Part 4.2: Data Analysis Worksheet and Reflection

 

Experimental procedure for measuring height of class members

As a group we discussed how we would go about measuring our heights accurately. There were a couple of suggestions as follows:

1.      1. Lying down and marking where top of our heads and the bottom of our feet were, then measuring between the marks with a tape measure.

2.      2. Standing against a wall, with our heels touching the wall, place a book or mark where the top of our heads was and then measure with a tape measure.

We decided to use No.2 - measuring against the wall. To avoid any random effect factors, we decided to do this without shoes on, and if the book method was used we would measure to the bottom of the book. The equipment needed would be tape measure and book, and/ or pencil.

The results were then put into a recording sheet as shown below:

Height recording sheet

Name	Height in cm’s
Megan	157
Naomi	163
Rosie	168
Beth	170
Amelia	178

 

To easily read the data collected, we can use a ‘Frequency Table’. This is especially useful when there is lots of data to analyse.

Frequency Tables use the following symbols:

X = each data value, in this case the height in cm’s.    

 f = frequency of each data value.

 n= number of samples collected (n-value). In this case there were five people so n= 5

 Σ = SIGMA – add all the values together.

 

Height Frequency Table

 

x	Tally	f
155 - 159	I	1
160 - 164	I	1
165 – 169	I	1
170 – 174	I	1
175 – 179	I	1

                                                              n= 5        Σx= 836     

Measure of Central Tendency.

Instead of having lots of data to look through we can use a single value to describe our results. The most common ones used are Mean (average), Median (the most common number in the dataset) and Mode (the middle number in a sorted list of numbers). These were worked out for our height results as follows:

Mean = 167.2

Mode = none - as there was not enough data to analyse.

Median = 168

We also worked out the range (the difference between the highest and lowest values):

Range = 21

 

Measuring the variation of normally distributed data.

We can measure the spread of the data around the mean value by working out the Standard Variation and then calculated the Standard Error with this information.

 From our data:

Standard Deviation = 7.85

Standard Error = 3.51

 

Reflection:

Going through some data analysis has been a way of developing my knowledge of spreadsheets, graphs and how to use Excel to achieve the results needed. This was an area on my Skills Audit that I felt quite confident in but needed a bit more practice. After being out of education and office work for a long time I realised that my skills were outdated so have had to go back to basics and relearn some areas especially Excel. This will be an important skill when dealing with survey data and trends in future studies and work. Many Conservation groups use data as a way of studying trends and geographic patterns in populations such as the RSPB (n.d.). 

References

RSPB, (n.d.), Mapping and GIS, www.rspb.org.uk/our-work/conservation/conservation-and-sustainability/mapping-and-gis. Viewed 22/03/2021.