- Organizational Behavior - Assessing structure, culture, leadership and team dynamics to improve organizational effectiveness

Session 6 - Introducing Organizational Culture

Spring 2021

Version 1

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Copyright © 2021 Thomas Mazzone

BIOL2610 2021 Experiment Report

Due Date: 5 p.m. Friday 21 May (mark before final exam)

Alternative Due Date: 5 p.m. Friday 28 May (no guarantee of mark before final exam)

Summary

For this assignment, you will need to design, run, and report on an experiment. A list of example questions is given below. You can choose to investigate one of these questions, or you can address another question of interest to you after receiving prior approval from me. You first need to develop a biological hypothesis that addresses your chosen question. You then need to develop a statistical null hypothesis and identify an appropriate statistical test to evaluate it. Next, you need to design an experiment that will yield data suitable for addressing your chosen biological and statistical hypotheses. Remember to carefully consider principles of experimental design such as replication, independence, and randomisation. You’ll then run your experiment and analyse your data. Finally, you will need to interpret the results of your statistical analyses in light of your biological and statistical hypotheses. The report you submit should be in the form of a journal article. You will also be required to submit your experimental data and accompanying metadata in archival format, print outs of your R output from the console, and a commented R script containing all of functions you used to generate the output. You are required to do your analyses in R.

Learning Objectives

This assignment is designed to help you meet some of the major learning objectives of this unit. In particular, through this assignment, you will go through all of the steps involved in designing and implementing a biological experiment:

1. Develop testable hypotheses based on general scientific questions.

2. Design an unconfounded experiment to test scientific hypotheses.

3. Choose an appropriate statistical test to analyse experimental data.

4. Carry out statistical tests using the computer package RStudio.

5. Correctly interpret results from statistical tests.

6. Clearly present the findings of the experiment and statistical analyses using figures, tables, and text.

Questions

Here is a list of biological questions you could investigate for your experiment:

1. What colour flowers do pollinators prefer?

2. Do snow pea sprouts grow faster with increases in light, water, or nutrients?

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3. Are germination rates of plants affected by the addition of salinity, coffee grounds and/or worm tea to soil?

4. Does body temperature affect the activity (e.g. running speed, time to leave a circle, time to first movement) of invertebrates (e.g. crickets, worms, ants, mealworms)?

5. Do different food types differ in their attractiveness to ants?

6. Are slugs more attracted to fresh or rotting vegetables, and do they show preference for particular vegetables?

7. How are hatching times/growth rates of brine shrimp (sea monkeys) influenced by environmental conditions (e.g. food availability, temperature, salinity, pH)?

8. Is formation of bread mould affected by factors such as bread type (e.g. white versus wheat), moisture level, or packaging (e.g. uncovered, plastic-bagged)?

If you would like to explore a different question, then discuss it with me. It should be OK, but I need to ensure first that it will allow you to meet the learning objectives. Also, please be aware that you are unable to due anything involving outdoor FIELDWORK (except in your yard at home) because doing fieldwork for anything, including a class assignment, requires extensive paperwork!

With all of the experiments, I strongly suggest having a trial to get your methods right before starting the main experiment! Here is some advice for setting up particular experiments:

(1) Flowers and pollinators

One possibility would be to make some mock-ups of flowers (e.g. coloured paper on paddle pop sticks) in different colours. To increase the attractiveness to pollinators, you could place a dab of honey at the centre of each flower. Some variants on this question would be to investigate factors attracting or repelling different types of insects. For example, are ants equally attracted to sugar and artificial sweeteners?

(2) – (3) Plant growth and germination

Plant seeds can be bought in packets at garden shops and Bunnings (and can be eaten at the end of the experiment!) You’ll also need seed trays and seed raising mix. There are a lot of possible variants on this question! Previously students have investigated questions such as: whether different types of fertiliser are more effective than others; whether watering plants with chlorinated water from the swimming pool is detrimental; whether adding sugar to soil is detrimental to plant growth; and whether fertilisation changes stomatal density on leaves.

(4) – (6) Activity of invertebrates

You could use a temperature manipulation (i.e. putting bugs in a fridge until they reach an internal thermal equilibrium, after about 20 mins or so. . . ) and compare aspects of their subsequent behaviour. For example, you could compare activity rates of bugs kept at room temperature with those subject to various periods of experimental cooling. Or, you could investigate rates of recovery from cold treatment among different sizes of invertebrate. For slugs or ants, you could put out food baits and count the number of organisms observed after leaving the bait out for a fixed period of time.

(7) Growth of sea monkeys

Brine shrimp are readily available at toy stores, and hatch within a few hours to a few days. You could investigate how hatching times vary depending on the temperature of the water. You could also rear sea

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monkeys at different densities and/or with different amounts of food to assess how competition and/or overall food availability influence growth rates.

(8) Bread mould

You could investigate effects of conditions such as light, temperature or moisture by placing slices of bread in clear or opaque bags, in locations with different temperatures (the fridge is an extreme example – you could also try direct sunlight vs indirect sunlight to raise temperature). You might want to compare different types of bread; or you could compare bread with toast; or you could investigate effects of different preservatives such as salt or cinamon. Some hints: You want to ensure before starting the experiment that mould spores are present. Watch out for breads with loads of preservatives: we’ve had some experiments fail because no mould grew at all. To quantify mould growth, you could place a bread-shaped grid over the slice of bread and count the number of grid squares containing mould. Please use rubber gloves and a mask when doing this - be careful not to breathe in the spores!

Requirements for your Experiment Report

You will be required to submit a report in the form of a manuscript (max 2500 words), along with your data and metadata in archival form, and your R output and commented script. Your experiment report should be submitted to Turnitin. I will provide a rubric to help you when writing your report. The report needs to have the following sections:

Title

The title should be informative, but not verbose.

Abstract

The abstract should be a concise summary of your experiment, including hypothesis, design, results and interpretation.

Introduction

The introduction needs to introduce your biological hypothesis. You’ll need to think a bit about the biology involved and come up with a prediction that you wish to test. I would like you to briefly discuss the biology and explain how you came up with your hypothesis. However, I am not expecting you to provide a review of other literature on the subject: this would typically form part of the Introduction to a scientific manuscript, but can be omitted for this unit.

Materials and Methods

You need to provide a lot of detail in this section about exactly how you set up and ran your experiment. Pay particular attention to discussing how many replicates you used, how you ensured independence among your replicates, and how you randomised your sampling. Each of the above experiments would be quite easy to pseudo-replicate, so please make sure you give careful thought to avoiding this error! Also clearly state how you made your measurements and discuss what steps you took to make them as precise as possible.

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Results

Present your results as clearly as you can. Use no more than 7 figures and no more than 3 tables. Use correct statistical language. Ensure that your figures meet the ‘good graph’ criteria (see Lecture 3). Make sure that figures and tables have captions. Use appropriate statistical tests and give confidence intervals for effect sizes where possible. Additionally, you should present results of an a priori or post hoc power analysis.

Discussion

In the discussion, interpret your results in terms of your original question. Typically, the Discussion section that puts results in the context of what is already known about the subject and refers to other literature – you do not need to do this for this report unless you want to. The discussion is also the place to mention any problems that you faced with your experiment and suggest how you would improve your experiment if you were to do it over again.

Acknowledgements

If you had help with your experiment, acknowledge this help in this section.

References

Only necessary if you have referred to books or articles in your text. For this assessment, I am not expecting you to review the literature: the focus is on your experiment.

Supplementary Material

Provide printouts of the RStudio output that you report on, along with a commented R script that contains all of the commands used to conduct your analyses. Evaluation of the assumptions underlying the test you used should be presented in this section.

Dataset and Metadata

Provide your data set as a .csv file, containing the data, and a .txt file, containing the metadata. The .csv file can be produced in Excel (Save As -> Save As Type -> Comma Delimited). This file type can be read by any program, not just Excel. Your .csv file should contain your data in carefully labelled columns and will look a bit like this:

name,gender,foot,hand Kate,F,27.5,17.1 Elisse,M,26.9,17.5 Alex,F,26.7,18.7 ...

The .txt file can be produced in Word (Save As -> Save As Type -> Plain Text Format). Your metadata needs to contain (at least) the following information (see Lecture 3):

• Name and contact details of person responsible

• Geographic information about where data were collected

• Description of methods used

• Types of experimental units

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• An explanation of each variable, including any abbreviations used for variable names

• Units of measurement for each variable

• Value used to indicate missing data (only if missing values are present)

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