it takes to analyse, interpret, and write up a research report. Figure 1.2 illustrates a timetable in the form of a Gantt chart for a research project expected to last 33 weeks – around a full academic year: many research projects are substantially shorter than this. Note how the time allocated to actually collecting the data is relatively short and overlaps with the ongoing literature review and writing process. Also, you are strongly urged to begin writing the report before completing your data collection; it should be possible at this stage for you to write the methods and introduction since the former will describe what you have been doing, whilst the latter reflects your background reading of the literature. Table 1.1 shows an example timescale for a research project lasting a week (e.g. on a field course). Note that even with such a short timescale, sufficient time needs to be devoted to planning the project to ensure the best chance of success (see Box 1.4).
Table 1.1 Example timescales for a short research project.
| Day | Morning | Afternoon | Evening |
| 1 | Select topic, identify aims and hypotheses, create programme of study, select sites and techniques, identify major resources required, complete risk and ethical assessments | Test, and become experienced in using, techniques and equipment, implement a pilot study | Evaluate pilot study, amend programme of study |
| 2 | Implement amended programme and collect data | Enter data into spreadsheet, write methods | |
| 3 | Collect data | Enter data into spreadsheet, write introduction | |
| 4 | Collect data | Enter data into spreadsheet, edit introduction and methods | |
| 5 | Collect data | Enter data into spreadsheet, plan results tables and figures | |
| 6 | Collect data | Analyse data | |
| 7 | Write results section | Write discussion | Complete report |
Box 1.4 Some tips on time management
Be realistic about what you can achieve in the time you have available and work within your strengths and weaknesses.
Plan your long‐term goals.
Have a weekly plan, with realistic and achievable targets, and update this on a regular basis to reflect your progress.
Identify not only the key phases of your research project, but also other areas that will take up your time (both in terms of study and general living) to ensure that your research timescales are realistic and that your aims are achievable.
Prioritise your work into that you have got to do, that you ought to do, and that you would like to do but may not have time.
Make good use of your time: a trip by train may be an ideal opportunity to read references or edit your manuscript.
Statistical considerations in project design
Since research is about asking questions, you need to design your project so that it will answer them effectively, without allowing your design to introduce ambiguous results, or results that are open to other interpretations. This is where the planning phase starts to define what you are going to measure and how. If, for example, we investigate the types of birds found inhabiting a woodland patch, then we have a choice of ways in which we record the data. We might note how many individual birds there are, or the numbers of each feeding type (insect feeders, seed feeders, etc.), or how many individuals there are in each species. These measurements enable us to obtain a picture of the birds found in a woodland patch. If we monitor birds only in a single woodland patch, we could worry that our chosen woodland is unusual in some way and therefore not representative of woodland patches in general. We could therefore examine a series of patches and obtain data for 10 or more of these. Now, if we wish to describe how many birds were found in all of these woodlands, we require some sort of descriptive statistic to summarise the information across 10 or more patches. Descriptive techniques include estimates of the average values per sampling unit (e.g. per site), population estimates and densities, methods of describing distributions (i.e. whether organisms are distributed randomly, evenly or in aggregations), and measures of community richness including diversity and evenness indices. These techniques are discussed in more detail in Chapter 5.
Most projects go beyond a simple description of particular species and sites in an attempt to make comparisons or generalisations that can hopefully have wider applicability. For example, if we decide to investigate whether the number of animals found under decaying logs on a woodland floor is influenced by the size of the log, we might approach this in one of three basic ways:
| 1. by looking at possible differences between samples; for example, if the logs were easily divided into two classes (large and small, i.e. <20 cm and ≥20 cm), we could compare the numbers of animals found under each size class; |
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| 2. by looking at possible relationships between variables; for example, we might have a wide range of sizes of logs and decide to examine whether the number of animals varies in some systematic way (either increasing or decreasing) as log size increases; |
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| 3. by looking at possible associations between frequency distributions; for example, we could compare the frequency of predators, herbivores, decomposers, etc. from under each of two size classes of logs (i.e. <20 cm and ≥20 cm). |
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From this simple example it can be seen that how we ask the question has an impact on how we design our study. The three different ways of looking at this study (listed above) also illustrate three broad
