1SLE251 Project Assignment– 40% of final markData collection and analysis, and professional contextDue date:Submission of final report: 8pm Friday 28th MaySubmit via Dropbox on the unit site – hardcopy or emailed reports will not beaccepted.NOTE: Although this project allows for the data collection to be done in groups of1,2 or 3 – ALL WRITE-UPS MUST BE INDIVIDUAL REPORTS.Objective:1) Data collection, analysis and write-up (20% of final unit mark)You will collect and analyse some biological data in order to answer a biologicalquestion. These data may be in the form of observations or experimentalmeasurements that you yourself have collected, or else taken from internetresources. DO NOT simply copy an analysis from another source, or from a previousassignment you have done for another unit.We strongly encourage you to formulate your own question that you wish to testand the practicals from Week 5 onwards are there to help you achieve this andprovide feedback for you. However, on the SLE251 unit site we have suggested someexperiments or online data sets, and relevant questions, that you may wish to test ifyou cannot think of an appropriate thing to measure and test yourself.2) Professional context (20% of final unit mark)As the SLE251 unit also forms part of your career education, we require you toidentify the professional context in which your work and the skills learnt are relevantand valued, and articulate how the project has helped provide you with evidence ofsome fundamental transferable skills: critical thinking, problem solving,communication, research skills and discipline specific knowledge; for inclusion inyour resumé or in job applications and interviews. You will also find 2 jobs currentlybeing advertised (one science-based, one non-science) and get you to address 42relevant selection criteria from the job ads (2 for each job) using the STAR techniqueyou will learn about in classes.Approach:For the data collection you can work in groups of up to three students – so either byyourself or with one or two other students. When you’ve collected and analysed thedata you will need to write a report about what you have done. NOTE: Even if youhave been doing this as a group project, you should each write a report in yourown words – DO NOT COPY EACH OTHER’S REPORTSEither by yourself, or with your group, plan a simple experiment or set of observations.Your study will take one of two forms: eithera) examine the difference between the mean measurements taken from two ormore groups/samples.b) examine whether two sets of continuous (scale) measurements are related toeach otherIn the type a) study ideally you should collect at least 20 measurements from eachgroup in your study, and that those measurements should be continuous, orapproximately continuous (for more information, see below). You need to follow the“scientific method”, recording your thoughts/methods/results at each step:In the type b) study you should collect at least 20 measurements for each of the twomeasurements you are making – each of these two types measurements should betaken from the same individuals/samples (e.g. measuring leaf litter cover andinvertebrate abundance, or measures of salinity and vegetation quality in differentstreams).Here’s how you should you approach the task if you are planning to test your ownquestion:1. Make an observation about the world – ideally this should be something vaguelybiological.2. Your observation should raise a question.33. Formulate a hypothesis which helps explain your observation. Your hypothesis may,or may not, be part of a larger theory that you have about the world. Be careful tomake the hypothesis clear, specific and plausible, and if it does not already read like aprediction, it should readily generate predictions.4. Your hypothesis may already be in the form of a prediction. If it is not, make aspecific prediction about an experiment you could perform or a set of observationsyou could make. In the type a) study, your predictions should take the form: “group A,B and C will be different”, “A will be bigger than B or C” or “A will be smaller than B orC”. In the type b) study your predictions should take the form “measurements X andY are related to each other”, “X is positively related to Y” or “X is negatively related toY”.5. Use your prediction and/or hypothesis to create a specific null hypothesis whichyour experiment/observations will test. The null hypothesis should be paired with thealternative hypothesis – the two should be mutually exclusive.6. Plan an experiment or a use a set of observations to test the prediction you havemade. The study should be as simple as possible, and should easily yield at least 20data points in each category. Think carefully about discriminating between your nulland alternative hypotheses: How accurate will your measurements need to be? Howcan you avoid measurement bias? What confounding variables will there be?Obviously, you needn’t worry too much about how generally informative your studywill be – it is necessarily going to be rather narrow in scope, but do consider biologicalrealism, especially if you’re planning an experiment.A note about ethicsDeakin University, as with all institutions in Australia, has very strict protocols aboutconducting studies on humans or vertebrate animals. Consequently, unfortunately,we cannot allow you to carry out experimental investigative studies on people orvertebrate animals. There are no exceptions to this. Even apparently innocuousstudies (such as seeing how long it takes for people to complete a crossword puzzleand relating this to age), fall under the Ethics guidelines. Note that using alreadycollected data (say from an internet resource) is fine, because these data will havebeen collected under appropriate regulations and are in the public domain.4Observational studies that involve no interference with the persons or animals beingobserved (such as bird watching, or observing people’s behaviour in supermarkets)may be acceptable. To know where the line is drawn consider whether your studyactively involves you addressing/interacting with a person (e.g. to ask a question), orhandling an animal. If so, then that is not acceptableExperimental work, however, is fine for plants and invertebrate animals (with theexception of cephalopods (octopus and squid) and decapod crustaceans (crabs,yabbies, lobster) – although we assume it is unlikely that any of you are planningexperiments on these animals).If you have any doubts about whether what you are planning is appropriate, pleaseask your demonstrators or Matt (Burwood) or Pete (Waurn Ponds).THERE will be severe penalties if you fail to comply with these ethics requirements.Two examplesHere is an example of how your project might evolve (note that when you come towrite this up, you should write in continuous prose in the past tense):A type a) study (a categorical predictor of continuous response variable)Observation: There seem to be a lot of magpies in suburban parks.Question: Are there magpies congregating where humans are more likely to bepresent?Theory: Magpies benefit from living in the city perhaps through the presence of foodand water made available by humansPrediction: There are more magpies in parks within the city boundary than areas ofparkland (bushland) outside of the city.Hypothesis: Same as prediction.Null hypothesis: Magpies in suburban parks and bushland areas exist at the samedensities.Experiment/Observations: Pick a suburban park and walk around it for 20 minutesand count the number of magpies that you see. Repeat this for other areas ofsuburban parkland, and then compare with areas of the bush outside the city.Examples of things to consider: measurement precision (e.g. are you counting the5same magpies twice, are you walking a continuous route, rather than doubling backon yourself) – confounding variables/effects (e.g. could weather conditions influencehow many magpies you see on a particular day? Perhaps bushland areas have thickervegetation making it more difficult to see magpies. Any difference you find in suchcircumstances may be the result of the type of habitat you sample (open versuswooded), not the identity of the site). Compare average magpie count (continuousresponse variable) for suburban vs. bushland areas (categorical predictor variable).A type b) study (a continuous predictor of a continuous response variable)Observation: Tall people seems to be better at sprinting than short peopleQuestion: Is it an advantage to be tall if you are a sprinter?Theory: Tall people have long legs, which means they have longer stride length and socan cover ground more quickly.Prediction: Taller people will run 100m in a faster time than shorter peopleHypothesis: There is a negative relationship between human height and time taken torun 100m (taller people take less time)Null hypothesis: There is no relationship between human height and time taken torun 100mExperiment/Observations: Analyse the results of all the Men’s 100m races at theLondon Olympics (http://www.london2012.com/athletics/event/men-100m/). Foreach heat click on the runners to get information on their height (in cm) and then alsonote their time they took to run 100m. Examples of things to consider: repeatsampling: some individuals will have run more than one heat (final, semi-final etc.) soyou may need to use a single measure (mean time), some individuals from well-fundedcountries are likely to have had access to better training than others – how could youcontrol for this?Doing the analysisThe data that you collect should be of a form that can be analysed using either be at-test, or analysis of variance (ANOVA) for a type a) study or correlation or linearregression for the type b) study. These topics are covered in Lectures in weeks 4-8and in Practicals 2-4. Don’t forget that when the predictor, explanatory variables are6categorical or have been chosen to be treated as categorical variables that ANOVA isused. A t-test is a particular form of ANOVA that compares two group means,whereas ANOVA more generally can compare multiple group means (it can be usedfor two as well, like a t-test). Correlation and regression are for when both thepredictor and response variables are numeric continuous variables. ANOVA is forwhen predictor variables are categorical (they are called ‘factors’).Remember to check the statistical assumptions of t-tests, ANOVAs and, correlationand regression (normality of the continuous (scale) variables, equal variances in theresponse variable for different factors, linear relationship) first – use boxplots andscatterplots. Use transformations (log or square root) if necessary – remember, youcan’t log zeros so if you have zero values, you should add a small constant to eachvalue. Always re-check the assumptions after a transformation to make sure ithelped meet assumptions.Writing your report:See the ‘Format and Layout’ pdf on the unit site for details of how to lay out yourproject. However, here are some other hints that may help you:Data analysis sectionThe Introduction should be brief paragraph or two (200 words) that includes thequestion you are trying to answer, some background to the study including thenature of the observations that led you to ask the question, and why the question isinteresting and what theory it provide an answer to. It should clearly lead to theaims of the project, and a clear statement of the hypothesis being tested. Thebackground should be supported with reference to two appropriate sources (e.g.book, journal article, government report)The Methods should include all the details of how you conducted your study andanalysed your data (e.g. what tests were used, what assumption tests were followed,what transformation were employed etc.). You should be concise, but give enoughinformation so that somebody else could repeat your work. Make sure that you write7in the past tense – you are writing a report on what you have done, not a set ofinstructions for someone in the future. If you are taking data from an online source,you do not need to provide every methodological detail of how those data werecollected in the first place, but some indication should be given so that the meaningand context of the data is clear – and you must of course describe how you extractedthe data and any criteria you used in processing those data.In the Results you need to state the results of the analysis of your data, and state whatthey mean (see examples below). You should be very concise, and should not includeany interpretation of your results in this section (that goes in the Discussion). Youshould include at least one figure showing the difference between the groups thatyou sampled (i.e. a box plot, or plot of means) for the type a) study, or the relationshipbetween the two measurements (i.e. a scatterplot with linear regression line shown)for the type b) study. Any figure shown should be referred to in the text and have clearfigure legends explaining the figure. You should give the results of all the statisticaltests you conducted (and state what these tests were). At a minimum, for the type a)analysis you should present the the t or F values, the sample sizes or degrees offreedom and the probability values associated with the test that there is no differencebetween the group means. It may also be appropriate to report the group means withassociated standard errors, if not clearly shown on the figure. For the type b) analysisyou should present the correlation coefficient (r) or the linear regression model (slopeand intercept) the sample size, plus the probability associated with the test that thecorrelation coefficient or slope is zero, and the measure of explained variance (r2) –see lectures and Worksheet 4 output. You may need to decide which is moreappropriate to report, the correlation test, or the regression equation (consider whatyou wish to infer). Also, please do not directly cut and paste statistical output from Rinto your report. You should extract the information needed from the output andinclude it in the text of your results section. However, appropriate graphs can becopied in to your report from R.For the Discussion: In two or three paragraphs (max 300 words), discuss thesignificance and meaning of your results (see examples below). Things to consider8include: If you found a significant difference between groups, or significant associationbetween two measurements, what does this tell you about the answer to the questionyou posed in the introduction? Do you think the effect is likely to be biologicallyimportant or typical? Is it possible that there is an alternative explanation for yourdata (think about confounding variables)? If you failed to find a significant result,should you scrap your theory/hypothesis, modify it, or collect more/different data?Would more accurate or precise measurements or a larger sample size have made adifference? If you identify problems, suggest what could be done to improve theanalysis. Be careful not to “waffle” in the conclusions: only mention the points thatyou judge are most important. The discussion should place the work in the context ofother studies in the field, and hence should also include 2 appropriate references (e.g.book, journal articles, government reports) – two references should be different fromthose mentioned in the introduction (although it is also fine to re-cite the introductoryreferences, if appropriate in addition to these extra two).Include a list of references after the discussion. Using a web search such as Web ofKnowledge or Google Scholar. Remember that you need to try and find at least fourscientific references that relate to the topic you are studying. Cite these in the textat appropriate parts of the introduction or discussion as either support for why yourquestion is interesting, or support for your conclusions.Professional context sectionAfter your project report you need to provide an evaluation of the professionalcontext of the skills you have acquired doing the project. This will be in three subsections.First, you need to describe how conducting the project developed your research skills,discipline specific knowledge, critical thinking, problem solving and communication.This will be in form of short (two sentences maximum) descriptions of how the projecthas helped you improve three of these skills.9The second bit of evaluation asks you to find two current jobs that are available, onein science, one not science-related, which have selection criteria that you couldrespond to, using example from this project to provide evidence of your skills. Use ajob search tool such as www.seek.com.au to find these advertisements. For thissection to provide a copy of the job advert (with selection criteria and weblinkapparent) in your write-up. Then you will need to identify 4 different selection criteriafrom the two job ads you selected (2 for each job) that you think you can appropriateaddress with the experience or skills you have acquired doing this project.For the third and final part, you will address In 100 words or less for each (400 wordstotal) you will describe how you meet each selection criterion highlighted in part 2above. You will use the STAR technique to frame your responses, where you describethe SITUATION (the context of the issue you addressed), the specific TASK that wasrequired of you, the ACTION you took and the RESULT (the outcome of that action andhow it provides evidence for the specific skill). You will receive more training on howto structure your responses in the Career Education class in weeks 6 and 8. Thechallenge here is to be concise but with the right level of detail demonstratingevidence that you really do fulfil the selection criteria.Ultimately, these tasks are designed to help you recognise and articulate thequantitative and analytical skills that you have developed, and how/why they arevalued, which will be critical for resumés, job applications and interviews withpotential employers.Some helpful advice: The difference between “results” and “discussion”People often get confused about the difference between the “results” and“discussion” sections of write-ups. The key point is that the results should simply statethe facts, while the discussion interprets those facts. There are some examples below:Results: “The difference in numbers of magpies in suburban parks compared tobushland areas was significant (independent samples t-test: t = 3.42, df = 45, p < 0.01).10On average, there were 1.7 more magpies observed in our suburban parkobservational periods than compared to bushalndDiscussion: “The difference in magpie numbers that was observed in this study isperhaps surprising, given that many of the bushland areas we collected data fromwere still frequently visited by humans, with many picnic grounds in greater densitiesin suburban areas, supporting our hypothesis. The observed difference was, however,small, and may be attributable to the fact that magpies in suburban parks are generallyfound in more open environments, while the magpies in bushland were present indenser vegetation which may have hindered clear observation”.Results: “We found no significant relationship between height and time taken tocomplete a 100m sprint (Pearson’s correlation: r = 0.210, n = 32, p = 0.082 – see Figure2)”.Discussion: “Our failure to find a relationship between height and sprinting abilitymeans that we cannot reject our null hypothesis. However, by using data fromOlympic athletes, our data set probably represents only a tiny subset of the truepopulation, representing those individuals with extremely good sprinting ability andthis may have obscured any trend that might exist across a wider population.Additionally the observations may not have been truly independent if winning athletestempered their sprint performance in heats to match other runners in their heats inorder to save energy for future races”.
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