tests, see the Supplement on Assessment. What educational methods are most effective at developing the dispositions, abilities and knowledge of a critical thinker?
Abrami et al. They also found that in these studies a combination of separate instruction in critical thinking with subject-matter instruction in which students are encouraged to think critically was more effective than either by itself. However, the difference was not statistically significant; that is, it might have arisen by chance. Most of these studies lack the longitudinal follow-up required to determine whether the observed differential improvements in critical thinking abilities or dispositions continue over time, for example until high school or college graduation. For details on studies of methods of developing critical thinking skills and dispositions, see the Supplement on Educational Methods.
Scholars have denied the generalizability of critical thinking abilities across subject domains, have alleged bias in critical thinking theory and pedagogy, and have investigated the relationship of critical thinking to other kinds of thinking. McPeck attacked the thinking skills movement of the s, including the critical thinking movement.
He argued that there are no general thinking skills, since thinking is always thinking about some subject-matter. It is futile, he claimed, for schools and colleges to teach thinking as if it were a separate subject. Rather, teachers should lead their pupils to become autonomous thinkers by teaching school subjects in a way that brings out their cognitive structure and that encourages and rewards discussion and argument.
As some of his critics e. To make his argument convincing, McPeck needs to explain how thinking differs from writing and speaking in a way that does not permit useful abstraction of its components from the subject-matters with which it deals. He has not done so. Nevertheless, his position that the dispositions and abilities of a critical thinker are best developed in the context of subject-matter instruction is shared by many theorists of critical thinking, including Dewey , , Glaser , Passmore , Weinstein , and Bailin et al. McPeck argued for a strong subject-specificity thesis, according to which it is a conceptual truth that all critical thinking abilities are specific to a subject.
He did not however extend his subject-specificity thesis to critical thinking dispositions. In particular, he took the disposition to suspend judgment in situations of cognitive dissonance to be a general disposition. Conceptual subject-specificity is subject to obvious counter-examples, such as the general ability to recognize confusion of necessary and sufficient conditions. A more modest thesis, also endorsed by McPeck, is epistemological subject-specificity, according to which the norms of good thinking vary from one field to another.
Epistemological subject-specificity clearly holds to a certain extent; for example, the principles in accordance with which one solves a differential equation are quite different from the principles in accordance with which one determines whether a painting is a genuine Picasso. But the thesis suffers, as Ennis points out, from vagueness of the concept of a field or subject and from the obvious existence of inter-field principles, however broadly the concept of a field is construed. For example, the principles of hypothetico-deductive reasoning hold for all the varied fields in which such reasoning occurs. A third kind of subject-specificity is empirical subject-specificity, according to which as a matter of empirically observable fact a person with the abilities and dispositions of a critical thinker in one area of investigation will not necessarily have them in another area of investigation.
The thesis of empirical subject-specificity raises the general problem of transfer. If critical thinking abilities and dispositions have to be developed independently in each school subject, how are they of any use in dealing with the problems of everyday life and the political and social issues of contemporary society, most of which do not fit into the framework of a traditional school subject? Proponents of empirical subject-specificity tend to argue that transfer is more likely to occur if there is critical thinking instruction in a variety of domains, with explicit attention to dispositions and abilities that cut across domains. But evidence for this claim is scanty. There is a need for well-designed empirical studies that investigate the conditions that make transfer more likely.
It is common ground in debates about the generality or subject-specificity of critical thinking dispositions and abilities that critical thinking about any topic requires background knowledge about the topic. For example, the most sophisticated understanding of the principles of hypothetico-deductive reasoning is of no help unless accompanied by some knowledge of what might be plausible explanations of some phenomenon under investigation. Critics have objected to bias in the theory, pedagogy and practice of critical thinking. Commentators e. The critics, however, are objecting to bias in the pejorative sense of an unjustified favoring of certain ways of knowing over others, frequently alleging that the unjustly favoured ways are those of a dominant sex or culture Bailin These ways favour:.
A common thread in this smorgasbord of accusations is dissatisfaction with focusing on the logical analysis and evaluation of reasoning and arguments. While these authors acknowledge that such analysis and evaluation is part of critical thinking and should be part of its conceptualization and pedagogy, they insist that it is only a part. Paul , for example, bemoans the tendency of atomistic teaching of methods of analyzing and evaluating arguments to turn students into more able sophists, adept at finding fault with positions and arguments with which they disagree but even more entrenched in the egocentric and sociocentric biases with which they began. Martin and Thayer-Bacon cite with approval the self-reported intimacy with their subject-matter of leading researchers in biology and medicine, an intimacy that conflicts with the distancing allegedly recommended in standard conceptions and pedagogy of critical thinking.
Thayer-Bacon contrasts the embodied and socially embedded learning of her elementary school students in a Montessori school, who used their imagination, intuition and emotions as well as their reason, with conceptions of critical thinking as. Thayer-Bacon — Students, she writes, should. Alston Some critics portray such biases as unfair to women. Her charge does not imply that women as a group are on average less able than men to analyze and evaluate arguments. Facione c found no difference by sex in performance on his California Critical Thinking Skills Test. Kuhn — found no difference by sex in either the disposition or the competence to engage in argumentative thinking. The critics propose a variety of remedies for the biases that they allege.
In general, they do not propose to eliminate or downplay critical thinking as an educational goal. Rather, they propose to conceptualize critical thinking differently and to change its pedagogy accordingly. Their pedagogical proposals arise logically from their objections. We would conclude that men and dogs do not significantly differ in the amount of dog-like behaviour they engage in.
The output also shows the results of bootstrapping. The confidence interval ranged from Therefore, this bootstrap confidence interval confirms our conclusion that men and dogs do not differ in amount of dog-like behaviour. Analyse the data on whether the type of music you hear influences goat sacrificing — DarkLord. The bootstrap confidence interval ranges from Therefore, this bootstrap confidence interval confirms our conclusion that there is a significant difference between the number of goats sacrificed when listening to the song containing the backward message compared to when listing to the song played normally.
Test whether the number of offers was significantly different in people listening to Bon Scott than in those listening to Brian Johnson, using an independent t -test and bootstrapping. Do your results differ from Oxoby ? Oxoby Offers. The bootstrap confidence interval ranged from We could report these results as:. McNulty et al. The data are in McNulty et al. Remember that you can move variables in the dialog box by dragging them, or selecting them and cliking. We need to specify three variables:. Simple moderation analysis is represented by model 1, so activate this drop-down list and select. The finished dialog box looks like this:. Click on and set these options:. Because our data file has variables with names longer than 8 characters, click on and set the option to allow long names:.
Back in the main dialog box, click to run the analysis. The first part of the output contains the main moderation analysis. To interpret the moderation effect we can examine the simple slopes, which are shown in the next part of the output. Essentially, the output shows the results of two different regressions: the regression for attractiveness as a predictor of support 1 when the value for gender is 0.
Because husbands were coded as zero, this represents the value for males; and 2 when the value for gender is 0. Because wives were coded as 1, this represents the female end of the gender spectrum. From what we have already learnt about regression we can interpret the two models as follows:. These results tell us that the relationship between attractiveness of a person and amount of support given to their spouse is different for men and women. Specifically, for women, as attractiveness increases the level of support that they give to their husbands increases, whereas for men, as attractiveness increases the amount of support they give to their wives decreases:. If you set the options that I suggested in task 1, your output should contain the values that you need to plot:.
Create a data file with a variable that codes Attractiveness as low, mean or high, a variable that codes Gender as husbands or wives, and a variable that contains the values of Support from the output. The data file will look like this:. Use the chart builder to draw a line chart with Attractiveness on the x -axis, Support on the y -axis and has different coloured lines for Gender. The dialog box will look like this:. The resulting graph confirms our results from the simple slops analysis in the previous task. The direction of the relationship between attractiveness and support is different for men and women: the two regression lines slope in different directions.
Specifically, for husbands blue line the relationship is negative the regression line slopes downwards , whereas for wives green line the relationship is positive the regression line slopes upwards. Additionally, the fact that the lines cross indicates a significant interaction effect moderation. So basically, we can conclude that the relationship between attractiveness and support is positive for wives more attractive wives give their husbands more support , but negative for husbands more attractive husbands give their wives less support than unattractive ones.
Using the same data as in Tasks 1 and 2, find out if this relationship is moderated by gender? In this chapter we tested a mediation model of infidelity for Lambert et al. Simple mediation analysis is represented by model 4 the default. If the drop-down list is not already set to then select this option. The first part of the output shows us the results of the simple regression of commitment predicted from pornography consumption. The next part of the output shows the results of the regression of number of hook-ups predicted from both pornography consumption and commitment. The negative b for commitment tells us that as commitment increases, number of hook-ups declines and vice versa , but the positive b for consumptions indicates that as pornography consumption increases, the number of hook-ups increases also.
These relationships are in the predicted direction:. The next part of the output shows the total effect of pornography consumption on number of hook-ups outcome. As is the case when we include relationship commitment in the model, pornography consumption has a positive relationship with number of hook-ups as shown by the positive b-value :. The next part of the output is the most important because it displays the results for the indirect effect of pornography consumption on number of hook-ups i. The first bit of new information is the Indirect Effect of X on Y , which in this case is the indirect effect of pornography consumption on the number of hook-ups.
Put another way, relationship commitment is a mediator of the relationship between pornography consumption and the number of hook-ups. The rest of the output contains various standardized forms of the indirect effect. In each case they are accompanied by a bootstrapped confidence interval. In other words, there is mediation. Fit a linear model predicting life satisfaction from the type of animal to which a person was married. Write out the final model. Looking at the coefficients, we can see that type of animal wife significantly predicted life satisfaction because the p-value is less than 0. Remember that goat was coded as 0 and dog was coded as 1, therefore as type of animal wife increased from goat to dog, life satisfaction also increased.
In other words, men who were married to dogs were more satisfied than those who were married to goats. By replacing the b -values in the equation for the linear model see the book , the specific model is:. Repeat the analysis in Task 6 but include animal liking in the first block, and type of animal in the second block. Do your conclusions about the relationship between type of animal and life satisfaction change? This means that even after adjusting for the effect of love of animals, type of animal wife still significantly predicted life satisfaction.
The values in this output tell us that love of animals explains When type of animal wife is factored in as well, Using the GlastonburyDummy. Back in the main dialog box click to fit the model. Tablets like the iPad are very popular. A company owner was interested in how to make his brand of tablets more desirable. Test his theory that the relationship between cool advertising and product desirability is mediated by how cool people think the product is Tablets. The first part of the output shows us the results of the simple regression of how cool the product is perceieved as being predicted from cool advertising. The next part of the output shows the results of the regression of Desirability predicted from both how cool people think the product is and how cool people think the advertising is.
The next part of the output shows the total effect of cool advertising on product desirability outcome. You will get this bit of the output only if you selected Total effect model. The total effect is the effect of the predictor on the outcome when the mediator is not present in the model. The next part of the output is the most important because it displays the results for the indirect effect cool advertising on product desirability i. The first bit of new information is the Indirect Effect of X on Y , which in this case is the indirect effect of cool advertising on the product desirability.
For one module I wandered around with a large cane and beat anyone who asked daft questions or got questions wrong punish. In the second I encouraged students to discuss things that they found difficult and gave anyone working hard a nice sweet reward. The data are in the file Teach. Fit a model with planned contrasts to test the hypotheses that: 1 reward results in better exam results than either punishment or indifference; and 2 indifference will lead to significantly better exam results than punishment.
The means should correspond to those plotted in the graph. These diagnostics are important for interpretation later on. It looks as though marks are highest after reward and lowest after punishment:. We should routinely look at the robust F s. Because the observed significance value is less than 0. Because there were specific hypotheses I specified some contrasts. The next part of the output shows the codes I used. Note that the codes for each contrast sum to zero, and that in contrast 2, reward has been coded with a 0 because it is excluded from that contrast.
It is safest to interpret the part of the table labelled Does not assume equal variances. Looking at the means, this tells us that the average mark after reward was significantly higher than the average mark for punishment and indifference combined. The second contrast together with the descriptive statistics tells us that the marks after punishment were significantly lower than after indifference again, significantly different because the value in the column labelled Sig. As such we could conclude that reward produces significantly better exam grades than punishment and indifference, and that punishment produces significantly worse exam marks than indifference.
So lecturers should reward their students, not punish them. Children wearing superhero costumes are more likely to harm themselves because of the unrealistic impression of invincibility that these costumes could create. I can relate to the imagined power that a costume bestows upon you; indeed, I have been known to dress up as Fisher by donning a beard and glasses and trailing a goat around on a lead in the hope that it might make me more knowledgeable about statistics.
Imagine we had data Superhero. Fit a model with planned contrasts to test the hypothesis that different costumes give rise to more severe injuries. The output tells us that wearing a Superman costume was significantly different from wearing either a Hulk or Ninja Turtle costume in terms of injury severity, but that none of the other groups differed significantly. The post hoc test has shown us which differences between means are significant; however, if we want to see the direction of the effects we can look back to the means in the table of descriptives Output 7. We can conclude that wearing a Superman costume resulted in significantly more severe injuries than wearing either a Hulk or a Ninja Turtle costume.
In Chapter 7 there are some data looking at whether eating soya meals reduces your sperm count. Why do you think this difference has arisen? A boxplot of the data suggests that 1 scores within conditions are skewed; and 2 variability in scores is different across groups. The table of descriptive statistics suggests that as soya intake increases, sperm counts decrease as predicted:.
Note that the Welch test agrees with the non-parametric test in Chapter 7 in that the significance of F is below the 0. However, the Brown-Forsythe F is non-significant it is just above the threshold. This illustrates the relative superiority with respect to power of the Welch procedure. The unadjusted F is also not significant. If we were using the unadjusted F then we would conclude that, because the observed significance value is greater than 0. This may seem strange because if you read Chapter 7, from where this example came, the Kruskal—Wallis test produced a significant result.
The reason for this difference is that the data violate the assumptions of normality and homogeneity of variance. As I mention in Chapter 7, although parametric tests have more power to detect effects when their assumptions are met, when their assumptions are violated non-parametric tests have more power! This example was arranged to prove this point: because the parametric assumptions are violated, the non-parametric tests produced a significant result and the parametric test did not because, in these circumstances, the non-parametric test has the greater power.
Also, the Welch F , which does adjust for these violations yields a significant result. If we wanted to test this experimentally, we could get six groups of people and strap a mobile phone on their heads, then by remote control turn the phones on for a certain amount of time each day. After six months, we measure the size of any tumour in mm3 close to the site of the phone antenna just behind the ear. The six groups experienced 0, 1, 2, 3, 4 or 5 hours per day of phone microwaves for six months. Do tumours significantly increase with greater daily exposure? The data are in Tumour. Because there were no specific hypotheses I just carried out post hoc tests and stuck to my favourite Games—Howell procedure because variances were unequal.
It is clear from that each group of participants is compared to all of the remaining groups. First, the control group 0 hours is compared to the 1, 2, 3, 4 and 5 hour groups and reveals a significant difference in all cases all the values in the column labelled Sig. In the next part of the table, the 1 hour group is compared to all other groups. Again all comparisons are significant all the values in the column labelled Sig. In fact, all of the comparisons appear to be highly significant except the comparison between the 4 and 5 hour groups, which is non-significant because the value in the column labelled Sig.
The effect size indicated that the effect of phone use on tumour size was substantial. Using the Glastonbury data from Chapter 11 GlastonburyFestival. Compare the results to those described in Chapter Compare this table to the one in Chapter 11, in which we analysed these data as a regression reproduced below :. The tables are exactly the same!
What about the contrasts? The table below shows the codes I used to get simple contrasts that compare each group to the no affiliation group, and the subsequent contrasts:. Again they are the same the values of the contrast match the unstandardized B , and the standard errors, t -values and p -values match :. Labcoat Leni 7. Read Labcoat Leni 7. The first part of the output tells usb that the group fetishistic, non-fetishistic or control group had a significant effect on the time spent near the terrycloth object. These results show that male quails do show fetishistic behaviour the time spent with the terrycloth.
Look at the output to see from where the values reported in the paper come. The first part of the output tells usb that the group fetishistic, non-fetishistic or control group had a significant effect on copulatory efficiency. A sociologist wanted to compare murder rates Murder each month in a year at three high-profile locations in London Street. Fit a model with bootstrapping on the post hoc tests to see in which streets the most murders happened. The data are in Murder. These means will be important in interpreting the post hoc tests later.
The next part of the output shows us the F -statistic for predicting mean murders from location. For all tests, because the observed significance value is less than 0. It is clear from the output that each street is compared to all of the remaining streets. If we look at the values in the column labelled Sig. The question asked us to bootstrap the post hoc tests and this has been done. We can see that the difference between Ruskin Avenue and Rue Morgue remains significant after bootstrapping the confidence intervals; we can tell this because the confidence intervals do not cross zero for this comparison.
Surprisingly, it appears that the difference between Acacia Avenue and Rue Morgue is now significant after bootstrapping the confidence intervals, because again the confidence intervals do not cross zero. The mean values in the table of descriptives tell us that Rue Morgue had a significantly higher number of murders than Ruskin Avenue and Acacia Avenue; however, Acacia Avenue did not differ significantly in the number of murders compared to Ruskin Avenue.
A few years back I was stalked. I imagined a world in which a psychologist tried two different therapies on different groups of stalkers 25 stalkers in each group — this variable is called group. To the first group he gave cruel-to-be-kind therapy every time the stalkers followed him around, or sent him a letter, the psychologist attacked them with a cattle prod. The psychologist measured the number of hours stalking in one week both before stalk1 and after stalk2 treatment Stalker.
Analyse the effect of therapy on stalking behaviour after therapy, covarying for the amount of stalking behaviour before therapy. First, conduct an ANOVA to test whether the number of hours spent stalking before therapy our covariate is independent of the type of therapy our predictor variable. Your completed dialog box should look like:. In other words, the mean number of hours spent stalking before therapy is not significantly different in the cruel-to-be-kind and psychodyshamic therapy groups.
This result is good news for using stalking behaviour before therapy as a covariate in the analysis. Click to access the options dialog box, and select these options:. The output shows that the covariate significantly predicts the outcome variable, so the hours spent stalking after therapy depend on the extent of the initial problem i. More interesting is that after adjusting for the effect of initial stalking behaviour, the effect of therapy is significant.
To interpret the results of the main effect of therapy we look at the adjusted means, which tell us that stalking behaviour was significantly lower after the therapy involving the cattle prod than after psychodyshamic therapy after adjusting for baseline stalking. To interpret the covariate create a graph of the time spent stalking after therapy outcome variable and the initial level of stalking covariate using the chart builder:. The resulting graph shows that there is a positive relationship between the two variables: that is, high scores on one variable correspond to high scores on the other, whereas low scores on one variable correspond to low scores on the other. A marketing manager tested the benefit of soft drinks for curing hangovers. He took 15 people and got them drunk.
Fit a model to see whether people felt better after different drinks when covarying for how drunk they were the night before. This result is good news for using the variable drunk as a covariate in the analysis. Click to access the contrasts dialog box. In this example, a sensible set of contrasts would be simple contrasts comparing each experimental group with the control group, water. Select simple from the drop down list and specifying the first category as the reference category. The final dialog box should look like this:. The output shows that the covariate significantly predicts the outcome variable, so the drunkenness of the person influenced how well they felt the next day.
The parameter estimates for the model selected in the options dialog box are computed having paramterized the variable drink using two dummy coding variables that compare each group against the last the group coded with the highest value in the data editor, in this case the cola group. The beta values literally represent the differences between the means of these groups and so the significances of the t -tests tell us whether the group means differ significantly.
From these estimates we could conclude that the cola and water groups have similar means whereas the cola and Lucozade groups have significantly different means. The contrasts compare level 2 Lucozade against level 1 water as a first comparison, and level 3 cola against level 1 water as a second comparison. The adjusted group means should be used for interpretation. The adjusted means show that the significant difference between the water and the Lucozade groups refelects people feeling better in the Lucozade group than the water group. To interpret the covariate create a graph of the outcome well , y -axis against the covariate drunk , x -axis using the chart builder:. The resulting graph shows that there is a negative relationship between the two variables: that is, high scores on one variable correspond to high scores on the other, whereas low scores on one variable correspond to low scores on the other.
The more drunk you got, the less well you felt the following day. Therefore we get:. The highlight of the elephant calendar is the annual elephant soccer event in Nepal google search it. A heated argument burns between the African and Asian elephants. In , the president of the Asian Elephant Football Association, an elephant named Boji, claimed that Asian elephants were more talented than their African counterparts. I was called in to settle things. I collected data from the two types of elephants elephant over a season and recorded how many goals each elephant scored goals and how many years of experience the elephant had experience.
Analyse the effect of the type of elephant on goal scoring, covarying for the amount of football experience the elephant has Elephant Football. This result is good news for using the variable experience as a covariate in the analysis. After adjusting for the effect of experience, the effect of elephant is also significant. In other words, African and Asian elephants differed significantly in the number of goals they scored. To interpret the covariate create a graph of the outcome goals , y -axis against the covariate experience , x -axis using the chart builder:. The resulting graph shows that there is a positive relationship between the two variables: the more prior football experience the elephant had, the more goals they scored in the season.
In Chapter 4 Task 6 we looked at data from people who had been forced to marry goats and dogs and measured their life satisfaction and, also, how much they like animals Goat or Dog. Fit a model predicting life satisfaction from the type of animal to which a person was married and their animal liking score covariate. First, check that the predictor variable wife and the covariate animal are independent. This result is good news for using the variable love of animals as a covariate in the analysis. After adjusting for the effect of love of animals, the effect of animal is also significant. In other words, life satisfaction differed significantly in those married to goats compared to those married to dogs.
The resulting graph shows that there is a positive relationship between the two variables: the greater ones love of animals, the greater ones life satisfaction. Compare your results for Task 6 to those for the corresponding task in Chapter What differences do you notice and why? Animal liking was entered in the first block, and type of animal wife in the second block:. In other words, after adjusting for the effect of love of animals, type of animal wife significantly predicted life satisfaction.
The conclusions are the same, but more than that:. Imagine we also had information about the baseline number of mischievous acts in these participants mischief1. Fit a model to see whether people with invisibility cloaks get up to more mischief than those without when factoring in their baseline level of mischief Invisibility Baseline. First, check that the predictor variable cloak and the covariate mischief1 are independent.
This result is good news for using baseline mischief as a covariate in the analysis. After adjusting for baseline mischief, the effect of cloak is also significant. In other words, mischief levels after the intervention differed significantly in those who had an invisibility cloak and those who did not. To interpret the covariate create a graph of the outcome mischief2 , y -axis against the covariate mischief1 , x -axis using the chart builder:. The resulting graph shows that there is a positive relationship between the two variables: the greater ones mischief levels before the cloaks were assigned to participants, the greater ones mischief after the cloaks were assigned to participants.
To test the idea I took two groups age : young people which I arbitrarily decided was under 40 years of age and older people above 40 years of age. Fit a model to test my idea Fugazi. Click to access the Post Hoc dialog box, and select these options:. The graph of the main effect of music shows that the significant effect is likely to reflect the fact that ABBA were rated overall much more positively than the other two artists.
First, ratings of Fugazi are compared to ABBA, which reveals a significant difference the value in the column labelled Sig. In the next part of the table, ratings of ABBA are compared first to Fugazi which repeats the finding in the previous part of the table and then to Barf Grooks, which reveals a significant difference the significance value is below 0. The final part of the table compares Barf Grooks to Fugazi and ABBA, but these results repeat findings from the previous sections of the table. The main effect of age was not significant, and the graph shows that when you ignore the type of music that was being rated, older people and younger people, on average, gave almost identical ratings. The interaction effect is shown in the plot of the data split by type of music and age.
Ratings of Fugazi are very different for the two age groups: the older ages rated it very low, but the younger people rated it very highly. A reverse trend is found if you look at the ratings for Barf Grooks: the youngsters give it low ratings, while the wrinkly ones love it. For ABBA the groups agreed: both old and young rated them highly. The interaction effect reflects the fact that there are age differences for some bands Fugazi, Garf Brooks but not others ABBA and that the age difference for Fugazi is in the opposite direction than for Barf.
First we use the mean squares and degrees of freedom in the summary table and the sample size per group to compute sigma for each effect:. We next need to estimate the total variability, and this is the sum of these other variables plus the residual mean squares:. In Chapter 5 we used some data that related to male and female arousal levels when watching The Notebook or a documentary about notebooks Notebook. Fit a model to test whether men and women differ in their reactions to different types of films. The graph of the main effect of sex shows that the significant effect is likely to reflect the fact that males experienced higher levels of psychological arousal in general than women when the type of film is ignored.
The main effect of the film was also significant, and the graph shows that when you ignore the biological sex of the participant, psychological arousal was higher during the notebook than during a documentary about notebooks. The interaction effect is shown in the plot of the data split by type of film and sex of the participant. Psychological arousal is very similar for men and women during the documentary about notebooks it is low for both sexes. However, for the notebook men experienced greater psychological arousal than women. The interaction is likley to reflect that there is a difference between men and women for one type of film the notebook but not the other the documentary about notebooks.
In Chapter 4 we used some data that related to learning in men and women when either reinforcement or punishment was used in teaching Method Of Teaching. The graphed means suggest that for men, using an electric shock resulted in higher exam scores than being nice, whereas for women, the being nice teaching method resulted in significantly higher exam scores than when an electric shock was used. At the start of this Chapter I described a way of empirically researching whether I wrote better songs than my old bandmate Malcolm, and whether this depended on the type of song a symphony or song about flies. The outcome variable was the number of screams elicited by audience members during the songs.
Draw an error bar graph lines and analyse these data Escape From Inside. To produce the graph, access the chart builder and selecta multiple line graph from the gallery. In the Element Properties dialog box remember to select to add error bars:. Therefore, although the main effect of songwriter suggests that Malcolm was a better songwriter than Andy, the interaction tells us that this effect is driven by Andy being poor at writing symphonies. Note that all we change is compare FaceType to compare Alcohol. The pertinent part of the output is:. Think back to the chapter. These tests reflect the fact that ratings of unattractive faces go up as more alcohol is consumed, but for attractive faces ratings are quite stable across doses of alcohol.
These injuries were attributed mainly to muscle and tendon strains. A researcher hypothesized that a stretching warm-up before playing Wii would help lower injuries, and that athletes would be less susceptible to injuries because their regular activity makes them more flexible. The outcome was a pain score out of 10 where 0 is no pain, and 10 is severe pain after playing for 4 hours injury. Fit a model to test whether athletes are less prone to injury, and whether the prevention programme worked Wii.
This design is a 2 Athlete: athlete vs. To fit the model, access the main dialog box and:. The graph shows that, on average, athletes had significantly lower injury scores than non-athletes. The graph shows that stretching significantly decreased injury score compared to not stretching. However, the two-way interaction with athletes will show us that this is true only for athletes and non-athletes who played on the Wii, not for those in the control group you can also see this pattern in the three-way interaction graph. This is an example of how main effects can sometimes be misleading. The graph shows not surprisingly that playing on the Wii resulted in a significantly higher injury score compared to watching other people playing on the Wii control.
The graph of the interaction effect shows that not taking into account playing vs. Parallel lines usually indicate a non-significant interaction effect, and so it is not surprising that the interaction between stretch and athlete was non-significant. The interaction graph shows that not taking stretching into account non-athletes had low injury scores when watching but high injury scores when playing whereas athletes had low injury scores when both playing and watching. The interaction graph shows that not taking athlete into account stretching before playing on the Wii significantly decreased injury scores, but stretching before watching other people playing on the Wii did not significantly reduce injury scores.
This is not surprising as watching other people playing on the Wii is unlikely to result in sports injury! What this actually means is that the effect of stretching and playing on the Wii on injury score was different for athletes than it was for non-athletes. In the presence of this significant interaction it makes no sense to interpret the main effects. The interaction graph for this three-way effect shows that for athletes, stretching and playing on the Wii has very little effect: their mean injury score is quite stable across the two conditions whether they played on the Wii or watched other people playing on the Wii, stretched or did no stretching.
However, for the non-athletes, watching other people play on the Wii compared to not stretching and playing on the Wii rapidly declines their mean injury score. The interaction tells us that stretching and watching rather than playing on the Wii both result in a lower injury score and that this is true only for non-athletes. In short, the results show that athletes are able to minimize their injury level regardless of whether they stretch before exercise or not, whereas non-athletes only have to bend slightly and they get injured! A group of students investigated the consistency of marking by submitting the same essays to four different lecturers.
The outcome was the percentage mark given by each lecturer and the predictor was the lecturer who marked the report TutorMarks. Compute the F -statistic for the effect of marker by hand. The mean mark that each essay received and the variance of marks for a particular essay are shown too. Now, the total variance within essay marks will in part be due to different lecturers marking some are more critical and some more lenient , and in part by the fact that the essays themselves differ in quality individual differences.
Our job is to tease apart these sources. This tells us, for example, that the grand mean the mean of all scores is We take each score, substract from it the mean of all scores The n s are the number of scores on which the variances are based i. To get the total degrees of freedom we add the df for each essay. The mean mark awarded by each tutor is:. We now know that there are units of variation to be explained in our data, and that the variation across our conditions accounts for units. Of these units, our experimental manipulation can explain units.
The F -statistic is calculated by dividing the model mean squares by the residual mean squares:. This value of F can be compared against a critical value based on its degrees of freedom which are 3 and 21 in this case. The first part of the output tells us about sphericity. The second part of the output tells us about the main effect of marker. If we look at the Greenhouse-Geisser corrected values, we would conclude that tutors did not significantly differ in the marks they award, F 1.
If, however, we look at the Huynh-Feldt corrected values, we would conclude that tutors did significantly differ in the marks they award, F 2. Which to believe then? The best course of action here would be report both results openly, compute some effect sizes and focus more on the size of the effect than its p -value. The final part of the output shows the post hoc tests. Assuming we want to interpret these which, if we do, we might be speculative unless the effect size for the main effect seems meaningul. The only significant difference between group means is between Prof Field and Prof Smith. Looking at the means of these markers, we can see that I give significantly higher marks than Prof Smith. However, there is a rather anomalous result in that there is no significant difference between the marks given by Prof Death and myself, even though the mean difference between our marks is higher The reason is the sphericity in the data.
You will find that there is a very high positive correlation between the marks given by Prof Smith and myself indicating a low level of variability in our data. However, there is a very low correlation between the marks given by Prof Death and myself indicating a high level of variability between our marks. It is this large variability between Prof Death and myself that has produced the non-significant result despite the average marks being very different this observation is also evident from the standard errors. However, we can obtain it as follows:. You should get:. The next step is to convert this to a mean squares by dividing by the degrees of freedom, which in this case are the number of essays minus Remember that because the main F -statistic was not significant we should not report further analysis.
I fitted 20 people with incredibly sophisticated glasses that tracked their eye movements yes, I am making this up …. Over four nights I plied them with either 1, 2, 3 or 4 pints of strong lager in a nightclub and recorded how many different people they eyed up i. Is there an effect of alcohol on the tendency to eye people up? The second part of the output tells us about the main effect of alcohol. These show that the only significant difference was between 2 and 3 pints of alcohol. In the previous chapter we came across the beer-goggles effect.
In that chapter, we saw that the beer-goggles effect was stronger for unattractive faces. We took a follow-up sample of 26 people and gave them doses of alcohol 0 pints, 2 pints, 4 pints and 6 pints of lager over four different weeks. We asked them to rate a bunch of photos of unattractive faces in either dim or bright lighting. The outcome measure was the mean attractiveness rating out of of the faces, and the predictors were the dose of alcohol and the lighting conditions BeerGogglesLighting.
Do alcohol dose and lighting interact to magnify the beer goggles effect? The second part of the output tells us about the main effects of alcohol and lighting, and also their interaction.Even a Cat Experimentation Argumentative Essay error in dosage or administration Cat Experimentation Argumentative Essay leave Cat Experimentation Argumentative Essay prisoner conscious but paralyzed while Cat Experimentation Argumentative Essay, a sentient witness of his or her own asphyxiation. State Defenders Assn. To interpret the covariate create a Cat Experimentation Argumentative Essay of the outcome goalsy -axis against the covariate experiencex Cat Experimentation Argumentative Essay using the chart builder:. The table Cultural Differences In Ancient Greece the diagram confirms this, and tells us Cat Experimentation Argumentative Essay significance values of the comparisons. My favourite subject is Creative writing. Ethos Pathos In 12 Angry Men experimentation and cruelty committed Psychodynamic Perspective Essay be banned Cat Experimentation Argumentative Essay animals share Apollo 13 Characteristics same basic […]. Why Cat Experimentation Argumentative Essay