In this topic, you will develop knowledge and understanding of data analysis in relation to key areas of physical activity and sport. Through this content, you should be able to link it to other topics.

Types of Data

 

There are two types of data that you must know about: qualitative data and quantitative data.

Qualitative Data

Qualitative data is a pattern described with words.

 

An example of this could be:

Person A's working heart rate is lower than Person B's.

             or

Person A ran further than Person B.

Qualitative may not be as reliable as quantitative data since it only gives a vague idea of trends of the results. 

 

Quantitative Data

Quantitative data is a pattern described with numbers.

 

An example of this could be:

The heart rate of the office worker was 20 beats lower than the athlete's in two minutes.

         or

The athlete's average heart rate is 75.8 beats per minute (rounded to one decimal place) however, the office worker's is 76.3 beats per minute.

 

Quantitative data is more reliable than qualitative data since it gives results for proof.

Collecting and Presenting Data

 

Collecting Data

When data is collected, you must check reliability. To do this, you must repeat each reading three times to check that they are similar.

 

Your data must also be accurate, the accuracy of your results usually depends on the method. An example is: counting your heart rate for six seconds and multiplying by ten, rather than counting for the full sixty seconds. This may be inaccurate as your heart rate may slow down (depending on your recovery rate) thus making it less accurate.

Data set 1 is more precise than data set 2 as the mean (the average) is closer to the other results.

Presenting Data

After collecting data, it must be organised so it's more useful. 

 

If your data comes in categories, present it in a bar chart:

The X-axis in a bar chart is either categorical (e.g. blood group or maximum heart rate of certain ages) or discrete which is data that can be counted in chunks (like the number of people since you cannot have 10.45 of a person).

 

When creating a bar chart you must remember the following things:

 

If your data is continuous, plot a graph:

 

If both axes of the graph are continuous (numerical data that can have any value within a range, e.g. distance or time) you should use a graph to display the data.

 

When creating a plotting point on a graph you must remember the following things:

Comparing Data

After collecting data, it can be compared against normative data to give you an idea of how your fitness is in comparison to the general population. However, for higher performing athletes normative data may not be as helpful as their results won't be as relevant to their own progress.

An athlete will only be interested in how their fitness data compares to other athletes and specifically to their own previous fitness test performances. As a result, continuing fitness can be tracked and improvements can be made in areas needed.

Interpreting Data

 

In order to interpret data accurately, you must spot the patterns of what it's presenting to you.

Examples of Qualitative Data

By looking at this graph you can come up with several statements describing the patterns.

 

E.g. The athlete's overall heart rate is lower                             

or 

The office worker's heart rate at the end was higher than the athlete's.

Examples of Quantitative Data

By looking at this graph you can come up with several statements with figures describing the patterns.

E.g. The athlete's overall heart rate is (on average) 0.5 beats lower.

or 

The office worker's heart rate at the end was 20 beats higher than the athlete's.

When passing a judgement on data that you have received you should:

introduce your point and combine both types of data together then finally add a justification/an alternative view.

For example, the athlete's cardiovascular fitness is better than the office worker's. This is apparent as the athlete's average heart rate is 0.5 beats lower than the office worker's. This could be due to the athlete doing an increased amount of exercise in their day to day activity, compared to the office worker who may (typically) spend a majority of the day sitting down at a desk. This could then lead to a sedentary lifestyle (if there is no other input of physical exercise) which could increase the risk of CHD (Coronary Heart Disease) as well as higher blood pressure which is evident above. 

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